Title: ZigzagPointMamba: Spatial-Semantic Mamba for Point Cloud Understanding
Venue: NeurIPS 2025
Accepted: yes
Keywords: Point cloud understanding

State-space models

Mamba

self-supervised learning

Masked Autoencoder
Abstract: State Space models (SSMs) like PointMamba provide efficient feature extraction for point cloud self-supervised learning with linear complexity, surpassing Transformers in computational efficiency. However, existing PointMamba-based methods rely on complex token ordering and random masking, disrupting spatial continuity and local semantic correlations. We propose \textbf{ZigzagPointMamba} to address these challenges. The key to our approach is a simple zigzag scan path that globally sequences point cloud tokens, enhancing spatial continuity by preserving the proximity of spatially adjacent point tokens. Yet, random masking impairs local semantic modeling in self-supervised learning. To overcome this, we introduce a Semantic-Siamese Masking Strategy (SMS), which masks semantically similar tokens to facilitate reconstruction by integrating local features of original and similar tokens, thus overcoming dependence on isolated local features and enabling robust global semantic modeling. Our pre-training ZigzagPointMamba weights significantly boost downstream tasks, achieving a 1.59\% mIoU gain on ShapeNetPart for part segmentation, a 0.4\% higher accuracy on ModelNet40 for classification, and 0.19\%, 1.22\%, and 0.72\% higher accuracies respectively for the classification tasks on the OBJ-BG, OBJ-ONLY, and PB-T50-RS subsets of ScanObjectNN. Code is available at https://github.com/Rabbitttttt218/ZigzagPointMamba.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes the ZigzagPointMamba framework. To address the spatial continuity destruction and random masking defects of PointMamba in point cloud self-supervised learning, the zigzag scan path and semantic twin mask strategy (SMS) are introduced. The zigzag path generates a spatially continuous token sequence on a three-dimensional plane through hierarchical sorting to enhance local geometric consistency; SMS performs masking based on token semantic similarity, forcing the model to use global semantic reconstruction to improve the robustness of feature representation. Experiments show that on datasets such as ModelNet40, ShapeNetPart, and ScanObjectNN, ZigzagPointMamba improves by 0.4%-1.59% in classification and segmentation tasks compared to PointMamba, verifying the effectiveness of the method.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**:\n1. Optimization of spatial continuity: The zigzag scanning path generates a structured token sequence in the XY, XZ, and YZ planes through layering and alternating sorting, effectively retaining the spatial proximity of adjacent points and alleviating the geometric fragmentation problem caused by traditional scanning methods (such as random and Z-order).\n2. Semantic-aware masking: SMS calculates token redundancy through the cosine similarity matrix, masks semantically similar areas, and prompts the model to rely on global context rather than local features, improving semantic modeling capabilities, especially in complex backgrounds (OBJ-BG) and disturbance scenes (PB-T50-RS).\n3. Lightweight and efficient: The state space model (SSM) based on PointMamba maintains linear computational complexity, and the parameter scale is comparable to the baseline (12.3M), but the performance is improved through structural optimization, which is suitable for large-scale point cloud processing.\n\n**Weaknesses**:\n1. Originality limitations: Zigzag scanning and semantic masking are both combined innovations of existing technologies, lacking theoretical breakthroughs. The zigzag path is based on two-dimensional image scanning, and the three-dimensional expansion is only plane splicing, and no new spatial sorting paradigm is proposed; SMS is essentially a masking strategy based on similarity, which is similar to the existing semantic perception methods (such as GeoMAE).\n2. The performance improvement is small: it only improves by 0.4% in ModelNet40, 1.59% in ShapeNetPart, and less than 1% in some scenarios (such as PB-T50-RS). The improvement effect has not significantly exceeded the baseline, and it is difficult to reflect a revolutionary breakthrough.\n3. Dependence on preprocessing steps: FPS is required to select key points and KNN to construct local neighborhoods. It is sensitive to sparse point clouds or non-uniformly sampled data, which may affect the generalization in real scenarios such as autonomous driving.\n4. Limitations of the masking strategy: The SMS threshold needs to be manually tuned (default 0.8), and it relies on the cosine similarity of the normalized feature vector, which is sensitive to changes in feature distribution and may fail in cross-modal or heterogeneous data.\n\nQUESTIONS:\n1. Extreme scenario robustness: Is the spatial continuity of the zigzag path still valid in scenarios with drastic changes in point cloud density (such as long-range scanning by drones) or dynamic occlusion?\n2. Interpretability of semantic masks: How does SMS ensure that the masked area corresponds to real semantic units (such as \"wheels\") rather than statistical redundancy? Are there any visualization or ablation experiments to verify semantic consistency?\n3. Computational efficiency trade-off: The hierarchical sorting of the zigzag path and the similarity matrix calculation of SMS increase the preprocessing time. Have you conducted latency tests in scenarios with high real-time requirements (such as robot navigation)?\n\nLIMITATIONS:\n1. Geometric prior dependency: Zigzag scanning assumes that local geometric proximity is equivalent to semantic relevance, but in non-rigid objects (such as vegetation and flexible robotic arms), spatial proximity may not reflect semantic associations, leading to feature confusion.\n2. Semantic mask bias: SMS's hard threshold mask based on cosine similarity (default 0.8) may mistakenly delete key semantic tokens (such as similar features between car tires and car bodies). Ablation experiments show that a threshold fluctuation of 0.1 leads to a 1.5% drop in accuracy.\n3. SSM architecture bottleneck: Continuing the recursive state transfer architecture of PointMamba, it is difficult to model long-range dependencies (such as global motion across frames), and performance may deteriorate in video-level point cloud sequences.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-06-04"}

comments: SUMMARY:
1. This paper proposes a zigzag scan path to preserve spatial proximity to mitigate discontinuities in the previous traversal order.
2. The authors introduce the semantic-siamese mask strategy to enhance token semantic associations for point MAE.

STRENGTHS AND WEAKNESSES:
Strengths:
1. The paper is well written and easy to follow.
2. ZigzagPointMamba achieves the state-of-the-art performance in ModelNet40, sanObjectNNDa and ShapeNetPart datasets.

Weaknesses:
Please refer to the questions.

QUESTIONS:
1. The novelty is limited. The exploration of traversal order does not present significant new insights, and relevant works are insufficiently cited [1][2][3]. Additionally, leveraging semantics for MAE has become quite common[4].
2. The proposed method is only validated on classification tasks, which are relatively small datasets and prone to overfitting. It is recommended to further evaluate the method on segmentation tasks to better demonstrate its effectiveness.
3. There is a figure citation error on page 4, second line from the bottom.

[1] Point Cloud Mamba: Point Cloud Learning via State Space Model.  
[2] Spectral Informed Mamba for Robust Point Cloud Processing.  
[3] Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection.  
[4] SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders.

LIMITATIONS:
Please refer to the questions.
TITLE: Official Review
CLARITY: 3
IMPACT: 2
ORIGINALITY: 2
DATE: 2025-07-02

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe paper proposes the ZigzagPointMamba method, a point cloud self-supervised learning model that integrates a zigzag scanning path with a Semantic Masking Strategy (SMS). It addresses two core issues of existing PointMamba methods: reliance on complex token ordering (e.g., random or Hilbert scans) that disrupts spatial continuity, and random masking strategies that harm local semantic correlations. By globally serializing point clouds via zigzag scanning to preserve spatial continuity and combining it with SMS to mask semantically similar regions, the method enhances global modeling.\n\nSTRENGTHS AND WEAKNESSES:\nStrength\n\n1. Clear innovations: The paper introduces a 3D zigzag scanning path, which hierarchically orders points in XY/XZ/YZ planes and generates spatially continuous sequences through alternating directional sorting, resolving the geometric discontinuity caused by random/Hilbert scans; SMS calculates redundancy scores based on cosine similarity, masking semantically similar regions (e.g., chair arms, wheels, or other holistic components), forcing the model to rely on global context for reconstruction and overcoming the limitations of random masking’s local dependency.\n\n2 . Experimental validation across diverse scenarios: The method demonstrates significant robustness in classification, segmentation, and few-shot learning tasks, particularly in noisy (ScanObjectNN-OBJ-BG) and deformed (PB-T50-RS) environments.\n\nWeakness\n\nMajor issues:\n\n 1.Table 6 only compares SMS with attention mechanisms but does not evaluate random masking or other strategies under the same scanning path. It is recommended to add ablation studies for the proposed ZigzagPointMamba method.\n\n2.No ablation experiments are designed to verify the synergistic gain of zigzag + SMS, leaving the \"1+1>2\" effect questionable. The individual contributions of zigzag scanning and SMS remain unquantified.\n\n3 .SMS is only described with basic formulas, algorithmic explanations, and experiments, lacking a complete derivation or proof of its mathematical optimality.\n\n4 .As shown in Table 5, SMS is sensitive to hyperparameters (thresholds). The authors do not provide an adaptive threshold mechanism.\n\nQUESTIONS:\nPlease see the weakness above\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-03"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper investigates novel scanning and masking strategies for mamba networks in the context of 3D point clouds. Specifically, the authors extend the 2D zigzag scan path [13] to point cloud tokens, thereby accounting for spatial proximity of 3D points and generating smoother, spatially coherent token sequences.  When dealing with 3D point cloud data, the problem becomes more complex, and the authors propose a zigzag scan path that extends the 2D approach by performing scanning operations on three planes (the XY-, XZ-, and YZ-planes), following key points selection with farthest point sampling. They perform hierarchical layering and alternating sorting to generate traversal paths, thereby generating ordered sequences, in which the feature vectors of adjacent points reflect their 3D neighborhood. They further enhance the model with a new masking strategy, which compute semantic similarity from token vectors, identifies redundant regions, and performs masking to discourages the model from over-relying on local information, hence capturing long-range relationships. They report comprehensive experiments and comparisons, including object classification on the ScanObjectNN Dataset, classification on the ModelNet40 Dataset, part segmentation on the ShapeNetPart Dataset, in addition to few-shot learning experiments  on ModelNet40.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n\n-\tOverall, a clear, well written and technically sound paper.\n\n-\tA well-executed engineering effort to customize zigzag scan paths to 3D point clouds. While zigzag scan path has been explored in the past for 2D image processing (e.g.,  [13]), the extension to 3D point clouds is, as pointed above, not trivial. \n\n-\tComprehensive experiments and comparisons.\n\nWeaknesses:\n\n-\tThe results are mixed: For example, in Table 2 (Classification on ModelNet40 Dataset), the proposed method underperforms PointM2AE [37] (NeurIPS 22) in term of the OA performance measure, while halving the number of parameters and FLOPs. While reducing the computational load might be interesting, the fact that the method is not competitive w.r.t to other non-Mamba SOTA methods might be a significant weakness. In fact, in terms of performance, the method beats the PointMamba baseline [16] (which has the same computational load) but not the other SOTA methods.  BTW, highlighting the performance of ZigzagPointMamba (the proposed method) in bold in all tables (Tables 1, 2, 3, and 4) is a bit misleading (as it does not correspond to the best performance). Note that a similar behaviour is also observed in the part segmentation experiments in Table 3.  Furthermore, in the few-shot learning experiments on ModelNet40 (Table 4), the proposed method underperforms PointMAE and other non-Mamba SOTA methods by near 10 percent points. This is a bit concerning.  \n\n-\tThe authors provide anonymous link to the code, but the repository is empty (it contains pointer to a zip file that could not be found).\n\nQUESTIONS:\nI do not have much technical questions, except the following:\n\nTable 3: Is there a reason why the computation measures (# of param and FLOPs) are not reported for the part segmentation experiments?\n\nI am hesitating for my overall recommendation for this paper, but leaning towards acceptance. There is a good engineering effort (with moderate technical novelty) but the main concern is that the method does not outperform non-Mamba SOTA methods in terms of performance measures (but just has the benefit of reducing the computation load, in comparison to these SOTA methods). It just outperforms the PointMamba baseline (which has the same computational load). If I am missing something in this regard, please clarify (and I would be happy to increase my score)?\n\nLIMITATIONS:\nThe authors did not address the limitations and potential negative societal impact of their work. However, I do not see any serious issue in regard potential negative societal impact.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-05"}