What Matters in Range View 3D Object Detection

Authors: benjamin  wilson
nicholas autio mitchell
jhony kaesemodel pontes
james  hays
Venue: robot-learning_org_CoRL_2024_Conference
Published: 2024-06-07 07:17:41

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.

Main paper: 
 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 as 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. Voxel-based and bird'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 representations. Currently, the research community focuses on bird's-eye view or voxel-based methods. 
related work: 
 Point-based Methods. Point-based methods aim to use the full point cloud without projecting to a 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 to the urban autonomous driving domain, point-based methods do not scale well with the number of points and size of scenes, which makes them unsuitable for real-time, low-latency safety-critical applications. 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 are assigned to the same grid cell. While some methods resolve these collisions by simply selecting the nearest point [12] , others use a max-pooled feature [14] or a learned feature fusion [19, 13] .Range-based Projections. A range view representation is a spherical projection that maps a point cloud onto a 2D image plane, with the result often referred to as a range image. Representing 3D points as a range image yields a few notable advantages: (a) memory-efficient representation, i.e., the image can 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 with dense 2D convolution, (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 to 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] .Multi-View Projections. To leverage the best of both projections, recent work has explored multiview methods [19, 13, 15, 24] . These methods extract features from both the bird's-eye 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 et al. [13] suggests fusing these features by projecting range view features to the bird's-eye view. Fadadu et al. [15] investigates the fusing of RGB camera features with multi-view lidar features. In this work, we explore how competitive range view representations can be without these additional views. 
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 a range-view-based 3D object detection model.Inputs: Range View Features. The range view is a dense grid of features captured by a lidar sensor. In Fig. 1 , we illustrate the range view, its 3D representation along with an explicit geometric correspondence of a building between views and the location of the physical lidar sensor which Figure 3 : 3D Object Detection in the Range View. We show a range image, the object confidences from a network, and their corresponding 3D cuboids shown in the bird's-eye view for a scene with multiple parked vehicles. For each visible point in the range image, our range view 3D object detection model learns (1) which category an object belongs to (2) the offset from the visible point to the center of the object, its 3D size, and its orientation. In the above example, we show one particular point (Point A) from two different perspectivesthe range view and the bird's-eye view. Blue boxes indicate the ground truth cuboids, green boxes indicate true positives, and red boxes indicate false positives. Importantly, each object can have many thousands of proposals -however, most will be removed through non-maximum suppression.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 a set of 3D "query" points (3D points which are pixels in range view features). For each 3D point stored as features in a range image, we predict a 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 is a natural candidate for 2D-based convolutional architectures. However, unlike 2D architectures, a range view architecture must reason in 3D space. Prior literature has shown that learning this mapping is surprisingly difficult [25] , which motivates the use of 3D encodings. 3D encodings incorporate explicit 3D information when processing features in the 2D neighborhood of a range image. For example, the Meta-Kernel from RangeDet [5] weights range view features by a relative Cartesian encoding. We include a 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 a 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, we compute classification targets by computing the spatial proximity between an object proposal and its assigned ground truth cuboid via a Gaussian likelihood: where d xyz i and g xyz i are the coordinates of the assigned object proposal and its corresponding ground truth annotation, and σ controls the width of the Dynamic 3D Centerness. We adopt σ = 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 IoU BEV method proposed in prior work [5] .EQUATIONFigure 5 : Dynamic 3D Classification Supervision. We decode object proposals at each 3D point in a range image during training in order to rank them and compute a soft classification target ti. In the above example, we show two object points, p1 (red) and p2 (blue), their corresponding proposals decoded from the network (colorcoded), the soft targets t1 and t2, and the radii computed for Dynamic 3D Centerness, r1 and r2. We illustrate the differences between IoU-based (left) and our proposed Dynamic 3D Centerness (right) rankings. IoU-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 centerness does not suffer from the same problem.Range Subsampling. The non-uniform density of lidar sensors causes nearby objects to have significantly more proposals since we make predictions at every observed point -with some objects containing many thousands of points.Processing large numbers of proposals is expensive, but also redundant since nearby objects have many visible points. We propose a straightforward Range Subsampling (RSS) method, which addresses runtime challenges without introducing any additional parameters and simplifies the overall network architecture. For a dense detection output from the network, we partition the object proposals by a set of nonoverlapping range intervals. Proposals closer to the origin are subsampled heavily, while proposals at the far range are not subsampled. Despite its simplicity, we will show that it outperforms complex multi-resolution, range-conditioned architectures [5] in our experimental section. 
experiments: 
 In this section, we present our experiments on two modern, challenging datasets for rangeview-based 3D object detection. Our experi-ments illustrate which decisions matter when designing a performant range-view-based detection model.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 × 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 a composite detection score (CDS). AP is a VOC-style computation with a true positive defined at 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 to Wilson et al. [6] for further details.Waymo Open. The Waymo Open dataset [7] contains three evaluation categories: Vehicle, Pedestrian, and Cyclist, evaluated at a maximum range of approximately 80 m. We use the training split (798 logs) and the validation split (202 logs) in our experiments. The dataset contains one medium-range and four near-range lidar sensors. The medium-range lidar sensor is distributed as a single, dense range image. We utilize the 2650 × 64 range image from the medium-range lidar for all experiments. We evaluate our Waymo experiments using 3D Average Precision (AP). Following RangeDet [5] , we report Level-1 (L1) results. Additional details can be found in the appendix and the original paper [7] .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 the high resolution pathway in both the backbone, the classification, and regression heads.In Table 1 , we find that performance on Argoverse 2 consistently improves when doubling the backbone feature dimensionality. Additionally, the error metrics continue to decrease despite increasingly challenging true positives being detected. We report a similar trend in Table 2 on Waymo Open. We suspect that the performance improvements are largely due to learning difficult 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-art comparison on Argoverse 2 and Waymo Open, respectively, to balance performance and runtime.3D Input Encoding. Close proximity of pixels in a 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-implement four of these methods: the Meta-Kernel from RangeDet [5] , the Range-aware Kernel (RAK) from RangePerception [2] , PointNet [8] , and EdgeConv [27] . We find that the Meta-Kernel outperforms our baseline by 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 Meta-Kernel yields a 3.57% and 5.03% improvement over the Vehicle and Pedestrian categories. Consistent with our results on Argoverse 2, 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.Dynamic 3D Classification Supervision. We compare four different strategies across the Argoverse 2 and Waymo datasets. In Fig. 5 , we illustrate the difference between IoU-based methods and our proposed Dynamic 3D Centerness. On Argoverse 2, our Dynamic 3D centerness outperforms IoU BEV by by 1.9% mAP and 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 IoU-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 as their object evaluation categories, which are larger on average than many of the smaller categories in Argoverse 2. We find that Dynamic IoU BEV and Dynamic 3D Centerness perform similarly at 60.85% and 60.30% AP; however, for smaller objects such as pedestrian, Dynamic 3D Centerness outperforms IoU BEV by 1.1%. Full tables are in the appendix.Our experiments suggest that IoU prediction is unnecessary for strong performance on either dataset. We adopt Dynamic 3D Centerness for our state-of-the-art comparison since it performs well on both datasets.Range-based Sampling. We compare our baseline architecture (single resolution prediction head with no sub-sampling) 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, it yields a modest improvement in runtime by reducing the total number of object proposals processed via NMS. By sampling object proposals as a postprocessing step, our method, RSS, performs on par with RCP and the baseline 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-pyramid-network (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-art comparison. Table 3 : Subsampling by Range: Argoverse 2. We compare different subsampling strategies on the Argoverse 2 validation set. The range-conditioned pyramid modifies the network architecture with each multi-resolution 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-art amongst range view models on the Waymo Open dataset. In Table 5 , we report our mAP over the 26 categories in Argoverse 2. We outperform VoxelNext [29] , the strongest voxel-based model, by 2.2% mAP with comparable runtime. In Table 6 , we show our L1 AP against a variety of different models on Waymo Open. Our method outperforms all existing range view models while also being multi-class.  
conclusion: 
 In this paper, we examine a diverse set of considerations when designing a range view 3D object detection model. Surprisingly, we find that not all contributions from past literature yield meaningful performance improvements. We propose a 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 is 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 at least as effective as recentlyproposed techniques, and that range view models are a promising avenue for future research. 
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. 
range view representation: 
 The range view representation, also known as a range image, is a 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:EQUATIONwhere (φ ij , θ ij , r ij ) are the inclination, azimuth, and range, and H, W are the height and width of the image. Importantly, the cells of a range image are not limited to containing only spherical coordinates. They may also contain auxiliary sensor information such as a lidar's intensity.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 D represent the predictions from a network.EQUATIONEQUATIONwhere x ego i , y ego i , z ego i are the coordinates of the object in the ego-vehicle reference frame, l i , w i , h i are the length, width, and height of the object, θ i is the counter-clockwise rotation about the vertical axis, and c i is the object likelihood. Similarly, we define the ground truth cuboids as:EQUATIONEQUATIONwhere q i is a continuous value computed dynamically during training. For example, q i may be set to Dynamic 3D Centerness or IoU BEV . The detected objects, D are decoded as the same parameterization as G.EQUATIONEQUATIONWe seek to predict a continuous representation of the ground truth targets as:EQUATIONEQUATIONwhere x ego k , y ego k , z ego k are the coordinates of the object in the ego-vehicle reference frame, l k , w k , h k are the length, width, and height of the object, θ k is the counter-clockwise rotation about the vertical axis, and c k is the object category likelihood.3D Anchor Points in the Range View. To predict objects, we bias our predictions by the location of observed 3D points which are features of the projected pixels in a range image. For all the 3D points contained in a range image, we produce a detection d k .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:EQUATIONEQUATIONwhere P and G are the sets of points in the range image and the ground truth cuboids in the 3D scene, ∆x i , ∆y i , ∆z i are the offsets from the point to the associated ground truth cuboid in the point-azimuth reference frame, log l i , log w i , log h i are the logarithmic length, width, and height of the object, respectively, and sin θ i , cos θ i are continuous representations of the object's heading θ i .Classification Loss. Once all of the candidate foreground points have been ranked and assigned, each point needs to incur loss proportional to its regression quality. We use Varifocal loss [26] with a sigmoid-logit activation for our classification loss:EQUATIONwhere c i is classification likelihood and q i is 3D classification targets (e.g., Dynamic IoU BEV or Dynamic 3D Centerness). Our final classification loss for an entire 3D scene is:EQUATION)where M is the total number of foreground points, N is the total number of objects in a scene, P j G is the set of 3D points which fall inside the j th ground truth cuboid, c j i is the likelihood from the network classification head, and q j i is the 3D classification target.Regression Loss. We use an ℓ 1 regression loss to predict the regression residuals. The regression loss for an entire 3D scene is:EQUATION)where N is the total number of objects in a scene, P j G is the set of 3D points which fall inside the j th ground truth cuboid, r j i is the predicted cuboid parameters from the network, and t j i are the target residuals to be predicted. Total Loss. Our final loss is written as:EQUATIONAdditional details on the evaluation metrics used in the Argoverse 2.• Average Precision (AP): VOC-style computation with a true positive defined at 3D Euclidean 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 at 2 m.• Average Orientation Error (AOE): Smallest yaw angle between the ground truth and prediction for true positives at 2 m.• Composite Detection Score (CDS): Weighted average between AP and the normalized true positive scores:EQUATIONWe refer readers to Wilson et al. [6] for further details.Additional details on the evaluation metrics used in the Waymo Open are listed below.1. 3D Mean Average Precision (mAP): VOC-style computation with a true positive defined by 3D IoU. The gravity-aligned axis is fixed.(a) Level 1 (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: x, y, z, range, and intensity, while for our Waymo experiments, we include six input features: x, y, z, 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 a OneCycle scheduler with AdamW using a learning rate of 0.03 across four A40 GPUs using Synchronized BatchNorm. Both the Argoverse 2 and Waymo models use standard data augmentation techniques such as: random axis flipping, scaling from 0.95 to 1.05, and rotation between -π 4 and π 4 . All models in the ablations are trained for 5 epochs on a uniformly sub-sampled fifth of the training set.State-of-the-art Comparison Model. We leverage the best performing and most general methods from our experiments for our state-of-the-art comparison for both the Argoverse 2 and Waymo Open dataset models. The Argoverse 2 and Waymo Open models use an input feature dimensionality of 256 and 128, respectively. Both models use the Meta-Kernel and a 3D input encoding, Dynamic 3D Centerness for their classification supervision, and we use our proposed Range-Subsampling with range partitions of [0 -30 m), [30 m, 50 m), [50 m, ∞) with subsampling rates of 8, 2, 1, respectively. For both datasets, models are trained for 20 epochs.  
oracle experiments: 
 While our current work only focuses on single-lidar-only detection, we do believe that sensor fusion is an important direction to explore. We ran some preliminary oracle experiments which use "ground truth" information during inference to correct for errors made via the network's classification or regression. We find that overall performance improves by roughly 5% when correcting for classification errors and 8% when correcting for regression errors. 
 
 Appendix: 3D AP L1 ↑ Vehicle Pedestrian Cyclist Dynamic IoU BEV [5] 60  


Decision: 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.

Reviews: review: title: title: Range View 3D Object Detection Review
paper_summary: paper_summary: In a field filled with overly complicated methods chasing the slightest improvements on metrics, they demonstrate the competitiveness of a simpler approach
main_review: main_review: The paper's premise is straightforward and clearly layed out. As promised in the introduction, the authors perform a series of ablation tests comparing different approaches at each step of their overall method.

Specifically they compare:

- Two existing methods for 3D Input Encoding
- A novel loss function against an existing one for better training 
- A novel (simple) subsampling step vs an existing one vs no subsampling at all.

The paper's straightforward premise, clear introduction, and experiments being repeated across two datasets are all strengths. However there are clear weaknesses in the form of:

- Each experiment only comparing two existing methods (three if counting the absence of a method)
- Novel methods proposed are all very straightforward and simple. 

The results showing that a simple method such as the one proposed can achieve competitive results is very interesting, and the strongest part of the paper. However it is hard to view that alone sufficient for acceptance. For the other contributions of comparing different steps of the process, more than two methodologies would have to be compared for the paper to be viewed as either a reliable "analysis of what matters" or "measure of the current field".
questions: questions: - 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.
- 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.
limitations: limitations: 1
review_summary: review_summary: 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.
title: title: Initial Review
paper_summary: paper_summary: 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.
main_review: main_review: ### Strengths
- The paper is well-written and clear. 
- The proposed method is simple, yet effective. The overall approach is simpler than prior works, yet outperforms baselines. 
- The experimental analysis is rigorous and convincing. The design decisions are motivated via ablation experiments.
- The code is released and has useful improvements (written in PyTorch, multi-class) over existing public codebases for range view detection.


### Weaknesses 
- While I believe the simplicity of the approach should be considered a positive, the overall novelty of the technical contributions of this work appear to be minimal. The model architecture is based on prior works and the key technical contributions are (1) a simplified classification loss and (2) a simple sub-sampling technique. 
- The details of the sub-sampling technique, one of the core technical contributions of the paper, are not clearly described. 
- The limitations of the method and of using range view in general are not well addressed in the paper. E.g., it's not clear how well the method can be extended to support fusing with camera data or supporting multiple lidars on a single vehicle which has become increasing common (e.g., multiple lidars are available in the dataset but only a single one is used)
questions: questions: - Can you please share the details the of the sub-sampling technique?
limitations: limitations: 2
review_summary: review_summary: 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.
title: title: Beat point-based and voxel-based SOTA methods using range-view for the 3D object detection task in Argoverse 2.
paper_summary: paper_summary: 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.
main_review: main_review: Quality:
  Strengths:
    1. The code is providied.
    2. Model model deteails, ablation studies, and Qualitative Results visualizations are provided in the supplementary materials.
    3. Experiments are conducted on both Argoverse 2 and Waymo Open Dataset, and the performance is good, especially beats many point-based and voxel-based SOTA on Argoverse 2.
  Weaknesses:
    It would benefit from additional experiments on a more diverse set of datasets to confirm the generalizability of the findings.

Clarity:
  Strengths:
    1. The paper is organized logically, with a clear introduction, and it's well-written and easy to understand.
    2. The figures and tables are clear and easy to understand. (e.g., diagrams illustrating range view representation)
  Weaknesses:
    It would be better to have more types of objects in the visualizations, like pedestrians, cyclists.

Originality:
  Strengths:
    Some novel modules like Dynamic 3D Centerness and Range Subsampling.
  Weaknesses:
    The novel modules are incremental on some exisiting fraemworks, like Meta-Kernel 3D Input Encoding, and Deep Layer Aggregation backbone from RangeDet.

Significance:
  Strengths:
    1. Significant performance improvements for range-view 3D object detection, especially on Argoverse 2, beating many point-based and voxel-based SOTA methods. SOTA for range-view methods.
    2. Provide open-source code for further research and application, enhancing the work's impact on the community.
  Weaknesses:
    1. Some performance improvements are only significant on the Argoverse 2 dataset, like Dynamic 3D Centerness, which lack of generalizability.
    2. I would recommend to provide the model size / runtime of your range-view model, the point-based and voxel-based methods in the performance comparison table, in order to further demonstrate the effeciency of your approach and improve the significance of the paper.
questions: questions: 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.

Is 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.

Is there any other dataset that can be used to prove the effectiveness of the range-view method?
limitations: limitations: 1
review_summary: review_summary: 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.