RangePerception: Taming LiDAR Range View for Efficient and Accurate 3D Object Detection
Authors: Yeqi BAI
Ben Fei
Youquan Liu
Tao MA
Yuenan Hou
Botian Shi
Yikang LI
Venue: NeurIPS 2023 poster (2023)
Keywords: 3D Detection
Autonomous Driving

Abstract: LiDAR-based 3D detection methods currently use bird's-eye view (BEV) or range view (RV) as their primary basis. The former relies on voxelization and 3D convolutions, resulting in inefficient training and inference processes. Conversely, RV-based methods demonstrate higher efficiency due to their compactness and compatibility with 2D convolutions, but their performance still trails behind that of BEV-based methods. To eliminate this performance gap while preserving the efficiency of RV-based methods, this study presents an efficient and accurate RV-based 3D object detection framework termed RangePerception. Through meticulous analysis, this study identifies two critical challenges impeding the performance of existing RV-based methods: 1) there exists a natural domain gap between the 3D world coordinate used in output and 2D range image coordinate used in input, generating difficulty in information extraction from range images; 2) native range images suffer from vision corruption issue, affecting the detection accuracy of the objects located on the margins of the range images. To address the key challenges above, we propose two novel algorithms named Range Aware Kernel (RAK) and Vision Restoration Module (VRM), which facilitate information flow from range image representation and world-coordinate 3D detection results. With the help of RAK and VRM, our RangePerception achieves 3.25/4.18 higher averaged L1/L2 AP compared to previous state-of-the-art RV-based method RangeDet, on Waymo Open Dataset. For the first time as an RV-based 3D detection method, RangePerception achieves slightly superior averaged AP compared with the well-known BEV-based method CenterPoint and the inference speed of RangePerception is 1.3 times as fast as CenterPoint.

LangePerception.. Taming LiDAR Range View for Efficient and Accurate 3D Object Detection
Yeqi Bai'
Ben Feil.2 Youquan Liu'
Tao Ma
Yuenan Hou* Botian Shi Yikang Li" Shanggaai AI Laboratory FFudan University 3 Multimedia Laboratory, The Chinese University of Hong Kong (baiyeqi, feiben, liuyouquan, matao] (houyuenan, shibotian, balaaann,,yyiaaaa kyy.kkngg)gpplaaae wrg.pp..............p...oo... cn
mataa]@pjlab @pjlab org.
Abstract
LiDAR-bace based 3D detection methods currently use bird's-eye view (BEV) or range view (RV) as their primary basis. The former relies on voxelization and 3D con- volutions, resulting in inefficient training and inference processes. Conversely, RV-based methods demonstrate higher efficiency due to their compactness and compatibility with 2D convolutions, but their performance still trails behind that of BEV-based methods. To eliminate this performance gap while preserving the effi ciency of RV-based methods, this study presents an efficient and accurate RV-based 3D object detection framework termed RengePerception. Through meticulous analysis, this study identifies two critical challenges impeding the performance of existing RV-based methods: 1) there exists a natural domain gap between the 3D world coordinate used in output and 2D range image coordinate used in input, generating difficulty in information extraction from range images; 2) native range images suffer from vision corruption issue, affecting the detection accuracy of the objects located on the margins of the range images. To address the key challenges above, we propose two novel algorithms named Range Aware Kernel (RAK) and Vision Restoration Module (VRM), which facilitate information flow from range image representation and world-coordinate 3D detection results. With the help of RAK and VRM, our RungePerception achieves 3.25/4. 18 higher averaged L./112 AP compared previous state-of-the-are RV-based method RangeDet, Open Dataset. For the first time as an RV-based 3D detection method, RangePer- ception achieves slightly superior averaged AP compared with the well-known BEV-based method CenterPoint and the inference speed of RangePerception is 1.3 times as fast as CenterPoint.
Waymo
Introduction
In recent years, LiDAR-based 3D perception 1 3, 4 5
has made tremendous advances in the
field of autonomous driving. One primary task in this area 3D detection [ 2 7 9 [0]. which involves predicting the 3D locations of objects of interest, well as their geometric and motional properties, including categories, 3D sizes, headings, and velocities. Despite sharing some similarities, there is fundamental difference between LiDAR-bases 3D detection and maae--aseed
2D detection [1] 13 14 [5] 16]: RGB images are inherently well-structured matrices, whereas LiDAR signals are sets of sparse and unordered points in 3D space. Considering modern computer vision techniques, such as Convolutional Neural Networks (CNNs) 11 [8] and Vision Transformers
*Coresponding author
37th Conference on Neural Information Processing Systems (NcurIPS 2023).

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(ViTs) 19 20 require well-structured matrices as inputs, an essential process in LiDAR- 3D detection is to effectively organize the unstructured LiDAR points.
To effectively organize LiDAR points and facilitate the use of established computer vision techniques, two major representations have been adopted: bird's-eye view (BEV) and range view (RV). Shown in Fig. a-d) is a frame of top LiDAR signal from Waymo Open Dataset (WOD) 211, represented in RV and BEV accordingly. BEV-b 3D detectors 2 convert sparse LiDAR points into 3D voxel grids, extract features with a 3D convolution backbone, and perform classification and regression after mapping the extracted 3D features to the BEV. With the aid of well-organized voxel representation and compatibility with well well-developed detection paradigms, BEV-based methods exhibit the highest detection accuracy among contemporary LiDAR- based 3D object detectors. However, it is worth pointing out that LiDAR sensor in autonomous driving scenarios [22 23 24 uniformly samples from spherical coordinate system, and the cartesian-coordinat incompatibility with LiDAR sensors, as evidenced by two factors. First, as the distance from LiDAR grows longer, the distribution of voxels becomes a lot sparser, despite that LiDAR beams are uniformly distributed in the spherical coordinate. Second, when the horizontal length of farthest beam extends to the time of I, the space of the voxel representation has to increase to the time of 12 to contain complete information. As result, BEV-based methods have certain drawbacks in their applications, such as the requirement for complex and time-consuming sparse 3D convolutions as their backbones, limiting their efficiency. Moreover, if users aim to augment horizontal perception range, the computational complexity of BEV-based detectors must increase quadratically, which poses a challenge for real-time implementation.
voxel representation has certain
The range-view (RV) representation, on the other hand, is naturally generated from the scanning mechanism of the LiDAR sensor (25 26 27 28). Each pixel in the range image corresponds to an optical response of the LiDAR beam, making the range view the most compact and informative way to represent LiDAR signals. The compactness of range images also enables RV-based 3D detectors 22 30
9 10 to enjoy more compact feature spaces and higher inference speeds, compared to the BEV-based 3D detectors. However, in the aspect of detection accuracy, pioneering RV-based 3D detectors significantly lag behind the top- nop-perfrmming BEV- based detectors, with a performance gap of more than 10 average L1 AP on WOD validation set. More recently, RangeDet [9] proposed several modules with stronger representation power, narrowing the performance gap to 2.77 average L1 3D AP on WOD, compared with state-of-the-an BEV-based method CenterPoint (6]. On top of the arts above, FCOS-LiDAR [0] develops a multi-frame RV-based detection pipeline on nuScenes dataset [1 32). Despite being 90% faster than CenterPoint, FCOS-LiDAR's overall validation AP is 3.32 lower, evaluated with multi-frame setting on nuScenes dataset.
To better exploit the potential of the range-view representation, detailed analysis of existing RV-based detection methods is conducted, which reveals two critical unsolved challenges.
Spatial Misalignment. Existing RV-based detectors treat range images the same way as RGB images, by directly feeding them into 2D convolution backbones. This workflow neglects the nature that range images contain rich depth information, and even two range pixels are adjacent in range coordinate, their actual distance in 3D space could be more than 30 meters. As visualized in Fig. [e). foreground pixels on the margins of vehicles and pedestrians are often far from their neighboring background pixels in 3D space. Directly processing such 3D-space-unee uncorrelated pixels with 2D convolution kernels can only produce noisy features, hindering geometric information extraction from the margins of foreground objects. This phenomenon will be termed as Spatial Misalignment in further discussion of this paper.
Vision Corruption. When objects of interest are located on the margins of range images, as shown in Fig. [c..), their corresponding foreground range pixels are separately distributed around the left and right borders of the range image. Since CNNs have limited receptive fields, features around the left border cannot be shared with features around the right border and vice versa, when 2D convolution backbones are used as feature extractors. This phenomenon, called Vision Corruption, can significantly impact the detection accuracy of objects on the margins of range images. Previous RV-based detection methods have overlooked this issue and directly processed range images with 2D convolution backbones without compensating for the corrupted areas.
In this paper, we demonstrate an efficient and accurate RV-based 3D detection framework, termed RangePerception. To overcome the key challenges above, two novel algorithms named Range Aware Kernel (RAK) and Vision Restoration Module (VRM) are proposed and integrated into RangePer-
2

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Range LiDAR points (RV)
(b) Intensity of LiDAR points (RV)
(c) Foreground LiDAR points of Vehieles (RV)
40
80
(c) Range distribution of foreground points (RV)
rig
(d) LiDAR points (BEV)
(f) Vision Corruption phenomena (RV)
Figure 1: (a-d) A sample frame of top LiDAR signal, represented in RV and BEV respectively. (e) Spatial Misalignment phenomena. (f) Vision Corruption phenomena.
ception framework, both facilitating information flow from range image representation and world- coordinate 3D detection results. With the help of RAK and VRM, our RangePerception presents 73.62 & 80.24 & 70.33 LI 3D AP for vehicle pedestrian & cyclist, on WOD, achieving state-of- the-art performance as range-view-based 3D detection method. The contributions of this paper presented as follows.
RangePerception Framework. A novel niigh-pefformigg 3D detection framework, named RangePer- ception, introduced in this paper. RangePerception the first RV-based 3D detector achieve 74.73/69.17 average L1/1.2 AP on WOD, outperforming the previous state-of-the-arr RV-hased detector RangeDet, which has average L./L22 APs of 71 11.48/64.99, presenting an improvement of 3.25/4.18. RangePerception also demonstrates slightly superior performance compared to widely- used BEV-based method CenterPoint (6). which has average L1/L2 APs of 74.25/68 04. Notably, RangePerception's inference speed is 1.3 times as fast as CenterPoint, justifying better fitness for real-time deployment on autonomous vehicles
Range Aware Kernel. As part of RangePerception's 's feature extractor, Range Aware Kernel (RAK) a trailblazing algorithm tailored RV-based networks. RAK disentangles the range image space into multiple subspaces, and overcomes the Spatial Misalignment issue by enabling independent feature extraction from each subspace. Experimental results show that RAK lifts the average L./I12 AP by 5.75/5.99, while incurring negligible computational cost.
Projectt website is available at htlttp::/rraggeprcpttoonn..iiiie 10 enhance the accessibility and comprehension of this study.

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Range Imsge
Donse Prediction
Vision Restoration Module
Range Aware
Range Backbone
Redundancy Pruner
Region Proposal
Kernel
Network
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De geonxe De geosn
Re[-Atr+a]
Subepaoes
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Figure 2: The RangePerception framework takes range image I as input and generates dense predictions. To improve representation learning. the framework sequentially integrates the VRM and RAK modules before the Range Backbone. Subsequently, specially devised Redundancy Pruner is used to remove redundancies in the deep features, thereby mitigating the computational cost in the subsequent Region Proposal Network and post-processe cost-processig layers.
Vision Restoration Module. To resolve the Vision Corruption issue, Vision Restoration Module (VRM) is brought to light in this study. VRM extends the receptive field of the backbone network by restoring previously corrupted areas. VRM is particularly helpful to the detection of vehicles, as will be illustrated in the experiment section.
2 Preliminary of Range-view Representation
This section provides brief overview of the range view representation of LiDAR data. Specifically, LiDAR data can be represented as an m x n matrix, known as range image, where m represents the number of beams emitted and represents the number of measurements taken during cycle. Each column the range image corresponds to a shared azimuth, while each row corresponds to shared inclination, indicating the relative vertical and horizontal angles of returned point with respect to the LiDAR's ' original point. Each pixel in the range image contains at least three geometric values, namely range r, azimuth 0, and inclination 6, which define spherical coordinate system. The widely-used point cloud data with Cartesian coordinates is derived from the spherical coordinate system: Cartesian coordinates of the points. Considering modern LiDAR sensors [3 34 35
cos( sin(0), z s()) where v. y, denote the often measure magnitude of the returned laser pulse named intensity 1) and elongation p. range view of LiDAR signal can be engineered as / E mxxxx with each pixel being 1j, = {r, y, x, 0, 6, p}. This study additionally defines range matrix as " with each pixel being RG r, for the sake of further discussion. To provide a better illustration, Fig. I[a) presents a sample frame of range matrix R and Fig. [b) shows intensity values in the corresponding range image I.
cos(() cos(0),
Methodology
The RangePerceeptio framework, depicted in Fig. takes range image input and produces dense predictions. To enhance representation learning, VRM and RAK are sequentially incorporated before the Range Backbone. Afterwards, a carefully designed Redundancy Pruner is employed to eliminate redundancies in deep features, which minimizes computational costs in subsequent Region Proposal Network (RPN) and post-processing layers. This section begins with comprehensive explanations of RAK and VRM, followed by an in-depth elaboration of RangePerception architecture.

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(c) Range LiDAR points in range image
(d) Intensity of LiDAR points in range image!
(a) Perception Windows for RAK
(c) Intensity of LiDAR points in subspace K
Remedy for Spatial Misalignment
Intensity of LiDAR points in subspace
Figure 3: Range Aware Kernel disentangles the range image space into multiple subspaces, and overcomes the Spatial Misalignment issue by enabling independent feature extraction from each subspace.
3.1 Range Aware Kernel
As key component of RangePerception's feature extractor, Range Aware Kernel is innovative algorithm specifically designed for RV-based networks. RAK disentangles the range image space into multiple subspaces, and overcomes the Spatial Misalignment issue by enabling independent feature extraction from each subspace. Shown in Fig. a), Range Aware Kernel comes with a set of I predefined Perception Windows, formulated as W W1, W2, ..., W1-1, wi), where each Perception
Window is a range-conditioned interval Wi [Te1, T:2..
Given a frame of range image I IR I RAK first caleulates binary mask M E Zixmxnx8 according to Perception Windows W = (Wi, W2, W1-1, wi). where each M, E g is pixel-wise mask for range image I, indicating whether each range value Ry Ij,8,1 stays in current Perception Window wy. Subsequently, RAK defines tensor K Rixmxxx8 representing I subspaces, and derives each subspace K; = M, @ I. Detailed computing logic of RAK is illustrated Algorithm [ note that though inference process of RAK seems incur (Imn) time complexity, GPU implementation of RAK can readily achieve ((1) with proper parallelism.
As elaborated above, RAK divides range image I into multiple subspaces K, where each subspace K; contains LiDAR points that belong to Perception Window w,. To provide a clearer visualization, Fig. c,d) displays range and intensity values of a frame of range image I. By further processing range image with RAK, tensor K is computed, from which intensity values of subspaces Ki and Ka presented in Fig- ee,f. RAK effectively separates foreground vehicle points from their background counterparts, thus minimizing Spatial Misalignment and facilitating feature extraction from range view. Fig. Bb) provides further evidence of the efficacy of RAK. by clearly disentangling previously indistinguishable vehicles from noisy background points.
In the architecture of RangePercepptin, Range Aware Kernel is positioned directly before backbone network. Subspaces K E R R!xmxixs generated from Range Aware Kernel, is subsequently fed into the backbone network for non- non-linear feature extraction. Experimental results demonstrate that RAK increases the average L1/L2 AP by 5.75/5.99, while incurring negligible computational cost.
3.2 Vision Restoration Module
As described Sec. each column in range image corresponds to a shared azimuth 0 E [0, 2m] indicating the spinning angle of LiDAR. Specifically, 0 = 0 at left margin of range image and 0 = 2n right margin of range image. Due the periodicity of LiDAR's scanning cycle, azimuth values 0 and 2T correspond to beginning and end of each scanning cycle, both pointing in the opposite
5

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Algorithm 1 Range Aware Kernel
1: input range image E R
2: init range matrix R E Rm*=
3: init i, A: E &
4: for j m.) k E [1, do
5: Rí.A < [jA.11
6: init tensor E R
7: init binary mask M E 2
8: for W/ E W do
9:
for RJA in R do
0 = 8
0 = 8
10:
if Rg k E wi then Mij.k f-1
11:
12:
else Rg $ w. then
13:
Mi f-
14:
K4 f M4 O
-=0
15:
output tensor K
Figure Spherical Coordinate of VRM.
direction of the ego vehicle. As illustrated in Fig. 4 objects located behind ego vehicle are often separated by ray with 0 = O, resulting in Vision Corruption phenomena elaborated in Fig. c..f).
To resolve Vision Corruption issue, Vision Restoration Module is brought light in this study. By predefining a restoration angle 8, VRM builds an extended spherical space with azimuth E [- + 8]. In this way, visual features originally corrupted by LiDAR's sampling process restored on both sides of range image /, significantly easing the feature extraction from the margins of I. This restoration process clearly visualized in Fig. (ja.), where VRM-extended range images can be termed as 1" In the architecture of RangePerception, restored range images I" are subsequently processed by RAK and range backbone for deeper pattern recognition. Note that in Sec. 3.1 input range image of RAK is still denoted as I instead of I", for the sake of simplicity.
It is straightforward to observe from Fig. 5 a) that VRM introduces redundancies to range view: region with azimuth 0 E [-6, 5], appears twice in restored range image I". Though this duplication helps information extraction from the margins of I, redundancies persist in feature space F" learnt by RAK and range backbone, leading to unnecessary computational costs for subsequent region proposal network and
layers. To address this issue and improve efficiency, Redundancy Pruner (RP) is designed and equipped with RangePerception framework. Operating on feature space F", RP performs inverse function of VRM, by pruning f" from azimuth interval [-ô, 2n + 8) back to [o, 2m)
To better explain the process above, a pseudo VRMMextended image is rendered in Fig. ()c) Vanilla pixels in I are filled with zeros, while pixels generated by VRM are filled with ones. A pseudo feature space F" is subsequently computed, via inputting pseudo image to RAK and backbone. Finally, RP drops redundancies in pseudo F", resulting in pruned feature space F. As observed from lower part of Fig. [(c), visual information that belongs to VRM-restored spaces readily flows to vanilla space, thanks to the layered convolution kernels. This justifies that Redundancy Pruner solely decreases computational costs, without causing any loss in visual features.
3.3 RangePerception Framework
For the sake of efficiency, RangePercepption designed as an anchor-fres single-star stage detector, as presented in Fig. 2 Given frame of range image I, Perceptornn framework first compensates the corrupted regions with VRM, resulting in restored image T" E Rmxxxx where μ m(6+m) Subsequently, RAK converts I" into subspaces K T disentangling misaligned pixels. Range Backbone, derived from DLA-34 (36), is adopted to extract non-linear features F E R muxx from subspaces. Further, RP eliminates redundant features, generating pruned features E Rmxnxe RPN learns dense predictions (C, B, U} on top of deep features F. representing class predictions, box predictions, and IoU predictions accordingly. Finally, Weight NMS employed aggregate dense predictions, where IoU predictions U are treated as per-box confidence scores.
"

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RAK & Backbone
(a) Vision Restoration of range matrix
F
Redundancy Pruner
(b) Vision Restoration of foreground points
(c) Pseudo feature propagation
Figure 5: Vision Restoration Module. By predefining restoration angle 8, VRM builds extended spherical space with azimuth 0 E [-ō, 2n + 6]. As result, Vision Corruption issues are resolved on both sides of range image I, significantly easing the feature extraction from the margins of I.
Related Work
BEV-based 3D Detection. The majority of the perfhrrippr LiDAR-based 3D detectors are categorized as BEV-based detection, where the initial step involves the conversion of the point cloud into BEV (Bird's Eye View) images. VoxelNet (37 is the pioneering end- and-to-end BEV-based detector that utilizes PointNet 38 for inner-voxel representation and 3D convolutions for high-level feature generation, where high-level features are further processed by region proposal network (RPN). To reduce the computational burden of 3D convolutions, SECOND (1] introduces the usage of sparse convolutions. Another common approach is to eliminate voxelization along the elevation axis and convert the point cloud into pillars instead of voxels, as proposed by 22] By leveraging either voxel- based or pillar-based BEV representation. CenterPoint [6] achieves state-of -f-toof-tt---a--- the-art performance levels center-based anchor-free manner. However, complex and time-consuming sparse 3D convolutions in these methods hinder their practical applications, and lightweight 3D detectors urgently need to be developed.
Multi-view-based 3D Detection. Most nop-performing multi- calti--vee-bbsedd (MV-based) 3D detec- 8 adopt two-stage approach, where the first stage typically employs BEV-based detector, and point-view features are subsequently utilized in the second stage for proposal refinement. PV- RCNN 7 combines 3D voxel CNN and PointNet-based (38) set abstraction to learn diseriminative features from point clouds. Part-A² [8] introduces part-awareness multi-view aggregation, achieving outstanding performance by sequentially predicting coarse 3D proposals and accurate intra-object part locations. However, despite the excellent detection performance achieved by MV-based methods, their inference speed remains impractical for real-time deployment on vehicles due to the computational complexity of their structure.
tors 7
Range-view-basee -aasd 3D Detection. In light of the compactness of the RV representation, certain approaches endeavor to perform detection based on RV. VeloFCN [3 was the first work to perform 3D object detection using RV, which involves transforming the point cloud range image and
then applying 2D convolution to detect 3D objects. Subsequent research endeavors (2 30
10 put forth improve the efficacy of RV-based detectors. For instance, LaserNet 29 models the
distribution of 3D box corners to capture their uncertainty, resulting in more accurate detections.
RCD [0 introduces the range-conditioned dilation mechanism to dynamically adjust the dilation rate based on the measured range, thereby alleviating the scale-sensitivity issue of RV-based detectors. Moreover, RangeDet [9] proposes the Range Conditioned Pyramid mitigate the scale-variation issue and utilizes the Meta-Ken Kernel convolution to better exploit the 3D geometric information of the points. Despite outperforming all previous RV-based methods, RangeDet lags behind the widely-used BEV- based method CenterPoint by 2.77 average AP, evaluated on WOD. Recently, FCOS-LiDAS [IO] proposes a novel range view projection mechanism, and demonstrates the benefits of fusing multi- frame point clouds for a Lange-view-based detector. Although FCOS-LiDAR is 90% faster than CenterPoint, its overall validation 3.32 lower, evaluated with multi-fa multi-frame setting on nuScenes dataset.
7

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Table 1: Detection performance measured by 3D AP/APH WOD validation set, along with inference speed measured by FPS. All experiments are conducted under single-frane frame setting.
Mathod
View
Stage
Vehiate
Pallstriin
Cyclist
Average
FPS
Li
L1
L.2
Socond
72.25/71
84163.33
60,7351
6066189
58: 18.35/57.11
1223302
60:9
20.51
PoinPillar
one 71.76/71
69.6544.41
61.6841.21
59.9952.05
66.81/57.00
60.6655
CenterPaint
D
one 74.58/4404
66/42/65.03
76.17769.90
68.3392..3
72.00/0..00
69.39968223
74.25/1.61
68,05163
34.73
PV-RCNN
8-P
two
76.70/76 11
68 68-44667.95
73.95/63.20 20
65.68/55.66
67.95666225
65.54M63.79
73,4788852 52
66,5562 53
3.17
Part-4 -mnchor
H+3
two 77.03/76.49
08.46/67.988
75,2666.89
06, 19/5.63
05.61/67.37
66.13164498
73.63/70.25
66,939338 85
8:25
RangeDet
R
72.85/72.33
64 64.03663.57
75,94/71.99 (94
67, 67.60663.0 89
65.67/64.39 39
63,33/62.08
71,4889,55 55
64,99/63.16
34.88
RungePascoption
73.62/73.11
66.47/66.00
80,3476.12
72.2
70.336833
68:75/67.43
74/7372.72
69.17/67.32
45.85
5
Experiments
The experiments in this study utilize the WOD dataset, which is the only dataset that provides native range images. WOD consists of 798 training sequences and 202 validation sequences, with each sequence containing approximately 200 frames. The 64-beam top LiDAR signals are utilized to train and evaluate the RangePerception and baseline models. Scan per cycle of WOD's top LiDAR is 2650, resulting in range image represented by / E 2664x250088 The metries of L1/L2 3D AP/APH are calculated and reported following the official evaluation protocol of WOD.
Dataset.
Data Augmentation. Data augmentation techniques are employed during training improve the model's ' generalization capabilities. Range images and point clouds are randomly flipped along both the x and y axes and rotated within the range of [-x /4 т. /4. Additionally, random global scaling factor between [0.95, 1.05] is applied. The ground-trud truth copy-paste data augmentation approach is also utilized.
Implementation Details. The RangePereeption framework is implemented on top of OpenPCDet codebase [40] Since OpenPCDet only supports voxel-based and point-based models, range-view data pipeline and detection models are built from scratch. For Vision Restoration Module, restoration angle 8 is predefined as 0.086T, generating I" E R For Range Aware Kernel, six Percep- tion Windows are adopted: W = {0, 15]. [10, 20), [15, 30), [20, 40]. [30, 60), [45, )o)) resulting in Subspaces K adopted as Range Backbone, by updating the input convolution kernel's fan in to 48 channels. Models learned using Adam optimizer with an initial learning rate of 3e-3, scheduled with the one-cycle learning rate policy. The decay weight is set to 0.01, and momentum range is [0.95, 0.85). All models trained with 30 epochs on WOD training set, where batch size is 32 and frame sampling rate is 100%. Inference speed is examined with one NVIDIA A100 GPU with batch size set to 1.
R [6x644 226x64x880x5 2880 and transformed Subspaces K' E R 644x x8800888222 48 DLA-34 network is
Baseline Methods. As shown in Table 1 state-of-the-art BEV-based 1 MV-based RV-based (9] detectors are selected as baseline methods. All BEV-based and MV-based baselines trained and evaluated with OpenPCDet's official Py implementation. Since official open- sourced version of RengeDet is coded with MxNet [41). we reimplement RangeDet with PyTorch and integrate RangeDet-Py Torch into OpenPCDet framework. We train RangeDet-Py
and
according to settings presented in their paper and measure its inference speed under OpenPCDet framework. The detection AP/APH of RangeDet is listed according to experimental results in their paper.
Main Results. Detection performance measured by 3D AP/APH is reported in Table [ where RangePerception is compared against state-of-the-art BEV-based (B), RV-based (R), and MV-based (B+P) methods. Inference speed measured by frame per second (FPS) also presented. is evident that RangePerception's 's average AP/APH surpass all baselines, which highlights the strong detection functionality of RangePerception framework. Furthermore, RangePerception achieves state-of-the-an performance in pedestrian AP/APH. We attribute this to the fact that range images better preserve visual features of small objects, while voxelization introduces quantization errors to originally sparse foreground points. The results also demonstrate that RangcPerception has the fastest inference speed among all methods. Specifically, RangePerception is 1.32 times as fast as CenterPoint, which is already highly efficient BEV-based detector. Additionally, RangePerception is the first RV-based detector to achieve higher average AP/APH compared to CenterPoint, outperforming the previous state-of-the-arr RV-based method by large margin.
Ablation Study. Presented Table 2 an ablation study is conducted to assess the effectiveness of proposed designs, specifically RAK and VRM. Firstly, the study investigates the impact of RAK
8

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Table 2: Ablation study of RangePerrception, measured by 3D AP/APH on WOD validation set.
Setting
Vehaclo
Pedestiion
Cyclist
Average
L2
A1
withouut RAK
70.46/70.01
63.45662.8 89
72.86/68.85
64.56/60.72
63.63/62-46
61.44660.25
68.98/67. 11
63.18/61.29
A2
20 Comvolution
70,48/70.03
63,46/62.99
72,84668 87
64.56/60.71
63 3366462.44 43
61.45/60.26
68.99667 11
63. 13.16/11.29
65.98/65.37
66.06/61.21
A4
Perception Windows
73.13/72.62
65.97/65.51
80.15/76.03
72.17/68.42
70.15568.85
68.42/67.10
74,48/72 50
68.85/67.01
A4
Perceptioe Windowss
73.59/73.08
66,5585598
80.25/76.13
72.30/88556
70.3166892
68.74/67.42
74.72/7..71
69.16/67.22
72.50/71977
66/40/65.91
70.31/68.92
68.74/67422
74.3472.52
69.13/67.29
RangoPenceplio
73.62/73.11
66.47/66.00
80.24/76.12
72.29/68.51
70.33/68.93
68.75/67.43
74.73/72.72
69.17/67.32
by removing it from our framework (AI) and replacing it with 1x] Convolution layer of equal dimension (A2). The results show significant decrease of more than 5 average L1 AP for both cases, highlighting the crucial role of RAK in achieving strong detection performance. Secondly, we compare the performance of RAK and Kernel by replacing RAK with Meta-Kernel in our setting by an
framework (A3). Comparison reveals that the RAK setting outperforms the improvement of 2.85 average L1 AP, further validating the efficacy of RAK in our approach. Thirdly, we investigate the optimality of the Perception Window setting in RAK by varying the number of Perception Windows to 4 and 8, respectively (A4, A5). Analysis indicates that reducing the number of Perception Windows leads to subtle decrease in detection AP, while increasing the number of Perception Windows brings no further performance gain. Lastly, to evaluate the efficacy of VRM, we disable VRM and RP during training and inference processes (A6). Notably, decreases in detection AP are observed for all classes, particularly for vehicles. We attribute this observation to the fact that vehicles, being relatively large objects, are more susceptible to Vision Corruption.
(a)
Figure 6: Qualitative detection results of RangePerception, on a validation frame of WOD. (a) Input range image. (b.c) Dense class predictions for vehicles and pedestrians. (d) Predicted boxes from BEV. (e) Ground-truth boxes from BEV. For (a-c), ego vehicle heads towards the middle of the range image. For (d.e), ego vehicle visually highlighted in red.

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(a) Intensity of input range image
(b) Dense class predictions for vehicles
(c) Predicted boxes from BEV
(d) Ground-truth boxes from BEV
Figure 7: Qualitative vehicle detection results from RangePerception, validation frame of WOD. Notably, the vehicles this frame are significantly occluded by trees and barriers. Despite these challenges, RangePerception exhibits remarkable detection performance by effectively extracting features from the range view.
Qualitative Results. Detection results WOD validation frame are visualized in Fig. Notably, the sparse pedestrian foreground points are accurately preserved in RV, which eases the detection process of RangePerceppion. Additionally, Fig. shoowases RangePerception' 's capability of handling highly occluded foreground objects.
Limitations
RangePerception is designed to be highly compatible with point clouds generated from single viewpoint. Similar to other existing RV-based detectors, RangePerceetiio may not be suitable for perception tasks involving point clouds generated from multiple viewpoints, such those obtained from multiple autonomous vehicles. However, it is important to emphasize that the occurrence of such vielii-iee point clouds is rare autonomous driving scenarios. Therefore, this limitation does not affect the real-world deployment and application of RangePerceptiin framework.
7 Conclusion
This paper presents RangePerception, an RV-based 3D detection framework that effectively addresses Spatial Misalignment and Vision Corruption challenges. By introducing RAK and VRM, RangePer- ception achieves superior detection performance on WOD, showcasing its potential for efficient and accurate real-world deployment.
10

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Acknowledgments and Disclosure of Funding
The research was supported by Shanghai Artificial Intelligence Laboratory. the National Key R&D Program of China (Grant No. 2022ZD01 (002000000104) and the Science and Technology Commission of Shanghai Municipality (Grant No. 22DZ1 100102).
The authors express their gratitude to Zhongxuan Hu from Huazhong University of Science and Technology for the valuable assistance in refining mathematical representations in this paper.
References
[1] Yan Yan, Yuxing Mao, and Bo Li. Second: Sparsely emhedded convolutional detection. In Sensors, 2018.
[2] Alex H Lang, Sourabh Vora, Holger Caesar, Lubing Zhou, Jiong Yang, and Oscar Beijbom. Pointpillars: Fast encoders for object detection from point clouds. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2019.
[3] Xinge Zhu, Hui Zhou, Tai Wang, Fangzhou Hong. Yuexin Ma, Wei Li, Hongsheng Li, and Dahua Lin. Cylindrical and asymmetrical 3d convolution networks for lidar segmentation. In Proceedings of the IEEF/CVF conference computer vision and pattern recognition, pages 9939-9948, 2021.
[4] Jianyun Xu, Ruixiang Zhang, Jian Dou, Yushi Zhu, Jie Sun, and Shiliang Pu. Rpvnet: A deep and efficient range-point-voxel fusion network for lidar point cloud segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 16024 16033, 2021.
Haotian Tang, Zhijian Liu, Shengyu Zhao, Yujun Lin, Ji Lin, Hanrui Wang, and Song Han. Searching efficient 3d architectures with sparse point-voxel convolution. In Computer Vision-ECCV 2020: 16th Luropean Conference, Glasgow, UK, August 23-28, 2020, Proceedings. Part XXVIII, pages 685-702. Springer, 2020.
[6] Tianwei Yin, Xingyi Zhou, and Philipp Krahenbuhl. Center-based 3d object detection and tracking. In Proceedings of the IEFE Conference Computer Vision and Pattern Recognition (CVPR), June 2021.
[7] Shaoshuai Shi, Chaoxu Guo, Li Jiang, Zhe Wang. Jianping Shi, Xiaogang Wang, and Hongsheng Li. Pv-renn: voxet-voxel feature abstraction for 3d object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2020.
[8] Shaoshuai Shi. Zhe Wang. Jianping Shi, Xiaogang Wang. and Hongsheng Li. From points to parts: 3d object detection from point cloud with part-aware and part-aggregation network. IEEE Trunsactions on Puttern Analysis and Machine Intelligence (TPAMI), 2020.
[9] Lue Fan, Xuan Xiong, Feng Wang, Naiyan Wang, and Zhaoxiang Zhang. Rangedet: In defense of range view for lidar-based 3d object detection. In Proceedings of the IEEE/CVE International Conference on Computer Vision, pages 291 2918-2927, 2021.
[10] Zhi Tian, Xiangxiang Chu, Xiaoming Wang, Xiaolin Wei, and Chunhua Shen. Fully convolutional onc- stage 3d object detection on lidar range images. In Advances in Neural Information Processing Systems, 2022.
[11] Ross Girshick. Jeff Donahue. Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2014.
[12] Ross Girshick. Fast r-cnn. In Proceedings of IEEE international conference on computer vision, pages 1440-1448, 2015.
[13] Joseph Redmon and Ali Farhadi. Yolo9000; better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 7263-7271, 2017.
[14] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yann Yang Fu, and Alexander C Berg. Ssd: Single shot multibox detector. In European conference on computer vision, pages 21 37. Springer, 2016.
[15] Xingyi Zhou, Dequan Wang, and Philipp Krhl. Objects as points. arXiv preprint urXiv: 2019.
[16] Zhi Tian, Chunhua Shen, Hao Chen, and Tong He. Foos: Fully convolutional one-stage object detection. In Proceedings of the JEEE/CVF international conference computer vision, pages 9627- 2019.
11

---

[17] Karen Simonyan and Andrew Zisserman. Very deep convolutional networks for large-scale image recogni- tion. arXiv preprint urXiv:1409.1553 1556, 2014.
[18] Kaiming He, Xiangyu Zhang. Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pottern recognition, pages 770-778, 2016.
[19] Kaiming He, Xinlei Chen, Saining Xie, Yanghao Li, Piotr Dollár, and Ross Girshick. Masked autoencoders are scalable vision learners. Proceedings of the IEEE/CVF Conference on Computer Vision and Puttern Recognition, pages 16000-16009, 2022.
[20] 7e Liu, Yutong I Lin, Yue Cao, Han Hu, Yixuan Wei, Zheng Zhang, Stephen Lin, and Baining Guo. Swin transformer: Hierarchical vision transformer using shifted windows. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pages 10012-10022, October 2021.
[21] Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurelien Chouard, Vijaysai Patnaik, Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Benjamin Caine, et al. Scalability in perception for autonomous driving: Waymo open dataset. Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pages 2454,2454, 2020.
[22] Ricardo Roriz, Jorge Cabral, and Tiago Gomes, Automotive lidar technology: survey, IEEE Transactions on Intelligent Transportation Systems, 23(7):6282-6297, 2021.
[23] Ralph H Rasshofer and Klaus Gresser. Automotive radar and lidar systems for next generation driver assistance functions. Advonces in Radio Science, 3:205-209, 2005.
[24] Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(1 1):2311-112 2013.
[25] Patrick Chazette, Julien Totems, Laurent Hespel, and Jean-Stééhhan Stéphane Bailly. Principle and physies of the lidar measurement. In Optical Remote Sensing of Land Surface, pages 201-247, Elsevier, 2016.
[26] George L Heritage and Andrew RG Large. Principles of 3d laser scanning. Laser scanning for the environmental sciences, 1:21-34, 2009.
[27] Trevor Moffiet, Kerrie Mengersen, Christian Witte. R King. and Robert Denham. Airborne laser scanning: Exploratory data analysis indicates potential variables for classification of individual trees forest stands according to species. ISPRS Journal of Photogranmetry and Remote Sensing, 59(5):289-309 2005.
[28] MARIA ANTONIA Brovelli, MASSIMILIANO Cannata, UM Longoni, et al. Managing and processing lidar data within grass. In Proceedings of the GRASS users conference, volume 29, pages 1-29, 2002.
[29] Gregory P. Meyer, Ankit I Laddha, Eric Kee, Carlos An efficient probabilistic 3d object detector for autonomous driving. In Proceedings the IEEE Conference Computer Vision and Pattern Recognition (CVPR), June 2019.
Gonzalez, and Carl K. Wellington. Lasernet:
[30] Alex Bewley, Pei Sun, Thomas Mensink, Dragomir Anguelov, and Cristian Sminchisescu. Range con- ditioned dilated convolutions for scale invariant 3d object detection. In Conference on Robot Learning. pages 627-641. PMLR, 2021.
[31] Holger Caesar, Varum Bankiti, Alex H Lang. Sourabh Vora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A multimodal dataset for autonomous driving. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 11621-11631, 2020.
[32] Whye Kit Fong, Rohit Mohan, Juana Valeria Hurtado, Lubing Zhou, Holger Cacsar, Oscar Beijbom, and Abhinav Valada. Panoptic nuscenes: A large-scale benchmark for lidar panoptic segmentation and tracking. IEEE Robotics and Automation Letters, 7())3795-3802, 2022.
[33] Liyong Qian, Decheng Wu, Dong Liu, Shalei Song, Shuo Shi, Wei Gong, and Le Wang. Parameter simulation and design of an airborne hyperspectral imaging lidar system. Remote Sensing, 13(24):5123,
2021.
[34] Chia-Yin Tsai, Ashok Veeraraghavan, and Aswin C Sankaranarayanan. Shape and reflectance from two-bounce light transients. In 2016 IEEE International Conference on Computational Photography (ICCP), pages 1-10. IEEE, 2016.
[35] Connor Henley, Joseph Hollmann, and Ramesh Raskar. Bounce- flash lidar. IEEE Transactions on Computational Imaging, 8:411-424 2022.
2022.
12

---

[36] Fisher Yu, Dequan Wang, Evan Shelhamer. and Trevor Darrell. Deep layer aggregation. In Proceedings of the IEEE Conference Computer Vision and Puttern Recognition (CVPR), June 2018.
[37] Yin Zhou and Oncel Tuzel. Voxelnet: End-to-end learning for point cloud based 3d object detection. In Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (CVPR). June 2018.
[38] Charles R Qi. Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning point sets for 3d classification and segmentation. In Proceedings of the IEEE Conference Computer Vision and Pattern Recognition (CVPR), June 2017.
[39] Bo Li, Tianlei Zhang, and Tian Xia. Vehicle detection from 3d lidar using fully convolutional network. arXiv preprint 1rXiv:1608.077
2016.
Development [40] OpenPCDet Deveomeee Team. Openpedet: An open-source toolbox for 3d object detection from point
clouds. https://giihhe http:p///ggttabb..cc//oeeeaeeae/ conp//pppaammmlaabpppppeCCeC 2020.
[41] Tianqi Chen, Mu Li, Yutian Li, Min Lin, Naiyan Wang. Minjie Wang, Tianjun Xiao, Bing Xu, Chiyuan Zhang, and Zheng Zhang. Mxnet: A flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arxiv: 2015.
13


Reviews:
[{"review_id": "6RVSkiZPGo", "reviewer": "Reviewer_ZtTP", "summary": "This paper proposed a new range view based 3D object detection algorithm, which achieved on-par performance compared to sota BEV/voxel based algorithm.", "questions": "1. Modern lidar sensors usually carry multiple return signals with each laser pulse. For example, WOD dataset has multiple returns, each return forming a unique range image. It is unclear how this framework takes multi-return into consideration.", "limitations": "No negative societal impact is noticed to the best of reviewer's knowledge.", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The paper starts from first principle, analyzes the possible information losses during the lidar data collection/analysis/transformation process, and comes up with corresponding strategies to enhance/improve range view perception performance.\n2. The paper comes up with effective strategies for training range view detection models, and achieved on-par performance with sota voxel based methods, and runs much faster.", "weaknesses": "1. The core idea of RAK is to divide the computation window based on the range. This is hardly entirely new/novel for range based methods, range conditioned convolution/detection has been widely studied before. The reviewer implemented very similar strategies but have not found experimental improvements in the range view detection task.\n2. The VRM module does not demonstrated experimental benefits.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate your positive acknowledgment of our work. We hope that the subsequent discussion effectively addresses the concerns you have raised.\n\n## Response 4.1 Incorporation of Multi-return Range Images (Question 1)\n\nThe incorporation of multi-return LiDAR data is extensively investigated and evaluated during the development of RangePerception. Actually, the LiDAR data in Waymo Open Dataset (WOD) consists of only two returns, namely first return and second return. First-return and second-return range images of several sampled frames in WOD are visualized in Fig.1 of our global response PDF file. Through this set of visualization, it is evident that the first return of WOD LiDAR is significantly more compact and informative; in contrast, the second return is highly sparse and contains little information.\n\nTo reaffirm this conclusion, we conduct a random sampling of 1000 foreground objects for each category in WOD. We then calculate the average number of first-return and second-return foreground points within these objects. The results, presented in Table F, clearly demonstrate that the second return is significantly sparser compared to the first return. Specifically, for vehicle category, the second-return points account for only 2.17\\% of the total points. This observation reinforces the conclusion that the first return contains more informative data, while the second return is sparse and provides limited information.\n\n### Table F: Statistics of sampled objects in WOD, in terms of first-return and second-return foreground points. The average numbers of first-return and second-return foreground points in each object are shown in the first and second data column. Additionally, the ratio of second-return points over all foreground points is shown in the third data column.\n\n| Class       | Avg. 1st Points | Avg. 2nd Points | 2nd Points Ratio |\n|:-----------:|:----------------:|:-----------------:|:------------------:|\n| Vehicle     | 613.97          | 13.68           | 2.17\\%            |\n| Pedestrian  | 95.46           | 5.60            | 5.54\\%            |\n| Cyclist     | 155.79          | 3.93            | 2.46\\%            |\n\nIndeed, the sparsity and limited information of the second return in the LiDAR data lead previous range-view-based detectors, such as RangeDet, to exclusively utilize the first return as both input data and for target assignment. To make a fair comparison with RangeDet, RangePerception is also implemented solely based on first-return range images. It is worth pointing out that in our main paper Table 1, the results of RangeDet and RangePerception are achieved solely based on the first-return LiDAR input, while other baseline methods are evaluated based on both returns of LiDAR input. Notably, RangePerception achieves state-of-the-art performance without the help of second-return data.\n\nDespite the facts above, explorations are also made to exploit the information contained in second-return range image. We adopt a straightforward approach that concatenates the second-return range image $S \\in \\mathbb{R} ^ {{m} \\times {n} \\times {8}}$ with transformed subspaces $K' \\in \\mathbb{R} ^ {{m} \\times {n} \\times {8l}}$, generating an augmented subspace tensor $A \\in \\mathbb{R} ^ {{m} \\times {n} \\times {8(l+1)}}$. Augmented subspace tensor $A$ is further input to Range Backbone for feature extraction. As shown in Table G, the performance gain introduced by this operation is relatively marginal, where the improvement to average L1 AP is only 0.08. Since the second-return data is initially sparse and hardly suffers from Spatial Misalignment, we do not attempt to process second-return data with Range Aware Kernel.\n\n### Table G: Comparision study of two-return range image input, measured by 3D AP/APH on WOD validation set. Experiments are conducted under single-frame setting.\n\n| Method | View | Stage | Vehicle | Vehicle | Pedestrian | Pedestrian | Cyclist | Cyclist | Average | Average | FPS |\n| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |\n|  |  |  | L1 | L2 | L1 | L2 | L1 | L2 | L1 | L2 |  |\n| First-return Input |  R  | one |  73.62/73.11  |  66.47/66.00  |  80.24/76.12  |  72.29/68.54  |  70.33/68.93  |  68.75/67.43  |  74.73/72.72  |  69.17/67.32  | **45.85** |\n| Two-return Input |  R  | one |  **73.71/73.20**  |  **66.55/66.08**  |  **80.27/76.15**  | **72.32/68.57**  |  **70.45/69.05**  |  **68.78/67.45**  |  **74.81/72.80**  |  **69.27/67.37**  | 44.73 |\n\nWe will include the analysis and experiment results above in the supplementary material of our paper. Since the incorporation of second-return range image is not the focus of this study, we leave methodology exploration for the utilization of second return to future work.\n\n## Response 4.2 Range Aware Kernel (Weakness 1)\n\nWe address your concerns regarding Range Aware Kernel in our global response 5.2. \n\n## Response 4.3 Vision Restoration Module (Weakness 2)\n\nThe experimental advantages resulting from the incorporation of the Vision Restoration Module (VRM) are elucidated through ablation study A6, as discussed in our main paper at L296-299. In accordance with the findings presented in A6, the integration of VRM leads to an enhancement in L1 AP/APH for vehicle detection, with improvements of 1.12/1.14. Notably, the observed enhancements in the cases of pedestrian and cyclist detection are comparatively limited. We attribute this observation to the fact that pedestrians and cyclists, being relatively small objects, are less susceptible to Vision Corruption.\n"}, {"review_id": "82jFADkFVb", "reviewer": "Reviewer_2sVP", "summary": "This paper proposes RangePerception, an RV-based 3D detection framework that aims to address the issues of Spatial Misalignment and Vision Corruption in range-view representation. The Range Aware Kernel (RAK) disentangles the range image space into multiple sub-spaces, and overcomes the Spatial Misalignment issue by enabling independent feature extraction from each subspace. The Vision Restoration Module (VRM) builds an extended spherical space by pre-defining a restoration angle to restore visual features originally corrupted by the LiDAR’s sampling process on both sides of range image.", "questions": "1. For RV-based 3D detection, the scale-variation and occlusion issues are also two major challenges. Is the proposed method helpful to them?\n2. For Range Aware Kernel, window partitioning inevitably leads to the problem of separating one object into different sub-spaces. Can you carefully analyze this issue?\n3. After disentangling into multiple sub-spaces, the range image will become extremely sparse. Why does the feature extraction network still use 2D dense convolution instead of 2D sparse convolution?", "limitations": "The authors discuss the limitations and there are no potential negative societal impacts.", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. This paper presents a novel high-performing RV-based 3D object detection framework.\n2. The specially designed RAK and VRM modules effectively solve the Spatial Misalignment and Vision Corruption problems in range-view representation.\n3. The paper is well-organized and clearly written especially in the introduction part.", "weaknesses": "1. The paper only provides the results on the Waymo validation set, lacking the results on the Waymo test set.\n2. Lack of evaluation based on different distances like RangeDet.\n3. No experiments are conducted on other mainstream datasets such as nuScenes and KITTI.\n4. Lack of ablation studies on restoration angle hyper-parameters.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for recognizing our work. We aim to address your concerns adequately in the following discussion.\n\n## Response 3.1 Occlusion and Scale Variation Phenomena (Question 1)\n\nThis topic is carefully discussed in Response 2.1 to reviewer PXtx.\n\n## Response 3.2 Performance on nuScenes Dataset (Weakness 3)\n\nWe include performance on nuScenes dataset in Response 2.2 to reviewer PXtx.\n\n## Response 3.3 Separation of Objects (Question 2)\n\nAs is introduced in our main paper L259, RAK's Perception Windows $W$ are designed as a sequence of overlapped range intervals $w_i = [r_{i1}, r_{i2}]$. The overlapped alignment of Perception Windows better handles the cases where objects are separated by margins of Perception Windows. For example, if a vehicle lies on the margin of $w_1 = [0, 15]$ and $w_3 = [15, 30]$, its feature can still be preserved in $w_2 = [10, 20]$. The benefit brought by overlapped window alignment is further validated by our extensive ablation study E8, presented in Response 1.1 to Reviewer GJQ1.\n\n## Response 3.4 Dense 2D Convolution (Question 3)\n\nAs illustrated in our main paper Fig. 2, transformed subspaces $K' \\in \\mathbb{R} ^ {{m} \\times {n} \\times {8l}}$ are processed by Range Backbone for non-linear feature extraction. We denote the first 2D Convolution Layer in Range Backbone as $\\mathcal{C}$, whose output dimension is $d$. Subsequently, the output feature from $\\mathcal{C}$ can be represented as $H \\in \\mathbb{R} ^ {{m} \\times {n} \\times {d}}$. Note that 2D Convolution Layer $\\mathcal{C}$ consists of $8l \\times d$ number of fully connected kernels. Due to the fully connected nature of $\\mathcal{C}$, feature $H = \\mathcal{C}(K')$ is a dense tensor, despite the sparsity of $K'$. As a result, the input and output features of each consecutive layer in Range Backbone are also dense tensors. It is therefore unfeasible to build Range Backbone with sparse 2D Convolutions.\n\n## Response 3.5 Choice of Restoration Angle (Weakness 4)\n\nWe randomly sample 200 frames of range images from Waymo Open Dataset. By visualization, we find that restoration angle $\\delta = 0.086\\pi$ is sufficient to resolve the Vision Corruption issues that occur in the sampled frames, without introducing unnecessary redundancy.\n\nAn extensive ablation study for Vision Restoration Module is also conducted. As presented in Table D, ablations are experimented by disabling VRM and RP (E11), reducing restoration angle to $0.043\\pi$ (E12), and increasing restoration angle to $0.172\\pi$ (E13). Comparison demonstrates that all three sizes of restoration angle $\\delta$ boost the detection accuracy, while the selected hyper-parameter $\\delta = 0.086\\pi$ generates the most ideal numerical results.\n\n### Table D: Ablation study of Vision Restoration Module, measured by 3D AP/APH on WOD validation set.\n\n|        | Setting               | Vehicle     | Vehicle | Pedestrian   | Pedestrian  | Cyclist      | Cyclist      | Average      | Average      |\n|:--------:|:-----------------------:|:-------------:|:---------:|:--------------:|:---------:|:--------------:|:---------:|:--------------:|:---------:|\n|        |                       | L1          | L2      | L1           | L2      | L1           | L2      | L1           | L2      |\n| E11    | without VRM & RP      | 72.50/71.97 | 66.40/65.91 | 80.20/76.08 | 72.27/68.53 | 70.31/68.92 | 68.74/67.42 | 74.34/72.32 | 69.13/67.29 |\n| E12    | $\\delta = 0.043\\pi$            | 73.46/73.03 | 66.45/65.93 | 80.22/76.10 | 72.28/68.53 | **70.33/68.93** | 68.74/67.43 | 74.67/72.69 | 69.15/67.30 |\n| E13    | $\\delta = 0.172\\pi$            | 73.60/73.09 | 66.46/65.99 | **80.24/76.12** | 72.29/68.53 | 70.32/68.92 | **68.75/67.43** | 74.72/72.71 | 69.16/67.31 |\n|        | RangePerception       | **73.62/73.11** | **66.47/66.00** | **80.24/76.12** | **72.29/68.54** | **70.33/68.93** | **68.75/67.43** | **74.73/72.72** | **69.17/67.32** | \n\n## Response 3.6 Evaluation based on Different Distances  (Weakness 2)\n\nShown in Table E, we conduct an extensive evaluation on the WOD validation set, measuring the detection performance in terms of L1 3D AP for different distance ranges. For comparison, we use RangeDet as the baseline method. RangePerception demonstrates superior performance compared to RangeDet across different distances for all classes. The results validate the effectiveness of RangePerception in handling diverse scenarios and addressing scale variation issues.\n\n### Table E: Detection performance measured by L1 3D AP on WOD validation set, based on different distances.\n\n| Class       | Method           | Overall | 0 - 30m | 30 - 50m | 50m - $\\infty$  |\n|-------------|------------------|:---------:|:---------:|:---------:|:---------:|\n| Vehicle     | RangeDet         | 72.85   | 87.96   | 69.03   | 48.88   |\n| Vehicle     | RangePerception  | **73.62**   | **88.79**   | **69.77**   | **49.45**   |\n| Pedestrian  | RangeDet         | 75.94   | 82.20   | 75.39   | 65.74   |\n| Pedestrian  | RangePerception  | **80.24**   | **86.87**   | **79.64**   | **69.36**   |\n| Cyclist     | RangeDet         | 65.67   | 79.33   | 55.80   | 45.00   |\n| Cyclist     | RangePerception  | **70.33**   | **82.95**   | **59.35**   | **50.19**   |\n\n## Response 3.7 Performance on Waymo Test Set (Weakness 1)\n\nQuantitative and qualitative results on Waymo test set will be presented and discussed in the next version of this paper."}, {"review_id": "eQVIWLIxbi", "reviewer": "Reviewer_PXtx", "summary": "The paper presents an efficient and accurate RV-based 3D object detection framework called *RangePerception*. The framework addresses two critical challenges impeding the performance of existing RV-based methods: 1) a natural domain gap between the 3D world coordinate used in output and 2D range image coordinate used in input, generating difficulty in information extraction from range images; and 2) native range images suffer from vision corruption issue, affecting the detection accuracy of objects located on the margins of the range images. To address these challenges, two novel algorithms named Range Aware Kernel (RAK) and Vision Restoration Module (VRM) are proposed. With the help of RAK and VRM, RangePerception achieves higher averaged L1/L2 AP compared to previous state-of-the-art RV-based method RangeDet on Waymo Open Dataset. For the first time as an RV-based 3D detection method, RangePerception achieves slightly superior averaged AP compared with the well-known BEV-based method CenterPoint and has an inference speed that is 1.3 times as fast as CenterPoint. The paper’s contributions include the RangePerception Framework, Range Aware Kernel and Vision Restoration Module.", "questions": "Range view detection could be inevitably affected by **occlusion** and **scale variation** due to the spherical projection. Does the proposed method consider this?", "limitations": "The limitations are adequately included in the Supplementary Material.", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "**Originality**: The paper introduces two modules, Range Aware Kernel (RAK) and Vision Restoration Module (VRM), to address the challenges of existing RV-based methods. The proposed RangePerception framework is original in its approach to efficiently and accurately detect 3D objects using LiDAR range view.\n\n**Quality**: The paper presents a thorough analysis of the challenges impeding the performance of existing RV-based methods and proposes solutions to address these challenges. The paper provides ablation studies to demonstrate the effectiveness of each component. Supplementary Material is well written.\n", "weaknesses": "**Major Issues**:\n\n1. In table 1, it seems that many related works [1-2] are missing. They also focus on Efficient and Accurate 3D Object Detection, and the authors should also compare with them.\n2. Experiments are insufficient. It's interesting to see the performance on other datasets (e.g., KITTI and nuScenes). For nuScenes dataset, they use a 32-beam LiDAR (KITTI and WOD both are 64-beam). Does the proposed RV-based method still performs well for extremely low resolution range images (32 or even 16-beam LiDAR)?\n\n**Minor Issues**:\n1. Inconsistent definition and dimension of *range image* in Algorithm 1 L.1 (range image $I \\in R^{m \\times n \\times 8}$) and in Paper L.119-L.121 (m x n matrix).\n2. Please add citations in Tab.1 first column (*Method* column).\n3. Although the illustration on Redundancy Pruning in Fig. 5(c) is straight forward, it's better to also include the real range image visualization for Redundancy Pruner in Fig. 5 (a) or (b).\n4. It could be easier to follow introducing the **Related Work** section ahead of the **Methodology**.\n\n\n[1] Liu, Zhijian, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela Rus, and Song Han. \"BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird's-Eye View Representation.\" In ICRA. 2023.\n\n[2] Sun, Pei, Weiyue Wang, Yuning Chai, Gamaleldin Elsayed, Alex Bewley, Xiao Zhang, Cristian Sminchisescu, and Dragomir Anguelov. \"Rsn: Range sparse net for efficient, accurate lidar 3d object detection.\" In CVPR. 2021.\n", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We appreciate your recognition of our work. We hope the following discussion adequately addresses your concerns.\n\n## Response 2.1 Occlusion and Scale Variation Phenomena (Question 1)\n\nAs is illustrated in our main paper L202-204 \\& Fig.2,  RangePerception learns the pixel-wise dense predictions, namely classification $C$, regression $B$, and confidence $U$. It is straightforward to observe from our main paper Fig.2 \\& Fig.6(b, c), where the class prediction head of RangePerception accomplishes the same task as foreground segmentation. Such segmentation-based learning principle adequately addresses the issue of occlusion and scale variation: even though some objects have limited foreground points due to far distance or strong occlusion, their geometric attributes can still be learned and predicted via the segmentation-based paradigm.\n\n\n## Response 2.2 Performance on nuScenes Dataset  (Major Weakness 2)\n\nThe results on nuScenes dataset are presented in this section.\n\n**Baseline Methods.** As shown in Table C, state-of-the-art BEV-based, MV-based, and RV-based detectors are selected as baseline methods. All BEV-based and MV-based baselines are trained and evaluated with OpenPCDet's official PyTorch implementation. We reimplement and examine RangeDet as described in our main paper L269-270. Since FCOS-LiDAR reports results on nuScenes without releasing the code, we reimplement FCOS-LiDAR in our codebase, and achieve similar single-frame results as reported in their paper.\n\n**Experiment Settings.** Training and evaluation are conducted with single-frame setting on nuScenes dataset. All models are trained with $20$ epochs on nuScenes training set, where batch size is $32$ and frame sampling rate is $100\\%$. Inference speed is examined with one NVIDIA 3090Ti GPU with batch size set to $1$.\n\n**Main Results.** The quantitative performance of RangePerception is surprisingly excellent, surpassing all baseline methods in terms of NDS and average AP. Particularly, our method exhibits outstanding performance in detecting small objects, such as pedestrians, motors, bicycles, and traffic cones. This observation reaffirms the analysis presented in our main paper L277-280 \\& L300-302, where the range-view representation better preserves visual features of small objects, while voxelization introduces quantization errors to the originally sparse foreground points. Additionally, RangePerception demonstrates superior performance in detecting construction vehicles, which we attribute to our segmentation-based learning paradigm being more sensitive to the distinctive shape of construction vehicles. The remarkable overall performance and balanced perception ability across all object categories further validate that the RangePerception framework effectively handles scale variation.\n\n### Table C: Detection performance measured by NDS and AP on nuScenes validation set. All experiments are conducted under single-frame setting. C.V. stands for construction vehicle, and T.C. stands for traffic cones.\n\n| Method             | View | Stage | NDS   | AP    | Car   | Truck | Bus   | Trailer | C.V. | Ped   | Motor | Bicycle | T.C.  | Barrier | FPS   |\n|--------------------|------|-------|-------|-------|-------|-------|-------|---------|------|-------|-------|---------|-------|---------|-------|\n| Second             | B    | one   | 50.48 | 50.03 | 75.24 | 46.04 | 54.17 | 47.99   | 16.73| 69.50 | 42.07 | 19.06   | 67.33 | 62.12   | 16.72 |\n| PointPillar        | B    | one   | 50.02 | 49.27 | 74.70 | 45.46 | 53.23 | 45.99   | 16.46| 68.71 | 40.96 | 18.73   | 66.51 | 61.96   | 23.89 |\n| CenterPoint        | B    | one   | 54.95 | 53.92 | 78.19 | 47.53 | 55.92 | 49.54   | 16.96| 77.09 | 49.99 | 22.62   | 70.98 | **65.69**   | 19.75 |\n| PV-RCNN            | B+P  | two   | 54.23 | 53.12 | 80.75 | 49.09 | 57.65 | 51.26   | 17.51| 74.84 | 47.27 | 21.54   | 66.78 | 64.47   | 1.85  |\n| Part-$A^2$-anchor  | B+P  | two   | 54.67 | 54.10 | **81.10** | **49.30** | **57.91** | **51.45**   | 21.58| 76.16 | 48.13 | 21.82   | 67.96 | 65.61   | 4.69  |\n| FCOS-LiDAR         | R    | one   | 53.41 | 53.43 | 72.95 | 42.33 | 46.95 | 43.31   | 25.56| 74.99 | 60.35 | 34.61   | 70.29 | 62.74   | 25.23 |\n| RangeDet           | R    | one   | 54.39 | 54.17 | 73.41 | 42.83 | 48.15 | 44.33   | 26.37| 75.46 | 60.58 | 34.58   | 71.52 | 64.50   | 19.87 |\n| RangePerception    | R    | one   | **55.65** | **55.31** | 75.68 | 43.91 | 48.71 | 44.93   | **26.51**| **78.80** | **60.61** | **35.90** | **72.93** | 65.10   | **26.17** |\n\n## Response 2.3 Comparison with BEVFusion and RSN (Major Weakness 1)\n\nWe will cite and compare with these two methods in the next version of our paper. Meanwhile, we hope to point out that the problem settings of BEVFusion and RangePerception are naturally different: (1) BEVFusion is a multi-modal joint-LiDAR-camera detection method; (2) RangePerception, on the other hand, is a single-modal detection method based on LiDAR signal.\n\n## Response 2.4 Range Image Definition (Minor Weakness 1)\n\nThe definition of range image $I \\in \\mathbb{R} ^ {{m} \\times {n} \\times {8}}$ and range matrix $R \\in \\mathbb{R} ^ {{m} \\times {n}}$ will be specifically clarified in the next version of this paper.\n\n## Response 2.5 Visualization for Redundancy Pruner (Minor Weakness 3)\n\nWe would like to clarify that Redundancy Pruner operates on feature space $F^r$ (described in main paper L191) instead of range image $I$, which is the reason why we choose to visualize redundancy pruner with pseudo feature in main paper Fig.5(c). That being said, we can include a new figure which visualizes how Redundancy Pruner operates on the real feature space, if you believe that would be helpful.\n\n## Response 2.6 Placement of Related Work (Minor Weakness 2 & 4)\n\nCitations will be added to Table 1 in the next version of this paper. Meanwhile, Related Work section will be placed ahead of the Methodology section.\n"}, {"review_id": "trxgCjfEBB", "reviewer": "Reviewer_GJQ1", "summary": "This paper studies range-view-based LiDAR 3D object detection. It proposes Range Aware Kernel and Vision Restoration Module to handle the problems of range view representation, such as inconsistent range distribution and boundary splitting. The main contributions are as follows:\n\n- The proposed module boosts the performance of range-view-based detection and the whole framework is very efficient, further closing the gap between range-view-based methods and BEV-based methods, which is meaningful in practical use.\n\n- Authors are willing to release the code based on the popular OpenPCDet codebase. Given that previous works either do not open source (e.g., LaserNet, RCD) or are based on a non-mainstream framework (RangeDet), it is valuable and admirable if authors could contribute the source code.", "questions": "My overall impression is positive and I believe this paper has significant practical meaning in LiDAR-based 3D object detection, especially industrial use. However, I think the experiments are not sufficient enough (but well supporting the claims) to meet the bar of the high-standard NeurIPS conference. I strongly encourage authors to add more ablation experiments to offer more insight into the proposed modules. If so, I will increase my rating.", "limitations": "Limitations have been adequately addressed.", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- The proposed modules well addressed the problem in range view. In particular, Range-aware Kernel is a more elegant and efficient solution than the Range-conditioned Pyramid in RangeDet.\n\n- Although not very extensive, the effectiveness of every proposed module is well grounded in the experiment section.\n\n- Performance is good and reaches SoTA performance among all range-view-based methods. Compared with other BEV-based methods, its performance is also promising, especially for a single-stage detector.\n\n- Authors are willing to release the code based on the popular OpenPCDet codebase. Given that previous works either do not open source (e.g., LaserNet, RCD) or are based on a non-mainstream framework (RangeDet), it would be valuable and admirable if authors could contribute the source code.", "weaknesses": "- Technical novelty is a little bit limited. The issue of spatial misalignment is in fact mentioned in RangeDet, where Meta Kernel is proposed to mitigate the problem. And the Vision Restoration is also a straightforward trick, and a similar trick (Ring CNN) is adopted in PolarNet. However, the proposed solution is indeed useful and practical. In my opinion, this point is not a fatal weakness.\n\n- Experiments seem not extensive. This paper only contains two tables. Although these tables are informative and well validate the proposed module, these experiments are not sufficient enough to reach the bar of a high-standard conference. For example, Table 2 could be split into several small tables for more detailed ablation on each component. RAK is the core design, and it deserves a more detailed ablation on various hyper-parameters (e.g., different range partition intervals, different placed layers/positions ) or be compared with other potential alternative designs, which is necessary to reveal the inner workings.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "Thank you for your positive recognition of our work. We hope the discussion below could address your concerns.\n\n## Response 1.1 Extensive Ablation Study for Range Aware Kernel (Question 1 & Weakness 2)\n\nWe have conducted thorough comparison experiments during the development process of the Range Aware Kernel (RAK). Below, we present the quantitative results of these experiments, which will serve as an extensive ablation study in the next version of this paper.\n\nThe results of the ablation study are presented in Table A (in global response 5.1), where we assess the effectiveness of RAK.\n\n**Comparison with Other Operators** Firstly, this study investigates the impact of RAK by removing it from our framework (E1) and replacing it with a 1$\\times$1 Convolution layer of equal dimension (E2). The results show a significant decrease of more than 5 average L1 AP for both cases, highlighting the crucial role of RAK in achieving strong detection performance. Secondly, we compare the performance of RAK and Meta-Kernel by replacing RAK with Meta-Kernel in our framework (E3). Comparison reveals that the RAK setting outperforms the Meta-Kernel setting by an improvement of 2.85 average L1 AP, further validating the efficacy of RAK in our approach. \n\n\n**Adjusting Number of Windows.** We investigate the optimality of the Perception Window setting in RAK by varying the number of Perception Windows (E4 - E7), where the corresponding hyper-parameters are presented in Table B. Reducing the number of Perception Windows to 2 and 4 leads to decreases in detection AP (E4, E5), indicating that an insufficient number of Perception Windows hinders the remedy for Spatial Misalignment. Increasing the number of Perception Windows to 8 and 10 brings no further performance gain (E6, E7), which we hypothesize is because an increased number of Perception Windows makes subspaces $K$ sparser and generates redundant convolution parameters.\n\n**Adjusting Placement of Windows.** We explore the optimality of the Perception Window setting in RAK by experimenting with different placements of Perception Windows (E8, E9). As is mentioned in our main paper and Table B, RAK's Perception Windows $W$ are designed as a sequence of overlapped range intervals $w_i = [r_{i1}, r_{i2}]$, where the length of each range interval $l_i = r_{i2} - r_{i1}$ gradually increases. The overlapped alignment of Perception Windows better handles the cases where objects are separated by margins of Perception Windows. For example, if a vehicle lies on the margin of $w_1 = [0, 15]$ and $w_3 = [15, 30]$, its feature can still be preserved in $w_2 = [10, 20]$. Further, the design of gradually increasing window length $l_i$ considers the fact that as the LiDAR beams reach farther, the distribution of LiDAR points becomes sparser, and our design balances the point density in each Perception Window, thus enabling more fluent feature extraction. To verify the intuitions above, experiments are conducted for non-overlapped windows (E8) and uniformly distributed windows (E9), subtle performance drops can be observed for both cases, justifying the effectiveness of our design.\n \n**Integrating RAK into Range Backbone.** Placing RAK in the mid of Range Backbone is also examined, where experiments are conducted for the case in which RAK is placed after the first convolution block of Range Backbone (E10). Qualitative result indicates that such network design leads to a minor performance degrading, which we attribute to the fact that Spatial Misalignment can be more effectively resolved at the earliest stage of feature extractor.\n\n### Table B: Hyper-parameters of Range Aware Kernel's Ablation Study.\n\n|  | Setting | Perception Windows $W$ |\n| :---: | :---: | :---: |\n| E4 | 2 Perception Windows | $\\{[0,40],[30, \\infty)\\}$ |\n| E5 | 4 Perception Windows | $\\{[0,30],[20,50],[30,60],[50, \\infty)\\}$ |\n| E6 | 8 Perception Windows | $\\{[0,10],[5,15],[10,20],[15,25],[25,35],[30,45],[35,60],[45, \\infty)\\}$ |\n| E7 | 10 Perception Windows | $\\{[0,10],[5,15],[10,20],[15,25],[25,35],[30,40],[35,45],[40,50],[45,55],[50, \\infty)\\}$ |\n| E8 | Non-overlapped Windows | $\\{[0,10],[10,20],[20,30],[30,45],[45,60],[60, \\infty)\\}$ |\n| E9 | Uniformly Distributed Windows | $\\{[0,20],[15,35],[30,50],[45,65],[60,80],[75, \\infty)\\}$ |\n|  | RangePerception | $\\{[0,15],[10,20],[15,30],[20,40],[30,60],[45, \\infty)\\}$ |\n\n\n## Response 1.2 Comparison with Existing Works (Weakness 1)\n\n**RangeDet.** We are aware that Spatial Misalignment issue is in also mentioned in RangeDet, where Meta-Kernel is proposed to mitigate the problem. We hope to bring to your attention that we are the first work to explicitly visualize and explain Spatial Misalignment issue (main paper L72-76 \\& Fig.1(e)). We also implement Meta-Kernel and compare it with RAK in our ablation study A3 (main paper L289-292). The empirical comparison demonstrates a significant 2.85 average L1 AP improvement of the RAK setting over the Meta-Kernel setting, further affirming the effectiveness of RAK in our approach.\n\n**PolarNet.** We appreciate your acknowledgment of the Ring CNN design proposed by PolarNet. We confirm that Ring CNN offers a viable solution to the Vision Corruption issue mentioned in our paper. Furthermore, we agree that our Vision Restoration Module, as proposed in our paper, is also an effective and practical approach to address the Vision Corruption issue. In the upcoming version of our paper, we will incorporate PolarNet into the related work section and provide a concise comparison between our approach and the Ring CNN design.\n"}]

Author rebuttal:
Thank you for acknowledging our work positively. We are pleased to provide the supplemental responses for part of the individual responses.

## Response 5.1 Supplemental Response for Reviewer GJQ1

### Table A: Ablation study of Range Aware Kernel, measured by 3D AP/APH on WOD validation set.

|  | Setting |   Vehicle   |   Vehicle   | Pedestrian | Pedestrian | Cyclist | Cyclist | Average | Average |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
|  |  | L1 | L2 | L1 | L2 | L1 | L2 | L1 | L2 |
| E1 | without RAK | 70.46/70.01 | 63.45/62.89 | 72.86/68.88 | 64.56/60.72 | 63.63/62.43 | 61.44/60.25 | 68.98/67.11 | 63.18/61.29 |
| E2 | 2D Convolution | 70.48/70.03 | 63.46/62.90 | 72.86/68.87 | 64.56/60.71 | 63.64/62.43 | 61.45/60.26 | 68.99/67.11 | 63.16/61.25 |
| E3 | Meta-Kernel | 72.95/72.43 | 65.98/65.37 | 75.95/71.96 | 67.63/63.90 | 66.76/65.46 | 64.57/63.38 | 71.88/69.95 | 66.06/64.21 |
| E4 | 2 Perception Windows | 71.34/70.85 | 64.31/63.75 | 73.63/69.65 | 65.37/61.48 | 65.17/64.09 | 63.10/61.95 | 70.04/68.20 | 64.26/62.39 |
| E5 | 4 Perception Windows | 73.13/72.62 | 65.97/65.51 | 80.15/76.03 | 72.17/68.42 | 70.15/68.85 | 68.42/67.10 | 74.48/72.50 | 68.85/67.01 |
| E6 | 8 Perception Windows | 73.59/73.08 | 66.45/65.98 | 80.25/76.13 | 72.30/68.56 | 70.31/68.92 | 68.74/67.42 | 74.72/72.71 | 69.16/67.32 |
| E7 | 10 Perception Windows | 73.58/73.07 | 66.43/65.97 | **80.26/76.14** | **72.32/68.57** | 70.29/68.89 | 68.72/67.41 | 74.71/72.70 | 69.16/67.32 |
| E8 | Non-overlapped Windows | 72.14/71.53 | 66.07/65.48 | 80.03/75.97 | 72.09/68.37 | 67.12/65.88 | 65.34/64.15 | 73.09/71.13 | 67.83/66.00 |
| E9 | Uniformly Distributed Windows | 73.12/72.86 | 66.12/65.52 | 79.88/75.79 | 71.91/68.13 | 69.45/68.04 | 67.82/66.51 | 74.15/72.23 | 68.62/66.72 |
| E10 | RAK After First Conv Block | 73.61/73.10 | 66.45/65.98 | 79.84/75.78 | 71.88/68.10 | 69.41/68.01 | 67.79/66.48 | 74.29/72.30 | 68.71/66.85 |
|  | RangePerception | **73.62/73.11** | **66.47/66.00** | 80.24/76.12 | 72.29/68.54 | **70.33/68.93** | **68.75/67.43** | **74.73/72.72** | **69.17/67.32** |

## Response 5.2 Supplemental Response for Reviewer ZtTP

We are aware that previous works such as Cylinder3D and PolarNet divide computation windows based on range. However, the computation windows of previous works are designed as a set of non-overlapped intervals. Unlike prior approaches, Range Aware Kernel (RAK) incorporates a distinct innovation by adopting a set of overlapped intervals as part of its window structure design. This strategic choice ensures that objects situated at the margins of Perception Windows are not subject to information loss, thereby enhancing the fidelity of feature extraction. We believe this nuanced design, illustrated in Fig.3(a) \& L258-260 of our main paper, fundamentally differentiates RAK from previous methods.

Furthermore, it's noteworthy that Cylinder3D and PolarNet are primarily developed for segmentation tasks operating on point views. RAK, on the other hand, is purposefully tailored for the range-view-based object detection task. This tailored focus underscores RAK's pioneering role in addressing Spatial Misalignment challenges within this specific context.

Moreover, as illustrated in Response 1.1 to Reviewer GJQ1, thorough window structure designing and hyper-parameter tuning are the key enabler for the optimal functionality of RAK. Both factors significantly impact RAK's performance, as demonstrated by our extensive ablation study in main paper L286-296 \& Response 1.1.

We recognize the necessity to address the concerns of novelty and experimental improvements thoroughly. We will carefully compare our method with Cylinder3D and PolarNet in the next version of our paper. Our supplementary material will include an in-depth analysis of the hyper-parameter tuning process, substantiated by comprehensive experiments that showcase the improvements attainable through RAK when the optimal configuration is achieved. The discussions above will facilitate a more nuanced understanding of RAK's potential and its applicability within the broader context of range-view-based methods.

In conclusion, we are committed to refining our manuscript to more accurately represent the significance and effectiveness of the Range Aware Kernel, and we are grateful for your guidance in this endeavor.


Meta-review: This paper finally received all four positive scores. In the first round, reviewers raised some critical questions about the novelty against existing works, the results on more datasets, and some experiment analyses. The authors carefully prepared the rebuttal and provided detailed one-to-one responses. After discussion, the reviewers agree that most concerns are well addressed, and the scores converge to accept. In summary, after revision, this paper is qualified to be published on the NeurIPS. The final recommendation is Accept.
Decision: Accept (poster)