What Matters in Range View 3D Object Detection
Authors: Benjamin Wilson
Nicholas Autio Mitchell
Jhony Kaesemodel Pontes
James Hays
Venue: CoRL 2024 (2024)
Keywords: 3D Object Detection
3D Perception
Autonomous Driving
TL;DR: Range view 3D detection shows competitive performance against other lidar representations while utilizing straightforward techniques.

Abstract: Lidar-based perception pipelines rely on 3D object detection models to interpret complex scenes. While multiple representations for lidar exist, the range view is enticing since it losslessly encodes the entire lidar sensor output. In this work, we achieve state-of-the-art amongst range view 3D object detection models without using multiple techniques proposed in past range view literature. We explore range view 3D object detection across two modern datasets with substantially different properties: Argoverse 2 and Waymo Open. Our investigation reveals key insights: (1) input feature dimensionality significantly influences the overall performance, (2) surprisingly, employing a classification loss grounded in 3D spatial proximity works as well or better compared to more elaborate IoU-based losses, and (3) addressing non-uniform lidar density via a straightforward range subsampling technique outperforms existing multi-resolution, range-conditioned networks. Our experiments reveal that techniques proposed in recent range view literature are not needed to achieve state-of-the-art performance. Combining the above findings, we establish a new state-of-the-art model for range view 3D object detection — improving AP by 2.2% on the Waymo Open dataset while maintaining a runtime of 10 Hz. We are the first to benchmark a range view model on the Argoverse 2 dataset and outperform strong voxel-based baselines. All models are multi-class and open-source. Code is available at https://github.com/benjaminrwilson/range-view-3d-detection.

What Matters in Range View 3D Object Detection
Benjamin Wilson Georgia Institute of Technology benj jannjamiwwsssnggaechhh edu
Nicholas Autio Mitchell University of Freiburg
naumitch@cs. uni mmi-freiburg, de
Jhony Kaesemodel Pontes
James Hays Georgia Institute of Technology hays@gatech. edu
Latitude
jpontes@lat.ai
Abstract: Lidar-based perception pipelines rely on 3D object detection models to interpret complex scenes. While multiple representations for lidar exist, the range view is enticing since it losslessly encodes the entire lidar sensor output. In this work, we achieve state-of-the-art amongst range view 3D object detection models without using multiple techniques proposed in past range view literature. We explore range view 3D object detection across two modern datasets with substantially different properties: Argoverse 2 and Waymo Open. Our investigation reveals key insights: (I) input feature dimensionality significantly influences the overall performance, (2) surprisingly, employing a classification loss grounded 3D spatial proximity works as well or better compared to more elaborate loU-based losses, and (3) addressing non-uniform lidar density via a straightforward range subsampling technique outperforms existing multi-resolution, range-conditioned networks. Our experiments reveal that techniques proposed in recent range view literature are not needed to achieve state-of -fht---rt- performance. Combining the above findings, we establish a new state-of-the-art model for range view 3D object detection - improving AP by 2.2% on the Waymo Open dataset while maintaining a runtime of 10 Hz. We are the first to benchmark range view model on the Argoverse dataset and outperform strong voxel-based baselines. All models are multi-class and open-source. Code is available https: / h//ggtttbbb.. com/ benj penjammnrwiison/ramnee www.--ddddeeeeeee
Keywords: 3D Object Detection, 3D Perception, Autonomous Driving
1 Introduction
Lidar-based 3D object detection enhances how machines perceive and navigate their environment enabling accurate tracking, motion forecasting, and planning. Lidar data can be represented in various forms such unordered points, 3D voxel grids, bird's-eye view projections, and range view representations. Each representation differs in terms of its sparsity and how it encodes spatial relationships between points. Point-based representations preserve all information but compromise on the efficient computation of spatial relationships between points. moxel-based and bird's hird's-eye view representations suffer from information loss and sparsity, yet they maintain efficient neighborhood computation. The range view representation preserves the data losslessly and densely in the "native" view of the sensor, but 2D neighborhoods in such an encoding can span enormous 3D distances and objects exhibit scale variance because of the perspective viewpoint.
The field of range-view-based 3D object detection is relatively less explored than alternative repre- sentations. Currently, the research community focuses on bird's bird's-eye view or voxel- based methods.
8th Conference Robot Learning (CoRL. 2024), Munich, Germany.

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This is partially due to the performance gap between these models and lange-view-bane models. However, we speculate that the lack of open-source range view models prevents researchers from easily experimenting and innovating within this setting [1, 2. 3, 4]. Our research reveals several unexpected discoveries, including: (1) input feature dimensionality significantly influences overall performance in 3D object detection in the range view by increasing network expressivity to capture object discontinuities and scale variance, (2) straightforward classification loss based on 3D spatial proximity yields superior generalization across datasets compared to intricate 3D Intersection over Union bous)- losses, (3) simple range subsampling outperforms complex, range-specifee net- work designs, and (4) range view 3D object detection can be competitive across multiple datasets. Surprisingly, we find without including certain contributions from prior work, we end up with a straightforward. 3D object detection model that pushes state-of-the-are amongst range view models on both the Argoverse and Waymo Open datasets.
While the goal of this work is not to set new state-of-the-art in 3D object detection, we show that range view methods are still competitive without the need for "bells and whistles" such as model ensembling, time aggregation, or single-category models.
Our contributions are outlined as follows:
1. Analysis of What Matters. We provide detailed analysis design decisions in range view 3D object detection. Our analysis shows that four key choices impact downstream performance and runtime- input feature dimensionality, 3D input encoding, 3D classification supervision, and range-hased subsampling. When these design decisions are optimized, we arrive at relatively simple range view architecture that is surprisingly competitive with strong baseline methods of any representation.
2. Simple, Novel Modules. We propose straightforward classification loss grounded in 3D spatial proximity yiclds superior generalization across datasets compared to more complex loU-based losses [5] which generalizes surprisingly well across the Argoverse 2 and Waymo Open datasets. We introduce a simple, range subsampling technique which outperforms multi-resolution, range-conditioned network heads [5].
3. High Performance without Complexity. Surprisingly, without range-specific network designs [5, 2], IoU prediction [5], we demonstrate that range view based models are competitive with strong voxel-based baselines on the Argoverse dataset and establish new state-of-the- amongst range view based 3D object detection models on the Waymo Open dataset - improving L1 mAP by 2.2% while running at 10 Hz.
4. Open Source, Multi-class, Portable. Prior range view methods have not provided open- source implementations [1. used single-class detection designs [5], or have been written in non-mainstream deep learning frameworks [5]. We provide multi-class, open-source models written in Pytorch on the Argoverse [6] and Waymo Open [7] datasets with
open-source implementations to facilitate range-view-based, 3D object detection research at
https://githu/: www/bemn//cccc atm////onaaaaaaaaaooaa/aaaaeeee
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2 Related Work
Point-based Methods. Point-based methods aim to use the full point cloud without projecting to lower dimensional space [8, 9, 10]. PointNet [8] and Deep Sets [11] introduced permutation invariant networks which enabled direct point cloud processing, which eliminates reliance on the relative ordering of points. PointNet++ [9] further improved on previous point-based methods, but still remained restricted to the structured domain of indoor object detection. While PointRCNN [10] extends methods the urban autonomous driving domain, point-based methods do scale well with the number of points and size of scenes, which makes them unsuitable for real-time, low-latency safety-critice applications.

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Range View
Elongation
Intensity
Range
Sensor
x
Y
z
Figure I: Range View Representation. We illustrate the connection between the range view representation (top) and love-the-shoulle view of 3D scene (bottom). The range view representation encodes large 3D scenes recorded by rotating lidar sensor into compact image which can be dircctly processed by CNN-based architectures. We show a building in both views in the top left (enclosed in black boxes). Warmer colors indicate points closer the lidar sensor, while cooler colors represent points distant from the sensor.
Figure Network Inputs: Range View Features. The input to our network for the Waymo Open dataset consists of auxiliary features (elongation and intensity) and geometric features (range, x, y, z). Each channel is re mapped to represent warmer colors the smallest values and cooler colors as the largest values within their respective domains. White pixels indicate invalid returns.
Grid-based Projections. Grid-based projection methods first discretize the world into either 2D Bird's Eye View [12, 13, 14, 15] or 3D Voxel [16, 17, 18, 19] grid cells, subsequently placing 3D lidar points into their corresponding 2D or 3D cell. These methods often result in collisions, where point density is high and multiple lidar points assigned to the same grid cell. While some methods resolve these collisions by simply selecting the nearest point [12], others use max-pooled feature [14] or a learned feature fusion [19, 13].
Range-based Projections. A range view representation is spherical projection that maps a point cloud onto a 2D image plane, with the result often referred to as range image. Representing 3D points range image yields few notable advantages: (a) memory-efficient representation, i.c., the image be constructed in a way in which few pixels are "empty", (b) implicit encoding of occupancy or "free-space" in the world, (c) compute-efficient - image processing can be performed (d) scaling to longer ranges is invariant of the discretization resolution. A range image can be viewed as a dense, spherical indexing of the 3D world. Notably, spherical projections are O(1) in space complexity as a function of range at a fixed azimuth and inclination resolution. Due these advantages, range view has been adapted in several works for object detection [1, 20, 21, 5, 3]. Subsequent works have explored the range view for joint object detection and forecasting [22, 23].
with dense 2D convolution,
Multi- View Projections. To leverage the best of both projections, recent work has explored multi- view methods [19, 13, 15, 24]. These methods extract features from both the bird's s-yd's-ey view and the range view and fuse them to generate object proposals. While Zhou et al. [19] proposes to fuse multi-view features in the point space, Laddha al. [13] suggests fusing these features by projecting range view features the bird's hird's-eye view. Fadadu et al. [15] investigates the fusing of RGB camera features with multi-view lidar features. In this work, explore how competitive range view representations can be without these additional views.
3 Range View 3D Object Detection
We begin by describing the range view and its features, then we outline the task of 3D object detection and its specific formulation in the range view. Next, we describe a common architecture for range-view- 3D object detection model.
Inputs: Range View Features. The range view is dense grid of features captured by lidar sensor. In Fig. 1, illustrate the range view, its 3D representation along with an explicit geometric correspondence of building between views and the location of the physical lidar sensor which

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Range
Point
Object Points
Bird's-eye eye View
D
Object Center
D
Point
Figure 3: 3D Object Detection in the Range View. We show range image, the object confidences from cuboids shown
network, and their corresponding vehicles. For each visible point in the range image, our range view 3D object detection model learns (1) which category object belongs to (2) the offiet from the visible point to the center of the object, its 3D size, and its orientation, In the above example, the range view and the hird's-eye view. Blue hoxes indicate the ground truth cubnids, green boxes indicate positives, and boxes indicate false positives. Importantly, each object can have many thousands proposals however, most will be removed through non- mon-maximum suppression.
srede view a scene with multiple parked
show one particular point (Point A) from two different perspectives
captures the visualized 3D data. Fig. 2 shows the range view features used for our Waymo Open experiments.
3D Object Detection. Given range view features shown in Fig. 2, we seek to map over 50,000 3D points to a much smaller set of objects in 3D and describe their location, size, and orientation. Given the difficulty of this problem, approaches usually "anchor" their predictions on set of 3D "query" points (3D points which are pixels range view features). For each 3D point stored as features range image, we predict 3D cuboid which describes a 3D object. Fig. 3 illustrates the range view input (top), the smaller set of object points (middle), and the regressed object cuboids prior to non-maximum suppression (bottom). Importantly, each object may contain thousands of salient points which are transformed into 3D object proposals.
3D Input Encoding. Feature extraction on 2D grids a natural candidate for 2D-based convolu- tional architectures. However, unlike 2D architectures, range view architecture must reason 3D space. Prior literature has shown that learning this mapping is surprisingly difficult [25], which moti- vates the of 3D encodings. 3D encodings incorporate explicit 3D information when processing features in the 2D neighborhood of range image. For example, the Meta-Kernel from RangeDet [5] weights range view features by relative Cartesian encoding. We include cross cross-dataset analysis of two different methods from prior literature in our experiments. Unexpectedly, we discover that not all methods lead to improvements over our baseline encoding.
Scaling Input Feature Dimensionality. The backbone stage performs feature extraction for range view features which have been processed by the 3D input encoding. We adopt strong baseline architecture, Deep Layer Aggregation (DLA), for all of our experiments following prior work [5]. We find that scaling feature dimensionality significantly impacts performance across datasets.
Dynamic 3D Centerness. We propose a dynamic 3D classification supervision method motivated by VarifocalNet [26]. During training, compute classification targets by computing the spatial proximity between object proposal and its assigned ground truth cuboid via Gaussian likelihood:
Can(d,, gi) exp
-r; 0²
where = ||d
9. zyz ?

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Training
Inference
Claciffeation Head
Chanileation Supersbius
Bung--iiw
SD Input
Dynande
Fosturea
Eusoding
Buckboau
Dynamie
Cencerazas
Range Sabssmpling
DLA
Rang--Asure
Lose
Prot-
poocensing
Detections
Regrosion
Prepossl
Head
Theoofling
Figure 4: Model Architecture. We explore a variety of design decisions in range view based 3D ohject detection models. Our overall framework is shown above. Range view features processed by 3D input encoding which modulates features by their proximity in 3D space. These features are subsequently passed backbone CNN for feature extraction and sharing. classification likelihoods and object regression parameters, respectively. The regression parameters are compared with their ground truth target assignments to produce classification targets which incorporate regression -uullitt... The classification scores and decoded bounding boxes subsampled by our Range Subsampling method and then finally clustered non-m oon-maxiuum supression produce the final of likelihoods and scores. Blue boxes indicate core components in the network boxes outlined in black indicate components which we explicitly ablate and explore.
classification and regression process these features and produce
where deus and 9, 9,"² are the coordinates the assigned object proposal and its corresponding ground truth annotation, and a controls the width the Dynamic 3D Centerness. We adopt o = 0.75 for all experiments. Importantly, our Dynamic 3D Centerness method is computed in 3D space, not pixel space, and it's a function of the object proposals produced during each step of training. We compare our Dynamic 3D Centerness approach to the Dynamic IoUBEV method proposed in prior work [5].
Range Subsampling. The non-uniform den- sity of lidar sensors causes nearby objects to have significantly more proposals since we make predictions at every observed point -antt-w with some objects containing many thousands of points. Processing large numbers of proposals is ex- pensive, but also redundant since nearby ob- jects have many visible points. We propose straightforward Range Subsampling (RSS) method, which addresses runtime challenges without introducing any additional parameters and simplifies the overall network architecture. For dense detection output from the network, we partition the object proposals by a set of non- overlapping range intervals. Proposals closer to the origin subsampled heavily, while propos- als at the far range are not subsampled. Despite its simplicity, we will show that it outperforms complex multi-resolution, range-conditioned ar- chitectures [5] in our experimental section.
IoU-based
3D Centerness
4
0.6 P1
0.75
P1
P2 12
0
P2 12
0.25
Figure 5: Dynamic 3D Classification Supervision. We decode object proposals at each 3D point in range im- age during training in order to rank them and compute soft classification target 4. In the above example, show two object points, pn (red) and pz (blue), their cor- responding proposals decoded from the network (color- coded). the soft targets i and fz, and the radii computed for Dynamic 3D Centerness, and r2. We illustrate the differences between loU-based (left) and our proposed Dynamic 3D Centerness (right) rankings. loU-based metrics are sensitive to translation error and can provide no signal when there is no overlap between the decoded proposal and the ground truth object. Dynamic 3D cen- terness does not suffer from the same problem.
4 Experiments
In this section, we present our experiments two modern, challenging datasets for range- view-based 3D object detection. Our experi-
5

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ments illustrate which decisions matter when designing a performant range-view-basee detection model.
4.1 Datasets
Argoverse 2. The dataset contains 1,000 sequences of synchronized, multi-modal data. The dataset contains 750 training sequences, 150 validation sequences, and 150 testing sequences. In our experiments, we use the top lidar sensor to construct an 1800 x 32 range image. The official Argoverse 2 3D object detection evaluation contains 26 categories evaluated at a 150 m range with the following metrics: average precision (AP), average translation (ATE), scaling (ASE), and orientation (AOE) errors, and composite detection score (CDS). AP is a VOC-style computation with a true positive defined 3D Euclidean distance averaged over 0.5 m, 1.0 m, 2.0 m, and 4.0 m. We outline additional information in the appendix and refer readers Wilson et al. [6] for further details.
Waymo Open. The Waymo Open dataset [7] contains three evaluation categories: Vehicle, Pedes- trian, and Cyclist, evaluated maximum range of approximately 80 m. We the training split (798 logs) and the validation split (202 logs) in experiments. The dataset contains one medium-range and four near-range lidar sensors. The medium-range lidar sensor is distributed as single, dense range image. We utilize the 2650 x 64 range image from the medium-rang' lidar for all experiments. We evaluate our Waymo experiments using 3D) Average Precision (AP). Following RangeDet [5], we report Level-1 (LI) results. Additional details can be found in the appendix and the original paper [7].
4.2 Experiments
In this section, we report our experimental results. Full details on our baseline model can be found in the appendix.
Input Feature Dimensionality. We find that
input feature dimensionality plays a large role in classification and localization performance. We explore scaling feature dimensionality of
mAP
ATE
ASE
AOE
CDS
Latency (ms)
Backhone
Head
16.3
0.83
0.50
1,25
12.3
10.55
3.60
18.7
1.19
14.51
256
20.2
0.56
24.52
the high resolution pathway in both the back- bone. the classification, and regression heads. In Table 1, we find that performance on Argov- erse 2 consistently improves when doubling the backbone feature dimensionality. Additionally, the error metrics continue to decrease despite increasingly challenging true positives being de- tected. We report a similar trend in Table 2 on Waymo Open. We suspect that the performance improvements are largely due to learning dif- ficult variances found in the range view (e.g. scale variance and large changes in depth). We choose an input feature dimensionality of 256 and 128 for our state-of-the-ar comparison on Argoverse and Waymo Open, respectively, to balance performance and runtime.
21.0
Table 1: Input Feature Dimensionality: Argoverse
2. Evaluation metrics across four input feature dimen- sionalities d shown on the Argoverse 2 validation set. Sealing the high resolution feature dimensionality of the network both the backhone and head leads to substan- tial performance improvements increase from 16.3% to 21.0% mAP while reducing true positive errors.
APr.
Latency (ms) Backbone
Vehicle Pedestrian
Cyclist
Head
24.74
128
64.00
71.77
42.37
40.31
23.98
Table Input Feature Dimensionality: Waymo Open. Level-1 Mean Average Precision across two input fea- ture dimensionalities d on the Waymo Open validation set. Using larger input feature dimensionality leads to notable improvement across categories. Further scal- ing was limited by available GPU memory.
3D Input Encoding. Close proximity of pix- els in range image does not guarantee that they are close in Euclidean distance. Previous literature has explored incorporating explicit 3D information to better retain geometric information into the input encodings. We re- re-implement four of these meth- ods: the Keta-Kernel from RangeDet [5]. the Range-aware Kernel (RAK) from RangePerception

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Dynamic 3D Classification Supervision. We compare four different strategies across the Argoverse 2 and Waymo datasets. In Fig. 5, illustrate the difference between loU-based methods and our proposed Dynamic 3D Centerness. On Argoverse 2, our Dynamic 3D centemess outperforms IOUBEV by by 1.9% mAP and 1 1.3% CDS. We speculate that this performance improvement occurs because Argoverse 2 contains many small objects e.g. bollards, construction cones, and construction barrels, which receive low classification scores due to translation error under loU-based metrics. Dynamic 3D Centerness also incurs less translation, scale, and orientation errors than competing rankings. The optimal ranking strategy remains less evident for the Waymo dataset. The official Waymo evaluation uses vehicle, pedestrian, and cyclist their object evaluation categories, which are larger on average than many of the smaller categories in Argoverse 2. We find that Dynamic IoUBEV and Dynamic 3D Centerness perform similarly at 60.85% and 60.30% AP; however, for smaller objects such as pedestrian, Dynamic 3D Centerness outperforms BOUBEV by 1.1% Full tables are in the appendix. Our experiments suggest that loU prediction is unnecessary for strong performance on either dataset. comparison since performs well on both [2]. PointNet [8], and EdgeConv [27]. We find that the 2.2% mAP and 1.9% CDS. Additionally, the average translation and orientation errors are reduced. Unexpectedly, we are unable to reproduce the performance improvement from the Range Aware Kernel. On the Waymo Open dataset, we find that the Kema-Kernel yields a 3.57% and 5.03% improvement over the Vehicle and Pedestrian categories. Consistent with our results on Argoverse the Range Aware Kernel fails to reach our baseline performance. Full results are in the appendix. We will adopt the Meta-Kernel for our state-of-the-art comparison.
outperforms our baseline by
We adopt Dynamic 3D Centerness for our datasets.
Range-based Sampling. We compare our baseline architecture (single resolution prediction head with no samp-ingl against the Range-conditioned Pyramid (RCP) [5] and our Range Subsampling (RSS) approach. In Table 3, we surprisingly find that RCP performs nearly identical to our baseline model in overall performance; however, yields a modest improvement in runtime by reducing the total number of object proposals processed via NMS. By sampling object proposals processing step, our method, RSS, performs on par with RCP and the bascline with no additional parameters or network complexity, and comparable runtime. Similarly, we examine the impact of range-based sampling across the Waymo Open dataset in Table 4. We find that the Range-conditioned pyramid performs worse than our baseline despite having 2.8x the number of parameters in the network heads. We speculate that feature-pyrant powuurr-pyrmiidneewwoooo (FPN) approaches are not as effective in the range view since objects cannot be normalized in the manner proposed by the original FPN [28]. We will adopt RSS in our state-of-the- comparison.
post-
Method
Head Params. (M)
mAP
ATE J
ASE J
AOE
CDS
FPS
Baseline
1.2
16.2
0.82
0.50
1.28
12.3
19.51
RCP [5]
3.1
15.9
0.83
0.51
1.28
12.0
26.0
RSS (ours)
1.2
16.3
0.83
0.50
1.28
12.3
27.49
Table Subsampling by Range: Argoverse 2. We compare different subsampling strategies the Argoverse 2 validation set. The range-conditioned pyramid modifies the network architecture with each multi-resllutinn head responsible for a range-interval. In contrast, our RSS approach is a parameter free approach which uses a single-resolution head and only changes the subsampling procedure before NMS.
Comparison against State-of-the-Art. Combining a scaled input feature dimensionality with Dynamic 3D Centerness and Range-based sampling yields a model which is competitive with existing voxel-based methods on the Argoverse 2 dataset, and state-of-the on the Waymo Open dataset. In Table 5, we report our mAP over the 26 categories in Argoverse We outperform VoxeINext [29], the strongest voxel-based model, by 2.2% mAP with comparable runtime. In Table 6, show our L1 AP against variety of different models on Waymo Open. Our method outperforms all existing range view models while also being multi-class.
amongst range view models
7

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Method
Head Params. (M)
3D APL1 Pedestrian
FPS
Vehicle
Cyclist
Baseline
1.2
60.09
67.46
28.55
4.87
[5]
3.4
58.97
66.91
16.74
14.08
RSS (ours)
1.2
60.34
68.57
28.47
19.3
Table Subsampling by Range: Waymo Open. We compare different subsampling strategies on the Waymo Open validation set. RSS outperforms approaches in both performance and runtime while requiring fewer
parameters.
R. Vehicle
Podestriam
Veliele
B. Treck
W. Deviee
Motoreydle
Bollard
S. Sien
Bicyydee
Cab
Sign
Bos
Mean
T.
FPS
(%)
1.06
mAP
(30)
FSD [31]
28.2
68.1
59.0
41.8
42.6
41.2
38.6
5.9
33.5
11.9
40.9
26.9
14.8
49.0
Next (29)
30.7
72.7
63.2
53.9
64.9
44.9
40.6
6.8
40.1
14.9
38.8
20.9
19.9
42.4
18
Ours (64)
28.8
74.4
65.9
45.6
70.8
48.4
30.0
31.4
4.3
35.6
16.9
17.5
41.0
15.9
21.1
39.3
16.7
31
Ours (128)
32.9
76.2
68.1
19.0
72.1
50.3
36.5
39.7
6.6
38.0
22.6
17.9
17.1
21.0
26.4
47.7
20.0
22
(256)
Table 5: Statc-of-the-Art Comparison: Argoverse 2. We compare range view model against different ofate-of-the-art peer-reviewed methods the Argoverse 2 validation dataset. We significantly outperform other methods on small retroreflective objects such construction barrels and cones. The full table will be available in the appendix.
Method
Open-source
Multi-class
3D APL1 f Pedestrian
Vehicle
Cyclist
Voxel-based
SWFormer [32)
77.8
80.9
Multi-view-based
RSN (20)
75.1
77.8
Range-view-bused
To the Point [4]
65.2
73.9
RangeDet [5]
72.85
75.94
65.67
RangePerception [2]
73.62
80.24
70.33
Ours
75.12
80.82
75.01
Table 6: Comparison against Statc-of-the-Art: Waymo Open. We compare range view model against different state-of-the-art methods the Waymo validation set. Our model outperforms all other range-view- based methods. best our knowledge, we are the only open-source, multi-class range view method. all methods report Cyclist performance and we' re unable to compare FPS fairly since we do not have access to their code.
5 Conclusion
In this paper, we examine a diverse set of considerations when designing range view 3D object detection model. Surprisingly, find that not all contributions from past literature yield meaningful performance improvements. We propose straightforward dynamic 3D centerness technique which performs well across datasets, and a simple sub-sampling technique to improve range view model runtime. These techniques allow us to establish the first range view method on Argoverse 2, which competitive with voxel-based methods, and a new state-of-the-art amongst range view models on Waymo Open. Our results demonstrate that simple methods are least as effective as recently- proposed techniques, and that range view models a promising avenue for future research.

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Appendix
Our supplementary materials cover the following: background on 3D object detection in the range view, additional quantitative results, qualitative results, dataset details, and implementation details for our models.
7 Range View Representation
The range view representation, also known range image, is 2D grid containing the spherical coordinates of an observed point with respect to the lidar laser's original reference frame. We define a range image as:
A ((Pi))
H: S s
where (Piy) 0.j. are the inclination, azimuth, and range, and W are the height and width of the image. Importantly, the cells of range image not limited to containing only spherical coordinates. They may also contain auxiliary sensor information such lidar's intensity.
7.1 3D Object Detection
Given a range image r, we construct a set of 3D object proposals which are ranked by a confidence score. Each proposal consists of a proposed location, size, orientation, and category. Let represent the predictions from a network.
D { E IR [R*)
K ii=1 where K C N, t00 V. 2 L, We, h4 ca.
(3)
d, A {0,T00
where 580 U; "000 1go are the coordinates of the object in the ego-vehicle reference frame, 4, We h; the length, width, and height of the object, 0 is the counter- clocer-clockkise rotation about the vertical axis, and ej is the object likelihood. Similarly, we define the ground truth cuboids as:
(g:
R J where M C N.
(5)
9:
ego 1
2g0 M/
ego 24
14 Wi, hi 0z, 9
(6)
where y is a continuous value computed dynamically during training. For example, q may be set to Dynamic 3D Centerness or IoUBEV. The detected objects, are decoded as the same parameterization as G.
D
A (d E :1 2 2 2x) xx) k= where K C N,
dę
A ego
ego _go Uk
=k l,, wh. 11, 0x)
(8)
We seek to predict continuous representation of the ground truth targets as:
A dg
E R :1 2 ... ......)) 20) where K C N,
K
(9)
9k a
ego WE =k .1, wh, hu, Cx
(10)
EgO Ego ego where x :3k 2% are the coordinates of the object in the ego-vehicle reference frame, 4x wa, la are the length, width, and height of the object, 0g is the counter-clockwin rotation about the vertical axis, and e₁ is the object category likelihood.
3D Anchor Points in the Range View. of observed 3D points which features of the projected pixels in range image. For all the 3D points contained in range image, produce detection de.
To predict objects, bias our predictions by the location
12

---

Regression Targets. Following previous literature, we do not directly predict the object proposal
representation in Section 7.1. Instead, we define the regression targets as the following:
T(P,9 (P [t<(ps. g.) I J==1: where E N,
(11)
4pp:. (:) (Ar, Ay; Az; log la, log wi, log h4 sin 0,, cos cos0;) 0:},
(12)
where and the sets of points in the range image and the ground truth cuboids in the 3D seene, Ar,, Ay,, Az; the offsets from the point the associated ground truth cuboid in the point-azimuth reference frame, log li, log wi, log h; are the logarithmic length, width, and height of the object, respectively, and sin 8, cos 0g are continuous representations of the object's 's heading 8,
Classification Loss. Once all of the candidate foreground points have been ranked and assigned, each point needs incur loss proportional its regression quality. We Varifocal loss [26] with sigmoid-logit activation for classification loss:
(-<( qi log(c,) + (1- 9.) log(1 c,)) if
VFL(C, (c)
(13)
acac log(1 (1 - c2) otherwise.
where G; is classification likelihood and 9i is 3D classification targets (e-g-, Dynamic IOUnEV Dynamic 3D Centerness). Our final classification loss for entire 3D scene is:
1 M
L.
VFL(C,,)).
(14)
i=
where . is the total number of foreground points, N is the total number of objects in scene, P the of 3D points which fall inside the fuh ground truth cuboid, of is the likelihood from the network classification head, and of is the 3D classification target.
Regression Loss We use & regression loss to predict the regression residuals. The regression loss for an entire 3D scene is:
Pa
C,
LILOSs (r, 1)),
(15)
where N is the total number of objects in scene, Pd is the set of 3D points which fall inside the jth ground truth cuboid, rf is the predicted cuboid parameters from the network, and t² are the target residuals to be predicted.
Total Loss.
Our final loss is written as:
C = La + brr
(16)
7.2 Argoverse
Additional details on the evaluation metries used in the Argoverse 2.
Average Precision (AP): VOC-style computation with true positive defined at 3D Eu- clidean distance averaged over 0.5 m, 1.0 m, 2.0 m, and 4.0 m.
Average Translation Error (ATE): 3D Euclidean distance for true positives at 2 m.
Average Scale Error (ASE): Pose-aligned 3D IoU for true positives 2 m.
Average Orientation Error (AOE): Smallest yaw angle between the ground truth and prediction for true positives
Composite Detection Score (CDS): Weighted average between AP and the normalized true positive scores:
CDS AP. I where & E (ATE ASE A0Fun))
(17)
ZEX'
We refer readers Wilson et al. [6] for further details.
13

---

7.3
Waymo Open
Additional details on the evaluation metrics used in the Waymo Open are listed below.
3D Mean Average Precision (mAP): VOC-style computation with true positive defined
by 3D IoU. The gravity-aligned axis fixed.
(a) Level (L1): All ground truth cuboids with at least five lidar points within them.
(b) Level 2 (L2): All ground cuboids with at least 1 point and additionally incorporates heading into its true positive criteria.
Following RangeDet [5], we report L1 results.
8 Range View 3D Object Detection
Baseline Model. Our baseline models are all multi-class and utilize the Deep Layer Aggregation
(DLA) [33] architecture with an input feature dimensionality of 64, In our Argoverse 2 experiments, we incorporate five input features: y, range, and intensity, while for our Waymo experiments, include six input features:
range, intensity, and elongation. These inputs are then transformed to the backbone feature dimensionality of 64 using a single basic block. For post-processing, we use weighted non-maximum suppression (WNMS). All models are trained and evaluated using mixed-precision with BrainFloat16 [34]. Both models use OneCycle scheduler with Adam W using learning rate of 0.03 across four A40 GPUs using Synchronized BatchNorm. Both the Argoverse and Waymo models use standard data augmentation techniques such as: random axis flipping, scaling from 0.95 to 1.05, and rotation between _ and - All models in the ablations are trained for epochs on a uniformly sub-sampled fifth of the training set.
State-of-the-an Comparison Model. We leverage the best performing and most general methods from experiments for state-of-the-arr comparison for both the Argoverse 2 and Waymo Open dataset models. The Argoverse 2 and Waymo Open models use input feature dimensionality of 256 and 128, respectively. Both models use the Meta-Kerne and a 3D input encoding, Dynamic 3D Centerness for their classification supervision, and we use our proposed Kange-Subsamplis with range partitions of [0 -0-33 30 m). [30m, For both datasets, models are trained 20 epochs.
m, 50 m), [50 m, oo) with subsampling rates of 8, 2, 1, respectively.
Method
mAP
ATE +
ASE 4
AOE 4
CDS
Dynamic IoUBEV [5]
14.4
0.85
0.51
1.29
11.0
Dynamic IoUnD
13.8
0.87
0.50
1.26
10.5
Dynamic BEV Centerness (ours)
16.2
0.76
0.48
1.30
12.2
Dynamic 3D Centerness (ours)
16.3
0.83
0.50
1.28
12.3
Table Classification Supervision: Argoverse Evaluation metrics errors using two different classifica- tion supervision methods on the Argoverse 2 validation set. We observe that our proposed centerness- methods outperform loU-hased methods. Dynamic 3D Centerness achieves the hest performing mAP and CDS with the BEV variant as close second. Surprisingly, Dynamic 3D centerness outperforms IoUREV in average translation, scale, orientation errors.
8.1 Oracle Experiments
While our current work only focuses on
detection, we do believe that sensor fusion an important direction to explore. We ran some preliminary oracle experiments which use "ground truth" information during inference correct for errors made via the network's classification regression. We find that overall performance improves by roughly 5% when correcting for classification errors and 8% when correcting for regression errors.
14

---

Method
3D AP L1 Pedestrian
Vehicle
Cyclist
Dynamic ToUBev [5]
60.54
67.45
23.53
Dynamic IoUgD
60.85
67.48
20.79
Dynamic BEV Centerness (ours)
59.92
68.56
28.45
Dynamic 3D Centerness (ours)
60.34
68.58
28.53
Table 8: Classification Supervision: Waymo Open. Evaluation metrics and emrors using two different classification supervision methods the Waymo Open validation Our results suggest that centerness-based approaches are a competitive alternative loUBEv, while being simpler.
Method
mAP 1
ATE 4
ASE
AOE
CDS
Basic Block
16.3
0.83
0.50
1.28
12.3
Range Aware Kernel" [2]
15.7
0.83
0.51
1.29
11.8
PointNet [8]
16.4
0.82
0.50
1.27
12.4
EdgeConv [27]
16.7
0.78
0.49
1.17
12.7
Meta Kernel [5]
18.5
0.80
0.50
1.21
14.2
Table 3D Input Encoding: Argoverse Mean Average Precision using different input feature encodings on the Argoverse 2 validation set. * Code unavailable. Re-implemented by ourselves.
Method
3D APL1 t
Vehicle
Pedestrian
Cyclist
Basic Block
60.34
68.58
28.53
Range Aware Kernel [2]
59.84
67.69
24.62
PointNet [8]
61.17
69.92
36.30
EdgeConv [27]
61.23
70.29
42.29
Meta Kernel [5]
63.92
73.61
52.84
Table 10: 3D Input Encoding: Waymo Open. I.I Average Precision (AP)
three different 3D input feature encodings on the Waymo validation set. The Meta Kernel outperforms all methods improving considerably across all categories. Surprisingly. the Range Aware Kernel performs worse than our baseline method. * Code unavailable. Re-implemented by ourselves based on details in the manuscript [2].
67.6
-611
32.4
6.3
789
301
Nachoo 123)
347
7237
65.2
539
645
400
415
63
+11
149
382
309
159
424
324
255
447
2011
254
1537
263
14.4
438
204
165
123
410
368
300
183
113
22
18-2
129
Den (23)
964
300
251
412
541
Table 11: State-of-the-Art Comparison: Argoverse 2 (All categories). We compare our range view model against different state-of-the th-the--rt art, peer-reviewed methods on the Argoverse 2 validation dataset. This table includes all categories some which were omitted due to space in the main manuscript.
8.2 Qualitative Results
We include qualitative results for both Argoverse 2 and Waymo Open shown in Figs. 6 and 7.
15

---

Figure 6: Qualitative Results: Argoverse 2. True positives (green) and ground truth cuboids (blue) are shown below for our best performing model. True positives shown using 2 Euclidean distance from the ground truth cuboid center.
16

---

Figure 7: Qualitative Results: Waymo Open. True positives (green), faase positives (red) and ground truth cuboids (blue) are shown below for best performing model. True positives are shown using 0.7 loU threshold.
17


Reviews:
[{"review_id": "VRwfqcErIx", "reviewer": "Reviewer_SCbK", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 2, "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": "In a field filled with overly complicated methods chasing the slightest improvements on metrics, they demonstrate the competitiveness of a simpler approach", "summary_of_recommendation": "Experiments section does not compare enough existing methods for me to consider it an \"analysis of what matters\". Proposed methods are too straightforward to accept as lone contribution.", "technical_quality": 4, "clarity_of_presentation": 4, "potential_impact": 2, "robotics_focus": 2, "quality_of_limitations": 1, "questions_for_rebuttal": "- For the \"Analysis of What Matters\" contribution why are so few options considered at each step? E.g. in a field filled with countless proposed supervision/loss methods it seems more could easily be considered.\n- This lack of options is especially concerning for \"3D Input Encoding\". Considering only two methods were compared, the fact one of them did not reproduce the claimed results, inevitably leads to the question of whether their paper exaggerated performance, or this paper's authors failed to reproduce it properly. If more methods were compared (and achieved results roughly consistent with their own paper's claims) then a single method performing worse than it's claims would not bring the comparison's reliability into question.", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "aKsg9ZA3r5", "reviewer": "Reviewer_2jY8", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 3, "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": "This paper studies the problem of 3d object detection from a lidar range view representation as input. The paper reviews existing architectures and through experiments demonstrates the modules and designs that most contribute to high performance. The paper then proposed simple alternatives (range subsampling, simplified classification loss)  to existing modules and demonstrates improved performance. Ultimately, the authors propose a simplified architecture is able to achieve state of the art performance.", "summary_of_recommendation": "Overall, I believe the technical contributions of the paper would be useful to the community, given that the paper is well-written, technically sound, and presents interesting insights on the problem of range view detection.", "technical_quality": 4, "clarity_of_presentation": 5, "potential_impact": 3, "robotics_focus": 3, "quality_of_limitations": 2, "questions_for_rebuttal": "- Can you please share the details the of the sub-sampling technique?", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "fRhzkWeakM", "reviewer": "Reviewer_sbqX", "summary": "", "questions": "", "limitations": "", "rating": 3, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": 3, "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": "In this paper, the authors explore the input feature dimensionality, the classification loss function (3D spatial proximity vs IoU-based), and the way to address non-uniform lidar density (proposed range subsampling vs multi-resolution, range-conditioned networks) to establish a range-view 3D object detection model that outperforms many point-based and voxel-based methods in the Argoverse 2 dataset and Waymo Open Dataset. The authors adopt Deep Layer Aggregation (from RangeDet) as backbone, Varifocal Loss as classification loss, L1 Loss as regression loss, and have ablation studies on Input Feature Dimensionality, 3D Input Encoding methods (Meta-Kernel vs Range-Aware Kernel), Dynamic 3D Classification Supervision (in supplementary materials), and Range-based Sampling.", "summary_of_recommendation": "Good performance for range-view 3D object detection especially in Argoverse 2 dataset. The paper is organized logically, with a clear introduction, and it's well-written and easy to understand. Further provement of significance by providing more experiment results is needed. Weak Accept.", "technical_quality": 3, "clarity_of_presentation": 4, "potential_impact": 3, "robotics_focus": 3, "quality_of_limitations": 1, "questions_for_rebuttal": "Is there some techniqes used in this paper can be used in point-based or voxel-based methods to improve the performance? Essentially in Argoverse 2, that would be a better apple-to-apple comparison or the prove of the significance of the techniqes in this paper.\n\nIs it possible to provide the model size / runtime of the range-view model, the point-based and voxel-based methods in the performance comparison table? To make sure the performance gain is not from the larger model size and make sure they are apple-to-apple comparisons.\n\nIs there any other dataset that can be used to prove the effectiveness of the range-view method?", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: Summarizing the strengths and weaknesses pointed out by the reviewers:

Strengths:
- Clear and straightforward premise
- Good ablation analysis and baseline comparisons
- Experimental results across 2 datasets
- Well-written and clear
- The proposed method is simple yet outperforms more complicated baselines

Weaknesses:
- Proposed methods are straightforward and simple, limiting their novelty
- Details of the sub-sampling method are not clearly explained
- Limitations of range-view methods are not well addressed
- Some performance improvements are only effective on the Argoverse dataset

The reviewers disagree on the rigor of the experiments. 1 found them "rigorous and convincing" while the other 2 wanted to see more, particularly against other methods or on other datasets. Since more experiments can't hurt, the authors might consider addressing the concerns of those 2 reviewers by running more experiments.

Overall, 2 reviewers recommended acceptance and 1 rejection, though all weakly. Given the specific feedback of the reviewers, the authors are advised to address their concerns as best as possible.

**Update after rebuttal**: The authors' rebuttal directly addressed many of the reviewers' concerns, including many new experiments. Only 2 of the 3 reviewers responded after the rebuttal. The first upgraded their score from weak reject to weak accept. The second maintained a recommendation of weak accept. The final reviewer, who did not respond, was already recommending weak accept, thus, given all reviewers recommend acceptance, I recommend this paper be accepted.
Decision: Accept