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LSANet: Feature Learning on Point Sets by Local Spatial Aware Layer
Lin-Zhuo Chen, Xuan-Yi Li, Deng-Ping Fan, Kai Wang, Shao-Ping Lu, Ming-Ming Cheng
TL;DR
Existing point-cloud feature extraction often neglects local spatial distribution, limiting fine-grained geometric perception. The paper introduces LSA layers that learn Spatial Distribution Weights and combines them with hierarchical spatial feature extraction in LSANet. LSANet achieves on-par or better performance than state-of-the-art methods across benchmark 3D recognition tasks.
Problem
Existing point-cloud feature extraction processes do not adequately model local spatial distribution, limiting fine-grained pattern perception.
Method
LSANet uses LSA layers that learn Spatial Distribution Weights from local-region structure and hierarchical Spatial Feature Extractors to integrate spatial information with features.
Results
LSANet achieves on-par or better results than state-of-the-art approaches across object classification, part segmentation, and semantic segmentation benchmarks.
Takeaways & Limitations
The design provides stronger spatial information extraction and supports fine-grained geometric pattern perception across multiple 3D recognition tasks.
Abstract
from arXiv · showhide
Directly learning features from the point cloud has become an active research direction in 3D understanding. Existing learning-based methods usually construct local regions from the point cloud and extract the corresponding features. However, most of these processes do not adequately take the spatial distribution of the point cloud into account, limiting the ability to perceive fine-grained patterns. We design a novel Local Spatial Aware (LSA) layer, which can learn to generate Spatial Distribution Weights (SDWs) hierarchically based on the spatial relationship in local region for spatial independent operations, to establish the relationship between these operations and spatial distribution, thus capturing the local geometric structure sensitively.We further propose the LSANet, which is based on LSA layer, aggregating the spatial information with associated features in each layer of the network better in network design.The experiments show that our LSANet can achieve on par or better performance than the state-of-the-art methods when evaluating on the challenging benchmark datasets. For example, our LSANet can achieve 93.2% accuracy on ModelNet40 dataset using only 1024 points, significantly higher than other methods under the same conditions. The source code is available at https://github.com/LinZhuoChen/LSANet.
1 Introduction
Point-cloud methods must handle permutation-invariant, irregular data while capturing local spatial structure. LSANet addresses this with LSA layers using spatial distribution weights and hierarchical spatial features.
- Point sets are permutation-invariant and cannot be accurately represented by regular lattices, complicating direct transfer of 2D CNN techniques.
- Voxel and multi-view conversions enable 2D-style operations but introduce quantization artifacts and inefficient computation.
- PointNet++ extracts local-region features, but shared MLP and max-pooling operations remain independent of local spatial structure.
- SpiderCNN relates weights to each point’s center vector but does not fully model whole-region distribution, making geometric transforms difficult to handle.
- LSA layers learn Spatial Distribution Weights from local-region structure and integrate them with feature operations to capture geometric patterns more accurately and robustly.
- LSANet combines LSA layers with hierarchical Spatial Feature Extractors to integrate coordinates and intermediate features, achieving state-of-the-art benchmark results.
2 Related Work
Related work includes volumetric, multi-view, and point-based approaches for processing irregular point sets. Point-based methods preserve direct point processing but vary in how they aggregate neighborhood and spatial information.
- Volumetric and Multi-view approach: Volumetric methods convert point sets to regular 3D grids for convolution but incur high computation costs and inefficiency from point-set sparsity.
- Point-based approach: PointNet directly processes points with shared MLPs and symmetric aggregation, but cannot combine neighboring-point information.
- Point-based approach: PointNet++ adds sampled local regions and neighborhood features, yet its shared MLP and max-pooling operations do not account for local spatial distribution.
- Point-based approach: Other point-based methods use self-organizing networks, recurrent networks, kernel correlation, or learned transformations to process neighborhood structure.
3 Our Method
LSANet uses Local Spatial Aware layers to connect feature-extraction operations with local spatial distributions through hierarchically generated Spatial Distribution Weights. Its architecture also integrates progressively enriched spatial representations with backbone features through a Spatial Feature Extractor branch.
- 3.1 Extract spatial distribution feature: The spatial-distribution feature concatenates each point’s spatial feature with a region-level representation computed using shared weights across points, preserving permutation invariance.Each point’s resulting representation is associated with both its own location and all points in the local region.
- 3.2 Generation of Spatial Distribution Weights (SDWs): LSA layers generate Spatial Distribution Weights from local spatial-distribution features, linking feature extraction to geometric structure and fine-grained shape patterns.The spatial-distribution feature combines each point’s spatial feature with the spatial feature of its entire local region.
- 3.2 Generation of Spatial Distribution Weights (SDWs): A fully connected nonlinear function generates initial SDWs from each point’s spatial-distribution feature, and repeated sub-layer processing produces weights for subsequent feature learning.The generator takes spatial-distribution features in R^128 as input and produces weights with the same dimension as the corresponding point features.
- 3.3 Combine SDWs with other operations: Combining SDWs with shared MLP weights makes feature-extraction weights depend on local spatial distribution, allowing different points to receive adaptive weights for local shape-pattern capture.The updated weights are shared across points before being modulated by their spatial-distribution weights.
- 3.3 Combine SDWs with other operations: SDWs also guide max pooling to select points according to spatial distribution rather than strongest channel response alone.The paper further combines SDWs with an additional Spatial Feature Extractor branch that progressively lifts raw coordinates into higher-dimensional spatial representations.
- 3.4 LSANet Architecture: LSANet uses LSA layers as basic units, adds the Spatial Feature Extractor branch, and retains PointNet++ sampling and grouping for classification and segmentation architectures.Classification aggregates the final LSA-layer features, while segmentation adds PointNet++ feature propagation to produce per-point semantic scores.
4 Experiments
Experiments evaluate LSANet across classification, part segmentation, and semantic segmentation benchmarks, alongside ablations, visualizations, and complexity analyses. Results show strong benchmark performance, effective spatially aware components, robustness to varying point density, and fast inference with proper parameter counts.
- Classification and Segmentation Tasks: ModelNet40 uses 1,024 sampled points, while ShapeNet, ScanNet, and S3DIS use 2,048, 8,192, and 8,192 points respectively with task-specific inputs.The evaluation spans CAD classification, part segmentation, and semantic segmentation datasets with the metrics listed in Table 1.
- Classification and Segmentation Tasks: LSANet outperforms compared point-based methods on ModelNet40 classification and ShapeNet, ScanNet, and S3DIS segmentation benchmarks.On ModelNet40 with 1024 points without surface normals in the unaligned setting, it achieves 1.6% higher overall accuracy than PointNet++’s MSG network; it also outperforms compared methods in segmentation.
- Analysis and Visualization: The LSA layer and SFE improve accuracy by 1.1% and 0.8%, while region spatial encoding and SDW-integrated max pooling further validate the design.The ablation study removes SDW integration from max pooling and the region spatial encoder to assess their effects.
- Complexity Analysis: LSANet performs well as input point counts decrease, while its classification network retains proper parameter counts and fast inference time.Sampling-density tests use models trained with 1,024 points and evaluate inputs of 1,024, 512, 256, 128, and 64 points.
- Analysis and Visualization: SDWs show different directional preferences across feature channels, supporting the LSA layer’s ability to perceive fine-grained local patterns.The visualization samples 512 representative points with neighbors and displays six of 64 channels before the shared MLP operation.
5 Conclusion
LSANet combines the proposed LSA layer with a network design that strengthens spatial information extraction, achieving on-par-or-better results across standard 3D recognition benchmarks.
- LSANet achieves on-par-or-better results than state-of-the-art approaches across object classification, part segmentation, and semantic segmentation benchmarks.Ablation experiments and visualizations further illustrate the effectiveness of the LSA layer and LSANet design.