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Camera-Only 3D Panoptic Scene Completion for Autonomous Driving through Differentiable Object Shapes

T0 review · 3 major / 3 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read OffsetOcc models each object's shape as a differentiable set of center-relative offsets, enabling panoptic 3D scene completion from cameras alone.

desk verdict The offset-set representation is genuinely novel, but the paper's central claim of panoptic scene completion via differentiable object shapes is not implemented: predicted object voxels are never written into the final grid, so the completion part is untested. read the letter →

arxiv 2505.09562 v1 pith:4T4PCRPR submitted 2025-05-14 cs.CV

Nicola Marinello , Simen Cassiman , Jonas Heylen , Marc Proesmans , Luc Van Gool This is my paper · ORCID
classification cs.CV
keywords 3Dpanopticscenecompletionoccupancypredictiondifferentiableobjectshapeoffsetrepresentationcamera-onlyperceptionautonomousdrivingsetvoxelgrid
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

This paper tries to establish that 3D panoptic scene completion from cameras alone can be solved by treating each object's shape as a differentiable set of 3D offsets from the object center, rather than decoding voxels bottom-up and clustering them into instances. The authors build OffsetOcc, a framework of two plug-in modules: an Object Module that predicts object class, center, and a set of occupied-voxel offsets for up to 900 queries in a set-prediction scheme, and a parameter-free Panoptic Module that fuses those instance shapes with a baseline occupancy grid by majority voting. The motivation is that scene completion needs to hallucinate occluded voxels, and distinguishing instances within a class matters for planning; current occupancy models trained with a visibility mask ignore occluded regions and can learn unrealistic object shapes. A sympathetic reader should care because the proposed representation is generic: it can sit on top of any camera-based occupancy decoder and adds only 2.3 million parameters, turning semantic occupancy into panoptic occupancy with modest latency overhead. The paper reports 28.0 mIoU on Occ3D-nuScenes under the standard visibility-masked evaluation and 29.4 PQ on a panoptic LIDAR proxy, while arguing that the masked metric hides poor completion of occluded regions.

What carries the argument

The load-bearing identity is the offset occupancy representation: an object is its center plus a set of vectors to occupied voxel centers, with per-offset occupancy scores, converted to a mask by voxelization. This replaces full per-object grid masks with a compact, differentiable description and gives a top-down, object-centric prior that bottom-up voxel decoders lack. The machinery also includes the two-level Hungarian assignment (objects, then voxels within each object) that lets the shape loss be computed against ground-truth voxel sets, and the location/offset decoupling that makes offset learning independent of center accuracy. The Panoptic Module's radius-r majority voting is the fusion mechanism that turns separate object masks plus a baseline grid into a single panoptic grid without learned parameters.

What would settle it

Compute occupancy IoU restricted to camera-occluded thing voxels, the complement of the visibility mask, on a held-out set with reliable occluded-object labels: if a model trained without the visibility mask performs no better than a constant-empty baseline in these regions, the claimed completion of occluded object shapes is not happening. A second check would compare PQ against true panoptic voxel ground truth instead of the LIDAR point proxy.

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Extended reading notes

Core claim

The central claim is that object occupancy can be represented as a set prediction: for each detected object the model outputs a 3D center and a fixed-size set of K offset vectors, each offset aimed at the center of a voxel the object occupies and paired with an occupancy score in [0,1]; voxels whose score passes 0.5 are kept and voxelized into an object mask. Because this mask is generated from continuous vectors, the whole shape estimate is differentiable and can be trained end-to-end from the semantic voxel annotations already present in occupancy benchmarks, without needing panoptic labels at training time. The Object Module uses learnable object queries with 3D deformable cross-attention to the voxel feature grid, and training matches predictions to ground truth twice with the Hungarian algorithm, once at the object level and once at the voxel level. A supervised decoupling trick anchors the predicted offsets to the ground-truth object center during training, so the shape loss does not absorb localization error. The Panoptic Module then assigns baseline object voxels to the most-voted nearby instance ID, producing a complete panoptic voxel grid.

Load-bearing premise

The approach assumes that panoptic training targets can be manufactured by intersecting semantic voxel labels with 3D bounding boxes, which is reliable for box-shaped vehicles but noisy for pedestrians, cones, and bicycles, and that labeling LIDAR points with voxel labels adequately measures panoptic scene completion, which never tests whether occluded voxels were actually completed.

Editorial extensions

If this is right

  • Existing camera-based 3D scene completion models can be upgraded to panoptic output by adding the two modules, without retraining the base occupancy decoder.
  • Since offsets are learned per class from occupancy annotations, objects that are partially occluded can, in principle, receive completed voxel shapes rather than just a bounding box.
  • Panoptic occupancy metrics should be reported without the visibility mask, because masked evaluation discards exactly the occluded regions scene completion is meant to fill.
  • The overhead is small enough (2.3 million parameters, and 714 ms versus 500 ms per six-camera frame on an L40S) to fit real-time autonomous-driving pipelines.

Reading between the lines

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

  • If object shapes are indeed learned as class-typical offset distributions, the same offset head could be conditioned on temporal features or depth estimates, letting a partially visible object trigger a complete shape prior without extra supervision.
  • The parameter-free voting fusion could be replaced by a learned refinement that estimates per-voxel instance confidence, which might close part of the gap to stronger panoptic baselines.
  • Because training is done without the visibility mask, the Object Module's losses may act as a shape regularizer that makes the baseline occupancy decoder more geometrically plausible even where no instance supervision exists; this is testable by ablating the frozen baseline.
  • A dataset with true panoptic voxel annotations would let the field decide whether the offset representation or bottom-up clustering is better for completing occluded instances.
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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

3 major / 3 minor

Summary. The paper proposes OffsetOcc, a camera-only framework for 3D panoptic scene completion. It augments a standard semantic scene completion (SSC) baseline with an Object Module, a DETR-style decoder that predicts object centers and a set of offsets that describe each object's occupied voxels, and a Panoptic Module that merges these predictions with the baseline occupancy grid to produce a panoptic voxel output. The authors train the Object Module on Occ3D-nuScenes by generating panoptic labels from box-voxel intersection, and evaluate the full model with mIoU/IoU on the occupancy task plus a panoptic LIDAR segmentation metric as a proxy for panoptic performance. They report mIoU 28.0, IoU 43.9 on the visibility-masked setting, and panoptic LIDAR PQ 29.4, with ablations on loss decoupling and voting radius.

Significance. The idea of learning object shapes as differentiable offset sets is interesting and could be a useful component for panoptic scene understanding. The paper is clearly written and the code is released, which is commendable. However, the implementation as described does not actually use the learned object shapes to add occluded occupancy: the Panoptic Module only assigns instance IDs to voxels already classified as objects by the baseline, and the only panoptic evaluation is on LIDAR points, which are observed surfaces. Consequently, the central claim of 'panoptic scene completion through differentiable object shapes' is not supported by the architecture or the experiments. If the authors were to redesign the fusion so that predicted object occupancy is written into the grid and to evaluate with a voxel-level panoptic metric, the contribution could be more meaningful, but the present manuscript does not deliver the promised capability.

major comments (3)
  1. [Sec. 3.3, Panoptic Module and Eq. (1)] The Panoptic Module uses the Object Module's predicted point cloud only as voting evidence: 'each voxel classified as an object by the baseline decoder is assigned an instance ID based on the Object Module prediction.' There is no union or OR operation that inserts the predicted offset-derived voxels (Eq. 1) into the final panoptic grid. Therefore, any occluded object voxel that the baseline misses is absent from the output, regardless of how accurate the learned shapes are. This is a load-bearing flaw: the paper's central claim is that differentiable object shapes enable 3D panoptic scene completion, but in the described architecture they only enable instance labeling of baseline predictions. To support the claim, the fusion must actually add the Object Module's predicted occupancy to the grid, and the experiments must show that occluded voxels are completed better as a result.
  2. [Sec. 4.3, Panoptic LIDAR segmentation and Table 2] The panoptic evaluation labels LIDAR points according to their corresponding voxel labels. LIDAR points are located only on observed surfaces, so this metric cannot measure whether occluded voxels have been correctly completed. This is particularly serious because the paper defines scene completion as 'reconstruct[ing] regions that are currently occluded' (Sec. 1). Even if the architecture were modified to write Object Module voxels into the grid, the current metric would remain blind to that improvement. A voxel-level panoptic metric on the full occupancy grid, ideally reported both with and without the visibility mask, is required to test the claimed capability.
  3. [Sec. 4.1, Datasets (panoptic ground truth)] The panoptic ground truth is generated 'by intersecting semantic voxel annotations with 3D bounding boxes.' For classes whose shapes are not well approximated by boxes, such as pedestrians, bicycles, and traffic cones, this intersection yields noisy or incorrect instance labels. Because the Object Module is trained to reconstruct exactly these voxel sets, the supervision may be too inaccurate to support the paper's claim that accurate object shapes are learned. The authors should quantify the error introduced by this label generation, for example by comparing against manually labeled instances on a subset, and discuss the impact on the object shape learning.
minor comments (3)
  1. [Eq. (1)] The notation is inconsistent: the occupancy score is written as s_i^k in some places and s^k_i in others; please use a consistent subscript/superscript convention for the object index and offset index.
  2. [Fig. 4] The x-axis label is 'd' while the text and Sec. 3.3 use 'r' for the voting radius; unify the notation to avoid confusion.
  3. [Sec. 4.3] The paper calls the metric 'panoptic LIDAR segmentation,' but the task under study is panoptic scene completion on a voxel grid; the relationship between the two should be clarified, and the proxy nature of the LIDAR metric should be stated more prominently.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: object shapes are learned from external Occ3D labels and nuScenes boxes; the Panoptic Module is a parameter-free ID-assignment post-process, so no prediction reduces to its input by construction.

full rationale

The paper's derivation chain is self-contained against external data and benchmarks. The Object Module is supervised directly from Occ3D-nuScenes semantic voxel annotations and nuScenes 3D bounding boxes (Sec. 4.1), with the occupancy loss in Eq. (4) comparing predicted offsets to matched ground-truth voxel centers; predicted occupancy in Eq. (1) is just a thresholded set of learned offsets, not a re-encoding of the paper's own claims. The Panoptic Module is parameter-free: it assigns instance IDs to voxels already classified as objects by the baseline decoder, then applies majority voting with radius r, and the only tuned quantity r is selected on a mini set for smoothing rather than being fitted to define the target metric. There is no load-bearing self-citation chain: citations to DETR and deformable attention are standard architectural components, and no uniqueness theorem is imported from the authors' prior work. The reviewer's and skeptic's concerns about the LIDAR-proxy metric and about the fusion step not writing Object Module voxels into the grid are correctness and validity concerns about whether the claimed completion capability is actually exercised, not instances of circularity: even if the architecture fails to deliver occluded-voxel completion, the method's outputs are not defined in terms of the conclusions it claims. No fitted input is renamed as a prediction, and no known result is re-labeled as a new contribution. Therefore the appropriate finding is no significant circularity, score 0.

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

The central claim rests on hand-chosen hyperparameters and on the quality of derived panoptic labels and proxy evaluation. No new physical entities, forces, or conserved quantities are introduced; the object center and offsets are computational representations rather than new entities.

free parameters (4)
  • Number of offsets K = 2197
    Chosen by hand as 'sufficiently large to describe objects of any size' (Sec. 3.3); the shape representation capacity depends on it.
  • Number of object queries Q = 900
    Chosen by hand in Sec. 4.2; sets the maximum number of objects the module can output in a scene.
  • Voting radius r = 9
    Selected using the mini set in Fig. 4, then used in all main results; affects panoptic ID assignment and reported PQ.
  • Loss weights lambda1..lambda4 = 0.5, 0.02, 0.125, 0.0125
    Set by hand in Sec. 4.2; balance classification, center distance, focal, and offset L2 components of L_objects.
assumptions (4)
  • domain assumption Occ3D semantic voxel labels and nuScenes 3D bounding boxes, when intersected, give valid panoptic ground truth for training object shapes.
    Sec. 4.1 generates panoptic labels this way; errors in this intersection propagate to object supervision, especially for non-box classes.
  • domain assumption Panoptic LIDAR segmentation, computed by labeling LIDAR points with predicted voxel labels, is a valid proxy for panoptic scene completion quality.
    Sec. 4.3 uses this proxy; it only measures visible surfaces, not occluded voxel completion.
  • domain assumption The baseline occupancy model is fixed after stage 1 and provides features sufficient for the object module to learn shapes.
    Sec. 3.4 freezes the baseline; the authors acknowledge this limits feature quality and contributes to the gap with PanoOcc.
  • standard math Hungarian bipartite matching can align predicted point clouds to ground truth voxel centers with L2 cost.
    Used in Sec. 3.4 for object-level and voxel-level matching; standard optimization, not questioned.

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Cite this review

Pith. "Pith review of Camera-Only 3D Panoptic Scene Completion for Autonomous Driving through Differentiable Object Shapes." pith.science (2026). https://pith.science/paper/4T4PCRPR

@misc{pith2026250509562,
  author       = {Pith},
  title        = {Pith review of: Camera-Only 3D Panoptic Scene Completion for Autonomous Driving through Differentiable Object Shapes},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/4T4PCRPR}},
  note         = {Machine review of arXiv:2505.09562}
}
read the original abstract

Autonomous vehicles need a complete map of their surroundings to plan and act. This has sparked research into the tasks of 3D occupancy prediction, 3D scene completion, and 3D panoptic scene completion, which predict a dense map of the ego vehicle's surroundings as a voxel grid. Scene completion extends occupancy prediction by predicting occluded regions of the voxel grid, and panoptic scene completion further extends this task by also distinguishing object instances within the same class; both aspects are crucial for path planning and decision-making. However, 3D panoptic scene completion is currently underexplored. This work introduces a novel framework for 3D panoptic scene completion that extends existing 3D semantic scene completion models. We propose an Object Module and Panoptic Module that can easily be integrated with 3D occupancy and scene completion methods presented in the literature. Our approach leverages the available annotations in occupancy benchmarks, allowing individual object shapes to be learned as a differentiable problem. The code is available at https://github.com/nicolamarinello/OffsetOcc .

Figures

Figures reproduced from arXiv: 2505.09562 by the authors.

Figure 1
Figure 1. OffsetOcc. Our method OffsetOcc models object oc￾cupancy as a set prediction task, where objects are represented by their 3D center and a set of offsets that describe their shape. The set of offsets is learned from the dataset annotations. Classifica￾tion head omitted for conciseness. One of the most useful applications may well be enabling autonomous agents, analyzing sensory data to build up an accurate representa… view at source ↗
Figure 2
Figure 2. Overview of our framework OffsetOcc. Our approach can be integrated with modern occupancy models and consists of two key components: an Object Module and a Panoptic Module. The Object Module utilizes features extracted by an uplifting encoder to detect objects in the voxel grid and predict their occupancy using the offset occupancy mechanism. The Panoptic Module then merges these object predictions with the occupanc… view at source ↗
Figure 3
Figure 3. Qualitative results on Occ3D-nuScenes validation set. Our model accurately differentiates and segments different objects in the scene. Method Image Backbone Temporal Train w/ mask Evaluate w/ mask mIoU IoU MonoScene [2] R101-DCN ✗ ✗ ✓ 6.1 - BEVDet [9] R101-DCN ✗ ✗ ✓ 19.4 - OccFormer [46] R101 ✗ ✗ ✓ 21.9 - BEVFormer [20] R101-DCN ✓ ✗ ✓ 26.9 - TPVFormer [10] R101-DCN ✓ ✗ ✓ 27.8 - CTF-Occ [34] R101-DCN ✗ ✗ ✓ 28.5 - Spa… view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Effect of the voting radius. Increasing r initially im￾proves performance by smoothing predictions. However, beyond the optimal value of 9, performance declines as the voting pro￾cess starts to incorporate irrelevant voxels, leading to incorrect ID assignments. Tested …
Figure 5
Figure 5. Figure 5: Additional qualitative results of our model OffsetOcc. [PITH_FULL_IMAGE:figures/full_fig_p012_5.png]

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Reference graph

Works this paper leans on

51 extracted references · 43 canonical work pages

  1. [1]

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

    Holger Caesar, Varun Bankiti, Alex H. Lang, Sourabh V ora, Venice Erin Liong, Qiang Xu, Anush Krishnan, Yu Pan, Giancarlo Baldan, and Oscar Beijbom. nuscenes: A mul- timodal dataset for autonomous driving. arXiv preprint arXiv:1903.11027, 2019. 6

    arXiv 1903

  2. [2]

    Monoscene: Monoc- ular 3d semantic scene completion

    Anh-Quan Cao and Raoul de Charette. Monoscene: Monoc- ular 3d semantic scene completion. In IEEE/CVF Confer- ence on Computer Vision and Pattern Recognition (CVPR) , pages 3991–4001, 2022. 2, 3, 5, 7, 13

    work page 2022

  3. [3]

    End-to- end object detection with transformers

    Nicolas Carion, Francisco Massa, Gabriel Synnaeve, Nicolas Usunier, Alexander Kirillov, and Sergey Zagoruyko. End-to- end object detection with transformers. In European confer- ence on computer vision, pages 213–229. Springer, 2020. 3, 4, 5

    work page 2020

  4. [4]

    Gridmask data augmentation

    Pengguang Chen, Shu Liu, Hengshuang Zhao, Xingquan Wang, and Jiaya Jia. Gridmask data augmentation. arXiv preprint arXiv:2001.04086, 2020. 6

    arXiv 2001

  5. [5]

    Imagenet: A large-scale hierarchical image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Fei-Fei Li. Imagenet: A large-scale hierarchical image database. In CVPR, pages 248–255. IEEE Computer Society,

    work page

  6. [6]

    Mamba: Linear-time sequence mod- eling with selective state spaces

    Albert Gu and Tri Dao. Mamba: Linear-time sequence mod- eling with selective state spaces. arXiv preprint, 2023. 2

    work page 2023

  7. [7]

    Deep residual learning for image recognition

    Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In Proceed- ings of the IEEE conference on computer vision and pattern recognition, pages 770–778, 2016. 6

    2016

  8. [8]

    Real- time 3d occupancy prediction via geometric-semantic disen- tanglement

    Yulin He, Wei Chen, Tianci Xun, and Yusong Tan. Real- time 3d occupancy prediction via geometric-semantic disen- tanglement. arXiv, 2024. 2

    work page 2024

Show all 51 references
  1. [9]

    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, 2021. 7, 13

    2021

  2. [10]

    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 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , pages 9223–9232, 2023. 2, 7, 13

    2023

  3. [11]

    Selfocc: Self-supervised vision-based 3d occupancy prediction

    Yuanhui Huang, Wenzhao Zheng, Borui Zhang, Jie Zhou, and Jiwen Lu. Selfocc: Self-supervised vision-based 3d occupancy prediction. In IEEE/CVF Conference on Com- puter Vision and Pattern Recognition (CVPR), pages 19946– 19956, 2024. 3

    2024

  4. [12]

    Gaussianformer: Scene as gaussians for vision-based 3d semantic occupancy prediction.arXiv, 2024

    Yuanhui Huang, Wenzhao Zheng, Yunpeng Zhang, Jie Zhou, and Jiwen Lu. Gaussianformer: Scene as gaussians for vision-based 3d semantic occupancy prediction.arXiv, 2024. 2

    2024

  5. [13]

    Poisson surface re- construction

    M Kazhdan, M Bolitho, and H Hoppe. Poisson surface re- construction. In Proceedings of the fourth Eurographics sym- posium on Geometry processing, 2006. 3

    2006

  6. [14]

    Panoptic segmentation

    Alexander Kirillov, Kaiming He, Ross Girshick, Carsten Rother, and Piotr Doll ´ar. Panoptic segmentation. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 9404–9413, 2019. 6

    2019

  7. [15]

    The hungarian method for the assignment problem

    Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly , 2(1-2):83–97,

  8. [16]

    Deep learning

    Yann Lecun, Yoshua Bengio, and Geoffrey Hinton. Deep learning. Nature 2015 521:7553, 521:436–444, 2015. 1

    2015

  9. [17]

    Occmamba: Semantic occupancy prediction with state space models

    Heng Li, Yuenan Hou, Xiaohan Xing, Xiao Sun, and Yany- ong Zhang. Occmamba: Semantic occupancy prediction with state space models. arXiv, 2024. 2

    2024

  10. [18]

    Panoptic-phnet: Towards real-time and high-precision lidar panoptic segmentation via clustering pseudo heatmap

    Jinke Li, Xiao He, Yang Wen, Yuan Gao, Xiaoqiang Cheng, and Dan Zhang. Panoptic-phnet: Towards real-time and high-precision lidar panoptic segmentation via clustering pseudo heatmap. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, pages ...

    2022

  11. [19]

    Alvarez, Sanja Fidler, Chen Feng, and Anima Anandkumar

    Yiming Li, Zhiding Yu, Christopher Choy, Chaowei Xiao, Jose M. Alvarez, Sanja Fidler, Chen Feng, and Anima Anandkumar. V oxformer: Sparse voxel transformer for camera-based 3d semantic scene completion. pages 9087– 9098, 2023. 3

    2023

  12. [20]

    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. ECCV 2022. Lec- ture Notes in Computer Science, 13669:1–18, 2022. 1, 6, 7, 13

    2022

  13. [21]

    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, 2023. 2

    2023

  14. [22]

    Feature pyra- mid networks for object detection

    Tsung-Yi Lin, Piotr Doll ´ar, Ross Girshick, Kaiming He, Bharath Hariharan, and Serge Belongie. Feature pyra- mid networks for object detection. In Proceedings of the IEEE conference on computer vision and pattern recogni- tion, pages 2117–2125, 2017. 6

    2017

  15. [23]

    Focal loss for dense object detection

    Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Doll´ar. Focal loss for dense object detection. In 2017 IEEE International Conference on Computer Vision (ICCV), pages 2999–3007, 2017. 5

    2017

  16. [24]

    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 European Conference on Computer Vision , pages 54–71. Springer,

  17. [25]

    Decoupled weight de- cay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight de- cay regularization. In International Conference on Learning Representations, 2019. 6

    2019

  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 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 19936–19945, 2024. 2, 3, 7, 13

    2024

  19. [27]

    3d object detection for autonomous driving: A comprehensive survey

    Jiageng Mao, Shaoshuai Shi, Xiaogang Wang, and Hong- sheng Li. 3d object detection for autonomous driving: A comprehensive survey. International Journal of Computer Vision, 131:1909–1963, 2023. 1

    1909

  20. [28]

    Uniocc: Unifying vision-centric 3d oc- cupancy prediction with geometric and semantic rendering

    Mingjie Pan, Li Liu, Jiaming Liu, Peixiang Huang, Lon- glong Wang, Shanghang Zhang, Shaoqing Xu, Zhiyi Lai, and Kuiyuan Yang. Uniocc: Unifying vision-centric 3d oc- cupancy prediction with geometric and semantic rendering. arXiv, 2023. 3

    2023

  21. [29]

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

    Mingjie Pan, Jiaming Liu, Renrui Zhang, Peixiang Huang, Xiaoqi Li, Hongwei Xie, Bing Wang, Li Liu, and Shanghang Zhang. Renderocc: Vision-centric 3d occupancy predic- tion with 2d rendering supervision. Proceedings - IEEE In- ternational Conference on Robotics and Automation ,...

    2024

  22. [30]

    Bo Peng, Eric Alcaide, Quentin Anthony, Alon Albalak, Samuel Arcadinho, Stella Biderman, Huanqi Cao, Xin Cheng, Michael Chung, Xingjian Du, Matteo Grella, G. V . Kranthi Kiran, Xuzheng He, Haowen Hou, Jiaju Lin, Przemysław Kazienko, Jan Kocon, Jiaming Kong, Bartłomiej Koptyra,...

  23. [31]

    Efficientlps: Efficient lidar panoptic segmentation

    Kshitij Sirohi, Rohit Mohan, Daniel B ¨uscher, Wolfram Bur- gard, and Abhinav Valada. Efficientlps: Efficient lidar panoptic segmentation. IEEE Transactions on Robotics, 38 (3):1894–1914, 2021. 7

    1914

  24. [32]

    Real-time 3d semantic occu- pancy prediction for autonomous vehicles using memory- efficient sparse convolution

    Samuel Sze and Lars Kunze. Real-time 3d semantic occu- pancy prediction for autonomous vehicles using memory- efficient sparse convolution. In IEEE Intelligent Vehicles Symposium, pages 1286–1293. Institute of Electrical and Electronics Engineers Inc., 2024. 2

    2024

  25. [33]

    Computer vision: algorithms and applica- tions

    Richard Szeliski. Computer vision: algorithms and applica- tions. Springer Nature, 2022. 1

    2022

  26. [34]

    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. Advances in Neural Information Processing Systems, 36:64318–64330, 2023. 1, 2, 6, 7, 13

    2023

  27. [35]

    Scene as occupancy

    Wenwen Tong, Chonghao Sima, Tai Wang, Li Chen, Silei Wu, Hanming Deng, Yi Gu, Lewei Lu, Ping Luo, Dahua Lin, and Hongyang Li. Scene as occupancy. In IEEE/CVF International Conference on Computer Vision (ICCV), pages 8406–8415, 2023. 2, 4, 6

    2023

  28. [36]

    Occgen: Gener- ative multi-modal 3d occupancy prediction for autonomous driving

    Guoqing Wang, Zhongdao Wang, Pin Tang, Jilai Zheng, Xi- angxuan Ren, Bailan Feng, and Chao Ma. Occgen: Gener- ative multi-modal 3d occupancy prediction for autonomous driving. arXiv, 2024. 3

    2024

  29. [37]

    Occrwkv: Re- thinking efficient 3d semantic occupancy prediction with lin- ear complexity

    Junming Wang, Wei Yin, Xiaoxiao Long, Xingyu Zhang, Zebin Xing, Xiaoyang Guo, and Qian Zhang. Occrwkv: Re- thinking efficient 3d semantic occupancy prediction with lin- ear complexity. arXiv, 2024. 2

    2024

  30. [38]

    Reliocc: Towards reli- able semantic occupancy prediction via uncertainty learning

    Song Wang, Zhongdao Wang, Jiawei Yu, Wentong Li, Bailan Feng, Junbo Chen, and Jianke Zhu. Reliocc: Towards reli- able semantic occupancy prediction via uncertainty learning. arXiv, 2024. 2

    2024

  31. [39]

    Openoccupancy: A large scale benchmark for surrounding semantic occupancy perception

    Xiaofeng Wang, Zheng Zhu, Wenbo Xu, Yunpeng Zhang, Yi Wei, Xu Chi, Yun Ye, Dalong Du, Jiwen Lu, and Xin- gang Wang. Openoccupancy: A large scale benchmark for surrounding semantic occupancy perception. In IEEE/CVF International Conference on Computer Vision (ICCV), pages 17850...

    2023

  32. [40]

    Panoocc: Unified occupancy representation for camera-based 3d panoptic segmentation

    Yuqi Wang, Yuntao Chen, Xingyu Liao, Lue Fan, and Zhaox- iang Zhang. Panoocc: Unified occupancy representation for camera-based 3d panoptic segmentation. In IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 17158–17168, 2024. 2, 3, 7, 13

    2024

  33. [41]

    Surroundocc: Multi-camera 3d oc- cupancy prediction for autonomous driving

    Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu, Jie Zhou, and Jiwen Lu. Surroundocc: Multi-camera 3d oc- cupancy prediction for autonomous driving. In IEEE/CVF International Conference on Computer Vision (ICCV), pages 21729–21740, 2023. 3

    2023

  34. [42]

    3d semantic scene completion and occupancy prediction for autonomous driving: A survey

    Guixing Xu, Wei Liu, Zuotao Ning, Qixi Zhao, Shuai Cheng, and Jiwei Nie. 3d semantic scene completion and occupancy prediction for autonomous driving: A survey. 2023 4th In- ternational Conference on Computers and Artificial Intelli- gence Technology, CAIT 2023, pages 181–188, 2023. 2

    2023

  35. [43]

    Lidarmultinet: Towards a unified multi-task network for lidar perception

    Dongqiangzi Ye, Zixiang Zhou, Weijia Chen, Yufei Xie, Yu Wang, Panqu Wang, and Hassan Foroosh. Lidarmultinet: Towards a unified multi-task network for lidar perception. arXiv preprint arXiv:2209.09385, 2022. 7

    2022 arXiv

  36. [44]

    Flashocc: Fast and memory-efficient occupancy prediction via channel-to-height plugin

    Zichen Yu, Changyong Shu, Jiajun Deng, Kangjie Lu, Zong- dai Liu, Jiangyong Yu, Dawei Yang, Hui Li, and Yan Chen. Flashocc: Fast and memory-efficient occupancy prediction via channel-to-height plugin. arXiv, 2023. 2

    2023

  37. [45]

    Panoptic-flashocc: An efficient baseline to marry semantic occupancy with panoptic via instance cen- ter

    Zichen Yu, Changyong Shu, Qianpu Sun, Junjie Linghu, Xi- aobao Wei, Jiangyong Yu, Zongdai Liu, Dawei Yang, Hui Li, and Yan Chen. Panoptic-flashocc: An efficient baseline to marry semantic occupancy with panoptic via instance cen- ter. arXiv, 2024. 2, 3

    2024

  38. [46]

    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 IEEE/CVF International Conference on Computer Vision (ICCV), pages 9433–9443, 2023. 2, 7, 13

    2023

  39. [47]

    Vision-based 3d occupancy prediction in au- tonomous driving: a review and outlook

    Yanan Zhang, Jinqing Zhang, Zengran Wang, Junhao Xu, and Di Huang. Vision-based 3d occupancy prediction in au- tonomous driving: a review and outlook. arXiv, 2024. 2

    2024

  40. [48]

    Lowrankocc: Tensor decomposition and low-rank recovery for vision-based 3d semantic occupancy prediction

    Linqing Zhao, Xiuwei Xu, Ziwei Wang, Yunpeng Zhang, Borui Zhang, Wenzhao Zheng, Dalong Du, Jie Zhou, and Jiwen Lu. Lowrankocc: Tensor decomposition and low-rank recovery for vision-based 3d semantic occupancy prediction. In IEEE/CVF Conference on Computer Vision and Pattern Re...

    2024

  41. [49]

    Monoocc: Digging into monocular semantic occu- pancy prediction

    Yupeng Zheng, Xiang Li, Pengfei Li, Yuhang Zheng, Bu Jin, Chengliang Zhong, Xiaoxiao Long, Hao Zhao, and Qichao Zhang. Monoocc: Digging into monocular semantic occu- pancy prediction. Proceedings - IEEE International Con- ference on Robotics and Automation , pages 18398–18405,

  42. [50]

    Panoptic- polarnet: Proposal-free lidar point cloud panoptic segmenta- tion

    Zixiang Zhou, Yang Zhang, and Hassan Foroosh. Panoptic- polarnet: Proposal-free lidar point cloud panoptic segmenta- tion. In Proceedings of the IEEE/CVF Conference on Com- puter Vision and Pattern Recognition , pages 13194–13203,

  43. [51]

    Temporal

    Xizhou Zhu, Weijie Su, Lewei Lu, Bin Li, Xiaogang Wang, and Jifeng Dai. Deformable detr: Deformable transformers for end-to-end object detection. In International Conference on Learning Representations, 2020. 4, 6 Camera-Only 3D Panoptic Scene Completion for Autonomous Driving...

    2020

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.