Pith. sign in

REVIEW 4 major objections 5 minor 45 references

LOMA: Language-assisted Semantic Occupancy Network via Triplane Mamba

T0 review · 4 major / 5 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 Language priors from text embeddings improve monocular 3D occupancy prediction to state-of-the-art results on two driving benchmarks.

desk verdict LOMA is a sensible integration of VLM features and Mamba into occupancy prediction, but the 'language geometric prior' is untested and the SOTA claim is overstated. read the letter →

arxiv 2412.08388 v1 pith:F57FOHBH submitted 2024-12-11 cs.CV

Yubo Cui , Zhiheng Li , Jiaqiang Wang , Zheng Fang This is my paper · ORCID
classification cs.CV
keywords 3Dsemanticoccupancypredictionscenecompletionvision-languagemodelsstatespacetri-planeMambaautonomousdrivingKITTISSCBench-KITTI360
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 argues that adding language priors to a monocular 3D semantic occupancy network improves both geometric and semantic completion in large outdoor driving scenes. The proposed LOMA builds a voxel-wise language feature by classifying each image pixel with a vision-language model and sampling the corresponding class text embedding into the 3D voxel grid, on the idea that class names like "car" encode implicit size and shape knowledge. It then fuses this language feature with the image-derived 3D vision feature through a Tri-plane Fusion Mamba block, which projects both modalities onto three orthogonal 2D planes, applies a global state-space scan on each plane, and back-projects to 3D. On SemanticKITTI it reports the best mIoU, and on SSCBench-KITTI360 it reports the best occupancy IoU; ablations attribute a large share of the gain to the language branch.

What carries the argument

The load-bearing mechanism is the pair formed by the VL-aware Scene Generator (VSG) and the Tri-plane Fusion Mamba (TFM) block. VSG takes an image and a list of class names, extracts aligned image and text embeddings from a pre-trained vision-language model, forms a per-pixel category map via a softmax inner product, and samples the text embedding into the 3D voxel grid so every voxel receives a language vector tied to the semantic class at its projected pixel. TFM concatenates the 3D vision and language features, flattens them onto three orthogonal planes through linear layers, runs a shared Mamba scan — an input-dependent state-space model that gives a global receptive field at linear cost — on each plane, and then back-projects and sums to update both modalities. A multi-scale version (MS-TFM) uses sparse 3D convolutions to downsample, applies TFM at each scale, and uses dense deconvolutions to upsample, carrying features from non-empty voxels into empty voxels and between modalities without quadratic attention.

What would settle it

Replace the text-embedding table in the trained VSG with random vectors of the same dimension, or with embeddings of scrambled class names, while keeping the VL-vision pathway and all other weights fixed, then re-measure IoU and mIoU on SemanticKITTI val; if the gain over the no-language baseline persists substantially, the claimed implicit geometric prior from language is not the cause.

Watch

Extended reading notes

Core claim

The central claim is that frozen vision-language text embeddings, lifted to 3D voxels through a per-pixel category map, carry usable geometric priors for scene completion, not just semantic labels. Concretely, the VL-aware Scene Generator computes a per-pixel category map from the inner product between VLM image and text features, then samples the text feature at each voxel by nearest-neighbor projection, producing a 3D language feature that points to the semantic class visible at that voxel. The Tri-plane Fusion Mamba block performs the subsequent 3D-to-3D propagation: it concatenates vision and language features along the channel dimension, projects them onto XY, YZ, and ZX planes, runs a shared input-dependent state-space scan globally on each plane, back-projects to 3D, and sums the results. The paper reports 43.01 IoU and 15.10 mIoU on SemanticKITTI test and 46.35 IoU and 18.28 mIoU on SSCBench-KITTI360 test, and in validation ablations adding the VSG language branch to the TFM+MS backbone improves IoU by 1.16 and mIoU by 0.88.

Load-bearing premise

The load-bearing premise is that the text embedding of a class name contains usable 3D geometric knowledge — for example, that "car" encodes a rough volume of about 4 by 2 by 2 meters — so sampling that embedding into voxels improves occupancy prediction; if the text embedding only encodes semantic identity, the reported gains could come from the added VLM vision pathway or extra parameters.

Editorial extensions

If this is right

  • If the language-prior claim holds, frozen text embeddings can serve as a cheap geometric prior for 3D occupancy, so changing the list of class-name prompts requires no retraining of the vision-language model.
  • Because the fusion is done on three 2D planes, the approach scales to large outdoor grids of shape 256 by 256 by 32 with global interaction at linear cost rather than quadratic attention.
  • On SemanticKITTI test, LOMA reports 43.01 IoU and 15.10 mIoU, giving the best semantic completion among the compared methods while also raising occupancy IoU by 0.82 over the previous best.
  • On SSCBench-KITTI360, LOMA reports 46.35 IoU and 18.28 mIoU, a 2.23-point occupancy IoU gain over the prior best, which the paper reads as evidence that language contributes geometric knowledge.
  • Ablations show both VSG sub-features help: using only the VL-language branch improves IoU by 0.17 and mIoU by 0.20 over using only the VL-vision branch, supporting the paper's claim that text, not just the VLM's image features, carries the signal.

Reading between the lines

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

  • One testable extension is to vary the text source: substituting random embeddings, scrambled class names, or class names from another language would isolate whether the gain comes from geometric meaning encoded in the class labels or merely from a learned per-class codebook.
  • The same VSG-style text sampling could be lifted into other 3D perception tasks such as monocular detection, panoptic mapping, or LiDAR-camera fusion, wherever a per-pixel category map is available; the paper only demonstrates occupancy completion.
  • The tri-plane Mamba fusion is modality-agnostic, so it could fuse any pair of 3D features, such as camera and LiDAR or two temporal frames, with the same linear-cost global scan; the paper only fuses vision and language.
  • The per-class results hint that language priors help most for geometrically stable classes like cars, buildings, and roads and least for rare or shape-variable classes; that is an inference from the class tables, not a claim the paper makes.
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

4 major / 5 minor

Summary. The paper proposes LOMA, a vision-language framework for monocular 3D semantic occupancy prediction. It introduces a VL-aware Scene Generator (VSG) that extracts pixel-level semantic labels and language features from a pretrained vision-language model, then broadcasts class text embeddings into 3D space (Eq. 3). A Tri-plane Fusion Mamba (TFM) block fuses the 3D language and vision features through projection onto three orthogonal planes and global SSM-based interaction, extended to multiple scales. Experiments on SemanticKITTI and SSCBench-KITTI360 report state-of-the-art or competitive IoU/mIoU, with ablations attributing gains to the language branch and the TFM module.

Significance. If the central claim holds, the paper introduces a genuinely novel use of language priors for 3D occupancy completion and an efficient SSM-based fusion alternative to attention. The ablations (Tables 3-5) provide some support that the proposed modules improve performance, and the efficiency analysis (FLOPs) is a useful contribution. However, the core mechanistic claim—that language features inject implicit 3D geometric knowledge—is not realized by the actual implementation, and the experimental comparisons have unresolved fairness issues. The absence of code, error bars, and a direct test of the language-prior mechanism limits the strength of the conclusions.

major comments (4)
  1. [Section 3.2, Eq. (3)] The central claim that language features provide 'implicit geometric information' (Section 1) is not supported by the implemented mechanism. In Eq. (3), the VL-language feature g_l = f_L(s(M, π(x_w))) is a fixed class-level text embedding indexed by the per-pixel argmax category map M from Eq. (1). Every voxel belonging to the same semantic class receives the identical C-dimensional vector, with no spatial extent, size, or 3D shape information. The only 3D structure entering this feature comes from the camera projection and the 2D semantic map, not from any geometric content in the text embedding. Therefore, the IoU gains attributed to language in Table 4 (e.g., 43.07→43.92 for VL-Language) are consistent with the model using class conditioning and additional parameters; the paper does not provide evidence that language encodes shape priors. Please include a control experiment that varies the information content of the text embedding (e.g., random embeddings, class hashes, or ablated class names) to directly test the geometric-prior hypothesis.
  2. [Table 1] The comparison in Table 1 mixes methods with different input modalities without annotation. Several entries (LMSCNet, AICNet, JS3C-Net) are traditionally evaluated with LiDAR or depth input, while LOMA uses monocular RGB plus a pretrained stereo-depth network, and other methods (MonoScene, TPVFormer, VoxFormer) are camera-based. Without clear markers for input type or a separated comparison, the reported 'state-of-the-art' claim is not an apples-to-apples comparison. Please annotate the input modality for every method or split the table into camera-based and LiDAR-based groups, and specify which methods use ground-truth depth versus estimated depth.
  3. [Tables 3 and 4] The ablation results are presented without error bars or multiple-seed statistics, and several gains are small relative to likely run-to-run variance. For example, Table 3 shows that adding TFM to the baseline changes mIoU from 13.36 to 13.44 (+0.08), and Table 4 reports an IoU gain of 0.85 when adding VL-Language; these numbers could fall within noise. Furthermore, the ablations remove entire modules rather than varying the information content of the language feature, so they do not isolate the 'language prior' effect from the added parameters or the extra visual pathway. Please provide mean±std over at least three seeds and an ablation that controls for capacity (e.g., replacing the text embedding with a learned class embedding of equal dimensionality).
  4. [Section 4.2, Tables 1-2] The paper claims 'new state-of-the-art performances in both geometric and semantic completion tasks' but the results do not uniformly support this. On SemanticKITTI (Table 1), LOMA's mIoU (15.10) is lower than HASSC (13.34? actually HASSC mIoU is 13.34, so LOMA is higher? Let me re-read: the table shows HASSC mIoU 13.34, LOMA 15.10; LOMA is higher, but IoU is 43.01 vs HASSC 43.40, so LOMA is second in IoU. The paper honestly states 'best mIoU and second IoU' for SemanticKITTI. On SSCBench-KITTI360 (Table 2), LOMA improves IoU over Symphonies by 2.23 but mIoU is 18.28 versus 18.58 for Symphonies, i.e., a decrease. The text acknowledges this only implicitly ('We also achieve 18.28 mIoU'). The claim of overall SOTA in both tasks should be qualified, and the mIoU regression on KITTI360 should be discussed.
minor comments (5)
  1. [Figure 1] The caption contains garbled symbols '99K and 99K' where arrows or flow indicators are intended; please fix the notation.
  2. [Section 3.1] There is a typo in the sentence 'align the feature channel of FI and fL to the the above 3D feature G'—duplicate 'the'.
  3. [Section 3.1] The temperature parameter t in Eq. (1) is described only as 'pre-defined'; please state its value or specify whether it is learned.
  4. [References] The reference list contains duplicate entries for Li et al. 2020a/2020b (same paper) and omits a few recent works discussed in the text, such as the full author list for some CVPR/ICCV papers; please clean up the bibliography.
  5. [Table 2] The table reports Precision and Recall for occupancy, but the main text does not define these metrics; please clarify the definitions and how they are computed.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: LOMA's gains are empirical network improvements; the language-feature attribution concern is an empirical validity issue, not a definitional reduction.

full rationale

The paper does not derive occupancy predictions from fitted parameters or self-citations. Its central contribution is an end-to-end learned network, and the reported IoU/mIoU gains in Tables 1-4 are benchmark measurements rather than consequences of an equation that equals its input. The VSG module (Eqs. 1-3) builds voxel-level language features by sampling a fixed class-text embedding using a VLM-derived pixel category map; this is a feature-construction step, not a derivation of the output. The class text embeddings are pre-trained and not fitted to the occupancy labels, and the no-language ablations in Tables 3-4 provide independent empirical evidence for the contribution of the added pathway. One might question whether the mechanism realizes 'implicit geometric knowledge' from text, since Eq. 3 broadcasts class-level embeddings without explicit shape priors; however, that concern is about whether the stated mechanism explains the observed gain, not about circularity in the derivation. There are no load-bearing self-citations or uniqueness theorems imported from the authors. Therefore no circularity is identified.

Assumptions & free parameters 2 free parameters · 3 assumptions · 0 invented entities

The central claim is an empirical performance claim, so the ledger captures the domain assumptions behind the language-prior idea: the VLM transfer, the depth reliability, and the projection validity. No invented entities and no ad hoc constants appear.

free parameters (2)
  • Temperature t in softmax (Eq. 1) = not specified
    Hyperparameter controlling sharpness of pixel-text alignment; chosen by hand and not reported.
  • Learnable 3D feature grid G = trained end-to-end
    Initial 3D feature volume used for 2D-to-3D lifting; its initialization is not described, and the final values are fitted to the training data.
assumptions (3)
  • domain assumption Pre-trained VLM (LSeg) text features encode implicit geometric priors that transfer to 3D occupancy prediction
    Core motivation (Sec. 1, 3.2); supported only indirectly by ablation.
  • domain assumption Camera intrinsics/extrinsics and depth from MobileStereoNet are accurate enough for correct 2D-to-3D lifting
    Used throughout the lifting (Eq. 2-3); standard for monocular SSC but an external dependency.
  • domain assumption Tri-plane projection and summation is approximately lossless for the features
    Eqs. 9-15; the paper asserts 'without losing information' but provides no proof.

how reviews work

0 comments
Cite this review

Pith. "Pith review of LOMA: Language-assisted Semantic Occupancy Network via Triplane Mamba." pith.science (2026). https://pith.science/paper/F57FOHBH

@misc{pith2026241208388,
  author       = {Pith},
  title        = {Pith review of: LOMA: Language-assisted Semantic Occupancy Network via Triplane Mamba},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/F57FOHBH}},
  note         = {Machine review of arXiv:2412.08388}
}
read the original abstract

Vision-based 3D occupancy prediction has become a popular research task due to its versatility and affordability. Nowadays, conventional methods usually project the image-based vision features to 3D space and learn the geometric information through the attention mechanism, enabling the 3D semantic occupancy prediction. However, these works usually face two main challenges: 1) Limited geometric information. Due to the lack of geometric information in the image itself, it is challenging to directly predict 3D space information, especially in large-scale outdoor scenes. 2) Local restricted interaction. Due to the quadratic complexity of the attention mechanism, they often use modified local attention to fuse features, resulting in a restricted fusion. To address these problems, in this paper, we propose a language-assisted 3D semantic occupancy prediction network, named LOMA. In the proposed vision-language framework, we first introduce a VL-aware Scene Generator (VSG) module to generate the 3D language feature of the scene. By leveraging the vision-language model, this module provides implicit geometric knowledge and explicit semantic information from the language. Furthermore, we present a Tri-plane Fusion Mamba (TFM) block to efficiently fuse the 3D language feature and 3D vision feature. The proposed module not only fuses the two features with global modeling but also avoids too much computation costs. Experiments on the SemanticKITTI and SSCBench-KITTI360 datasets show that our algorithm achieves new state-of-the-art performances in both geometric and semantic completion tasks. Our code will be open soon.

Figures

Figures reproduced from arXiv: 2412.08388 by the authors.

Figure 1
Figure 1. (a) Previous vision-only framework. (b) Our pro [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Architecture of the proposed LOMA. We input the image and categories text as inputs. The image encoder extracts [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. (a) Architecture of the proposed TFM module. We concatenate the two different modality features along the feature [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: Illustration of the proposed MS-TFM layer. [PITH_FULL_IMAGE:figures/full_fig_p005_4.png]
Figure 5
Figure 5. Figure 5: Qualitative visualizations on SemanticKITTI val. Our proposed LOMA generates more refined predictions for objects and also preserves organized designs for structures. Method IoU mIoU Param FLOPs Conv 40.98 10.89 17.8M 34.0G Deformable 43.70 14.51 17.5M 26.8G Swin2D 43.…

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

45 extracted references · 26 canonical work pages

  1. [1]

    Behley, J.; Garbade, M.; Milioto, A.; Quenzel, J.; Behnke, S.; Stachniss, C.; and Gall, J. 2019. Semantickitti: A dataset for semantic scene understanding of lidar sequences. In Proceedings of the IEEE/CVF international conference on computer vision, 9297--9307

    work page 2019

  2. [2]

    Cao, A.-Q.; and De Charette, R. 2022. Monoscene: Monocular 3d semantic scene completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3991--4001

    work page 2022

  3. [3]

    Chen, X.; Lin, K.-Y.; Qian, C.; Zeng, G.; and Li, H. 2020. 3d sketch-aware semantic scene completion via semi-supervised structure prior. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 4193--4202

    work page 2020

  4. [4]

    Cheng, R.; Agia, C.; Ren, Y.; Li, X.; and Bingbing, L. 2021. S3cnet: A sparse semantic scene completion network for lidar point clouds. In Conference on Robot Learning, 2148--2161. PMLR

    work page 2021

  5. [5]

    Ding, Z.; Wang, J.; and Tu, Z. 2023. Open-Vocabulary Panoptic Segmentation Mask CLIP

    work page 2023

  6. [6]

    Y.; Dao, T.; Saab, K

    Fu, D. Y.; Dao, T.; Saab, K. K.; Thomas, A. W.; Rudra, A.; and R \'e , C. 2022. Hungry hungry hippos: Towards language modeling with state space models. arXiv preprint arXiv:2212.14052

    arXiv 2022

  7. [7]

    Geiger, A.; Lenz, P.; and Urtasun, R. 2012. Are we ready for autonomous driving? the kitti vision benchmark suite. In 2012 IEEE conference on computer vision and pattern recognition, 3354--3361. IEEE

    2012

  8. [8]

    Ghiasi, G.; Gu, X.; Cui, Y.; and Lin, T.-Y. 2022. Scaling open-vocabulary image segmentation with image-level labels. In European Conference on Computer Vision, 540--557. Springer

    2022

Show all 45 references
  1. [9]

    Gu, A.; and Dao, T. 2023. Mamba: Linear-time sequence modeling with selective state spaces. arXiv preprint arXiv:2312.00752

    2023 arXiv

  2. [10]

    Gu, A.; Goel, K.; and R \'e , C. 2021. Efficiently modeling long sequences with structured state spaces. arXiv preprint arXiv:2111.00396

    2021 arXiv

  3. [11]

    Gu, A.; Johnson, I.; Goel, K.; Saab, K.; Dao, T.; Rudra, A.; and R \'e , C. 2021. Combining recurrent, convolutional, and continuous-time models with linear state space layers. Advances in neural information processing systems, 34: 572--585

    2021

  4. [12]

    Gupta, A.; Gu, A.; and Berant, J. 2022. Diagonal state spaces are as effective as structured state spaces. Advances in Neural Information Processing Systems, 35: 22982--22994

    2022

  5. [13]

    He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, 770--778

    2016

  6. [14]

    Huang, T.; Pei, X.; You, S.; Wang, F.; Qian, C.; and Xu, C. 2024. LocalMamba: Visual State Space Model with Windowed Selective Scan

    2024

  7. [15]

    Huang, Y.; Zheng, W.; Zhang, Y.; Zhou, J.; and Lu, J. 2023. Tri-perspective view for vision-based 3d semantic occupancy prediction. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 9223--9232

    2023

  8. [16]

    Jiang, H.; Cheng, T.; Gao, N.; Zhang, H.; Lin, T.; Liu, W.; and Wang, X. 2024. Symphonize 3d semantic scene completion with contextual instance queries. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 20258--20267

    2024

  9. [17]

    Q.; Belongie, S.; Koltun, V.; and Ranftl, R

    Li, B.; Weinberger, K. Q.; Belongie, S.; Koltun, V.; and Ranftl, R. 2022. Language-driven Semantic Segmentation. In International Conference on Learning Representations

    2022

  10. [18]

    Li, J.; Han, K.; Wang, P.; Liu, Y.; and Yuan, X. 2020 a . Anisotropic convolutional networks for 3d semantic scene completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3351--3359

    2020

  11. [19]

    Li, J.; Han, K.; Wang, P.; Liu, Y.; and Yuan, X. 2020 b . Anisotropic convolutional networks for 3d semantic scene completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 3351--3359

    2020

  12. [20]

    Li, J.; Liu, Y.; Gong, D.; Shi, Q.; Yuan, X.; Zhao, C.; and Reid, I. 2019. Rgbd based dimensional decomposition residual network for 3d semantic scene completion. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 7693--7702

    2019

  13. [21]

    Li, Y.; Li, S.; Liu, X.; Gong, M.; Li, K.; Chen, N.; Wang, Z.; Li, Z.; Jiang, T.; Yu, F.; Wang, Y.; Zhao, H.; Yu, Z.; and Feng, C. 2023 a . SSCBench: Monocular 3D Semantic Scene Completion Benchmark in Street Views. arXiv:2306.09001

    2023 arXiv

  14. [22]

    M.; Fidler, S.; Feng, C.; and Anandkumar, A

    Li, Y.; Yu, Z.; Choy, C.; Xiao, C.; Alvarez, J. M.; Fidler, S.; Feng, C.; and Anandkumar, A. 2023 b . Voxformer: Sparse voxel transformer for camera-based 3d semantic scene completion. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 9087--9098

    2023

  15. [23]

    Liu, Y.; Tian, Y.; Zhao, Y.; Yu, H.; Xie, L.; Wang, Y.; Ye, Q.; and Liu, Y. 2024. VMamba: Visual State Space Model

    2024

  16. [24]

    Mei, J.; Yang, Y.; Wang, M.; Huang, T.; Yang, X.; and Liu, Y. 2023. SSC-RS: Elevate LiDAR semantic scene completion with representation separation and BEV fusion. In 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 1--8. IEEE

    2023

  17. [25]

    W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al

    Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021. Learning transferable visual models from natural language supervision. In International conference on machine learning, 8748--8763

    2021

  18. [26]

    Roldao, L.; de Charette, R.; and Verroust-Blondet, A. 2020. Lmscnet: Lightweight multiscale 3d semantic completion. In 2020 International Conference on 3D Vision (3DV), 111--119. IEEE

    2020

  19. [27]

    Shamsafar, F.; Woerz, S.; Rahim, R.; and Zell, A. 2022. Mobilestereonet: Towards lightweight deep networks for stereo matching. In Proceedings of the ieee/cvf winter conference on applications of computer vision, 2417--2426

    2022

  20. [28]

    Shi, Y.; Dong, M.; and Xu, C. 2024. Multi-Scale VMamba: Hierarchy in Hierarchy Visual State Space Model

    2024

  21. [29]

    T.; Warrington, A.; and Linderman, S

    Smith, J. T.; Warrington, A.; and Linderman, S. W. 2022. Simplified state space layers for sequence modeling. arXiv preprint arXiv:2208.04933

    2022 arXiv

  22. [30]

    X.; Savva, M.; and Funkhouser, T

    Song, S.; Yu, F.; Zeng, A.; Chang, A. X.; Savva, M.; and Funkhouser, T. 2017. Semantic scene completion from a single depth image. In Proceedings of the IEEE conference on computer vision and pattern recognition, 1746--1754

    2017

  23. [31]

    N.; Kaiser, L

    Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A. N.; Kaiser, L. u.; and Polosukhin, I. 2017. Attention is All you Need. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc

    2017

  24. [32]

    Wang, S.; Yu, J.; Li, W.; Liu, W.; Liu, X.; Chen, J.; and Zhu, J. 2024. Not All Voxels Are Equal: Hardness-Aware Semantic Scene Completion with Self-Distillation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14792--14801

    2024

  25. [33]

    Wei, Y.; Zhao, L.; Zheng, W.; Zhu, Z.; Zhou, J.; and Lu, J. 2023. Surroundocc: Multi-camera 3d occupancy prediction for autonomous driving. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 21729--21740

    2023

  26. [34]

    Yan, X.; Gao, J.; Li, J.; Zhang, R.; Li, Z.; Huang, R.; and Cui, S. 2021. Sparse single sweep lidar point cloud segmentation via learning contextual shape priors from scene completion. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 35, 3101--3109

    2021

  27. [35]

    Yang, X.; Zou, H.; Kong, X.; Huang, T.; Liu, Y.; Li, W.; Wen, F.; and Zhang, H. 2021. Semantic segmentation-assisted scene completion for lidar point clouds. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 3555--3562. IEEE

    2021

  28. [36]

    Yao, J.; Li, C.; Sun, K.; Cai, Y.; Li, H.; Ouyang, W.; and Li, H. 2023. Ndc-scene: Boost monocular 3d semantic scene completion in normalized device coordinates space. In 2023 IEEE/CVF International Conference on Computer Vision (ICCV), 9421--9431. IEEE Computer Society

    2023

  29. [37]

    Zhang, Y.; Zhu, Z.; and Du, D. 2023. Occformer: Dual-path transformer for vision-based 3d semantic occupancy prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, 9433--9443

    2023

  30. [38]

    Zheng, Y.; Li, X.; Li, P.; Zheng, Y.; Jin, B.; Zhong, C.; Long, X.; Zhao, H.; and Zhang, Q. 2024. Monoocc: Digging into monocular semantic occupancy prediction. arXiv preprint arXiv:2403.08766

    2024 arXiv

  31. [39]

    C.; and Liu, Z

    Zhou, K.; Yang, J.; Loy, C. C.; and Liu, Z. 2022 a . Conditional prompt learning for vision-language models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, 16816--16825

    2022

  32. [40]

    C.; and Liu, Z

    Zhou, K.; Yang, J.; Loy, C. C.; and Liu, Z. 2022 b . Learning to prompt for vision-language models. International Journal of Computer Vision, 130(9): 2337--2348

    2022

  33. [41]

    Zhu, L.; Liao, B.; Zhang, Q.; Wang, X.; Liu, W.; and Wang, X. 2024. Vision mamba: Efficient visual representation learning with bidirectional state space model. arXiv preprint arXiv:2401.09417

    2024 arXiv

  34. [42]

    Zhu, X.; Su, W.; Lu, L.; Li, B.; Wang, X.; and Dai, J. 2021. Deformable DETR: Deformable Transformers for End-to-End Object Detection. In ICLR

    2021

  35. [43]

    Zou, H.; Yang, X.; Huang, T.; Zhang, C.; Liu, Y.; Li, W.; Wen, F.; and Zhang, H. 2021. Up-to-down network: Fusing multi-scale context for 3d semantic scene completion. In 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 16--23. IEEE

    2021

  36. [44]

    , " * write output.state after.block = add.period write newline

    ENTRY address archivePrefix author booktitle chapter edition editor eid eprint howpublished institution isbn journal key month note number organization pages publisher school series title type volume year label extra.label sort.label short.list INTEGERS output.state before.all...

  37. [45]

    write newline

    " write newline "" before.all 'output.state := FUNCTION n.dashify 't := "" t empty not t #1 #1 substring "-" = t #1 #2 substring "--" = not "--" * t #2 global.max substring 't := t #1 #1 substring "-" = "-" * t #2 global.max substring 't := while if t #1 #1 substring * t #2 gl...

Pith tools

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