Point-Focused Attention Meets Context-Scan State Space: Robust Biological Visual Perception for Point Cloud Representation
Authors: Kanglin Qu
Pan Gao
Qun Dai
Yuanhao Sun
Venue: ICLR 2026 Poster (2026)
Keywords: Point cloud learning
Attention mechanism
State space model
Biomimetic vision
Primary area: applications to computer vision, audio, language, and other modalities
TL;DR: A point cloud representation learning network inspired by biological vision mechanisms.

Abstract: Synergistically capturing intricate local structures and global contextual dependencies has become a critical challenge in point cloud representation learning. To address this, we introduce PointLearner, a point cloud representation learning network that closely aligns with biological vision which employs an active, foveation-inspired processing strategy, thus enabling local geometric modeling and long-range dependency interactions simultaneously. Specifically, we first design a point-focused attention, which simulates foveal vision at the visual focus through a competitive normalized attention mechanism between local neighbors and spatially downsampled features. The spatially downsampled features are extracted by a pooling method based on learnable inducing points, which can flexibly adapt to the non-uniform distribution of point clouds as the number of inducing points is controlled and they interact directly with point clouds. Second, we propose a context-scan state space that mimics eye's saccade inference, which infers the overall semantic structure and spatial content in the scene through  a scan path guided by the Hilbert curve for the bidirectional S6. With this focus-then-context biomimetic design, PointLearner demonstrates remarkable robustness and achieves state-of-the-art performance across multiple point cloud tasks. The code is available at https://github.com/Point-Cloud-Learning/PointLearner.

Published as a conference paper at ICLR 2026
POINT-FOCUSED ATTENTION MEETS CONTEXT-SCAN STATE SPACE: ROBUST BIOLOGICAL VISUAL PERCEP-
TION FOR POINT CLOUD REPRESENTATION
Kanglin Qu'?, Pan Gaol?2, Qun Dai and Yuanhao Sun'
'College of Artificial Intelligence, Nanjing University of Acronautics and Astronautics
KKey Laboratory of Brain-Machine Intelligence Technology, Ministry of Education School of Mathematical Sciences, Beijing University of Posts and Telecommunications kl inquu 63. com, {Pan. Cao, da qun) edu. en, sunyhēbupt. edu on
ABSTRACT
Synergistically capturing intricate local structures and global contextual depen- dencies has become a critical challenge in point cloud representation learning. To address this, we introduce PointLearner, point cloud representation learn- ing network that closely aligns with biological vision which employs an active, foveation-inspired processing strategy, thus enabling local geometric modeling and long-range dependency interactions simultaneously. Specifically, we first de- sign a point-focused attention, which simulates foveal vision at the visual focus through a competitive normalized attention mechanism between local neighbors and spatially downsampled features. The spatially downsampled features are ex- tracted by pooling method based on learnable inducing points, which can flexi- bly adapt to the non-uniform distribution of point clouds as the number of induc- ing points is controlled and they interact directly with point clouds. Second, we propose a context-scan state space that mimics eye's s saccade inference, which in- fers the overall semantic structure and spatial content in the path guided by the Hilbert curve for the bidirectional S6. With this focus-then- context biomimetic design, Pointl demonstrates remarkable robustness and achieves state-of-the-art performance across multiple point cloud tasks. The code is available https://gituub.com/ooinne.c Leanndn//oeannnnnnnntt earner.
through
INTRODUCTION
As fundamental data form in 3D vision, point clouds have been widely applied in numerous tasks such as autonomous driving, robot navigation, and augmented reality due to their ability to pre- cisely represent the geometric structure and spatial details of objects (Yan al. 2024a| Zhou al. 2024a Yan et al.
20246b An et al.] 2025 Resani & Nasihatkon 2025 et al., 2025b
b He et al.
2024
Xu et al.
2024 /hou et al. 2025a Zhang al.) 2023 2024a Liang al., 2025b 2026).
Currently, local attention networks (Zhao et al. 2021 Wu et al. (2024a) represent the mainstream
paradigm for point cloud representation learning. By ingeniously computing attention within local neighbors/windows, they successfully reduce computational complexity to a linear relationship with the number of input points. However, such networks inevitably narrow the perceptual field, sacri- ficing the global perception capability of the attention mechanism, thereby hindering the effective modeling of long-range dependencies between objects in a scene. Recently, inspired by the excep- tional long-range modeling capability with linear complexity of the selective state space model (S6) in Mamba (Gu Dao 2023), several studies (Zhang et al. 2024b Han et al. 2024: Köprücti et al.) 2024 Schöne al. 2024 Wang al. 2024) have attempted to introduce into point cloud rep- resentation learning to overcome the trade-off between long-range interactions and computational resources. However, the bidirectional S6 still relies compressing all contextual information into the history-hidden state for global connectivity, resulting in insufficient locality learning. Based on the above analysis, how synergistically capture local fine-grained structures and global contextual dependencies has become a critical challenge in point cloud representation learning.
"Corresponding author.

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Published as a conference paper at ICLR 2026
(o) Point cloud object
(b) Foveal vision
(c) Point-focused attcation
(d) Local neighbor attention
(c) Spatial downsampling attention
Figure I: A comparison of different information perception ways, where the red dot indicates the visual focus and the orange circle denotes the local awareness range. Our point-focused attention aligns more closely with natural foveal vision than the perceptual modes in (d) and (e).
In the biological visual system, foveal vision is responsible for perceiving the most significant in- formation within the visual field, exhibiting pronounced spatial non-uniformity: as shown in Fig- [bb, the region near the visual focus possesses extremely high acuity, enabling fine discrimination of detailed features; while visual acuity decreases with increasing eccentricity, resulting in coarser processing of peripheral features. This pattern not only optimizes the utilization of finite neural resources but also aligns with the intrinsic structure of information distribution natural scenes (Wandell 1995). What is more, biological vision is not static process rather than dynamic one that acquires information on a series of serialized visual foci via eye's continuous saccade move- ments, thereby inferring the overall semantic structure and spatial content within scene (Stewart et al. 2020). The above operational mechanism endows the biological visual system with powerful perceptual capability synergistically model local geometries and long-range dependencies.
Inspired by this, we explore PointLearner, a bionic-desiged network that closely aligns with biolog- ical vision, enabling local geometric modeling and global context awareness simultaneously. In our network, the proposed point-focused attention emulate foveal vision perception at the visual focus, and context-scan state space used implement inference process during eye saccade:
The point-focused attention adopts dual-branch design, where one branch performs fine- mne-grained attention modeling for each point's s local neighbors, while the other branch establishes coarse- grained attention relationships between each point and spatially downsampled features. By computing attention weights from both branches within a single softmax calculation, the point- focused attention couples coarse- and fine-grained features in competitive normalized manner with linear complexity. This makes each point adaptively and efficiently fuse local structures with global semantics, aligning with the intrinsic information distribution in natural scenes like foveal vision perception. Notably, we develop an induced point pooling method, which is able to flexibly adapt to the non-uniform distribution of point clouds, through trainable vectors (dubbed inducing points) directly performing attention interactions with data points, as well as a controllable number of inducing points, thereby effectively downsampling spatial features.
The context-scan state space utilizes the Hilbert curve for serialization of the foint-focused at- tention feature, and further employs the the bidirectional S6 for geometric inference. As demon- strated in the eye tracking machine vision experiments (Newport al.) 2023), the Hilbert curve more aligined with eye's saccade inference, having the property of locality preservation and the peculiarity of its self-similar rotating replication. By employing biological inspired focus-ther then- context pipeline, the overall semantic structure and fine-gra fine-grained spatial content can be efficiently integrated for point cloud representation learning.
To validate the effectiveness of PointLearner, conduct extensive experiments multiple stan- dard point cloud datasets, including ModelNet40 (Wu et al. (2015). ScanObjectNN (Uy et al. 2019). ShapeNet (Yi et al.) 2016), and S3DIS (Armeni al., 2016). Experimental results demonstrate that our network, by emulating the operational mechanism of biological vision, effectively captures detail-rich local structures and coherent global context. It exhibits remarkable robustness and point cloud representation capability, yielding state-of-the-art results across multiple point cloud tasks.
In summary, the contributions of this paper stem from the following aspects:
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Published as a conference paper at ICLR 2026
(1) We propose PointLearner for point cloud learning, bottom-to-up framework that aligns with biological vision, which achieves local refinement modeling and long-range dependency interactions by natural visual perception. This bio-inspired network attains ofate-of-the-art results on various point cloud tasks and demonstrates significant robustness.
(2) We design point-focused attention simulating foveal vision perception at the visual focus. By computing attention weights for a point's local neighbors and spatially downsampled features within single softmax calculation, it enables each point to adaptively and efficiently select the most effective receptive field information from both local structures and global semantics via a competitive normalized attention mechanism with linear complexity.
(3) We introduce a context-scan state space mimicking eyc's saccade inference. By the Hilbert curve with excellent locality-preserving property, it guides the bidirectional S6 to accurately infer the entire scene along a scanning path that maintains high-fidelity spatial proximity between points.
RELATED WORK
2.1 ATTENTION BASED NETWORKS
The attention mechanism (Vaswani et al. 2017 Shi 2024
Su et al., 2024) has been widespread in
point cloud representation learning, due to its ability to enable dynamic interactions between ele- ments and global modeling. Several studies have enhanced the performance of the attention mecha- nism in point cloud tasks by refining attention modules (Guo et al. M
Mazur & Lempitsky [202]]
Yan al. 2020) or designing pre-training strategies (Chen al., 2023 Yu et al.
[022] Pang al, 2022 Liu et al. 2022 Qi et al., 2023). Although these global attention networks have achieved
impressive results, they perform attention computations directly on the entire point cloud, regarding each point as token. This incurs prohibitive computational overheads due to the quadratic com- plexity of the attention mechanism and the large number of points in point clouds. Thus, some works ingeniously design local attention networks, which can be categorized into local neighbor-base and window-based methods. Local neighbor-baas based methods (Zhao et al.) 2021 Wu et al.) [022] Nic et al. 2022 Xiang et al.] 2023 Zhang et al.] 2022: Liu et al. 2024b) apply the attention mechanism to point neighborhoods constructed for each point using neighbor search techniques such as the K- Means, ball query, and K-nearest neighbors (KNN), while window-based methods (Lai al. 2022
Park et al. 2022] He et al., 2022] Fan et al.) 2022] Sun et al., 2022, Liu et al., 20236] Wu et al, 2024a) partition the 3D space into non-overlapping windows through voxelization or space-filling curves, transforming attention computations on all points to these spatial windows. Although local attention networks exhibit linear complexity, setting on the number of neighbors or window size constrains the receptive field of the attention mechanism, hindering the modeling of long-range de- pendencies. Departing from local neighbors, this paper incorporates spatial downsampling branch and context-scan state space to extract global dependencies with the biological visual mechanism, overcoming the trade-off between global modeling and computational resources.
2.2 SSM-BASED NETWORKS
With excellent long-range modeling capability with linear complexity, the state space model (SSM)
(Gupta al. 2022 Gu et al. 2022 Smith et al.) 2023 Mehta et al." 2023 Gu et al. 2020 2021 Gu
& Dao 2023) has gained significant prominence in the field of natural language processing (NLP).
Among these, Mamba (Gu & Dao 2023) stands out as the most influential work. Its core component called 56 introduces an input-driven selective mechanism, enabling flexible selection of relevant in- formation and achieving breakthrough performance in long sequence modeling. Furthermore, S6 incorporates a hardware-awand algorithm inspired by FlashAttention (Dao et al. (022). significantly improving both training and inference efficiencies. These outstanding properties have motivated the extension of S6 from NLP to computer vision, including image recognition (Liu et al.
2025
Shaker et al. 2025 Fu et al. 2025 He et al., (025), video understanding (Chen et al. 2025
Wang
et al. 2023 Li et al. (202a)). and medical image segmentation (Xing al. 2024 Ma et al. 2024
Ruan & Xiang 2024). Recently, S6 has also been applied to point cloud representation learning.
To adapt to the causal nature and unidirectional modeling of S6, existing methods design serializa-
tions strategies based on space-filling curves (Liang et al. 2024: Li et al. 2025 Liu et al. 2024a)
or axis ordering (Zhang al. 2024b Han et al. 2024
Köprücu et al.) 2024 Schöne et al.
2024
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Point Cloud
Point- Roint-Focused Focused Attention
Context- Scan State Space
LayerNorm
LayerNorm
Embedding
MLP
Encoder
M
Downsampling
earner Block
Serialization
State Space Model
Decoder
Upsampling
Parner Block
Task Head
Figure 2: Left: Pipeline of PointLearner. Right: Architecture of PointL block, where the line between the red dots represent the saccade path guided by the serialization, which is used for geometric inference by the state space model.
to establish inter- anter-point structural dependencies for the bidirectional S6's geometric inference. How- ever, the bidirectional S6 still relies on compressing all context information into the history-hidden state to achieve global connectivity, leading to insufficient locality learning. Our work captures lo- cal structure information by a point-focused attention that matches foveal vision the visual focus, addressing the above shortcoming.
3
METHODOLOGY
3.1
OVERVIEW
As shown Fig. earner follows the standard Point Transformer-style architecture Zhao et al. 2021 Wu et al. 2024a). A point cloud is first fed into an embedding layer formed by an MLP to be projected into a high-dimensional space, followed by an encoder-decoder structure that performs residual-based hierarchical feature aggregation, where the downsampling and upsampling layers use the Farthest Point Sampling (FPS) and linear interpolation (Qi al. 20176), respectively, and finally appropriate task head is invoked based on specific requirements. In this study, network is validated on point cloud recognition and segmentation tasks, where the recognition head first applies average pooling the encoder's output, then produces global category logits by MIP; the segmentation head processes the decoder's output with an MI.P to predict per-point cat- egory logits. As the component of the encoder-decoder structure, Pointl earner block adopts the Transformer architecture for flexible integration into the network. By incorporating the point- focused attention first and then context-scan state space, it endows the network with information perception capability akin to the biological vision system, achieving local geometric modeling and long-range dependency interactions simultaneously. We will elaborate on these key modules next.
3.2 POINT-ROCUSED ATTENTION
The point-focused attention adopts dual-branch design to emulate foveal vision at the visual focus, where the local neighbor branch provides fine-grained perception near each query point, while the spatial downsampling branch simultaneously maintains each query point's boarse-grained awareness for global semantics, i.e., high acuity at the focus and low acuity in the periphery.
Fine-grained perception. Given a point set P = {p: (2,, Wi, =)), e Nx3 with a corre-
sponding point feature set F ( NDD the Local Neighbor Branch point p; is
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Leumuble Pooiing
Valoe
Coarse-Graleed
Spatial Dowasampling
Downsamp Tokens
K-Neared
Tokens
Local Neighbors Gruined Perception
Ouapal Faster
Figure 3: Diagram of point-focused attention with the competitive normalized attention mechanism.
computed as follows:
(Q' K K v') (W, W W:) F
A softmax (( KV.) IVD)
(I)
LNB (p:) = AAVV
where W RDxD represents transformation matrix, and N; E R. denotes the indices of the local neighbors of point in P, determined by KNN.
Coarse-grained awareness. Given point feature set F (f.) E RRD and its corresponding
spatially downsampled feature set = {s.) M RDMxD the Spatial Downsampling Branch on point p; is computed follows:
Q''K" V*) (W.F, W.S.W.S)
A soflmax K*//K*/VD
SDB (p.) A.
Notably, the non-uniformity of point clouds prevents them from achieving spatial downsampling through simple and effective average pooling like 2D images. Moreover, as a commonly used down- sampling method for point clouds, FPS often requires setting small sampling rate attain suffi- cient coverage for extracting global information, which significantly increase computational cost in Eq. (2). Thus, develop an induced point pooling inspired by the inducing point method in the sparse Gaussian (Snelson & Ghahramani] 2005). Specifically, M D-dimensional vectors defined I RD are termed inducing points, and they are trainable parameters. The Induced Point Pooling on a point feature set F {f: NRD computed as follows:
(K", VP) = (W W F
S = (//D/DD) V
where S E R&XD represents the spatially downsampled features extracted by the induced point pooling on By using controllable number of trainable inducing points to directly learn how to induct point clouds adaptively through attention interactions, the induced point pooling flexibly adapts to the non-umte uii--unifomm distribution of point clouds to integrate global semantics, thereby effec- tively downsampling spatial features, shown in Appendix C.2
Competitive normalized fusion. straightforward approach for the point-focused attention is to sum the attention outputs from both branches to achieve the multi-scale fusion PFA (p,) of the local structures and global semantics at a point p,, thereby matching foveal vision perception, as follows:
PFA (p.) LNB (p.) + SDB (p:) = A + A
(4)
However, such shallow multi-scale feature fusion struggles to align with deep dynamic interactions between local fine-grained features and global coarse-grained semantics inherent in foveal vision
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Published as a conference paper at ICLR 2026
we update the point-focuse attention competitive normalized attention variant by computing the attention weights of both branches within single softmax calculation, thereby coupling fine- and coarse gransee features, follows:
perception. To address this, illustrated in Fig. 3
A = softmar (Concat K'. K.KKK) /VD)
A Ar split (A,, [K, M))
(5)
PFA (p.) LNB (p:) SDB (p:) = A. A,V*
where Concat denotes channel-level concatenation, split serves to partition channels according to specified sizes, and K and M represent the number of local neighbors determined by KNN and the number of inducing points used for downsampling spatial features, respectively. Compared to the simple version in Eq. 4 demonstrated in Tab. Eq. (5) enhances deep dynamic interactions through the competitive mechanism between fine- and coarse-grained features, without introducing additional computational overheads. This effectively simulates the perceptual process of foveal vision adaptively selecting the most effective receptive field information, thereby better aligning with the intrinsic structure of information distribution in natural scenes.
Complexity analysis. Based on the above settings and considering the feature transformation, the computational complexity Ω (PFA) of the point-focun attention
follows:
Ω (PFA) = Ω (LNB) + Ω (SDB) + Q (IPP)
(6)
6N + 2M + 2NKD + 4NMD
Since K and M are typically small, it can be observed that Ω (PFA) scales linearly with the number of points, indicating that PFA aligns with the optimized utilization of finite neural resources in foveal
visual. For the complexity of each module in PFA, please refer Appendix D
3.3 CONTEXT-SCAN STATE SPACE
The context-scan state space simulates eye's saccade inference through dynamic saccade mech- anism. It serializes a point cloud to provide a scanning path for the state space model, enabling the inference of the overall semantic structure and spatial content in a scene, i.e., the serialization performs continuous scanning and the state space model responsible for information integration, as illustrated in Fig. 2 (right).
Serialization. Space-filling curves are trajectories that cover high-dimensional regions by continu- ous parametric mapping. Their core function is to transform high-dimensional geometric structures into one-dimensional sequences while preserving local neighhorhood relationships, meaning that spatially adjacent elements remain adjacent in the sequence. When applied to point clouds, the high-dimensional space refers to the 3D Euclidean space containing point coordinates. Common space-filling curves include the Hilbert curve and Z-Order curve. The former is highly valued for its superior bocality-peeserving property, while the latter is renowned for its high efficiency.
Inspired by the spatial proximity of space-filling curves, recent S6-based works (Liang al. 2024
Li et al.; 2025 Liu et al.) 2024a) utilize them to serialize point clouds, establishing more reliable inter-point structural dependencies for geometric reasoning compared to axis-ordering based serial- ization (Zhang et al.) 2024b Han et al. 2024 Köprücü et al., 20244 Sccöne et al. 2024). However, provide richer spatial information, these serialization strategies often concatenate the results from multiple space-filling curves. This concatenation introduces a longer sequence, leading to redun- dancy and negatively impacting efficiency. More importantly, concatenating sequences with differ- ent spatial relationships can easily cause confusion. In addition, by serializing randomly located points using the Hilbert and Z-Order curves, respectively, can be intuitively observed that the Hilbert curve outperforms the Z-Order curve in preserving spatial proximity, as shown in Appendix
T
This finding is consistent with the prior research (Nordin & Telles 2023) and also better aligns with the inherent pattern of eye movements during visual search, which typically involve continuous scanming along spatially adjacent regions. Therefore, the Hilbert curve is employed for serialization to establish reliable inter-point point structural dependencies while guiding high-fidelity spatially adja- cent scanning path for accurate scene inference.
State space model. S6 is forward recurrence process based on hidden states, where each position in input sequence can only access prior information and cannot obtain content from subsequent po- sitions, shown in Appendix A This unidirectional modeling is unsuitable for visual data requiring

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Published as a conference paper at ICLR 2026
Network
Operator OA
Network
Operator
Ins. mloU
TIPT (Zha al. 2023
Attention 93.4
APES (W
2023a
Attention
85.8
Inter-MAE LLu ct al. 2023a
Attention 93.6
TACT Dong et a..
2025
Attention
86.1
CrrosNNet (Wi et sl. 2023b
Attention 93.4
PointGPP (Chen et al.
2023
Attention
86.2
1ACT (Dong al. 2023
Attention 93.7
ReeCon (Qi Ct al. 2023
Attention
86.4
LFT-Ner Gao al 2202
Attention 93.2
OctFormer
Attention 92.7
1Point2 (Zed el al.
2023
Atlention
86.3
&ReCon Oi al. 20233
Attention 92.5
tIDPT (Zha el al. 2023
Atlention
85.7
IDAPT (Zoou et 12204bb
Attention 93.5
GAD (Li el al. 2024b
Attention
86.3
TLCM (Zha et al. 2024
Attention 93.6
MMakk 3D Yan et al.
2024b
Attention
86.3
tPoint-PES Tang
2024
Attention 93.4
TMVNet Yan et al. 2024a
Attention
86.1
PointStack Wijaya et a..
2024
Attention 93.3
LLCM (Zha et 2024
Attention
86.3
GAD (Li
Attention
93.8
Point Tuu era
2024h
Attention
93.5
DAPT Zhou et al. 2024b
Attention
85.5
PoonntGST Liang of all. 20025a
Attention
93.4
TPoint-PEI-T Tang al.
2024
Attention
85.1
Point (Liang al. 2024
SSM
93.6
PointGST (Liang et al.
2025a
Attention
85.7
OctMamha (Libeend 20124
SSM
92.7
1Mamba3D (Han et al. 2024
SSM
85.6
PCM (Zhang et
20124
SSM
93.4
*PointMamba (Laang
2024
SSM
86.2
et MMamba3D
2024
SSM
93.4
PCM (Zhang al. 2024h
SSM
84.3
NIMBA (Köprucu cl al. 2024
SSM
92.1
STREAM (Schone
2024
SSM
92.7
NIMBA (Koprucu 2024
SSM
85.5
PrinTramba (Wang et al. 2024
Hybrid
92.7
PoinTramba (Wang 2024
Hybrid Hybrid
85.7
PointLearner
Hybrid
94.2
PointLearner
86.9
Table 1: Experimental results on Model- Table 2: Experimental results on ShapeNet dataset.
Net40 dataset. i; Pre-training strategy.
f; Pre-training strategy.
global learning. To address this, most works (Li et al. 2025 Han al. 2024 Köprücü al. 2024 Wang et al. 2024) propose the bidirectional S6, by introducing the bidirectionality from Vision Mamba (Zhu al.) 2024) into S6, to achieve global modeling over point sequences. Specifically, two S6 modules are deployed in parallel: forward S6 and backward S6. The former performs forward-scanning recurrent along the input sequence, while the latter processes reverse order. In this way, each point in the input sequence possesses global receptive field, which aligns with how the eye perform back-and- object. Hence, we adopt the bidirectional S6 for scene inference.
scanning to infer information when recognizing an indistinct
EXPERIMENTS
To validate PaintLearner, we conduct experimental comparisons on multiple point cloud tasks. Ad- ditionally, we perform robustness checking, and explore the effectiveness and characteristics of these biomimetic designs through extensive ablation studies. For detailed descriptions on datasets and evaluation metrics, please refer to Appendix B
4.1 EXPERIMENTAL COMPARISONS
Object recognition. Table T lists the quantitative results of network and recent works Model- Net40 dataset. It can be observed that the performance of the previous tftt-of-theear attention net- works has been saturated, confined through this performance bottleneck, achieving a state-of-the-art 94.2% OA. This result demon- strates that integrating the advantages of both attention and SSM paradigms within biological visual system framework is an effective avenue to advancing point cloud representation learning.
narrow range 93.2% to 93.8%. Our PointLearner breaks
Part segmentation. Table S lists the quantitative results of our network and recent works on ShapeNet dataset. Overall, the performance of emerging SSM-based methods confirms their limita- tions in handling fine-grained local features. The proposed Pointl Leanner achieves 86.9% Ins. mloU, significantly outperforming existing state-of-the-an methods based either attention SSM. This strongly demonstrates that the hybrid paradigm of the attention and SSM, guided by the biolog- ical vision system, effectively synergize the strengths of both operators, exhibiting powerful capabilities in local geometric modeling and long-range dependency interactions.
Semantic segmentation. Table lists the quantitative results of our network and recent works S3DIS dataset. In the more challenging task of point cloud semantic segmentation, the performance of the networks with different architectures exhibits significant disparities, highlighting the dual

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Published as a conference paper at ICLR 2026
Network
Operator
mloU
Network
Operator OA
Point Vector (Dnng al.
2023
Attention
72.3
ADS (Hong el al
2023
Attention 87.5
SPT (Robert et al. 2023
Attention
68.9
IDPPT (Zha et al.
2023
Attention 84.9
Spolf Park al.
2025
Attention
70.8
TACT Dong ct 2023
Attention 88.2
TACT (Dong et al. 2023
Attention
61.2
Joint-MAH: ( Gue al.
2023
Attention 86.1
Spu'Tr (Park ct 2023
Attention 88.6
ReConn (Qi
al.
2023
Attention
60.8
LLCM (Zha et al 2024
Attention 87.8
IDPT Zha et al.
2023
Attention
53.1
Inter-MAES (Liu et al.
2023a
Attention 85,4
MMM-3Dscene (Xu al.
2023
Attention
71.9
PointGPT (Chen
202.3
Attention 86.9
Retro-FPN (Xiang
2023
Attention
73.0
PointDif (Zheng et g1 2024
Attention 87.6
PTv3 (Wu et al. 2024a
Attention
73.4
TDAPT (Zhou
2024h
Attention 85.1
KPConvX-L (Thomas al. 2024
Attention
73.5
GAD (LF al. 2024:
Attention 82.6
DAPT (Zhou et al. 2024b
Attention
56.2
Point-PPFFT [Tang et al.
2024
Attention 85.0
PPontt-PPe PEFT [ang al.
2024
Attention
56.0
MaskFeattD (Yan
20246
Attention 87.7
MVNett Yan ct al. 2024a
Attention
73.8
MMVNet (Yan al. 2024a
Attention 86.7
GAD (Li cra 202-4
Attention
62.9
1 PointGST [Lang
2025a
Attention
85.6
Swin3D Yang al. 2025
Attention
72.5
T PointMamha (I.ing et al. 2024
SSM
89.3
*PonnGST (Liang al. 2025a)
Attention
58.6
PCM (Zhang
20224
SSM
88.1
1 Mamba3D (Han et
2024
SSM
88.2
PCM (Zhang el al. 2024b
SSM
63.4
NIMBA (Köprucu m..
2024
SSM
84.2
HydraMamba (Ou el al.
2025
SSM
73.6
STREAM (Schone et al.
2024
SSM
85.3
Pamba (Li ct al. 2025
SSM
73.5
Poin (Wang el al.
2024
Hybrid
88.9
PointLearner
Hybrid
74.3
Pointl_camner ceamer
Hybrid
89.8
Table 3: Experimental results on S3DIS dataset.
Table Experimental results on ScanOb-
t; Pre-training strategy.
jectNN dataset. i; Pre-training strategy.
challenges of local geometric modeling and global contextual inference in this task. Our network attains state-of-ther ofe-the-art performance with 74.3% mloU, which benefits from its bio-inspired visual perception design, maintaining the sensitivity of the attention mechanism to both local geometries and global semantics while incorporating the scene inference capability of SSM.
4.2 ROBUSTNESS CHECKING
Biological vision exhibits remarkable robustness to low-quality scenes. verify that our biomimetic network inherits this property, we conduct experiments from two perspectives: strong noise corruption and varying sampling densities.
Robustness to strong noise. ScanObjectNN is challenging dataset collected from real-world scenes. We conduct experiments on its most difficult variant, PB_T50_RS RS, to validate ParneLerrner'' robustness to strong noise. Tab. [ lists the quantitative results of our network and recent works ScanObjectNN dataset. The performance of the hybrid architecture PoinTramba (88.9% OA) has demonstrated the potential of hybrid methods in terms of robustness. Our Pointl further elevates the performance to 89.8% OA, surpassing all the existing models. This result indicates that the synergistic mechanism of the attention and SSM adopted by PointLearner effectively inherits the robust inference capability of the biological vision system under low-quality perception conditions.
Robustness varying sampling densities. Sensor data captured directly from the real world often suffers from severe irregular sampling issues. Consequently, during the testing phase, we randomly discard data points, as shown Fig. (left), to validate our network's robustness non- uniformly sparse data. Fig. [4 (right) presents the quantitative results of our network alongside the top-performing attention method (GAD (Li al., (024b)) and SSM method (PCM (Zhang et al,, 2024b)) from Tab. on ModelNet40 dataset with varying numbers of sampling points. Intuitively, our network exhibits superior robustness to variations sampling density compared to the other methods, with only a 2.2% performance drop when the number of test points decreases from 1024 to 256. This result benefits from the competitive normalized attention in the point-focused atten- tion. which balances descriptiveness and robustness by appropriately weighting local structures and global perception, as well as the powerful global inference capability of the sontext-scan state space.
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1024 polnts
fe a6 WiRe
163 pomes
95
90
(A.
83
512 points
256 prints
128 points
80
75
70
PrinLLemer
1024
768
512
256
128
Number points
Figure 4: Left:
Point clouds with different point densities. Right: Quantitative results of our net-
work and other works on ModelNet40 dataset with different numbers of sampling points.
Networks
Operator Params
Latency
Memory
mlo
HydraMamba (Qu ct al. 2025
SSM 63.14M
54ms
5.9G
73.6
PTv3 (Wu et al., 2024a
Attention 46.17M
49ms
6.3G
73.4
Swin3D ( Yang al. 2025
Attention 71.15M
365ms
10.7G
72.5
PointLearner
Hybrid 52.78M
63ms
6.5G
74.3
Table 5: Params, latency, and memory footprint of our network and previ-
ous state-of-the-art methods single inference on S3DIS dataset.
4.3 EFFICIENCY ANALYSIS
To further analyze the computational overheads of PointLeamner we compared it with several pre- vious state-of-the-an methods S3DIS dataset, and the params, latency, and memory footprint in a single inference are selected as evaluation metrics. Specifically, to ensure a fair comparison, the taken as the average values obtained over
latency and memory footprint in a single inference the entire S3DIS test set with each scene on the same RTX 4090 GPU. Tab. 5 presents the params, latency, and memory footprint of our network and multiple previous state-of-the-art methods in single inference. Swin3D is a heavy attention network that, unlike PTv3, does not incorporate effi- ciency improvements for attention computations. Furthermore, it can be observed that, with simi- lar number of parameters, Pointleearner, as hybrid network, achieves superior trade-off between computational overheads and performance compared to the fully optimized PTv3 and the Hydra- Mamba that benefits from S6's excellent properties. We attribute this to the following two aspects: (I) PointLearner is able to achieve powerful local geometric modeling and long-range dependency interactions from the perspective of biological vision through hybrid operators, which simultane- ously requires less layer stacking; (2) Although our basic block contains multiple components, they are essentially lightweight with linear complexity, while also benefiting from the hardware- optimized algorithms of FlashAttention (Dao et al. 2022
4.4 ABLATION STUDY
We conduct ablation study on ModelNet40 dataset, to investigate the effectiveness of the designed bio-inspired visual processing modules. To ensure fair comparison, all experiments are conducted on an RTX 4090 GPUs with identical configurations, and results are averaged over three runs.
Local neighbor branch. The point-focused attention employs the local neighbor branch to achieve the fine-grained perception, akin to foveal vision. To validate its necessity, we compare ablation results with and without the local neighbor branch in Tab. 6 The trade-off between accuracy and efficiency further indicates that the local neighbor branch enhances the network's ability to learn local geometric structures while maintaining low computational complexity. This closely aligns with the biological vision mechanism where refined local perception optimizes global inference.
Spatial downsampling branch. Through the spatial downsampling branch, the foint-focused atten- tion simultancously maintains coarse- coarse-grained perception of global semantics for each query point. To validate its importance, we compare ablation results with and without this branch in Tab.

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Published as a conference paper at ICLR 2026
LNB
Params
FLOPs
Throughput OA
SDB
Params
FLOPs
Throughput OA
7.36M
0.610G
163FPS
94.17
7.36M
0.610G
163FPS
94.17
x
6.57M
0.504G
221FPS
92.11
x
6.35M
0.578G
183FPS
93.06
Table 6: Ablation results w/ or w/o the local neighbor branch.
Table 7: Ablation results w/ or w/o the spatial downsampling branch.
Fusion Params FLOPs Throughput OA
SSM
Params FLOPs
Throughput
OA
Competitive 7.36M 0.6I0G
163FPS
94.17
Bid. S6 7.36M 0.610G
163FPS
94.17 93.08
Additive 7.36M
0.610G
166FPS
93.43
Uni. S6
6.06M 0.553G
181FPS
Table 8: Ablation results with different multi- Table 9: Ablation results with both state space models.
scale fusion.
PFA CSSS Params
FLOPs
Throughput OA
4.57M
0.487G
198FPS
92.93
5.37M
0.463G
231FPS
91.94
7.36M
0.610G
163FPS
94.17
Table 10: Ablation results with PFA and CSCC.
Quantitative analysis demonstrates that this branch effectively establishes coarse paase-grained associa- tions between query points and global semanties a low computational cost, simulating the inte- gration mechanism of peripheral information in biological vision.
Normalized fusion. In the point-focused attention, by coupling attention weights within single softmax calculation, the proposed competitive normalized fusion replaces simple additive multi- scale fusion to enhance deep dynamic interactions. To validate its advantage, Tab. compares the ablation results of both multi-scale fusion strategies. Quantitative analysis demonstrates that the competitive fusion mechanism effectively simulates the dynamic interaction and adaptive selection between fine- and coarse- grained features in foveal vision. This achieves superior semantic fusion effect while maintaining nearly identical computational overheads.
State space model. The context-scan state space leverages the state space model to infer the en- tire scene along the scanning path provided by the serialization, based on inter-point structural de- pendencies. To validate that the adopted bidirectional S6 achieves stronger inference performance compared to the unidirectional S6, we compared the ablation results of both state space models. As shown in Tab. D. although the bidirectional S6 introduces slight increase in parameters and com- putational complexity. it constructs a global receptive field for each point through the forward and backward scanning. This effectively simulates the eye's back-and-forth saccade, thereby enhancing inference ability in complex scenes.
PFA & CSSS. The proposed network incorporates two key modules: Point-Focused Attention (PFA) and Contextual Scan State Space (CSSS). PFA fuses local neighbors and spatially downsampled fea- tures based on competitive normalized attention mechanism, simulating foveal vision at the visual focus. On this basis, CSSS further infers the overall semantic structure and spatial content within scene through point cloud serialization and state space model, mimicking eye's ' saccade inference. The combination of PFA and CSSS closely aligns with biological vision, and their complementarity fully validated by the ablation results presented in Tab. 10
CONCLUSION
In this paper, we introduce PointLearner, a point cloud representation learning network inspired by biological vision mechanisms. First, we propose a point-focused attention architecture that mimics the foveal vision of the human eye, enabling fine-grained perception around each query point while maintaining coarse-grained awareness of more distant regions. Building on this locally attentive representation, we further introduce a context-scan state space model to globally scan point clouds, drawing inspiration from the saccadic movements of the human eye during visual inference. This allows the model to integrate local details and global context, leading to a comprehensive under- standing of the underlying geometrie structure. Extensive experiments across multiple datasets and tasks demonstrate that PointLearner outperforms existing methods and exhibits strong robustness to noise and varying sampling densities.
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ACKNOWLEDGMENTS
This work is supported in part by the National Natural Science Foundation of China under Grant (62476126, 62272227).
REFERENCES
Z. C. An, L. Sun, Y. Liu, R. J. Li, M. Wu, M. M. Cheng, E. Konukoglu, and S. J. Belongie. Multi- modality helps few-shot 3d point cloud semantic segmentation. In Proc. International Conference on Learning Representations (ICLR), pp. 1-13, Singapore, Apr. 2025.
I. Armeni, O. Sener, A. R. Zamir, H. L.. Jiang, 1. K. Brilakis, M. Fischer, and S. Savarese. 3d semantic parsing of large-scale indoor spaces. In Proc. IEEE/CVF Conference on Computer Vision and Puttern Recognition (CVPR), pp. 1534-1543, Las Vegas, NV, USA, Jun. 2016.
G. Y. Chen, M. L. Wang, Y. Yang, K. Yu, L. Yuan, and Y. F. Yue. Pointgpt: Auto-regressively generative pre-training from point clouds. In Proc. Advances in Neural Information Processing Systems (NeurIPS), pp. 1-13, New Orleans, LA, USA, Dec. 2023.
S. R. Chen, Y. X. Luo, Y. Ma. Y. Qiao, and Y. L. Wang. H-mba: Hierarchical mamba adaptation for multi-modal video understanding in autonomous driving.
2025.
T. Dao, D. Y. Fu, S. Ermon, A. Rudra, and C. Ré. Flashattention: Fast and memory- exact attention with io-awareness. In Proc. Advances in Neural Information Processing Systems (NeurIPS), pp. 1-16, New Orleans, LA, USA, Dec. 2022.
X. Deng, W. Y. Zhang, Q. Ding, and X. M. Zhang. Pointvector: A vector representation in point cloud analysis. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 9455-9465, Vancouver, BC, Canada, Jun. 2023.
R. P. Dong. Z. K. Qi. L. F. Zhang, J. B. Zhang. J. Sun, Z. Ge. L. Yi, and K. S. Ma. Autoencoders as cross-modal teachers: Can pretrained 2d image transformers help 3d representation learning? In Proc. International Conference on Learning Representations (ICLR), pp. 1-16, Kigali, Rwanda, May 2023.
L. Fan, 7.0 Q. Pang. T. Zhang, Y. Wang, Zhao, Wang, N. Wang, and X. Zhang. Em- bracing single stride 3d object detector with sparse transformer. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8448-8458, New Orleans, LA, USA, Jun. 2022.
Y. X. Fu, M. Lou, and Y. Yu. Segman: Omni-scale context modeling with state space models and local attention for semantic segmentation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19077-19087, Nashville, TN, USA, Jun. 2025.
Y. B. Gao, X. B. Liu, J. Li, Z. J. Fang. X. Y. Jiang, and K. M. S. Huq. Lft-net: Local feature transformer network for point clouds analysis. IEEE Transactions Intelligent Transportation Systems (TITS), (422)2158-2166 Feb. 2023.
A. Gu and T. Dao. Mamba: Linear-time sequence modeling with selective state spaces. arXiv: 2312.00752. 2023.
A. Gu, T. Dao, S. Ermon. A. Rudra, and C. Ré. Hippo: Recurrent memory with optimal polynomial projections. In Proc. Advances in Neural Information Processing Systems (NeurIPS), pp. 1474- 1487, Virtual Event, Dec. 2020.
A. Gu, I. Johnson, K. Goel, K. Saab, T. Dao, A. Rudra, and C. Ré. Combining recurrent, convolu- tional, and continuous- models with linear state space layers. In Proc. Advances in Neural Information Processing Systems (NeurIPS), pp. 572-585, Virtual Event, Dec. 2021.
A. Gu, K. Goel, and C. Ré. Efficiently modeling long sequences with structured state spaces. In Proc. International Conference on Learning Representations (ICLR), pp. 1-15, Virtual Event, Apr. 2022.
11

---

Published as a conference paper at ICLR 2026
M. H. Guo, X. Cai, Z. Liu, J.J. Mu, R. R. Martin, and S. M. Hu. Pet: Point cloud transformer. Computational Visual Media (CVM), 7(2):187-19, Apr. 2021.
Z. Y. Guo, R. R. Zhang, L. T. Qiu, X. Z. Li, and P. A. Heng. Joint-mae: 2d-3d joint masked autoencoders for 3d point cloud pre-training. In Proc. International Joint Conference on Artificial Intelligence (IJCAI), pp. 791-799, Macao, SAR, China, Aug. 2023.
A. Gupta, A. Gu, and J. Berant. Diagonal state spaces are as effective as structured state spaces. In Proc. Advances in Neural Information Processing Systems (NeurIPS), pp. 1-12, New Orleans, LA, USA, Dec. 2022.
X. Han, Y. Tang, Z. X. Wang, and X. Z. Li. Mamba3d: Enhancing local features for 3d point cloud analysis via state space model. In Proc. ACM International Conference on Multimedia (MM), pp. 4995-5004, Melbourne, Australia, Oct. 2024.
C. H. He, R. H. Li, S. Li, and L. Zhang. Voxel set transformer: set-to-set approach to 3d object detection from point clouds. In Proc. IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 8407-8417, New Orleans, LA, USA, Jun. 2022.
H. Y. He, J. N. Zhang. Y. X. Cai, H. X. Chen, X. B. Hu, Z. Y. Gan, Y. B. Wang, J. Wang, Y. S. Wu, and L. Xie. Mobilemamba: Lightweight multi-receptivve visual mamba network. In Proc. Computer Vision and Pattern Recognition (CVPR), pp. 4497-4507, Nashville, TN, USA, Jun. 2025.
IEEE/CVF Conference
X. W. Hc, S. L. Cheng, D. K. Liang, S. Bai, X. Wang, and Y. Y. Zhu. Latformer: Locality point-view fusion transformer for 3d shape recognition. Pattern Recognit (PR), 151:1 10413, Jul. 2024.
C. Y. Hong, Y. Y. Chou, and T. L. Liu. Attention discriminant sampling for point clouds. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 14383-14394, Paris, France, Oct. 2023.
N. Köprücü, D. Okpekpe, and A. Orvieto. Nimba: Towards robust and principled processing of point clouds with ssms.
2024.
X. Lai, J. H. Liu, L. Jiang, L. W. Wang, and H. S. Zhao. Stratified transformer for 3d point cloud seg- mentation, In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8490-8499 New Orleans, LA, USA, Jun. 2022.
K. C. Li, X. H. Li, Y. Wang. Y. N. He, Y. L. Wang, L. M. Wang, and Y. Qiao. Vidcomamba: State space model for efficient video understanding. LtXiv:2403.06977, 2024a.
Z. H. Li, P. Gao, K. You, C. Yan, and M. Paul. Global attention-guided dual-domain point cloud feature learning for classification and segmentation. IEEE Transactions Artificial Intelligence (TAI), 5(10):5167-51) 5167, Oct. 2024b.
Z. Y. Li, Y. B. Ai. J. H. Lu, C. X. Wang, C., Deng, H. Z. Chang, Y. Z. Liang, W. F. Yang, S. F. Zhang, and T. Z. Zhang. Pamba: Enhancing global interaction in point clouds via state space model. In Proc. AAAI Conference Artificial Intelligence (AAAI), pp. 5092-5100, Philadelphia, PA, USA, Feb. 2025.
D. K. Liang, X. Zhou, W. Xu, X. K. Zhu, Z. K. Zou, X. Q. Ye, X. Tan, and X. Bai. Pointmamba: A simple state space model for point cloud analysis. In Proc. Advances in Neural Information Processing Systems (NeurIPS). pp. 1-14, Vancouver, BC, Canada, Dec. 2024.
D. K. Liang, T. R. Feng, X. Zhou, Y. M. Zhang, Z. K. Zou, and X. Bai. Parameter-efficient fine- tuning in spectral domain for point cloud learning. IEEE Transactions Pattern Analysis and Machine Intelligence (TPAMI), 1999))))999999996 Aug. 2025a.
D. K. Liang, W. Hua, C. S. Shi, Z. K. Zou, X. Q. Ye, and X. Bai. Sood++: Leveraging unlabeled data to boost oriented object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), (11):840-888. Sep. 2025b.
12

---

Published as a conference paper at ICLR 2026
D. K. Liang, D. Y. Zhang, X. Zhou, S. F. Tu, T. R. Feng, Fi, Y. M. Zhang, M. Y. Du, X. Tan, and X. Bai. Seeing the future, perceiving the future: A unified driving world model for future generation and perception. In Proc. IEEE International Conference on Robotics and Automation (ICRA), pp. 1-11, Vienna, Austria, Jun. 2026.
H. T. Liu, M. Cai, and J. Masked discrimination for self-supervised learning point clouds. In Proc. European Conference on Computer Vision (ECCV), pp. 657-675, Tel Aviv, Israel, Oct. 2022.
M. Liu, Y. Wu, M. G. Gong, Z. X. Liu, Q. G. Miao, and W. Pa... Inter-modal masked autoencoder for self-supervised learning on point clouds. IEEE Transactions Multimedia (TMM), 26:3897- 3908, Sep. 2023a.
J. M. Liu, R. J. Yu, Y. Wang. Y. Zheng, T. C. Deng, W. C. Ye, and H. S. Wang. Point mamba: A novel point cloud backbone based on state space model with octree-based ordering strategy. arXiv:2403.06467 2024a.
L. Y Liu, M. Zhang, J. H. Yin, T. W. Liu, Ji, R. Piao, and H. C. Lu. Defmamba: Deformable visual state space model. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recog- nition (CVPR), pp. 8838-8847, Nashville, TN, USA, Jun. 2025.
Y. H. Liu, B. Tian, Y. L. L. X. Li, and F. Y. Wang. Point cloud classification using content-based transformer via clustering in feature space. IEEE/CAA Journal of Automatica Sinica (JAS), 11(1): 231-239, Jan. 2024b.
Z. J. Liu, X. Y. Yang, H. T. Tang, S. Yang, and S. Han. Flatformer: Flattened window attention for efficient point cloud transformer. In Proc. IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 1200-1211, Vancouver, BC, Canada, Jun. 2023b.
J. Ma, F. F. Li, and B. Wang. U-mamba: Enhancing long-range dependency for biomedical image segmentation. arXiv:2401.0477 2024.
K. Mazur and V. Lempitsky. Cloud transformers: A universal approach point cloud processing tasks. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 10695- 10704, Montreal, QC, Canada, Oct. 2021.
H. Mehta, A. Gupta, A. Cutkosky, and B. Neyshabur. Long range language modeling via gated state spaces. In Proc. International Conference Learning Representations (ICLR), pp. 1-16, Kigali, Rwanda, May 2023.
Robert Ahadizad Newport, Sidong Liu, and Antonio Di leva. Integrating eye gaze into machine learning using fractal curves. In Gaze Meets Machine Learning Workshop, pp. 113-126. PMLR, 2023.
D. Nie, R. Lan, L. Wang, and X. F. Ren. Pyramid architecture for multi-scale processing in point cloud segmentation. In Proc. IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 17263-17273, New Orleans, LA, USA, Jun. 2022.
A. Nordin and A. Telles. Comparing the locality preservation of z-order curves and hilbert curves. 2023.
Y. T. Pang, W. X. Wang, F. E. H. Tay, W. Liu, Y. H. Tian, and L. Yuan. Masked autoencoders for point cloud self-supervised learning. In Proc. European Conference on Computer Vision (ECCV), pp. 604-621, Tel Aviv, Israel, Oct. 2022.
C. Park, Y. Jeong, M. S. Cho, and J. Park. Fast point transformer. Proc. JEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16928-16937. New Orleans, LA, USA, Jun. 2022.
J. Park, S. Lee. S. Kim, Y. Y. Xiong, and H. J. Kim. Self-positioning basnt-based transformer for point cloud understanding. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 21814-21823, Vancouver, BC, Canada, Jun. 2023.
13

---

Published as a conference paper at ICLR 2026
C. R. Qi. H. Su, K. C. Mo, and L. J. Guibas. Pointnet: sification and segmentation. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 77-85, Honolulu, HI, USA, Jul. 2017a.
Deep learning point sets for 3d clas-
C. R. Qi, L. Yi, H. Su, and L. J. Guibas. +ointnet++: Deep hierarchical feature learning on point sets in a metric space. In Proc. Advances in Neural Information Processing Systems (NeurIPS), pp. 5099-5108, Long Beach, CA, USA, Dec. 2017b.
Z. K. Qi, R. Dong, G. F. Fan, Z. Ge. X. Y. Zhang, K. S. Ma, and L. Yi. Contrast with reconstruct: Contrastive 3d representation learning guided by generative pretraining. In Proc. International Conference on Machine Learning (ICML), pp.
Honolulu, HI, USA, Jul. 2023.
K. L. Qu, Gao, Q. Dai, and Y. H. Sun. Hydramamba: Multi-head state space model for global point cloud learning. In Proc. ACM International Conference on Multimedia (MM), pp. 333-342, Dublin Ireland, Oct. 2025.
H. Resani and Nasihatkon. Miracle3d: Memory-efficient integrated robust approach for continual learning 3d point clouds via shape model construction. In Proc. International Conference Learning Representations (ICLR), pp. 1-14, Singapore, Apr. 2025.
D. Robert, H. Raguet, and L. Landricu. Efficient 3d semantic segmentation with superpoint trans- former. In Proc. IEEE/CVF International Conference Computer Viston (ICCV), pp. 17149- 17158, Paris, France, Oct. 2023.
J. C. Ruan and S. C. Xiang. Vm-unet: Vision mamba unet for medical image segmentation. arXiv: 2402.02491, 2024.
M. Schöne, Y. Bhisikar, K. Bania, K. K. Nazeer, C. Mayr, A. Subramoney, and D. Kappel. Stream:
A universal state-space model for sparse geometric data. arXiv:2411 2024.
A. M. Shaker, S. T. Wasim, S. H. Khan, J. Gall, and F. S. Khan. Groupmamba: Efficient group- based visual state space model. Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 14912-14922 Nashville, TN, USA, Jun. 2025.
D. Shi. Transnext: Robust foveal visual perception for vision transformers. In Proc. IEEF/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 17773-17783, Seattle, WA, USA, Jun. 2024.
J. T. H. Smith, A. Warrington, and S. W. Linderman. Simplified state space layers for sequence modeling. Proc. International Conference on Learning Representations (ICLR), pp. 1-13, Kigali, Rwanda, May 2023.
E. L. Snelson and Z. Ghahramani. Sparse gaussian processes using pseudo-inputs In Proc. Advances in Neural Information Processing Systems (NeurIPS), pp. 1257-1264, Vancouver, BC, Canada, Dec. 2005.
E. E. M. Stewart, M. Valsecchi, and A. C. Schütz. review of interactions between peripheral and foveal vision. Journal of Vision (JVISION), 20(12):2, Nov. 2020.
J. Y. Su, J. B. Ma, S. Y. Tong, E. Z. Xu, and M. H. Chen. Multiscale attention wavelet neural operator for capturing steep trajectories in biochemical systems. In Proc. AAAI Conference on Artificial Intelligence (AAAI), 1 15100-15107, Vancouver, BC, Canada, Feb. 2024.
P. Sun, M. X. Tan, W. Y. Wang, C. X. Liu, F. Xia, Z. Q. Leng, and D. Anguelov. Swformer: Sparse window transformer for 3d object detection in point clouds. Proc. European Conference on Computer Vision (ECCV), pp. 442, Tel Aviv, Israel, Oct. 2022.
Y. W. Tang, R. Zhang, 7. Guo, X. 7. Ma, B. Zhao, Z. G. Wang, D. Wang, and X. L. Li. Point-peft: Parameter-efficieet fine-tuning for 3d pre-trained models. In Proc. AAAI Conference on Artificial Intelligence (AAAI), pp. 5171-5179, Vancouver, BC, Canada, Feb. 2024.
H. Thomas, Y. H. Tsai, T. D. Barfoot, and J. Zhang. Kpconvx: Modernizing kemnel point convo- lution with kernel attention. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5525-5535, Seattle, WA, USA, Jun. 2024.
14

---

Published as a conference paper at ICLR 2026
M. A. Uy Q. H. Pham, B. S. Hua, D. T. Nguyen, and S. K. Yeung. Revisiting point cloud classifica- tion: A new benchmark dataset and classification model on real-world data. In Proc. IEEE/CVF International Conference Computer Vision (ICCV), pp. 1588-1597, Scoul, South Korea, Oct. 2019.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polo- sukhin. Attention is all you need. In Proc. Advances in Neural Information Processing Systems (NeurIPS), pp. 5998-6008, Long Beach, CA, USA, Dec. 2017.
B. A. Wandell, Foundations of Vision. Sinauer Associates, 1995.
J. Wang, W. T. Zhu, P. C. Wang, X. Yu, L. D. Liu, M. Omar, and R. Hamid. Selective structured state-spaces for long-form video understanding. In Proc. IEEE/CVF Conference on Computer Vision and Paltern Recognition (CVPR), pp. 6387-6397, Vancouver, BC, Canada, Jun. 2023.
P. S. Wang. Octformer: Octree-based transformers for 3d point clouds. ACM Transactions Graphics (TOG), 12(4):155, Jul. 2023.
7. C. Wang. Z. H. Chen, Y. M. Wu, 7. Zhao, I.. Zhou, and D. Xu. Pointramba: hybrid wransfrmer--ambl framework for point cloud analysis. arXiv:2405.154m 2024.
K. T. Wijaya, D. H. Pack, and S. H. Kong. Advanced feature learning on point clouds using multi- resolution features and learnable pooling. Remote Sensing (RS), 16(11):1835, May 2024.
C. Z. Wu, J. W. Zheng, J. Pfrommer, and J. Beyerer. Attention- based point cloud edge sampling. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5333- 5343, Vancouver, BC, Canada, Jun. 2023a.
X. Y. Wu, Y. X. Lao, L. Jiang, X. H. Liu, and H. S. Zhao. Point transformer v2: Grouped vector at- tention and partition- pooling. Proc. Advances in Neural Information Processing Systems (NeurIPS), pp. 1-13, New Orleans, LA, USA, Dec. 2022.
X. Y. Wu, L. Jiang, P. S. Wang, Z. J. Liu, X. H. Liu, Y. Qiao, W. L. Ouyang, T. He, and H. S. Zhao Point transformer v3: Simpler, faster, stronger. In Proc. JEEE/CVF Conference on Computer Vision and Patlern Recognition (CVPR), pp. 4840-4851, Seattle, WA, USA, Jun. 2024a.
X. Y. Wu, Z. T. Tian, X. Wen, B. H. Peng, X. H. Liu, K. C. Yu, and H. S. Zhao. Towards large- scale 3d representation learning with multi-dataset point prompt training. In Proc. JEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19551-19562, Seattle, WA, USA, Jun. 2024b.
X. Y. Wu, D. DeTone, D. Frost, T. Shen, C. Xie, N. Yang, J. Engel, R. Newcombe, S.Zao, and J. Straub. Sonata: Self-supervised learning of reliable point representations. In Proc. IEEF/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 22193-22204, Nashville, TN, USA, Jun. 2025.
Y. Wu, J. M. Liu, M. G. Gong, P. R. Gong. X. L. Fan, A. K. Qin, Q. G. Miao, and W. P. Ma. Self- supervised intra-modal and cross-modal contrastive learning for point cloud understanding. IEEE Transactions on Multimedia (TMM), 26: 1628-1638, Jun. 2023b.
Z. R. Wu, S. R. Song, A. Khosla, F. Yu, L. G. Zhang, X. O. Tang, and J. X. Xiao. 3d shapenets: A deep representation for volumetric shapes. In Pruc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1912-1920, Boston, MA, USA, Jun. 2015.
Xiang, X. Wen, Y. S. Liu, H. Zhang, Y. Fang, and Z. Z. Han. Retro-fpn: Retrospective fea- ture pyramid network for point cloud semantic segmentation. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 17826-17838 Paris, France, Oct. 2023.
Z. H. Xing, T. Ye, Y. J. Yang, G. Liu, and I.. 7hu. Segmamba: Long-range sequential modeling mamba for 3d medical image segmentation. arXiv:2401. 2024.
M. Y. Xu, M. T. Xu, T. He, W. Ouyang, Y. L. Wang, X. G. Han, and Y. Qiao. Mm- Mm-3dscene: 3d scene understanding by customizing masked modeling with informative- presserved reconstruction and self- distilist consistency. In Proc. JEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4390- Vancouver, BC, Canada, Jun. 2023.
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W. Xu, C. S. Shi, S. Tu, X. Zhou, D. K. Liang, and X. Bai. A unified framework for 3d scene understanding. In Proc. Advances in Neural Information Processing Systems (NeurIPS), pp. 1-14, Vancouver, BC, Canada, Dec. 2024.
S. M. Yan, C. Song, Y. K. Kong, and Q. X. Huang. Multi-view representation is what you need for point-cloud pre-training. Proc. International Conference on Learning Representations (ICLR), pp. 1-15, Vienna, Austria, May 2024a.
M. Yan, Q. Yang, Y. X. Guo, H. Pan, S. Wang. Tong, Liu, and Q. X. Huang. 3d feature prediction for masked- cateecddaaateencder-----see point cloud pretraining. In Proc. International Conference on Learning Representations (ICLR), pp. 1-12, Vienna, Austria, May 2024b.
X. Yan, C. D. Zheng, Z. Li, S. Wang, and S. G. Cui. Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5588-5597, Virtual Event Seattle, WA, USA, Jun. 2020.
Y. Q. Yang, X. Guo, Y. Xiong, Y. Liu, H. Pan, P. Wang. X. Tong, and B. Guo, Swin3d: A pretrained transformer backbone for 3d indoor scene understanding. Computational Visual Media (CVM), 11(1)83-101, Feb. 2025.
L. Yi, V. G. Kim, D. Ceylan, I. C. Shen, M. Y. Yan, H. Su, C. Lu, Q. X. Huang, A. Sheffer, and L. Guibas. A scalable active framework for region annotation in 3d shape collections. ACM Transactions Graphics (TOG), 35(6):210, Dec. 2016.
X. M. Yu, L. Tang, Y.M. Rao, T. J. Huang, J. Zhou, and J. W. Lu. Point-bert: Pre-training 3d point cloud transformers with masked point modeling. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19291-19300, New Orleans, LA, USA, Jun. 2022.
K. A. Zeid, J. Schult, A. Hermans, and B. Leibe. Point2vec for self-supervised representation learning point clouds. In Proc. DAGM German Conference (DAGM), pp. 131-146, Heidelberg, Germany, Sep. 2023.
Y. H. Zha, J. P. Wang, T. Dai, B. Chen, Z. Wang, and S. T. Xia. Instance-aware dynamic prompt tuning for pre-trained point cloud models. In Proc. 1EEE/CVF International Conference on Com- puter Vision (ICCV), pp. 14161-14170, Paris, France, Oct. 2023.
Y. H. Zha, N. Q. Li, Y. Z. Wang, T. Dai, H. Guo, B. Chen, Z. Wang, Z. H. Ouyang, and S. T. Xia. Lem: Locally constrained compact point cloud model for masked point modeling. In Proc. Ad- vances in Neural Information Processing Systems (NeurIPS), pp. 1-14, Vancouver, BC, Canada, Dec. 2024.
Y. H. Zha, Y. 7. Wang, H. Guo, P. Wang, T. Dai, B. Chen, Z. H. Ouyang, Y. R. Xue, K. Chen, and S. T. Xia. Pma: Towards efrfieter-efficient point cloud understanding via point mamba adapter. In Proc. JEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16976-16988) Nashville, TN, USA, Jun. 2025.
C. Zhang, H. C. Wan, X. Y. Shen, and Z. Z. Wu. Patchformer: An efficient point transformer with patch attention. In Proc. IEEE/CVF Conference Computer Vision and Pattern Recognition (CVPR), pp. 11798, New Orleans, LA, USA, Jun. 2022.
D. Y. Zhang, D. K. Liang, Z. K. Zou, J. Y. Li, X. Q Ye, Z. Liu, X. Tan, and X. Bai. A simple vision transformer for weakly semi-supervised 3d object detection. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 8339-8339, Paris, France, Oct. 2023.
D. Y. Zhang, D. K. Liang, Z. C. Tan, X. Q. Ye, C. Zhang, J. D. Wang, and X. Bai. Make your vit-based multi-view 3d detectors faster via token compression. In Proc. European Conference on Computer Vision (ECCV), pp. 56-72, Milan, Italy, Sep. 2024a.
Zhang, X. T. Li, H. B. Yuan, S. P. Ji, and S. C. Yan. Point cloud mamba: Point cloud learning via state space model. HxXiv:2403.00762. 2024b.
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H. S. Zhao, L. Jiang, J. Y. Jia, P. Torr, and V. Koltun. Point transformer. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV), pp. 16239-16248, Montreal, QC, Canada, Oct. 2021.
X. Zheng, X. S. Huang, G. F. Mei, N. Hou, Z. Lyu, B. Dai, W. L. Ouyang, and Y. S. Gong. Point cloud pre-training with diffusion models. In Proc. IEEE/CVE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 19935-22945.5 Seattle, WA, USA, Jun. 2024.
Zhou, S. Wang. B. Ma, Y.S. Liu, T. Huang, and L. Wang. Uni3d: Exploring unified 3d representation at scale. In Proc. International Conference Learning Representations (ICLR), pp. 1-14, Vienna, Austria, May 2024a.
X. Zhou, D. K. Liang, W. Xu, X. K. Zhu, H. Xu, Z. K. Zou, and X. Bai. Dynamic adapter meets prompt tuning: Parameter-efficies transfer learning for point cloud analysis. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 14707-14717, Seattle, WA, USA, Jun. 2024b.
X. Zhou, D. K. Liang, S. Tu, W. Chen, Ding, Zhang. F. Y. Tan, H. S. Zhao, and X. Bai. Hermes: A unified self-driving world model for simultaneous 3d scene understanding and generation. In Proc. IEEE/CVE International Conference on Computer Vision (ICCV), pp- 27817-27821 Honolulu, HI, USA, Oct. 2025a
Y. C. Zhou, J. Y. Gu, T. Y. Chiang, F. B. Xiang, and H Su. Point-sam: Promptable 3d segmentation model for point clouds. In Proc. International Conference on Learning Representations (ICLR), pp. 1-14, Singapore, Apr. 2025b.
L. H. Zhu, B. C. Liao, Q. Zhang, X. L. Wang, W. Liu, and X. G. Wang. Vision mamba: Effi- cient visual representation learning with bidirectional state space model. In Proc. International Conference on Machine Learning (ICML), pp. 1-10, Vienna, Austria, Jul. 2024.
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A STATE SPACE MODELS
SSMs are cyclic processes with latent states, which map a 1-D equation or sequence x (t) E IR to y (t) IRN by latent state h (t) IRN. The process is mathematically denoted as linear ordinary differential equation as follows
(t) - (h(),, (t) (t),
where the three parameters A E BRXN B E IRN, and C E R represent the state matrix, input matrix, and output matrix, respectively. Since the above SSMs run on continuous inputs and are not applicable to discrete inputs such as images and text, they cannot be introduced into deep models. Thus, it is necessary to discretize them, and the zero-order hold is commonly used as discretization method. The discretized formulas
follows
h4 Ah4-1 + BrG y1 = Ch,,
where Å and B are the results of discretizing the continuous parameters A and by a time scale A, denoted
(9) Since processing the input and latent state equally, previous approaches focusing linear time- invariant SSMs (where A and are invariant) may fail to capture critical information from con- text. Hence, Mamba proposes novel SSM termed S6 by integrating an input-dependent selective mechanism into SSMs, where A and B are the functions of inputs, indicating Mamba is linear time-variant.
A C- = (-(A ()BB)
B DATASETS AND IMPLEMENTATION
B.I DATASETS
ModelNet40 dataset contains 12,311 CAD models across 40 categories, with 9,843 samples in the training set and 2,468 samples in the test set. Data preprocessing follows the method of Qi et al. (017a). where 1,024 points and their normal vectors are uniformly sampled from each sample as input. As per most relevant studies in the literature, the overall accuracy (OA) is adopted as an evaluation metric.
ShapeNet dataset comprises 16,878 samples from 50 parts across 16 categories, with 14,005 samples in the training set and 2,873 in the test set. Data preprocessing is consistent with that applied to ModelNet40 dataset. As per most relevant studies the literature, the instance mloU (Ins. mloU) is adopted as evaluation metric.
S3DIS dataset comprises point cloud data from 271 indoor scenes across 6 areas, with each point annotated with one of 13 semantic labels. Data preprocessing follows the method of Qi et al.) 2017a), where input features include point coordinates, RGB color, and normalized positions. As most relevant studies in the literature, area 5 is used the test set, while the remaining areas used for training, and the mean IoU (mloU) is adopted as an evaluation metric.
ScanObjectNN (PB_T50_RS variant) contains 14,450 valid samples across 15 categories, with 11,636 samples for training and 2,814 for testing. Except for using only coordinates as input, data preprocessing and evaluation metric are consistent with those used for ModelNet40 dataset.
B.2 IMPLEMENTATION
To better understand the model's structure and implementation, we list detailed network architec- tures and training settings across different datasets in Tab. 11
C MORE ABLATION STUDIES
C.I ABLATION COMPARISON ON THE SERIALIZATION
Serialization. Based on point cloud serialization, the context-scan state space generates a continu- scanning path with inter-point structural dependencies. To validate the rationale behind selecting
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Configurations
ModelNet40
ScanOhjectNN
ShapeNet
S3DIS
Training epochs
500
500
600
Optimizer & Scheduler
Adamw & CosLR AdamW & CosLR Adamw & CosLR
Adamw & Cos R
Weight decay
0.01
0.01
0.01
0.01
Learning rate
Se-4
4e-4
le-3
le-3
Warmup epochs
10
20
10
Batch size
24
24
24
Embedding channels
48
48
96
48
KNN
8
8
8
8
IPP ratio
8
8
8
16
Encoder depth
11 1,
[1. 1, 1,
[2, 2, 6, 2]
[1,
Encoder channels
[48, 96, 192, 384]
[48,
192, 384]
[96, 192, 384, 768]
[96, 192, 384, 768]
Decoder depth
[1. 1. 1J [768, 384, 192, 96]
[1. 1, 1, 1]
Decoder channels
[768, 384, 192, 96]
Downsampling stride
[4. 4, 4] 4 True 0.3
[4. 4.4]
[4. 4, 4] 4 Truc 0.3
[4. 4. 4]
MLP ratio
4
4 Truc
QKV bias
True 0.3
Dropout
0.3 RandomScale
RandomFlip
RandomScale RandomShift ShufflePoint
ShufflePoint RandomScale RandomRotate
RandomScale RandomShift ShufflePoint
RandomJitter
Augmentation
ChromaticAAtoC AutoContrast
ChromaticTranslatiin
ChromaticJitter
Table 11: Detailed implementation configurations.
(a) Serialization/rcordering randomly located points by the Hilbert curve
(b) perialization/roorerrnn of randomly located points by the Z-order curve
Figure 5: Comparison of the serialization of randomly located points by the Hilbert curve (top) and Z-Order curve (bottom).
the Hilbert curve, Tab, 12 compares the ablation results of various serialization strategies. When two scrialization methods are employed, they are applied separately to the two directions of the bidi- rectional S6. Quantitative analysis indicates that while combining multiple serialization strategies can provide richer spatial information, differences in spatial relationships between these strategies may introduce confusion and interfere with the learning of spatial consistency. By leveraging its exceptional spatial locality property, shown in Fig. 5) the Hilbert curve establishes a high-fidelit spatially adjacent scanning path with reliable inter-point structural dependencies for the state space model. This aligns with the visual search pattern of eye movements scanning tinuously along spatially adjacent regions, thereby achieving an optimal balance between accuracy and efficiency. To further discuss the specific advantages of the Hilbert curve over learnable serial-
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Serialization
Params
FLOPs
Throughput
0A
None
7.36M
0.610G
219FPS
91.34
Hilbert
7.36M
0.610G
163FPS
94.17
Z-OOrder
7.36M
0.610G
209FPS
93.06
Hilbert & Trans-Hilbert
7.36M
0.610G
133FPS
93.78
Hilbert & Z-Order
7.36M
0.610G
155FPS
93.52
Learnable Serialization
8.04M
0.723G
168FPS
92.78
Table 12: Ablation results with multiple serialization strategies.
ization strategies in terms of spatial locality preservation, continuity, and computational efficiency, we compare with the learnable serialization strategy from the latest research (Zha al. 2025). is intuitively observed that, compared to the learnable serialization, the Hilbert curve exhibits higher computational efficiency and superior spatial locality preservation and continuity. We attribute the poorer performance of the learnable serialization to the fact that it is an adaptive method for de- termining geometric correlation between points, but this approach possesses much less geometry- specific inductive biases compared to space- falliin curves. In summary, the Hilbert curve introduces more precise inductive bias regarding geometric correlation compared to the Z-Order curve and learnable serialization strategies.
C.2 ABLATION COMPARISON OF IPP AND FPS
In our work, we employ the proposed Induced Point Pooling (IPP) for spatial downsampling. To investigate its ability to flexibly adapt to the non- uniform distribution of point clouds for global semantic integration, we present ablation results comparing IPP with the Farthest Point Sampling (FPS) at different sampling rates in Fig. 6 where IN denotes the sampling rate relative to in- put number of points, and None indicates the ab- sence of the spatial downsampling branch. In- tuitively, low sampling rates, both methods exhibit comparable performance, indicating that FPS can obtain better global semantics with its ex- cellent spatial coverage when sufficient sampling points are available. However, the downsam- pling rate increases, the performance of FPS de- clines sharply. At sampling rate of /256, its ac- curacy approaches the baseline without the downsampling branch, suggesting its inability to capture critical semantics from non-uniform point clouds at high sparsity. contrast, IPP exhibits a more gradual performance decrease, maintaining pling rate. This confirms that IPP, through trainable induced points that adaptively learn the point cloud distribution, can flexibly and robustly integrate global semanties, providing effective coarse-gres -aarse-grained information for the point-focused attention.
942
93.9
(%6)
93.6
93.3
IPP
FPS
93.0
/4
/8
/16 /32
/64 /128
/256 None
Sampling rates
Figure 6: Quantitative results of IPP and FPS.
excellent accuracy of 93.39% even the /256 sam-
D COMPLEXITY ANALYSIS
Following the same settings as Eq. and considering the feature transformation, the computational complexities of each module in the foint-focused attention are as follows:
Ω (LNB) D22 2NKD
Ω (SSD) .D2 2 2NMD
(10)
Ω (IPP) = 2
Finally, have complexity of Ω (PFA) = 6 2M + 2N 4NMD for the point- focused attention. Since the context-scan state space inherits the linear complexity inherent in the state space model, the overall network exhibits linear complexity.
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0.8
0.6
0.4
0.0
Figure 7: Visualization results of attention heatmaps.
E VISUALIZATIONS
E. ATTENTION HEATMAPS
To better understand the attention responses and the advantages of the proposed method, we present in Fig. 7 the attention heatmaps of PointLearner for different objects, generated from attention weights in the local neighbor branch of the last point-focused attention layer within the decoder. These attention heatmaps illustrate that, through fully understanding of the bio-inspired visual perception, our method effectively focuses critical information for semantic inference to achieve outstanding performance, such as the tires and seat on the motorcycle, as well as the base and cover of the lamp.
E.2 QUALITATIVE COMPARISON
To intuitively demonstrate the performance of network, we present in Fig. 8 the visualization results of our network alongside the top-performing SSM method (PointMamba (Liang et al.) (024)) and attention method (GAD et. 2024b)) from Tab. on ShapeNet dataset, where the red points denote that these points are misclassified. The comparison of the visualization results reveals that our network is able achieve better part segmentation results at the boundaries of objects.
F FUTURE WORK
In our experimental comparisons, it is observed that most existing attention models employ pre- training methods improve performance, with the self-supervised pre-training paradigm dominat- ing. Self-supervised pre-training methods can leverage large amounts of unlabeled data to enhance feature modeling capabilities, as well as help Transformer models with large receptive fields achieve effective local structural modeling by increasing data scale. Although self-supervised pre-training on large-scale point cloud datasets has been proven effective for improving the accuracy of Trans- former models, the compatibility of existing Transformer self-supervised pre-training methods hybrid architectures, as well as self-supervised pre-training strategies specifically tailored for hybrid architectures, remain underexplored. Hence, it is promising direction for future research to collect
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more data and design self- supervised learning methods for hybrid models, in PPT (Wu et al. 2024b) and Sonata (Wu et al. 2025) designed for Transformer models.
G THE USE OF LARGE LANGUAGE MODELS (LLMS)
The authors utilized large language models (LLMs) to a limited extent for proofreading and improv- ing grammatical correctness. All key aspects of the research, encompassing innovation, conceptual development, and literature discovery, were solely driven by the authors without L.M assistance.
Mug
Ground Truth
Input
PointLearner
Prediction
Misclassification
PointMamba
Prediction
Misclassification
GAD
Prediction
Misclassification
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Earphone
Ground Truth
Input
PointLearner
Prediction
Misclassification
PointMamba
Prediction
Misclassification
GAD
Prediction
Misclassification
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Lamp
Ground Truth
Input
PointLearner
Prediction
Misclassification
PointMamba
Prediction
Misclassification
GAD
Prediction
Misclassification
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Knife
Ground Truth
Input
PointLearner
Prediction
Misclassification
PointMamba
Prediction
Misclassification
GAD
Prediction
Misclassification
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Chair
Ground Truth
Input
PointLearner
Prediction
Misclassification
PointMamba
Prediction
Misclassification
GAD
Prediction
Misclassification
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Guitar
Ground Truth
Input
PointLearner
Prediction
Misclassification
PointMamba
Prediction
Misclassification
GAD
Prediction
Misclassification
Figure 8: Visualization results of PointLearner, PointMamba, and GAD.
27


Reviews:
[{"review_id": "FrjnNw5zE3", "reviewer": "Reviewer_9P6s", "summary": "This paper proposes PointLearner, a point cloud representation learning network that closely aligns with biological vision by employing an active, foveation-inspired processing strategy, thus enabling simultaneous local geometric modeling and long-range dependency interactions.", "questions": "see weakness", "limitations": "", "rating": 4, "confidence": 4, "soundness": 1, "presentation": 3, "contribution": 2, "strengths": "1.The motivation of explaining the network design from a biological vision perspective is interesting;\n2.The argument in the Introduction regarding the limitations of local attention is correct;\n3.The goal of \"synergistically capturing local fine-grained structures and global contextual dependencies\" is valid;\n4.The experimental results demonstrate the effectiveness of the proposed network architecture;\n5.The paper is well-written and easy to understand.", "weaknesses": "1.Although the motivation and starting point of the paper are sound, the core ideas of the proposed solution lack novelty;\n2.The Fine-grained Perception module, which updates the center token using k neighboring tokens, is very common apart from the novel use of attention;\n3.The Coarse-grained Awareness module is essentially a standard approach that uses learnable queries with cross-attention to aggregate features;\n4.The Competitive Normalized Fusion is a commonly adopted strategy when a network has multiple parallel branches;\n5.Most of the datasets used are pure point cloud data. To better align with the biological vision motivation, it is recommended to include more datasets with RGB information.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "N4u4zced4m", "reviewer": "Reviewer_5iH6", "summary": "This paper proposes PointLearner, a biologically inspired point cloud learning framework that integrates local geometric modeling and global contextual reasoning. The key idea is to mimic human visual perception, which combines a point-focused attention mechanism  with a context-scan state space. Experiments on several benchmarks show state-of-the-art performance and strong robustness to noise and varying point densities.", "questions": "Why did the authors choose to apply this biomimetic architecture specifically to point clouds rather than to 2D images or videos, where visual perception analogies might seem even more natural?\n\nHave the authors considered testing or conceptually discussing how this framework would perform on other vision data (e.g., images or video) ?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. A novel biomimetic perspective. The motivation of this paper is very interesting, which is derived from biological visual analogies, effectively balancing local attention and the understanding of global features.\n\n2. Robust architectural design. The proposed point-focused attention module and state space module are well integrated and can effectively handle noise and sparse sampling, demonstrating good generalization ability.\n\n3. Clear structure and writing. This paper is logically clear and easy to follow.", "weaknesses": "Lack of domain-specific motivation. Although the biological vision analogy is creative, the paper does not clearly explain why this mechanism is particularly suitable for point clouds. The proposed “foveation + saccade” process seems domain-agnostic and could also be applied to images or videos. Without a clear justification, the choice of point cloud data feels somewhat arbitrary.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iN0e1Je14m", "reviewer": "Reviewer_i3Jg", "summary": "This paper proposes PointLearner, a point cloud representation learning network that is inspired by biological vision mechanisms.\nThe method introduces two main components: (1) a point-focused attention module that mimics foveal vision by combining fine-grained local neighbor attention with coarse-grained global semantics from a novel induced point pooling strategy, and (2) a context-scan state space module that serializes point clouds using a Hilbert curve and applies bidirectional S6 to simulate saccadic eye movements for global scene inference. \nThe authors demonstrate state-of-the-art performance on ModelNet40, ShapeNet, S3DIS, and ScanObjectNN datasets, with particular robustness to strong noise and varying point densities.", "questions": "What are the specific advantages of using the Hilbert curve over the Morton curve or learned serialization strategies in terms of spatial locality preservation, continuity, and computational efficiency?", "limitations": "", "rating": 8, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The analogy to foveal vision and saccadic eye movements is well-motivated, intuitively aligning with the challenges of point cloud learning, including local detail preservation and global context integration. \n\n2. The work effectively combines the strengths of attention (local geometry sensitivity) and state space models (long-range dependency modeling) within a biologically plausible framework, demonstrating the potential of such hybrid paradigms.", "weaknesses": "1. The biological analogy is intuitive but somewhat heuristic. \nA more rigorous theoretical or empirical analysis of how closely the modules mimic biological vision would strengthen the motivation and be helpful for understanding.\n\n2. Visualizing heat maps could be useful for better understanding the attention responses and the advantage of the proposed method.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "B1r7kvCmJ1", "reviewer": "Reviewer_Qkeu", "summary": "This paper proposes PointLearner, a biomimetic point cloud representation learning network that aims to simulate the \"focal-context\" perception mechanism of biological visual systems to collaboratively capture both the local fine structure and global context dependencies of point clouds. Specifically, Point-focused attention models local fine details and global semantic dependencies through a local neighbor branch and a spatial downsampling branch. Context-scan state space uses a Hilbert curve to serialize the point cloud and integrates a bidirectional S6 model to dynamically scan and integrate the global semantic structure.", "questions": "1. The serialization strategy in this paper converts 3D data into a 1D sequence, which may disrupt the adjacency relationships between points. Mamba3D argues that serialization is unnecessary, and StructMamba3D also forgoes serialization in favor of spatial state modeling to preserve adjacency relationships. Why does this paper still choose to adopt the Hilbert curve serialization strategy? Are there sufficient experimental results to demonstrate its advantage in terms of model performance?\n2. Can the proposed method be applied in the context of self-supervised learning? Leveraging large amounts of unlabeled data to enhance feature modeling capabilities is an important direction. Increasing the dataset size can also help transformer models with large receptive fields to achieve good local or structural modeling. Investigating the impact of self-supervised pre-training on model performance and validating the model’s scalability would further enhance its applicability.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "1. The dual-path design of local neighbor branch and spatial downsampling branch effectively balances the contribution of local structure and global semantics.\n2. To address the non-uniform distribution of point clouds, the paper introduces a learnable inducing points pooling method. By enabling attention interaction between inducing points and the original point cloud, this method flexibly extracts global semantics and shows more stable performance at higher sampling rates compared to the farthest point sampling method.\n3. The model demonstrates promising performance improvements over existing methods on four mainstream point cloud datasets (ModelNet40, ShapeNet, S3DIS, ScanObjectNN).", "weaknesses": "1. The paper does not provide a comparison of the computational cost of the proposed model with previous methods. Each encoder block not only incorporates a two-branch architecture but also adds a state space model for global feature modeling. This may lead to a significant increase in computational overhead, which requires further clarification.\n2. The spatial downsampling branch of the Point-focused attention is capable of capturing global features, so the motivation for introducing the state space model for additional global feature modeling is not well justified. While the authors mention simulating eye jump inference, this design still seems redundant and lacks sufficient novelty, as it closely resembles the design of PointMamba.\n3. The details of model training are not sufficiently clear. It is recommended to include a more detailed description of the model architecture across different datasets in the form of tables to help readers better understand the model’s structure and implementation.\n4. The experimental comparison is flawed, as it does not distinguish self-supervised models, which could lead to unfair comparisons. For example, pre-trained models on ShapeNet, such as PointGST, may not directly apply to segmentation tasks in indoor scenes. To make the comparison fairer on the S3DIS dataset, it would be more appropriate to include self-supervised methods pre-trained on indoor scene data, such as Sonata.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)