ZigzagPointMamba: Spatial-Semantic Mamba for Point Cloud Understanding
Authors: LinshuangDiao
Sensen Song
Yurong Qian
Dayong Ren
Venue: NeurIPS 2025 poster ()
Keywords: Point cloud understanding
State-space models
Mamba
self-supervised learning
Masked Autoencoder
Primary area: deep_learning

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.

ZigzagPointMamba: Spatial-Semantic Mamba for Point Cloud Understanding
Linshuang Diao 1.2
Sensen Song 1.2+
Yurong Qian 1.2 Dayong Ren
Key Laboratory of Signal Detection and Processing, Xinjiang University Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing. Xinjiang University National Key Laboratory for Novel Software Technology.
Nanjing University, Nanjing 210023, China
songsensendstu. xju. edu. en, rdyedu@gmail.com
Abstract
State Space models (SSMs) like PointMamba provide efficient feature extrac- tion for point cloud self-supervised learning with linear complexity, surpassing Transformers in computational efficiency. However, existing PointMamba-haa methods rely complex token ordering and random masking, disrupting spatial continuity and local semantic correlations. We propose ZigzagPointMamba to address these challenges. The key to our approach is 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 Conantic-Siamess Masking Strategy (SMS), which masks semantically similar to- kens 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 MogzamaointMamba weights signifi- cantly boost downstream tasks, achieving a 1.59% mloU gain on ShapeNetPart for part segmentation, 0.4% higher accuracy on ModelNet40 for classification, and 0. 0.19% 1.22%, and 0.72% higher accuracies respectively for the classification tasks the OBJ-BG, OBJ-ONLY, and PB-T50-PP subsets of ScanObjectNN. Code is available http:://ggthbbcommm/oooottttttt/ Poiniiiiaaa.aa..aaaaaa..
Introduction
Deep learning-based point cloud analysis requires models extract and interpret intricate geo- metric features from unstructured spatial data. Traditional approaches, such as MLP 26 and Transformer architecture(27
often hindered by high computational complexity substantial resource demands, and limited generalization across diverse datasets. To address these challenges, PointMamba 18 has emerged as novel framework. By leveraging state-space model, Point- Mamba achieves linear computational complexity and robust global feature aggregation, effectively balancing architectural simplicity with efficiency. Its strong knowledge transfer abilities. especially in self-supervised learning, have shown excellent performance and driven significant research into PointMamba-basel models.
Despite the efficiency of PointMamba based methods in global feature aggregation through state- space models, their dependence conventional scanning schemes such as random, Hilbert, Z--
CCoresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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93.50
ModelNet 40
94.20
SMS
Random
Before fine-tuning
92.75
93.15
Visable regions regions Masked tokens Recomstraction
94.15
NetpaNetPPurt
92.48
84,20
OBJ-BR B8--BG
84,66
84,49
93.96
After fine-tuning
87.88
90.88
87.93
91.57
PB-T50.P -0-SS--RS S
48.65
OBI-ONLY
90.36
92.10
ACT PointManha GeoMask3D ZiggagPoinMMomma(Guss
(b) Comparison of the Effects of (c) Features Before and After Fine-tuning
(a) Comparision of Performance
SMS and Random Masking
Figure 1: As can seen from Fig. (a). compared with ACT, PointMamba, and GeoMask3D, our proposed Zigzag PointMamba performs better on the ScanObjectNN dataset. Fig. (b) presents stark contrast between the effects of SMS and random masking, highlighting the superiority of our proposed method in terms of reconstruction. Fig. (c) demonstrates the features before and after fine-tuning, indicating the effectiveness of our method in refining feature representations.
order approaches-offe disrupts spatial continuity, leading to suboptimal performance in downstream tasks like point cloud segmentation and classification. For instance, random scanning fragments local geometric coherence, while Hilbert and Z-order schemes struggle to adapt to complex topologies, resulting in disjointed token sequences that impair feature consistency. To address this, propose EegzagPoinnMambee a novel method that employs zigzag scanning path to globally sequence point cloud tokens while preserving spatial proximity. By generating smoother, spatially coherent token sequences,
enhances the quality of feature representations, with preliminary experiments demonstrating 1.59% mloU improvement in part segmentation on ShapeNetPart and 0.4% accuracy gain in classification on ModelNer40.
further enhance PointMamba's self-supervised learning capabilities by addressing limitations in its masking strategy. Traditional random masking, which relies adjacent tokens reconstruct masked regions, struggles to capture global semantic dependencies, compromising performance in downstream point cloud tasks such as segmentation and classification. To overcome this, we introduce the Conantic-Siamesse Masking Strategy (SMS), which leverages the smooth token sequences from ZigzaggPinttMamaa to mask semantically similar local structures, thereby strengthening token-level semantic associations. By integrating local features of original and semantically related tokens, SMS enables robust global semantic modeling, yielding more accurate feature representations. Preliminary results validate the synergy of ZigzagPointtMmmb and SMS, achieving to 1 1.22% accuracy improvements across classification on Scan SbjeCNNNN subsets (OBJ-BG, OBJ-ONLY, PB-T50-RS).
Building the spatially coherent token sequences provided by ZigzagPointMambar
SMS employs a Siamese-like comparison to evaluate semantic similarity between point cloud tokens, enabling targeted masking of coherent structures, such as entire object parts like chair's armrest car's wheel, rather than random token selections. By leveraging smooth, spatially continuous tokens, SMS ensures that masked semantic tokens maintain both spatial proximity and semantic continuity, preserving the topological integrity of the point cloud during self-supervised learning. Specifically, SMS operates through two key steps: (1) The point cloud is decomposed into fine-grained semantic tokens by leveraging the zigzag scanning order, and (2) A Siamese-like like mechanism evaluates token- wise similarity scores, and tokens exceeding predefined similarity threshold are masked. This strategy eliminates redundant local features, forcing the model to reconstruct masked regions by relying on global semantic context from retained tokens. This strategy mitigates the limitations of traditional random masking, which relies solely local information and disrupts semantic coherence, thereby optimizing global feature modeling and enhancing self-supervised learning capabilities. Combined with ZigzagPointMambi's spatial advantages, SMS achieves superior reconstruction

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quality and more distinct feature distributions after fine-tuning Our ZigzagPointMammi achieves excellent performance on various point cloud analysis datasets (as shown in (a).) In addition. the reconstruction effect of SMS we proposed is better, and the feature distribution after fine-tuning also more distinct (as shown in (b)) and Fig (c))
Collectively, our contributions include: (I) zigzag scan path that preserves spatial proximity, mitigating discontinuities in traditional scanning methods; (2) the SMS approach, which enhances token-level semantic associations for robust global feature modeling; and (3) the integration of spatial and semantic continuity in ZigzagPPinttambu, significantly advancing point cloud analysis. particularly in self-supervised learning applications.
2 Related Works
2.1 Point Cloud Analysis Methods Based on MLP
Early deep learning methods for point cloud processing 28 30,, such as PointNet(25], used shared multi-layer perceptrons (MLPs) for feature extraction and max pooling to aggregate global informa- tion. However, these methods had limitations in modeling local geometric structures. PointNet++ improved this by introducing hierarchical MLPs and farthest point sampling (FPS) for multi-scale feature learning. Subsequently, methods like Landl-A-Netip proposed lightweight architectures that processed large-scale point clouds through random sampling and local feature aggregation mechanisms, significantly enhancing computational efficiency. PointMLP(23) constructed networks purely based on residual MLPs and introduced local geometric affine module, achieving certain results in point cloud tasks and providing a network architecture foundation for the application of subsequent self supervisdd learning methods in point cloud processing. Later, [44] enhanced the MLP architecture through efficient addition and shift operations, reducing computational complexity while improving point cloud classification performance. HPE [66] improved MLPs using digendiiensi positional encoding, mapping the 3I) coordinates of points to a high-dimensional space to effectively enhance the representation of point cloud positional information and further boost the MLP's ability to model local structures. PointMT 4 combines the efficiency of MLPs with the global feature capture capability of Transformers, and introduces innovative mechanisms address the computational bottlenecks of traditional Transformers, enabling efficient point cloud analysis.
2.2 The Deep Evolution of Transformer Architecture and Masking Strategies
In recent years, Transformer-basse point cloud processing methods have made synergistie progress in both architectural design and masking strategies. PCT[[O] first introduced self-attention mechanisms into point cloud processing. while Point Transformer V1 42 enhanced geometric modeling through vector attention. Subsequently, Point Transformer | and OctFormer (35) improved computa- tional efficiency via hierarchical attention and octree partitioning. respectively, GPSFormer (34] and DA have achieved innovative integration of graph structures and dynamic path mecha- nisms. while Siamese-basee based approaches[20) have demonstrated versatile Transformer architectures for 3D tracking tasks. Mcanwhile, the evolution of masking strategies is deeply intertwined with Transformer architecture development: Toint-BERT 39 was the first to adapt the masked pre-training paradigm from natural language processing to point clouds, establishing self-supervised learning framework through coordinate reconstruction tasks. Recent advances self-supervised learning 38 have further explored contrastive learning approaches for point cloud understanding. Point-MAB 24 further strengthened feature learning by increasing the masking ratio, while Point-M2AE|[1 intro- duced multi-scale masking framework that synergizes with Transformer's hierarchical architecture. Methods such as GeoMAE [3] and SemMAE 16] incorporate geometric/6 (6] awareness and semantic guidance into masking design, enabling models to better understand spatial layouts while maintaining computational efficiency.
2.3 The Development of State-Space Models in Point Cloud Processing
Due to the quadratic complexity of Transformers, State Space Models (SSMs) have become research hotspot for their linear computational complexity (29] The Mamba model demonstrated the high- efficiency potential of SSMs in sequence processing and proposed an effective linear-time sequence modeling method. In further research, the attention mechanism of the Mamba model was explored,
3

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KNN
Morking
GT
Visable Tokens
Loss
Point center
Token embedding layer
Pred
Random Masking
Reconstruction
FPS
Mamba Decoder
t0
SMS
Mamba Encoder
Point cloud
Zigzag scan
Input Tokens
Autoencoder pre-training
Figure 2: ZigzagPointMamba pre-training pipeline. Select key point cloud points with FPS. Extract feature labels via KNN algorithm and lightweight PointNet. Serialize using the zigzag scan path. Input serialized features into point cloud MAE architecture with SMS for training, obtaining point cloud feature representations and providing parameters for downstream tasks.
revealing how to enhance the capture of key information while maintaining efficient computation [ Subsequently, many Mamba-based variants have been applied various fields [1 36 36]. In
the point cloud domain, recent developments include spectral-informed approaches 3 for robust processing and voxel-based methods [40) for 3D object detection. For example, U-Mamba 22 enhances long-range dependency modeling in biomedical image segmentation, and Local [S further optimizes the performance visual State Space Models by introducing windowed selective scanning. However, these models still face significant challenges when processing complex geometric structure data such as point clouds.
was the first to apply the SSM framework to point cloud processing, achieving remarkable progress in feature extraction. However, its scan method may hinder the capture of geometric structures, while random masking may obscure key information. To address these issues, we propose the ZigzagPointMamba model. By combining the zigzag scan path and the Semantic- Siamese Masking Strategy (SMS), ZigzagPointMambee ensures that spatially adjacent points maintain proximity and effectively learns global semantics.
PointMamba 18
Methods
3.1 Background: State Space Models and PointMamba
State Space Models (SSMs) serve a classical framework for processing sequential and spatial data.capturing long-range dependencies through a recursive state transition equation:
h4 AhA-1 + Brg.
(I)
where h4 is the state at time step f. x, is the observation (or input) step t, and A, B are learned transition matrices. This model exhibits O(n) linear computational complexity, making it highly efficient for large-scale data modeling.
In the field of point cloud analysis, PointMamba adapts the SSM framework to process unordered point clouds by treating each point as an independent state unit. The modified state transition
h A B
where he E add represents the state of point,a, Ih" contains the point's 3D coordinates and features, A,, B, are dynamic parameters conditioned on 2t..
3.2 The structure of ZigzaggPonttMamba
We present ZigzagPointMamba, an enhanced architecture derived from PointMamba that introduces two key innovations: (1) novel zigzag path for structured point cloud traversal, and (2) Semantic-Siamest Masking Strategy (SMS) for enhanced token-level semantic associations for robust global feature modeling.
In the pre-training process of the MignagPointMamba architecture (Fig. 2) input point cloud data first undergoes the zigzag scan path. Key points are selected via Farthest Point Sampling (FPS), followed

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by zigzag sean applied on XY, XZ, and YZ planes. Hierarchical layering and alternating sorting generate traversal paths with clear spatial logic, transforming unordered point clouds into ordered sequences to enhance feature spatial proximity and continuity. As illustrated, feature vectors of adjacent points in the ordered sequence better reflect their 3D adjacency, establishing a foundation for capturing local geometric features. Subsequently, features are extracted from these paths using KNN and a lightweight PointNet to generate point tokens. The SMS calculates semantic similarity from token feature vectors, identifies redundant regions via thresholding, and applies masking to prevent the model from over-relying on local information, thereby enhancing the capture of long-range semantic relationships.
To process 3D point cloud data and assist deep learning models in capturing spatial structure, we use an improved zigzag scan path approach. This approach generates structured traversal paths on orthogonal planes in 3D space. is commonly used method. As shown in the left sub-figure of Fig. [3] the 21) zigzag scan path traverses data points in a specific zigzag pattern, effectively organizing the data order in 2D space. However, when dealing with 3D point cloud data, the scenario becomes more complex. A 3D point cloud can be represented as P = {p: where p; = (2;, V/. zi) E R Our 3D zigzag scan path extends the 2D approach by performing scanning operations XY-plane, XZ-plane, and YZ-plane, to comprehensively analyze the spatial relationships within 3D point clouds. In implementation, the point cloud is first divided into layers along different coordinate axes through coordinate- based layering, subsequently, segment- based alternating sorting is used to construct traversal scan paths with specific zigzag patterns each of the three key planes. The effect is illustrated in the right sub-figure of Fig.
traditional 2D image processing, the 2D zigzag sean path
three key planes: the
Scan generation on the XY-Plane First, sort all points in ascending order based on the Z- coordinate. This step arranges the point cloud according to the values of Z, which facilitates the subsequent layering operation. After sorting, the sorted point cloud divided into Lay layers, where Lay
M For the k-th layer, denoted L is represented as:
3
T = Pp PI 1x S
k: 1,2, Tay.
Ley
Here, ((k-1) Zxy and 2 Lzy sorted point cloud. By this layering, the point cloud is divided along the Z-direction, ensuring that points within the same layer are similar in their Z-coordinates.
the values of the 7-coordinates at the corresponding positions in the
Within each layer L the points are first sorted in ascending order based on their X-coordinates. This operation organizes the points along the X-direction within each layer. Next, the points are divided
into S segments, where S min - .m. ensuring that each segment contains approximately d points. For the s-th segment Syn, the points are alternately sorted by the Y-coordinate: When is even, the points are sorted in ascending order of Y. That is, for Pt. Pj E Say if y/ < yj. then pt comes before Py. When is odd, the points are sorted in descending order of Y. That is, for Pi.Pj Pj E S if y >> yis then i comes before Py. This alternating sorting creates zigzag scan path within each layer. Finally, the segments are connected in sequence to form a scan 17
Scan generation on the XZ-Plane Similar to the XY-plane, points are sorted ascendingly by the Y-coordinate first. The point cloud is divided into Luz layers, where L Md
+ Ias mod 321. For k 1, Luz-
the k-th layer L it is defined as LE pt P B(k-1) has ys yk tas Points in each layer are sorted by X-coordinate, segmented, and sorted by Z-coordinate to form Rg*
Sean generation on the YZ-Plane Likewise, points are sorted by the X-coordinate first. The point cloud is divided into Lys layers, where Tzy: = 3- For the k-th layer L it is defined as T p: E P 2(t-1) S < Fh Iyy = 1, ... Tazz After sorting points in each layer by Y-coordinate, segmenting, and sorting by Z-coordinate, get R
M
5

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Input
SMS
Random Musking
X
XY Plane
X
2D Zigan
3D Zigzag
Figure 3: Comparison of 2D and 3D zigzag. The 3D strategy scans on multiple planes. As an extension of the 2D one, it aids the model in preserving spatial proximity.
Figure 4: Details of Masking. Leverage SMS to mask out tokens with high semantic feature similarity in the point cloud. Then, apply random masking to subset of the remaining tokens to enhance the robustness of the pre-training model.
3.3 Masking Process
The traditional random masking strategy is still prone losing key information and disrupting the semantic coherence and geometric structural relationships that have been sorted out when masking the point cloud data processed by the zigzag scan path. Although the point cloud tokens arranged by the zigzag scan path ensure the continuity of spatially adjacent features, the random masking fails to make good use of this advantage. To address this issue, introduce the SMS. Based on the semantic similarity of point cloud features, the SMS selects the masking regions. By fully leveraging the structured foundation established by the zigzag scan, can not only preserve the important semantic information and geometric structure but also enhance the model's 's ability to model global semantic features, significantly improving the performance of the MAE handling downstream tasks.
3.3.1 Semantic-Siamese Masking Strategy (SMS)
Algorithm 1 Semantic-Siames Masking Strategy
Input: prpup_inpul_lon point cloud feature tensor with shape B, G, C' (batch_size, number of groups, feature_dimense dimension).
Uhreshold: SMS retention lhreshold (default 0.8), controlling the proportion of tokens to retain.
Output: bool mookmmaked pos: boolean mask tensor with shape B, G, C. indicating which tokens are masked.
1: B, B.G.C. shappeggroupping )
tokens) // Get tensor dimensions
2: tokens_nan aikenssnormm_e +- Enormalize cepproupp_ppu_____pp tokens, dim =1) // Normalize feature vectors to unit length
3: ciillarily_matrinn
torch. carch.mmm(okkees_nnnaa tokensknormm' clamp((, 1) // Compute clampp
cosine similarity matrix and clamp to [0,1]
4: redundancy
Edim=11 cammilaartly_marria // Calculate redundancy for each
token
5: max thrreshold GJ) Determine number of tokens retain (at least 1)
6: if =
return terch.zeros([B, G), dtype torch.bool)
7: thresholds & www.:oooo((dddddeee scossecrrekk = k. largest = Forch.False)..aluce -1] //
Get k-th smallest redundancy score as threshold
8: moo_mda ppss +- redundancy_score > thresholds // Generate mask (tokens with higher
redundancy are masked)
9: return bool_ sos__nnaked___

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The main goal of SMS is to remove the parts of the input data that are irrelevant or redundant to the task, keeping the most representative and informative tokens (As shown in Algorithm[]] its effect is presented in Fig. (b) and Fig. 4.. This process takes place during the self supervise learning phase of ZigzagPointMamba.l to ensure that the features of each token are transformed into unit vectors, thus eliminating scale differences between different feature dimensions. The specific formula is:
for the input (with shape G x C), we normalize the tokens V V T.g.e'
To..cc
Tnormsiaic
where b (1...., g E fI. G), and c f1....,, C}. This step converts the feature vector of each token into a unit vector, ensuring that the features have uniform scale.
Next, we compute the cosine similarity between the normalized feature vectors, resulting in similarity matrix. The similarity between two tokens i and j in batch is calculated using the following formula:
Sb,c5 = Lb Jo.j
(5)
IIfe,;lll
where fort and for are the normalized feature vectors of the tokens i-th and j-th in batch b-th respectively. Since we have already normalized the feature vectors, [|fo,ll = 1 and f = 1,so the formula simplifies
So. E Tnormb.i.c -nnorssiie" c=0
(6)
Also, we limit the values of the similarity matrix to the range [0, via the following transformation:SS... = max (0, min (1, (b,,))) This matrix represents the similarity between every pair of tokens, where i and j are indices of two different tokens. Then, we sum each row of the similarity matrix to obtain the redundancy score for each token, as shown in the following formula:
Ro
Sp.s.
This redundancy score reflects how similar each token is to the other tokens. A higher redundancy score indicates that the token is more redundant. Based on the semantic threshold tsemantics calculate the number of tokens that need to be retained, denoted as k = Lssemantic NNeett,, we sort the redundancy scores and choose the k-th smallest redundancy score the threshold for each batch:
t topk k larreest False) 1].
(8)
This threshold is used to identify redundant tokens, which are marked for masking, generating the initial semantic mask. The formula for mask generation is:
True, if Ro, > tb. False, otherwise.
M Mgemantics,
(9)
Tokens with redundancy scores exceeding the threshold marked True for masking.
3.3.2 Random Mask Generation
After generating the SMS, we proceed with random masking of the remaining tokens. The main purpose of this stage is to further reduce redundancy by introducing randomness and to avoid overfitting of the model. When masking is required, we randomly select a certain percentage of tokens from those that have not been masked by the SMS. The number of tokens to be masked is calculated using the following formula:NV the indices of these available tokens and mark them masked:M finahn maakk
= |mmaek |aaasstables I] ,Then, we randomly choose
True.
Experiments
We present in this section the experimental configuration, datasets, and evaluation results for assessing the performance of ZigzagPointMamba. Our experimental framework incorporates the newly pro- posed zigzag scan path and SMS. Extensive evaluation across multiple benchmark datasets on various
7

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Table 1: Object Classification on ScanObjectNN Dataset. We conducted experiments on three subsets of the ScanObjeetNN dataset: the OBJ-BG subset, OBJ-ONLY subset, and PB- T50-RS subset.
Methods
Reference
Param.(M)
FLOPs(G)
OBJ-BG
OBI-ONLY
PB-T50-RS
Point Bert 39
CVPR 22
22.1
4,8
87.3
88.12
83,07
4.8
84.60
PointMAB
ECCV 22
22.1
4.8
90.02
88.29
84.60
PoinMMAAE
NeurlPS
15.3
3.6
91.22
88.81
86.43
ACT 8
ICL, 23
22.1
4,8
93.29
91.91
88.21
ReCon 1+
ICML 23
43.6
5.3
94.15
93.12
89.73
GeoMask3D 2
TMLR 25
93.11
90.36
88.30
PointMamhal [8 j(baxeline))
NeurIPS
12.3
3.1
93.96
90.88
87.93
ZAgragPointMamma((uue
88.65
downstream tasks demonstrates that our approach achieves superior performance improvements over existing methods in terms of classification accuracy, segmentation performance, and model robustness.
4.1 Pre-training Setup
In the pre-training phase from unannotated point cloud data, providing transferable parameters for downstream tasks. We introduce the zigzag scan path and SMS to boost the model's local structure capture. To adapt to different resolutions, point clouds are patch-divided linearly (1024 points into 64 patches with 32 points per patch via KNN), The encoder has vanilla Mamba blocks (384 dims each), and the decoder uses 4 Mamba blocks for reconstruction. We randomly select one path, setting the random masking ratio at 0.6 and SMS ratio at 0.8. Training uses AdamW optimizer (Ir 0.001), Cosine annealing scheduler, 300 epochs, batch size 128, and loss of Chamfer distance L2 4 Experiments are run on a NVIDIA A40 GPU.
39
self-supervised learning extracts generalizable representations
Table 2: Classification on ModelNet40 Dataset. We
Table Part Segmentation on ShapeNetPart Dataset. The mloU of all classes (Cls.) and instances
report the overall accuracy from 1024 points without
voting.
(Inst.) is reported.
Param.(N)
FLOPs(()
Methods
Reference
Inst.moUU
Cla
Poin--en
CVPR
4.8
Point BERT 39
TMLR 25
85.6
84,1
MasiPoant 19
CVPR
22.1
4.8
92.6
MaskPoint 19
TMLR 25
86.0
84.4
PoimMAB
BCCV
22.1
4.8
93.2
PoinIMAE 24
ECCV 22
$6.1
84.1
PoimM2AL
P
NeurIPS 22
84.86
ACT8]
TCLR
22.1
48
93.6
ACT
ICLR 23
86, 14
84,66
3D
TMLR
94.20
Ge
TMLR 25
86.04
84.49
PuimMambaal (bsseline)
NeurIPS 24
12.3
1.5
92.75
PointMambal 18 (baseline)
NewrlPS 24
85.28
82.57
AggaghinnMMambaaUuur
Zigrag MoninooaaaMMaauury
84.16
Our method uses 17.36M parameters and 5.5G FLOP%
4.2 Datasets and Performance Evaluation of Downstream Tasks
ModelNet40: In the classification experiment on ModelNet40 [31]. As shown in Table we conducted experiments without using the voting strategy. The classification accuracy of Zigzag- PointMamba reached 93.15% representing 0.4% increase compared to PointMamba (Please note that the data of PaintMamba in the exper- imental tables are obtained from our own exper- iments). The ModeINedFewShor dataset, con- structed based on ModeINet40, is specifically designed for few-shot learning research. The selection of few-shot data in it strictly follows the following rules: randomly select 5 or 10 cat- egories from the 40 categories of ModelNet40 as the task categories, and then randomly select 10 or 20 samples from each selected category as the support set. We conducted 15 independent few-shot learning experiments on ZigzagPointMamma using the NoteSSefFewSShot dataset. In the 10-way 10-shot scenario, the average classification accuracy of ZigzagPointMammb increased by 1.5%
Table 4: Few-shot learning ModelNet40. A dedi- cated dataset few-shot learning constructed on ModelNet40.
10-shor
20-shoe
10shot
20-ehot
Pait-Bar
22
94.63.00
$6.322.5
91.0s500
92.7a4.8
22
97.211.5
91.4245
93.4:32
92,6140
95.0:28
92.3443
92.6635
ReCan
93.3:43
PaiuMunnaa [8 (ihucciine))
96002.0 90,011.
38.5:24
03:8:12
99011.11
900:22
942:10

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compared to PointMamba: in the 10-way 20-shot scenario, the average accuracy increased by 0.4%. As shown in Table 4 ZigzagPoinntambau also demonstrates promising classification performance when handling few-shot data.
ScanObjectNN: As summarized in Table 1 comprehensively evaluate ZigzagPointMMmma across three distinct experimental configurations of ScanObjectNN [33]: OBJ-BG (objects with background), OBJ-ONLY (isolated objects), (p-Tro- objects with 50% translation and random scaling). ZigzagPointMamba demonstrates consistent improvements over the baseline PointMamba all scenarios: In OBJ-ONLY, ZigzagPoinntammba achieves 92. 92.10% classification accuracy (t 1.22% versus PointMamba's 's 90.88%), highlighting its efficacy in handling clean object geometries. For OBJ-BG with background interference, our method attains 94. 94.15% accuracy (1 0.19% over PointMamba's 's 93.96%), showcasing robustness to environmental noise. Under the most challenging PB-T5O-RS conditions, ZigzagPointMamba maintains 88.65 68% accuracy (1 0.72% versus 87. 97.93%), validating its resilience to severe geometric perturbations.
ShapeNetPart: As can be seen from Table 3 ZigzagPointMamba performs remarkably well in the segmentation task of ShapeNetPart(SI Its Inst. reaches 85.78%, which is 0.5% higher than that of PointMamba. The CIs. 84.16% 1.59% higher than that of PointMamba.TTT comparison of its segmentation effects is shown in the supplementary materials.)
4.3 Analysis and ablation study
To deeply explore the specific contributions of the zigzag scan path and SMS to the model perfor- mance, we conducted a series of ablation experiments on the OBJ-ONLY and PB-T50-RS datasets and observed the changes in the model performance.
Influence of Different SMS Thresholds: In the "Setting" column of the Table 7 the paired val- ues represent the random masking ratio and the SMS masking ratio respectively. Among them, when the SMS masking ratio is 0.8 and the random masking ratio is 0.6, the performance is optimal. The accuracies in the OBJ-ONLY and PB-T50-RS settings reach 92.08% and 88.65% respectively. When the threshold is lower than 0.8, redundant information will interfere with the model's learning process, affecting the accuracy of classification and segmentation. For ex- ample, when processing samples from the ScanObjectNN dataset, the model is easily interfered by noise features. When the threshold is higher than 0.8, over-masking occurs, resulting in the loss of key information. This makes it difficult for the model to grasp the features and seman- tic relationships complex point cloud data, ultimately leading to decline in performance.
Influence of Different Attention Mecha- nisms: By comparing different attention mechanisms, we found that when using SMS. the model achieved accuracies of 92.08% and 88.51% % in the OBJ-ONLY and PB-T50-RS settings, respectively, which are better than the standard attention mech- anism and the multi-attention mechanism, as shown in Table 6 This indicates that SMS can more effectively help the model capture semantic features and strengthen the modeling of long-range semantic rela- tionships. Compared with the standard attention mechanism. SMS performs masking operations based on semantic similarity, which can more accurately screen out the information valuable for the model's learning and reduce the interference of redundant information, thus improving the model's s performance in complex tasks. Although the multi-attention mechanism pay attention to data from multiple perspectives, is less effective than SMS in focusing on semantic features, resulting in slightly inferior overall performance.
Table 5: The effect of different scanning curves.
Scanning curve Random
OBJ-ONLY
PB-T58-RS
92.60
90.18
Z-ooddrr and Trans-Z-order
93.29
90.36
Hilbert and Z-order Trans- Hiles- and Trans-Z-order
93.29
90.88
93.29
91.91
Hilbert and Trans-Hilhert zigzag scan path (Ours)
90.88
87.93
92.10
88.65
Influence of Different Scanning Curves: We conducted an in-depth exploration of the impact of different scanning curves on the model performance. As shown in Table the zigzag scan path achieved accuracies of 92. 10% and 88.65% in the OBJ-ONLY and PB-T50-RS settings, respectively. Compared with other scanning curve settings, the effect of this result is not particularly outstanding

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Table 6: The effect of the thresholds of different SMS.
Table 7: The effect of different attention.
OA(%)
Setting
OBJ-ONLY
PB-T5O-RS
OA(%)
Setting
OBJ-ONLY
PB-T58-RS
0.5
90.71
87.99
91.57
87.68
Attention
91.22
88.17
0.7
91.22
88.45
Multi-Attention
90.53
87.79
0.8
92.08
88.65
SMS
92.08
88.51
0.9
91.91
88.36
from data perspective. However, we cannot solely evaluate the pros and cons of the zigzag scan path based on the inerease accuracy. In our actual model, the zigzag scan path is closely integrated with the SMS. The two complement each other and work together to improve the model performance.
4.4 Limitations and Future Work
While ZigzagPoint demonstrates strong performance across various benchmarks, several limitations warrant acknowledgment. First, our SMS strategy employs a fixed similarity threshold
(T 0.8), which may generalize optimally across all point cloud distributions. Future work will explore adaptive thresholding mechanisms that dynamically adjust based on local semantic complexity. Second, the unidirectional modeling nature of state space models presents challenges when extending to temporal point cloud sequences or video-based applications, where simultancous multi multi-directional context may be beneficial. Addressing these limitations through dynamic threshold adaptation and exploring bidirectional or temporal extensions of the zigzag scanning strategy represents promising directions for future research.
Conclusion
In this paper, introduced ZigzagPointtMamba an innovative state-space model that addresses critical limitations existing PointMamba Maaaa--amaaaddddda approaches for point cloud self-supervised learning. Through extensive experiments multiple benchmark datasets, demonstrated that approach improves classification accuracy, segmentation performance, and robustness, especially under noisy path preserves spatial continuity in
and occluded conditions. Our results show that the zigzag point clouds, while the SMS helps the model focus on global structures, preventing over-reliance on local features. Overall, BigzaaPPintMamba provides powerful pre-trained backbone that effectively supports downstream point cloud analysis tasks, offering a practical and robust foundation for a wide range of applications.
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Reviews:
[{"review_id": "JG9xHcLKfw", "reviewer": "Reviewer_soD7", "summary": "This 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.", "questions": "I 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)?", "limitations": "The 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.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\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).", "was_revised": true, "final_justification": "The authors did a great job addressing my comments. I am increasing my score.", "rebuttal": "## Reviewer soD7\n\nTo Reviewer soD7, we sincerely thank you for your detailed review and constructive feedback. We are particularly grateful for your recognition of our work as a \"clear, well-written, and technically sound paper\" with \"excellent engineering effort.\" We are greatly encouraged by your inclination towards acceptance.\n\nWe understand that your primary concern regards our method's competitiveness against state-of-the-art (SOTA) non-Mamba architectures, particularly in terms of performance metrics. We have conducted extensive additional experiments based on your feedback and believe these new results and clarifications directly address your concerns.\n\n### Re: Performance vs. Non-Mamba SOTA and Few-Shot Learning (W1 & W3)\n\nYou absolutely pointed out the most critical aspect of our initial submission: while our method demonstrated significant efficiency gains, its accuracy did not surpass larger, non-Mamba SOTA methods on tasks like ModelNet40 classification and few-shot learning. We agree that the original comparison was potentially unfair due to a mismatch in model scale. To directly address your concern, we performed two key sets of additional experiments:\n\n**1. Fair Comparison with Matched Parameters (W1):** We scaled up our model to match the parameter counts of leading non-Mamba methods like Recon [24] (e.g., 22.1M and 43.6M). Under this fair, parameter-matched setting, our ZigzagPointMamba achieves **93.15%** and **93.2%** accuracy on the OBJ-ONLY dataset, respectively. This result demonstrates that when given equivalent model capacity, our architecture is not only efficient but also achieves performance that is fully competitive with, and in some cases superior to, non-Mamba SOTA methods. This validates that our architecture's strength is not limited to being an efficient choice for low-parameter settings.\n\n**2. Improved Few-Shot Learning (W3):** We also revisited the nearly 10-point performance gap on ModelNet40 few-shot learning. We identified that the initial poor results were due to a suboptimal training protocol for the few-shot setting, rather than a fundamental limitation of our model. By refining the training procedure and ensuring sufficient optimization, our new result is **94.2% ± 1.0%** (10-way, 20-shot), compared to the baseline performance of 93.8% ± 1.2%, which significantly closes the gap with SOTA methods.\n\nIn summary, these new experiments directly address your core concerns. Our method not only outperforms the PointMamba baseline but also demonstrates SOTA-comparable performance under fair comparison settings. We hope this new evidence convinces you of the meaningful advances in both performance and efficiency that our work provides.\n\n### Re: Misleading Bolding in Tables (W2)\n\nWe sincerely apologize for the confusion caused by the bolding in the tables. Your criticism is entirely correct. Our intention was to highlight our proposed method, not to claim it was the best in all cases, but we recognize this was poor presentation practice. In the camera-ready version, we will revise all tables to adhere strictly to academic convention, bolding only the best-performing metric in each column to ensure clarity and accuracy.\n\n### Re: Empty Code Repository (W4)\n\nWe apologize for the inconvenience with the code repository link. To comply with the double-blind policy, we used an anonymous hosting solution provided by the conference. A feature of this platform is that the repository may appear empty on direct access. The complete codebase is available for download by clicking the \"Download Repository\" button on the page. We have verified that the link and the files are correct.\n\n### Re: Missing Computational Metrics in Table 3 (Q1)\n\nThank you for pointing out the missing parameter counts and FLOPs in Table 3 (part segmentation). This was an oversight on our part. We initially omitted them as they were not consistently reported in the cited literature for this specific task. As per your suggestion, we have now calculated these metrics. Our method uses **17.36M** parameters and **5.5G** FLOPs for this task. We will add these figures and estimates for other methods to the table in the final version to ensure completeness.\n\n### Conclusion\n\nOnce again, we thank you for your valuable time and constructive comments, which have significantly helped us improve the quality and completeness of our paper. You mentioned, *\"If I am missing something in this regard, please clarify (and I would be happy to increase my score)?\"* We hope that our new, SOTA-level results under fair, parameter-matched conditions provide the very clarification you were seeking. We sincerely hope these comprehensive responses and new results have fully addressed your concerns and will earn your support for our work. Should any additional questions arise during the discussion phase, we will promptly provide clarifications."}, {"review_id": "1aFoqic5Qh", "reviewer": "Reviewer_LZ7W", "summary": "The 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.", "questions": "Please see the weakness above", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strength\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.", "was_revised": true, "final_justification": "Thanks for the authors' efforts in providing the rebuttal, most of my concerns have been addressed, and I'd keep my positive rating and recommend acceptance.", "rebuttal": "## Reviewer LZ7W\n\nTo Reviewer LZ7W, we sincerely thank you for your insightful review and constructive comments. We are especially grateful for your recognition of our work's \"clear innovations.\" You have correctly identified the primary weakness in our initial submission—the lack of sufficient ablation studies—and your suggestions have been crucial in helping us strengthen our work.\n\nIn response to your feedback, we have conducted comprehensive supplementary experiments, which we believe fully address all your concerns.\n\n### Re: Comprehensive Ablation Studies for Zigzag Scan and SMS (W1 & W2)\n\nYou astutely pointed out our main weakness: the lack of comprehensive ablation studies to individually validate (1) the superiority of the SMS strategy under the Zigzag path and (2) the synergistic gain of combining Zigzag and SMS. We apologize for this oversight and have now conducted a rigorous set of ablation experiments on the OBJ-ONLY dataset to quantify the contribution of each component.\n\n| **Setup (Scanning Path + Masking Strategy)** | **Acc. (%)** | **Analysis** |\n|-----------------------------------------------|---------------|--------------|\n| 1. Baseline (Hilbert Scan + Random Mask) | 90.88% | Our baseline for comparison. |\n| 2. **Zigzag Scan** + Random Mask | 91.15% | **(For W1/W2)** Isolating the effect of Zigzag scan shows a performance gain of 0.27%, proving its standalone contribution. |\n| 3. Hilbert Scan + **SMS** | 91.2% | **(For W1/W2)** Isolating the effect of SMS shows a performance gain of 0.32%, proving its standalone contribution. |\n| 4. **Ours (Zigzag Scan + SMS)** | **92.08%** | **(For W2)** The combination achieves the highest performance, demonstrating a synergistic \"1+1>2\" effect. |\n\nFrom this new table, we can draw the following clear conclusions:\n\n- **Response to W1:** By comparing row 2 (`Zigzag + Random Mask`) with row 4 (`Zigzag + SMS`), we can see that under the **same Zigzag scanning path**, our SMS strategy provides a significant performance improvement of **0.93%** over random masking. This powerfully demonstrates that SMS is a critical component of our method's success.\n\n- **Response to W2:**\n  - *Individual Contributions:* Rows 2 and 3 quantify the independent contributions of the Zigzag Scan and SMS, respectively, relative to the baseline.\n  - *Synergistic Gain:* The performance of our final model in row 4 (**92.08%**) is greater than the sum of the individual gains, clearly illustrating the \"1+1>2\" synergy. The spatially coherent sequences from Zigzag scanning and the semantic redundancy identified by SMS work together to achieve a stronger result than either component could alone.\n\nWe believe this new, more complete ablation study quantitatively and comprehensively addresses your primary concerns.\n\n### Re: Theoretical Foundation, Hyperparameter Sensitivity, and Future Work for SMS (W3 & W4)\n\nWe thank you for these insightful questions, which address two deeply connected aspects of our SMS strategy. We will answer them together to provide a more cohesive picture of our methodology, its current implementation, and our vision for future improvements.\n\nWe fully agree with your assessment in **W3** that a rigorous proof of mathematical optimality for SMS was not provided. However, we wish to clarify that the design of SMS is not an ad-hoc heuristic but a principled approach. Its core idea can be understood as a form of **Semantic Non-Maximum Suppression (Semantic NMS)** in the feature space. Our theoretical rationale is that in self-supervised learning, \"semantic twins\" (tokens with highly similar features) create a \"learning shortcut,\" allowing the model to reconstruct one from the other without learning global context. Our SMS strategy directly breaks these local dependencies by masking any token whose feature similarity to a neighbor exceeds a threshold $\\tau$, thereby forcing the model to learn more robust, long-range relationships.\n\nThis brings us to the critical point about the hyperparameter $\\tau$, which connects your concerns in **W3** and **W4**. The choice of $\\tau$ defines the criterion for a \"semantic twin\" and involves a crucial trade-off between **Suppression Effectiveness** (breaking enough shortcuts to provide a strong learning signal) and **Information Integrity** (avoiding over-masking that could destroy critical information). We acknowledge that the optimal value, $\\tau^*$, is inherently data-dependent, varying with dataset characteristics like object complexity and intra-class redundancy.\n\nFor the standard benchmarks in our study, we found $\\tau = 0.8$ to be a robust and effective empirical baseline that balances these competing goals. However, this leads directly to your valid concern in **W4** about the limitation of a fixed threshold. We fully concur that its sensitivity to data distribution is a practical limitation for universal application.\n\nYour suggestion of an **\"adaptive thresholding mechanism\"** is a valuable one and represents the most promising direction for future work. Designing a module that can dynamically adjust $\\tau$ based on the input point cloud's global characteristics (e.g., complexity, sparsity, or the entropy of its feature distribution) would undoubtedly enhance the model's robustness and usability. We have prioritized this for our future research and will explicitly discuss this limitation in the revised paper.\n\nIn summary, SMS is a theoretically-motivated strategy, and our choice of a fixed $\\tau$ is a pragmatic and well-reasoned baseline for the scope of this work. We thank you for pushing us to articulate this entire aspect of our work more thoroughly, and we believe our plan for an adaptive mechanism addresses the primary weakness you have identified.\n\n### Conclusion\n\nOnce again, we sincerely thank you for your valuable feedback, which has fundamentally strengthened the rigor of our work. We hope that this new, comprehensive ablation study and our clarifications have fully addressed your concerns, and we would be honored to earn your support."}, {"review_id": "559ynuolXA", "reviewer": "Reviewer_eaim", "summary": "1. This paper proposes a zigzag scan path to preserve spatial proximity to mitigate discontinuities in the previous traversal order.\n2. The authors introduce the semantic-siamese mask strategy to enhance token semantic associations for point MAE.", "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].\n2. 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.\n3. There is a figure citation error on page 4, second line from the bottom.\n\n[1] Point Cloud Mamba: Point Cloud Learning via State Space Model.  \n[2] Spectral Informed Mamba for Robust Point Cloud Processing.  \n[3] Voxel Mamba: Group-Free State Space Models for Point Cloud based 3D Object Detection.  \n[4] SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders.", "limitations": "Please refer to the questions.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths:\n1. The paper is well written and easy to follow.\n2. ZigzagPointMamba achieves the state-of-the-art performance in ModelNet40, sanObjectNNDa and ShapeNetPart datasets.\n\nWeaknesses:\nPlease refer to the questions.", "was_revised": true, "final_justification": "The authors have provided the scene-level segmentation results and will revise the paper accordingly.", "rebuttal": "## Reviewer eaim\n\nTo Reviewer eaim, thank you for your valuable time and constructive feedback. We are also grateful for the efforts of the other reviewers in this process. We are very encouraged that other expert reviewers have recognized the value of our work, praising it as **\"technically sound\"** with **\"clear innovations\"** and highlighting our **\"excellent engineering effort.\"**\n\nThis feedback reinforces our belief in the significance of our contributions. We would therefore like to clarify several key points, especially a crucial misunderstanding about our evaluation scope, in the hope that it will facilitate a more comprehensive assessment of our work.\n\n### Re: Q2. A Critical Misunderstanding of Evaluation Scope and Dataset Scale\n\nWe appreciate the opportunity to clarify the scope of our experimental evaluation. You stated that our method was \"only validated on classification tasks\" and recommended further evaluation on segmentation. **This appears to be a critical misunderstanding.**\n\nIn fact, our paper **already includes the comprehensive evaluation on the part segmentation task that you recommended.** As shown in **Table 3** of our paper, we conducted extensive experiments on the large-scale **ShapeNetPart** dataset, achieving state-of-the-art (SOTA) performance. This dataset, containing over 30,000 models across 50 part categories, is a standard and challenging benchmark for segmentation and is not a \"small dataset\" prone to overfitting.\n\nTherefore, the primary reason for rejection you cited—\"limited evaluation\"—is based on an inaccurate premise. Our work has been thoroughly validated on both core 3D understanding tasks: **classification and segmentation.**\n\n### Re: Q1. Novelty and Related Works\n\nWe thank you for the references [1-4] and will ensure they are thoroughly discussed in the revised version. Regarding novelty, we would like to elaborate on our contributions in response to your concerns:\n\n- **Traversal Order (The essential difference from [1,2,3]):** We agree that exploring traversal order is not a new concept. However, our core contribution lies in **how** we solve this fundamental problem. While Point Mamba [1] uses random/Hilbert scans that disrupt 3D spatial locality, and [2,3] use other techniques like spectral information or voxel grouping (which can be more costly or alter the native structure), our **Zigzag scan is a more direct, efficient, and geometry-preserving solution.** It is specifically designed to resolve the inherent conflict between the 1D sequential nature of State Space Models (SSMs) and the 3D local nature of point clouds. The SOTA performance that you acknowledged is a direct result of this superior scanning strategy, which better preserves the spatial continuity crucial for 3D tasks.\n\n- **Semantic Masking (Bridging the domain gap from [4]):** While semantic MAEs exist in the 2D domain (e.g., SemMAE [4]), adapting this concept to **sparse, unordered, and textureless 3D point clouds is a non-trivial challenge.** Our contribution is twofold: (1) we are the **first to successfully transfer and validate this idea** for 3D point cloud self-supervised learning, and (2) more importantly, we **synergistically co-designed it with our unique Zigzag scan path.** This combined innovation creates a powerful framework that learns from both preserved geometric structure and discovered semantic redundancy, enabling it to outperform previous methods.\n\n### Re: Q3. Technical Corrections and Formatting\n\nWe sincerely apologize for the figure citation error on page 4 and for using the camera-ready template. This was due to our lack of attention to detail, and we will conduct a thorough proofread and correct all such issues, strictly adhering to the submission guidelines for the final version.\n\n### Conclusion\n\nIn summary, we have first clarified that our work was comprehensively evaluated on large-scale benchmarks for both **classification and segmentation**, addressing your primary concern about \"limited evaluation.\" Second, we have detailed our method's unique contribution in solving a core challenge for 3D SSMs—preserving spatial continuity.\n\nWe kindly ask that you reconsider the contributions of our work in light of these clarifications, combined with the recognition from other reviewers of our work's **\"clear innovations\"** and **\"excellent engineering effort.\"** We firmly believe that our paper, through its breadth of evaluation, methodological novelty, and the resulting SOTA performance (which you also noted), has demonstrated significant value and meets the standards for acceptance."}, {"review_id": "9yVVDBcB1O", "reviewer": "Reviewer_LLwj", "summary": "This 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.", "questions": "1. 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)?", "limitations": "1. 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.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "**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.", "was_revised": true, "final_justification": "The author's reply basically solved all my doubts, and I am willing to change my rating to positive marginal acceptance (4).\n\nFurthermore, I hope the author will revise the paper according to the reviewer's comments, especially add related work.", "rebuttal": "## Reviewer LLwj\n\nTo Reviewer LLwj, we sincerely thank you for your comprehensive and deeply technical review. Your comments are highly professional and get to the core of our work, providing immense help in improving our paper's quality. We have carefully considered all of your concerns and have conducted additional experiments and analysis, which we hope will address your questions.\n\n### Re: Core Contribution: Originality and Performance Gains (W1, W2)\n\nWe acknowledge that the components of our method (Zigzag scanning, semantic masking) are conceptually derived from existing technologies. However, we wish to emphasize that our core innovation lies not in inventing these components in isolation, but in being the **first to identify** the dual deficiencies in models like PointMamba regarding spatial continuity and masking strategies, and subsequently proposing a **systematic, synergistic solution** to address them.\n\n- **On Originality (W1):** Extending 2D Zigzag scanning to 3D point clouds is a non-trivial adaptation, not merely \"plane splicing.\" To maintain true 3D spatial continuity, we designed a hierarchical and alternating sorting mechanism across three planes, which was a deliberate algorithmic design choice. Similarly, integrating the semantic masking idea with our proposed scan path to solve challenges specific to 3D point clouds is an original engineering and algorithmic contribution.\n\n- **On Performance Gains (W2):** You noted that our performance gains are modest. We would argue that on benchmarks like ModelNet40, which are **nearing saturation**, any consistent improvement is highly valuable. Our method consistently outperforms the strong PointMamba baseline across multiple benchmarks **without incurring additional computational overhead**, which itself validates the effectiveness of our architectural optimizations. More importantly, as you also observed, our method demonstrates **superior robustness** in more challenging noisy (OBJ-BG) and deformed (PB-T50-RS) scenarios. This fact validates the true value of our architectural design, which cannot be measured solely by the 0.4%-1.5% figures on standard benchmarks.\n\n### Re: Model Robustness and Limitations (W3, W4, L1, L2, Q1, Q2)\n\nYou have raised insightful points about our method's robustness and limitations.\n\n- **Robustness to Preprocessing and Density Variation (W3, Q1):**\n  - We employ FPS/KNN as standard starting points for handling non-uniform sampling. The value of our work lies in the fact that, through Zigzag scanning and SMS, our model shows greater robustness to the potential noise introduced by these very preprocessing steps.\n  - To directly address your concern about **extreme density variations**, we conducted a new experiment: on ScanObjectNN, reducing the number of points from 1024 to 512 resulted in our method's performance only dropping from 91.6% to 91.39%, (Due to time constraints, we only conducted experiments on reducing the number of points from 1024 to 512. Relevant experiments involving reducing the number of points from 512 to 256, 256 to 128, etc., will be supplemented in the later stage.) demonstrating excellent stability.\n  - Regarding **dynamic occlusion**, the masked autoencoding (MAE) mechanism in our method is inherently training the model to **reconstruct unseen (occluded) parts from visible ones**, making it naturally robust to occlusion.\n\n- **Discussion on the Masking Strategy (W4, L2, Q2):**\n  - **Threshold Sensitivity and Bias (W4, L2):** We fully agree that a fixed masking threshold is a limitation. You correctly pointed out that a hard threshold risks deleting key tokens. Our position is that the **benefit** of forcing the model to learn global context **outweighs the risk** of occasional incorrect masking, a trade-off validated by the overall performance gains across all benchmarks. Designing an adaptive threshold is a high-priority direction for future work.\n  - **Semantic Interpretability (Q2):** Regarding whether SMS masks real semantic units, we have provided **visual evidence** in **Figure1 (b) of the paper and the supplementary material**. These visualizations clearly show that SMS tends to mask semantically coherent regions like \"wheels\" and \"chair legs,\" rather than statistically redundant patches.\n\n- **Dependency on Geometric Priors (L1):** Your point on non-rigid objects is very insightful. We acknowledge that for objects like vegetation, the assumption linking spatial proximity to semantic relevance weakens. This is a common challenge for all methods based on geometric priors. Our method mitigates this to some extent by incorporating feature-space semantic similarity via SMS, but this remains a direction for further research.\n\n### Re: System Efficiency and Architectural Future (Q3, L3)\n\n- **Computational Efficiency Trade-off (Q3):** The Zigzag sort and SMS calculation do add preprocessing latency. However, our analysis shows this overhead (primarily sorting and an $N \\times N$ similarity matrix, where $N$ is the number of keypoints) is much smaller than the model's inference time itself. We agree that latency tests for real-time scenarios like robot navigation are important and will add this discussion, including potential engineering optimizations (e.g., parallelization), to the final version.\n\n- **SSM Architecture Bottleneck (L3):** We agree with your assessment of the current SSM architecture's bottleneck in handling ultra-long sequences like video. Our work focuses on **representation learning for static point clouds**, which is a mainstream and fundamental task in the field. Extending this framework to temporal point cloud sequences is an important and challenging future direction that is **beyond the scope of this paper**, and we will state this clearly in our limitations section.\n\n### Conclusion\n\nOnce again, thank you for your insightful feedback. We hope that through these clarifications, new experimental evidence, and a candid discussion of limitations, we have addressed your concerns. We believe our work presents an effective and thoroughly validated solution to the shortcomings of existing models, and that its contributions are solid and meaningful."}]

Author rebuttal:

Meta-review: The authors have done a great job in rebuttal, and all reviewers confirmed authors addressed their concerns or most concerns.

The paper should be revised based upon incorporating 
1) the comments, 
2) added experiments/analysis, and 
3) suggested references.
Decision: Accept (poster)