Pith. sign in

REVIEW 3 major objections 7 minor 6 cited by

EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene Understanding

T0 review · 3 major / 7 minor · reviewed 2026-08-11 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read EmbodiedOcc claims an embodied agent can build a consistent global 3D occupancy map of an indoor scene online from single-camera RGB streams by maintaining and refining an explicit Gaussian memory.

desk verdict New task and benchmark, strong local results, but 'global' occupancy is only measured on explored frustums—worth reviewing with that caveat. read the letter →

arxiv 2412.04380 v3 pith:TGVPL6KE submitted 2024-12-05 cs.CV cs.AIcs.LG

Yuqi Wu , Wenzhao Zheng , Sicheng Zuo , Yuanhui Huang , Jie Zhou , Jiwen Lu This is my paper · ORCID
classification cs.CVcs.AIcs.LG
keywords embodied3DoccupancypredictionsemanticGaussiansGaussianmemorymonocularRGBperceptiononlinesceneunderstandingindoorcompletiondepth-awarerefinementOcc-ScanNetbenchmark
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 proposes a new task, embodied 3D occupancy prediction: an agent moving through an unseen indoor scene must produce a global semantic occupancy map of the room online, using only single-camera color images and their poses. To solve it, the authors introduce EmbodiedOcc, which represents the whole room as a fixed set of uniform 3D semantic Gaussians and refines only the Gaussians inside the current camera frustum at each frame. A depth-aware local refinement module injects predicted depth into the Gaussian features, and a confidence mechanism blends stored memory with the new observation so that revisiting a region improves it. On the reorganized EmbodiedOcc-ScanNet benchmark the method reports substantially higher IoU and mIoU than splicing per-frame predictions and than existing occupancy baselines, and a look-back evaluation shows that repeated observations improve accuracy. The interest is that an explicit, continuously updated geometric memory, rather than frame-by-frame inference, gives a practical route to room-scale understanding for embodied agents.

What carries the argument

The central object is the Gaussian memory: a world-coordinate set of 3D semantic Gaussians covering the whole scene, plus per-Gaussian confidence tags. The local refinement module carries the update: an embedding layer lifts Gaussian vectors to queries, a depth-aware branch adds an MLP feature built from the predicted depth at the projected pixel and the Gaussian's camera-space z-coordinate, sparse 3D convolution lets nearby Gaussians interact, and deformable cross-attention integrates image features. A confidence refinement sets the update coefficient to zero for never-visited Gaussians and a fixed value for previously visited ones, so memory persistence is controlled; Gaussian-to-voxel splatting renders the occupancy.

What would settle it

Take a held-out scene, predict occupancy for the entire room volume, and compute IoU separately in voxels that no input frame observed. If accuracy in those unobserved voxels is at chance, the 'global' occupancy claim fails; if it is high, the Gaussian memory has genuinely extrapolated beyond its observations.

Watch

Extended reading notes

Core claim

EmbodiedOcc's central discovery is that a global 3D Gaussian memory can be updated locally and incrementally to yield coherent online occupancy prediction. The model initializes the entire room with uniform semantic Gaussians in world coordinates, each with mean, scale, rotation, opacity, semantics, and a visited tag. Given a posed frame, it reads out the Gaussians in the frustum, refines them with deformable cross-attention to image features, and uses a predicted depth map to supply per-Gaussian structural cues; updated Gaussians are written back with confidence weights that down-weight stale memory. Gaussian-to-voxel splatting converts the memory into global voxel occupancy at any time. The paper argues this matches human exploration: the representation is initially unknown and uniform, becomes structured where observed, and retains consistency across revisits, outperforming both single-view baselines and simple splicing of local predictions.

Load-bearing premise

The load-bearing premise is that the benchmark measures success only in the parts of the room that the camera actually saw; if the goal is the whole room, including unvisited corners, the paper does not show that its memory is correct there.

Editorial extensions

If this is right

  • If the claim holds, an agent with only a monocular camera can maintain a room-scale semantic occupancy map online, removing the need for depth sensors in indoor perception pipelines.
  • Re-observing an area should improve the map rather than degrade it; the paper's look-back tests support this, which matters for agents that revisit locations.
  • The stopping mechanism, based on the fraction of Gaussians with visited tags, gives a principled online criterion for deciding when a room is sufficiently explored.
  • The reported runtime breakdown identifies image and depth encoders as the bottleneck, so the Gaussian update itself is cheap enough for further scaling.
  • On the local single-frame task the same module also outperforms prior monocular occupancy methods, so the gains are not only from memory.

Reading between the lines

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

  • A test the paper does not run: score the full room volume rather than the explored frustum union; this would reveal whether unvisited Gaussians hold any meaning.
  • The confidence-weighted update is essentially a learned gated memory; one could train the confidence values per-Gaussian or couple the memory to a navigation policy that selects viewpoints to maximize expected information gain.
  • The same explicit Gaussian representation could feed downstream embodied tasks such as object search, frontier exploration, or collision-free navigation, since it is a geometry rather than a latent feature.
  • Pose noise is not modeled; testing with perturbed extrinsics would show how much of the claimed consistency depends on perfect localization.
Share X Bluesky LinkedIn Reddit HN

Signed reviews

No signed human review yet.

Editorial analysis

A structured set of objections, weighed in public.

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

Referee Report

3 major / 7 minor

Summary. The paper formulates an 'embodied 3D occupancy prediction' task as online, vision-only estimation of an indoor scene from a posed monocular RGB stream, and proposes EmbodiedOcc, which maintains a global set of 3D semantic Gaussians as an explicit memory. At each frame, Gaussians in the current frustum are read from memory, refined by a depth-aware local module using deformable cross-attention and sparse convolution, and written back with confidence-scaled updates; a Gaussian-to-voxel splatting head produces occupancy predictions. The authors construct the EmbodiedOcc-ScanNet benchmark from Occ-ScanNet, report local and embodied IoU/mIoU against several baselines, and provide ablations of the memory, the depth-aware branch, Gaussian parameters, and runtime.

Significance. If the results are taken at face value, the paper makes a useful contribution: it defines a practical task, releases a benchmark and code, and shows that a memory-based Gaussian representation can integrate repeated monocular observations better than simple splicing. The ablations isolating the memory (Table 4) and the depth-aware branch (Table 5) are informative, and the runtime decomposition (Table 7) identifies the main bottlenecks. However, the central quantitative claim of 'global occupancy prediction of the whole scene' is evaluated only on the union of camera frustums, not on the full room volume, which is a substantial gap between the claim and the evidence.

major comments (3)
  1. [Sec. 4.1; Sec. 3.1, Eq. (2)] The evaluation metric for embodied prediction is defined on the union of the 30 camera frustums, not on the full room volume: 'the global occupancy used here is the union of the frustums corresponding to 30 frames of each scene' (Sec. 4.1). Eq. (2), in contrast, defines Y_t as the occupancy prediction of the whole scene in Xroom×Yroom×Zroom, and the Abstract claims 'global 3D occupancy' of the scene. Because the model initializes Gaussians across the entire room (Sec. 3.3) but never receives a loss or evaluation signal in unobserved regions, the reported IoU/mIoU do not test whether the memory produces meaningful occupancy in unexplored areas. I request either an evaluation on the full room volume (with unobserved voxels treated as unknown or reported separately) or a revision of the claims to 'explored-region occupancy.'
  2. [Sec. 4.4, Table 3] The Look-Back experiment does not isolate the benefit of memory consistency. For K=3, First-Time processes frames [0,1,2], while Look-Back processes [0,1,2,1,0]; similarly, K=5 uses 5 versus 9 frames and K=8 uses 8 versus 15 frames. Look-Back therefore has strictly more observations of the same frustums, so the higher IoU/mIoU could come from additional refinement opportunities rather than from the memory mechanism. A controlled comparison should process the same number of frames (for example, a First-Time run on the concatenated sequence without repetition, or a no-memory baseline that ingests the same five frames) before concluding that 'EmbodiedOcc has met our expectations.'
  3. [Sec. 4.3, Table 2] The strongest baseline, SplicingOcc, is constructed from the authors' own local module and is not an existing method; the 'large margin' over existing methods claimed in the Abstract is therefore mostly against driving-scene methods adapted by voting. This is acceptable as an internal ablation, but the text should state clearly that SplicingOcc is not a prior method and that the comparison to prior work is not a like-for-like evaluation under a common training budget or architecture. Moreover, because the metric only scores the frustum union, the advantage of EmbodiedOcc over SplicingOcc may partly reflect better fusion in seen regions rather than a genuinely global representation; the full-room metric requested in Comment 1 is needed to separate these factors.
minor comments (7)
  1. [Sec. 3.4, Eq. (7)] The balance factor λ1 is introduced in the loss but its value is not reported in Section 4.2.
  2. [Sec. 4.4, Analysis of the Depth-Aware Branch] The text 'Indo-orDepth' should read 'IndoorDepth' (reference [6]).
  3. [Sec. 3.3, Eq. (5)] The operation ⊕ is defined as quaternion composition plus addition for the other components, but the notation is nonstandard; please define it at first use.
  4. [Sec. 4.2] The sentence 'the confidence value θ of well-updated Gaussians is set to 0 in the first two refinement layers (frozen) and 0.5 in the final refinement layer' is ambiguous: are the first two layers frozen only during training or also at inference?
  5. [Sec. 4.1, Tables 1 and 2] The input column 'xrgb' is not explained; presumably it indicates RGB-only input, but this should be stated.
  6. [Sec. 4.4, Figure 5] The x-axis label says 'stopping ratios' but the plotted values appear to be thresholds; please label the axes and state the ground-truth region used for the reported IoU and mIoU.
  7. [Sec. 3.4] The training procedure for EmbodiedOcc is described only in prose; a short pseudocode block stating how many updates are performed per scene, when the memory is re-initialized, and how gradients flow through the detached memory would improve reproducibility.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: supervised benchmark evaluation with standard train/val splits; the frustum-union metric is an evaluation-scope caveat, not a fitted prediction.

full rationale

The paper's central claim is empirical and self-contained: the local refinement module is trained on the Occ-ScanNet train split and evaluated on its validation split (Table 1), and the embodied framework is trained on EmbodiedOcc-ScanNet train scenes and evaluated on held-out validation scenes (Table 2). The Gaussian-to-voxel splatting is adopted from GaussianFormer [13] as an implementation component, not as a load-bearing justification of the main result; even though [13] has overlapping authors, the paper's contribution is the Gaussian memory, depth-aware update, and the new benchmark, and the reported gains are measured against external baselines and a spliced baseline. The confidence value θ and the stopping threshold are selected via ablations on the mini-validation set (Figure 6 and Figure 5), which is standard model selection rather than fitting the reported metric. The only notable gap is that the embodied evaluation region is defined as the union of the 30 frustums (Section 4.1), so the claim of whole-scene global prediction is not tested in unexplored room regions; however, this is an evaluation-scope limitation, not a circular derivation, because no equation defines the predicted output in terms of that metric and no parameter is fitted to the target IoU/mIoU values.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The paper introduces no physical or external entities. Its central claim rests on standard deep-learning assumptions, a set of hand-chosen Gaussian hyperparameters, and a domain assumption that the explored frustum union is an adequate target for 'global' occupancy. The confidence threshold θ is tuned on a mini validation split.

free parameters (4)
  • Confidence value θ for previously updated Gaussians = 0.5
    Chosen from ablation on EmbodiedOcc-ScanNet-mini (Figure 6); affects the memory update magnitude and the final embodied performance.
  • Gaussian interval for global scene initialization = 0.16 m
    Initialization spacing for the global Gaussian memory; sensitivity shown in Table 6 ('Gaussian Interval') and the paper notes 0.16m is used.
  • Number of local Gaussians = 16200
    Used for local frustum prediction; ablation in Table 6 shows performance degrades with 8100.
  • Gaussian scale range = min 0.01, max 0.08
    Table 6 reports these as the scale hyperparameters; variations affect IoU.
assumptions (5)
  • domain assumption The union of the 30 per-frame ground-truth frustums is a valid ground truth for the explored region and a sufficient training target for 'global' occupancy.
    Section 4.1 defines the global occupancy GT as the union of frustums; this excludes all unobserved areas from both training and evaluation.
  • domain assumption The agent's camera intrinsics and extrinsics are known exactly at every step.
    Section 3.1 includes Mt in the input and Section 3.3 uses coordinate transforms and frustum masks that assume perfect poses.
  • domain assumption The frozen DepthAnything-V2 model produces sufficiently accurate metric depth for indoor scenes.
    Section 3.2 and 4.2: the depth-aware branch relies on this; Table 5 shows embodied mIoU drops from 41.45 to 30.73 without it.
  • domain assumption A fixed set of uniformly initialized 3D Gaussians (with chosen count, interval, and scale) can represent the scene well enough for occupancy prediction.
    This is the core representation assumption, supported only by the narrow ablations in Table 6 and the voxel comparison in Table 4.
  • standard math Gaussian-to-voxel splatting and the sparse-convolution/deformable-attention feature integration behave as described in GaussianFormer [13].
    The paper directly reuses these modules and does not re-derive them (Sections 3.2 and 3.4).

how reviews work

0 comments
Cite this review

Pith. "Pith review of EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene Understanding." pith.science (2026). https://pith.science/paper/TGVPL6KE

@misc{pith2026241204380,
  author       = {Pith},
  title        = {Pith review of: EmbodiedOcc: Embodied 3D Occupancy Prediction for Vision-based Online Scene Understanding},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/TGVPL6KE}},
  note         = {Machine review of arXiv:2412.04380}
}
read the original abstract

3D occupancy prediction provides a comprehensive description of the surrounding scenes and has become an essential task for 3D perception. Most existing methods focus on offline perception from one or a few views and cannot be applied to embodied agents that demand to gradually perceive the scene through progressive embodied exploration. In this paper, we formulate an embodied 3D occupancy prediction task to target this practical scenario and propose a Gaussian-based EmbodiedOcc framework to accomplish it. We initialize the global scene with uniform 3D semantic Gaussians and progressively update local regions observed by the embodied agent. For each update, we extract semantic and structural features from the observed image and efficiently incorporate them via deformable cross-attention to refine the regional Gaussians. Finally, we employ Gaussian-to-voxel splatting to obtain the global 3D occupancy from the updated 3D Gaussians. Our EmbodiedOcc assumes an unknown (i.e., uniformly distributed) environment and maintains an explicit global memory of it with 3D Gaussians. It gradually gains knowledge through the local refinement of regional Gaussians, which is consistent with how humans understand new scenes through embodied exploration. We reorganize an EmbodiedOcc-ScanNet benchmark based on local annotations to facilitate the evaluation of the embodied 3D occupancy prediction task. Our EmbodiedOcc outperforms existing methods by a large margin and accomplishes the embodied occupancy prediction with high accuracy and efficiency. Code: https://github.com/YkiWu/EmbodiedOcc.

Figures

Figures reproduced from arXiv: 2412.04380 by the authors.

Figure 1
Figure 1. Given streaming monocular RGB inputs, our EmbodiedOcc conducts embodied occupancy prediction in an online manner [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Framework of our EmbodiedOcc for embodied 3D occupancy prediction. We maintain an explicit global memory of 3D Gaussians during the exploration of the current scene. For each update, the Gaussians within the current frustum are taken from the memory and updated using semantic and structural features extracted from the monocular RGB input. Each Gaussian has a confidence value to integrate information from both the me… view at source ↗
Figure 3
Figure 3. Motivation of the depth-aware branch. Along a spe￾cific ray, Gaussians distributed in front of the true depth point are likely to model the empty semantic (A). Gaussians distributed be￾hind the true depth point closely are likely to model valid seman￾tics (B). Gaussians that are distributed behind the true depth point but are too far away require more information to guide their up￾dates (C). During the embodied expl… view at source ↗
Figures from the paper (4 more)
Figure 4
Figure 4. Figure 4: Illustration of our Gaussian memory. During each update, the Gaussians within the current frustum are taken from the memory. Confidence values of those well-updated Gaussians are used to integrate information from both the memory and the current input. Then we put thes…
Figure 5
Figure 5. Figure 5: Performance with different stopping ratios. 50.78 41.45 41.05 40.80 50.15 mIoU 50.53 IoU 0.3 0.5 0.7 [PITH_FULL_IMAGE:figures/full_fig_p007_5.png]
Figure 7
Figure 7. Figure 7: Visualization of the embodied occupancy prediction. We visualize the update of Gaussian memory and corresponding global occupancy. As the Gaussians transition from random to ordered, the occupancy of the current scene becomes more accurate and complete. Image Ground Tr…
Figure 8
Figure 8. Figure 8: Visualization of local occupancy prediction. Occ-ScanNet-mini2 and the EmbodiedOcc-ScanNet-mini datasets. We find that depth information will significantly benefit the local and embodied occupancy prediction. As shown in the second row, without the assistance of depth …

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 6 Pith papers

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

  1. Occupancy World Model for Robots

    cs.CV 2025-05 conditional novelty 6.0 of 10

    RoboOccWorld predicts future 3D occupancy for indoor robots by conditioning an autoregressive transformer on the next camera pose, outperforming OccWorld on a restructured ScanNet benchmark.

  2. RoboOcc: Enhancing the Geometric and Semantic Scene Understanding for Robots

    cs.RO 2025-04 conditional novelty 6.0 of 10

    RoboOcc uses opacity-guided and geometry-aware encoding of 3D Gaussian representations to achieve state-of-the-art indoor 3D semantic occupancy prediction from monocular RGB.

  3. Doe-1: Closed-Loop Autonomous Driving with Large World Model

    cs.CV 2024-12 conditional novelty 6.0 of 10

    Doe-1 unifies perception, prediction, and planning in autonomous driving into a single autoregressive next-token generation model over image, text, and action tokens.

  4. Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots

    cs.RO 2025-07 conditional novelty 5.0 of 10

    A humanoid-specific multimodal occupancy perception system with a new dataset, sensor layout, and a fusion network that claims state-of-the-art results on its own benchmark.

  5. OmniIndoor3D: Comprehensive Indoor 3D Reconstruction

    cs.CV 2025-05 conditional novelty 5.0 of 10

    OmniIndoor3D jointly optimizes appearance, geometry, and panoptic labels in a single set of 3D Gaussians initialized from RGB-D camera depth, reporting state-of-the-art numbers on ScanNet and ScanNet++.

  6. SliceOcc: Indoor 3D Semantic Occupancy Prediction with Vertical Slice Representation

    cs.CV 2025-01 conditional novelty 5.0 of 10

    A camera-only indoor 3D semantic occupancy model using paired-horizontal-plane features achieves 15.45% mIoU on EmbodiedScan, best among evaluated RGB baselines.

Reference graph

Works this paper leans on

59 extracted references · 32 canonical work pages · cited by 6 Pith papers

  1. [1]

    Semantic scene com- pletion via integrating instances and scene in-the-loop

    Yingjie Cai, Xuesong Chen, Chao Zhang, Kwan-Yee Lin, Xiaogang Wang, and Hongsheng Li. Semantic scene com- pletion via integrating instances and scene in-the-loop. In CVPR, pages 324–333, 2021. 2

    work page 2021

  2. [2]

    Monoscene: Monoc- ular 3d semantic scene completion

    Anh-Quan Cao and Raoul de Charette. Monoscene: Monoc- ular 3d semantic scene completion. In CVPR, pages 3991– 4001, 2022. 2, 5, 6

    work page 2022

  3. [3]

    Scannet: Richly-annotated 3d reconstructions of indoor scenes

    Angela Dai, Angel X Chang, Manolis Savva, Maciej Hal- ber, Thomas Funkhouser, and Matthias Nießner. Scannet: Richly-annotated 3d reconstructions of indoor scenes. In CVPR, pages 5828–5839, 2017. 2

    2017

  4. [4]

    Scancomplete: Large- scale scene completion and semantic segmentation for 3d scans

    Angela Dai, Daniel Ritchie, Martin Bokeloh, Scott Reed, J¨urgen Sturm, and Matthias Nießner. Scancomplete: Large- scale scene completion and semantic segmentation for 3d scans. In CVPR, pages 4578–4587, 2018. 2

    work page 2018

  5. [5]

    Compact 3d gaussian splatting for dense visual slam

    Tianchen Deng, Yaohui Chen, Leyan Zhang, Jianfei Yang, Shenghai Yuan, Jiuming Liu, Danwei Wang, Hesheng Wang, and Weidong Chen. Compact 3d gaussian splatting for dense visual slam. arXiv preprint arXiv:2403.11247, 2024. 2

    arXiv 2024

  6. [6]

    Deeper into self-supervised monocular indoor depth estimation

    Chao Fan, Zhenyu Yin, Yue Li, and Feiqing Zhang. Deeper into self-supervised monocular indoor depth estimation. arXiv preprint arXiv:2312.01283, 2023. 8

    arXiv 2023

  7. [7]

    Gaussianflow: Splatting gaussian dynamics for 4d content creation

    Quankai Gao, Qiangeng Xu, Zhe Cao, Ben Mildenhall, Wen- chao Ma, Le Chen, Danhang Tang, and Ulrich Neumann. Gaussianflow: Splatting gaussian dynamics for 4d content creation. arXiv preprint arXiv:2403.12365, 2024. 2

    arXiv 2024

  8. [8]

    Two stream 3d semantic scene completion

    Martin Garbade, Yueh-Tung Chen, Johann Sawatzky, and Juergen Gall. Two stream 3d semantic scene completion. In CVPRW, pages 0–0, 2019. 2

    work page 2019

Show all 59 references
  1. [9]

    3d semantic segmentation with submanifold sparse convolutional networks

    Benjamin Graham, Martin Engelcke, and Laurens Van Der Maaten. 3d semantic segmentation with submanifold sparse convolutional networks. In CVPR, pages 9224–9232,

  2. [10]

    Gaussian frosting: Editable complex radiance fields with real-time rendering

    Antoine Gu ´edon and Vincent Lepetit. Gaussian frosting: Editable complex radiance fields with real-time rendering. arXiv preprint arXiv:2403.14554, 2024. 2

    2024 arXiv

  3. [11]

    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, pages 9223–9232,

  4. [12]

    Selfocc: Self-supervised vision-based 3d oc- cupancy prediction

    Yuanhui Huang, Wenzhao Zheng, Borui Zhang, Jie Zhou, and Jiwen Lu. Selfocc: Self-supervised vision-based 3d oc- cupancy prediction. In CVPR, pages 19946–19956, 2024

    2024

  5. [13]

    Gaussianformer: Scene as gaussians for vision-based 3d semantic occupancy prediction

    Yuanhui Huang, Wenzhao Zheng, Yunpeng Zhang, Jie Zhou, and Jiwen Lu. Gaussianformer: Scene as gaussians for vision-based 3d semantic occupancy prediction. In ECCV, pages 376–393, 2025. 2, 3, 4, 6, 7

    2025

  6. [14]

    Semantically-aware spatio-temporal rea- soning agent for vision-and-language navigation in continu- ous environments

    Muhammad Zubair Irshad, Niluthpol Chowdhury Mithun, Zachary Seymour, Han-Pang Chiu, Supun Samarasekera, and Rakesh Kumar. Semantically-aware spatio-temporal rea- soning agent for vision-and-language navigation in continu- ous environments. In ICPR, pages 4065–4071. IEEE, 2022. 1

    2022

  7. [15]

    3d gaussian splatting for real-time radiance field rendering

    Bernhard Kerbl, Georgios Kopanas, Thomas Leimk ¨uhler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. TOG, 42(4), 2023. 2

    2023

  8. [16]

    Unidet3d: Multi- dataset indoor 3d object detection

    Maksim Kolodiazhnyi, Anna V orontsova, Matvey Skripkin, Danila Rukhovich, and Anton Konushin. Unidet3d: Multi- dataset indoor 3d object detection. In AAAI, 2025. 2

    2025

  9. [17]

    Instance-aware exploration-verification- exploitation for instance imagegoal navigation

    Xiaohan Lei, Min Wang, Wengang Zhou, Li Li, and Houqiang Li. Instance-aware exploration-verification- exploitation for instance imagegoal navigation. In CVPR, pages 16329–16339, 2024. 1

    2024

  10. [18]

    Rgbd based dimensional decomposi- tion residual network for 3d semantic scene completion

    Jie Li, Yu Liu, Dong Gong, Qinfeng Shi, Xia Yuan, Chunxia Zhao, and Ian Reid. Rgbd based dimensional decomposi- tion residual network for 3d semantic scene completion. In CVPR, pages 7693–7702, 2019. 2

    2019

  11. [19]

    Depth based semantic scene completion with position importance aware loss

    Jie Li, Yu Liu, Xia Yuan, Chunxia Zhao, Roland Siegwart, Ian Reid, and Cesar Cadena. Depth based semantic scene completion with position importance aware loss. RAL, 5(1): 219–226, 2019. 2

    2019

  12. [20]

    Sgs-slam: Se- mantic gaussian splatting for neural dense slam

    Mingrui Li, Shuhong Liu, Heng Zhou, Guohao Zhu, Na Cheng, Tianchen Deng, and Hongyu Wang. Sgs-slam: Se- mantic gaussian splatting for neural dense slam. In ECCV, pages 163–179, 2025. 2

    2025

  13. [21]

    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. 2

    2023

  14. [22]

    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 preprint arXiv:2307.01492, 2023. 2

    2023 arXiv

  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 ICCV, pages 2980–2988, 2017. 5

    2017

  16. [24]

    Infusion: Inpainting 3d gaussians via learn- ing depth completion from diffusion prior

    Zhiheng Liu, Hao Ouyang, Qiuyu Wang, Ka Leong Cheng, Jie Xiao, Kai Zhu, Nan Xue, Yu Liu, Yujun Shen, and Yang Cao. Infusion: Inpainting 3d gaussians via learn- ing depth completion from diffusion prior. arXiv preprint arXiv:2404.11613, 2024. 2

    2024 arXiv

  17. [25]

    Sgdr: Stochas- tic gradient descent with warm restarts

    Ilya Loshchilov and Frank Hutter. Sgdr: Stochas- tic gradient descent with warm restarts. arXiv preprint arXiv:1608.03983, 2016. 6

    2016 arXiv

  18. [26]

    Decoupled weight decay regularization

    Ilya Loshchilov and Frank Hutter. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 6

    2017 arXiv

  19. [27]

    3d geometry-aware deformable gaussian splatting for dynamic view synthesis

    Zhicheng Lu, Xiang Guo, Le Hui, Tianrui Chen, Min Yang, Xiao Tang, Feng Zhu, and Yuchao Dai. 3d geometry-aware deformable gaussian splatting for dynamic view synthesis. In CVPR, pages 8900–8910, 2024. 2

    2024

  20. [28]

    Mmscan: A multi-modal 3d scene dataset with hierarchical grounded language annota- tions

    Ruiyuan Lyu, Tai Wang, Jingli Lin, Shuai Yang, Xiao- han Mao, Yilun Chen, Runsen Xu, Haifeng Huang, Chen- ming Zhu, Dahua Lin, et al. Mmscan: A multi-modal 3d scene dataset with hierarchical grounded language annota- tions. arXiv preprint arXiv:2406.09401, 2024. 2

    2024 arXiv

  21. [29]

    Gsedit: Efficient text-guided edit- ing of 3d objects via gaussian splatting

    Francesco Palandra, Andrea Sanchietti, Daniele Baieri, and Emanuele Rodol `a. Gsedit: Efficient text-guided edit- ing of 3d objects via gaussian splatting. arXiv preprint arXiv:2403.05154, 2024. 2

    2024 arXiv

  22. [30]

    Pointnet: Deep learning on point sets for 3d classification and segmentation

    Charles R Qi, Hao Su, Kaichun Mo, and Leonidas J Guibas. Pointnet: Deep learning on point sets for 3d classification and segmentation. In CVPR, pages 652–660, 2017. 1, 2

    2017

  23. [31]

    Deep hough voting for 3d object detection in point clouds

    Charles R Qi, Or Litany, Kaiming He, and Leonidas J Guibas. Deep hough voting for 3d object detection in point clouds. In ICCV, pages 9277–9286, 2019. 2

    2019

  24. [32]

    Mopa: Modular object nav- igation with pointgoal agents

    Sonia Raychaudhuri, Tommaso Campari, Unnat Jain, Mano- lis Savva, and Angel X Chang. Mopa: Modular object nav- igation with pointgoal agents. In WACV, pages 5763–5773,

  25. [33]

    Semantic scene completion using local deep implicit functions on lidar data

    Christoph B Rist, David Emmerichs, Markus Enzweiler, and Dariu M Gavrila. Semantic scene completion using local deep implicit functions on lidar data. TPAMI, 44(10):7205– 7218, 2021. 2

    2021

  26. [34]

    Fcaf3d: Fully convolutional anchor-free 3d object detection

    Danila Rukhovich, Anna V orontsova, and Anton Konushin. Fcaf3d: Fully convolutional anchor-free 3d object detection. In ECCV, pages 477–493, 2022. 1

    2022

  27. [35]

    Indoor segmentation and support inference from rgbd images

    Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus. Indoor segmentation and support inference from rgbd images. In ECCV, pages 746–760, 2012. 6

    2012

  28. [36]

    Contrastive gaussian clustering: Weakly supervised 3d scene segmentation

    Myrna C Silva, Mahtab Dahaghin, Matteo Toso, and Alessio Del Bue. Contrastive gaussian clustering: Weakly supervised 3d scene segmentation. arXiv preprint arXiv:2404.12784 ,

    arXiv

  29. [37]

    Semantic scene comple- tion from a single depth image

    Shuran Song, Fisher Yu, Andy Zeng, Angel X Chang, Mano- lis Savva, and Thomas Funkhouser. Semantic scene comple- tion from a single depth image. In CVPR, pages 1746–1754,

  30. [38]

    3dgstream: On-the-fly training of 3d gaussians for efficient streaming of photo-realistic free- viewpoint videos

    Jiakai Sun, Han Jiao, Guangyuan Li, Zhanjie Zhang, Lei Zhao, and Wei Xing. 3dgstream: On-the-fly training of 3d gaussians for efficient streaming of photo-realistic free- viewpoint videos. In CVPR, pages 20675–20685, 2024. 2

    2024

  31. [39]

    Efficientnet: Rethinking model scaling for convolutional neural networks

    Mingxing Tan and Quoc Le. Efficientnet: Rethinking model scaling for convolutional neural networks. In ICML, pages 6105–6114, 2019. 6

    2019

  32. [40]

    Scene as occupancy

    Wenwen Tong, Chonghao Sima, Tai Wang, Li Chen, Silei Wu, Hanming Deng, Yi Gu, Lewei Lu, Ping Luo, Dahua Lin, et al. Scene as occupancy. In ICCV, pages 8406–8415, 2023. 2

    2023

  33. [41]

    Softgroup for 3d instance segmentation on point clouds

    Thang Vu, Kookhoi Kim, Tung M Luu, Thanh Nguyen, and Chang D Yoo. Softgroup for 3d instance segmentation on point clouds. In CVPR, pages 2708–2717, 2022. 1, 2

    2022

  34. [42]

    Ca- group3d: Class-aware grouping for 3d object detection on point clouds

    Haiyang Wang, Lihe Ding, Shaocong Dong, Shaoshuai Shi, Aoxue Li, Jianan Li, Zhenguo Li, and Liwei Wang. Ca- group3d: Class-aware grouping for 3d object detection on point clouds. NeurIPS, 35:29975–29988, 2022. 1, 2

    2022

  35. [43]

    Occsora: 4d occupancy generation models as world simulators for au- tonomous driving

    Lening Wang, Wenzhao Zheng, Yilong Ren, Han Jiang, Zhiyong Cui, Haiyang Yu, and Jiwen Lu. Occsora: 4d occupancy generation models as world simulators for au- tonomous driving. arXiv preprint arXiv:2405.20337, 2024. 2

    2024 arXiv

  36. [44]

    Embodiedscan: A holistic multi-modal 3d perception suite towards embodied ai

    Tai Wang, Xiaohan Mao, Chenming Zhu, Runsen Xu, Ruiyuan Lyu, Peisen Li, Xiao Chen, Wenwei Zhang, Kai Chen, Tianfan Xue, Xihui Liu, Cewu Lu, Dahua Lin, and Jiangmiao Pang. Embodiedscan: A holistic multi-modal 3d perception suite towards embodied ai. In CVPR, 2024. 2

    2024

  37. [45]

    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. arXiv preprint arXiv:2303.03991, 2023. 2

    2023 arXiv

  38. [46]

    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, pages 21729–21740, 2023. 2, 6, 7

    2023

  39. [47]

    Scfusion: Real-time incremental scene recon- struction with semantic completion

    Shun-Cheng Wu, Keisuke Tateno, Nassir Navab, and Fed- erico Tombari. Scfusion: Real-time incremental scene recon- struction with semantic completion. In 3DV, pages 801–810. IEEE, 2020. 2

    2020

  40. [48]

    Bridging 3d gaussian and mesh for freeview video rendering

    Yuting Xiao, Xuan Wang, Jiafei Li, Hongrui Cai, Yanbo Fan, Nan Xue, Minghui Yang, Yujun Shen, and Shenghua Gao. Bridging 3d gaussian and mesh for freeview video rendering. arXiv preprint arXiv:2403.11453, 2024. 2

    2024 arXiv

  41. [49]

    Memory-based adapters for online 3d scene perception

    Xiuwei Xu, Chong Xia, Ziwei Wang, Linqing Zhao, Yueqi Duan, Jie Zhou, and Jiwen Lu. Memory-based adapters for online 3d scene perception. arXiv preprint arXiv:2403.06974, 2024. 2

    2024 arXiv

  42. [50]

    Gs-slam: Dense visual slam with 3d gaussian splatting

    Chi Yan, Delin Qu, Dan Xu, Bin Zhao, Zhigang Wang, Dong Wang, and Xuelong Li. Gs-slam: Dense visual slam with 3d gaussian splatting. In CVPR, pages 19595–19604, 2024. 2

    2024

  43. [51]

    Depth any- thing v2

    Lihe Yang, Bingyi Kang, Zilong Huang, Zhen Zhao, Xiao- gang Xu, Jiashi Feng, and Hengshuang Zhao. Depth any- thing v2. arXiv preprint arXiv:2406.09414, 2024. 6

    2024 arXiv

  44. [52]

    Driving in the occupancy world: Vision-centric 4d occupancy forecast- ing and planning via world models for autonomous driving

    Yu Yang, Jianbiao Mei, Yukai Ma, Siliang Du, Wenqing Chen, Yijie Qian, Yuxiang Feng, and Yong Liu. Driving in the occupancy world: Vision-centric 4d occupancy forecast- ing and planning via world models for autonomous driving. arXiv preprint arXiv:2408.14197, 2024. 2

    2024 arXiv

  45. [53]

    4d gaussian splatting: Model- ing dynamic scenes with native 4d primitives.arXiv preprint arXiv:2412.20720, 2024

    Zeyu Yang, Zijie Pan, Xiatian Zhu, Li Zhang, Yu-Gang Jiang, and Philip HS Torr. 4d gaussian splatting: Model- ing dynamic scenes with native 4d primitives.arXiv preprint arXiv:2412.20720, 2024. 2

    2024 arXiv

  46. [54]

    Ndc-scene: Boost monocular 3d semantic scene completion in normalized de- vice coordinates space

    Jiawei Yao, Chuming Li, Keqiang Sun, Yingjie Cai, Hao Li, Wanli Ouyang, and Hongsheng Li. Ndc-scene: Boost monocular 3d semantic scene completion in normalized de- vice coordinates space. In ICCV, pages 9455–9465, 2023. 2

    2023

  47. [55]

    Gspn: Generative shape proposal network for 3d instance segmentation in point cloud

    Li Yi, Wang Zhao, He Wang, Minhyuk Sung, and Leonidas J Guibas. Gspn: Generative shape proposal network for 3d instance segmentation in point cloud. In CVPR, pages 3947– 3956, 2019. 2

    2019

  48. [56]

    Monocular occupancy prediction for scalable indoor scenes

    Hongxiao Yu, Yuqi Wang, Yuntao Chen, and Zhaoxiang Zhang. Monocular occupancy prediction for scalable indoor scenes. arXiv preprint arXiv:2407.11730, 2024. 2, 5, 6, 7

    2024 arXiv

  49. [57]

    Gaussian-slam: Photo-realistic dense slam with gaus- sian splatting

    Vladimir Yugay, Yue Li, Theo Gevers, and Martin R Os- wald. Gaussian-slam: Photo-realistic dense slam with gaus- sian splatting. arXiv preprint arXiv:2312.10070, 2023. 2

    2023 arXiv

  50. [58]

    Occworld: Learning a 3d occupancy world model for autonomous driving

    Wenzhao Zheng, Weiliang Chen, Yuanhui Huang, Borui Zhang, Yueqi Duan, and Jiwen Lu. Occworld: Learning a 3d occupancy world model for autonomous driving. In ECCV,

  51. [59]

    Pointocc: Cylindrical tri-perspective view for point-based 3d semantic occupancy prediction

    Sicheng Zuo, Wenzhao Zheng, Yuanhui Huang, Jie Zhou, and Jiwen Lu. Pointocc: Cylindrical tri-perspective view for point-based 3d semantic occupancy prediction. arXiv preprint arXiv:2308.16896, 2023. 2

    2023 arXiv

Pith tools

Reviewed August 11, 2026 · model on record in the stance chip above.