Pith. sign in

REVIEW 2 major objections 4 minor 2 cited by

GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model

T0 review · 2 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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

Pith's one-line read GAPrompt claims that geometry-aware prompting—trainable point clouds, instance-specific point shifts, and shape-feature propagation—lets a frozen 3D vision model beat full fine-tuning with 2.19% of the parameters.

desk verdict Genuinely 3D-aware PEFT with a solid PEFT-vs-PEFT story; the 'surpasses full fine-tuning' claim for ReCon and Point-FEMAE is weakened by the stripped-backbone protocol disclosed only in Appendix A. read the letter →

arxiv 2505.04119 v3 pith:3OSMMVB7 submitted 2025-05-07 cs.CV

Zixiang Ai , Zichen Liu , Yuanhang Lei , Zhenyu Cui , Xu Zou , Jiahuan Zhou This is my paper · ORCID
classification cs.CV
keywords pointcloudclassificationparameter-efficientfine-tuningprompttuninggeometry-awareprompting3Dvisiontransformerspre-trainedmodelsShiftPrompterPropagation
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

GAPrompt is a parameter-efficient fine-tuning method for pre-trained 3D point cloud vision models. It freezes the backbone and trains only about 0.6M parameters, yet reports 90.67% accuracy on the hardest ScanObjectNN variant starting from Point-FEMAE, above the 90.22% of full fine-tuning. The central idea is that prompts for point clouds should carry geometry—a small learnable point cloud, instance-specific shifts of the input points, and a global shape feature propagated through the encoder—instead of only generic token prompts. If the claim holds, users can swap full fine-tuning for a tiny trainable prompt that preserves the pre-trained model and costs little extra compute.

What carries the argument

The central object is the GAPrompt module trio. The Point Prompt is a small set of learnable 3D coordinates initialized uniformly and concatenated with the raw point cloud, giving the model explicit tunable geometry at the input; the Point Shift Prompter uses multi-resolution grouping with farthest point sampling and k-nearest neighbour search, a lightweight PointNet encoder, and a shift head to produce both a global shape feature $f$ and a shifted point cloud; the Prompt Propagation mechanism then runs FPS and KNN over the token set, randomly injects the shape-enhanced prompt tokens into the center and neighbour tokens, and propagates interpolated features back to all tokens. The shape feature $f$ also scales the prompt tokens and adapters through factors $\beta_p$ and $\beta_a$. This carries the argument by moving prompting from token space into point-level and feature-propagation space, which the paper argues is where point cloud geometry lives.

What would settle it

Fully fine-tune the complete, unmodified ReCon and Point-FEMAE models (residual modules included) and run GAPrompt on those same complete backbones; if the 0.6M-parameter prompt no longer matches or exceeds full fine-tuning on ScanObjectNN PB T50 RS, the headline claim is refuted. A simpler check is to fully fine-tune the stripped Point-MAE-style backbones and see whether the 90.22% Point-FEMAE baseline itself shifts once its residual components are removed.

Watch

Extended reading notes

Core claim

The paper claims that a prompting scheme built around geometry, not just latent tokens, can adapt frozen pre-trained 3D vision models to downstream classification at a fraction of the parameter cost. With 0.6M trainable parameters (2.19% of Point-FEMAE), GAPrompt reaches 90.67% on ScanObjectNN PB T50 RS, surpassing full fine-tuning at 90.22%, and it improves on or matches full fine-tuning on Point-MAE, ReCon, and PointGPT-L as well. The authors trace this to three mechanisms: a Point Prompt that concatenates learnable 3D coordinates directly into the input point cloud, a Point Shift Prompter that extracts a global shape feature and produces per-point coordinate shifts, and a Prompt Propagation step that injects shape-enhanced prompt tokens into local feature interpolation inside each transformer block.

Load-bearing premise

The comparison against full fine-tuning of ReCon and Point-FEMAE assumes that loading only their pre-trained weights into a Point-MAE-style backbone, without their extra residual modules, preserves the representational capacity those models would have if fully fine-tuned in their original architecture.

Editorial extensions

If this is right

  • With 0.6M trainable parameters, GAPrompt matches or exceeds full fine-tuning on four pre-trained backbones across ScanObjectNN and ModelNet40, so downstream users could freeze a 3D encoder and store only a small prompt per task.
  • The added compute is modest (5.0G FLOPs versus 4.8–5.3G for the base backbones), keeping inference close to the frozen-model cost, in contrast to IDPT (7.2G) and Point-PEFT (7.0G).
  • On PointGPT-L, GAPrompt reports 98.97% on ScanObjectNN OBJ BG and 96.2% on ModelNet40 without voting, which would make it the best reported efficient-tuning result on that backbone.
  • The same global shape feature feeds the downstream head together with the [CLS] token and max-pooled patch tokens, so instance geometry contributes directly to the final decision.

Reading between the lines

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

  • If geometry-aware prompting is the active ingredient, the same Point Shift and Prompt Propagation designs should transfer to segmentation and detection, where instance geometry is even more discriminative than in object classification.
  • A testable extension is to ablate the PointNet-style prompter against a simpler or fully symmetric shape encoder to separate the contribution of global shape extraction from the propagation mechanism itself.
  • The 'surpasses full fine-tuning' result for ReCon and Point-FEMAE rests on a stripped-backbone comparison; a direct run on the complete unmodified models would show whether the gain is due to the prompt or to dropping residual modules.
  • One could check whether the learned Point Prompt settles on semantically meaningful surface regions by measuring overlap with human keypoint annotations, which would directly test the claim that it captures fine-grained geometry.
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

2 major / 4 minor

Summary. The paper introduces GAPrompt, a parameter-efficient fine-tuning (PEFT) method for pre-trained 3D point cloud transformers. Three components are proposed: a learnable Point Prompt appended to the raw input coordinates, a Point Shift Prompter that extracts instance-specific shape features and shifts input points, and a Prompt Propagation mechanism that injects the shape-enhanced prompt tokens into the token-wise FPS/KNN hierarchy of each transformer block. Experiments report classification accuracy on ScanObjectNN variants and ModelNet40 using four pre-trained backbones (Point-MAE, ReCon, PointGPT, Point-FEMAE). GAPrompt is reported to outperform existing PEFT methods (IDPT, DAPT, Point-PEFT) and in some cases to surpass full fine-tuning while using about 2% of trainable parameters. The paper includes component ablations, hyperparameter studies, and visualizations.

Significance. If the reported results are taken at face value, GAPrompt is a useful contribution to 3D PEFT: it explicitly exploits geometry at the point level, unlike token-only prompting, and it is shown to be parameter-efficient and computationally light. The paper provides careful component ablations (Tables 3 and 4), hyperparameter sensitivity plots (Figures 4, 7, 8), and a public code repository, which are strengths. However, the headline claim of surpassing full fine-tuning rests on a comparison whose fairness is compromised for two of the four backbones, as stated in Appendix A. The core PEFT comparison against IDPT/DAPT/Point-PEFT on the same protocol is meaningful, but the claim of beating full fine-tuning for ReCon and Point-FEMAE is not supported by the current evidence. The absence of variance reporting is an additional concern for the small reported margins.

major comments (2)
  1. [Appendix A / Table 1] The comparison against full fine-tuning of ReCon and Point-FEMAE is not apples-to-apples. Appendix A states that for these two models the authors 'only load pre-trained weights into a Point-MAE model for efficient fine-tuning, while excluding the residual components of ReCon and Point-FEMAE.' Thus the GAPrompt rows in Table 1 labeled 'ReCon' and 'Point-FEMAE' use a stripped Point-MAE backbone initialized with the pre-trained weights, whereas the 'Full Fine-Tuning' rows use the complete original architectures. The reported margins over full fine-tuning (0.03 points for ReCon on PB T50 RS, 0.45 points for Point-FEMAE on PB T50 RS) are therefore not attributable to the GAPrompt adaptation alone; they may reflect the backbone choice and the pre-training weights. The paper should either run GAPrompt on the full ReCon and Point-FEMAE architectures, or limit the claim to 'competitive with full fine-tuning' for these rows and make the protocol disclosure in the main text and table caption explicit.
  2. [Table 1 / Section 4.2] Table 1 reports a single accuracy per configuration without error bars or standard deviations. The central empirical claim of surpassing full fine-tuning relies on differences as small as 0.03 and 0.45 percentage points, which are typically within seed-to-seed variance for point cloud classification. Since the abstract and Section 4.2 explicitly state that GAPrompt 'surpasses' full fine-tuning for ReCon and Point-FEMAE, the authors should provide results over multiple seeds (at least for the key comparisons) or confidence intervals. Without this, the claimed advantage over full fine-tuning for these two backbones is not statistically established.
minor comments (4)
  1. [Section 4 / Table 5] Section 4 states that the hyperparameters are set as βa = 0.5, βp = 0.5, and P = 20, but Table 5 shows different Point Prompt numbers for each dataset (20, 10, 20, 5). This is confusing; either the main text should acknowledge that P is tuned per dataset, or Table 5 should be reconciled with the stated default.
  2. [Appendix A] The phrase 'sight computational saving' in Appendix A should be 'slight computational saving.'
  3. [Table 1] The full fine-tuning row is labeled 'PointGPT-L' in Table 1 but the text in Section 4.2 and elsewhere uses 'Point-GPT'; please unify the naming.
  4. [Table 1 caption] The table caption does not disclose that the ReCon and Point-FEMAE GAPrompt rows use a Point-MAE backbone with only the pre-trained weights loaded. Adding this caveat in the caption would prevent misreading of the results.

Circularity Check

0 steps flagged · score 2.0 of 10

No circular derivation found; GAPrompt is benchmarked empirically. Self-citations are non-load-bearing, and the Appendix A backbone-stripping disclosure is a fairness concern rather than circularity.

full rationale

The paper does not derive its headline accuracies from its inputs by construction. GAPrompt is an empirical method whose three trainable modules (Point Prompt, Point Shift Prompter, Prompt Propagation) are measured on ScanObjectNN and ModelNet40 against external baselines; the ablations in Tables 3 and 4 are incremental empirical evaluations, not fitted-then-predicted quantities. Eq. 19 is imported from He et al. (2021) as an equivalent transformation of attention with prompt tokens and is not used to fit or predict any reported number. The only self-citations by the corresponding author (Li & Zhou 2025; Liu et al. 2024) occur in related-work enumerations of prompt tuning and are not load-bearing for any claim. One caveat, flagged explicitly per review rules: Appendix A states, 'we follow the approach of DAPT by only loading pre-trained weights into a Point-MAE model for efficient fine-tuning, while excluding the residual components of ReCon and Point-FEMAE.' This makes the 'surpasses full fine-tuning' rows for ReCon and Point-FEMAE comparisons across architectures rather than within the exact original models, which is a comparison-fairness / correctness risk, not a circularity: those full-fine-tuning numbers are external measurements, and GAPrompt's design is not defined in terms of them. Hyperparameters such as beta_a, beta_p, and P are tuned on the benchmark, but the resulting accuracies are fresh test-set measurements, not algebraically forced by those choices.

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

No new physical entities or ontic postulates are introduced. The new components (Point Prompt, Point Shift Prompter, Prompt Propagation) are trainable modules rather than scientific entities with falsifiable handles outside the paper. The main free parameters are the hyperparameters adjusted via ablation, and the axioms are standard domain assumptions for PEFT that the experiments are designed to test.

free parameters (3)
  • beta_p (prompt enhancing factor) = 0.5
    Chosen by ablation on ScanObjectNN PB T50 RS (Figure 4); scales the shape feature added to prompt tokens in Eq. 12.
  • beta_a (adapter enhancing factor) = 0.5
    Chosen by ablation (Figure 8); scales the shape feature added to adapters in Eq. 5.
  • P (number of point prompt points) = 20/10/20/5 per dataset
    Ablated in Figure 7; the number of learnable point prompts varies by dataset and is listed in Table 5.
assumptions (3)
  • domain assumption Pre-trained point cloud transformers can be frozen and adapted via input-level and token-level prompts without catastrophic forgetting.
    This is the core PEFT premise. It is not proven theoretically but is supported by the experiments in the paper.
  • domain assumption The token embedding module of each pre-trained model can accept input point clouds with S+P points (original plus point prompts) and maintain meaningful token semantics.
    Required for concatenating [x;P] as described in Section 3.1, but not verified per backbone.
  • domain assumption The He et al. 2021 equivalent transformation of attention with prompts (Eq. 19) applies to the frozen transformer with the injected and propagated tokens used in GAPrompt.
    The paper borrows this theoretical result and applies it to the new prompting scheme without re-deriving it. The assumption is that the transformation remains valid under the FPS/KNN token propagation.

how reviews work

0 comments
Cite this review

Pith. "Pith review of GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model." pith.science (2026). https://pith.science/paper/3OSMMVB7

@misc{pith2026250504119,
  author       = {Pith},
  title        = {Pith review of: GAPrompt: Geometry-Aware Point Cloud Prompt for 3D Vision Model},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3OSMMVB7}},
  note         = {Machine review of arXiv:2505.04119}
}
read the original abstract

Pre-trained 3D vision models have gained significant attention for their promising performance on point cloud data. However, fully fine-tuning these models for downstream tasks is computationally expensive and storage-intensive. Existing parameter-efficient fine-tuning (PEFT) approaches, which focus primarily on input token prompting, struggle to achieve competitive performance due to their limited ability to capture the geometric information inherent in point clouds. To address this challenge, we propose a novel Geometry-Aware Point Cloud Prompt (GAPrompt) that leverages geometric cues to enhance the adaptability of 3D vision models. First, we introduce a Point Prompt that serves as an auxiliary input alongside the original point cloud, explicitly guiding the model to capture fine-grained geometric details. Additionally, we present a Point Shift Prompter designed to extract global shape information from the point cloud, enabling instance-specific geometric adjustments at the input level. Moreover, our proposed Prompt Propagation mechanism incorporates the shape information into the model's feature extraction process, further strengthening its ability to capture essential geometric characteristics. Extensive experiments demonstrate that GAPrompt significantly outperforms state-of-the-art PEFT methods and achieves competitive results compared to full fine-tuning on various benchmarks, while utilizing only 2.19% of trainable parameters. Our code is available at https://github.com/zhoujiahuan1991/ICML2025-GAPrompt.

Figures

Figures reproduced from arXiv: 2505.04119 by the authors.

Figure 1
Figure 1. Our GAPrompt compares to full fine-tuning and existing PEFT methods. We compare the classification accuracy on the hardest variant of ScanObjectNN (Uy et al., 2019) based on pre￾trained Point-FEMAE (Zha et al., 2024). 1. Introduction The advent of scanning sensor devices has significantly fa￾cilitated the acquisition of 3D point cloud data, an inherently irregular and unstructured geometric representation. This adva… view at source ↗
Figure 2
Figure 2. Methods for adapting pre-trained 3D vision models. (a) Fine-tuning updates entire model parameters. (b) Prompt-based methods adapt the model to downstream tasks by reformulating the input at the token level. (c) Our proposed GAPrompt adapts the model by tuning explicit learnable point clouds and token prompts, enhanced with instance-specific shape features extracted by a geometry-aware prompter. He et al., 2021). Ho… view at source ↗
Figure 3
Figure 3. The overall pipeline of GAPrompt. The raw input point clouds are processed by Point Shift Prompter, generating instance￾specific shape features and shifted points. These shifted points are then combined with the Point Prompt and embedded into input tokens. The shape features are further utilized to enhance the prompt tokens, which are concatenated with the input tokens. Subsequently, the concatenated tokens are fed … view at source ↗
Figures from the paper (5 more)
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6 [PITH_FULL_IMAGE:figures/full_fig_p008_6.png]
Figure 7
Figure 7. Figure 7: Ablation study of Point Prompt number P and ini￾tialization distribution. 0.10 90.0 89.5 90.5 0.20 0.30 0.50 0.70 1.40 Adapter Enhancing Factor Accuracy (%) 89.86 89.98 90.47 90.67 90.38 90.00 Adapter Tuning [PITH_FULL_IMAGE:figures/full_fig_p012_7.png]
Figure 9
Figure 9. Figure 9: The t-SNE visualizations from the test sets of ScanObjectNN (PB T50 RS) using a pre-trained Point-FEMAE with different tuning strategies. We extract the final classification features from the top linear layer for t-SNE visualizations. Accuracy (%) 90.67 90.04 90.0 90.5…
Figure 10
Figure 10. Figure 10: Ablation study on different input for downstream head. C.2. Analysis on Adapter Enhancing Factor βa. As shown in [PITH_FULL_IMAGE:figures/full_fig_p013_10.png]

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 2 Pith papers

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

  1. UPP: Unified Point-Level Prompting for Robust Point Cloud Analysis

    cs.CV 2025-07 conditional novelty 6.0 of 10

    UPP unifies denoising and completion as point-level prompts in a frozen pre-trained point cloud transformer, improving classification accuracy on noisy and incomplete point clouds without updating backbone weights.

  2. Fast 3D Foundation Model Initialized Gaussian Splatting

    cs.CV 2026-07 conditional novelty 4.0 of 10

    VGGT-X initialization plus joint MCMC pose–Gaussian optimization and depth-guided loss yields usable 3DGS models in ~3 minutes at 23.61 dB average validation PSNR without COLMAP.

Reference graph

Works this paper leans on

42 extracted references · 20 canonical work pages · cited by 2 Pith papers

  1. [1]

    Pointgpt: Auto-regressively generative pre-training from point clouds

    Chen, G., Wang, M., Yang, Y., Yu, K., Yuan, L., and Yue, Y. Pointgpt: Auto-regressively generative pre-training from point clouds. Advances in Neural Information Processing Systems, 36, 2024

    work page 2024

  2. [2]

    Adaptformer: Adapting vision transformers for scalable visual recognition

    Chen, S., Ge, C., Tong, Z., Wang, J., Song, Y., Wang, J., and Luo, P. Adaptformer: Adapting vision transformers for scalable visual recognition. Advances in Neural Information Processing Systems, 35: 0 16664--16678, 2022

    2022

  3. [3]

    Autoencoders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning? arXiv preprint arXiv:2212.08320, 2022

    Dong, R., Qi, Z., Zhang, L., Zhang, J., Sun, J., Ge, Z., Yi, L., and Ma, K. Autoencoders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning? arXiv preprint arXiv:2212.08320, 2022

    arXiv 2022

  4. [4]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., and Houlsby, N. An image is worth 16x16 words: Transformers for image recognition at scale. ICLR, 2021

    2021

  5. [5]

    Towards a unified view of parameter-efficient transfer learning

    He, J., Zhou, C., Ma, X., Berg-Kirkpatrick, T., and Neubig, G. Towards a unified view of parameter-efficient transfer learning. arXiv preprint arXiv:2110.04366, 2021

    arXiv 2021

  6. [6]

    Parameter-efficient transfer learning for nlp

    Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., and Gelly, S. Parameter-efficient transfer learning for nlp. In International conference on machine learning, pp.\ 2790--2799. PMLR, 2019

    2019

  7. [7]

    W., Ouyang, W., and Zuo, W

    Huang, T., Dong, B., Yang, Y., Huang, X., Lau, R. W., Ouyang, W., and Zuo, W. Clip2point: Transfer clip to point cloud classification with image-depth pre-training. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp.\ 22157--22167, October 2023

    work page 2023

  8. [8]

    Visual prompt tuning

    Jia, M., Tang, L., Chen, B.-C., Cardie, C., Belongie, S., Hariharan, B., and Lim, S.-N. Visual prompt tuning. In European Conference on Computer Vision, pp.\ 709--727. Springer, 2022

    2022

Show all 42 references
  1. [9]

    and Deng, Z.-H

    Jie, S. and Deng, Z.-H. Fact: Factor-tuning for lightweight adaptation on vision transformer. In Proceedings of the AAAI conference on artificial intelligence, volume 37, pp.\ 1060--1068, 2023

    2023

  2. [10]

    Compacter: Efficient low-rank hypercomplex adapter layers

    Karimi Mahabadi, R., Henderson, J., and Ruder, S. Compacter: Efficient low-rank hypercomplex adapter layers. Advances in Neural Information Processing Systems, 34: 0 1022--1035, 2021

    2021

  3. [11]

    The power of scale for parameter-efficient prompt tuning

    Lester, B., Al-Rfou, R., and Constant, N. The power of scale for parameter-efficient prompt tuning. arXiv preprint arXiv:2104.08691, 2021

    2021 arXiv

  4. [12]

    and Zhou, J

    Li, Q. and Zhou, J. Caprompt: Cyclic prompt aggregation for pre-trained model based class incremental learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 39, pp.\ 18421--18429, 2025

    2025

  5. [13]

    Li, X. L. and Liang, P. Prefix-tuning: Optimizing continuous prompts for generation. arXiv preprint arXiv:2101.00190, 2021

    2021 arXiv

  6. [14]

    Scaling & shifting your features: A new baseline for efficient model tuning

    Lian, D., Zhou, D., Feng, J., and Wang, X. Scaling & shifting your features: A new baseline for efficient model tuning. Advances in Neural Information Processing Systems, 35: 0 109--123, 2022

    2022

  7. [15]

    Liu, H., Cai, M., and Lee, Y. J. Masked discrimination for self-supervised learning on point clouds. In European Conference on Computer Vision, pp.\ 657--675. Springer, 2022

    2022

  8. [16]

    Relation-shape convolutional neural network for point cloud analysis

    Liu, Y., Fan, B., Xiang, S., and Pan, C. Relation-shape convolutional neural network for point cloud analysis. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 8895--8904, 2019

    2019

  9. [17]

    Insvp: Efficient instance visual prompting from image itself

    Liu, Z., Peng, Y., and Zhou, J. Insvp: Efficient instance visual prompting from image itself. In Proceedings of the 32nd ACM International Conference on Multimedia, pp.\ 6443--6452, 2024

    2024

  10. [18]

    and Hutter, F

    Loshchilov, I. and Hutter, F. Decoupled weight decay regularization. In International Conference on Learning Representations, 2019

    2019

  11. [19]

    and Hutter, F

    Loshchilov, I. and Hutter, F. Sgdr: Stochastic gradient descent with warm restarts. In International Conference on Learning Representations, 2022

    2022

  12. [20]

    Scaffold-gs: Structured 3d gaussians for view-adaptive rendering

    Lu, T., Yu, M., Xu, L., Xiangli, Y., Wang, L., Lin, D., and Dai, B. Scaffold-gs: Structured 3d gaussians for view-adaptive rendering. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp.\ 20654--20664, June 2024

    2024

  13. [21]

    E., Liu, W., Tian, Y., and Yuan, L

    Pang, Y., Wang, W., Tay, F. E., Liu, W., Tian, Y., and Yuan, L. Masked autoencoders for point cloud self-supervised learning. In European conference on computer vision, pp.\ 604--621. Springer, 2022

    2022

  14. [22]

    V., Le Nguyen, M., Nguyen, Y

    Phan, A. V., Le Nguyen, M., Nguyen, Y. L. H., and Bui, L. T. Dgcnn: A convolutional neural network over large-scale labeled graphs. Neural Networks, 108: 0 533--543, 2018

    2018

  15. [23]

    R., Su, H., Mo, K., and Guibas, L

    Qi, C. R., Su, H., Mo, K., and Guibas, L. J. Pointnet: Deep learning on point sets for 3d classification and segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 652--660, 2017 a

    2017

  16. [24]

    R., Yi, L., Su, H., and Guibas, L

    Qi, C. R., Yi, L., Su, H., and Guibas, L. J. Pointnet++: Deep hierarchical feature learning on point sets in a metric space. Advances in neural information processing systems, 30, 2017 b

    2017

  17. [25]

    Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining

    Qi, Z., Dong, R., Fan, G., Ge, Z., Zhang, X., Ma, K., and Yi, L. Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining. In International Conference on Machine Learning, pp.\ 28223--28243. PMLR, 2023

    2023

  18. [26]

    Shapellm: Universal 3d object understanding for embodied interaction

    Qi, Z., Dong, R., Zhang, S., Geng, H., Han, C., Ge, Z., Yi, L., and Ma, K. Shapellm: Universal 3d object understanding for embodied interaction. arXiv preprint arXiv:2402.17766, 2024

    2024 arXiv

  19. [27]

    Dropout: a simple way to prevent neural networks from overfitting

    Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R. Dropout: a simple way to prevent neural networks from overfitting. The journal of machine learning research, 15 0 (1): 0 1929--1958, 2014

    1929

  20. [28]

    Point-peft: Parameter-efficient fine-tuning for 3d pre-trained models

    Tang, Y., Zhang, R., Guo, Z., Ma, X., Zhao, B., Wang, Z., Wang, D., and Li, X. Point-peft: Parameter-efficient fine-tuning for 3d pre-trained models. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp.\ 5171--5179, 2024

    2024

  21. [29]

    A., Pham, Q.-H., Hua, B.-S., Nguyen, T., and Yeung, S.-K

    Uy, M. A., Pham, Q.-H., Hua, B.-S., Nguyen, T., and Yeung, S.-K. Revisiting point cloud classification: A new benchmark dataset and classification model on real-world data. In Proceedings of the IEEE/CVF international conference on computer vision, pp.\ 1588--1597, 2019

    2019

  22. [30]

    and Hinton, G

    Van der Maaten, L. and Hinton, G. Visualizing data using t-sne. Journal of machine learning research, 9 0 (11), 2008

    2008

  23. [31]

    Wang, H., Liu, Q., Yue, X., Lasenby, J., and Kusner, M. J. Unsupervised point cloud pre-training via occlusion completion. In Proceedings of the IEEE/CVF international conference on computer vision, pp.\ 9782--9792, 2021

    2021

  24. [32]

    3d shapenets: A deep representation for volumetric shapes

    Wu, Z., Song, S., Khosla, A., Yu, F., Zhang, L., Tang, X., and Xiao, J. 3d shapenets: A deep representation for volumetric shapes. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp.\ 1912--1920, 2015

    1912

  25. [33]

    Point-nerf: Point-based neural radiance fields

    Xu, Q., Xu, Z., Philip, J., Bi, S., Shu, Z., Sunkavalli, K., and Neumann, U. Point-nerf: Point-based neural radiance fields. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 5438--5448, 2022

    2022

  26. [34]

    Point-bert: Pre-training 3d point cloud transformers with masked point modeling

    Yu, X., Tang, L., Rao, Y., Huang, T., Zhou, J., and Lu, J. Point-bert: Pre-training 3d point cloud transformers with masked point modeling. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp.\ 19313--19322, 2022

    2022

  27. [35]

    B., Ravfogel, S., and Goldberg, Y

    Zaken, E. B., Ravfogel, S., and Goldberg, Y. Bitfit: Simple parameter-efficient fine-tuning for transformer-based masked language-models. arXiv preprint arXiv:2106.10199, 2021

    2021

  28. [36]

    Instance-aware dynamic prompt tuning for pre-trained point cloud models

    Zha, Y., Wang, J., Dai, T., Chen, B., Wang, Z., and Xia, S.-T. Instance-aware dynamic prompt tuning for pre-trained point cloud models. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pp.\ 14161--14170, 2023

    2023

  29. [37]

    Towards compact 3d representations via point feature enhancement masked autoencoders

    Zha, Y., Ji, H., Li, J., Li, R., Dai, T., Chen, B., Wang, Z., and Xia, S.-T. Towards compact 3d representations via point feature enhancement masked autoencoders. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp.\ 6962--6970, 2024

    2024

  30. [38]

    Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training

    Zhang, R., Guo, Z., Gao, P., Fang, R., Zhao, B., Wang, D., Qiao, Y., and Li, H. Point-m2ae: multi-scale masked autoencoders for hierarchical point cloud pre-training. Advances in neural information processing systems, 35: 0 27061--27074, 2022

    2022

  31. [39]

    Pnerfloc: Visual localization with point-based neural radiance fields

    Zhao, B., Yang, L., Mao, M., Bao, H., and Cui, Z. Pnerfloc: Visual localization with point-based neural radiance fields. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 38, pp.\ 7450--7459, 2024

    2024

  32. [40]

    Dynamic adapter meets prompt tuning: Parameter-efficient transfer learning for point cloud analysis

    Zhou, X., Liang, D., Xu, W., Zhu, X., Xu, Y., Zou, Z., and Bai, X. Dynamic adapter meets prompt tuning: Parameter-efficient transfer learning for point cloud analysis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp.\ 14707--14717, 2024

    2024

  33. [41]

    Pointclip v2: Prompting clip and gpt for powerful 3d open-world learning

    Zhu, X., Zhang, R., He, B., Guo, Z., Zeng, Z., Qin, Z., Zhang, S., and 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

  34. [42]

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