Low-Latency Neural LiDAR Compression with 2D Context Models
Authors: Rui Song
Yan Wang
Tongda Xu
Zhening Liu
Zehong Lin
Jun Zhang
Venue: ICLR 2026 Poster (2026)
Keywords: Data Compression
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Context modeling is fundamental to LiDAR point cloud compression. Existing methods rely on computationally intensive 3D contexts, such as voxel and octree, which struggle to balance the compression efficiency and coding speed. In this work, we propose a neural LiDAR compressor based on 2D context models that simultaneously supports high-efficiency compression, fast coding, and universal geometry-intensity compression. The 2D context structure significantly reduces the coding latency. We further develop a comprehensive context model that integrates spatial latents, temporal references, and cross-modal camera context in the 2D domain to enhance the compression performance. Specifically, we first represent the point cloud as a range image and propose a multi-scale spatial context model to capture the intra-frame dependencies. Furthermore, we design an optical-flow-based temporal context model for inter-frame prediction. Moreover, we incorporate a deformable attention module and a context refinement strategy to predict LiDAR scans from camera images. In addition, we develop a backbone for joint geometry and intensity compression, which unifies the compression of both modalities while minimizing redundant computation. Experiments demonstrate significant improvements in both rate-distortion performance and coding speed. The code is available at: https://github.com/rrui-song/RangeCM.

Published as a conference paper at ICLR 2026
LOW-LATENCY NEURAL LIDAR COMPRESSION WITH 2D CONTEXT MODELS
Rui Song', Yan Wang?, Tongda Xu², Zhening Liu', Zehong Lin Jun Zhang"
1 The Hong Kong University of Science and Technology
2 Institute for Al Industry Research (AIR), Tsinghua University
3 School of Data Science, Lingnan University
rrui songeconnect. ust. nk, wangyanbair www.nnguaaeduu... x, tongdaenyu..dduu zhening. cucconnect. hk, zehonglin@in www.innnnnnnnnnnnnemmmmmmme ok, eejahanglust.hr
ABSTRACT
Context modeling fundamental LiDAR point cloud compression. Existing methods rely on computationally intensive 3D contexts, such as voxel and octree, which struggle balance the compression efficiency and coding speed. In this work, we propose a neural LiDAR compressor based on 2D context models that simultaneously supports high-efficiency compression, fast coding, and universal geometry-intensity compression. The 2D context structure significantly reduces the coding latency. We further develop comprehensive context model that in- tegrates spatial latents, temporal references, and cross-modal camera context in the 2D domain to enhance the compression performance. Specifically, we first represent the point cloud range image and propose multi-scale spatial con- text model to capture the intra-frame dependencies. Furthermore, we design an ppoibal-lowwbbased temporal context model for inter-frame prediction. Moreover, we incorporate deformable attention module and context refinement strategy to predict LiDAR scans from camera images. addition, we develop backbone for joint geometry and intensity compression, which unifies the compression of both modalities while minimizing redundant computation. Experiments demonstrate significant improvements in both rate-distortion performance and coding speed. The code is available at: https:: //t/t/giittt com rui www.:///iioog//aaaeeaeeeoeeee /RangeCM
INTRODUCTION
LiDAR point cloud, an effective data structure to represent real-world scenes, has been used in wide range of applications such as autonomous driving and robotics (Guo et al.) (222)). The large volume of LiDAR data creates a strong demand for effective compression algorithms. In recent years, neural networks have significantly promoted performance LiDAR compression. A common approach is to predict symbols based on previously decoded contextual features. Since the bitstream length is determined by the cross-entropy between the ground truth and the estimated dis- tribution, accurate neural context model can effectively reduce the bitrate in lossless compression. The context structure is particularly crucial for improving the density estimation accuracy, leading to the development of various context types (Gao al. 2025 Huang et al. 2020 Wang et al.) (022a)).
Although these learning- based models have greatly improved the rate-distortion performance, the coding speed remains an issue. State-of-the-ar models typically rely on an informative 3D context to capture detailed local geometric features (Wang et al., 2025a Wang & Liu 2022 Zhou et al.) 2022). Nevertheless, the heavy computational burden of processing these 3D features results in run- times that can reach or exceed hundreds of milliseconds, making them impractical for low- low-latency applications. For instance, a Velodyne HDL-64E LiDAR can generate point clouds rate of 10 frames per second (FPS). On the other hand, although recent works Yoou et al., 2025) deliver real-time coding speeds, their compression performance lags behind other state-of-the models. Therefore, reducing the coding latency while preserving high compression efficiency remains an open challenge. Besides, existing methods typically employ two separate deep neural networks to
"Corresponding Authors.

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calculate the dedicated context for geometry and intensity compression (Wang al. 2025b). We argue that a single hybrid context can be applied to effectively predict both geometry and intensity, thereby reducing redundant computation and improving the coding speed.
2D range images provide a more compact and computationally efficient representation of the LiDAR point cloud. Intuitively, 2D context models enable faster compression by operating directly on range-view features. However, extracting features from the range view is challenging due to the lack of precise 3D local contexts (Fan et 2021), and naively replacing the 3D context model with 2D backbone causes severe performance degradation. To yield superior compression ratio, state- of-the-art range image compression methods opt use a 3D feature extractor (Zhou et al. 2022 Wang & Liu 2022), which in turn compromises the coding speed.
In this work, we propose RangeCM, fast and efficient 2D context model for LiDAR compres- sion. It performs probability estimation based on latent features derived from a variational auto- encoder (VAE), where transforms and context models are built by the 2D convolutional neural net- work (CNN), By getting rid of computationally expensive 3D operators and directly working on 2D range-view features, RangeCM achieves much faster inference speed. Meanwhile, RangeCM jointly predicts geometry and intensity by integrating the context modeling of both attributes, which avoids recomputing contexts and further accelerates the inference process.
To enhance the compression performance of the 2D context model, propose comprehensive spatio-temporal cross cross-modal context structure. We first design multi-scale context for intra-frame prediction, which decomposes the range image into sketch map and detail map. The estima- tion of details is conditioned on the sketch, which enables a coarse-to-fin next-scale prediction strategy. For inter-frame prediction, we formulate temporal context by warping features from the reference frame to the current frame using a range-view optical flow. Furthermore, as the RGB camera is often jointly deployed with the LiDAR sensor in autonomous driving and robotic appli- cations Yeong et al. 2021), we develop a cross-modal context that predicts LiDAR features based on camera images. The camera context is generated using deformable attention (Zhu et al.) [02T), which adaptively projects camera features onto the range view. In addition, we employ a context refinement strategy to precisely align LiDAR and camera features under the causality constraint. By aggregating diverse spatial, temporal, and camera contexts, our 2D context model even outperforms the 3D counterparts by a large margin.
We evaluate RangeCM on the Waymo Open Dataset (Sun et al. 2020) and the SemanticKITTI benchmark (Behley et al. 2019).. Experiments demonstrate that RangeCM achieves significant im- provements both rate-distortion performance and coding speed. Compared to the state-of ooffthe-art art geometry compression method (Zhou et al., 2022), RangeCM yields an average BD-Rate improve- ment of 14.9% and 3.5 faster speed. Meanwhile, RangeCM reduces the inference latency by more than 100x compared to the state-of-the-al intensity compression model (Wang et al. (025b), while maintaining comparable compression efficiency. Our key contributions are as follows:
We develop a new paradigm for low-latency LiDAR compression, where all computations are performed the 2D domain. The proposed framework achieves state-of-the-arr rate- distortion performance and practical coding speed while supporting both geometry and intensity compression in a unified manner.
We propose comprehensive context model that integrates spatial, temporal, and camera features for LiDAR compression. To align these distinct features, we devise multi- context model for fitta- prediction, flow-based model for spatio-tempore temporal aggrega- tion, and a deformable attention module for
fusion.
We design a joint compression backbone that predicts LiDAR geometry and intensity based on a hybrid context, which merges the context modeling of geometry and intensity to im- prove computational efficiency.
RELATED WORK
2.1
POINT CLOUD COMPRESSION
Point clouds possess geometry (i.c., point coordinates) and attribute (e.g.. reflecting intensities, RGB colors, and normals) information. Specialized methods have been developed to compress these two
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feature types respectively. Geometry compression methods encode the orderless point cloud more regular data structures, such as octrees (Schnabel & Klein 2006), voxel grids (Quach et al. 2019). and range images (Wang et al.] 2022b). The MPEG Geometry- based Point Cloud Compression (G-PCC) standard (Schwarz et al. 2018) follows the octree-based pipeline, where the octree is encoded by rule-based context model losslessly. Other octree-based approaches predict the octree symbol distribution with a Seaarnnng-bsee context model (Huang et al.) 2020] Fu et al. 2022] Song ct al., 2023a [uo al., 2024). Voxel-based methods quantize the point cloud into discrete voxels and predict the occupancy status of each voxel grid with multi-scale context model (Wang et al., et al. (02T). Besides, range image is another remory-eeficiiee data structure t0 organize the point cloud. State-of-the- art range image compression methods adopt auto-regressive 2022 or multistage (Wang Liu 2022) context models to encode range values.
2025a
2025a|202aa
(hhou et al.
Furthermore, geometry compression can be improved by introducing temporal references. Existing methods mostly build the temporal context by searching for K-nearest neighbors (KNN) in the ref-
erence frame (Biswas et al. 2020 Song et al., 2023b Wang et al. 2025a Zhou et al. 2022). The
symbol distribution is then predicted based on both the spatial and temporal contexts.
Traditional attribute compression methods adopt handcrafted transforms to remove the redundancy in the signal. For example, G-PCC uses region-adaptio adaptive hierarchical transform (RAHT) (De Queiroz & Chou 2016) and predicting transform (MPEG 2021b) to analyze attribute features. Recently, neural networks have been introduced to develop more powerful transforms and context models
(Sheng et al. 2021: Fang al., [2022] [Zhan al. 2023 Wang et al.. (0255] Zhuu et al.] (025)_
However, these models need to recompute contextual features for attribute prediction after geometry
compression, which slows down the coding speed.
2.2 CAAR-CAMERA FUSION
Multi modii- fusion has attracted growing interest in the point cloud compression community. Sev- eral works introduce depth images as an additional prior (Wang et al., 2024 Zheng et al. 2024). However, the depth images here are only 2D projections of the point cloud, which do not introduce additional information helpful for enhancing compression. In contrast, camera images present a more promising modality, because they provide dense semantic features that the original point cloud lacks (Liu et al., 2024b). To the best of our knowledge, there is only one existing work that attempts to utilize the camera context for point cloud compression (Lin et al., (023). This approach first uses a depth estimation network to lift the image to 3D space, then fuses camera and octree node features enhance octree-based point cloud compression. Nonetheless, its performance is limited by the inaccurate depth estimation and unreliable LiDAR-camert alignment, achieving only marginal im- provements (c.g., around 2% bitrate reduction compared the baseline (Fu et al. 2022) at an octree depth of 10). Therefore, how to effectively utilize the camera context remains an open question.
3 PRELIMINARIES
3.1 RANGE IMAGE
The LiDAR sensor generates a point cloud by emitting H x W laser shots along H elevation angles 0 (..,... .... } and W azimuth angles Φ {6), ... Φw). To produce H x W range image, each point is projected to a unique pixel coordinate (m, n) according to the angles (0m, Φx) of the corresponding laser beam. As shown in Fig. [ range image pixel records the range value reflected intensity s, and other optional attributes of the corresponding point. The Cartesian coordinates of the point can be losslessly recovered from the range value by:
Fi.j cos 02 cos Φ) y = Ti.j cos 02 sin Φ3. = = ri.j sin Os.
where (i.j) denotes the coordinates of the corresponding pixel in the range image. The emission angles @ and Φ are fixed and determined by the predefined sensor scanning pattern, which is known a priori at the receiver. Therefore, the point coordinates can be determined by the scalar range value r, which is more efficient than transmitting the original 3D Cartesian positions.
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Point
LiDAR
Intensity
Range Value
Figure 1: Illustration of range image representation of the LiDAR point cloud.
3.2 CONTEXTUAL VIDEO COMPRESSION
Contextual video compression employs a conditional variational auto-encoder to exploit the tempo- ral context (Li et al. 202 2024 Jia al. 2025). which typically consists of previously decoded frames that are closely related to the current frame (Liu et al. (222a)). Given the decoded reference frame ú and the current frame æ, the model extracts an optical flow v to represent the motion be- tween the two frames. This flow is then encoded by a hyperprior-based image compression model (Ballé et al. 2018). Subsequently, the features ú are extracted and warped to the current frame using the reconstructed optical flow ú. The warped features serve as the temporal context uht, which fed into transform coding modules and the entropy model. Specifically, the analysis transform produces latent embedding of based on s/, y= 9a (ax <b2). The latent vector y is quantized into ý and subsequently encoded by conditional context model formulated as:
p 2 bb)) II (?) * (((-0.5,..5))...
(3) where & is the quantized hyperprior encoded by a fully factorized density model p(#), and u(-0.55 0.5) denotes a uniform distribution centered at 0 with a width of 1. Besides, ha h2 and hat are neural networks that predict distribution parameters based on the hyperprior and temporal context. Finally, the synthesis transform reconstructs the current frame as á = 9= ab))
p.o hac ha),
4 COMPREHENSIVE CONTEXT MODELING
4.1 OVERVIEW
RangeCM jointly compresses LiDAR geometry and intensity using a 2D comprehensive context. Its overall architecture is illustrated in Fig. 2 The continuous range image æ = {r, 8} is quantized into = (f, $), where multi-scale representation of the range value map, and is the quantized intensity map. In particular, & = { )2) is given by two-stage quantization as:
i = [r/bll A2 [(r - ////b/l.
(4)
Here, F is the sketched range value map, while A2 is an enhancement layer, referred to as the detail map, which conveys more details. The reconstructed range value map is recovered as c. + Pzz
RangeCM encodes ÅL, P2, and å sequentially. It first adopts deformable attention module to generate the basic camera context th based on the full-precision range image f. Then, a 2D CNN is employed to extract spatial features from ă. These features, along with the basic camera context vec. are encoded by VAE (Ballé et al. (018). The spatial context tb. derived from the synthesis transform of the VAE, which aggregates spatial priors and the basic camera context. The temporal context th is generated by a flow-based model. Subsequently, the distribution of the sketch map A is estimated based on both th, and t/,
Although 2/. is produced by accurate geometry, fine-grained camera features may lost during the transform coding of VAE. To overeome this issue, after recovering Å1, we use another deformable attention module to compute refined camera context v/. based on the LiDAR features provided by f1. Then, the detail map F. is predicted using a comprehensive context tb, which incorporates the spatial context th., the temporal context æh, and the refined camera context J... Finally, RangeCM predicts the intensity map based the diverse context sb and the geometric features f.
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Feature Extraction
Context Model
Warp
Concat
AE/AD
Flow Estimation
Sketch Map #1
Reference Irame i
tb
Context Model
Concat
AE/AD
Detail Map t
%/
Current Frame &
Base CCM
Context Model
4
AEEAD
Extraction
Refine CCM
Intensity Map š
Camera Image
Architecture of RangeCM. Blue blocks indicate the spatial context model. Green blocks constitute the temporal context model. Red blocks represent the camera context model (CCM). AT and ST indicate analysis and synthesis transform. AE/AD stands for arithmetic encoding/decoding.
Figure 2:
4.2 CAMERA CONTEXT MODEL
Practical autonomous driving and robotic systems commonly rely on the combined deployment of LiDAR and RGB cameras robust perception. LiDAR and cameras provide complementary scene descriptions: LiDAR offers accurate geometric features, while cameras capture dense semantic in- formation. These semantics are informative for range value prediction as well. For example, points from the same semantic instance generally have similar range values. It may be difficult to distin- guish whether two points belong to the same instance from the sparse point cloud, while the camera images provide critical disambiguation. In this work, assume that camera images separately encoded by another image codec and that they have been decoded before LiDAR compression.
The camera context model first utilizes 2D CNNs extract features from the range image and the camera images, respectively. Then, it adopts deformable attention (Zhu et al. 2021 1 to align these two modalities, using LiDAR features the query Q and camera features as the key K. For specific query token 4u (which corresponds to pixel in the range image), the deformable attention module adaptively samples N key tokens and computes cross-attention as follows:
M >
9n
M
Aigo V K(pn APsjn),
(5)
where is the index of the attention head and j is the index of the sampled key. Here, K Kp.+AAP +APijnn represents the jth sampled key token in the 7th head, where the sampling position is specified by the reference point pn and the learnable offset APsnn. Besides, U, and V," learnable weights of two linear layers, while A represents the weights between the query and the sampled key tokens. Both A and AP are predicted based on qn using linear layers. Therefore, the functionality of deformable attention is to dynamically aggregate N sampled camera tokens with the aggregation weights and sampling positions determined by the LiDAR query 4n- We further embed the deformable attention layer into Transformer block structure (Vaswani al. 2017). shown in Fig. 3
The reference point p.. a critical parameter in deformable attention, since directly determines the correspondence between range-view and camera-view pixels. We calculate ys, using the trans- formation matrix between LiDAR and camera. For a range image pixel, we lift it to the 3D space using Eq. (T). and project its 3D coordinates onto the camera coordinate system to obtain pa. This approach provides deformable attention with an inductive bias to aggregate features from camera image pixels that are spatially close the queried range image pixel.
Notably, LiDAR geometry is necessary to generate queries and reference points. Therefore, we can- not simply perform deformable attention using f. which is unavailable at the receiver. To maintain causality, must transmit tb. side information and compute xb. after the decoding of A. On the
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Deformable Attention
Conv
Conv
Conv
Add and Norm
abb
π
ab
2
3 4/
Concat
Concat
+ 4b
Concat
MLP
Y
Conv
Conv
Conv
Conv
4n
K(pml
Add and Norm
K (pr + APin)
Figure 3: Left: illustration of the deformable attention block. Right: coding pipeline of the multi- scale context model.
other hand, both e and ê preserve sighqqualiyy LiDAR geometrie features, which enable accurate and effective LDAR-ccamer alignment in the context model.
4.3 FLOW-BASED TEMPORAL CONTEXT MODEL
The temporal context model uses an optical flow capture the accurate motion between the current and reference frames. Here, the reference frame refers to the decoded previous frame. Given the current frame and the reference frame ú. it first extracts range-view optical flow using lightweight flow estimation model (Ranjan & Black 2017). Since this flow is not available at the receiver, a VAE is employed to encode as side information. Specifically, the analysis transform encodes v into latent embedding You which is quantized into D. and compressed based on hyperprior 2, The synthesis transform then restores è from Dv. Finally, the temporal context sb, is produced by warping the features of ú to the current view using 8.
4.4 SPATIAL PRIOR
RangeCM extracts convolutional features from è to serve as the spatial prior. This prior is then jointly encoded with the basic camera context v. into latent embedding using VAE (Ballé al. 2018 Inspired by the contextual video compression framework (Li et al. 2021a), the transform coding is conditioned on the temporal context 4h. Specifically, we employ an analysis transform to extract the latent embedding y = 9a (ă, tb., wb,) and generate the hyperprior as z ha (у). Then, the quantized latent ý encoded according to Eq. (2 and Eq. (3). Finally, the synthesis transform generates the spatial context as vb,- (g)))
4.5 MULTI-SCALE CONTEXT MODEL
We adopt multi-scale context model to predict e in a coarse-to-fine manner, where ê is utilized additional context enhance the prediction of Å2. Each map is further decomposed into two groups using checkerboard pattern (He et al. 2021 as follows:
F (1l f2)
A [A], m2)
where it and i are anchors, while 7 and A3 are non-anchors. After this group partition, the context model predicts each group based on the spatio-temporaa temporal--a-erreeaa context and the causal context from previous groups. The pipeline of coding f illustrated in Fig. 3 The estimation Á conditioned the spatial context vh., the temporal context th, and the causal context πh In contrast, A is predicted based on the comprehensive context zb and the causal context π22-
As 2b combines both geometry and intensity features, this hybrid context be applied to predict both f2 and 8. Therefore. we adopt lightweight prediction head to directly infer based on tb instead of using another heavy network recompute contextual features. For intensity compression,

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Table Comparison context types, BD-Rate gains to G-PCC (%), and runtimes (in seconds). For intensity compression, we report the total runtime for coding both geometry and intensity. The best results are marked in bold.
Geometry Compression
KITTI
WOD
Method
Context Type
Encoding
Decoding BD-Rate
Encoding
Decoding
BD-Rate
Infer. Total Infer. Total
Infer.
Total
Infer. Total
G-PCC
Spatial
0
0.95
0.48
0
1.24
0.62
ВНEM
Spatial
-31.12
1.38
1.61
RENO
Spatial
-12.47
0.04
0.07
Unicorn
Spatio-Temp
-27.34
2.65
2.83
2.36
2.50
RICNet
Spatial
45.82
0.40
0.63
0.40
0.43
RIDDLE
Spatio-Temp.
48.05
-54.21
0.49
0.53
0.97
RangeCM-G
Comprehensive
-56.07
0.04
0.09
0.03
0.14
-61.96
0.14
0.20
0.09
0.20
RangeCM-Gi
Comprehensive
-5155
0.04
0.09
0.03
0.14
-59.94
0.14
0.20
0.09
0.20
Intensity Compression
G-PCC
Spatial
0
0.84
0.75
0
0.59
0.65
Unicorn
Spatial
-12.16
14.84
13.04
RangeCM Comprehensive
-6.96
0.05
0.10
0.17
-20.93
0.22
0.10
0.27
we first use a checkerboard pattern to decompose into two groups 81 and 82. Then, we predict each group &, based on the comprehensive context v/, the geometry context A, and the causal context &ci- This workflow eliminates redundant computations and significantly improves network efficiency.
4.6 LOSS FUNCTION
The training objective of RangeCM is to minimize the overall bitrate of encoding range values, intensity map, spatial latent, and optical flow. The corresponding loss function is:
2
2
C = -Bapp())
((x)
log p (f | th., 1b.) +
log pl T vb) +
logp($1182s,f)
+ log p 2) + log p(2) + log pl Uo (2,) + log 1aggp(22 ((..)).
We adopt the discretized Logistic mixture (Salimans al. 2017) to fit the distribution of & and s. Latent variables ÿ and ÿe are modeled by a Gaussian distribution convolved with a uniform distribution, specified in Eq. (2). Hyperpriors 2 and 2, are fitted using fully factorized density model (Ballé et al. 2018).
EXPERIMENTS
5.1 EXPERIMENTAL SETUP
Datasets. We conduct evaluations on the Waymo Open Dataset (WOD) (Sun et al. 2020) and the SemTnticKIITI dataset (Behley et al. 2019). WOD provides raw range images and RGB camera images from 5 different views. It also offers accurate emission angles of LiDAR beams, which ensures lossless transformation between the range image and the point cloud. KITTI provides point cloud data along with camera images from 2 views. However, it does not provide transformation matrices between LiDAR and camera in the testing set. To strictly follow the official dataset division, we do not use camera priors for experiments on KITTI. A camera-involved RangeCM model is trained and evaluated using a different dataset partition, which is reported in Appendix [.1] Besides, beam emission angles. Following the settings in existing works (Wang Liu 2022 Zhou et al. 2022), our experiments are conducted on pseudo range images derived from estimated emission angles.
KITTI provides neither range images

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Bitrale vs. DI PSNR (WOD)
Biirate vs. D2 PSNR (WOD)
Bitrate vs. Intensily PSNR (WOD)
D1 PSNN (00)
Intenity PSNR
G-PCC
G-PCC
RIDDLE
RIDULE
Mang
RangeCM- RangeC'M
GJPCC RangeCM-Gi
RangeCM
Bits Per Point
Bas Per Point
Bits Per Poins
Bitrate vs. DI PSNR (KITTI)
Bitrate vs. D2 PSNR (KITTI)
Bitrate vs. Intensity PSNR (KITT))
D1 PSNK (009)
131 PSNN (035)
PCC
G PCC
EHEM
BHEM
RENO
RENO
Unicom
Unizoen
RICNNe
RICNet
RIDDLE
RIDDLE
BangeCMGG
RangeCM-GG
Unicocn RangeCM GI
RanpoCM-Gl
Ranget M-Gi
Bis Per Point
Bibs Par Point
Biis Per Point
Figure 4: Rate-distortion curves WOD and SemanticKITTI.
Baselines. For geometry compression, RangeCM compared against octree- based schemes G- PCC v23 (MPEG 2023) and EHEM (Song al.) (023a), voxel-based models Unicorn (Wang al,,
2025ab and RENO You et al. 2025). and range image compressors RICNet (Wang & Liu (022) and RIDDLE Zhou et al. (022). Notably, RIDDLE and Unicorn are spatio-temporal context models, while other baseline methods only exploit the spatial context. Since RIDDLE only evaluates its temporal context model on WOD, we compare intra-frame prediction mode on KITTI instead. Meanwhile, G-PCC v23 (MPEG 2023) and Unicorn (Wang et al.) (025b) are selected as basclines for intensity compression, where we compare RangeCM against the lossy compression modes of G- PCC and Unicorn. We also compare RangeCM with the state-of-the- art lossless LiDAR reflectance compressor SerLiC (Zhu et al. 2025) Appendix D.6
Implementation Details. For each dataset, we train two models named RangeCM-G and RangeCM-CG respectively. RangeCM-G is exclusively optimized for geometry compression, and RangeCM-GI is trained for joint geometry- iintenttyy compression. To avoid training multiple mod- els for different bitrates, we randomly sample the quantization step by during training. We evaluate single NVIDIA RTX A6000 GPU. Following the common test conditions of G-PCC (MPHG) 2221a),, we adopt Point-to- Pointtto-Ponnt PSNR (D1 PSNR) and Point-to-Plans PSNR (D2 PSNR) to measure the reconstruction quality. Please refer to Appendix B for more details.
RangeCM based
5.2 PERFORMANCE EVALUATION
The rate-distortion performance of RangeCM is shown in Fig. and Table | Regarding geome- try compression, RangeCM outperforms existing methods by remarkable margin. Compared to the state-of-the-art model RIDDLE, RangeCM-G and RangeCM-GI achieve the BD-rate gains of 17.14% and 12.59% on the WOD, respectively. This demonstrates the effectiveness of the proposed comprehensive context model. Besides, it is shown that octree-based and voxel-based methods (i.c., G-PCC. EHEM, and Unicorn) are more effective at low bitrates, while range image compressors (i.e., RICNet, RIDDLE, and RangeCM) perform better at high bitrates. A reasonable explanation that octree and voxel structures can represent the point cloud with only a few symbols for coarse reconstructions at low bitrates, but the number of required symbols quickly increases as the PSNR grows, leading to inferior performance at high bitrates. contrast, the symbol number in the range image is always fixed, thus range image compression methods more robust to the variation of bi- trate. For intensity compression, RangeCM surpasses G-PCC and achieves comparable performance to the state-of-the-ae thr method Unicorn.
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Table Ablation on geometry compression.
Table Ablation on intensity compression.
BD-Rate
BD-Rate to RangeCM-Gr
Model
Model
RangeCM-G
w/o CC w/o CC and TC w/o CC, TC, and MSC
+6.85%
w/o CC w/o CC and TC w/o CC, TC, and MSC
+2.30%
+22.02%
+21.88%
+34.19%
+31.75%
Furthermore, RangeCM greatly reduces the coding latency compared to existing methods, which demonstrates the advantages of the proposed 2D context model. Its coding latency is around 0.1 seconds on KITTI, which satisfies the requirements of real-time applications. Compared to the real-time compressor RENO, RangeCM achieves comparable coding latency with significantly bet- ter compression efficiency. For intensity compression, RangeCM over 100 times faster than the learning-based baseline Unicorn. Given the hybrid context, the inference latency of intensity com- pression is only around 10 milliseconds, because RangeCM only uses several additional layers to predict the intensity values. In contrast, Unicorn takes around 5 seconds to recompute contextual features for intensity prediction.
Since RangeCM utilizes the camera context, it requires a serial coding of camera images and Li- DAR point clouds, while other methods may process these two modalities in parallel. However, image compression can be quite fast on the GPU platform. For example, coding all 5 camera views with a GPU-accelerated JPEG codec (Nvidia (025c) takes only 2 milliseconds on WOD. There- fore, the serial camera-LiDAR compression RangeCM remains much faster than the LiDAR-only compression of baseline methods.
the other hand, RangeCM-G slightly outperforms RangeCM-GI in geometry compression, which implies that the joint geometry-intensity context modeling influences the geometry compression performance. This is probably due to the difficulty of training a versatile model. However, this performance gap is actually marginal, while the improvements on inference speed much more significant. Thus, is worthwhile to introduce the joint compression pipeline.
5.3 ABLATION STUDIES
We conduct ablation studies on WOD to validate the effectiveness of the proposed camera context model (CC), temporal context model (TC), and multi-scale context (MSC). We gradually remove these models from RangeCM-G to investigate their contributions to geometry compression. Then, sequentially remove these models from RangeCM-GI examine their benefits on intensity com- pression. The experimental results are shown in Tables
and
The camera context model obviously benefits geometry compression, yielding a BD-Rate improve- ment of 6.85%. This suggests that the proposed model effectively exploits the cross-modal depen- dency between camera and LiDAR. However, the improvement in intensity compression relatively modest, which is reasonable given the weak correlation between camera images and reflectance in- tensity. For example, the reflectance intensity is closely related to the material of real-world ob- jects, which may be difficult to identify only from camera images. Please refer to Appendix C for detailed discussions. Meanwhile, the temporal context model significantly enhances compres- sion performance, with Rate improvements of 15.17% and 19.58% for geometry and intensity compression, respectively. Moreover, the multi-scale intra-frame context model leads to significant improvements well.
CONCLUSION
In this work, we propose a fast and computationally efficient 2D context model for LiDAR point cloud compression. All computations
executed in the 2D domain, which yields a significantly faster inference speed compared to the 3D context models. Furthermore, the proposed method integrates the features of the current frame, reference frame, and camera images, constituting hy- brid context to facilitate effective compression. Moreover, we develop a joint snometty-intensinn compression workflow by predicting both modalities based the same hybrid context, thereby sig-

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nificantly accelerating the coding process. Extensive experiments on the WOD and SemanticKITTI datasets demonstrate that the proposed universal 2D context model achieves state-of-the-ut com- pression performance and delivers a fast coding speed that is applicable to low-latency applications.
ACKNOWLEDGMENTS
This work was supported by the General Research Fund (Project No. 16209622) from the Hong Kong Research Grants Council and by the Wuxi Research Institute of Applied Technologies, Ts- inghua University under Grant 20242001120.
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Appendix
A MODEL ARCHITECTURE
Spatial Context Extraction. The architecture of the VAE for spatial context extraction is shown in Fig. 5 This model aims to compress the spatial embedding of the range image along with the basic camera context. To produce the latent embedding y. the H x W range image is downsampled by 4 x 64 times, and the derived contains 96 channels. Distribution ý is estimated based on the hyperprior 2 and the temporal context 1h, The dimension of the spatial context 2, is 64.
Temporal Context Model. The temporal context model consists of a flow estimation module, VAE for flow compression, and a motion compensation module. The flow prediction module follows the design of the spatial pyramid network (Ranjan & Black] (017). The structure of the VAE and motion compensation module is shown in Fig. [6] The latent variable ye and the temporal context 2b include 128 and 64 channels, respectively.
Camera Context Model. The camera context model first employs CNN to extract features from the range image and camera images, shown in Fig. Then, it adopts two deformable attention blocks to align LiDAR and camera features. The architecture of the attention block represented in Fig. in the main paper. The deformable attention calculated in a 128-dimensional feature space, while the camera context the contains 64 channels. The head number in deformable attention 4, and we sample 8 key tokens in each attention head. For those range image pixels that are not projected onto any camera views, their camera features the are set to a zero tensor.
Multi-Scale Context Model. After obtaining the spatial, temporal. and camera contexts, the multi-scale context model aggregates these features and predicts the distribution of f1, f2. and sequentially. The logistic mixture consists of 3 components, with each component characterized by mean, scale, and weight. Therefore, we predict 9 parameters for each symbol. The architecture of the context fusion model is shown in Fig. g
B IMPLEMENTATION DETAILS
B. EXPERIMENT DETAILS
RangeCM-G and RangeCM-Gi are trained using the same settings. The model is optimized for 2M and 0.7M steps on the WOD and SemanticKITTI, respectively. We use an AdamW optimizer to train the model, and the batch size is set to During training, the learning rate is initially set to le 4, while it decreases to 5e - 5 after 60% training steps. All network modules are trained in an end-to-end manner following the loss function of Eq. 7
For geometry compression, RangeCM uses two-stage quantization, as defined by Eq. where the quantization steps are assigned as b,
and b2 = {1/5, 1/10, 1/25, 1/50, 1/100]. For intensity compression, we adopt different settings on different datasets. The original intensity values in KITTI have been quantized into 100 levels in [0, 1]. We further apply another quantization with a step of 2 x by to trade off the bitrate and reconstruction quality. For example, when the range values quantized by b2 = 1 100 in geometry compression, the intensity values are quantized with a step of 1/50.
Intensity values in the WOD are unbounded and continuous, so we first preprocess the data as:
[clip(s, 0, 1) x 2
To adjust the bitrate and reconstruction quality, ş is further quantized using step of b2-
Some pixels in the range image may be empty, where the laser beam does not detect any object along the corresponding direction. We represent these empty pixels with particular symbol and encode them as ordinary pixels. In this way, the decoder can recognize these pixels.
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Analysis Transform 9
Res c192, s1x4
Res Block, c96, si 1
Atteation Block, c96
Conv Coav3x3 696, x2
Attention Block c96
Comv3x3, x2x2
Context Model
Block, cl6
Res Block, 196
Res Bleck, c96
Conv3x3, c96
d: V. 1
Comv343,
Convlx4, Conv|x4,c192,s114 slx4
ConvIx4, c192,s14
Conv3x3, c192, s2x2
AE
Conv3x3, c192, x2x2
Synthesis Transform
Concat
Res Block, cbt, s
Atlention Block, c96
Res Block, c9,, x2x2
Attention c96
s2x2
Cony1x1, c192
Block, ets
Ros Block. c96
Res Block. c96
Ros Block, e96
SPCony, Cony c9t
Conv3 e/
AD
Conv1x1,
Contest Model
Convixi, e192
14g.09
Figure Architecture of the spatial context extraction model. Res Block indicates the residual
block. SPConv denotes the subpixel convolution layer. Attention Block is the convolution- boaeeetto----eed attention block (Cheng et al. 2020). c represents the output dimension of the corresponding block. indicates the stride of the downsampling or upsampling layer.
Analysis Transform
Cony 1av1x1 144, e128, s1x4
Conv 1 94, e128, $184
x2>2
Conv3x3 064, c64, x2x2
Conv3r3, c64
Cony3 c128,
y
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Conv3x3, c64
AE
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Synthesis Tramsfrm
Block,
x (C Cono3333 c64)
TConvl 14, c2, 11x4
TCnay] X4, c128, xX
SPCon 353 c128, x2
(Convi==1), c256
SPConv3x3, c128, s2
Cxnv3x3, c64
Conv3=3, c64
Conv333, c256
4/
Cony
Concat
Figure 6: Left: Architecture of the optical flow compression network. TConv indicates the transpose
convolution layer. Right: Structure the motion compensation module.
Camera Feature Extraction
LIDAR Feature
Extraction
Can-1e c64 sivt
Cama cl2 x2x2
Cam3333 c192 s2x2
Com333 (26 s2x
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Com.33 c192
Com c19
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Can 3x3 c192
Ranpe Imuge
Caw5X c6l
Ros Block, c61
Concal
Figure 7: Architecture of the camera and LiDAR feature extraction network the camera context model.
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Predicting F1
Predicting 222
Predicting
f
Convix1 c64
Conv3x3, c64
Convix1, c64
3x/Cony3x3, c64)
Cony Lomy1x1.cm co4
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MConv5#5, c64
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ConvIXI, c64
Concat +
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Convix1, c128
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ConvIx1,e9
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p(F)
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Figure 8: Architecture of the context fusion model. MConv indicates the convolution layer with checkerboard mask (He et al. 2021).
Figure 9: Visualization of the LiDAR point cloud and camera images. Points colored according to their range value. Best viewed zoomed in.
B.2 BASELINE SETTINGS
In geometry compression, the PSNR between the original point cloud P and the reconstructed point cloud P is calculated by:
3r
PSNR(P, P) 10 log10
max MSE(P, P), MSE P. P)
where is user-specified parameter called peak value. A common practice is setting to the maximum nearest neighbor distance across the entire dataset D (Biswas et al. 2020):
min |pp -P
(10)
PED PEEP j71
Following this formulation, we most baselines (e.g., EHEM (Song et al. 2023a and RICNet (Wang & Liu 2022) use the same peak values as ours, the settings Unicorn Wang et al., 2025a) and RIDDLE Zhou et al. 2022) are different. Therefore, we recompute their PSNRs under our setting to guarantee a fair comparison. Unicorn presents the MSE results, so we directly recompute the PSNR according to Eq 9 RIDDLE releases neither source codes MSE results, while the chamfer distance results are provided. As the distortion of RIDDLE is completely determined by the quantization applied to the range image, first find the quantization steps that match the reconstructed point cloud with the provided chamfer distance. Then, we use the above steps to reproduce their reconstructed point clouds, and calculate the PSNR using our peak values accordingly.
57.41 and 59.70 on WOD and KITTI, respectively, While
C DISCUSSION ON CAMERA CONTEXT MODEL.
effective solution to improve the perception algorithms in autonomous driving (Li et al. 2023b Vora et al.) 2020 Liu et al., 2023). Motivated by these multi-modal perception models, the proposed comprehensive context introduces camera images as additional contexts for LiDAR compression. Although it necessitates serial camera-LiDAR com- pression workflow, the image coding time is negligible using well-optimized aPU-accelerated
LiDAR-cament fusion has emerged
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Bitrate vs. DI PSNR (KITTI)
Bitrates vs. Detection Accuracy (KITTI)
D1 PSNN (88)
Precision 12
03
13
74
RangeCM-G wio CCM
RangeCM-Gi G-PCC
RangeCM-G
15
Point
Figure 10: Left: Rate-distortion curves the KITTI dataset. CCM means the camera context
model. Right: Downstream task performance on the KITTI.
codec (Nvidia 2025c). and we can deploy this image codec and RangeCM on the same GPU to minimize the overall coding latency. The success of these multi-modal perception methods also proves the practicality of deploying this LiDAR-camert fusion system in real-world applications.
Although most autonomous driving vehicles are equipped with both camera and LiDAR sensors, there may be special applications that require the standalone LiDAR compression (e.g.. in-the-wild LiDAR compression). In this case, we can simply remove the camera context, which corresponds to the RangeCM w/o CC model in Table and T Notably, this RangeCM variant maintains BD-rate gain of 11.18% compared to the sfate-of-the-art method RIDDLE. Therefore, RangeCM is flexible in choosing whether to utilize the camera context, and it is not limited to applications where the camera context available.
Besides, Table 2 and Table show that the camera context is more effective on geometry compres- sion. This is because the camera data provides dense semantic features that the sparse LiDAR point clouds lack, and these semantics are correlated to the range values, shown in Fig. 9 However, predicting the reflectance intensity from images challenging. Light reflectance closely related to the materials of real-world objects. Nevertheless, it is difficult to predict the object materials from visual images. For example, wax, plastic, and crystal may appear similar the image, while they have different reflectance properties (Li et al. 2023a). Even though the materials known, it still difficult to estimate the reflectance intensity without referring to the corresponding physical models. Therefore, camera context has more significant impact geometry compression than intensity compression.
D ADDITIONAL EXPERIMENTS
D.1 EVALUATING COMPREHENSIVE CONTEXT MODEL ON KITTI
The KITTI dataset does not provide the transformation matrices between LiDAR and camera on the testing set (i.e., sequences 1l to 21). Since all baseline methods follow the official dataset partition for training and evaluation, we exclude the camera context our main paper to ensure the comparison is conducted on the same testing set. Here, we reorganize the KITTI dataset to evaluate the performance of the comprehensive context model. Specifically, sequences 00, 01, 02, 04. 05, 06 are selected for training. while sequences 07, 08, 09, 10 constitute the testing set. Furthermore, we train baseline model without the camera context under this dataset division. Results in Fig. TO show that the camera context model leads to a 4.77% BD-Rate reduction compared to the baseline.
This improvement slightly decreases compared to the one on WOD, because KITTI only includes 2 camera views, while WOD comprises 5 cameras. Consequently, fewer points can find the camera references in KITTI, thereby the benefits of the camera context are weakened. Nevertheless, modern autonomous vehicles are commonly equipped with numerous cameras (c.g., the sixth-gent seth-eeneration Waymo Driver incorporates 13 cameras (Waymo) 20244). As a result, most points can find the camera context in practice, leading an effective camera context.
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Table Runtime (in seconds) comparison among different methods. The coding times of RangeCM are evaluated on the RTX 3080 GPU. Best results are marked in bold.
Geometry Compression
SemanticKITTI
WOD
Method
Encoding Infer.
Decoding Infer.
Encoding Infer.
Decoding
Total
Total
Total
Infer.
Total
G-PCC
0.95
0.48
1.24
0.62
EHEM
1.38
1.61
RENO
0.04
0.07
Unicorn
2.65
2.83
2.36
2.50
RICNet
0.40
0.63
0.40
0.43
RIDDLE
0.49
0.53
0.97
RangeCM-G
0.05
0.09
0.05
0.09
0.15
0.21
0.10
0.23
RangeCM-Gi
0.05
0.09
0.05
0.09
0.15
0.21
0.11
0.24
Intensity Compression
G-PCC
0.84
0.75
0.59
0.65
Unicorn
14.84
13.04
RangeCM-GI
0.06
0.12
0.04
0.12
0.16
0.24
0.12
0.27
D.2 ABLATION STUDIES ON CONTEXT REFINEMENT
In this section, validate the effectiveness of the context refinement strategy. Specifically, train RangeCM-G model without the refined camera context 2b. while preserving the basic camera context &h.. The context refinement strategy improves the BD-Rate by 5. 11%, which verifies its importance. On the other hand, this model still outperforms the baseline without the entire camera context model (CCM), proving the benefits of the basic camera context.
D.3 DOWNSTREAM TASK PERFORMANCE
We conduct object detection based on the decoded point clouds to investigate how compression influences the downstream task performance. Specifically, compress both the geometry and intensity information the point cloud, and evaluate the accuracy of the PointPillars detector (Lang al. 2019). Results are shown in Fig. [T0 Compared to G-PCC, RangeCM yields higher detection accuracy at similar bitrates.
D.4 CODING SPEED ON ENTRY-LEVEI. GPU
In the main paper, we evaluate RangeCM with an A6000 GPU. Here, we further test its coding latency with RTX 3080 GPU. Results in Table show that RangeCM still preserves faster speed than most baseline methods on this less powerful GPU.
D.5 EVALUATION ON DIFFERENT LIDAR TYPES
In addition to 64-line LiDAR, we evaluate the performance of RangeCM on 32-line LiDAR dataset NuScenes (Caesar al. 2020) and 128-line dataset DurLAR (Li et al. 2021b). Experimental results in Table 5 and Table demonstrate that RangeCM maintains satisfactory performance on these
different DAA types.
D.6 LOSSLESS REFLECTANCE INTENSITY COMPRESSION
In this section, compare RangeCM to the lossless LiDAR reflectance compressor SerLiC (Zhu et al., 2025) on KITTI and NuScenes datasets. Results Table 7 show that RangeCM yields comparable compression ratio SerLiC with faster coding speed. The joint geometry -intenstyyi compression of RangeCM remains faster than the reflectance compression of SerLiC, which demon- strates the superiority of the proposed 2D context model.
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Table 5: dataset.
BD-Rate improvements over G-PCC and total coding time (in seconds) NuScenes
Geometry Compression
Intensity Compression
Method
Encoding Decoding
Encoding Decoding Time
BD-Rate
BD-Rate
Time
Time
Time
G-PCC RangeCM-Gi
0.25
0.17
0
0.47
0.32
-37.89
0.08
0.07
-16.89
0.09
0.08
Table 6: BD-Rate improvements over G-PCC and total coding time (in seconds) on DurLAR dataset.
Geometry Compression
Intensity Compression
Method
Encoding Decoding
Encoding Decoding
BD-Rate
BD-Rate
Time
Time
Time
Time
G-PCC
0
1.30
0.64
0
1.93
1.33
RangeCM-GI
-42.71
0.22
0.20
-9.34
0.26
0.26
Table Bits per point and total coding time (in seconds) for lossless reflectance compression on Se- mantcKITTI (left) and NuScenes (right) dataset. Please note that the coding time of RangeCM is for joint geometry-intensity compression, while the time of SerLiC for reflectance-only compression.
Method BPP Enc. Time Dec. Time
Method BPP Enc. Time Dec. Time
SerLiC 3.64
0.18
0.23
SerLiC 2.52
RangeCM 3.66
0.11
0.12
RangeCM 2.75
0.09
0.10
Table 8: Bitrate distribution over different variables.
Rate Point
2g
y
8
f5
$2
by=1/5
0.57%
1.61%
1.24%
9.47%
15.09%
12.18%
24.68%
18.75%
8.84%
7.51%
1y=1/10
0.42%
1.20%
0.94%
7.79%
10.75%
8.82%
27.64%
22.20%
10.93%
9.29%
1y=1/25
0.27%
0.76%
0.60%
5.89%
6.79%
5.59%
29.81%
25.12%
13.55%
11.57%
by51/50
0.19%
0.55%
0.44%
4.54%
4.87%
4.02%
30.34%
26.41%
15.38%
13.26%
b2=1/100
0.14%
0.41%
0.33%
3.42%
3.62%
2.99%
30.13%
27.01 111
17.04%
14.87%
D.7 BIT ALLOCATION OVER VARIABLES
Table 8 presents the bitrate consumption distribution symbol groups and latent variables. Most bits are spent on coding i and 8, while the latent variables 2., ÿv. 2, and ÿ only constitute a minor proportion of the bitstream.
D.8 ROBUSTNESS TO SCANNING PATTERN
In this section, validate the robustness of RangeCM different LiDAR scanning patterns (i.e., laser elevation and azimuth angles). We fine-tune RangeCM-GI on the SemanticKITTI dataset based on the model trained on WOD, as the laser emission angles are different for these two datasets. In particular, this experiment is conducted on the reorganized KITTI dataset (as introduced in Appendix D.I. because the pretrained model exploits the camera context while the testing set of the original dataset does not provide the transformation matrices between LiDAR and camera sensors. The pretrained model is fine-tuned for 9K steps. Compared to the model trained on the reorganized KITTI, the fine-tuned model yields minor BD-Rate increase of 3.84% and 3.25% for geometry and intensity compression, respectively. The results demonstrate that the pretrained RangeCM model can be readily extended to different scanning patterns based simple fine-tuning process.
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Table Performance comparison of RangeCM-GI when compressing complete and partial LiDAR seans. For partial scan compression, we record the total bitrate for coding both left and right parts, and compute the BD-Rate against the performance of the complete sean compression. The coding time represents the runtime (in seconds) for coding complete or partial scans. Results are evaluated on the WOD.
Geometry BD-Rate
Intensity BD-Rate
Encoding Time
Decoding
Data
Time
Complete Scan
0%
0%
0.22
0.27
Partial Scan
+0.24%
+0.18%
0.14
0.16
Table 10: The BD-Rate improvements (%) over baseline methods achieved by RangeCM-G and RangeCM-GI, which are evaluated on the SemanticKITTI (left) and WOD (right), respectively.
Geometry Compression
Geometry Compression
Method
RangeCM-G
RangeCM-GI
Method
RangeCM-G RangeCM-Gi -61.96
G-PCC
-56.07
-51.56
G-PCC
-59.94
EHEM
-36.23
-26.98
EHEM
RENO
-49.12
-43.52
RENO
Unicorn
-38.78
-32.43
Unicorn
RICNet
-12.99
-2.90
RICNet
RIDDLE -15.41
-6.53
RIDDLE
-17.14
-2.59
Intensity Compression
Intensity Compression
G-PCC
-6.96
G-PCC
-20.93
Unicorn
+7.03
Unicorn
D.9 PARTIAL SCAN COMPRESSION
The common practice of LiDAR point cloud compression is to encode the complete sean. this setting, the codec compresses the current frame 2; when the sensor is scanning the next frame 2t+1, and the quantized previous frame - is used as the temporal context. Nevertheless, some latency-sensitive applications, the LiDAR compressor needs to compress the partial point cloud during the scanning of the sensor. To validate the performance of RangeCM on encoding the partial scan, we split the range image vertically into two uniform parts. Then, RangeCM- to encode the left and right parts of the image independently. The experimental results are shown in Table 9 When compressing the partial scan, RangeCM achieves almost the same performance as encoding the complete scan. Therefore, RangeCM can be directly applied to coding partial point clouds. Moreover, the coding latency for partial scan compression decreases accordingly.
employed
D.10 MORE BASELINES
In the main paper, RangeCM is compared against state-of-the-art LiDAR compression methods. Here, we additionally compare with another baseline MuSCLE (Biswas et al. 2020). MuSCLE utilizes spatio-temporal context and supports both geometry and intensity compression. However, it did not provide the PSNR results on intensity compression or the coding latency, so we only compare RangeCM with regarding geometry compression performance. On the SemanticKITTI dataset, RangeCM outperforms MuSCLE with BD-Rate of -49.69%, which verifies the strength of RangeCM.
D.11 BD-RATE TO BASELINES
In the main paper, compare the BD-Rate improvements G-PCC yielded by different meth- ods. Here, we provide the specific BD-Rate improvements of RangeCM compared to each baseline method. The corresponding results shown in Table TO
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Figure 11: Visualization of camera images captured in the dark scene.
E VISUALIZATION
We visualize the sampling locations and attention weights predicted by the deformable attention model in Fig. 12 Specifically, these results collected from the last attention block in the basic camera context model. is shown that deformable attention tends to aggregate features from similar semantic objects. In addition, the sampling points close to the reference point generally have higher attention scores.
Furthermore, we visualize the reconstructed optical flow ô given by the temporal context model, as shown in Fig. 13 We also present range value maps of the current and reference frame to visualize the ground-truth motion patterns. These results prove that the estimated flow effectively captures the motions between adjacent frames, which provides accurate correlations for temporal context modeling. Besides, we also visualize the original and decoded point clouds in Fig. 14
F DISCUSSION ON IN- VEHICLE DEPLOYMENT
This paper evaluates RangeCM's efficiency on the general-purpose GPU such RTX A6000 and RTX 3080. In practice, the in-vehicle GPU platform provides comparable even stronger comput- ing performance. For example, the Nvidia Drive AGX Thor platform offers 1000 Tera Operations Per Second (TOPS) for processing INT8 data (Nvidia 2025b). In contrast, the RTX A6000 and RTX 3080 GPU offer only 619.4 and 476 TOPS (Nvidia 2025a). Furthermore, the AGX Thor plat- form is built the latest Blackwell architecture, while the RTX A6000 and RTX 3080 are based on the previous- geroouuttnnntttionn Ampere architecture. Besides, the peak memory consumption of RangeCM only 3GB, while AGX Thor has 64GB system memory (shared by CPU and GPU), which suf- ficient to deploy our model. Finally, the AGX Thor platform adopts hardware-level optimization to accelerate the I/O of camera and LiDAR data, which enables fast I/O speed. Therefore, believe RangeCM can preserve low-latency coding capability in practical in-vehicle deployment.
G LIMITATION
As RangeCM utilizes the camera images as the context, its performance may be affected by the image quality. For example, the camera images are less informative in low visibility weather condi- tions (e.g., fog and rain) or dark scenes (e.g., night), which in turn influences the effectiveness of the proposed method. Compared its average performance, found that RangeCM costs 24.4% more bitrates to encode sequence captured in the dark environment. The images from this sequence are visualized Fig.
Furthermore, the intensity compression performance may decline when dealing with real-world ob- jects that have complex reflectance properties. For instance, difficult to predict the accurate reflectance intensity from woodland, water, and snow.
H LLM USAGE STATEMENT
LLM is not applied for any research ideation. We only use the LLM to detect grammar errors and polish the human-written manuscript.
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Visualization of the deformable attention model. Red triangle indicates the reference point, i.e., the 2D projection of the query. Dots represent the predicted sampling locations, and they are colored according to their attention scores.
Figure 12:
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Current Frame
Current Frame
Reference Frame
Reference Frame
Optical Flow
Optical Flow
Current Frame
Current Frame
Reference Frame
Reference Frame
Optical Flow
Optical Flow
Current Frame
Current Frame
Reference Frame
Reference Frame
Optical Flow
Optical Flow
Current Frame
Current Frame
Reference Frame
Reference Frume
Optical Flow
Optical Flow
Figure 13: Visualization the current frame, reference frame, and reconstructed optical flow.
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Input Point Cloud
Bits Point: 1.0, PSNR: 64.66
Bits Point: 2.01, PSNR: 77.66
Bits Point: 3.70, PSNR: 96.49
Cloud
PSNR:
Input Point Cloud
Bits Per Point: 1,27, PSNR: 64.68
Bits Per Point: 2.41, PSNR: 78.68
Bits Per Point: 4.17, PSNR: 91,53
Input Point Cloud
Bits Per Point: 1.27. PSNR: 64.62
Bits Per Point: 2.60, PSNR: 78.61
Bits Per Point: 4.46, PSNR: 93.53
Input Point Cloud
Bits Per Point: 0.79, PSNR: 64,76
Bits Per Point: 1.72, PSNR: 78.67
Bits Per Point: 3.40, PSNR: 90.51
Input Cloud
Figure 14: Visualization of the ground truth and reconstructed point clouds different bitrates. Color indicates the chamfer distance between the ground truth and the decoded point cloud.
24


Reviews:
[{"review_id": "3GrglUzzAb", "reviewer": "Reviewer_9wkT", "summary": "This paper presents a neural LiDAR point cloud compression method based on 2D context modeling, addressing the trade-off between compression efficiency and coding speed that limits existing 3D context-based methods such as voxel or octree approaches. By leveraging a 2D context structure, the proposed method achieves fast and efficient compression while supporting joint geometry and intensity compression. Experimental results show substantial gains in both rate-distortion performance and coding speed.\nThe contributions of the paper can be summarized as follows: 1. Develop a new paradigm for low-latency LiDAR compression. 2. Propose a comprehensive context model that integrates spatial, temporal, and camera features for LiDAR compression. 3. Design a joint compression backbone that predicts LiDAR geometry and intensity based on a hybrid context.", "questions": "1. Training and hyperparameter details: Please provide a complete description of the training hyperparameters for RangeCM-G and RangeCM-GI (e.g., batch size, optimizer, learning rate schedule, and total training steps). If any baseline methods were re-implemented, please clarify the details of their re-training procedure.\n\n2. Adaptation to different scanning patterns: The appendix presents evaluations on 32-/128-line LiDARs (Tables 6 and 7). Could the authors clarify the robustness of the range image mapping when the sensor’s emission pattern differs from that during training (e.g., different elevation angles)? Would retraining be necessary for each sensor, or could fine-tuning suffice?\n\n3. Limitations and failure cases: In which real-world scenarios do the authors expect RangeCM’s performance to degrade significantly (e.g., extreme weather, reflections from water or snow)? Could some failure cases and analyses be provided to illustrate the system’s limitations?", "limitations": "", "rating": 6, "confidence": 1, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The method integrates multi-scale spatial context, flow-based temporal context, and deformable-attention camera context, with context refinement addressing causality and alignment issues. Moreover, the hybrid context jointly supports geometry and intensity compression, reducing redundant computation and significantly improving practical efficiency compared to prior methods such as Unicorn.\n\n2. The paper presents thorough evaluations, including different LiDAR resolutions, coding latency, and impact on downstream tasks like PointPillars, demonstrating robustness and transferability. Ablation studies clearly quantify the contributions of camera, temporal, and multi-scale contexts, showing the effectiveness of each component.", "weaknesses": "1. It is unclear whether the VAE used for compressing range-view optical flow was specifically trained on flow data. Without training on range-view optical flow, the VAE may not be able to effectively encode the unique spatial patterns and dynamics of LiDAR flow, potentially leading to high reconstruction error and suboptimal bit allocation. The authors are encouraged to clarify whether the VAE was trained on optical flow and, if not, to discuss the potential impact on compression performance.\n\n2. Material properties and reflections do not directly correspond to visual appearance, which limits the effectiveness of using deformable attention to predict intensity from camera images. The authors are encouraged to discuss whether incorporating more physics- or sensor-based priors (e.g., material classification combined with simplified BRDF models) could improve intensity prediction, or to analyze the potential upper bound achievable by purely data-driven approaches.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "ZiYxouOfGU", "reviewer": "Reviewer_uEV9", "summary": "The paper proposes RangeCM, a neural LiDAR compression framework that integrates spatial, temporal, and camera contexts to achieve low-latency and high-efficiency range–intensity compression. It introduces deformable attention for LiDAR–camera alignment and a dual-stage quantization strategy for geometry encoding. Experiments on Waymo and SemanticKITTI show rate–distortion and latency improvements over G-PCC, RIDDLE, and Unicorn.", "questions": "1. The method claims early scene prediction before full scanning, yet all tests use complete point clouds. Does low latency refer only to reduced inference time rather than partial-scan prediction?\n2. Since each frame uses the ground-truth reference instead of reconstructed data, has the impact of error propagation in continuous decoding been evaluated?\n3. Why are recent temporal compression baselines like MuSCLE[1] and BIRD-PCC[2] omitted from comparisons or tables?\n4. Were all latency comparisons conducted on the same hardware (e.g., RTX A6000)? If not, how are timing results normalized?\n5. The experimental results in Table 1 contain many missing entries, and several baselines are not uniformly reproduced across datasets. Given that the paper’s main contribution lies in introducing the Camera Context, the KITTI evaluation omits this component and instead reuses baseline results due to unavailable calibration matrices. Does this inconsistent experimental design—using different model implementations for different datasets—undermine the fairness and reliability of the reported comparisons?\n6. The appendix reports downstream task performance on KITTI, which is informative. However, the results suggest that G-PCC achieves nearly identical downstream accuracy when doubling data usage (6→12), while also benefiting from simple parallelization, low end-to-end latency, and broad generalization without GPU dependency. Considering modern increases in communication bandwidth, under what specific conditions does the proposed method provide a clear practical advantage over G-PCC—particularly in real-time deployment scenarios where latency and generality may outweigh compression ratio improvements?\n7. The paper repeatedly claims real-time capability, yet all experiments are conducted on an RTX A6000 GPU—a configuration rarely available in real-world automotive or mobile platforms. Considering that true real-time performance requires end-to-end processing at sensor-level frame rates (≥10 FPS) under resource-constrained conditions, can the authors clarify the specific hardware assumptions and computational settings under which their method achieves real-time operation (e.g. memory, computational requirements)? If such conditions cannot be met on embedded or on-vehicle systems, should the claim be limited to low-latency rather than real-time performance?\n8. Other problems.\n1) [152] Equation (1) misuses angle indices: x and y depend on \\( \\theta_i \\), but \\( z\\) incorrectly uses \\(\\theta_j\\) instead of \\(\\theta_i\\), causing an inconsistency in coordinate conversion.\n2) [197] The phrase “full-precision range value map can be recovered as \\hat r = \\hat r_1 + \\hat r_2” is misleading because both terms are quantized. It should be described as a “reconstructed” or “approximate” range map .\n3) Figure 2 contains a typographical error “Intenstiy Map”.\n\n[1] Biswas S, Liu J, Wong K, et al. Muscle: Multi sweep compression of lidar using deep entropy models[J]. Advances in Neural Information Processing Systems, 2020, 33: 22170-22181.\n[2] Liu C S, Yeh J F, Hsu H, et al. Bird-pcc: Bi-directional range image-based deep lidar point cloud compression[C]//ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2023: 1-5.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "1. The paper presents a coherent end-to-end architecture integrating spatial, temporal, and camera contexts for LiDAR compression, showing a thoughtful system design.\n2. It consistently outperforms prior methods like RIDDLE and Unicorn in both compression ratio and reconstruction quality, demonstrating effectiveness across datasets.", "weaknesses": "1. The claimed low-latency perception is not experimentally validated, as all tests rely on complete point clouds rather than partial-scan or early-sensing data.\n2. The use of ground-truth references for each frame hides cumulative decoding errors, leaving long-term stability and error propagation unexamined.\n3. Inference latency comparisons may be biased, since the hardware configuration of baseline methods is unspecified.\n4. The absence of released models or code prevents independent verification and limits the reproducibility of results.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "O3h2j23Y1q", "reviewer": "Reviewer_MSXZ", "summary": "The paper addresses the LiDAR point cloud compression problem via context modeling. The method yields a significant performance increase and faster runtime than its competitors. In order to achieve that,\nall computations are performed in the 2D domain (images from the camera and range images from LiDAR). Furthermore, the proposed method utilizes context from camera features for LiDAR compression. \nEvaluations were performed on WOD and KITTI datasets, and comparisons to prior works are properly presented.", "questions": "1. How would the authors address imperfect data? For example, there might be misalignments between the camera and LiDAR or fast motions that cause smeared images.\n2. Why are some prior works not included in Figure 4?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper clearly makes and supports its claims through empirical evaluations.\n2. Components of the proposed method are clearly explained.", "weaknesses": "1. Storage is a major point of compression. What is the effective storage saving that the proposed method offers, compared to prior works, if any?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)