Venue: CoRL 2022 Conference

Reviews:

final_score: rating: Weak Reject: I recommend rejecting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: This paper proposes a model that projects projective images from cameras mounted on a car to a polar birds-eye-view parametersization. The polar parameterization results in a non-uniform discretization of the space which has a higher resolution near the vehicle. The results show the parameterization achieves better segmentation accuracy compared to other methods such as FIERY and CVT. 
strengths_and_weaknesses: Strengths
=========
- As far as I’m aware this is the first method that uses a polar parameterization for a birds-eye-view representation of the area around a vehicle. This parameterization is ideally suited for this application as it greatly simplifies the processing and display of information.
- The method is very simple and accounts for things like non-planar surfaces so it doesn’t make any assumptions about the curvature of the road. 
- The results show that the approach achieves better performance compared to other birds-eye-view approaches such as FIERY and CVT. 

Weaknesses
=========
- Apart from the detection of other car bounding boxes, there isn’t much explanation of what this parameterization and method can be used for. At the moment the functionality seems a bit limited. 
- How does the approach work for obstacles that don’t have standard configurations, such as boom barriers that don’t necessarily lie on supporting surfaces?
- In terms of the evaluation, it’s not clear what contributes to the improvement in the accuracy in the segmentation task. This could either be the polar parameterization or the model itself.
- There are quite a few grammar errors that need to be fixed. Eg. “besides” is repeatedly used in a strange manner. Line 128 starts with “And”.

robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: Overall, I think this type of representation is very suitable for autonomous vehicles and wheeled robots and the proposed method seems to perform well compared to existing methods. However, apart from the improved performance on bounding box estimation, it doesn't seem to show any impressive results or a significant advance in terms of theory, methods or model architectures.
issues: - Clarify what parts of the method are leading to the improved segmentation accuracy compared to CVT and FIERY
- Explain whether the method can be used for other tasks such as estimating the road curvature.
- Fix the grammar errors
review_title: null

final_score: rating: Weak Accept: I recommend accepting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: The paper proposes a method for projecting features from 2D images into a polar bird eye's view grid. This grid is then used for estimating a 2.5D height surface of what is around the cameras. Features are then re-projected into an array-like polar representation and fed into an encoder-decoder based on FIERY [2].
strengths_and_weaknesses: The authors propose to use polar rasterization for images inputs. This is a novel approach that changes how the inputs of a neural network can be encoded. The method is well evaluated and show some improvements when compared to the state of the art.

The clarity of the method section needs to be improved as some of the descriptions are confusing and could interpreted in different ways.

robotics_focus: Sufficient demonstration on hardware
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: I do think the paper proposes an interesting contribution with is novel and interesting. I do think the writing needs to be improved for a more clear, simpler, and more consistent methodology. That aside the experiments are pretty thorough with ablation studies of several of the parameters and comparison with state of the art.
issues: ## Major comments
* The notations for variables have several typos, which makes it confusing to follow, e.g., line 111 and 112, are {$z_{inf}, Z_{inf}$} and {$z_{sup}, Z_{sup}$} the same things? Same goes for line 100, and equation (1) with $p_{ang}$ and $q_{ang}$.
* I do not understand how adding $q_{rad}$ and $q_{ang}$ allows forming a meaningful query system. How do you distinguish {$q_{ang} = 10^\circ, q_{rad} = 1m$} from {$q_{ang} = 1^\circ, q_{rad} = 10m$} (obviously things might be quantified differently but I think I missed something).
* The methodology is hard to follow, I would encourage the author to clarify the distinction between the two types of projections (the first one with 3.2 and the second one in 3.4) by making the headings consistent with figure 1. Also, specify explicitly before equation (5) that you are doing the image re-projection.
* Clarify what is the variable t, is equation (2) solved as a sliding window between frames, or is wrt to cells with closer radius, or is solved for each frame as an iterative optimisation starting from h_hypo until convergence?

## Minor comments
* line 150, do you mean 30k epochs? How do you track overfitting?
* If setting 1 and 2 are used for evaluation, which settings do you use for training (Or how are the datasets divided between training and evaluation)?
* Line 168, you compare with FIERY static, and not FIERY
* Line 177, no need to redefine acronyms
* Is using a constant resolution for $D_{rad}$ the best approach?
review_title: null

final_score: rating: Weak Reject: I recommend rejecting the paper, but will not argue for my recommendation if the majority of other reviewers have a different opinion.
review_content: summary: This paper tackles vision-based BEV semantic segmentation for on-road driving. It improves the previous work, FIERY, in two aspects: (i) using polar grid instead of a rectangular grid, and (ii) estimating the height of each map location instead of estimating the depth of each image pixel. Experiments validate the design choice and show that both the polar representation and the height estimation improve the BEV segmentation results on the nuscenes dataset.
strengths_and_weaknesses: Strengths:

- The proposed ideas are intuitive, effective and easy to implement.
- The idea of iteratively estimating the height is interesting.
- Experiments are thorough with detailed ablation study.

Weaknesses:

The idea of using polar representation and height-based projection are not that new. There has been plenty of work of adopting polar representation for on-road driving (the authors have mentioned in the related work). Recently, there is also work that proposes height estimation for BEV segmentation [1]. While this work shows that the proposed methods are better than the baseline, I have several concerns of the proposed approach.

[W1] Although the empirical results show that the polar representation is better, it is not clear why. Polar representation would have higher resolution close to the center, but its resolution at the edge of the map is lower than the rectangular representation. I think there is a tradeoff, and I wonder how the polar representation would perform as a function of the distance to the center. Is it always better? Or is it better near the center, and worse when far from the center than the rectangular representation? Does the dataset have a bias like this? It would be great if the authors could elaborate on this.

[W2] While adding height estimation seems effective, it is unclear how well the height estimation works. Does it predict reasonable height values? Currently, it shows that the height estimation helps, but it is unclear if it is helping in the right way, i.e. predicting reasonable height values.

[W3] The height estimation only estimates a single height value for each map cell. This discards a lot of the information from images. Does this have any potential drawbacks? A similar work [1] alleviates this issue by maintaining multiple height values in each map cell. It would be great if the authors could clarify the difference between this work and [1].

[W4] The technical section could be made clearer. 
- In Sec 3.4, it is unclear how the sampled features $f_i$ be used to produce the query embedding $q$ in the next iteration.
- I wonder if the comparison with previous work FIERY is fair. Are you using the same network architecture and parameter settings?

[1] BEVFormer: Learning Bird's-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers arXiv preprint arXiv:2203.17270

robotics_focus: Highly relevant to robotics but no hardware experiments
quality_of_the_limitations_section: Limitations are addressed clearly
summary_of_recommendation: The paper contains some interesting ideas and is enjoyable to read. While the ideas are not entirely novel, they are applied to a slightly different task or in a different way, and detailed experiments have shown some advantages over existing methods. However, I would like to see some clarifications about where the performance gain comes from, and the relation to related work. It would be great if the authors could provide some additional details about the issues raised.
issues: * Please address the issues raised the previous section, namely i) why polar is better than rectangular and ii) the relation to existing work.
* Make the technical section (3.4) clearer.
review_title: null

Meta-review: metareview: Dear Authors,

Thank you for submitting your manuscript to CoRL. I'm happy to inform you that your paper has been accepted. We have completed the review of your manuscript and a summary is appended below. The reviewers have advised accepting your manuscript as a poster considering your revised version and improvement of the quality of the manuscript based on the comments. Please make sure you incorporated all provided explanations and recommended editing into the final manuscript.


Regards,
confidence: 3: The area chair is somewhat confident
Decision: Accept (Poster)