Title: QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Prediction
Venue: NeurIPS 2025
Accepted: yes
Keywords: autonomous driving
Abstract: 3D occupancy prediction is crucial for robust autonomous driving systems as it enables comprehensive perception of environmental structures and semantics. Most existing methods employ dense voxel-based scene representations, ignoring the sparsity of driving scenes and resulting in inefficiency. Recent works explore object-centric representations based on sparse Gaussians, but their ellipsoidal shape prior limits the modeling of diverse structures. In real-world driving scenes, objects exhibit rich geometries (e.g., cuboids, cylinders, and irregular shapes), necessitating excessive ellipsoidal Gaussians densely packed for accurate modeling, which leads to inefficient representations. To address this, we propose to use geometrically expressive superquadrics as scene primitives, enabling efficient representation of complex structures with fewer primitives through their inherent shape diversity. We develop a probabilistic superquadric mixture model, which interprets each superquadric as an occupancy probability distribution with a corresponding geometry prior, and calculates semantics through probabilistic mixture. Building on this, we present QuadricFormer, a superquadric-based model for efficient 3D occupancy prediction, and introduce a pruning-and-splitting module to further enhance modeling efficiency by concentrating superquadrics in occupied regions. Extensive experiments on the nuScenes and KITTI-360 datasets demonstrate that QuadricFormer achieves state-of-the-art performance while maintaining superior efficiency. Code is available at https://github.com/zuosc19/QuadricFormer.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel method for 3D occupancy prediction that leverages superquadrics as a more efficient and expressive scene representation compared to traditional voxels or Gaussians. The core motivation is that the greater representative power of superquadrics allows them to model fine-grained scene geometry with fewer primitives, thereby reducing computational requirements. The authors propose a probabilistic superquadric mixture model, where each superquadric defines an occupancy probability distribution. This is integrated into a query-based network, QuadricFormer, which features a pruning-and-splitting module for efficient prediction. The method reports state-of-the-art (SOTA) results on the nuScenes dataset while demonstrating superior computational efficiency.\n\nSTRENGTHS AND WEAKNESSES:\n# Strengths\n\n- The paper is well-written and effectively illustrated. Figures 1, 2, and 3 are particularly helpful for building intuition, clarifying the differences between primitives, and explaining the method's architecture.\n\n- While superquadrics have been explored for object-centric and indoor scene modeling, this paper presents a novel and timely application to the challenging domain of 3D occupancy prediction for autonomous driving.\n\n- The central narrative -- that the superior representational capacity of superquadrics enables more efficient scene modeling by requiring fewer primitives -- is clearly articulated and consistently woven throughout the paper, making the motivation and design choices easy to follow.\n\n\n# Weaknesses and Questions\n- The paper's central argument is that superquadrics are inherently more efficient due to their expressive power. However, the evidence does not fully substantiate this claim. A key concern is that the observed reduction in primitives may stem more from the aggressive pruning-and-splitting module rather than the intrinsic properties of superquadrics. It is unclear if a similar architecture using Gaussians with the same pruning strategy would also achieve a comparable reduction in primitives.\n    \n    - Suggestion: To isolate the benefit of superquadrics, please replace the superquadrics in QuadricFormer with Gaussians, keeping the rest of the architecture (including the pruning module, query design, and training strategy) as identical as possible. This would directly test whether the representational choice itself is the primary driver of the efficiency gains.\n        \n- While the qualitative results in Figures 1 and 4 are visually appealing, they do not strongly demonstrate the claimed advantage of superquadrics in capturing fine-grained geometric details. The rendered superquadrics often appear as simple cuboids, quite similar in shape to what Gaussians would produce, just at a larger scale. The \"deforming\" capability that gives superquadrics their power (e.g., creating boxier or more cylindrical shapes) is not apparent. They do not seem to wrap around object corners or capture complex surfaces any better than their Gaussian counterparts in the visualizations provided. This weakens the core motivation of the paper.\n    - Suggestion: Please provide more detailed visualizations that highlight the advantages of superquadrics in capturing fine-grained geometric details.\n    \n- The evaluation is currently limited to the nuScenes dataset. To demonstrate the generalizability and robustness of the proposed method, it is crucial to include results on other standard benchmarks for this task.\n    \n    - Suggestion: Please add results on SemanticKITTI and/or SSCBench-KITTI-360. Given that recent works like GaussianFormer-2 also report on SSCBench.\n        \n- Table 2 presents a confusing picture regarding efficiency. A superquadric contains more parameters than a Gaussian (e.g., shape parameters in addition to pose and scale). Therefore, it is counter-intuitive that a model with N superquadrics would be faster and require less memory than a model with N Gaussians, as the table seems to imply.\n    \n    - Suggestion: Please clarify this by augmenting Table 2. It would be very helpful to include columns that distinguish between network parameters and primitive parameters, and to explicitly state the number of primitives used in each configuration being compared. This would help readers understand the true source of the efficiency gains.\n        \n- The comparison with GaussianFormer-2 appears to be against a weaker variant (Ch=128, mIoU=20.02) rather than its best-performing published model (Ch=192, mIoU=20.82), which has a higher mIoU than the proposed method.\n    \n    - Suggestion: For a fair and transparent comparison, the paper should include the strongest published version of prior works. \n\n\n- Several key hyperparameters are missing. Please specify:\n    - The initial number of quadrics before the pruning-and-splitting stage.\n    - The value of B, the number of blocks in the decoder (line 209). It is also unclear what a \"block\" refers to in this context.\n    - The definition of \"small-scale\" and \"large-scale\" quadrics used for the pruning criteria (line 207).\n        \n- Recent relevant LiDAR-based methods are missing: PaSCo (CVPR'24), SCPNet (CVPR'23), and CAL (ICML'25).\n\nQUESTIONS:\nI already included all questions in Strengths And Weaknesses section. \n\nOverall, while this paper introduces a compelling and novel approach by leveraging superquadrics for efficient 3D occupancy prediction, its central claim -- that the observed gains stem directly from the superior representational power of superquadrics -- is not yet fully substantiated. The primary concern is that the performance and efficiency improvements may originate from the QuadricFormer architecture and its aggressive pruning module rather than the choice of primitive itself, a suspicion reinforced by qualitative results (Figs. 1 & 4) where the advertised \"deforming\" capability of superquadrics is not apparent. They often appear as simple, large-scale cuboids rather than adaptively capturing fine-grained geometric details. To resolve this, the authors should replace superquadrics with Gaussians within the same architectural framework is essential. The authors should provide new visualizations that offer close-up, side-by-side comparisons of a geometrically complex object (e.g., a vehicle, a traffic sign, or a building corner) as reconstructed by your method versus a Gaussian-based method. This direct comparison would make any superior detail-capturing ability immediately apparent. Finally, adjust the table to have fairer SOTA comparisons and evaluate on an additional dataset like SSCBench as in GaussianFormer2.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-13"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper QuadricFormer proposes a novel and efficient object-centric scene representation for 3D semantic occupancy prediction using superquadrics instead of traditional dense voxels or Gaussian primitives. By leveraging the geometric flexibility of superquadrics, the proposed QuadricFormer framework efficiently models complex scene structures with significantly fewer primitives. The paper introduces a probabilistic superquadric mixture model and design a pruning-and-splitting module to further enhance representation efficiency. Extensive experiments on the nuScenes dataset demonstrate that QuadricFormer achieves state-of-the-art performance while substantially reducing computational cost and memory usage.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. This paper proposes a novel and efficient object-centric scene representation for 3D semantic occupancy prediction based on superquadrics.\n2. The proposed model achieves superior performance while significantly reducing computational cost and memory usage compared to previous methods.\n\nWeaknesses:\n1. **The idea lacks sufficient impact**.\n\nWhile this paper provides a valuable attempt to apply superquadrics to the occupancy prediction task, the performance improvement over previous methods, such as GaussianFormer-2, appears to be limited.\n\nRegarding efficiency, memory usage is influenced by various factors, including the backbone architecture, the number of layers, and other design choices. Therefore, the reported efficiency improvements are difficult to attribute solely to the use of superquadric representations. Based on the current information, the comparison in Table 2 does not appear to be entirely fair.\n\nOverall, this paper seems to have made a modest contribution and, at best, would likely receive only a lukewarm response within the community.\n\n2. **Insufficient experiments**.\n\nThe experiments are conducted only on the nuScenes dataset and primarily compared with outdated methods such as TPVFormer and SurroundOcc. Several recent state-of-the-art approaches, including SparseOcc [1], FB-Occ [2], CVT-Occ [3], and many others, are notably missing from the comparisons. Additionally, evaluations on more widely adopted benchmarks, such as Occ3D-nuScenes and Occ3D-Waymo [4], are essential to convincingly demonstrate the advantages of the proposed method.\n\nFurthermore, SparseOcc [1] has challenged the use of IoU and mIoU as evaluation metrics, advocating for the adoption of RayIoU instead. An update of the evaluation metrics is therefore necessary to ensure fair and meaningful comparisons.\n\n**Reference**: \n\n[1] Fully Sparse 3D Occupancy Prediction, ECCV 2024.\n\n[2] FB-OCC: 3D Occupancy Prediction based on Forward-Backward View Transformation, arXiv 2023.\n\n[3] CVT-Occ: Cost Volume Temporal Fusion for 3D Occupancy Prediction, ECCV 2024.\n\n[4] Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving, arXiv 2023.\n\nQUESTIONS:\nThe main concerns, as outlined in the Weaknesses section, can be summarized as follows:\n\n1. The proposed idea lacks sufficient impact.\n\n2. The efficiency comparison requires further clarification.\n\n3. The experiments are insufficient in terms of benchmark diversity and baseline comparisons.\n\n4. The evaluation metrics are inadequate and need to be updated.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-20"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper replace the Gaussian with superquadrics as scene primitives in GaussianFormer to overcome the ellipsoidal shape limitation of Gaussian-based methods By leveraging a probabilistic superquadric mixture model and a pruning-splitting module, it concentrates primitives in occupied regions for better efficiency. Experiments on nuScenes show QuadricFormer achieves good performance with fewer primitives.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n- The motivation to replace Gaussians with a more general and expressive representation is commendable.\n- The presentation of the paper is clear.\n\nWeaknesses:\n- The experimental evaluation in the current version is insufficient. Most existing occupancy prediction methods report results on benchmarks such as OpenOccupancy or Occ3D, which facilitates fair and consistent comparison across approaches. However, the authors only evaluate their model on the dataset introduced by SurroundOcc, limiting the ability of the community to properly assess the model’s effectiveness. I strongly encourage the authors to include additional evaluations to compare with other sparse occupancy prediction methods.\n- The performance claims regarding QuadricFormer are misleading. The authors state in the main results (Tab. 2) that their model achieves state-of-the-art (SOTA) performance. However:\n  - In Table 2, QuadricFormer achieves 31.22 IoU and 20.12 mIoU, which are both lower than those of SurroundOcc (31.49 IoU, 20.30 mIoU) and OccFormer (31.39 IoU). Highlighting QuadricFormer as the best-performing model is potentially misleading to readers.\n  - Additionally, the reported numbers for GaussianFormer-2 (30.56 IoU, 20.02 mIoU) are not its best-performing results. According to its original paper, the best configuration of GaussianFormer-2 achieves 31.74 IoU and 20.82 mIoU, which outperforms QuadricFormer. The authors should ensure fair and accurate comparisons by reporting the strongest results for all baselines.\n\nQUESTIONS:\n- Could the authors provide more details on the pruning-and-splitting module? According to Table 4, this component plays a significant role in the model’s performance. However, the current version of the paper lacks sufficient explanation regarding its design and implementation.\n- [minor] In Table 1, the caption mentions “Ch”, but this notation does not appear anywhere in the table itself. \n- [minor] The statements in lines 244–245 do not align with the results reported in Table 1. The authors should revise the text to accurately reflect the experimental outcomes.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces a novel vision-based 3D semantic scene completion method that employs geometrically expressive superquadrics as scene primitives instead of traditional voxel-based representations. Extensive experiments on the nuScenes dataset demonstrate that this new representation enables a more lightweight 3D SSC framework with improved latency and memory efficiency, while maintaining competitive performance.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths\n- The paper is well-written and the methodology is easy to understand with clear intuition.\n- Applying superquadrics to 3D semantic scene completion is a novel, effective, and highly interesting approach.\n- The experimental results are promising, particularly in terms of efficiency, and support the proposed approach.\n\nWeaknesses\n- The proposed method shows lower IoU and mIoU performance compared to SurrondOcc. However, this gap is acceptable considering the improved efficiency in terms of latency and memory usage.\n- To further demonstrate the robustness of the proposed method, evaluations on more diverse datasets beyond a single benchmark, nuScene, would be beneficial.\n\nQUESTIONS:\n- In Table 1, the authors claim to achieve state-of-the-art performance. However, both the IoU and mIoU scores are lower than those of existing methods. Please clarify this discrepancy.\n- I would like to see results on additional datasets such as SemanticKITTI. This would be necessary to better demonstrate the generalizability and practical effectiveness of the proposed method.\n- In Table 4, the best performance is achieved when the Crop & Split Number is set to 800. Would increasing this number further lead to better performance? Additionally, how does the Crop & Split Number affect inference speed and memory usage?\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work proposes QuadricFormer, a novel 3D occupancy prediction model that uses probabilistic superquadrics instead of Gaussians to efficiently represent complex scene geometries. It achieves state-of-the-art performance on the nuScenes dataset with significantly improved representation efficiency.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n\n1.The introduction of superquadrics for scene geometry representation is the core innovation of this paper. Compared to previous 3D Gaussian-based representations, superquadrics offer significantly greater geometric expressiveness, allowing complex structures—such as cuboids, cylinders, and planes—to be modeled with far fewer primitives.\n\n2.The experimental results are impressive, particularly in terms of speed improvement, where the proposed method significantly reduces latency and memory consumption.\n\n3.The paper is logically well-structured and clearly written, making it easy to follow and understand.\n\n**Weaknesses**:\n\n**1. Issue with SOTA Claim in Table 1**\n\nWhile the paper claims state-of-the-art performance, the results in Table 1 do not fully support this assertion. Specifically:\n\nOccFormer reports a higher IoU (31.39 vs. 31.22) than QuadricFormer.\n\nSurroundOcc achieves both higher mIoU (20.30 vs. 20.12) and IoU (31.49 vs. 31.22).\n\nAlthough the differences are relatively minor, the current claim of SOTA is not strictly accurate based on these metrics. The authors are encouraged to revise the statement to more precisely reflect the strengths of QuadricFormer—such as efficiency-performance trade-off or performance with fewer primitives—to ensure consistency between the claims and the presented results.\n\n2.**The evaluation is limited to nuScenes.** Results on KITTI-360 or other datasets would help demonstrate the generalization and robustness of the proposed method.\n\n3.**Pruning-and-Splitting Module Is Somewhat Confusing.**\nThe pruning-and-splitting module is described as a key innovation, yet its implementation is underspecified, and Table 4 contains a puzzlingly high IoU (39.77 for number=0) that contradicts other results. The efficiency gain attributed to this module is not clearly quantified. More clarity, quantitative impact, and validation are needed to justify this component.\n\nQUESTIONS:\nsee weakness. If these concerns are clarified and the results remain solid, I would be happy to increase my score.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-02"}