4DPC$^2$hat: Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping

Hehe Fan; Ming Li; Tao He; Weilong Yan; Xindan Zhang; Xuerui Qiu; Ying Li; Yufei Shi

REVIEW 3 major objections 4 minor 2 cited by

4DPC$^2$hat: Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping

T0 review · 3 major / 4 minor · reviewed 2026-08-03 · deepseek-v4-flash

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Pith's one-line read A multimodal language model trained on 44K animated objects can caption and answer questions about dynamic 4D point cloud sequences, outperforming static 3D baselines by wide margins.

desk verdict First 4D point-cloud MLLM with a genuinely useful dataset recipe and large claimed gains, but the headline numbers rest on an unreported train/test split and single-run evaluation. read the letter →

arxiv 2602.03890 v3 pith:3HOVOPKH submitted 2026-02-03 cs.CV

Xindan Zhang , Weilong Yan , Yufei Shi , Xuerui Qiu , Tao He , Ying Li , Ming Li , Hehe Fan This is my paper · ORCID

classification cs.CV

keywords 4Dpointcloudunderstandingmultimodallargelanguagemodeltemporalreasoningcaptioningquestionansweringfailure-awarebootstrappingstate-spacemodelingsyntheticdataset

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 is trying to establish that dynamic point cloud understanding — reading a sequence of 3D point sets over time and answering language questions about actions and motion — can be brought to multimodal large language models. To do this it builds a large synthetic dataset of animated objects with captions and question-answer pairs, and trains a model that combines per-frame point encoding with bidirectional state-space temporal modeling. It also introduces an iterative bootstrapping loop that mines the model's own failure cases and generates targeted training questions to strengthen weak abilities. If the central claim is right, it would give robotics and embodied AI a way to reason in language about observed motion from raw sensor-like 3D data.

What carries the argument

The central mechanism is a bidirectional state-space sequence model applied to per-frame point tokens. Each frame is encoded by a frozen point transformer that outputs several group-level tokens plus one global token; keeping the group tokens avoids a 'spatial over-compression' that loses where within the object motion is happening. The bidirectional state-space module then processes the whole token sequence in both time directions, capturing long-range dependencies at linear cost. Around this sits a failure-aware bootstrapping loop: after supervised training, the model is run on a reference set, the worst-scoring answers (by semantic similarity to ground truth) are classified into error cat

What would settle it

Take a random sample of a few hundred sequences from the dataset, have human annotators check the reference captions and QA answers against the original animations for hallucinated colors, counts, or motions, and compare the model's outputs on those items to human judgments. If the machine-generated references are frequently wrong, the reported margins would not survive.

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Extended reading notes

Core claim

The paper's central claim is that 4DPC2hat is the first multimodal large language model able to reason over dynamic point cloud sequences, and that it does so substantially better than static 3D models adapted by frame-wise aggregation. The authors report large margins on both captioning and question answering — about 18.6 points on the language-model-judge caption score and about 18.9 points on the QA score over the strongest 3D baseline — and attribute these gains to three components: a topology-consistent point representation that tracks point identity across frames, a bidirectional state-space temporal module that preserves localized motion cues instead of collapsing a frame into a singl

Load-bearing premise

The load-bearing premise is that the machine-written captions and question-answer pairs used as ground truth accurately describe what actually moves in the point cloud sequences; if they do not, both training and evaluation are measuring imitation of an annotator language model rather than 4D understanding.

Editorial extensions

If this is right

Static 3D multimodal models, even when aggregated over frames with a temporal summarizer, are insufficient for dynamic point cloud understanding; sequence-aware temporal modeling is required.
A bidirectional state-space temporal module outperforms a temporal transformer on both captioning and QA, with the gap attributed to better preservation of motion continuity and past-future context.
Failure-aware bootstrapping yields larger and more balanced gains than adding the same targeted data through naive augmentation, and gains saturate after two rounds.
The dataset enables joint captioning and question answering on 4D assets for the first time, with question types covering counting, temporal relations, actions, spatial relations, and appearance.
Freezing the encoder and language backbone during failure-aware refinement avoids catastrophic forgetting while localized corrections improve reasoning.

Reading between the lines

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

If the reported margins hold up under human evaluation, the same data-generation pipeline — captions from rendered frames, QA from captions, failure-selected retraining — could transfer to other spatio-temporal modalities such as LiDAR video or RGB-D streams, where manual annotation is expensive.
Because the ground truth is entirely machine-generated, a human-checked subset of captions and answers is the natural next validation; without it, the margins may partly reflect imitation of the annotation model's language style rather than true 4D reasoning.
The failure-selection sample in the paper may overlap with the test IDs used for evaluation; a deliberately disjoint split with documented ID separation would make the bootstrapping gains a more reliable estimate.
The saturation of bootstrapping gains after two rounds suggests a ceiling on self-correction without new external signal; combining failure-aware data with human-verified corrections may be the next step.

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Editorial analysis

A structured set of objections, weighed in public.

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

Desk Editor's Note

read the letter

The thing to know: this is the first MLLM trained for open-ended captioning and QA over dynamic point cloud sequences, and it comes with a new 200K QA dataset built from Objaverse animations. The reported margins over adapted static 3D MLLMs are huge — GPT-4 captioning 73.27 vs. 54.70, QA 78.01 vs. 59.08. If those numbers are real, it is a new capability and a reusable benchmark.

What is genuinely new and well done: the dataset construction. The topology-consistent point sampling via barycentric coordinates on the first frame and re-evaluation on later frames is a clean way to get point-wise temporal correspondence. Two-level captioning and five QA categories give the benchmark breadth that existing 4D datasets lack. The architecture — Point-BERT encoder, bidirectional Mamba temporal module, LLaMA backbone — is a sensible recipe, and the appendices give enough detail to re-implement. The ablation of bootstrapping vs. naive data augmentation is the right experiment to isolate the method's contribution.

The soft spots are real and load-bearing. First, the paper never states that the 4,000 test object IDs are disjoint from the 44K sequences used in instruction tuning, nor that the failure-selection set in App. D (bottom 40% of a 20% slice) is disjoint from the test IDs. The dataset has roughly 44K sequences; SFT uses 44K sequences; if test IDs come from that pool, the reported scores are partly memorization and the comparison against baselines collapses. This is not necessarily fatal — the authors may have held out IDs — but they need to say so explicitly. Second, every number in Tables 2–5 and Figs. 5–6 is a single run with no error bars or significance tests, and GPT-4 scores rest on 200 IDs. Third, the ground truth is entirely Qwen-generated (captions, QA pairs) and evaluated by GPT-4 against those same references, with no human-validated subset. That makes the absolute scores hard to interpret as 4D understanding; they partly measure imitation of LLM text style. Fourth, no code or data is released, despite the abstract promising the dataset for the community.

I agree with the stress-test note: the split issue is the first question I would ask the authors. The circularity concern is real but somewhat softened because all baselines are graded on the same references; still, it limits what the absolute numbers mean.

This paper deserves a serious referee — it is a coherent, first-in-category systems contribution. Send it to peer review, but the reviewers must push for a clear statement of the train/test split, multi-seed runs, a human-evaluated subset, and a release plan. If the split is clean, this is a solid contribution; if not, the headline margins are void.

Referee Report

3 major / 4 minor

Summary. The paper introduces 4DPC2hat, an MLLM for dynamic point cloud sequence understanding, together with a new dataset 4DPC2hat-200K built from over 44K animated Objaverse/Objaverse-XL assets. The dataset pipeline converts meshes to topology-consistent point cloud sequences, uses Qwen2.5-VL to generate brief and detailed captions, and uses Qwen to generate 145K QA pairs across appearance, counting, action, temporal, and spatial categories. The model uses a frozen Point-BERT encoder, per-frame group/global tokens, a bidirectional Mamba temporal module, and a LLaMA backbone. Training proceeds through feature alignment, instruction tuning, and a failure-aware bootstrapping stage in which low-scoring predictions on a reference slice are used to synthesize targeted QA pairs. Experiments compare against frame-wise adapted 3D MLLMs and report large gains, e.g., GPT-4 captioning score 73.27 vs. 54.70 for MiniGPT-3D and QA score 78.01 vs. 59.08, plus ablations favoring Mamba over Transformer and bootstrapping over naive augmentation.

Significance. If the empirical results are trustworthy, the paper makes a useful contribution: it is the first systematic attempt at open-ended language reasoning over dynamic 4D point clouds, the dataset could be a valuable community resource, and the architecture and bootstrapping procedure are described with unusually concrete implementation detail in Appendices B-D. The claimed gains are large and the ablations are informative. However, the central empirical claim depends entirely on two currently unverified conditions: that the test set is disjoint from all training and bootstrapping data, and that the machine-generated captions/QA used for supervision are also valid references for evaluation. As presented, the reported margins may substantially overstate true 4D understanding, so the evidence does not yet support the headline claims.

major comments (3)

[§3.3, §4.1, App. D] The paper never establishes that the 4,000 test IDs in §4.1 are disjoint from the training data used in §3.3. The dataset contains 'over 44K dynamic object sequences' (§2) and §3.3 trains on 44k sequences with 145k QA pairs, so unless an explicit object-level holdout is reported, the test IDs may be a subset of the training pool, making Tables 2-3 measures of memorization rather than generalization. Additionally, App. D selects failure cases by evaluating the model on 'the 20% dataset' and taking the lowest-performing 40%; no statement ensures this selection is disjoint from the 4,000 test IDs. If it overlaps, the bootstrapping gains (Bs1/Bs2) in Fig. 5 are inflated by training on test-distribution examples. Please report the exact identities and counts of training, bootstrapping, and test object IDs, and confirm disjointness.
[§2.2, §2.3, §4.1, App. E] All ground-truth text is machine-generated: Qwen2.5-VL writes captions from rendered frames (§2.2), Qwen writes QA pairs from those captions (§2.3), and GPT-4 scores model outputs against the same references (§4.1, App. E). No human verification is reported for whether captions or answers correctly describe the animations. If Qwen hallucinates colors, counts, or motions — a known failure mode for such models — the model is trained to imitate and evaluated against text that may not describe the point cloud at all. The large margins in Tables 2-3 could then reflect imitation of LLM-generated text style rather than 4D understanding. Please provide a human-validated subset (e.g., 200-500 samples with agreement statistics) and, ideally, an independent human-written reference set for the GPT-4 evaluation.
[Tables 2-5, Figs. 5-6] Every quantitative result is a single run with no seeds, error bars, or significance tests. The GPT-4 scores in Tables 2 and 3 are point estimates computed on only 200 object IDs (§4.1), yet the central claim is a margin of roughly 18-19 points. Without variance across multiple seeds or confidence intervals for the 200-ID subset, it is impossible to assess whether the reported improvements are meaningful or within run-to-run noise. Please report mean±std over at least three seeds and interval estimates for the GPT-4 evaluation.

minor comments (4)

[Abstract / §1] The term 'first MLLM' for dynamic point clouds should be qualified in light of VG4D (Deng et al., 2024), which already combines 4D point clouds with vision-language modeling. The distinction between task-specific video recognition and open-ended language reasoning should be stated explicitly.
[Table 1] The '4D Asset' column is ambiguous: Diffusion4D and DeformingThings4D are marked with a checkmark, but the text says they lack language annotations. The column definition should clarify whether it means dynamic sequences, generated 4D content, or something else.
[Fig. 5] The merged ablation figure is too small and the legend/axis labels are hard to read, making the comparison between Bs and DA difficult to follow. Please enlarge the figure or split it into two panels with legible labels.
[§4.4] 'We further conduct qualitative evaluate' should read 'quantitative evaluation'. Also, Appendix E says 'GPT-4 compares scores how well...' — please revise the wording and specify the exact GPT-4 model version, temperature, and prompt reproducibility details.

Circularity Check

1 steps flagged · score 6.0 of 10

The reported gains in Tables 2–3 are scored against Qwen-generated 'ground truth' that is also the source of the training supervision, making the headline evaluation self-referential.

other [Sec. 2.2–2.3, Sec. 4.1 and App. E]
"After obtaining the rendered consistent image sequences from the chosen assets, we leverage Qwen2.5-VL (Bai et al., 2025) to generate two-level captions ... The QA pairs are generated based on the previously obtained complex dynamic captions, which are fed back into the LLM for QA generation. ... We adopt GPT-4 as an auxiliary evaluator to assess the semantic alignment between model-generated captions and ground-truth descriptions for dynamic 3D objects."

The 'ground-truth descriptions' used by GPT-4 are the same Qwen-generated captions from which the training QA/caption supervision was derived. The model is trained to produce Qwen-style text and then evaluated on how well it matches that same Qwen-style text. The reported GPT-4 and similarity scores therefore measure imitation of the annotation LLM, not independently verified facts about the dynamic point clouds. No human validation of any caption or answer is reported, so the claimed action-understanding and temporal-reasoning gains are self-referential to the Qwen annotation pipeline.

full rationale

The architecture (Point-BERT encoding, bidirectional Mamba, LLaMA decoding) and the dataset construction (Objaverse assets, Poisson sampling, barycentric correspondence) are not circular in themselves; they use standard externally validated components and a genuine preprocessing pipeline. The circularity lies in the validation loop. Sec. 2.2 has Qwen2.5-VL generate the captions, Sec. 2.3 has Qwen generate QA pairs from those captions, and App. E uses GPT-4 to score model outputs against those same Qwen-generated captions. Thus the numbers in Tables 2–3 measure how well a model reproduces the annotation LLM's text, not independently established facts about dynamic point clouds. A separate leak risk—the 4,000 test IDs are never stated to be disjoint from the 44K training sequences or from the 20% failure-selection set used in bootstrapping (App. D)—would, if real, make the bootstrapping gains circular as well, but the paper provides no evidence of overlap, so I do not count it as a demonstrated circular step. Because the central empirical claim rests on this self-referential evaluation while the method itself has independent content, the score is 6.

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

The central claim rests on (1) a dataset whose references are LLM-generated without human verification, (2) an unstated split between the failure-selection set and the test set, (3) a semantic-similarity metric serving as both the bootstrapping training signal and the evaluation metric, and (4) a fully synthetic data source. The free parameters are hand-chosen operational decisions (16 frames, 8192 points, failure fraction, 12K budget, two rounds) that jointly determine the reported gains; the number of group tokens G and Mamba blocks K are never disclosed. No new physical or conceptual entities are invented.

free parameters (5)

T = 16 sampled frames per sequence = 16
Chosen by hand as an 'optimal balance between computational efficiency and temporal coverage' (Sec. 2.1); all results depend on this sampling density.
N = 8192 points per frame = 8192
Fixed input size (App. D); affects geometry fidelity and temporal correspondence quality.
Failure-selection budget (bottom 40% of a 20% reference slice) = 40% of 20%
App. D: failure-set size chosen by hand; directly determines the bootstrapping training set and the reported Bs1/Bs2 gains.
Bootstrapping sample budget = 12,000
Selected from the Fig. 6 saturation curve (Sec. 4.5); the choice of 12k samples controls the scale of the refinement phase.
Number of group tokens G and Mamba blocks K = not disclosed
The paper never gives G or K (Eq. 1, Sec. 3.1), so core architectural degrees of freedom are unstated.

assumptions (5)

domain assumption Qwen2.5-VL captions and Qwen QA pairs are factually correct descriptions of the Objaverse animations, with no human verification
Sec. 2.2-2.3 and App. B: all ground-truth captions and QA pairs are LLM-generated from rendered frames/captions; if the LLMs hallucinate counts, colors, or motions, the training references and the GPT-4 evaluation targets are jointly wrong.
domain assumption The reference set used for failure identification is disjoint from the 4,000-ID test set
App. D describes failure selection on the 20% dataset but never states it is disjoint from the test IDs; the bootstrapping gains (Fig. 5) depend on this.
domain assumption Semantic-similarity scores (S-BERT, SimCSE, GPT-4) are valid proxies for spatio-temporal reasoning quality
Sec. 3.2 defines the failure ranking via cosine similarity of sentence embeddings; the entire bootstrapping signal and most metrics rest on this correlation.
domain assumption Objaverse synthetic animations are representative of real dynamic point cloud understanding
The dataset is entirely synthetic (Sec. 2.1); no evaluation on real LiDAR/scan sequences or existing 4D action benchmarks validates transfer.
standard math Standard deep-learning machinery (Point-BERT tokenization, state-space models, autoregressive LLM training) behaves as documented
Used in Sec. 3.1; prior work, external to this paper.

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Cite this review

Pith. "Pith review of 4DPC$^2$hat: Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping." pith.science (2026). https://pith.science/paper/3HOVOPKH

@misc{pith2026260203890,
  author       = {Pith},
  title        = {Pith review of: 4DPC$^2$hat: Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/3HOVOPKH}},
  note         = {Machine review of arXiv:2602.03890}
}

abstract

Point clouds provide a compact and expressive representation of 3D objects, and have recently been integrated into multimodal large language models (MLLMs). However, existing methods primarily focus on static objects, while understanding dynamic point cloud sequences remains largely unexplored. This limitation is mainly caused by the lack of large-scale cross-modal datasets and the difficulty of modeling motions in spatio-temporal contexts. To bridge this gap, we present 4DPC$^2$hat, the first MLLM tailored for dynamic point cloud understanding. To this end, we construct a large-scale cross-modal dataset 4DPC$^2$hat-200K via a meticulous two-stage pipeline consisting of topology-consistent 4D point construction and two-level captioning. The dataset contains over 44K dynamic object sequences, 700K point cloud frames, and 200K curated question-answer (QA) pairs, supporting inquiries about counting, temporal relationship, action, spatial relationship, and appearance. At the core of the framework, we introduce a Mamba-enhanced temporal reasoning MLLM to capture long-range dependencies and dynamic patterns among a point cloud sequence. Furthermore, we propose a failure-aware bootstrapping learning strategy that iteratively identifies model deficiencies and generates targeted QA supervision to continuously strengthen corresponding reasoning capabilities. Extensive experiments demonstrate that our 4DPC$^2$hat significantly improves action understanding and temporal reasoning compared with existing models, establishing a strong foundation for 4D dynamic point cloud understanding.

Figures

Figures reproduced from arXiv: 2602.03890 by the authors.

**Figure 1.** (a) We present 4DPC2 hat, the first multimodal large language model specially tailored for 4D point cloud sequence understanding, which supports the answering of diverse challenging question types, from action recognition to temporal relations, etc. (b) Comparing with existing state-of-the-art models, 4DPC2 hat demonstrates significantly superior performance on the comprehensive metric set. Abstract Point clouds pro… view at source ↗

**Figure 2.** Illustration of the 4DPC2 hat-200K dataset collection pipeline. The dataset contains 700K temporally ordered point cloud frames and 200K high-quality question–answer pairs, enabling both 4D object captioning and 4D object QA tasks. pipeline that iteratively analyzes model deficiencies and generates targeted question–answering samples to progressively strengthen the corresponding abilities. • We curate 4DPC2hat-200K,… view at source ↗

**Figure 3.** The 4DPC2 hat framework. Dynamic point cloud frames are first encoded by Point-BERT into group-level and global tokens, followed by bidirectional Mamba-based temporal modeling across frames. The resulting spatio-temporal representation is aligned with LLM for 4D captioning and question-answering. Failure-Aware Bootstrapping Learning utilizes model’s errors with semantic-based evaluation and selection, making analysi… view at source ↗

Figures from the paper (10 more)

**Figure 5.** Figure 5: Merged comparison ablation on Naive Data Augmentation (DA) and Bootstrapping Learning (Bs) [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]

**Figure 6.** Figure 6: The Impact of Data Scale on Bootstrapping. underrepresented failure modes rather than diluting them in a uniformly mixed corpus. Consequently, bootstrapping achieves substantially larger and more balanced improvements: under GPT-4 evaluation, Bs 2 increases the overal…

**Figure 1.** Figure 1: Prompt for the multilingual large language model to generate detailed and brief descriptions of 4D objects. Within this prompt, we describe the object’s actions, appearance, and changes over time. All QA pairs are automatically generated using Qwen based on the previou…

**Figure 2.** Figure 2: Distribution of the five subtasks within the 4D object question-answering task, with a total of 145k question-answer pairs. C. Data Construct from Bootstrapping Learning We design a structured prompt to support failure-aware bootstrapping learning by explicitly diagnos…

**Figure 3.** Figure 3: Prompts for the multilingual large language model to generate 4D question-answer pairs. Within this prompt, we examine five distinct perspectives to comprehensively formulate questions. 14 [PITH_FULL_IMAGE:figures/full_fig_p014_3.png]

**Figure 4.** Figure 4: failure-aware prompt for Bootstrapping QAs generation. Based on the identified error type, the model generates exactly one new question–answer pair that directly probes the deficient reasoning capability. Strict constraints prevent paraphrasing, meta-questions, or hall…

**Figure 5.** Figure 5: The prompt used in GPT-4 metric. GPT-4 compares scores how well the key information in the ground truth is correctly or partially reflected in the model output, producing a normalized score from 0 to 100. and produces a coherent description or answer for the entire seq…

**Figure 6.** Figure 6: Temporal aggregation prompt for enabling fair evaluation of static 3D models on dynamic point cloud sequences by summarizing frame-wise predictions with Qwen3. 18 [PITH_FULL_IMAGE:figures/full_fig_p018_6.png]

**Figure 7.** Figure 7: Qualitative comparison of 4D object captioning and QA across dynamic point cloud sequences. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_7.png]

**Figure 8.** Figure 8: Results on 4D object captioning and QA across dynamic point cloud sequences. 20 [PITH_FULL_IMAGE:figures/full_fig_p020_8.png]

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Forward citations

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Reference graph

Works this paper leans on

21 extracted references · 10 linked inside Pith · cited by 2 Pith papers

[5]

Fan, H., Yang, Y ., and Kankanhalli, M

URL https://arxiv.org/abs/1910.08287. Fan, H., Yang, Y ., and Kankanhalli, M. Point 4d transformer networks for spatio-temporal modeling in point cloud videos. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14204–14213, June 2021a. Fan, H., Yu, X., Ding, Y ., Yang, Y ., and Kankanhalli, M. Pstnet: Point spa...

arXiv 1910
[8]

URL https://arxiv.org/abs/2309. 00615. 9 Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping Hong, Y ., Zhen, H., Chen, P., Zheng, S., Du, Y ., Chen, Z., and Gan, C. 3d-llm: Injecting the 3d world into large language models.NeurIPS, 2023a. Hong, Y ., Zhen, H., Chen, P., Zheng, S., Du, Y ., Chen, Z., and Gan, C. 3d-llm: Injecting the...

2024
[9]

ISBN 978-3-031-72673-6

Springer Nature Switzerland. ISBN 978-3-031-72673-6. Li, Y ., Ma, L., Zhong, Z., Liu, F., Chapman, M. A., Cao, D., and Li, J. Deep learning for lidar point clouds in au- tonomous driving: A review.IEEE Transactions on Neu- ral Networks and Learning Systems, 32(8):3412–3432, 2021a. doi: 10.1109/TNNLS.2020.3015992. Li, Y ., Takehara, H., Taketomi, T., Zheng...

arXiv 2020
[10]

Papineni, K., Roukos, S., Ward, T., and Zhu, W

URL https:// arxiv.org/abs/2303.08774. Papineni, K., Roukos, S., Ward, T., and Zhu, W. J. Bleu: a method for automatic evaluation of machine translation. InProc Meeting of the Association for Computational Linguistics,

arXiv
[12]

Satanjeev, B

URL https://arxiv.org/abs/1908.10084. Satanjeev, B. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments.ACL- 2005, pp. 228–231,

arXiv 1908
[14]

ISBN 9798400706868

Association for Comput- ing Machinery. ISBN 9798400706868. doi: 10.1145/ 3664647.3681257. URL https://doi.org/10. 1145/3664647.3681257. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi `ere, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G. Llama: Open and efficient foundation l...

arXiv
[15]

Wang, J., Li, X., Sullivan, A., Abbott, L., and Chen, S

URL https://arxiv.org/ abs/2302.13971. Wang, J., Li, X., Sullivan, A., Abbott, L., and Chen, S. Pointmotionnet: Point-wise motion learning for large- scale lidar point clouds sequences. InProceedings of the 10 Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Wo...

arXiv
[16]

Pointllm-v2: Empowering large language models to better understand point clouds.IEEE Transactions on Pattern Analysis and Machine Intelligence, pp

Xu, R., Yang, S., Wang, X., Wang, T., Chen, Y ., Pang, J., and Lin, D. Pointllm-v2: Empowering large language models to better understand point clouds.IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–15, 2025a. doi: 10.1109/TPAMI.2025.3590784. Xu, Z., Yan, W., Shi, Y ., Meng, X., He, T., Zhuang, H., Li, M., and Fan, H. Scieducator: Sc...

arXiv 2025

Show all 21 references

[17]

Yu, X., Tang, L., Rao, Y ., Huang, T., Zhou, J., and Lu, J

URL https://arxiv.org/ abs/2505.09388. 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. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1...

arXiv
[18]

Zhu, Z., Ma, X., Chen, Y ., Deng, Z., Huang, S., and Li, Q

URL https: //arxiv.org/abs/2401.09417. Zhu, Z., Ma, X., Chen, Y ., Deng, Z., Huang, S., and Li, Q. 3d-vista: Pre-trained transformer for 3d vision and text alignment. InProceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2911–2921, October

arXiv
[19]

11 Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping Appendix A. Related Work 3D Multimodal Large Language Models.Building on multi-modal LLMs, recent work (Zhu et al., 2023; Qi et al., 2024b; Hong et al., 2023b)has explored integrating 3D vision with ...

2023
[20]

However, these models operate on single-frame or static point clouds

leverages 2D visual priors to improve 3D–language alignment, demonstrating that cross-modal knowledge can enhance instruction-following with limited 3D supervision. However, these models operate on single-frame or static point clouds. Their architectures and training data do n...

2019
[21]

Following PointLLM (Xu et al., 2024), we employ a two-level captioning instruction design for dynamic point cloud sequences

as the annotation model. Following PointLLM (Xu et al., 2024), we employ a two-level captioning instruction design for dynamic point cloud sequences. We construct brief captioning instructions that require concise descriptions of the dynamic point cloud, primarily used to faci...

2024
[2002]

Shapellm: Universal 3d object understanding for embodied interaction.arXiv preprint arXiv:2402.17766, 2024a

Qi, Z., Dong, R., Zhang, S., Geng, H., Han, C., Ge, Z., Wang, H., Yi, L., and Ma, K. Shapellm: Universal 3d object understanding for embodied interaction.arXiv preprint arXiv:2402.17766, 2024a. Qi, Z., Fang, Y ., Sun, Z., Wu, X., Wu, T., Wang, J., Lin, D., and Zhao, H. Gpt4poi...

arXiv
[2005]

R., Li, M., and Yeo, S

Shi, Y ., Yan, W., Xu, G., Li, Y ., Chen, Y ., Li, Z., Yu, F. R., Li, M., and Yeo, S. Y . Pvchat: Personalized video chat with one-shot learning.arXiv preprint arXiv:2503.17069,

arXiv
[2007]

ISBN 9781450347266

Associa- tion for Computing Machinery. ISBN 9781450347266. doi: 10.1145/1278780.1278807. URL https://doi. org/10.1145/1278780.1278807. Chen, S., Garcia, R., Laptev, I., and Schmid, C. Sugar: Pre- training 3d visual representations for robotics. InProceed- ings of the IEEE/CVF ...
[2019]

doi: 10.1109/TITS.2019. 2892405. Aygun, M., Osep, A., Weber, M., Maximov, M., Stachniss, C., Behley, J., and Leal-Taixe, L. 4d panoptic lidar seg- mentation. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5527–5537, June

2019 doi
[2022]

URL https: //arxiv.org/abs/2104.08821. Gu, A. and Dao, T. Mamba: Linear-time sequence mod- eling with selective state spaces. InFirst conference on language modeling,

arXiv
[2023]

Gao, T., Yao, X., and Chen, D

doi: 10.1109/TPAMI.2022.3161735. Gao, T., Yao, X., and Chen, D. Simcse: Simple contrastive learning of sentence embeddings,

2022
[2024]

doi: 10.1109/ ICRA57147.2024.10610217. Fan, H. and Yang, Y . Pointrnn: Point recurrent neural network for moving point cloud processing,

2024
[2025]

Bridson, R

URL https: //arxiv.org/abs/2502.13923. Bridson, R. Fast poisson disk sampling in arbitrary dimen- sions. InACM SIGGRAPH 2007 Sketches, SIGGRAPH ’07, pp. 22–es, New York, NY , USA,

2007 arXiv

Pith tools

Reviewed August 3, 2026 · model on record in the stance chip above.

[1] [5]

Fan, H., Yang, Y ., and Kankanhalli, M

URL https://arxiv.org/abs/1910.08287. Fan, H., Yang, Y ., and Kankanhalli, M. Point 4d transformer networks for spatio-temporal modeling in point cloud videos. InProceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14204–14213, June 2021a. Fan, H., Yu, X., Ding, Y ., Yang, Y ., and Kankanhalli, M. Pstnet: Point spa...

arXiv 1910

[2] [8]

URL https://arxiv.org/abs/2309. 00615. 9 Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping Hong, Y ., Zhen, H., Chen, P., Zheng, S., Du, Y ., Chen, Z., and Gan, C. 3d-llm: Injecting the 3d world into large language models.NeurIPS, 2023a. Hong, Y ., Zhen, H., Chen, P., Zheng, S., Du, Y ., Chen, Z., and Gan, C. 3d-llm: Injecting the...

2024

[3] [9]

ISBN 978-3-031-72673-6

Springer Nature Switzerland. ISBN 978-3-031-72673-6. Li, Y ., Ma, L., Zhong, Z., Liu, F., Chapman, M. A., Cao, D., and Li, J. Deep learning for lidar point clouds in au- tonomous driving: A review.IEEE Transactions on Neu- ral Networks and Learning Systems, 32(8):3412–3432, 2021a. doi: 10.1109/TNNLS.2020.3015992. Li, Y ., Takehara, H., Taketomi, T., Zheng...

arXiv 2020

[4] [10]

Papineni, K., Roukos, S., Ward, T., and Zhu, W

URL https:// arxiv.org/abs/2303.08774. Papineni, K., Roukos, S., Ward, T., and Zhu, W. J. Bleu: a method for automatic evaluation of machine translation. InProc Meeting of the Association for Computational Linguistics,

arXiv

[5] [12]

Satanjeev, B

URL https://arxiv.org/abs/1908.10084. Satanjeev, B. Meteor: An automatic metric for mt evaluation with improved correlation with human judgments.ACL- 2005, pp. 228–231,

arXiv 1908

[6] [14]

ISBN 9798400706868

Association for Comput- ing Machinery. ISBN 9798400706868. doi: 10.1145/ 3664647.3681257. URL https://doi.org/10. 1145/3664647.3681257. Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M.-A., Lacroix, T., Rozi `ere, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., and Lample, G. Llama: Open and efficient foundation l...

arXiv

[7] [15]

Wang, J., Li, X., Sullivan, A., Abbott, L., and Chen, S

URL https://arxiv.org/ abs/2302.13971. Wang, J., Li, X., Sullivan, A., Abbott, L., and Chen, S. Pointmotionnet: Point-wise motion learning for large- scale lidar point clouds sequences. InProceedings of the 10 Towards Dynamic Point Cloud Understanding with Failure-Aware Bootstrapping IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Wo...

arXiv

[8] [16]

Pointllm-v2: Empowering large language models to better understand point clouds.IEEE Transactions on Pattern Analysis and Machine Intelligence, pp

Xu, R., Yang, S., Wang, X., Wang, T., Chen, Y ., Pang, J., and Lin, D. Pointllm-v2: Empowering large language models to better understand point clouds.IEEE Transactions on Pattern Analysis and Machine Intelligence, pp. 1–15, 2025a. doi: 10.1109/TPAMI.2025.3590784. Xu, Z., Yan, W., Shi, Y ., Meng, X., He, T., Zhuang, H., Li, M., and Fan, H. Scieducator: Sc...

arXiv 2025