OPUS: Occupancy Prediction Using a Sparse Set

Summary

The paper presents OPS, a novel framework for occupancy prediction in autonomous driving. It formulates the task as a direct set prediction problem, using a transformer encoder-decoder architecture to predict occupied locations and classes simultaneously. This approach eliminates the need for explicit space modeling or complex sparsification. The main results of the paper demonstrate that the OPS framework outperforms previous state-of-the-art methods in occupancy prediction on the Occ3D-nuScenes dataset. It achieves superior RayIoU scores, a metric designed to address the overestimation issues of the traditional mIoU metric. The paper also includes an ablation study highlighting the effectiveness of the various strategies incorporated in OPS, such as adaptive re-weighting, consistent point sampling, and coarse-to-fine prediction. These strategies contribute to improved performance, particularly in terms of mIoU and RayIoU.

Strengths

- **[S1] Clarity:** The paper is well-written and easy to follow. The problem formulation, methodology, and experimental results are clearly presented. Overall, the figures are informative and complement the text well. - **[S2] Quality:** The proposed OPS framework is thoroughly described, and the experimental results on the Occ3D-nuScenes dataset shows merits of the proposed method in terms of RayIoU. The ablation studies further validate the contribution of each component in the framework.

Weaknesses

- **[W1] Technical soundness:** The paper proposes to use Chamfer distance as the main objective function, which is known to be sub-optimal. Although several strategies including FocalLoss, coarse-to-fine refinement have been proposed, it makes the system overly complicated and potentially unfair for comparison. For example, it is unclear whether the baseline methods can benefit from the proposed strategies as well. Please comment this in the rebuttal. - **[W2] Limited Evaluation:** The experimental evaluation is solely conducted on the Occ3D-nuScenes dataset. While this is a standard benchmark, evaluating the method on additional datasets, such as SemanticKITTI or Waymo Open Dataset, would provide a more comprehensive assessment of its generalizability and robustness. - Reference: [NewRef1] Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving - **[W3] mIoU Performance:** - [W3.1] Although OPS excels in RayIoU, its performance on the mIoU metric lags behind some dense models. Given that mIoU is a widely used metric in occupancy prediction, addressing this weakness would make the method more appealing to a broader audience. In comparison, RayIoU is only introduced in a recent paper that has not been peer reviewed. - [W3.2] As the paper is focusing on autonomous driving applications, it is unclear whether mIoU performance can cause safety-critical problems for downstream tasks such as behavior prediction and planning. It would be good to discuss this aspect in the rebuttal and next version of the paper. - [W3.3] In Table 3, the reference number for FB-OCC is wrong. - **[W4] Lack of Qualitative Analysis:** While the paper provides some visualizations, a more thorough qualitative analysis of the predictions would be insightful. Analyzing failure cases, comparing predictions across different classes and distances, and examining the impact of the proposed strategies on the quality of predictions would provide a deeper understanding of the method's strengths and weaknesses.

Questions

Please check the weakness section, especially W1 and W3.

Rating

5

Confidence

4

Soundness

2

Presentation

3

Contribution

2

Limitations

Yes, the limitations have been discussed in the paper.

Summary

This paper focuses on the sparsity property in occupancy prediction, given that most voxels are occupied. In order to reduce computation costs on empty voxels, this paper introduces a set prediction paradigm to explicitly model sparsity. OPS, the proposed framework, utilizes the encoder-decoder architecture to jointly reconstruct and classify point clouds. Chamfer distance is used as the supervision. For experiments, OPS outperforms state-of-the-art occupancy methods by 4.9 RayIoU.

Strengths

1. Good motivation. The introduction of sparsity as the representation of scenes is with a great motivation. 2. Novel architecture. Using the point cloud + set prediction approach for occupancy is novel, elegant, and makes sense. 3. Strong performance. The proposed OPS with RayIoU achieves significantly better performance and inference speed compared to the current state-of-the-art. Although the authors mentioned the current limitation is the need for longer epochs, it is not a major issue, as most set-prediction methods face this challenge.

Weaknesses

1. Error citation. I think the "sparseocc" mentioned frequently in this paper should be [1], instead of [2]. [1.] Fully sparse 3D panoptic occupancy prediction [2.] SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction 2. Is there a typo in Line 245? The query count for OPS-tiny is only 0.6k, while for OPS-S, OPS-M, and OPS-L, the query counts are as high as 12K, 24K, and 48K respectively. 3. If the query count is as high as 48K, self-attention would not be able to handle such a large query size. How did the authors manage to do this? The design details are not elaborated on. 4. Since the authors have replaced the basic representation of occupancy with point cloud, they should discuss the differences with point cloud forecasting tasks, such as 4D-Occ [3] and ViDAR [4], especially ViDAR paper which also constructs point clouds from visual images. Discussions on [3] and [4] are suggested to be included in the main paper. [3.] Point Cloud Forecasting as a Proxy for 4D Occupancy Forecasting [4.] Visual Point Cloud Forecasting enables Scalable Autonomous Driving.

Questions

See Weakness.

Rating

5

Confidence

4

Soundness

3

Presentation

2

Contribution

2

Limitations

N/A

Summary

This paper considers the problem of occupancy prediction from multi-view images for autoonomous driving. One of the main challenges in the occupancy prediction task is the high computational demand incured by discretizing the 3d space. Traditional methods typically predict the occupancy of each voxel individually and assign it a semantic class. The key idea of this work if to reduce computational burden by formulating occupancy prediction as a set prediction task. The authors employ the Chamfer distance in the loss term and propose a novel point sampling process, termed Consistent Point Sampling, to make the SparseBEV decoder applicable. In experiments on the Occ3d-nuScenes dataset, their method demonstrates superior performance with regard to the RayIoU metric while being computationally more efficient than the state-of-the-art methods.

Strengths

I believe the proposed general approach is interesting and the work seems well evaluated (to the extent that I can judge). The paper is largely well-written and seems easily understandable for someone working in that space. I also want to applaud the authors for committing to making their code publicly available.

Weaknesses

This topic seems to be traditionally mainly covered in Computer Vision venues. And while the contribution of this work may also be of interest to the ML community, the writing needs to provide more background and context to be more accessible to the NeurIPS community. E.g. the readers at NeurIPS may not be familiar with the broader tasks and some more specialized architectures such as SparseBEV. The work contains numerous writing and grammar issues. Some examples are mentioned in the minor comments section below. However, the work should undergo a careful proofread in its entirety. Some of the mathematical notation seems off. E.g. {$\mathbb{P}^g,\mathbb{C}^g$} is denoted as a set containing two sets while it is treated as a set cotaining tuples of point locations and semantic classes. Some more minor math notation issues are given in the comments below. The first bullet point in the contribution list is somewhat broad and focuses more on the properties of the method. It does not mention some of the new key technical ideas underlying the method such as CPS. I would recommend fully rephrasing the contribution list and focusing on what is technically new (or at least for the first time applied in the context of occupancy prediction). Simply mentioning that there are several strategies that are introduced to boost performance makes the reader wonder what these strategies are. Minor Comments: * l.3 "discretize 3D environment" -> "discretize the 3D environment" * l.33/34 "Alternative sparse latent representations has been explored" -> "Alternative sparse latent representations have been explored" * l. 37 What is meant by "necessitating complex intermediate designs and explicit"? Complex architecture? * l.42/43 "Our OPS eliminates" should be "Our method eliminates" or "OPS eliminates" * l.48 "unable to tacke tremendous voxels" maybe something like "unable to handle a very high number of voxels" * l.69/70 "all our model configurations easily surpass all prior arts" -> "all our model variants easily surpass all prior work". * l.83 "This task recently becomes a foundational perception task in autonomous driving" -> "This task has recently become a foundational perception task for autonomous driving" * Consider using $N_g$ instead of $N^g$ for the number of occupied voxels in ground truth. The latter looks like $N$ to the power of $g$. Same for all other occurrences of $g$ in a superscript. * If $\mathbf{c}^g$ denotes a semantic class, it should not be defined as element of $\mathbb{R}$. This is not necessarily wrong but may be confusing as it is likely represented as an integer if not a one-hot vector? * Using $N$ to represent the number of semantic classes may be confusing given that $N^g$ is used to represent the number of occupied ground truth voxels.

Questions

* From the writing, it is not really clear to me, whether some of the competing methods are also set based? I would not see why the Hungarian algorithm would be needed otherwise. If those methods are set-based, the writing might clarify this. From the abstract it seems like competing approaches perform classification of each voxel individually. * In the right most panel of fig 2, why is "Consistent Point Sampling" written in a green box while everything else is in grey? * What is meant by a set of learnable queries? Are these simply the queries that would be used in the traditional transformer except that they are now learned?

Rating

6

Confidence

3

Soundness

3

Presentation

2

Contribution

3

Limitations

To the extent that I can understand it, the authors have properly addressed the limitations of this work.

Summary

This paper introduces OPS, a novel framework that treats occupancy prediction as a set prediction problem. The approach leverages a transformer encoder-decoder architecture and Chamfer distance loss to align predicted and ground-truth points. The model improves performance with strategies like coarse-to-fine learning, consistent point sampling, and adaptive re-weighting. OPS achieves superior RayIoU and faster FPS compared to state-of-the-art methods on the Occ3D-nuScenes dataset. In summarize, this paper is technically sound and shows strong and convincing performance on a commonly used dataset.

Strengths

1. This paper formulates occupancy prediction as a sparse set prediction problem, which is technically sound and interesting. Indeed, predicting a sparse set introduces several efficiency and memory benefits, as long as a significant performance improvement. 2. Several proposed techniques, including coarse-to-fine learning, consistent point sampling, and adaptive re-weighting, are effective and have the potential to generalize to even other methods. Also, these techniques are validated by extensive ablation studies on the nuScenes dataset. 3. Experiments are carefully designed and adequate ablation studies are provided, though only on the nuScenes split.

Weaknesses

1. In a high level, the proposed method is similar to SparseOcc, although implementation details can be different. 2. Although this method has strong performance on Occ3D-nuScenes dataset and several ablation studies are provided, I am still concerned that this method can overfit to this relatively small dataset. Can authors provide further evidences on the Occ3D-Waymo split?

Questions

1. Can authors provide results on the Waymo split? Or at least can authors provide a convincing explanation why such experiments cannot be conducted on Waymo? As far as I know, Occ3D-Waymo is on a par with Occ3D-nuScenes in terms of data scale, thus computation should not be a significant concern. 2. Can authors comment on the difference between SparseOcc and OPS? I'd like to understand both the detail differences and the high-level differences.

Rating

5

Confidence

4

Soundness

3

Presentation

3

Contribution

3

Limitations

No potential negative societal impact is detected by reviewer.

Response

Thanks for the authors' rebuttal. I will maintain my rate.