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REVIEW 3 major objections 5 minor 41 references

SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation

T0 review · 3 major / 5 minor · reviewed 2026-08-06 · deepseek-v4-flash

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Pith's one-line read The paper claims that a frozen 3D vision-language model can detect real out-of-distribution point clouds by smoothing text-similarity scores over latent-space neighborhoods, without any training, and that this closes most of the gap…

desk verdict SODA delivers a credible training-free OOD improvement for synthetic-to-real point clouds, but the SOTA claim and the clustering-support evidence both need tightening before I'd sign off. read the letter →

arxiv 2506.21892 v1 pith:SINHCFQK submitted 2025-06-27 cs.CV cs.AI

Adam Goodge , Xun Xu , Bryan Hooi , Wee Siong Ng , Jingyi Liao , Yongyi Su , Xulei Yang This is my paper · ORCID
classification cs.CVcs.AI
keywords pointcloudOODdetection3Dvision-languagemodelssynthetic-to-realdomainshiftneighborhoodpropagationsourcesimilarityzero-shottransductiveinference
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 claims that synthetic-to-real domain shift, not semantic novelty, is the main obstacle to detecting out-of-distribution (OOD) real point clouds with a frozen 3D vision-language model, and that this obstacle can be overcome at inference time without any training. On ScanObjectNN, the proposed SODA method reaches 90.5 average AUC and 40.4 FPR95, against 82.1 and 71.0 for the best pretrained baseline. If correct, this means a pre-trained 3D VLM plus a neighborhood-aware scoring scheme can serve as a practical, label-efficient OOD detector for robotics and autonomous driving. The method also improves OOD scores consistently on corrupted point clouds and across different backbone models.

What carries the argument

The load-bearing mechanism is score propagation on a cosine-similarity graph combined with source-similarity reweighting. Test embeddings are nodes, edges exist when cosine similarity exceeds $\varepsilon = \text{percentile}(S, 100(1-\eta))$, and each sample's score is updated as $s^{(t)}(x_i) = \alpha s^{(0)}(x_i) + \frac{1-\alpha}{|\mathcal{N}_i|}\sum_{j\in\mathcal{N}_i} s^{(t-1)}(x_j)$, then multiplied by an iteratively propagated source similarity $d_{\mathrm{src}}^{(t)}(x_i)$. This smooths noisy text-based scores over class-consistent neighborhoods while giving more weight to samples that lie close to the synthetic source domain.

What would settle it

Compute, on ScanObjectNN with ground-truth labels, the fraction of each test sample's k nearest neighbors in ULIP-2 embedding space that share its ID/OOD status; if ID samples systematically have many OOD neighbors, or OOD samples have many ID neighbors, the propagation in Eq. 4 would smooth scores in the wrong direction and the reported AUC gains would shrink or reverse.

Watch

Extended reading notes

Core claim

The central discovery is that although real point clouds are poorly aligned with their class text embeddings in the ULIP-2 latent space, with classification accuracy dropping from 94.3% on synthetic ModelNet40 to 73.8% on real ScanObjectNN, real samples still cluster strongly by class. The paper turns this clustering into a detection rule: initialize each test sample's OOD score from text similarity, reweight it by closeness to synthetic source-domain reference samples, and then iteratively smooth scores across neighbors within an epsilon-similarity graph. The resulting score separates ID from OOD far better than text similarity alone, and the same propagation step also improves other OOD scores such as MSP when applied to them.

Load-bearing premise

The load-bearing premise is that real-world test point clouds form class-consistent clusters in the frozen ULIP-2 latent space, so neighbors of an ID sample are mostly ID and neighbors of an OOD sample are mostly OOD; the paper supports this only with a UMAP visualization and no quantitative neighborhood purity measure.

Editorial extensions

If this is right

  • A 3D VLM pre-trained on synthetic objects can detect real OOD objects without fine-tuning, removing the need for labeled ID examples or task-specific adaptation.
  • The neighborhood propagation component transfers across scoring functions: applying it to MSP, cosine-prototype, and source-similarity scores improves average AUC by 3.5 points and FPR95 by 14.7 points on ScanObjectNN.
  • SODA is robust across corruption types in ModelNet-C, improving AUC on most corruption types and across three different 3D VLM backbones, with the largest gains on the strongest backbone.
  • Because the method is inference-based, it can be deployed with any frozen point-cloud encoder, and its runtime cost after feature extraction is small, with graph construction and propagation taking under a second.
  • The full SODA variant also requires a set of source-domain reference samples, but the zero-shot variant ZS-SODA still beats pretrained baselines without such reference data.

Reading between the lines

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

  • The method is transductive: it needs the whole test batch to build the similarity graph, so single-sample real-time deployment would require a buffering or streaming strategy that the paper does not address.
  • If the latent-space clustering assumption holds more broadly, the same source-similarity-weighted propagation could be applied to OOD detection in other modality-shifted settings, such as lidar data from a different sensor or images under heavy corruption.
  • The dependency on synthetic reference samples means the full SODA version is only available when such reference data exists; the zero-shot variant avoids this but shows a smaller gain, suggesting source-similarity weighting is a major contributor.
  • A natural testable extension is to replace the top-10 source-similarity estimate with a learned or calibrated reliability measure, which could reduce sensitivity to the choice of reference set.
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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 / 5 minor

Summary. The paper proposes SODA, a training-free method for out-of-distribution (OOD) detection on real point clouds under synthetic-to-real domain shift. The method uses a frozen 3D vision-language model (ULIP-2) to compute initial per-sample scores from text-prototype similarity, optionally reweights these scores by a source-similarity measure computed against reference synthetic samples, and then refines both scores through a transductive neighborhood propagation on an epsilon-similarity graph. Experiments on ScanObjectNN and ModelNet-C show consistent gains over several baselines and across two backbones, with ablations examining the number of propagation iterations, the anchor weight alpha, the graph density percentile eta, and different backbone models.

Significance. If the reported results hold, SODA is a practically valuable contribution: it improves OOD detection on real point-cloud data without any fine-tuning or model modification, which is attractive for deployment. The paper provides code, uses standard benchmarks, reports multiple metrics, and includes ablations over hyperparameters and backbones. The source-similarity reweighting is a sensible, data-driven way to cope with synthetic-to-real shift. However, the paper's central claim of state-of-the-art performance is broader than the evidence supports, and the load-bearing mechanism of neighborhood propagation rests on an assumption that is currently supported only by a visualization. These issues need to be addressed before the contribution is fully convincing.

major comments (3)
  1. [Abstract / §5.3 / Supplementary Table 7] The abstract and conclusion state that SODA 'achieves state-of-the-art performance over existing approaches across datasets and problem settings.' This is contradicted by the paper's own Real benchmark results in Supplementary Table 7, where SODA obtains an average AUC of 81.6 and FPR95 of 74.5, while Cosine Proto obtains 83.6/64.9 and ReAct obtains 82.7/67.4. Those baselines are trained on real ID samples, so the scope of the claim should be narrowed to the source-free/domain-shifted setting, or the discrepancy should be explicitly discussed in the main text.
  2. [§3, Observation 2 and Eq. (4)] The propagation step in Eq. (4) is the core innovation, and Table 3 shows that removing it reduces SODA's average AUC from 90.5 to 86.7. The premise that real-domain neighborhoods are ID/OOD-pure is supported only by the UMAP visualization in Figure 3 and a qualitative statement. UMAP projections can create visually separated clusters even when no clean high-dimensional separation exists, and the figure does not display OOD samples or report any numeric purity measure. Please provide a quantitative neighborhood-purity analysis on the ScanObjectNN test splits, and show how the improvement from propagation correlates with this purity measure. Without such evidence, the central mechanism remains unverified on new domains.
  3. [§4.3, Eq. (6) and Table 3] The product formulation s^{(t)}(x_i) = d^{(t)}_{src}(x_i) * s^{(t)}_{text}(x_i) multiplies two separately propagated quantities, yet the text does not explain why the product of propagated scores should be preferred over, for example, propagating the product directly. Table 3 shows that the product yields a larger gain than either component alone, but a reader cannot tell whether this is principled or an artifact of the specific benchmark. Please provide a brief analysis or ablation comparing Eq. (6) against propagating the product once.
minor comments (5)
  1. [§5.2] The name 'Mahanalobis' is a typo; it should be 'Mahalanobis'.
  2. [Table 3] The header 'A vg. change' should be 'Avg. change'.
  3. [§5.4, Figure 5] The text says performance 'slightly drops before converging at around T=6 iterations,' but this transient drop is not clearly visible in the left panel of Figure 5. Please report the exact AUC values at each relevant T or clarify the statement.
  4. [Supplementary Table 5] The row label '1:9 ( Ours)' is unclear; it likely means the average over templates 1 through 9, which should be written out explicitly.
  5. [§4.3, Eq. (5)] The number of reference neighbors k is fixed to 10 without a sensitivity study. Since d_{src} directly affects the final score, please add a small ablation or a note on how k was chosen.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity found: SODA's scoring and propagation are not fitted to the OOD predictions, and the SOTA claim is benchmarked against external datasets.

full rationale

The paper proposes SODA as a training-free, transductive scoring procedure: initial text-similarity scores (Eq. 2), optional source-similarity reweighting using a fixed reference set (Eqs. 5-6), and neighborhood score propagation (Eqs. 3-4). No parameter is fitted to the OOD labels; the hyperparameters T=5, alpha=0.2, and eta=0.02 are fixed and their sensitivity is reported in the ablation study. The motivating observations in Section 3 are empirical descriptions of the ULIP-2 embedding geometry, not fitted components of the scoring function. The source-similarity weighting is a heuristic motivated by Observation 1, but the same weighting is also evaluated on ModelNet-C, and the propagation component is evaluated across multiple initial scorers (Table 3) and backbones (Table 5), so the central SOTA claim does not reduce by construction to its inputs. The only self-citation ([9]) appears in a related-work survey and is not load-bearing. The neighborhood-purity assumption underlying Eq. 4 is supported only by a UMAP figure, which is a weakness in evidence quality, but it is not circular because the paper does not define the score in terms of that purity or fit the purity to the outcome. The comparison against external public benchmarks and baselines makes the reported improvement independently checkable.

Assumptions & free parameters 4 free parameters · 3 assumptions · 0 invented entities

All scores rest on the pretrained ULIP-2 embedding space being semantically clustered for real point clouds. The method itself contributes no new entities and only four hand-set hyperparameters; the main additional input is a set of source-domain reference samples (ModelNet40) for the full SODA variant.

free parameters (4)
  • k (top-k source neighbors) = 10
    Number of nearest source reference embeddings averaged to define dsrc in Eq. 5; fixed without ablation.
  • eta (graph density percentile) = 0.02
    Percentile parameter for the epsilon threshold in Eq. 3; ablation Figure 5 shows a peak near eta=0.015 but the default is 0.02.
  • alpha (self-anchor weight) = 0.2
    Weight on the sample's own initial score in the propagation update of Eq. 4; ablation optimum is near 0.25.
  • T (propagation iterations) = 5
    Number of propagation iterations; ablation shows convergence around T=6 and no further change.
assumptions (3)
  • domain assumption ULIP-2's latent space clusters real point clouds by class despite synthetic-to-real shift.
    Motivates neighborhood propagation; based on UMAP visualization (Figure 3) and Observation 2, not a formal guarantee.
  • domain assumption Text-based initial scores are more reliable for samples with higher source similarity dsrc.
    Derived from Observation 1 and Figure 2, which show a linear relationship between source similarity and classification accuracy; used to reweight in Eq. 6.
  • standard math Iterative score propagation in Eq. 4 converges to a stable solution within T iterations.
    A linear recurrence with self-anchoring; convergence is observed empirically in Figure 5 (left) rather than proven.

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Cite this review

Pith. "Pith review of SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation." pith.science (2026). https://pith.science/paper/SINHCFQK

@misc{pith2026250621892,
  author       = {Pith},
  title        = {Pith review of: SODA: Out-of-Distribution Detection in Domain-Shifted Point Clouds via Neighborhood Propagation},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/SINHCFQK}},
  note         = {Machine review of arXiv:2506.21892}
}
read the original abstract

As point cloud data increases in prevalence in a variety of applications, the ability to detect out-of-distribution (OOD) point cloud objects becomes critical for ensuring model safety and reliability. However, this problem remains under-explored in existing research. Inspired by success in the image domain, we propose to exploit advances in 3D vision-language models (3D VLMs) for OOD detection in point cloud objects. However, a major challenge is that point cloud datasets used to pre-train 3D VLMs are drastically smaller in size and object diversity than their image-based counterparts. Critically, they often contain exclusively computer-designed synthetic objects. This leads to a substantial domain shift when the model is transferred to practical tasks involving real objects scanned from the physical environment. In this paper, our empirical experiments show that synthetic-to-real domain shift significantly degrades the alignment of point cloud with their associated text embeddings in the 3D VLM latent space, hindering downstream performance. To address this, we propose a novel methodology called SODA which improves the detection of OOD point clouds through a neighborhood-based score propagation scheme. SODA is inference-based, requires no additional model training, and achieves state-of-the-art performance over existing approaches across datasets and problem settings.

Figures

Figures reproduced from arXiv: 2506.21892 by the authors.

Figure 1
Figure 1. Example of a synthetic chair sample (left) and real chair sample (right). [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Classification accuracy (%) of real ScanObjectNN point clouds sorted by [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 3
Figure 3. UMAP projections of real domain test point clouds colored by their class. [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗
Figures from the paper (2 more)
Figure 4
Figure 4. Figure 4: An overview of SODA. Test samples are assigned initial scores based on [PITH_FULL_IMAGE:figures/full_fig_p007_4.png]
Figure 5
Figure 5. Figure 5: Average AUC performance with different numbers of iterations, [PITH_FULL_IMAGE:figures/full_fig_p013_5.png]

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Reference graph

Works this paper leans on

41 extracted references · 31 canonical work pages

  1. [1]

    NeurIPS 35, 21228--21240 (2022)

    Alliegro, A., Cappio Borlino, F., Tommasi, T.: 3dos: Towards 3d open set learning-benchmarking and understanding semantic novelty detection on point clouds. NeurIPS 35, 21228--21240 (2022)

    work page 2022

  2. [2]

    Pattern Recognition Letters 151, 172--179 (2021)

    Bhardwaj, A., Pimpale, S., Kumar, S., Banerjee, B.: Empowering knowledge distillation via open set recognition for robust 3d point cloud classification. Pattern Recognition Letters 151, 172--179 (2021)

    work page 2021

  3. [3]

    arXiv preprint arXiv:1512.03012 (2015)

    Chang, A.X., Funkhouser, T., Guibas, L., Hanrahan, P., Huang, Q., Li, Z., Savarese, S., Savva, M., Song, S., Su, H., et al.: Shapenet: An information-rich 3d model repository. arXiv preprint arXiv:1512.03012 (2015)

    arXiv 2015

  4. [4]

    IEEE Transactions on Pattern Analysis and Machine Intelligence 44(11), 8065--8081 (2021)

    Chen, G., Peng, P., Wang, X., Tian, Y.: Adversarial reciprocal points learning for open set recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 44(11), 8065--8081 (2021)

    work page 2021

  5. [5]

    In: Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XI 16

    Deng, J., Guo, J., Liu, T., Gong, M., Zafeiriou, S.: Sub-center arcface: Boosting face recognition by large-scale noisy web faces. In: Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XI 16. pp. 741--757. Springer (2020)

    work page 2020

  6. [6]

    In: AAAI

    Esmaeilpour, S., Liu, B., Robertson, E., Shu, L.: Zero-shot out-of-distribution detection based on the pre-trained model clip. In: AAAI. vol. 36, pp. 6568--6576 (2022)

    work page 2022

  7. [7]

    In: CVPR

    Fontanel, D., Cermelli, F., Mancini, M., Caputo, B.: Detecting anomalies in semantic segmentation with prototypes. In: CVPR. pp. 113--121 (2021)

    work page 2021

  8. [8]

    NeurIPS 34, 7068--7081 (2021)

    Fort, S., Ren, J., Lakshminarayanan, B.: Exploring the limits of out-of-distribution detection. NeurIPS 34, 7068--7081 (2021)

    work page 2021

Show all 41 references
  1. [9]

    arXiv preprint arXiv:2407.17083 (2024)

    Goodge, A., Hooi, B., Ng, W.S.: When text and images don't mix: Bias-correcting language-image similarity scores for anomaly detection. arXiv preprint arXiv:2407.17083 (2024)

    2024 arXiv

  2. [10]

    In: International conference on machine learning

    Guo, C., Pleiss, G., Sun, Y., Weinberger, K.Q.: On calibration of modern neural networks. In: International conference on machine learning. pp. 1321--1330. PMLR (2017)

    2017

  3. [11]

    arXiv preprint arXiv:1610.02136 (2016)

    Hendrycks, D., Gimpel, K.: A baseline for detecting misclassified and out-of-distribution examples in neural networks. arXiv preprint arXiv:1610.02136 (2016)

    2016 arXiv

  4. [12]

    arXiv preprint arXiv:1812.04606 (2018)

    Hendrycks, D., Mazeika, M., Dietterich, T.: Deep anomaly detection with outlier exposure. arXiv preprint arXiv:1812.04606 (2018)

    2018 arXiv

  5. [13]

    In: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)

    Huang, C., Abdelzad, V., Mannes, C.G., Rowe, L., Therien, B., Salay, R., Czarnecki, K., et al.: Out-of-distribution detection for lidar-based 3d object detection. In: 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). pp. 4265--4271. IEEE (2022)

    2022

  6. [14]

    NeurIPS 34, 677--689 (2021)

    Huang, R., Geng, A., Li, Y.: On the importance of gradients for detecting distributional shifts in the wild. NeurIPS 34, 677--689 (2021)

    2021

  7. [15]

    In: ICCV

    Huang, T., Dong, B., Yang, Y., Huang, X., Lau, R.W., Ouyang, W., Zuo, W.: Clip2point: Transfer clip to point cloud classification with image-depth pre-training. In: ICCV. pp. 22157--22167 (2023)

    2023

  8. [16]

    Sensors 23(24), 9913 (2023)

    Huch, S., Lienkamp, M.: Towards minimizing the lidar sim-to-real domain shift: Object-level local domain adaptation for 3d point clouds of autonomous vehicles. Sensors 23(24), 9913 (2023)

    2023

  9. [17]

    In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition

    Lang, A.H., Vora, S., Caesar, H., Zhou, L., Yang, J., Beijbom, O.: Pointpillars: Fast encoders for object detection from point clouds. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 12697--12705 (2019)

    2019

  10. [18]

    NeurIPS 31 (2018)

    Lee, K., Lee, K., Lee, H., Shin, J.: A simple unified framework for detecting out-of-distribution samples and adversarial attacks. NeurIPS 31 (2018)

    2018

  11. [19]

    arXiv preprint arXiv:1706.02690 (2017)

    Liang, S., Li, Y., Srikant, R.: Enhancing the reliability of out-of-distribution image detection in neural networks. arXiv preprint arXiv:1706.02690 (2017)

    2017 arXiv

  12. [20]

    NeurIPS 33, 21464--21475 (2020)

    Liu, W., Wang, X., Owens, J., Li, Y.: Energy-based out-of-distribution detection. NeurIPS 33, 21464--21475 (2020)

    2020

  13. [21]

    In: ICIP

    Masuda, M., Hachiuma, R., Fujii, R., Saito, H., Sekikawa, Y.: Toward unsupervised 3d point cloud anomaly detection using variational autoencoder. In: ICIP. pp. 3118--3122. IEEE (2021)

    2021

  14. [22]

    arXiv preprint arXiv:1802.03426 (2018)

    McInnes, L., Healy, J., Melville, J.: Umap: Uniform manifold approximation and projection for dimension reduction. arXiv preprint arXiv:1802.03426 (2018)

    2018 arXiv

  15. [23]

    NeurIPS 30 (2017)

    Qi, C.R., Yi, L., Su, H., Guibas, L.J.: Pointnet++: Deep hierarchical feature learning on point sets in a metric space. NeurIPS 30 (2017)

    2017

  16. [24]

    arXiv preprint arXiv:2310.03388 (2023)

    Rabino, P., Alliegro, A., Borlino, F.C., Tommasi, T.: Openpatch: a 3d patchwork for out-of-distribution detection. arXiv preprint arXiv:2310.03388 (2023)

    2023 arXiv

  17. [25]

    In: ICML

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: ICML. pp. 8748--8763. PMLR (2021)

    2021

  18. [26]

    arXiv:2202.03377 (2022)

    Ren, J., Pan, L., Liu, Z.: Benchmarking and analyzing point cloud classification under corruptions. arXiv:2202.03377 (2022)

    2022 arXiv

  19. [27]

    NeurIPS 34, 144--157 (2021)

    Sun, Y., Guo, C., Li, Y.: React: Out-of-distribution detection with rectified activations. NeurIPS 34, 144--157 (2021)

    2021

  20. [28]

    In: ICCV (2019)

    Uy, M.A., Pham, Q.H., Hua, B.S., Nguyen, D.T., Yeung, S.K.: Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data. In: ICCV (2019)

    2019

  21. [29]

    Vaze, S., Han, K., Vedaldi, A., Zisserman, A.: Open-set recognition: A good closed-set classifier is all you need? arXiv preprint arXiv:2110.06207 (2021)

    2021 arXiv

  22. [30]

    In: ICCV

    Wang, H., Li, Y., Yao, H., Li, X.: Clipn for zero-shot ood detection: Teaching clip to say no. In: ICCV. pp. 1802--1812 (2023)

    2023

  23. [31]

    ACM Transactions on Graphics 38(5), 1--12 (2019)

    Wang, Y., Sun, Y., Liu, Z., Sarma, S.E., Bronstein, M.M., Solomon, J.M.: Dynamic graph cnn for learning on point clouds. ACM Transactions on Graphics 38(5), 1--12 (2019)

    2019

  24. [32]

    arXiv preprint arXiv:2302.02914 (2023)

    Wu, Q., Chen, Y., Yang, C., Yan, J.: Energy-based out-of-distribution detection for graph neural networks. arXiv preprint arXiv:2302.02914 (2023)

    2023 arXiv

  25. [33]

    In: CVPR

    Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., Xiao, J.: 3d shapenets: A deep representation for volumetric shapes. In: CVPR. pp. 1912--1920 (2015)

    2015

  26. [34]

    In: CVPR

    Xue, L., Gao, M., Xing, C., Mart \' n-Mart \' n, R., Wu, J., Xiong, C., Xu, R., Niebles, J.C., Savarese, S.: Ulip: Learning a unified representation of language, images, and point clouds for 3d understanding. In: CVPR. pp. 1179--1189 (2023)

    2023

  27. [35]

    arXiv preprint arXiv:2305.08275 (2023)

    Xue, L., Yu, N., Zhang, S., Li, J., Mart \' n-Mart \' n, R., Wu, J., Xiong, C., Xu, R., Niebles, J.C., Savarese, S.: Ulip-2: Towards scalable multimodal pre-training for 3d understanding. arXiv preprint arXiv:2305.08275 (2023)

    2023 arXiv

  28. [36]

    Advances in neural information processing systems 34, 29393--29405 (2021)

    Yang, S., Van de Weijer, J., Herranz, L., Jui, S., et al.: Exploiting the intrinsic neighborhood structure for source-free domain adaptation. Advances in neural information processing systems 34, 29393--29405 (2021)

    2021

  29. [37]

    In: CVPR

    Zhang, R., Guo, Z., Zhang, W., Li, K., Miao, X., Cui, B., Qiao, Y., Gao, P., Li, H.: Pointclip: Point cloud understanding by clip. In: CVPR. pp. 8552--8562 (2022)

    2022

  30. [38]

    In: Proceedings of the IEEE/CVF International Conference on Computer Vision

    Zhu, X., Zhang, R., He, B., Guo, Z., Zeng, Z., Qin, Z., Zhang, S., Gao, P.: Pointclip v2: Prompting clip and gpt for powerful 3d open-world learning. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 2639--2650 (2023)

    2023

  31. [39]

    In: (ICML-03)

    Zhu, X., Ghahramani, Z., Lafferty, J.D.: Semi-supervised learning using gaussian fields and harmonic functions. In: (ICML-03). pp. 912--919 (2003)

    2003

  32. [40]

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

    ENTRY address author booktitle chapter doi edition editor eid howpublished institution journal key month note number organization pages publisher school series title type url volume year label INTEGERS output.state before.all mid.sentence after.sentence after.block FUNCTION in...

  33. [41]

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