Venue: NeurIPS 2022 Conference

Reviews:

final_score: rating: 6: Weak Accept: Technically solid, moderate-to-high impact paper, with no major concerns with respect to evaluation, resources, reproducibility, ethical considerations.
review_content: summary: This paper demonstrates a simple baseline range-based 3D detector for multi-frame inputs, which shows the potential way to achieve efficient 3D detection compared with current BEV pipelines. Experiments show the proposed method achieves comparable results with BEV methods such as CenterPoint with a faster speed.
strengths_and_weaknesses: Pros:

1. The paper writing is smooth and organized well.

2. The paper comprehensively investigates some basic designs (3D inputs, network backbone, detection head) of range-based 3D detectors and compares the advantages and disadvantages of range-view detectors. Some interesting points such as multi-frame point encoding and modality-wise convolution are proposed and discussed. Overall, it provides a simple baseline approach for RV detectors.

Cons:

The paper lacks a direct comparison with other range-based 3D detectors. Further comparison with RangeDet or RCD or RSN (with the same multi-round range projection inputs) at the level of the operator could further enhance the paper statements, specifically in their speed/runtime comparison. If not, please give the explanations.

questions: 1. Please see the cons as above.
2. Why FCOS-LiDAR(c128) gets better results than CenterPoint on nuScenes test set and worse results on the val set? 
3. As the feature map of each level has to be resized to the original image size, is it necessary to apply FPN to generate multi-level prediction? An ablation study compared with one-level (concatenation or summation) prediction can be provided.
4. Random scale augmentation involved in training might affect all point angles in range view projection. Would it cause object artifacts during projection?

limitations: Yes, in the conclusion and experiment parts. MRV's performance in multi-frame settings still falls behind BEV's.
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final_score: rating: 4: Borderline reject: Technically solid paper where reasons to reject, e.g., limited evaluation, outweigh reasons to accept, e.g., good evaluation. Please use sparingly.
review_content: summary: In this paper, the authors propose a range-view CNN based 3D detector. Instead of using a bird-eye-view representation (like most of current models), the sensor data are encoded into a range view. It allows a simple multi-round range view to consider a temporal information. The network applies modality-wise convolution, a kind of depth-wise convolution adapted to the proposed encoding. Experiment shows that the simple proposed model performs well 
strengths_and_weaknesses: Strengths:
Even if other range-view model already exists, the contribution of the paper is the combination of this kind of representation with an encoding of the data combining cartesian + spherical coordinates + a multi-round range model that uses modality-wise convolution. Modality-wise convolution is a nice idea in order to reduce the size of the model (like with depth wise) but using an inductive bias assuming that channels encoding different information can be split in the convolution.

Weaknesses: 
Regarding the ablation study of table 3, it seems that the best combination for modality groups is multi-frame on each channel. However, the mAP is slightly higher (less than 1%). 

There is another issue with the experimental part.  The baseline models you compare with should be increased with the one given on the nuScene benchmark: https://www.nuscenes.org/object-detection?externalData=no&mapData=no&modalities=Any

Moreover, It should be better to give the performances of the models reported on this page instead of giving the one reported in the original papers to have updated criteria. If you do that, you will see that your model is not SOTA. However, it remains simple and this is interesting in some applications. 

questions: Regarding the ablation study of table 3:  why do we have a higher time for Multi-Frame? Can you give more details about this? Do you use a sliding temporal window to compute one output for each time or do you compute some batches? 
limitations: /
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final_score: rating: 7: Accept: Technically solid paper, with high impact on at least one sub-area, or moderate-to-high impact on more than one areas, with good-to-excellent evaluation, resources, reproducibility, and no unaddressed ethical considerations.
review_content: summary: Because 3D detectors for autonomous driving need to perform quickly and have good detection performance in a limited computational environment, Voxel-based and BEV-based neural detectors have been suggested in existing works. However, the authors proposed a novel type of 3D detector that can maximize the utility of range view inputs. They additionally propose a multi-round range view projection to contain more points within a single range view image and the corresponding stem layer which is called modality-wise convolution.
strengths_and_weaknesses: Their view of LiDAR points will have implications for other researchers as well, and the proposed method is sufficient to serve as a baseline for range-view-based 3D detectors in terms of performance.

The proposed module and its motivation are well aligned. In particular, the backbone network for range view was reconstructed based on resnet50, and the explanation of its motivation is very interesting.

The authors designed a range view-based neural network through multi-round range view projection (MRV) that minimizes the point loss that may occur in the process of converting to range view images, and a specified 2D CNN network for lidar points.

However, when generating range-view images, a single pixel can contain multiple points as a range-view image is generated through multiple projections. In this regard, it is necessary to explain the target assignment process more clearly. For example, when two objects are assigned to one pixel, the authors need to describe how they are handled. 

questions: **How to find the points corresponding to a given range view pixel?** I reckon that the method of finding a point corresponding to a pixel in a range view image with various scales is more complicated, unlike BEV. For feature maps that are smaller than the original size, there may be various options for how to determine the depth for a pixel. This is because points with different depths can be assigned simultaneously within one pixel in this case. In other words, it can introduce ambiguity when dealing with training targets. How you find the points that map to a pixel also affects dynamic allocation. Please provide a detailed explanation on this.

Are the authors willing to release the code for active research on range views?

limitations: They are aware of the dangerous uses of object detection. In addition, it would be good if future works parts were added for this field.
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Meta-review: metareview: After the rebuttal and discussion two reviewers recommend acceptance, one rejection. In their rebuttal, the authors were able to convincingly resolve all issues raised. Thus the AC sees no reason to reject this paper.
confidence: Certain
Decision: Accept