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REVIEW 4 major objections 5 minor 1 cited by

STCOcc: Sparse Spatial-Temporal Cascade Renovation for 3D Occupancy and Scene Flow Prediction

T0 review · 4 major / 5 minor · reviewed 2026-08-16 · deepseek-v4-flash

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Pith's one-line read STCOcc claims that camera-only 3D occupancy and scene flow prediction improves by explicitly renovating features with the occupied state of 3D space, reporting 41.7% RayIoU on Occ3D-nus and 0.44 mAVE on OpenOcc while cutting training…

desk verdict A plausible, well-engineered occupancy/flow system whose headline SOTA numbers are single runs and whose two main tables disagree on the camera-mask training protocol. read the letter →

arxiv 2504.19749 v1 pith:Q4RTUBNH submitted 2025-04-28 cs.CV

Zhimin Liao , Ping Wei , Shuaijia Chen , Haoxuan Wang , Ziyang Ren This is my paper · ORCID
classification cs.CV
keywords 3Doccupancypredictionsceneflowcamera-onlyperceptionsparseattentiontemporalfusionstateRayIoUautonomousdriving
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper is trying to establish that the missing ingredient in camera-only 3D occupancy and scene flow prediction is explicit use of the occupied state of space, rather than reliance on implicit loss-driven feature learning. It proposes STCOcc, a cascade decoder that repeatedly predicts occupancy weights and uses them to reweight spatial and temporal attention, plus a sparse long-term temporal fusion. The reported payoff is a RayIoU of 41.7% on Occ3D-nus and 40.8% RayIoU with 0.44 mAVE on OpenOcc, exceeding prior camera-only methods, while training memory drops to 7.7–8.9GB. If the numbers hold, the paper offers a recipe for more geometric, memory-lean 3D perception from cameras alone.

What carries the argument

The load-bearing object is the occupied state $W$, a per-voxel occupancy weight produced by the Self-Recursive Occupancy Predictor (SROP), which iteratively refines $W$ across transformer layers via $W^i_{t,l} = f^i(B^i_{t,l}) + \alpha^i_l W^i_{t,l-1}$. This state drives three components: Occupancy-Aware Spatial Cross-Attention (OA-SCA) reweights sampled 3D reference points by $\Omega_x = w_x \beta_x$, combining occupancy with a Gaussian depth-matching factor $\beta_x$, to resolve the ambiguity in ordinary spatial cross-attention; Occlusion-Aware Temporal Self-Attention (OA-TSA) reweights deformable temporal attention by occupancy; and Sparse Temporal Fusion samples top-k occupied seeds into long-term and short-term streams fused by MLP and written back into the voxel feature. The same occupancy state also supplies per-stage supervision through the occupancy loss.

What would settle it

Run the published Occ3D-nus recipe (ResNet-50, 704×256, 36 epochs) several times and measure the spread of RayIoU; if seed-to-seed variation approaches the 0.5–1.7 point margins over the strongest prior methods, the claimed lead is not established. Also verify whether the Table 1 row without a camera-mask marker and the Table 2 row with a camera-mask marker use identical training data, since the mask changes supervision.

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Extended reading notes

Core claim

The central claim is that replacing implicit, loss-supervised refinement with explicit renovation guided by an iterated occupancy state makes camera-only 3D perception both more accurate and cheaper. On Occ3D-nus the method reports RayIoU 41.7% with a ResNet-50 at 704×256 input, and 42.1% at 1408×512; on OpenOcc it reports RayIoU 40.8% and mAVE 0.44, all while training in 7.7–8.9GB. The paper attributes the gain to using occupied-state guidance to resolve the ray-level ambiguity in spatial cross-attention and to focus temporal modeling on non-empty regions.

Load-bearing premise

The reported wins over prior methods are real rather than run-to-run noise, which matters because the main tables give single runs and the training protocol differs across comparison settings.

Editorial extensions

If this is right

  • Camera-only occupancy prediction can reach RayIoU above 41% on Occ3D-nus with a ResNet-50 at 704×256 input, a regime previously dominated by heavier backbones and larger images.
  • Training memory below 9GB means the method fits on a single consumer-grade GPU, making occupancy pretraining and longer temporal histories accessible without large-scale hardware.
  • Scene flow can be predicted jointly with occupancy at mAVE 0.44 under 3D supervision only, without lidar or rendering supervision.
  • Explicit occupancy-guided reweighting, not just depth, is what resolves ray-level ambiguity in spatial cross-attention, so the volume-rendering analogy transfers to occupancy perception.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • Inference: The same occupancy-weight-gated attention could be applied to other sparse 3D tasks such as object detection or motion forecasting, where ray ambiguity and empty-space redundancy also dominate.
  • Inference: Because SROP is recursive and shares weights across layers, it resembles an unrolled refinement; one testable extension is whether more stages or shared weights across stages yield further gains or saturate.
  • Inference: The memory reduction depends on top-k sparsity, so the 8.7GB figure is a point on a curve rather than a fixed property; varying the sampling ratio should trade memory against RayIoU.
  • Inference: The volume-rendering analogy suggests fusing occupancy and depth into a single transmittance-like weight could unify depth estimation and occupancy prediction in one decoder.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 5 minor

Summary. STCOcc proposes an explicit state-based modeling framework for camera-only 3D occupancy prediction and scene flow prediction. The method uses a cascade decoder with a Self-Recursive Occupancy Predictor (SROP), Occupancy-Aware Spatial Cross-Attention (OA-SCA), Occupancy-Aware Temporal Self-Attention (OA-TSA), and a sparse temporal fusion module that separates non-empty and empty regions into long-term and short-term streams. The paper reports state-of-the-art RayIoU/mIoU on Occ3D-nus and RayIoU/mAVE on OpenOcc, with substantially reduced training memory, and releases code.

Significance. If the reported results are robust, STCOcc is a meaningful advance: the explicit use of predicted occupancy state to renovate spatial and temporal features is a plausible and well-motivated alternative to purely implicit or depth-only feature fusion, and the sparse temporal fusion is a computationally attractive design. The paper also ships code, which supports reproducibility. However, the central claim is an empirical benchmark measurement, and the decisive margins over prior methods are small (0.1–0.5 RayIoU/mIoU points in Tables 1–3), while every reported number is a single run without error bars or seed information. The protocol inconsistency about camera-mask training between Tables 1 and 2 is load-bearing for the comparison, and the OpenOcc mAVE gain may be affected by a changing true-positive evaluation set. These issues need to be addressed before the headline SOTA claim can be accepted as established.

major comments (4)
  1. [§4.2, Tables 1 and 2] The camera-mask notation is directly inconsistent for the same configuration. Table 1 lists STCOcc (R50, 704×256) without the ∗ symbol, while Table 2 lists STCOcc (R50, 704×256) with ∗, and the footnote says ∗ means "trained with camera mask." Both entries report the same 7.7 GB memory and the same backbone/input size, but the RayIoU 41.7 and mIoU 44.6 cannot both come from the same training protocol unless the authors used two different protocols and did not say so. If the Occ3D-nus RayIoU result was produced with camera-mask supervision, its comparison against the unstarred baselines in Table 1 is not well-defined. Please clarify which setting produced each number and justify the comparison.
  2. [§4.2, Tables 1–3] All headline results are single runs with no error bars, seed information, or variance estimates. The reported SOTA margins are small: Table 1 gives 41.7 vs. 41.2 (OPUS-L) and 41.0 (COTR); Table 2 gives 44.6 vs. 44.5 (COTR); Table 3 gives 40.8 vs. 40.5 (Let Occ Flow). If run-to-run variance is comparable to these gaps, which is typical in this setting, the SOTA claim is not established. Please report mean and standard deviation over at least three seeds for the main comparison tables, or otherwise provide evidence that the differences are outside noise.
  3. [§4.3, Tables 4–7] The ablations are performed on only the first 300 training sequences, while the main results are on the full 600-sequence training set, so the component contributions in Table 4 may not transfer to the full-data setting. More importantly, each ablation is a single run with no variance. Table 7, for example, shows OA-SCA at 38.3 vs. DA-SCA at 37.7 and SCA at 37.5; the 0.6–0.8 point differences, on half the training data and without repetitions, are within plausible seed noise and do not support the statement that SCA and DA-SCA "do not significantly improve performance." Please rerun these comparisons with multiple seeds or report variance, and state the training-set size in the table captions.
  4. [§4.1 and Table 3] The mAVE metric is computed only over true positives within a query-ray threshold of 2 meters, as stated in Section 4.1. Since STCOcc also changes occupancy predictions (RayIoU 40.8 vs. 33.4 for SparseOcc), the set of objects over which mAVE is averaged can differ between methods. The large mAVE improvement from 0.83–0.87 to 0.44 could partly reflect a different denominator rather than better scene flow. Please provide a matched evaluation that conditions on the same true-positive set, or report mAVE with recall/coverage statistics so the reader can separate flow quality from occupancy-quality effects.
minor comments (5)
  1. [Abstract and Tables 1/3] The abstract says training memory is reduced to 8.7 GB, but Table 1 reports 7.7 GB for the main Occ3D-nus setting and Table 3 reports 8.7 GB for OpenOcc; please clarify which configuration the abstract refers to.
  2. [Table 2] CTF-Occ is cited as [40], but reference [40] is the Occ3D dataset paper; please provide the correct source for CTF-Occ.
  3. [§4.3 text] The phrases "3.7% increase in mA VE" and "45.5% increase in mA VE" mix relative improvements with percentage points; please report changes consistently (e.g., absolute differences or explicit relative percentages) to avoid confusion.
  4. [§3.2.3, Eq. (7)] The description of d_r and d'_r as "analogous to z and d in Eq. (4)" is unclear because z is a world coordinate while d is an image-plane depth; please define both quantities in a self-contained way.
  5. [Throughout] There are several minor typos and grammatical issues, including "corrreponds" (page 5), "paradism" (Section 2.2), "offical" (Table 3 caption), and "V ora" in reference [4]; a careful proofread is needed.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: the paper's claims are empirical benchmark measurements, and the self-referential occupancy predictor is architectural recursion, not logical circularity.

full rationale

The paper's central claims are empirical results on the Occ3D-nus and OpenOcc benchmarks, measured against held-out validation data with external ground-truth labels. No parameter is fitted to the target metric and then renamed as a prediction; hyperparameters such as the number of stages, transformer layers, and historical frames are hand-set, and the reported ablations vary individual components rather than tuning to the headline number. The Self-Recursive Occupancy Predictor (Eq. 1) reuses the model's own intermediate occupancy weights across transformer layers, but this is an iterative refinement architecture, not a derivation in which the conclusion is assumed as an input. The only self-citation, reference [18] (CascadeFlow), appears in the related-work discussion and is not load-bearing for any claimed result. The camera-mask discrepancy between Table 1 and Table 2, and the absence of error bars, are reproducibility and comparability concerns, not circularity. The derivation chain is therefore self-contained with respect to the benchmark evaluation protocol.

Assumptions & free parameters 6 free parameters · 4 assumptions · 0 invented entities

The central claims rest on the trustworthiness of the benchmarks and the assumption that the model's self-predicted occupancy is a usable prior. No new physical entities are introduced.

free parameters (6)
  • sigma in OA-SCA depth reweighting = 2 (default)
    Eq. 7; controls the tolerance for depth matching; set by hand, not derived.
  • alpha_l residual weight in SROP = 0.5 (initialization, learnable)
    Eq. 1; controls the influence of the previous layer's occupancy weights.
  • ux sampling threshold in OA-SCA = 0.5 at inference
    Section 3.2.3; used to select reference points; training uses truncated normal with mean 0.5.
  • Number of stages and transformer layers = 3 stages, 2 layers per stage
    Section 4.1; architecture hyperparameters, not validated across choices.
  • Historical frames per stage = 16, 8, 4
    Section 4.1; selected for the three stages.
  • lambda_f loss weight for scene flow = not specified
    Eq. 10; the weight for L1 flow loss is not reported in the paper.
assumptions (4)
  • domain assumption The Occ3D and OpenOcc datasets and their metrics (RayIoU, mAVE) are reliable measures of occupancy and scene flow quality.
    The central SOTA claim is evaluated on these benchmarks; if the metrics are biased, the conclusion is not meaningful.
  • domain assumption LIDAR projections onto images provide adequate depth supervision for the depth network.
    Section 4.1 states depth labels are produced by projecting LIDAR points; noisy depth labels would degrade the OA-SCA weighting in Eq. 7.
  • standard math The pinhole projection model in Eq. 4 correctly relates 3D points to 2D image features for the multi-camera rig.
    Used in OA-SCA and in the lift-splat encoder; if calibration is wrong, the spatial renovation is misaligned.
  • domain assumption The model's own predicted occupied state is a sufficiently accurate prior for feature renovation; otherwise the feedback loop can reinforce errors.
    The method explicitly feeds occupancy weights from SROP into attention and temporal fusion; this is a design assumption not proven in the paper.

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Cite this review

Pith. "Pith review of STCOcc: Sparse Spatial-Temporal Cascade Renovation for 3D Occupancy and Scene Flow Prediction." pith.science (2026). https://pith.science/paper/Q4RTUBNH

@misc{pith2026250419749,
  author       = {Pith},
  title        = {Pith review of: STCOcc: Sparse Spatial-Temporal Cascade Renovation for 3D Occupancy and Scene Flow Prediction},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/Q4RTUBNH}},
  note         = {Machine review of arXiv:2504.19749}
}
read the original abstract

3D occupancy and scene flow offer a detailed and dynamic representation of 3D scene. Recognizing the sparsity and complexity of 3D space, previous vision-centric methods have employed implicit learning-based approaches to model spatial and temporal information. However, these approaches struggle to capture local details and diminish the model's spatial discriminative ability. To address these challenges, we propose a novel explicit state-based modeling method designed to leverage the occupied state to renovate the 3D features. Specifically, we propose a sparse occlusion-aware attention mechanism, integrated with a cascade refinement strategy, which accurately renovates 3D features with the guidance of occupied state information. Additionally, we introduce a novel method for modeling long-term dynamic interactions, which reduces computational costs and preserves spatial information. Compared to the previous state-of-the-art methods, our efficient explicit renovation strategy not only delivers superior performance in terms of RayIoU and mAVE for occupancy and scene flow prediction but also markedly reduces GPU memory usage during training, bringing it down to 8.7GB. Our code is available on https://github.com/lzzzzzm/STCOcc

Figures

Figures reproduced from arXiv: 2504.19749 by the authors.

Figure 1
Figure 1. (a) Explicit versus Implicit Modeling: We propose a novel explicit state-based modeling approach that explicitly lever￾ages the occupied state to maintain feature sparsity and model spatial details. (b) Comparison with Different Methods: Our approach achieves state-of-the-art performance of RayIoU and mAVE with lower training costs. space, employing efficient and robust approaches for 3D feature processing is critic… view at source ↗
Figure 2
Figure 2. The overall architecture of STCOcc. The STCOcc framework is primarily composed of four integral modules: a feature extractor that captures image features and depth distribution, a 3D coarse encoder that generates multi-resolution coarse voxel features, a multi-stage spatial-temporal cascade decoder that incrementally renovates these coarse voxel features in both spatial and temporal dimen￾sions, and a head module de… view at source ↗
Figure 4
Figure 4. Illustration of Sparse Temporal Fusion. We imple￾ment temporal fusion using a parallel strategy in a sparse manner, focusing only on modeling the sampled features. and the distance of the ray’s corresponding object from the ego-vehicle, respectively. It should be noted that dr is de￾rived from the predicted bin depth distribution Dt and is transformed into relative depth, while ∆d represents the bin interval. The pa… view at source ↗
Figures from the paper (2 more)
Figure 5
Figure 5. Figure 5: Ablation on the OA-SCA module. We visualize the features after refinement with and without the OA-SCA module. These results surpass those of both OccNet [38] (which uses ResNet-101 with an input size of 1600 × 900) and Let Occ Flow [24] (which also uses ResNet-101 with…
Figure 6
Figure 6. Figure 6: Qualitative results on Occ3d-nus validation set. As depicted in the red circle, our method delivers detailed predictions for objects such as cars and trucks, while also offering clear boundary delineations for structures like buildings and vegetation. The Effectiveness…

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Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. SHTOcc: Effective 3D Occupancy Prediction with Sparse Head and Tail Voxels

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Reference graph

Works this paper leans on

53 extracted references · 35 canonical work pages · cited by 1 Pith paper

  1. [1]

    https://www.youtube.com/watch? v=j0z4FweCy4M, 2021

    Tesla AI Day. https://www.youtube.com/watch? v=j0z4FweCy4M, 2021. 1

    work page 2021

  2. [2]

    Se- mantickitti: A dataset for semantic scene understanding of lidar sequences

    Jens Behley, Martin Garbade, Andres Milioto, Jan Quen- zel, Sven Behnke, Cyrill Stachniss, and Jurgen Gall. Se- mantickitti: A dataset for semantic scene understanding of lidar sequences. In ICCV, 2019. 1

    work page 2019

  3. [3]

    The lov ´asz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks

    Maxim Berman, Amal Rannen Triki, and Matthew B Blaschko. The lov ´asz-softmax loss: A tractable surrogate for the optimization of the intersection-over-union measure in neural networks. In CVPR, 2018. 6

    work page 2018

  4. [4]

    Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom

    Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Gi- ancarlo Baldan, and Oscar Beijbom. nuscenes: A multi- modal dataset for autonomous driving. In CVPR, 2020. 1, 6

    work page 2020

  5. [5]

    Monoscene: Monoc- ular 3d semantic scene completion

    Anh-Quan Cao and Raoul De Charette. Monoscene: Monoc- ular 3d semantic scene completion. In CVPR, 2022. 2, 6

    work page 2022

  6. [6]

    Slowfast networks for video recognition

    Christoph Feichtenhofer, Haoqi Fan, Jitendra Malik, and Kaiming He. Slowfast networks for video recognition. In ICCV, 2019. 5

    work page 2019

  7. [7]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In CVPR,

  8. [8]

    Bevdet: High-performance multi-camera 3d object detection in bird-eye-view

    Junjie Huang, Guan Huang, Zheng Zhu, Yun Ye, and Da- long Du. Bevdet: High-performance multi-camera 3d object detection in bird-eye-view. arXiv:2112.11790, 2021. 1, 6

    arXiv 2021

Show all 53 references
  1. [9]

    Tri-perspective view for vision-based 3d se- mantic occupancy prediction

    Yuanhui Huang, Wenzhao Zheng, Yunpeng Zhang, Jie Zhou, and Jiwen Lu. Tri-perspective view for vision-based 3d se- mantic occupancy prediction. In CVPR, 2023. 1, 2, 6

    2023

  2. [10]

    Sym- phonize 3d semantic scene completion with contextual in- stance queries

    Haoyi Jiang, Tianheng Cheng, Naiyu Gao, Haoyang Zhang, Tianwei Lin, Wenyu Liu, and Xinggang Wang. Sym- phonize 3d semantic scene completion with contextual in- stance queries. In CVPR, 2024. 2

    2024

  3. [11]

    Viewformer: Exploring spatiotem- poral modeling for multi-view 3d occupancy perception via view-guided transformers

    Jinke Li, Xiao He, Chonghua Zhou, Xiaoqiang Cheng, Yang Wen, and Dan Zhang. Viewformer: Exploring spatiotem- poral modeling for multi-view 3d occupancy perception via view-guided transformers. In ECCV, 2024. 6

    2024

  4. [12]

    Bevstereo: Enhancing depth estimation in multi-view 3d object detection with temporal stereo

    Yinhao Li, Han Bao, Zheng Ge, Jinrong Yang, Jianjian Sun, and Zeming Li. Bevstereo: Enhancing depth estimation in multi-view 3d object detection with temporal stereo. In AAAI, 2023. 2, 3, 6, 7

    2023

  5. [13]

    Bevdepth: Acquisition of reliable depth for multi-view 3d object detec- tion

    Yinhao Li, Zheng Ge, Guanyi Yu, Jinrong Yang, Zengran Wang, Yukang Shi, Jianjian Sun, and Zeming Li. Bevdepth: Acquisition of reliable depth for multi-view 3d object detec- tion. In AAAI, 2023. 2, 3, 6

    2023

  6. [14]

    V oxformer: Sparse voxel transformer for camera- based 3d semantic scene completion

    Yiming Li, Zhiding Yu, Christopher Choy, Chaowei Xiao, Jose M Alvarez, Sanja Fidler, Chen Feng, and Anima Anand- kumar. V oxformer: Sparse voxel transformer for camera- based 3d semantic scene completion. In CVPR, 2023. 1, 2, 4

    2023

  7. [15]

    Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers

    Zhiqi Li, Wenhai Wang, Hongyang Li, Enze Xie, Chong- hao Sima, Tong Lu, Yu Qiao, and Jifeng Dai. Bevformer: Learning bird’s-eye-view representation from multi-camera images via spatiotemporal transformers. In ECCV, 2022. 2, 3, 4, 6, 7, 8

    2022

  8. [16]

    Fb-occ: 3d occupancy prediction based on forward-backward view transformation

    Zhiqi Li, Zhiding Yu, David Austin, Mingsheng Fang, Shiyi Lan, Jan Kautz, and Jose M Alvarez. Fb-occ: 3d occupancy prediction based on forward-backward view transformation. arXiv:2307.01492, 2023. 1, 6, 7, 8

    2023 arXiv

  9. [17]

    Fb-bev: Bev representa- tion from forward-backward view transformations

    Zhiqi Li, Zhiding Yu, Wenhai Wang, Anima Anandkumar, Tong Lu, and Jose M Alvarez. Fb-bev: Bev representa- tion from forward-backward view transformations. In ICCV,

  10. [18]

    Cascadeflow: 3d occupancy and flow prediction with cascaded sparsity sampling refinement framework

    Zhimin Liao and Ping Wei. Cascadeflow: 3d occupancy and flow prediction with cascaded sparsity sampling refinement framework. In CVPR2024 Autonomous Grand Challenge Track On Occupancy and Flow, 2024. 2

    2024

  11. [19]

    Sparse4d: Multi-view 3d object detection with sparse spatial-temporal fusion

    Xuewu Lin, Tianwei Lin, Zixiang Pei, Lichao Huang, and Zhizhong Su. Sparse4d: Multi-view 3d object detection with sparse spatial-temporal fusion. arXiv:2211.10581, 2022. 2, 4

    2022 arXiv

  12. [20]

    Sparsebev: High-performance sparse 3d object detec- tion from multi-camera videos

    Haisong Liu, Yao Teng, Tao Lu, Haiguang Wang, and Limin Wang. Sparsebev: High-performance sparse 3d object detec- tion from multi-camera videos. In ICCV, 2023. 2, 3

    2023

  13. [21]

    Fully sparse 3d occupancy prediction

    Haisong Liu, Yang Chen, Haiguang Wang, Zetong Yang, Tianyu Li, Jia Zeng, Li Chen, Hongyang Li, and Limin Wang. Fully sparse 3d occupancy prediction. In ECCV,

  14. [22]

    Petr: Position embedding transformation for multi-view 3d object detection

    Yingfei Liu, Tiancai Wang, Xiangyu Zhang, and Jian Sun. Petr: Position embedding transformation for multi-view 3d object detection. In ECCV, 2022. 2

    2022

  15. [23]

    Petrv2: A unified framework for 3d perception from multi-camera images

    Yingfei Liu, Junjie Yan, Fan Jia, Shuailin Li, Aqi Gao, Tian- cai Wang, and Xiangyu Zhang. Petrv2: A unified framework for 3d perception from multi-camera images. InICCV, 2023. 2

    2023

  16. [24]

    Let occ flow: Self-supervised 3d occupancy flow prediction

    Yili Liu, Linzhan Mou, Xuan Yu, Chenrui Han, Sitong Mao, Rong Xiong, and Yue Wang. Let occ flow: Self-supervised 3d occupancy flow prediction. arXiv:2407.07587, 2024. 7

    2024 arXiv

  17. [25]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv:1711.05101, 2017. 7

    2017 arXiv

  18. [26]

    Cotr: Compact occupancy transformer for vision-based 3d occupancy prediction

    Qihang Ma, Xin Tan, Yanyun Qu, Lizhuang Ma, Zhizhong Zhang, and Yuan Xie. Cotr: Compact occupancy transformer for vision-based 3d occupancy prediction. In CVPR, 2024. 6

    2024

  19. [27]

    N. Max. Optical models for direct volume rendering. TVCG,

  20. [28]

    Camera- based 3d semantic scene completion with sparse guidance network

    Jianbiao Mei, Yu Yang, Mengmeng Wang, Junyu Zhu, Xian- grui Zhao, Jongwon Ra, Laijian Li, and Yong Liu. Camera- based 3d semantic scene completion with sparse guidance network. IEEE Transactions on Image Processing, 2024. 1, 2, 4

    2024

  21. [29]

    Occupancy networks: Learning 3d reconstruction in function space

    Lars Mescheder, Michael Oechsle, Michael Niemeyer, Se- bastian Nowozin, and Andreas Geiger. Occupancy networks: Learning 3d reconstruction in function space. In CVPR,

  22. [30]

    Occdepth: A depth-aware method for 3d semantic scene completion

    Ruihang Miao, Weizhou Liu, Mingrui Chen, Zheng Gong, Weixin Xu, Chen Hu, and Shuchang Zhou. Occdepth: A depth-aware method for 3d semantic scene completion. arXiv:2302.13540, 2023. 1 9

    2023 arXiv

  23. [31]

    Nerf: Representing scenes as neural radiance fields for view syn- thesis

    Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes as neural radiance fields for view syn- thesis. Communications of the ACM, 2021. 5

    2021

  24. [32]

    Renderocc: Vision-centric 3d occupancy prediction with 2d rendering supervision

    Mingjie Pan, Jiaming Liu, Renrui Zhang, Peixiang Huang, Xiaoqi Li, Li Liu, and Shanghang Zhang. Renderocc: Vision-centric 3d occupancy prediction with 2d rendering supervision. arXiv:2309.09502, 2023. 6, 7

    2023 arXiv

  25. [33]

    Time will tell: New outlooks and a baseline for temporal multi-view 3d object detection

    Jinhyung Park, Chenfeng Xu, Shijia Yang, Kurt Keutzer, Kris Kitani, Masayoshi Tomizuka, and Wei Zhan. Time will tell: New outlooks and a baseline for temporal multi-view 3d object detection. arXiv:2210.02443, 2022. 2, 3, 8

    2022 arXiv

  26. [34]

    Learning occupancy for monocular 3d object detection

    Liang Peng, Junkai Xu, Haoran Cheng, Zheng Yang, Xiaopei Wu, Wei Qian, Wenxiao Wang, Boxi Wu, and Deng Cai. Learning occupancy for monocular 3d object detection. In CVPR, 2024. 2

    2024

  27. [35]

    Convolutional occupancy networks

    Songyou Peng, Michael Niemeyer, Lars Mescheder, Marc Pollefeys, and Andreas Geiger. Convolutional occupancy networks. In ECCV, 2020. 2

    2020

  28. [36]

    Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d

    Jonah Philion and Sanja Fidler. Lift, splat, shoot: Encoding images from arbitrary camera rigs by implicitly unprojecting to 3d. In ECCV, 2020. 3

    2020

  29. [37]

    Occupancy as set of points

    Yiang Shi, Tianheng Cheng, Qian Zhang, Wenyu Liu, and Xinggang Wang. Occupancy as set of points. In ECCV,

  30. [38]

    Scene as occupancy

    Chonghao Sima, Wenwen Tong, Tai Wang, Li Chen, Silei Wu, Hanming Deng, Yi Gu, Lewei Lu, Ping Luo, Dahua Lin, and Hongyang Li. Scene as occupancy. In ICCV, 2023. 1, 2, 3, 6, 7

    2023

  31. [39]

    Sparseocc: Re- thinking sparse latent representation for vision-based seman- tic occupancy prediction

    Pin Tang, Zhongdao Wang, Guoqing Wang, Jilai Zheng, Xi- angxuan Ren, Bailan Feng, and Chao Ma. Sparseocc: Re- thinking sparse latent representation for vision-based seman- tic occupancy prediction. In CVPR, 2024. 2

    2024

  32. [40]

    Occ3d: A large-scale 3d occupancy prediction benchmark for autonomous driving

    Xiaoyu Tian, Tao Jiang, Longfei Yun, Yucheng Mao, Huitong Yang, Yue Wang, Yilun Wang, and Hang Zhao. Occ3d: A large-scale 3d occupancy prediction benchmark for autonomous driving. In NeurIPS, 2023. 1, 2, 6

    2023

  33. [41]

    Opus: Occupancy prediction using a sparse set

    Jiabao Wang, Zhaojiang Liu, Qiang Meng, Liujiang Yan, Ke Wang, Jie Yang, Wei Liu, Qibin Hou, and Ming-Ming Cheng. Opus: Occupancy prediction using a sparse set. In NeurIPS, 2024. 6

    2024

  34. [42]

    Bev-lanedet: An efficient 3d lane detection based on virtual camera via key-points

    Ruihao Wang, Jian Qin, Kaiying Li, Yaochen Li, Dong Cao, and Jintao Xu. Bev-lanedet: An efficient 3d lane detection based on virtual camera via key-points. In CVPR, 2023. 2

    2023

  35. [43]

    Detr3d: 3d object detection from multi-view images via 3d-to-2d queries

    Yue Wang, Vitor Campagnolo Guizilini, Tianyuan Zhang, Yilun Wang, Hang Zhao, and Justin Solomon. Detr3d: 3d object detection from multi-view images via 3d-to-2d queries. In CoRL, 2022. 2

    2022

  36. [44]

    Surroundocc: Multi-camera 3d occu- pancy prediction for autonomous driving

    Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, and Jiwen Lu. Surroundocc: Multi-camera 3d occu- pancy prediction for autonomous driving. In ICCV, 2023. 1

    2023

  37. [45]

    Heightformer: Explicit height modeling without extra data for camera-only 3d object detection in bird’s eye view

    Yiming Wu, Ruixiang Li, Zequn Qin, Xinhai Zhao, and Xi Li. Heightformer: Explicit height modeling without extra data for camera-only 3d object detection in bird’s eye view. IEEE Transactions on Image Processing, 2024. 4

    2024

  38. [46]

    Regulating intermediate 3d features for vision-centric au- tonomous driving

    Junkai Xu, Liang Peng, Haoran Cheng, Linxuan Xia, Qi Zhou, Dan Deng, Wei Qian, Wenxiao Wang, and Deng Cai. Regulating intermediate 3d features for vision-centric au- tonomous driving. In AAAI, 2024. 2

    2024

  39. [47]

    Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective su- pervision

    Chenyu Yang, Yuntao Chen, Hao Tian, Chenxin Tao, Xizhou Zhu, Zhaoxiang Zhang, Gao Huang, Hongyang Li, Yu Qiao, Lewei Lu, et al. Bevformer v2: Adapting modern image backbones to bird’s-eye-view recognition via perspective su- pervision. In CVPR, 2023. 2

    2023

  40. [48]

    Widthformer: Toward efficient transformer-based bev view transformation

    Chenhongyi Yang, Tianwei Lin, Lichao Huang, and Elliot J Crowley. Widthformer: Toward efficient transformer-based bev view transformation. In IROS, 2024. 4

    2024

  41. [49]

    Visual point cloud forecasting enables scalable autonomous driving

    Zetong Yang, Li Chen, Yanan Sun, and Hongyang Li. Visual point cloud forecasting enables scalable autonomous driving. In CVPR, 2024. 1

    2024

  42. [50]

    Occnerf: Self- supervised multi-camera occupancy prediction with neural radiance fields

    Chubin Zhang, Juncheng Yan, Yi Wei, Jiaxin Li, Li Liu, Yansong Tang, Yueqi Duan, and Jiwen Lu. Occnerf: Self- supervised multi-camera occupancy prediction with neural radiance fields. arXiv:2312.09243, 2023. 7

    2023 arXiv

  43. [51]

    Beverse: Unified per- ception and prediction in birds-eye-view for vision-centric autonomous driving

    Yunpeng Zhang, Zheng Zhu, Wenzhao Zheng, Junjie Huang, Guan Huang, Jie Zhou, and Jiwen Lu. Beverse: Unified per- ception and prediction in birds-eye-view for vision-centric autonomous driving. arXiv:2205.09743, 2022. 2

    2022 arXiv

  44. [52]

    Occformer: Dual-path transformer for vision-based 3d semantic occu- pancy prediction

    Yunpeng Zhang, Zheng Zhu, and Dalong Du. Occformer: Dual-path transformer for vision-based 3d semantic occu- pancy prediction. In ICCV, 2023. 1, 2

    2023

  45. [53]

    Deformable detr: Deformable transformers for end-to-end object detection

    Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. In ICLR, 2021. 4 10

    2021

Pith tools

Reviewed August 16, 2026 · model on record in the stance chip above.