Efficient LiDAR Reflectance Compression via Scanning Serialization
Authors: Jiahao Zhu
Kang You
Dandan Ding
Zhan Ma
Venue: ICML 2025 poster (2025)
Keywords: lidar point cloud; attribute compression; lossless coding; probability prediction
Primary area: general_machine_learning->representation_learning

Abstract: Reflectance attributes in LiDAR point clouds provide essential information for downstream tasks but remain underexplored in neural compression methods. To address this, we introduce SerLiC, a serialization-based neural compression framework to fully exploit the intrinsic characteristics of LiDAR reflectance. SerLiC first transforms 3D LiDAR point clouds into 1D sequences via scan-order serialization, offering a device-centric perspective for reflectance analysis. Each point is then tokenized into a contextual representation comprising its sensor scanning index, radial distance, and prior reflectance, for effective dependencies exploration. For efficient sequential modeling, Mamba is incorporated with a dual parallelization scheme, enabling simultaneous autoregressive dependency capture and fast processing. Extensive experiments demonstrate that SerLiC attains over 2$\times$ volume reduction against the original reflectance data,  outperforming the state-of-the-art method by up to 22\% reduction of compressed bits while using only 2\% of its parameters. Moreover, a lightweight version of SerLiC achieves $\geq 10$ fps (frames per second) with just 111K parameters, which is attractive for real applications.

Efficient LiDAR Reflectance Compression via Scanning Serialization
Jiahao Zhu
Kang You *2 Dandan Ding'
Zhan Ma
Abstract
Reflectance attributes in LiDAR point clouds pro- vide essential information for downstream tasks but remain underexplored in neural compression methods. To address this, we introduce SerLiC, serialization-basee neural compression frame- work to fully exploit the intrinsic characteristics of LiDAR reflectance. SerLiC first transforms 3D LiDAR point clouds into 1D sequences via scan-order serialization, offering a device-centric perspective for reflectance analysis. Each point is then tokenized into contextual representa- tion comprising its sensor scanning index, radial distance, and prior reflectance, for effective de- pendencies exploration. For efficient sequential modeling, Mamba is incorporated with a dual parallelization scheme, enabling simultaneous au- toregressive dependency capture and fast process- ing. Extensive experiments demonstrate that Ser- LiC attains over 2x volume reduction against the original reflectance data, outperforming the state-of-the-ane method by up to 22% reduction of compressed bits while using only 2% of its parameters. Moreover, a lightweight version of SerLiC achieves 2 10 fps (frames per second) with just 111K parameters, which is attractive for real-world applications.
(a] LIDAR Point Cloud
(b) Detection wn Reflectance
(c) Detection w/o Refectance
Number of Paramstees
Usizon 111.50
Smaaeer
Gen
(100
40
60
80
100
120
140
160
Encoding Latency [s]
Docoding Latanoy [8]
d.PO0 (Poosit)
6.POC (Fredin)
ruSpinss
unaom
Unicon
16c
Ford
550
50
BSerLiC E Unicom ::G-POC (d) Compression Performance
(e) Consumption Details
Figure I. (a-c) Reflectance plays an indispensable role in down- stream tasks such 3D object detection; (d) The proposed SerLiC establishes new state-of-thc-an by outperforming the latest com- pression standard G-PCC (Zhang et al., 2024b) and the learning- based work Unicorn on widely accepted datasets KITTI (Behley et al., 2019), Ford (Pandey et al., 201 I), and nuScenes (Caesar et al., 2020) (the number denotes sequence number); (e) SerLiC achieves ultra-low coding latency with an exceptionally lightweight model.
producing vast amounts of data points that form the point cloud a digital representation of the surrounding 3D envi- romment for downstream perception tasks (Chen et al., 2024; Hu et al., 2023). The massive volume of such data, often reaching gigabytes per minute, highlights the development of efficient LiDAR point cloud compression (PCC) tech- niques to enable effective storage and transmission (Li et al., 2024; Zhang et al., 2024b; Liu et al., 2020).
1. Introduction
Over the past two decades, the light detection and ranging (LiDAR) sensors have become indispensable components in a diverse array of applications, including autonomous driving, robotics, etc (Baur et al., 2025; Liang et al., 2024a; Li & Ibanez-Guzman, 2020). These sensors work by period- ically emitting laser beams and capturing their reflections,
Point cloud reflectance, derived from the intensity of re- turned laser pulses LiDAR, serves as a crucial descriptor of the physical properties of objects. It is widely used in downstream tasks such as object detection and semantic segmentation (Viswanath et al., 2024; Tatoglu & Pochiraju, 2012). For instance, experiments show that removing the reflectance causes dramatic drop in pedestrian (and cyclist) detection accuracy incorporating the pre-trained PointPillar (Lang et al., 2019) detection model, having AP (average precision) from 51.4 (62.8) to 14.1 (34.3) on the KITTI dataset (see Figure 1), making impractical for use.
'Equal contribution 'School of Information Science and Tech- nology, Hangzhou Normal University, Hangzhou, China "School of Electronic Science and Engineering, Nanjing University, Nan- jing, China. Correspondence to: Dandan Ding <DandanD- u ednm.edu.cn...
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

---

Efficient LIDAR Reflectance Compression via Scanning Serialization
While existing neural compression methods have achieved significant performance gains, they mostly focus the ge- ometry or RGB color attributes (Wang et al., 2025; You et al., 2024: Zhang et al., 2023; Wang et al., 2023), and the compression of LiDAR reflectance is relatively under- explored with uncompetitive efficiency. This compression gap is from the intrinsic challenges of reflectance data.
Extensive experiments on benchmark datasets show that SerLiC achieves remarkably high performance, over 2x volume reduction against the original reflectance data and up to 22% bit rate reduction compared to the state-of-the-an Unicorn method (Wang et al., 2025), while using only 2% of the parameters and 10% of the GPU memory, as illustrated in Figure 1. Moreover, SerLiC is hardware-friende relying solely on simple networks rather than specific convolution libraries (e.g., Minkowski Engine (Choy et al., 2019)) com- monly used in existing methods. Our key contributions are summarized as follows:
The first challenge lies in the inherent sparsity of LiDAR scans. Unlike object point clouds comprising relatively dense points, LiDAR point clouds are much sparser, as laser pulses are reflected only from a limited subset of distant points in the environment (Kong et al., 2023; Raj et al., 2020). To address this, convolution based methods ( (Wang et al., 2025; 2023) have to employ large-kernel convolu- tions (e.g., 7 increase the receptive field to aggregate sufficient points in 3D space for analysis. However, this incurs an expensive computational burden while yielding only marginal performance gains, as the fixed-size large kernel remains insufficient to capture dynamic context.
We propose SerLiC, a lossless reflectance compression method for LiDAR point clouds, leveraging scan-order serialization to transform a 3D point cloud to ID point sequences for efficient representation.
We generate LiDAR information (scanning index and radial distance) for each point, along with the previous decoded reflectance, as context to exploit point depen- dencies in a sequence, supported by the selective state space model with dual parallelization mechanism.
The second challenge is the intricate physics of underlying LiDAR reflectance. Reflectance intensity influenced by various factors, such as the material properties of objects, the angle of incidence of laser pulses, the distance between the sensor and the target, and hardware variations (Liang et al., 2024a; Viswanath et al., 2024; Fang et al., 2014). These introduce intertwined correlations across points that extend beyond simple spatial proximity. For instance, reflectance values from surfaces with similar material properties may exhibit strong correlations even when spatially distant. How- ever, existing methods largely neglect these physical inter- actions inherent in LiDAR reflectance. They typically adopt modeling strategies designed for color attributes in object point clouds and rely on geometric 3D spatial proximity to define LiDAR point relationships. Consequently, their performance remains limited.
SerLiC delivers notable performance on benchmark datasets, offering high compression efficiency, ultra- low complexity, and strong robustness. Its light version runs 30 fps with frame pipelining and 10 fps without, with only 1 111K model parameters.
2. Related Work
2.1. Serialization- Point Cloud Analysis
Transformers. Serialization has shown remarkable efficacy in point cloud analysis tasks (Wu et al., 2024; Liu al., 2023; Wang, 2023), owing to the inherent simplicity and computational efficiency of structured data representations. Notable studies, including OctFormer (Wang, 2023), Flat- Former (Liu et al., 2023), and Point Transformer v3 (Wu et al., 2024), have utilized serialization- Transformer architectures to implement attention mechanisms in struc- tured spaces, achieving high-performance representation. However, Transformers bring quadratic computational com- plexity, which poses challenges in long sequence model- ing (Liu et al., 2024).
Therefore, this work proposes a serialization-based neu- ral compression framework, dubbed SerLiC, that utilizes sequence modeling to improve the efficiency of LiDAR re- flectance compression. In contrast to current techniques that directly work in 3D space for spatial correlation exploration, SerLiC follows the LiDAR scan order to serialize 3D points into ID point sequences, for which expensive 3D operations are removed. The serialization aligns with the LiDAR laser scanning mechanism, allowing for device-centric rather than spatial perspective in reflectance analysis. Upon this representation, we devise an entropy model to capture point dependencies in a window of each sequence for effective contextual modeling. Within each window, the selective state space model (a.k.a. Mamba (Gu & Dao, 2023)) is employed for autoregressive coding. Additionally, LiDAR scanning-relevant information is derived based the input point cloud as context to improve the modeling accuracy.
Mamba. Recently, Mamba (Gu & Dao, 2023) introduced the selective state space model (SSM) mechanism, garner- ing significant attention for its linear computational com- plexity and superior performance. Numerous works (Liang et al., 2024b; Zhang et al., 2025; Han et al., 2024; Zhang et al., 2024a; Wang et al., 2024) have successfully integrated Mamba into point cloud analysis to enhance efficiency. For instance, PointMamba (Liang et al., 2024b) applies selec- tive SSM in point cloud analysis by reorganizing key point features into sequences for efficient processing within the
2

---

Efficient LIDAR Reflectance Compression via Scanning Serialization
Mamba framework. Extending this paradigm, several stud- ies (Zhang et al., 2024a; Zeng et al., 2024) have introduced Mamba to LiDAR point clouds. However, their serial- ization approaches are limited to traditional fpace-filling curves (Morton, 1 1966; Hilbert, 2013), overlooking unique scanning mechanisms and physical information inherent in LiDAR point clouds.
3. Method
3.1. Problem Definition
Given a LiDAR point cloud with N points, we denote its reflectance intensities as X {x:]== and the geometry coordinates as C {ct) i=1- Here, x; and e; represent the reflectance attribute and the spatial coordinate of the i-th point. Existing approaches (Wang et al., 2025; 2023; You et al., 2024) rely exclusively on the unordered geo- metric coordinates C to model point correlations, thereby neglecting the inherent sequential scanning characteristies of LiDAR data. In contrast, this paper introduces serializa- tion for leveraging the sequential nature of LiDAR scans, This is because 1) Serialization transforms point clouds into a I ID representation, enabling more efficient and structured modeling from a device-centric perspective; and 2) Serial- ization supports the efficient processing of information that more closely reflects the physical characteristies of LiDAR scanning (C- C*), resulting in more accurate analysis.
2.2. Point Cloud Attribute Compression
Rules-based Methods. Recent advancements in Point Cloud Attribute Compression (PCAC) have led to the stan- dardization of techniques, with MPEG's G-PCC (Zhang et al., 2024b) serving as the benchmark. G-PCC in- corporates two primary methods for LiDAR reflectance compression: Region-Adaptive Hierarchical Transform (RAHT) (De Queiroz& & Chou, 2016) and Predicting/Lifie Transform (Predlift) (Mammou et al., 2018). To address G- PCC's latency and complexity, L3C2 (Sébastien & Jonathan, 2021) was introduced in 2021, specifically tailored for Li- DAR point clouds. However, its dependence on detailed sensor configurations (e.g., the precise pitch angles of indi- vidual lasers) limits its applicability.
The serialization process reorganizes the input point cloud (X, C) into I. point sequences as follows (see Figure 2):
{X,, C = Serialize (X, C),
(I)
Learning-basee -baed Methods. Neural models have revolution- ized PCAC by incorporating deep-learning techniques to capture attribute dependencies. CNeT (Nguyen & Kaup, 2023) employs an autoregressive framework with causal priors for sequential prediction, achieving high compres- sion gains at the expense of extremely high computational cost. Building on this, MNeT (Nguyen et al., 2023) adopts multiscale strategy to enhance computational efficiency but the compression gain is largely compromised. Additionally, CNeT and MNeT are deigned for color attributes, failing to compress the LiDAR reflectance. PoLoPCAC (You al., 2024) introduces point-based compression pipeline that models attributes in groups based on antecedents; however, it shows certain dependence on training data, limiting its generalizability. Unicorn (Wang et al., 2025), the latest framework, introduces a universal multiscale coding mech- anism using 3D sparse convolution library - Minkowski Engine (Choy et al., 2019) to predict attribute values across multiple scales, achieving state-of-the-art performance. To attain high performance, Unicorn devise large kernel sizes in 3D convolution, resulting in intensive complexity.
where L denotes the number of laser beams emitted by the LiDAR sensor. Each sequence X, = { No the reflectance intensities from the (-th laser beam, and No
represents
encodes the contextual information of the Ci = { c7 (=1 corresponding points.
In this context, we define LiDAR information ci.; (vr,i, Wr, Pr,)), including the laser index or,i, the azimuth angle index u,, and the radial distance Pt, for each point. These information can be entirely derived from the geo- metric coordinates C during the serialization process, as detailed in Section 3.2.
With the serialized representation (X,, Cj), our objective is to design parameterized entropy model that incorporates the priors Cj to exploit spatial and contextual dependencies in the reflectance data X,. This model aims to approximate the conditional probability distribution p(X/|C;) as closely as possible to the true distribution g (() The total encoded bit rate R is the sum of bit rates for all sequences, which can be expressed using the Shannon cross-entropy between two distributions:
Remarks. While these learning-based methods have demon- strated promising performance improvements, they mainly focus on general-purpose attributes (e.g., color intensities), which limits their effectiivness in specialized scenarios such as LiDAR reflectance. As the industrial adoption of Li- DAR technology grows across various applications like tonomous driving and urban planning (Liang et al., 2024a), developing specialized codecs for LiDAR point clouds is essential to meet practical requirements.
R
R
Ex (X](C])) (2)
3.2. Serialization
Serialization has demonstrated significant efficacy in a wide range of point cloud analysis tasks, owing to the inher- ent simplicity and computational efficiency of structured
3

---

Efficient LiDAR Reflectance Compression via Scanning Serialization
LIARR Point
Serialization
Sequences
oro
oo oo
Enc. Process
0
Dec. Process
AD
AD
Piakded
O
Enc. & Dec. Prooess
Mamba
AE
AE
Arihmmetic Encoder
Classifier
Classifie
MDAC
MDAC
Parulokked
AD
Arthmmetic Decoder
Mambe-driven Autoregressive Coding
Window-based Persilelization
Figure 2. SerLiC Framework. The input 3D LiDAR point cloud is first serialized into 1D ordered point sequences, which then divided into windows for parallel processing. For cach window, Mamba-driven autoregressive coding (MDAC) scheme is employed. which embeds scanning index (FF""), radial distance (FF), and prior reflectance (F context generate the probability function (PMF) for the reflectance intensity of the target (i-th) point.
data (Liang et al., 2024b; Zhang et al., 2025; Wu et al., 2024). Unlike conventional point cloud serialization strate- gies, such as the space-filling curves (Morton, 1966; Hilbert, 2013; Liang et al., 2024b; Zhang et al., 2025), our pro- posed SerLiC introduces LiDAR scan-order serialization, specifically designed effectively preserve and leverage the intrinsic regularities within LiDAR point cloud.
where I, and W represent the angular resolutions of the elevation and azimuth angles, respectively; L is the number of laser channels; [] denotes the floor operation; (Pup and Pdown are the maximum and minimum elevation angles, defining the upward and downward view field of LiDAR sensor. Notably, the values e; and u; clipped ranges [1, L] and [1, W]. respectively. As such, the contextual information of of each point is aggregated as (U: us Pi).
Coordinate Mapping. Specifically, the Cartesian coordi- nate C; of a point is first converted to spherical coordinate space, yielding the elevation angle Fi E [-g. z. ] azimuth angle Φ, (-m, x). and radial distance Ps E (0, +oc)'. The transformation is expressed as:
Reordering, Based on the angular indices and u, the point cloud data are organized and sorted as follows:
Points with the same laser index = 1(1 E [1, L) are aggregated into subsets (X,, C7), with (X,, CD) {xx,, = 4 []]
P:
27
42: aresin
24
(3)
Within each subset, points are ordered by increasing azimuthal index ug yielding sequence (X,, Cf) ((x;, ct) E (X,, (7,, | u; uj Vi j).
Pi
Ø; alan2 (y))
where Fi, ys, and 2, denote the Cartesian coordinate of c,- Next, each point is mapped to a discrete grid by assigning laser index i; and an azimuthal index u, respectively, based on its elevation angle s0, and azimuth angle c;:
In this way, the proposed serialization method restructures the point cloud into internally ordered sequences, enabling efficient autoregressive coding using the state-space model.
|
Pi idown
3.3. Contextual Construction
U/-
L
+1,
Pup - Pdown
(4)
Figure 2 illustrates the state space model- autoregres- sive encoder for sequentially compressing the reflectance attributes of the point sequence. Each point is tokenized into a contextual combination of scanning index, radial distance, and prior reflectance, with the aim exploring correlations to its neighbor for accurate probability estimation.
di
U;
W.
2TT
In practical systems, the maximum detection range of LiDAR constrained by sensor specifications, typically up 400 me- ters (Liang et al., 2024a).

---

Efficient LIDAR Reflectance Compression via Scanning Serialization
Scanning Index. Previous methods usually explicitly lever- age geometry coordinates to identify correlated neighbors for point. However, spatially close points may not be highly correlated as the LiDAR points are derived follow- ing its unique scanning mechanism. To this end, leverag- ing the scanning index for point context will yield better results since points with the same index are captured sim- ilarly. Specifically, the scanning index feature F of the i-th point is derived by directly embedding the laser and azimuth indices (i.e., and u.) obtained in Section 3.2:
P
Concal
Concat
Masked Multi-
Head Attention
Lineer
Linear
Moos
DWVComV
LayerNorm
Selective
F Embed (1:) Embed (u), 1 isis N.
(5)
SSM
Fead-forward
Attention Blook
Blook
where Embed denotes the embedding layer, and + refers to the concatenate operation.
Linear
LayerNorn
Radial Distance. LiDAR reflectance essentially represents the backscattered intensity of LiDAR signals, which are intrinsically influenced by the distance between the ob- ject and the sensor, based on the light transmission the- ory (Viswanath et al., 2024; Fang et al., 2014). Therefore, SerLiC explicitly incorporates radial distance as context to enhance the reflectance compression. Specifically, the radial distance pj (see Eq. (3)) is normalized and transformed into high dine-diimesional spatial feature Fy using Lincar layer:
Classifier
Classifier
(a) Mamba Block
(b) Attention Block
Figure 3. Basic Mamba block and Attention block. "LN" refers to Layer Norm; a denotes SiLU activation; @ means Hadamard product; represents element wise addition.
where F; denotes the output of the s-th Mamba layer; DWConv represents the depth-wise convolution; o refers to the SiLU activation funetion; @ means Hadamard product. The initial input F is the token feature Ftoken.
Fl- Linear (Norm (p.)). isis No,
(6)
where Norm refers to the min-max normalization that re- scales P to the range of (0, 1).
Let Fout be the output of the final Mamba layer. Then clas- sifier is used to predict the probability mass function (PMF) for the target (i-th) point, leveraging the Linear transform and Softmax function:
Prior Reflectance. Naturally, the reflectance value of previ- point in the sequence is embedded (F(_)) context.
We then combine all context for subsequent processing:
ptaken
P PT-1), 1 1sis Ni-
Pl (- (1, ci Softmax (Linear (Fqu))).
(9)
For the case i=1, where prior reflectance is unavailable, the value of Fø is estimated by the neural network.
Dual Parallelization. SerLiC proposes a dual paralleliza- tion strategy at both the sequence and window levels to accelerate the coding process. First, as shown in Figure 2, SerLiC processes point sequences in parallel, disregarding correlations across sequences. This is justified, point sequences are scanned by separate laser sensor rotations, inherently lacking strong inter-sequence dependencies. As result, sequence parallelization does not compromise coding efficiency but greatly enhances processing speed.
3.4. Entropy Coding
Mamba Coder. Given the collected context token ploken of point sequence, Mamba is ideally suited for autoregressive coding due to its ability model sequential dependencies and capture correlations between points.
Thus, the token Floken is passed through several Mamba blocks. A standard Mamba layer is shown in Figure 3(a), and the processing flow can summarized as follows:
Furthermore, SerLiC introduces window-based paralleliza- tion within each point sequence. LiDAR scanning systems, particularly those utilizing mechanical rotation, produce se- quences with extensive spatial coverage, often spanning full 360-degree field of view. Within such sequences, spatial regions exhibit varying degrees of correlation regions (8) like the area directly in front of the sensor have stronger internal correlations than distant regions located behind or to the sides. This localized correlation characteristic enables
F LayerNorm (F
F (DWConv (Linear (F))),
F; (Linear (F:)),
F Linear (SelecLiveSSM((e)) & P::) +F
5

---

Efficient LIDAR Reflectance Compression via Scanning Serialization
Table 1. Quantitative compression gains against other methods
BITS PER POINT (BPP)
COMPRESSION GAIN
CLASS
SEQ.
G-PCC (RAHT)
G-PCC
UNICORN
SERLIC (LIGHT)
SERLIC
VS.
VS.
vs.
VS.
(PREDLIFT)
RAHT
PREDLIFT
UNICORN
LIGHT
5.10
5.01
4.59
4.06
3.80
25.49%
-24.15%
-17.21%
-6.40%
4.56
4.51
4.14
3.68
3,45
-24.34%
-23.00%
-16.67%
-6.55%
4.79
4.62
4.15
3.63
3.35
-30.06%
-27.49%
-19.28%
-7.71%
5.24
5.22
4.82
4.35
4.16
-20.61%
-20.31%
-13.69%
-4.37%
5.11
5.03
4.61
4.13
3.87
-24.27%
-23.06%
-16.05%
-6.30%
5.00
4.96
4.55
4.11
3.85
-23.00%
-22.38%
-15.38%
-6.33%
KITTI
4.80
4.73
4.38
3.87
3.64
-24.17%
-23.04%
-16.89%
-5.94%
5.06
4.97
4.56
4.08
3.80
-24.90%
-23.54%
-16.67%
-6.66%
19
4.63
4.53
4.11
3.50
3.20
-30.99%
-29.36%
22..14%
-8.57%
20
4.51
4.35
3,98
3.52
3.28
-2..77%
-24.60%
17.59%
-6.82%
21
4.83
4.78
4.41
3.93
3.69
-23.00%
-22.80%
-6.33%
-6.11%
AVG.
4.88
4.79
4.39
3.90
3.64
-25.41% %
-24.01%
-17.08%
-6.67%
5.18
5.03
4.89
4.29
3.57
-3.00%%
-28.94%
26.999%
-1..78%
5.13
5.05
5.04
4.46
4.11
19.91%
-18.65%
-18.45%
-7.85%
AVG.
5.16
5.04
4.97
4.29
3.84
25.58%
-23.11%
-22.74%
-2.333%
01
3.93
3,61
3.13
2.89
-26.46%
-19.94%
-4.30%
02
3.41
3.08
2.46
2.58
-33.14%
-25.97%
-7.32%
03
3.18
2.77
2.23
2.34
-34.59%
-24.91%
-6.73%
NUSCENES
04
3.05
2.69
2.28
2,41
-22.62%
-12.27%
-3.51%
05
4.40
4.04
3.23
3.33
-31.82%
-25.74%
-7.12%
AVG.
3.59
3.24
2.78
2.52
-29.81%
-22.22%
-9.35%
sensor. There are around 120k points average per frame. These raw foating-poon coordinates are quan- tized to Imm precision (18 bits) with 7-bit reflectance.
Table 2. Quantitative compression gains against L3C2
BPP SERLIC
CR GAIN
CLASS
SEQ.
L3C2
vs. L3C2
Ford is also collected using Velodyne HDL-64E. The common test condition (CTC) defined by MPEG (WG 07 MPEG Graphics Coding and Haptics Coding, 2024b) utilizes three Ford sequences, each having 1 1,500 frames at Imm precision with 8-bit reflectance.
02
4.83
3.57
-56.15%
FORD
03
4.98
4.11
-17.39%
AVG.
4.90
3.84
-21.63%
The first sequence is for training, and the remaining two are for testing.
effective compression when processing is limited to smaller windows rather than the entire sequence. Accord- ingly. SerLiC partitions each sequence into independent win- dows for parallel processing, ensuring both efficiency and performance. Figure 2 depicts the window slicing method. Each sequence is partitioned equally. Padding points are appended the end to maintain a uniform window size for consistent processing.
nuScenes is a large-scale dataset collected for au- tonomous driving using Velodyne HDL-32E. It has ten subsets, each containing 85 scenes. For training, we ex- tract the first 100 frames from the first 12 scenes the first five subsets, resulting in 6,000 frames. For testing, we select the first 90 frames from the first scene of each of the last five subsets, yielding 450 frames numbered as sequences #01 to #05. Also, we quantize them to I mm geometric precision with 8-bit reflectance.
4. Experiment and Analysis
4.1. Datasets
We conducted experiments on well-known LiDAR datasets, including KITTI (Behley et al., 2019), Ford (Pandey al., 2011), and nuScenes (Caesar et al., 2020).
4.2. Experimental Details
Training Setting. We implement SerLiC using Python 10 and PyTorch 2.5. The model is trained with AdamW optimizer (Loshchilov & Hutter, 2019), using a learning rate of 2 x 10-4 and a batch size of 64. We employ a cosine annealing strategy (Loshchilov & Hutter, 2017) to gradually reduce the learning rate to 5 10 The model is randomly initialized and trained for 25 epochs for each dataset. For
KITTI or SemanticKITTI is large-scale LiDAR dataset used for semantic scene understanding. It con- tains 22 sequences, a total of 43,552 frames of outdoor scenes collected using the Velodyne HDL-64E LiDAR
6

---

Efficient LIDAR Reflectance Compression via Scanning Serialization
fair comparisons, all experiments are conducted on the same platform, equipped with an NVIDIA RTX 4090 GPU, Intel Core 19-13900K CPU, and 64GB of memory.
Table 3. Computational complexity on the KITTI dataset
The optimization objective is to minimize the bit rate (as defined in Equation (2)) for transmitting reflectance data, which can be further formulated as the negative log- likelihood of the observed reflectance values across all se- quences & [1, L] and points i Ni]:
METHOD
MEM.
PARAM.
ENC.
DEC.
RAHT
0.36s
0.34s
PREDLIFT
0.748
0.748
UNICORN
4.36GB
111.5M
2.77s
2.77s
SERLIC
0.41GB
2.2M
0.095"/0.18%
0.13s"/0.23s
SERLIC (LIGHT)
0.25GB
111K
0.035' 10.088
0.038' 70.098
RESULTS OF USING THE FRAME-LEVEL PIPELINING
L
N
4.4. Computational Complexity
C =-
log2 a (21, contexty,)), (10)
Table 3 reports the computation complexity. SerLiC uses only 2.2M parameters, which is only 2% of Unicomn
where Pt, (Z),5 .ontexti.)) denotes the conditional prob- ability estimated by the parametrized entropy model pi, based on the contexti,s-
For runtime, the coding process consists of three compo- nents: context construction (CC, on CPU), neural network (NN, on GPU), and arithmetic coding (AC, on CPU). By uti- lizing a three-stage pipeline in frame-leved to arrange these components, SerLiC achieves an encoding/decoding speed of 0.09/0.13 seconds per frame, i.e., 117 frames per sec- ond (fps). Even when the frame-level pipelining is disabled, the total runtime remains much shorter (< 10%) than that of Unicorn, achieving 4 to 5 fps. Note that our experiments use point clouds with 18-bit geometry. containing hundreds of thousands of points per frame. If applied to 12-bit point clouds, which have far fewer points but still maintain high accuracy for downstream tasks, the coding speed of SerLiC would be even faster. Detailed runfime and analysis can be found in supplementary material.
Testing Setting. The testing conditions strictly follow the CTC of MPEG AI-PCC (WG 07 MPEG 3D Graphics Cod- ing and Hapties Coding, 2024a). Quantitative analysis is measured in bits per point (bpp) and compression ra- tio (CR). The latest G-PCC version TMC13v233, which provides state-of-the-art performance through RAHT and Predlift modes, is compared. Also, we compare SerLiC with L3C2 (Sébastien & Jonathan, 2021), a profile dedicated for LiDAR PCC in MPEG, and existing learning-basse Uni- corn (Wang et al., 2025) which shows superior performance on LiDAR reflectance compression. All methods are evalu- ated under the same training/testing datasets and conditions.
Regarding GPU memory usage, SerLiC uses 0.41 GB while Unicorn uses 4.36 GB, only 9% of Unicorn. The number of parameters in SerLiC is also much smaller: the full version is only 2% of Unicorn while the light version only 1%c All these confirm the ultra-low complexity of SerLiC.
4.3. Compression Performance
Table 1 presents a detailed comparison of the overall bit rate and CR gains of SerLiC against G-PCC (RAHT), G-PCC (Predliftt, and Unicorn. As observed, SerLiC consistently outperforms both G-PCC (RAHT) and G-PCC (Predlift), across all three tested datasets, e.g., SerLiC rivals G-PCC (RAHT) by 25.41%, 25.58% and 29.81% on three datasets. Substantial performance improvements (17.08% on KITTI and 22.74% on Ford) are observed when compared with Unicorn and L3C2 (21.63% on Ford).
4.5. Ablation Study
Ablation studies are conducted on the KITTI dataset to validate SerLiC. Default settings are marked in gray. The results of CR gains are over the G-PCC (RAHT) anchor.
Notice that the compression of L3C2 relies heavily on de- tailed parameters of LiDAR sensor, including the elevation angle of each laser sensor and their respective distances to the LiDAR center. For datasets lacking such sensor data, such as KITTI and nuScenes, L3C2 cannot function effec- tively (hence we only provide L3C2 results on Ford). In contrast, SerLiC only requires basic LiDAR information such as the angular resolutions of the elevation and azimuth angles. These results are evidence of the superior effective- ness of SerLiC as well as its robustness on various datasets.
Table Ablation study on contextual construction
VARIANTS
+ F
+ FF
FN
BPP
CR GAIN
X
X
3.92
-19.67%
x
/
3.82
-21.72%
/
X
3.73
-23.57%
3.64
-25.41%
Contextual Construction. When certain components are disabled, we expand the embedding dimension of the re- maining components to ensure that the overall network di-
Pttpps:// //gi thub. com /MP RGGroup/ soeeg-pcc-toe tmcl3
7

---

Efficient LIDAR Reflectance Compression via Scanning Serialization
mension entering the Mamba block remains consistent. In Table 4, when both radial distance and scanning index em-
Docoding Latenoy 10)
AAttention
(GB)
GRU Memory (GB)
aMamba
beddings are disabled, the only available prior information is the reflectance of previous point F 1- As result, the
30
20
gains decrease from 25.41% % to 19.67%. Also, disabling
10
either FỤ or F causes loss. These results confirm the significance of utilizing LiDAR physical information.
64
120
256
512
1024
64
128
258
512
1024
Window Size
Window Size
(a) Decoding Latency
(b) Maximum GPU Memory
Table Ablation study on parallel window size, maximum GPU memory (Mem.), neural network (NN) latency (encod- ing/dccoding), and total coding time (encoding/decodiing
Figure 4. Comparison of decoding latency and running memory between Attention and Mamba implementations in SerLiC
SIZE
BPP
MEM. (GB)
NN (S)
TOTAL (S)
increases from 64 to 512. These results are in line with the general expectation that larger models have better capacity. However, higher complexity is required.
64
3.68
0.61
0.08/0.09
0.16/0.18
128
3.64
0.41
0.09/0.13
0.18//.23
256
3.64
0.32
0.17/0.28
0.32/0.43
512
3.64
0.29
0.33/0.57
0,57/0.60
Attention-based Coding. Attention represents another prevalent neural structure for sequence modeling, as ex- emplified by OctAttention (Fu et al., 2022) and Point Trans- former v3 (Wu et al., 2024). For fair comparison, we sub- stitute the original Mamba module with Masked Multi-Head Attention (Vaswani et al., 2017) (detailed in Figure 3(b)) while maintaining strict consistency in key hyperparame- ters, such as the number of layers and channels. We ob- serve that using Attention receives similar coding gains to Mamba but higher complexity. Figure 4 illustrates the com- plexity of Attention brased and Mamba-based SerLiC under different configurations. It is observed that Attention has much higher complexity, in both decoding delay and GPU memory consumption. This is because Attention computes point dependencies within a sequence window W via matrix multiplication, resulting in quadratic complexity O(W)). Partieularly for decoding which requires point-by-point pro- cessing in window, the complexity increases to ((W*).
1024
3.66
0.28
0.66/1.03
1.07/1.43
Window-based Parallelization. We further investigate the impact of window-based parallelization encod- ing/decoding runtime and memory usage. For fair com- parisons, we disable the frame pipelining in this ablation. Table 5 presents the results for various parallel window sizes, ranging from 64 to 1024. Increasing the window size substantially extends the encoding and decoding time due to the involvement of more autoregressive steps in a win- dow. At the same time, GPU memory usage decreases the number of parallel windows is reduced. As a result, we set the window size as 128 by default for SerLiC, striking balance between coding time and memory consumption.
Table 6. Ablation study on the Mamba network
MODULE
SETS.
PARAM.
BPP
CR GAIN
In contrast, Mamba consistently maintains linear computa- tional complexity. For example, when the parallel window size is 128, Attention needs 5.12 seconds and 1.46 GB mem- ory for decoding a KITTI point cloud, while Mamba needs only 0.23 seconds with 0.41 GB memory, which is much more beneficial for resource-constrained applications.
488K
3.84
-21.31%
1.4M
3.68
-24.48%
LAYERS
5
2.2M
3.64
-25.41%
7
3.1M
3.61
-26.02%
64
176K
3.76
-22.5%%
128
609K
3.69
-24.39%
DIMENSION
256
2.2M
3.64
-25.41%
Light Version. A light version of SerLiC is implemented by reducing the network dimension of standard SerLiC from 256 to 64, the number of layers from 5 to 3, and window size from 128 to 32. As presented in Table 1, our SerLiC (light) receives 6.67% loss against the standard version on the KITTI dataset. However, the computational complexity is greatly reduced, with the number of parameters substan- tially decreased to just 111K over 1,000 smaller than that of Unicorn and the coding speed at > 10 fps (0.08-0.09 seconds per frame) even without the frame pipelining imple- mentation. Under the frame-level pipeline, SerLiC (light) achieves a real-time processing speed of over 30 fps (0.03 seconds per frame).
512
8.6M
3.61
-26.02%
Mamba Network. To study the effect of the number of Mamba layers on model performance, we conducted experi- ments with layer configurations {1, 3, 5, 7]. For each con- figuration, the network dimension is set to 256. As shown in Table 6, the CR gains against G-PCC (RAHT) increase from 21.31 % to 26.02% as the number of layers increases from to 7. We also investigate the impact of network dimension by setting it to (64, 128, 256, 512}. Here, the number of Mamba layers is fixed at 5. As reported in Table 6, the gains increase from 22.95% to 26.02% as the network dimension
8

---

Efficient LiDAR Reflectance Compression via Scanning Serialization
Impact Statement
Table Quantitative compression gains against G-PCC (RAHT)
on the non-rotational dataset
This paper presents work whose goal is to advance the field of Machine Learning. There are many potential societal consequences of our work, none which we feel must be specifically highlighted here.
BPP
CR GAIN vs. RAHT
CLASS
SEQ.
RAHT
SERLIC
02
3.71
2.28
-38.54%
INNOVIZQC
03
3.82
2.25
-41.10%
A Acknowledgments
AVG.
3.77
2.27
-39.71% %
This research was supported by National Natural Science Foundation of China (62171174) and Natural Science Foun- dation of Jiangsu Province (BK20243038), We are grateful to the anonymous reviewers for comments early drafts of this paper. We also extend our sincere thanks to the authors of the relevant works used in our comparative studies for providing the latest results for evaluation.
Table Quantitative compression gains against G-PCC (RAHT) on various density scenarios in KITTI
BPP
CR GAIN vs. RAHT
CLASS
SEQ.
RAHT
SERLIC
CITY
4.67
3.24
-30.62%
RESIDENTIAL
4.96
3.81
-23.19%
KITTI
ROAD
5.41
4.39
-18.85%
CAMPUS
4.86
3.43
-29 299.42%
References
AVG.
4.98
3.72
-25.28%
Baur, S. A., Moosmann, F,, and Geiger, A. LISO: LiDAR- only self-supervised 3D object detection. In European Conference on Computer Vision, pp. 253-270. Springer, 2025.
Non-Rotational Adaptation. In addition to the KITTI, Ford, and nuScenes datasets that are captured by rotational LiDAR scanning, we also evaluated SerLiC on the non- rotational InnovizQC dataset provided by MPEG. It pro- vides three sequences, each having 300 frames with 16-bit coordinates and 8-bit reflectance. We used the first sequence for training and the remaining two for testing. Results shown in Table 7 demonstrate SerL.C''s strong generalization capa-
Behley. J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., and Gall, J. SemanticKITTI: A dataset for semantic scene understanding of LiDAR sequences. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 9296-9306, 2019.
bility to non-rotational LiDAR systems.
Caesar, H., Bankiti, V., Lang, A. H., Vora, S., Liong, V. E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. nuScenes: A multimodal dataset for autonomous driving. Proceedings of the IEEF/CVF Conference on Computer Vision and Pattern Recognition, pp. 11621- 11631, 2020.
Scenario Robustness. To further validate the robustness of SerLiC, we provide its detailed performance on KITTI across various scenarios, as reported Table 8. Results indicate that SerLiC consistently outperforms G-PCC in all scenes. Relatively, SerLiC achieves better performance in City and Campus scenes compared to Residential and Road scenes. This occurs due to dense roadside vegetation interfering with sensor-recorde reflectance characteristics and thus increasing compression difficulty.
Chen, L., Wu, P., Chitta, K., Jaeger, B., Geiger, A., and Li, H. End-to-end autonomous driving: Challenges and frontiers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024.
5. Conclusion
Choy, C., Gwak, J., and Savarese, S. 4D spatio-temporal convnets: Minkowski convolutional neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pateer Recognition, pp. 3075-3084, 2019.
This paper presents SerLiC, a serialization-basee neural compression framework tailored for LiDAR reflectance at- tribute. By leveraging scan-order serialization, SerLiC trans- forms 3D point clouds into ID sequences, aligning with LiDAR scanning mechanisms and enabling efficient sequen- tial modeling. The Mamba model with physics-inforee informed tokenization further enhances its ability to capture points comelations autoregressively, while maintaining linear-time complexity. Its high efficiency, ultra-low complexity, and strong robustness make a practical solution for real Li- DAR applications. Future work will extend SerLiC to lossy compression for higher compression efficiency.
De Queiroz, R. L. and Chou, P. A. Compression of 3D point clouds using a region-adaptive hierarchical transform. IEEE Transactions on Image Processing, 25(8):3947- 3956, 2016
Fang, W., Huang, X., Zhang, F., and Li, D. Intensity correc- tion of terrestrial laser scanning data by estimating laser transmission function. IEEE Transactions on Geoscience and Remote Sensing, 53(2):942-99 -951,, 2014.

---

Efficient LIDAR Reflectance Compression via Scanning Serialization
Fu, C., Li, G., Song, R., Gao, W., and Liu, S. OctAttention: Octree-based large-scale contexts model for point cloud compression. In Proceedings of the AAAI conference artificial intelligence, volume 36, pp. 625-633, 2022.
MPEG 3D point cloud compression. IEEE Transactions Broadcasting, 66(3):701-717, 2020.
Liu, X., Zhang, C., and Zhang, L. Vision Mamba: comprehensive survey and taxonomy. arXiv preprint arXiv:2405.04444, 2024.
Gu, A. and Dao, T. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint 17X10:2312.00752. 2023.
Liu, Z., Yang, X., Tang, H., Yang, S., and Han, S. Flat- Former: Flattened window attention for efficient point cloud transformer. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, PP. 1200-1211, 2023.
Han, X., Tang, Y., Wang, 7., and Li, Mamba3DD: En- hancing local features for 3D point cloud analysis via state space model. In Proceedings of the 32nd ACM In- ternational Conference Multimedia, pp. 4995-5004, 2024.
Loshchilov, I. and Hutter, F. SGDR: stochastic gradient de- scent with warm restarts, In 5th International Conference on Learning Representations, ICLR 2017,2017
Hilbert, D. Dritter Band: Analysis- Grundlagen der Mathematik- Physik Verschiedenes: Nebst Einer Lebens- geschichte. Springer- Verlag, 2013.
Loshchilov, I. and Hutter, F. Decoupled weight decay regu- larization. In 7th International Conference on Learning Representations, ICLR 2019, 2019.
Hu, S., Liu, W., Li, M., Zhang, Y., Liu, X., Wang, X., Zhang, L. Y., and Hou, PointCRT: Detecting backdoor in 3D point cloud via corruption robustness. In Proceedings of the 31st ACM International Conference on Multimedia, pp. 666 100,67,222 2023.
Mammou, K., Tourapis, A., Kim, J., Robinet, F,, Valentin, V., and Su, Y. Lifting scheme for lossy attribute encod- ing in TMC1. Document ISO/TE JTC1/S22//GG1 m42640, San Diego, CA, US, 2018.
Kong, L., Liu, Y., Chen, R., Ma, Y., Zhu, X., Li, Y., Hou, Y., Qiao, Y., and Liu, Z. Rethinking range view represen- tation for LiDAR segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 228-2408 2023.
Morton, G. M. A computer oriented geodetic data base and a new technique in file sequencing. 1966.
Nguyen, D. T. and Kaup, A. Lossless point cloud geometry and attribute compression using a learned conditional probability model. IEEE Transactions on Circuits and Systems for Video Technology, 33(8):4337-4344 2023.
Lang. A. H., Vora, S., Caesar, H.. Zhou, L.. Yang, J., and Beijhom, O. PointPillars: Fast encoders for object detec- tion from point clouds. In Proceedings of the IEEE/CVF conference computer vision and pultern recognition, pp. 12697-12705, 2019.
Nguyen, D. T., Nambiar, K. G., and Kaup. A. Deep proba- bilistic model for lossless scalable point cloud attribute compression. In ICASSP 2023-202. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1-5, IEEE, 2023.
Li, G., Gao, W., and Gao, W. Point Cloud Compres- sion: Technologies and Standardization. Springer Nature, 2024.
Li, Y. and (banez-Guzman J. LiDAR for autonomous driv- ing: The principles, challenges, and trends for automotive LiDAR and perception systems. IEEE Signal Processing Magazine, 37(4):50-61, 2020.
Pandey, G., McBride, J. R., and Eustice, R. M. Ford campus vision and LiDAR data set. International Journal of Robotics Research, 3044351553--1552 2011.
Raj. T., Hanim Hashim, F., Baseri Huddin, A., Ibrahim, M. F., and Hussain, A. A survey on LiDAR scanning mechanisms. Electronics, 9(5):441, 2020.
Liang, D., Zhang, C., Zhang, P., Liu, S., Li, H., Niu, S., Rao, R. Z.. Zhao, L., Chen, X., Li, H., al. Evolution of laser technology for automotive LiDAR, industrial viewpoint. nature communications. 15(1):7660, 2024a.
Sébastien, L. and Jonathan, T. A point cloud codec for Li- DAR data with very low complexity and latency. ISO/IEC JICI/SC29/WG0 MPEG, 2021.
Liang, D., Zhou, X., Xu, W., Zhu, X., Zou, 7 Ye, X., Tan, X., and Bai, X. PointMamba: simple state space model for point cloud analysis. In Advances in Neural Information Processing Systems, 2024b.
Tatoglu, A. and Pochiraju, K. Point cloud segmentation with LiDAR reflection intensity behavior. In 2012 IEEE International Conference on Robotics and Automation, pp. 786-790. IEEE, 2012.
Liu, H., Yuan, H., Liu, Q., Hou, J., and Liu, J. A compre- hensive study and comparison of core technologies for
10

---

Efficient LiDAR Reflectance Compression via Scanning Serialization
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L.,, Gomez, A. N., Kaiser, t.., and Polosukhin, I. Atten- tion is all you need. In Advances in Neural Information Processing Systems, volume 30, 2017.
Zhang, J., Wang, J., Ding, D., and Ma, Z. Scalable point cloud attribute compression. IEEE Transactions Mul- timedia, 2023.
Zhang. T., Yuan, H., Qi, L.,, Zhanng, J., Zhou, Q., Ji, S., Yan, S., and Li, X. Point Cloud Mamba: Point cloud learning via state space model. AAAI, 2025.
Viswanath, K., Jiang, P., and Saripalli, S. Reflectivity is all you need!: Advancing LiDAR semantic segmentation. arXiv preprint arXiv:2403.. /3888, 2024.
Zhang, W., Yang. F., Xu, Y., and Preda, M. Standardization status of MPEG geometry- -bseed point cloud compression (G-PCC) edition In 2024 Picture Coding Symposium (PCS), pp. 1-5. IEEE, 2024b.
Wang, J., Ding, D., and Ma, Z. Lossless point cloud attribute compression using cross-scale, cross-group, and cross- color prediction. In 2023 Data Compression Conference (DCC). pp. 228 227, 2023.
Wang. J., Xue, R., Li, J., Ding, D., Lin, Y., and Ma, Z. ver- satile point cloud compressor using universal multiscale conditional coding- part ii: Attribute. IEEE Transac- tions Pattern Analysis and Machine Intelligence, 47 (1):252-200) 268, 2025. doi: 10.1 10//////M.
Wang, P.-S. OctFormer: Octree-based transformers for 3D point clouds. ACM Transactions on Graphics (TOG), 42 202-11,,0023
Wang, Z. Chen, Z., Wu, Y., Zhao, Z. Zhou, L., and Xu, D. PoinTramba: A hybrid transformer-mamba framework for point cloud analysis. arXiv preprint arXiv:2405. 2024.
WG 07 MPEG 3D Graphics Coding and Haptics Coding. CTC on AI-based point cloud coding. Output document N01058, ISO/IBC JTC1/SC29/WG7, 148th MPEG meet- ing, Kemer, November 2024a.
WG 07 MPEG 3D Graphics Coding and Haptics Coding. Common test conditions for G-PCC. Output document N00944, ISO/EE JTC1/SC22/WG7, 147th MPEG meet- ing, Sapporo, September 2024b.
Wu, X., Jiang, L.,, Wang, P.-S., Liu, Z., Liu, X., Qiao, Y., Ouyang, W., He, T., and Zhao, H. Point Transformer V3: Simpler faster stronger. In Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pp. 4840 4851, 2024.
You, K., Gao, P., and Ma, Efficient and generic point model for lossless point cloud attribute compression. arXiv preprint axXhv:240.006936, 2024.
Zeng, K., Shi, H., Lin, J., Li, S., Cheng, J., Wang, K., Li, Z., and Yang, K. MambaMOS: LiDAR- based 3D mov- ing object segmentation with motion-aware state space model. In Proceedings of the 32nd ACM International Conference on Multimedia, pp. 1505-1513, 2024.
Zhang, G., Fan, L., He, C., Lei, Z., Zhang, Z., and Zhang, L. Voxel Mamba: Group-free state space models for point cloud based 3D object detection. arXiv preprint arXiv:2406. 10700, 2024a.

---

Efficient LiDAR Reflectance Compression via Scanning Serialization
A. Testing Dataset Details
Three widely accepted datasets, including KITTI, Ford, and nuScenes, are used as testing datasets this work. visualize typical samples from these three datasets in Figure 1. Accordingly. Table 1 provides their detailed information.
(a) KITTI
(b) Ford
nuScenes
Figure 1. Visualization of samples in KITTI, Ford, and nuScenes LiDAR point cloud datasets. The color indicates the value of reflectance, ranging from blue (low) red (high).
Table 1. Detailed information of testing datasets
CLASS
SEQ.
FRAMES
REFLECTANCE VALUE
POINTS PER FRAME
11
90
0-99
124,130
12
90
0-99
110,921
13
90
0-99
111,997
0-99
124,628
15
90
0-99
122,315
KITTI
16
90
0-99
125,622
17
90
0-99
113,687
18
0-99
124,873
19
90
0-99
121,239
20
90
0-99
116,382
21
90
0-99
123,307
02
1500
0-255
83,834
FORD
03
1500
0-255
84,063
01
90
0-255
29,606
02
90
0-255
29,568
NUSCENES
03
0-255
27,560
0-255
26,854
05
90
0-255
30,478
B. Complexity and Analysis
All the computational complexity reported in the following derived experiment platform equipped with an NVIDIA RTX 4090 GPU, an Intel Core 19-03K CPU, and 64GB of memory.
B.I. SerLiC Runtime Analysis
The coding process of SerLiC consists of three stages: context construction (CC, executed on CPU), neural network (NN, executed on GPU), and arithmetic coding (AC, executed CPU). These three stages work following the proposed Dual Parallelization mechanism when processing frame, including the sequence level and the window level, as illustrated in Figure 2(a). Each sequence divided into windows of equal size and all windows are processed in parallel.
Based on the above Dual Parallelization within a frame, we offer two ways to implement SerLiC across point cloud frames:
12

---

Efficient LiDAR Reflectance Compression via Scanning Serialization
Window
S. W
0
1D point sequences
S: W
W
O
O
W
5 W
O
W O
W
S W,
W O
W
S, W o
W O
W
S, W, O
5, W,
O
5 W
(a) Details of Dual Parallclization in Frame
Frames
Frames
(72)
1.9
2.4
1.2
2.1
Elepsed Time (8)
Elapsed Time
(b) Example of w/o Frame-level Pipeline
(c) Example of w/ Frame-level Pipeline
Figure (a) Proposed Dual Parallelization within a frame. (b) Sequential implementation across frames. (c) Frame-level pipeline across frames. S; indicates the point sequence. W; denotes the window. F; denote the i-th point cloud frame. We use SerLiC (light) show the examples in (b) and (c).
Frame-level sequential implementation. We implemented a frame-level sequential version of SerLiC, running point cloud frames sequentially on our platform, without any pipeline structure, as illustrated in Figure 2(b). As reported in Table and Figure 3(a)-(b), our SerLiC attains 0. 1/0.23 seconds per frame for encoding/decoding. i.e., >5/4 fps (frames per second). SerLiC (light) achieves faster speed of 10 fps. Even using the sequential version, our SerLiC achieves saate-of-th-ass processing speed. Its light version meet typical demands in applications such as conventional autonomous driving, LiDAR-bases based mapping, and geospatial surveying.
Frame-level pipeline implementation. To accelerate processing, we arrange CC, NN, and AC on three pipeline stages for frame-level parallelism, as illustrated in Figure 2(c). As presented in Table and Figure 3(c)-(d), using the three-stage pipelining across frames, SerLiC runs approximately at speed of 0.09/0.13 seconds per frame for encoding/decooing. i.e., 11/7.7 fps. The lightweight version of SerLii,, Serl.iC (light), is even faster, achieving over 30 fps, meeting the real-time requirements of many LiDAR-related applications like real-time obstacle detection in autonomous driving, virtual and augmented reality (VR/AR), and high-fidelity 3D reconstruction.
Table Analysis of encoding/decnding time (seconds per frame), including context construction (CC) latency, neural network (NN) latency, and arithmetic coding (AC) latency
METHOD
CC (S)
NN (s)
AC (S)
ToTAL (8) w/ PIPELINE
TOTAL (S) W/O PIPELINE
SERLIC
0.03/0.03
0.09/0.11
0.06/0.07
0.09/0.13 0.03/0.03
0//8/3
SERLIC (LIGHT)
0.03/0.03
0.02/0.03
0.03/0.03
0.08/0.09
Table 3. Quantitative compression results of Attention- and Mamba-based implementations
BPP
CR GAIN vs. ATTENTION- BASED
CLASS
ArTENTION-BASED
MAMBA-BASED
KITTI
3.80
3.64
-4.21%
13

---

Efficient LiDAR Reflectance Compression via Scanning Serialization
G-PCC (Predlit) FPS)
G-PCC (Prediit) (- FPS)
Unicam (5 FPS)
Unicom (s FPS)
SerLiC FPS)
(-4 FPS)
SerLICT (- FPS)
SerLICT FPS)
0
5 10
15
20 25
40
45
50
0
5
10
15
20
25
40
45
50
Encoding Speed w/o Frame-level Pipeline
Decoding Speed w/o Frame-level Pipeline
G-PCC (Predift) :- FPS)
GPCCC (Predlit) (- FPS)
Unicom
Unicom
SerLIC 11 FPS)
SerLIC FPS)
SerLICt (- FPS)
SerLIC1 (- FPS)
0
5
10
15
20
25
30
35
40 45
50
0
5
10
15
20
25
30
35
40
45
50
(c) Encoding Speed w/ Frvee-level Pipeline
(d) Decoding Speed w/ Frame- level Pipeline
Figure 3. The coding speed of SerLiC w/o and w/ frame-level pipeline implementation.
Table Computational complexity of Attention-based SerLiC different window sizes. Maximum GPU memory (Mem.) and encoding/decoddig time (seconds per frame) are analyzed. CC, NN, and AC refer to context construction, neural network, and arithmetic coding latencies, respectively.
SIZE
MEM. (GB)
CC (S)
NN (s)
AC (S)
TOTAL(S)
64
1.31
0.03/0.03
0.03/2.14 14
0.04/0.00
0.10/2.21
128
1.46
0.03/0.03
0.04/5.02
0,06/0.07
0.13/5.12
256
2.03
0.03/0.03
0.05/13.2
0.09/0.09
0. 0.17/13.3
512
3.23
0.03/0.03
0.09/39.8
0.18/0.18
0.30/40.0
1024
5.84
0.03/0.03
0.15/137
0.32/0.33
0.50/138
B.2. Attention Performance and Complexity
As described in the main manuscript, Attention can also be used in SerLiC for context modeling. However, compared to Attention, Mamba is more suited to our framework due its inherent ability capture point dependencies in a sequence.
To support our claim above, we provide detailed analysis of both the coding performance and computational complexity when using Attention. This allows readers to better understand the comparative impact of Attention and Mamba on the overall system performance.
Implementation Details. Given the parallel window size W, the computational complexity self-attention mechanism ((W?) due the matrix-based multiplication computation between Q. K, and V in self-attention. As we use the autoregressive coding method, causal mask with the size of W W (as indicated in Figure 3b of main manuscript) is applied to ensure that each token in the sequence only uses its preceding tokens (its following tokens unavailable as they are not coded yet) for context modeling. During the encoding process, since all points are known the encoder, the model can process all tokens in parallel, and the complexity is accordingly O ( However, during decoding, to generate an output sequence consistent with the encoding, the model must decode each point sequentially in a window W. As such, the attention mechanism results in a cubic time complexity (W'3) in decoding.
By contrast, the autoregressive nature Mamba naturally aligns with our SerLiC in the ID point sequence. Its complexity linear to the window size, i.e., ((W). Therefore, using Mamba is more efficient in our implementation.
Coding Performance. Table 3 reports the compression performance of Attention the KITTI dataset when using the same window size (128) and parameter settings as Mamba. It is observed that Mamba even attains better results than Attention on KITTI, gaining 4.21% % on average.
Complexity. Table 4 details the computational complexity of using the Attention block instead of Mamba SerLiC. For fair comparisons, we list the runtime of sequential implementation (w/o frame pipelining). In comparison with Table in our
14

---

Efficient LIDAR Reflectance Compression via Scanning Serialization
main manuscript where the computational complexity of Mamba is presented, we observe that using Attention requires only much higher GPU memory (1.46 GB 0.41 GB when window size is 128) but also longer processing time (0. 10.13/5.12 vs. 0.16/0.18 seconds per frame when window size is 128). Obviously, for the encoding time, Attention is comparable to Mamba. But for the decoding time, Attention is much longer, 28x of Mamba.
B.3. L3C2 Analysis and Complexity
Limited by pages, the details of the L3C2 method are not provided in our main manuscript. In the following section, we will elaborate on L3C2 and its complexity for comparison with our SerLiC
L3C2 Introduction, MPEG introduced 1,322 (Sébastien & Jonathan, 2021) in 2021, specifically tailored for the compression of LiDAR point clouds. However, its dependence on detailed sensor configurations (e.g., the precise pitch angles of individual lasers) limits its applicability on various datasets. Specifically, L3C2 requires the following inputs (refer Figure 4):
(1) numlasers: the number of laser sean lines from the LiDAR:
(2) lasersTheta: the tangent of the pitch angle for each line, where negative values indicate scan direction towards the ground, and positive values indicate scan towards the sky;
(3) lasersZ: the distance from the laser head to the LiDAR center for each line, measured in millimeters;
(4) lasersNummPhiPerTunr the number of points each line can scan in one full rotation.
For readers' convenience, we capture the configuration of L3C2 codec in Figure 4 to demonstrate the parameters required by L3C2. Since MPEG provides detailed LiDAR configuration parameters only for the Ford dataset, we are unable to test on KITTI and nuScenes in this work. Moreover, these parameters are only applicable to raw, unprocessed LiDAR point cloud data. However, the point cloud samples in nuScenes and KITTI datasets have undergone preprocessing before release. As result, even if their LiDAR parameters could be derived, they would not be compatible with these preprocessed samples.
numLasers:
lasersTheta: -0.461611, -0. -0.451281, -0. 440090, -0. 430000, -0. 418945, -0. -0.408657, -0.398
lasersz: 900000, 26. 600000, , 28.300000 24. 600000, 26. 200800, 100000, 24. 84.00000,
LasersNumPhiPerTuum 500, 800, 800, 800, 800, 800, 800, 800, 800, 800, 800,
Figure 4. Contiguration of L3C2 codec, where four parameters obtained from LiDAR physical parameters.
Table
Runtime comparison against L3C2 on the Ford dataset
METHOD
ENC.
DBC.
L3C2
0.11s
0.06s
SERLIC
0.19s
0.22s
SERLIC (LIGHT)
0.08s
0.08s
Complexity. In Table 2 of our main manuscript, we compare the coding performance of SerLiC with that of L,C2. Furthermore, we compare their runtime in Table 5. It is observed that our SerLiC (light) runs slightly faster than L3C2 in encoding process, while slightly lower decoding. Notice that L3C2 is implemented using C language and well optimized on CPU, whereas our SerLiC uses Python language and executes on both CPU and GPU. Thus, this comparison just serves reference for intuitive observation about the runtime of both methods.
C. G-PCC Configuration
G-PCC offers an optional configuration, called the Angular mode, which includes detailed LiDAR parameters similar to those used in L3C2. Since these parameters are only available on the Ford dataset, we enable the Angular mode only when testing on the Ford dataset, and disable it for KITTI and nuScenes datasets.
15

---

Efficient LiDAR Reflectance Compression via Scanning Serialization
Table 6. Detection results using the classical detection models w/ and w/o reflectance on the KITTI dataset
CAR MOD.
PED. MOD.
CYC. MOD.
MAP
METHODS
EASY
HARD
EASY
HARD
EASY
HARD
MOD.
POINTPILLAR (W/
87.75
78.40
75.18
57.30
$1.41
46.87
81.57
62.81
58.83
64.21
POINTPILLAR (W/o R)
83.85
74.21
70.36
21.56
14.08
12.83
47.79
34.28
32.13
40.86
SECOND (W/ R)
90.55
81.61
78.61
55.95
$1.15
46.17
82.97
66.74
62.78
66.50
SECOND (W/O R)
87.87
78.92
75.58
40.46
35.66
31.88
70.42
50.64
47.70
55.07
POINTRCNN R)
91.47
80.54
78.05
62.96
$5.04
48.56
89.17
70.89
65.64
68.82
POINTRCNN (W/O R)
88.03
77.00
72.85
42.90
35.28
30.68
56.58
42.51
40.07
51.60
D. Downstream Tasks
In our introduction, we mention that "...our experiments show that removing the reflectance causes dramatic drop in pedestrian (and cyclist) detection accuracy incorporating the pre-trained PointPillar (Lang al., 2019) detection model, having AP (average precision) from 51.4 (62.8) 14.1 (34.3) the KITTI dataset (see Figure I).." To support our statement, we offer our full results on the KITTI dataset in Table 6.
The results in Table 6 provide a detailed comparison of the detection performance of three classical models PointPillar, SECOND, and PointRCNN- wwtt and without reflectance data. Note that our experiments are conducted on pre-trained models. The table shows the Average Precision (AP) for three categories: Car, Pedestrian (Ped.), and Cyclist (Cyc.), across different difficulty levels (Easy, Moderate, Hard), as well the mean Average Precision (mAP) for the Moderate category.
It is observed that, reflectance data is essential for maintaining high accuracy and robustness in object detection tasks, particularly for pedestrians and cyclists. The substantial drop in AP for these categories underscores the importance of preserving reflectance information in point cloud data for downstream applications.
(a) Overall
Zoom-in (region)
(c) Zoom-in (line)
Figure 5. Intuitive observation on reflectance characteristies in KTTTI. (a) shows holistic perspective. (b) shows zoom-in the region details. (c) further zooms the reflectance each sensor line observation. The color indicates the value of reflectance, ranging from blue (low) to red (high).
Table Ablation study on sequence-level parallelization: exploiting correlations across sequences. "Intra" indicates intra-sequence coding, while "Inter (1)" and "Inter (3)" represent inter sequence coding based on the preceding three sequences, respectively.
BPP
MEM. (GB)
NN (s)
TOTAL (S)
INTRA INTER (1)
3.64
0.41
0.09/0.13 13
0.18/0.20 23
3.64
0.04
5.04/5.82
5.20/9.88 884
INTER (3)
3.63
0.04
5.25/6.18
5.48/10.16
E. More Ablation Studies
Sequence Parallelization. In Section 3.4-Dual Parallelization, we propose the dual parallelization scheme. including sequence level and window level, to accelerate coding speed of SerLiC within frame. The ablation of window-level parallelization is presented in Section 4.5 Window-based Parallelization. Limited by page number, we depict the ablation study of Sequence-level Parallelization here.
16

---

Efficient LiDAR Reflectance Compression via Scanning Serialization
On the other hand, in Section 3.4 Dual Parallelization, state that "SerLiC processes point sequences in parallel, disregarding correlations across sequences. This is justified, point sequences scanned by separate laser sensor rotations, inherently lacking strong inter-sequence dependencies. As result, sequence parallelization does not compromise coding efficiency but greatly enhances processing speed." Our ablation study here supports our statement above.
Figure 5 visualizes the reflectance of a point cloud frame. For better observation, we color the reflectance values using different colors. It clear that reflectance values are close to each other in a scan line while different to some extent across sean lines. This intuitively reveals that using point correlations within a scan line leads to better performance. Furthermore, we conduct experiments which exploit correlations not only across points in a sequence but also across sequences for reflectance compression. In this way, the sequence-level parallelization is disabled. That is, we apply the autoregressive coding both across sequences (called inter-sequence coding in the following) and within a sequence (called intru-sequence coding in the following).
Compression Performance. Table 7 presents the experimental results. When leveraging correlations between two sequences, i.e. using the previous sequence for context modeling while encoding the current one, no gain attained. The same holds when utilizing the previous three sequences. These findings is consistent with our observation that points are more strongly correlated within single sequence. This further validates the rationale behind our sequence-level parallelization approach, which effectively maintains high coding performance while keeping the complexity low.
Computational Complexity. As the inter-sequence coding disable sequence-level parallelism, its memory cost is reduced. As reported in Table 7, the memory usage of inter-sequence coding is only 0.04 GB. However, the runtime is accordingly remarkably increased due to the autoregressive coding across sequences. For example, the total encoding/decoding time 5.20/9..4 84 seconds per frame when using the previous sequence as context of the current sequence. When using three sequences, the coding time is even longer (5.48/10.16) longer time required by the context construction stage.
17


Reviews:
[{"review_id": "3CTK8ZuJY9", "reviewer": "Reviewer_GMXH", "summary": "This paper proposes an algorithm for LiDAR point compression using LiDAR reflectance and serialization. While many studies only use the point location which is one part of the LiDAR sensor measures, this work focuses on the necessity of the LiDAR reflectance that may involve the surface attribute, which is also one important factor for the various downstream tasks, such as 3D object detection task.\n\nAlong with this concept, this paper extends to use the Mamba architecture for more efficiency and propose the entropy coding in Section 3.4.\n\nThe authors provide experimental results in terms of pointcloud compression in Table 1, and this method presents a plausible compression ratio compared to the other studies.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your thoughtful feedback. Thank you for recognizing the *highly admirable compression benefit* presented in our study and acknowledging its potential to be *influential to future researchers* and *impactful to this field*. We highly value this opportunity to address your concerns.\n\n**1. Relation Between LiDAR Point Cloud Compression, LiDAR Reflectance Compression, and 3D Perception Task**\n\n**LiDAR point cloud compression** reduces data volume from 3D LiDAR scans, which consist of spatial coordinates (geometry) and reflectance values (attributes), each requiring distinct compression methods:\n\n- **LiDAR geometry compression** encodes coordinates into a compact bitstream.\n- **LiDAR reflectance compression** is dedicated to optimizing attribute data into another bitstream, using geometry as conditional prior.\n\nThe divergent statistical properties of geometric and reflectance data have historically motivated their independent compression solutions. This paper focuses on the **LiDAR reflectance compression**, significantly improving compression efficiency for reflectance data.\n\nExisting research predominantly advances LiDAR reflectance compression through technical refinements, while overlooking a fundamental systems-level inquiry: *\"Is reflectance compression necessary, and how does it impact downstream tasks?\"* We first address this gap through task-driven analysis, as illustrated in the introduction section and supplementary. Our evidence confirms that reflectance data significantly influences perception performance, thereby validating its compression and transmission necessity. Having established this critical premise, we subsequently propose the coding paradigm. Our contribution not only advances compression technology but also formally links reflectance fidelity to perception needs.\n\n**2. Logical Relation Between LiDAR Reflectance Compression and Mamba Architecture**\n\nWe sincerely thank the reviewer for the constructive feedback. We would like to clarify that in the compression domain, efficiency and performance are equally critical considerations. Specifically, the autoregressive coding employed in SerLiC is similar to the next token prediction in natural language processing (NLP). Autoregressive models are widely recognized for their exceptional contextual modeling capabilities [1][2], but their practical application has been constrained by the high computational complexity. While Mamba is not the only option for sequential modeling, our findings in Supplementary section C.2 demonstrate that Mamba exhibits much lower complexity-by several orders of magnitude-compared to attention-based architectures under equivalent layer stacking and channel dimensions. By combining Mamba’s linear complexity with our proposed dual-parallel strategy, SerLiC achieves real-time processing capabilities. This is the key reason for our choice of Mamba.\n\n**3. Quantitative Experiments for the Importance of the LiDAR Reflectance**\n\nFollowing the reviewer's suggestion, we have conducted additional experiments using another two classical models (SECOND and PointRCNN), with the experimental results presented in the table below (\"w/o R\" denotes the performance of the complete removal of reflectance information).\n\n||Car|Pedestrian|Cyclist|**mAP**|\n|:-|:-:|:-:|:-:|:-:|\n|PointPillar| 78.40 | 51.41 | 62.81 | **64.21** |\n|PointPillar (w/o R)| 74.21 | 14.08 | 34.28 | **40.86** |\n|SECOND| 81.61 | 51.15 | 66.74 | **66.50** |\n|SECOND (w/o R)| 78.92 | 35.66 | 50.64 | **55.07** |\n|PointRCNN| 80.54 | 55.04 | 70.89 | **68.82** |\n|PointRCNN (w/o R)| 77.00 | 35.28 | 42.51 | **51.60** |\n\nNonetheless, we respectfully clarify that the core contribution of our work lies in the development of a high-performance real-time reflectance compressor. The extensive utilization of reflectance in downstream tasks provides substantial validation for the significance of our work.\n\n**4. Compression Using Serialization Methods in Point Transformer v3**\n\nWhile Point Transformer v3 (PTv3) is designed for perception tasks and its shift-based patch interaction isn’t directly applicable to compression, its serialization method offers an alternative to our scan-order approach. Following the reviewer's suggestion, We conduct experiments in SemanticKITTI. Results show that Hilbert Curve (3.74 Bpp) and Z-order Curve (3.70 Bpp) underperform our scan-order serialization (3.64 Bpp), confirming the efficacy of our method. The detail table is available in [this anonymous website](https://anonymous.4open.science/r/11079-88B1/readme.md).\n\n[1] David Minnen, et al. \"Joint Autoregressive and Hierarchical Priors for Learned Image Compression.\" NeurIPS, 2018. \\\n[2] Chunyang Fu, et al. \"OctAttention: Octree-based large-scale contexts model for point cloud compression.\" AAAI, 2022.", "claims_and_evidence": "Overall, the authors well describe the importance of the LiDAR reflectance and the authors relate the LiDAR reflectance in the compression process using the Scan serialization. Serialization is recently used in the 3D scene understanding task, and this is also addressed in the Point Transformer v3 paper. In this perspective the authors well introduce this concept into the LiDAR point compression, so I believe that the overall claim is clear.", "methods_and_evaluation_criteria": "While the claim itself is clear, I wonder about the logical relation between LiDAR point compression and Mamba architecture.\n\nAs the title said, this paper is about LiDAR point compression using LiDAR reflectance and the Scan serialization. Here, the Scan serialization is not the only property that Mamba architecture pursues. In my understanding, Transformer architecture can also encapsulate pointcloud as a 1D array, which is addressed by Point Transformer v3. Accordingly, the necessity of the Mamba architecture looks redundant and irrelevant to what the authors originally addressed in the title and the beginning of the introduction section.\n\nIn terms of evaluation criteria, the authors provide profound results in table 1. I can clearly see the compression ratio of this method and it achieves a promising result compared to results from others.\n\nIn the abstract and the beginning of the introduction section, the authors address the importance of LiDAR reflectance in the 3D perception task. Moreover, the teaser figure provides a failure case when the method does not use the LiDAR reflectance. However, I cannot find any quantitative experiments about this claim in the manuscript and the supplementary material. So, I would like to say that __some of the evaluation criteria are missing.__", "theoretical_claims": "There are no theoretical claims that the authors provide. The authors revisit the concept of the serialization and the LiDAR reflectance. I believe that this paper is more closer to the technical paper, rather than to the theoretical paper.\n\n__So, I wonder whether this paper deserves to be reviewed as an ICML submission paper.__ If this paper is submitted to other computer vision conferences, it is okay for me. But I will __not__ consider this issue in my rating and will check the feedback from AC or PC.", "experimental_designs_or_analyses": "In terms of the compression, the authors well provide the quantitative results which are included in Table 1. However, the authors did not provide the results of the 3D perception task which can be an important factor why the LiDAR reflectance is a necessary measurement. In the introduction section, the authors said \n\n_\"For instance, our experiments show that removing the reflectance causes a dramatic drop in pedestrian (and cyclist) detection accuracy incorporating the widely used PointPillar (Lang et al., 2019) detection model, having\nAP (average precision) from 51.4 (62.8) to 14.1 (34.3) on the KITTI dataset (see Fig. 1), making it impractical for use.\"_\n\nWithout this, I cannot find any additional results in the manuscript and the supplementary material. Table 6 of the manuscript only provides results using PointPilar. Claiming the importance of the LiDAR reflectance solely from using the PointPillar results is not enough, in my opinion. It can mislead the readers.\n\nAlso, I am quite not sure why the Mamba architecture is a necessary condition for the LiDAR compression task. In my understanding, this formulation can be feasible when using Transformer architecture, like Point Transformer v3. In this perspective, the authors should provide a comparison with Scan Serialization with the conventional serialization methods. But, I cannot find the experimental results about this issue.", "relation_to_broader_scientific_literature": "This paper can be influential to future researchers who may use LiDAR sensors for their downstream tasks. As the authors mentioned, previous LiDAR-based perception methods mainly use the point location without using the LiDAR reflectance. Moreover, this paper also well designs the LiDAR point serialization while considering the LiDAR scan itself. So, I believe it is impactful to this field.", "essential_references_not_discussed": "I believe that the authors should provide a technical comparison and experimental comparison with Point Transformer v3, which firstly provides a pointcloud serialization using conventional space carving algorithms. According to the authors' claims, I believe that this method can be utilized for not just 3D perception tasks, but also LiDAR compression using their serialization method. __If my understanding is correct, please provide the authors' analysis and comparison in the rebuttal.__", "other_strengths_and_weaknesses": "The authors well describe the way of using the LiDAR reflectance for the LiDAR compression scheme. The scan serialization looks interesting since it respects the inherent properties that LiDAR sensors have. The proposed methodology looks okay and the resulting compression benefit is highly admirable as stated in Table 1. \n\nHowever, my concern is closer to the necessity of introducing Mamba architecture into this compression task. With the provided writing in the introduction section, I cannot clearly catch why the authors should leverage the Mamba architecture to the task. I understand that the Mamba is more efficient than the Transformer architecture thanks to their linear computational complexity. However, such a viewpoint is more likely to say that the authors want to emphasize efficiency, rather than focusing on effective compression. I hope that the authors can describe their opinion of this issue in the rebuttal.\n\nAnother weakness is that the authors address the importance of using LiDAR reflectance in the 3D perception task, which is also stated in the previous section by the reviewer. Table 6 of the supplementary material only provides results using PointPilar. Except for this, I cannot be sure of the authors' claim. \n\nMeanwhile, I am also confused about why the LiDAR compression is important to the 3D perception task itself. The experimental results imply that the authors put more weight on the efficiency. But, Table 6 and Line 53 are more about the performance itself. I am a bit confused about this. Can authors clarify this issue as well? I believe that if LiDAR reflectance itself is proven to be effective for LiDAR compression, the logic itself is good enough. What I wondered is why the statement about 3D perception is needed. I hope that I clarified my confusion to the authors.", "other_comments_or_suggestions": "I have no further comments. I wrote my questions and issues in the previous sections.", "questions_for_authors": "I have no further comments. I wrote my questions and issues in the previous sections.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {"ethical_review_concerns": "There is no ethical issue in this paper."}}, {"review_id": "5HiPqcpI1N", "reviewer": "Reviewer_Kwag", "summary": "The paper presents SerLiC, a novel serialization-based neural compression framework specifically designed for LiDAR reflectance data. The main contributions and findings of the study include:\n\n1. Serialization of LiDAR Data: SerLiC transforms 3D LiDAR point clouds into 1D sequences through scan-order serialization, aligning with the LiDAR scanning mechanism. This approach allows for more efficient modeling and analysis of reflectance attributes.\n\n2. Contextual Tokenization: Each LiDAR point is tokenized into a contextual representation that incorporates its sensor scanning index, radial distance, and prior reflectance values. This enhances the framework's ability to explore dependencies effectively.\n\n3. Efficient Sequential Modeling: The framework utilizes the Mamba model, which operates with a dual parallelization scheme, facilitating simultaneous autoregressive dependency capture while ensuring fast processing.\n\n4. Performance Improvements: SerLiC achieves over 2× volume reduction compared to the original reflectance data, with up to a 22% reduction in compressed bits relative to existing state-of-the-art methods like Unicorn. It operates with only 2% of the parameters used in these methods, making it highly efficient.\n\n5. Real-world Applicability: A lightweight version of SerLiC, with 111K parameters, achieves processing speeds of ≥ 10 frames per second, making it practical for real-world applications such as autonomous driving and urban planning.\n\n6. Experimental Results: Extensive tests demonstrate that SerLiC consistently outperforms existing compression methods (including G-PCC and Unicorn) across widely used datasets like KITTI, Ford, and nuScenes, reinforcing its effectiveness for LiDAR reflectance compression.\n\nOverall, the paper highlights the need for specialized codecs tailored to LiDAR technology and presents SerLiC as a robust solution to meet this requirement.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "**1. Deeper Analysis of SerLiC’s Limitations**\n\nWe appreciate your insightful observations and are pleased to provide a comprehensive response below.\n\n1.1 Adaptability to Non-Rotational LiDAR Systems.\n\nNon-rotational LiDAR's scanning also follows a specific order, and SerLiC maintains strong compatibility. To validate this, we have conducted additional experiments using the non-rotational InnovizQC dataset from MPEG. The dataset visualization is available in [this anonymous website](https://anonymous.4open.science/r/11079-88B1/readme.md). The compression results presented below demonstrate SerLiC's robust generalizability for non-rotational LiDAR systems. \n\n|| RAHT | Predlift | SerLiC |\n|-:|:-:|:-:|:-:|\n| InnovizQC 02| 3.71 | 3.10 | 2.28 |\n| InnovizQC 03| 3.82 | 3.17 | 2.25 |\n| **Average** | **3.77** | **3.14** | **2.27** |\n\n1.2 Performance Under Highly Variable Point Cloud Densities.\n\nWe provide detailed performance across distinct scenarios in the table below. Results show that SerLiC consistently outperforms G-PCC in all scenes. Relatively, SerLiC performs better in City and Campus scenes than in Residential and Road scenes. This occurs due to dense roadside vegetation interfering with sensor-recorded reflectance characteristics and increasing compression difficulty. We will add these results to the supplementary to demonstrate the method's applicability across diverse real-world scenarios.\n\n|| RAHT | Predlift | Ours |\n|-:|:-:|:-:|:-:|\n| City | 4.67 | 4.55 | 3.24 |\n| Residential | 4.96 | 4.94 | 3.81 |\n| Road | 5.41 | 5.36 | 4.39 |\n| Campus | 4.86 | 4.73 | 3.43 |\n\n**2. Pseudocode for the Serialization Process**\n\nThe following Python-style code demonstrates the serialization of LiDAR point clouds. The function takes a point cloud of shape (N, 4) as input and outputs a list of point sequences, where each sequence corresponds to a specific laser emitter and represents an ordered point set scanned by that laser.\n\n```python\ndef scan_order_serialization(points, L=64, W=1024, pitch_up=3.0, pitch_down=-24.0):\n    \"\"\"\n    Params:\n        points (torch.Tensor): Input point cloud of shape (N, 4), with columns (x, y, z, reflectance);\n        L (int): Number of lasers (vertical resolution);\n        W (int): Horizontal resolution;\n        pitch_up (float): Maximum elevation angle;\n        pitch_down (float): Minimum elevation angle;\n    Returns:\n        list[torch.Tensor]: List of point sequences. Each sequence corresponds to a laser beam, sorted by horizontal angle.\n    \"\"\"\n    # Separate coordinates and reflectance values\n    coords, refl = points[:, :3], points[:, 3:4]\n\n    # Coordinate mapping (This step involves using Eq. 3 and 4 \n    # from the manuscript to calculate rho, v, and u)\n    rho, v, u = coords_mapping(coords, L, W, pitch_up, pitch_down)\n\n    # Concatenate (rho, v, u) as auxiliary data\n    points = torch.cat([rho, v, u, coords, refl], axis=-1)\n\n    # Process each laser\n    seq_list = []\n    for laser_idx in range(1, L+1):\n        # Mask to filter points in the current laser\n        mask = (v == laser_idx).view(-1)\n        seq = points[mask]\n\n        # Sort points by horizontal index (u) to simulate spin order\n        spin_order = torch.argsort(u[mask].view(-1))\n        seq = seq[spin_order]\n        seq_list.append(seq)\n    return seq_list\n``` \n\nOur serialization approach transforms 3D LiDAR point clouds into 1D sequences for efficient modeling. Our serialization focuses on intra-frame organization, preserving each LiDAR frame's spatial structure losslessly. Temporal order is maintained by the system’s sequential storage/transmission of frames. For example, points from a moving car in frame $t$ retain their spatial layout after serialization; their temporal continuity across frames $t$, $t+1$, etc., is ensured by the ordered bitstream arrangement, unaffected by intra-frame encoding. This decoupling guarantees both spatial fidelity and temporal coherence.\n\nWe will open-source our code to ensure transparency and reproducibility for the community.\n\n**3. Comparison of Mamba Network and Transformer**\n\nSpecifically, the autoregressive coding employed in SerLiC is similar to the next token prediction in natural language processing (NLP). Autoregressive models are widely recognized for their exceptional contextual modeling capabilities, but their practical application is constrained by the high complexity. While Attention is also an alternative option for sequential modeling, our findings in Supplementary section C.2 show that Mamba exhibits significantly lower complexity-by several orders of magnitude-compared to attention-based architecture. This conclusion aligns with prior works [1]. Mamba’s linear complexity and dual-parallel strategy enable real-time processing of LiDAR reflectance compression. This is the key reason for our choice of Mamba.\n\n[1] Albert Gu, et al. \"Mamba: Linear-Time Sequence Modeling with Selective State Spaces.\" Arxiv, 2023.", "claims_and_evidence": "(1) The paper introduces serialization to leverage the sequential nature of LiDAR scans, enabling more efficient modeling and processing. This approach is both logical and well-motivated.\n\n(2) The paper thoroughly explores the intrinsic characteristics of LiDAR reflectance. Experiments demonstrate that removing reflectance leads to a significant decline in pedestrian and cyclist detection accuracy when using the widely adopted PointPillar model (Lang et al., 2019). Specifically, on the KITTI dataset, the average precision (AP) drops sharply from 51.4 (62.8) to 14.1 (34.3) for pedestrians (and cyclists), as shown in Figure 1, rendering it impractical for practical applications.", "methods_and_evaluation_criteria": "The proposed method is both logical and well-motivated.\n\n(1) Specifically, it introduces a lossless compression technique tailored for LiDAR point cloud reflectance data. Unlike previous approaches, which predominantly concentrate on general-purpose attributes such as color intensities or spatial coordinates, this method addresses a critical gap by focusing on the unique properties of LiDAR reflectance. General-purpose compression techniques, while versatile, often fail to fully exploit the specialized characteristics of reflectance data, such as its intensity distribution and correlation with surface materials. This limitation reduces their effectiveness in scenarios where reflectance plays a pivotal role, such as in autonomous driving, robotics, or environmental mapping. By prioritizing reflectance preservation without data loss, the proposed method offers a more efficient and targeted solution, enhancing the utility of LiDAR point clouds in these specialized applications.\n\nThe evaluation criteria are both reasonable and well-designed.\n\n(1) The testing conditions adhere strictly to the Common Test Conditions (CTC) outlined by the MPEG AI-based Point Cloud Compression (AI-PCC) framework. This standardized approach ensures consistency and comparability with existing benchmarks in the field of point cloud compression. The CTC provides a rigorous set of guidelines, including predefined datasets, compression ratios, and performance metrics such as bitrate and reconstruction quality, allowing for an objective assessment of the method’s effectiveness. By aligning with these conditions, the evaluation not only validates the method’s performance under controlled and reproducible settings but also demonstrates its potential applicability within the broader context of AI-driven point cloud processing standards. This adherence strengthens the credibility of the results and facilitates future comparisons with other state-of-the-art techniques.", "theoretical_claims": "There isn’t too much theoretical proof.", "experimental_designs_or_analyses": "Yes, ablation experiment.\n\nThe ablation studies in the paper are well-designed, systematically evaluating the impact of contextual construction, window-based parallelization, the Mamba network, and attention mechanisms on the performance and computational complexity of the SerLiC model, thereby thoroughly validating the effectiveness of each component. In the contextual construction experiments, by individually disabling components such as scanning index, radial distance, and prior reflectance, the study clearly demonstrates their critical role in capturing LiDAR reflectance correlations. For instance, a noticeable performance drop occurs when the scanning index and radial distance are disabled, aligning with the physical characteristics of LiDAR scanning. The window-based parallelization experiments, by adjusting window sizes (e.g., from 64 to 1024), reveal a trade-off between performance and computational resources (e.g., encoding time and memory usage), identifying 128 as the optimal balance point, which reflects consideration for real-world deployment needs. The Mamba network experiments, varying the number of layers (1 to 7) and network dimensions (64 to 512), show that increased model capacity enhances performance but also raises complexity, a finding consistent with expectations and providing a basis for resource optimization. The attention mechanism experiments, by replacing the Mamba module, compare the performance and complexity of both architectures, proving that Mamba significantly reduces computational overhead (e.g., decoding time drops from 5.12 seconds to 0.23 seconds at a window size of 128) while maintaining comparable performance. Overall, these ablation studies comprehensively cover the model’s core components, employing a controlled variable approach to deliver clear causal insights. They not only confirm the rationality of the SerLiC design but also offer valuable guidance for practical applications in LiDAR reflectance compression. Future work could further explore cross-dataset generalization and the scalability of lossy compression to enhance the method’s applicability.", "relation_to_broader_scientific_literature": "The paper \"Efficient LiDAR Reflectance Compression via Scanning Serialization\" introduces SerLiC, advancing LiDAR point cloud compression by focusing on reflectance data, an underexplored area in prior work. Its key contributions—serializing point clouds by scanning order, using Transformer-based sequence modeling, implementing window-based parallelization, and integrating the Mamba network—build on and extend the scientific literature. Traditional methods, like those in Zhu et al. (2018) and de Queiroz and Chou (2016), prioritize spatial coordinates with general-purpose encoding, often neglecting reflectance-specific traits. SerLiC’s serialization leverages LiDAR’s sequential nature, akin to raster scans in image processing, enabling novel sequence modeling previously underutilized in this domain. The Transformer approach adapts established NLP techniques (e.g., Vaswani et al., 2017) to LiDAR data, while window-based parallelization ensures practical efficiency, a common deep learning practice. The Mamba network, a recent innovation (Gu & Dao, 2023), offers linear complexity, potentially marking its debut in LiDAR compression and outperforming prior methods like Unicorn (Wang et al., 2025) by achieving over 2x volume reduction and 22% bit rate improvement with minimal parameters. These advancements enhance compression efficiency and real-time applicability, contributing significantly to autonomous driving and 3D mapping research. Future work could explore broader generalization and lossy compression extensions.", "essential_references_not_discussed": "No.", "other_strengths_and_weaknesses": "The concept of introducing serialization to leverage the sequential nature of LiDAR scans is insightful, enabling efficient modeling and processing. By transforming 3D LiDAR data into 1D sequences, it exploits inherent correlations, proving practical for real-world applications like autonomous driving. The writing is clear and accessible, effectively conveying complex ideas with concise explanations and figures, making it easy for readers, including non-specialists, to understand the methodology and its significance in LiDAR compression.", "other_comments_or_suggestions": "Discuss Limitations:\nThe paper should provide a deeper analysis of SerLiC’s limitations to enhance its credibility. For instance, its adaptability to non-rotational LiDAR systems, such as solid-state sensors with different scanning patterns, remains unaddressed. Additionally, the method’s performance under highly variable point cloud densities—common in complex environments like dense urban areas or sparse rural settings—needs exploration. These factors could affect serialization efficacy and compression quality. Discussing such constraints would clarify the method’s practical scope, guide future improvements, and help readers assess its applicability across diverse real-world scenarios.", "questions_for_authors": "【For Serialization Process:】\nCould the authors provide a more detailed description or pseudocode for the serialization process to clarify how the LiDAR scanning order is transformed into a one-dimensional sequence?\nA step-by-step explanation or pseudocode would help readers understand the exact mechanism behind converting the inherently spatial and temporal LiDAR scanning order into a 1D sequence, making the process more transparent and reproducible.\nHow does the serialization process specifically preserve the spatio-temporal correlations of LiDAR data? Please illustrate with a concrete example.\nLiDAR data contains critical spatial and temporal relationships, and it’s unclear how these are maintained during serialization. A specific example—perhaps showing how a sequence of LiDAR points from a moving object retains its spatial continuity and temporal order—would make this preservation mechanism more tangible and convincing.\n\n【For Comparison of Mamba Network and Transformer:】\nCould the authors explain why the Mamba network is better suited for processing LiDAR reflectance data compared to Transformers? Please specify which characteristics of LiDAR data make Mamba more advantageous.\nWhile both architectures are designed for sequence modeling, the authors should clarify why Mamba outperforms Transformers in this context. For instance, is it due to LiDAR data’s long-range dependencies, high sparsity, or variable sequence lengths? Highlighting these data-specific traits would strengthen the argument for choosing Mamba.\nAre there experimental results or theoretical foundations that support the superiority of Mamba in long-sequence modeling? Please provide relevant data or literature references.\nClaims of Mamba’s advantages need substantiation. Including experimental evidence (e.g., performance metrics like accuracy or efficiency on LiDAR datasets) or citing theoretical studies (e.g., prior work on Mamba’s efficiency in long-sequence tasks) would bolster the credibility of this comparison and provide a solid foundation for the authors’ conclusions.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9btqiWf5ob", "reviewer": "Reviewer_wVmh", "summary": "This paper proposes a reflectance compression method based on serialized LiDAR data. Specifically, the method first converts 3D LiDAR point clouds into 1D sequences through scanning order serialization, where each point is labeled with a context representation that includes the sensor scan index, radial distance, and previous reflectance values, thereby establishing dependencies within the sequence. To achieve efficient sequence modeling, the paper combines Mamba with a two-level parallelization scheme to accelerate the autoregressive processing speed. Experimental results show that this method outperforms the latest GPCC and deep learning-based Unicorn in terms of model size, encoding/decoding time, and compression performance. Additionally, the authors propose a pipelined compression strategy, enabling the encoding speed to reach thirty frames per second, which has practical significance for real-world applications.", "questions": "", "limitations": "", "rating": 2, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We sincerely appreciate your insightful comments and constructive suggestions. Thank you for recognizing this paper *provides substantial guidance for future research* with *reasonable writing* and *sufficient experimental evidence*. Below, we provide detailed responses in the hope of addressing your concerns.\n\n**1. Clearer Comparative Experiments for Downstream Detection Task**\n\n1) The visualizations and quantitative results in our manuscript show that simply removing reflectance data (setting values to 0) in pre-trained models leads to significant performance degradation. As suggested, expanded comparisons now include SECOND and PointRCNN (see table below), showing detection accuracies for cars/pedestrians/cyclists at multiple AP levels (\"w/o R\" = reflectance set to 0).\n\n||Car Easy|Car Mod.|Car Hard|Ped. Easy|Ped. Mod.|Ped. Hard|Cyc. Easy|Cyc. Mod.|Cyc. Hard|**mAP Mod.**|\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:| \n|PointPillar |87.75| 78.40| 75.18| 57.30| 51.41| 46.87| 81.57| 62.81| 58.83| **64.21**|\n|PointPillar (w/o R)   |83.85| 74.21| 70.36| 21.56| 14.08| 12.83| 47.79| 34.28| 32.13| **40.86**|\n|SECOND |90.55| 81.61| 78.61| 55.95| 51.15| 46.17| 82.97| 66.74| 62.78| **66.50**|\n|SECOND (w/o R)   |87.87| 78.92| 75.58| 40.46| 35.66| 31.88| 70.42| 50.64| 47.70| **55.07**|\n|PointRCNN   |91.47| 80.54| 78.05| 62.96| 55.04| 48.56| 89.17| 70.89| 65.64| **68.82**|\n|PointRCNN (w/o R)|88.03| 77.00| 72.85| 42.90| 35.28| 30.68| 56.58| 42.51| 40.07| **51.60**|\n\n2) We retrained the detection models from scratch without reflectance. The table below shows the performance of reflectance-ablated (*) models, where performance degradation is reduced but persists (and is non-trivial).\n\n||Car Easy|Car Mod.|Car Hard|Ped. Easy|Ped. Mod.|Ped. Hard|Cyc. Easy|Cyc. Mod.|Cyc. Hard|**mAP Mod.**|\n|:-|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:| \n|PointPillar*  | 87.86| 78.39| 75.71| 59.28| 52.48| 48.04| 76.18| 52.96| 50.17| **61.28**|\n|SECOND*  | 88.70| 79.47| 77.86| 57.52| 52.79| 48.38| 78.72| 56.92| 53.26| **63.06**|\n|PointRCNN*    | 89.46| 79.84| 77.58| 64.94| 54.62| 47.66| 89.16| 65.54| 60.92| **66.67**|\n\n3) To more adequately reflect the role of reflectance, we revised our original wording from \"It is crucial in downstream tasks...\" to \"It is widely used in downstream tasks...\". All results will be detailed in the revised manuscript and supplementary.\n\nFinally, we respectfully emphasize that the core contribution of this work lies in the development of a high-performance real-time reflectance compressor. The extensive utilization of reflectance in downstream tasks provides substantial validation for the significance of our work.\n\n**2. Lossless Reflectance Compression vs. Quantization-Induced Precision Loss**\n\nWe respectfully clarify that all comparisons were conducted in lossless mode, consistent with other lossless compression works (such as G-PCC and Unicorn). \n\nAlthough distinct coordinates may project to identical (u,v) pairs, these pairs only serve as auxiliary priors. The original 3D coordinates are preserved for each point through decoding the geometry bitstream (which aligns with G-PCC’s separate lossless geometry/attribute pipeline). Therefore, even though points may be mapped to the same (u,v) pair, they remain distinct points in 3D space, and we can clearly differentiate them based on their geomtry coordinates.\n\nBelow we show an example. Consider two points in a point cloud, $p_1$=(3.12, 8.65, 0.50, 80) and $p_2$=(3.13, 8.63, 0.50, 82), where each point is represented as ($x$, $y$, $z$, $reflectance$). After coordinate transformation (in Eq. 3) and quantization (in Eq. 4), both points are mapped to the same (u,v) pair (712, 58). However, this spatial projection does not compromise the integrity of reflectance values. Specifically:\n\n- The encoder utilizes statistical patterns from the (712, 58) pair to optimize entropy coding for both values (80 and 82).\n\n- The decoder reconstructs reflectance values (80 and 82) by resolving the entropy-coded symbols using the same (u,v)-derived priors and the lossless geometry data.\n\nIn short, SerLiC uses (u,v) as contextual priors for entropy coding while retaining raw reflectances. Please refer to the Python-style serialization code in our response to reviewer #Kwag for more details.\n\n**3. Additional Explanation on Bit Rate Points and Lossy Compression**\n\nAlthough different quantizations can adjust bitrates in lossless models, this approach differs fundamentally from lossy compression. \n\nWe tested the quantization-driven lossy compression (suggested by the reviewer) against G-PCC, with RD curves available in [this anonymous website](https://anonymous.4open.science/r/11079-88B1/readme.md). It is observed that although SerLiC is designed for lossless compression, it remarkably outperforms lossy methods in the high bitrate range. Moreover, SerLiC operates in real-time, much faster than these lossy methods. Extending SerLiC to lossy compression is our future work.", "claims_and_evidence": "Yes.", "methods_and_evaluation_criteria": "Yes.", "theoretical_claims": "There are no apparent errors in the theoretical proof.", "experimental_designs_or_analyses": "There are no significant issues with the experimental design and analysis.", "relation_to_broader_scientific_literature": "I believe the main contribution of this paper is the proposal that the reflectance in different sequences of LiDAR may not be correlated. The authors have demonstrated this point through ablation experiments, which provides guidance for the parallelization of future LiDAR reflectance compression.", "essential_references_not_discussed": "None", "other_strengths_and_weaknesses": "Strengths:\n(1) The paper points out that the correlation of reflectance between different sequences may not be high, allowing for the parallel processing of point clouds from different sequences. This is a significant finding, as current research in both image compression and point cloud attribute compression has focused heavily on the similarity of local information (e.g., studies based on context-based entropy models). The discovery of the unique distribution pattern of LiDAR reflectance in point clouds provides substantial guidance for future research, particularly in real-time data encoding and decoding.\n(2) The paper demonstrates potential for real-time encoding and decoding. The method employs parallelization between different sequences and within multiple windows of a single sequence, referencing the pipelining concept from computer architecture, transforming the encoding process into a three-stage pipeline, achieving a decoding speed of thirty frames per second.\n(3) The paper includes comprehensive ablation experiments: (a) it compares the decoding time, model size, and runtime memory usage of the core module Mamba with that of Transformer; (b) the ablation experiments regarding the correlation of reflectance between different sequences are particularly convincing, as they visually illustrate the differences between sequences and experimentally demonstrate that referencing more sequences does not lead to performance improvements.\n(4) The method proposed in this paper is specifically designed for LiDAR point clouds. Unlike the L3C2 method, which requires specific sensor parameter information, this method is more applicable to various datasets, needing only the most basic geometric information (xyz) to function.\n\nOverall, the proposed method exhibits excellent compression performance and encoding/decoding speed. The writing of the paper is also quite reasonable, supported by sufficient experimental evidence to substantiate the authors' claims.\n\nWeaknesses:\n(1) The proposed reflectance is significant for downstream detection tasks. The experimental results indicate that the accuracy for bicycles and pedestrians drops to less than half. I would like to know whether the authors retrained the PointPillar model under the same settings without considering reflectance during this experiment. Additionally, I hope to see more comprehensive comparative results, such as detection accuracies for cars, pedestrians, and cyclists at different AP levels. Since the impact of reflectance on downstream tasks is one of the core points of this paper, I would like this section of the comparative experiments to be clearer and more thorough.\n(2) In the process of data serialization, according to the formula provided by the authors in eq(4), there is an issue. When quantizing based on the elevation angle, due to noise during the sampling of LiDAR point clouds, it is inevitable that some different points will be mapped to the same (u, v) coordinates during the actual conversion process, which undoubtedly leads to a loss of precision. However, it seems that the paper claims to achieve lossless reflectance compression (as the authors do not provide RD curves in the subsequent results, only bpp and bpp-gain). I am concerned about the correctness of this part when compared to other results and hope the authors can provide a detailed explanation regarding this issue.\n(3) The paper does not conduct experiments at multiple bitrate points, which raises some confusion. Other methods, such as G-PCC and Unicorn, provide results at multiple bitrate points. When comparing with these methods, did the authors set them all to lossless mode? Similarly, why did the authors not conduct tests at multiple bitrate points? Even with lossless compression, different quantizations of attributes can achieve various bitrate points.", "other_comments_or_suggestions": "None", "questions_for_authors": "Please see the weakness.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper received divergent scores after the rebuttal. Reviewer Kwag maintained Accept, and  GMXH promised to raise to Weak Accept rating in discussion, while Reviewer wVmh remained at Weak Reject.

Reviewers GMXH and Kwag praised the paper for its novel approach to LiDAR reflectance compression via scan-order serialization and the incorporation of the Mamba architecture. They liked the balance between efficiency and performance for real-time compression, as well as the extensive quantitative results and ablation studies. There are also concerns raised regarding the theoretical motivation for introducing the Mamba architecture into the compression task, as well as the comprehensive evaluation of the impact on downstream tasks. Despite these issues, the rebuttal largely addressed the concerns.  

After carefully reading the paper, reviews, and rebuttal, the AC concludes that although the remaining concerns are valid, the paper demonstrates sufficient technical novelty and strong empirical performance. Based on these considerations, the AC would like to recommend acceptance, with the expectation that the authors will address the comments in the final version.
Decision: Accept (poster)