Source-linked AI summary
Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud
Mutian Xu, Junhao Zhang, Zhipeng Zhou, Mingye Xu, Xiaojuan Qi, Yu Qiao
TL;DR
Existing point-cloud networks treat points or local patches equally, leaving complementary contour and flat geometry insufficiently disentangled and largely local. GDANet dynamically separates these geometries into sharp and gentle components, then attentively fuses them with original features. Across classification and segmentation benchmarks, it achieves state-of-the-art performance with fewer parameters and reported robustness to density, rotation, and noise.
Problem
Previous point-cloud networks treat all points or local patches equally, while contour and flat regions provide distinct complementary geometric information.
Method
GDANet uses a Geometry-Disentangle Module to produce sharp contour and gentle flat-area components, then applies complementary attention to fuse them with original point features.
Results
GDANet achieves state-of-the-art performance on 3D classification and segmentation benchmarks with fewer parameters and reported robustness to density, rotation, and noise.
Takeaways & Limitations
Disentangled sharp and gentle components provide complementary geometric information that supplements local neighboring semantics.
Abstract
from arXiv · showhide
In 2D image processing, some attempts decompose images into high and low frequency components for describing edge and smooth parts respectively. Similarly, the contour and flat area of 3D objects, such as the boundary and seat area of a chair, describe different but also complementary geometries. However, such investigation is lost in previous deep networks that understand point clouds by directly treating all points or local patches equally. To solve this problem, we propose Geometry-Disentangled Attention Network (GDANet). GDANet introduces Geometry-Disentangle Module to dynamically disentangle point clouds into the contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components. Then GDANet exploits Sharp-Gentle Complementary Attention Module that regards the features from sharp and gentle variation components as two holistic representations, and pays different attentions to them while fusing them respectively with original point cloud features. In this way, our method captures and refines the holistic and complementary 3D geometric semantics from two distinct disentangled components to supplement the local information. Extensive experiments on 3D object classification and segmentation benchmarks demonstrate that GDANet achieves the state-of-the-arts with fewer parameters. Code is released on https://github.com/mutianxu/GDANet.
1 Introduction
GDANet addresses redundant and predominantly local point-cloud processing by disentangling contour and flat geometry into complementary holistic representations. It uses these components to supplement local information and reports state-of-the-art, lightweight, and robust performance.
- Motivation: Previous methods treat all points or local patches equally, creating redundancy and making key geometric relationships harder to capture.They also primarily model geometric information in local areas.
- Motivation: Contour areas provide structural information, while flat areas provide geometric manifold context as distinct but complementary representations.This separation is motivated by analogous edge and smooth components in image processing.
- Method overview: The Geometry-Disentangle Module dynamically factorizes point clouds into sharp contour and gentle flat-area variation components at different semantic levels.It extends graph signal processing to analyze graph signals on 3D point clouds.
- Method overview: The Sharp-Gentle Complementary Attention Module assigns different attentions to the two components and fuses them with original point features.This operates on holistic component representations rather than only local patches.
- Results: GDANet captures and refines complementary holistic geometries to supplement local neighboring information, achieving state-of-the-art results with fewer parameters.The reported model is also described as robust to density, rotation, and noise.
2 Related Work
Related work develops local geometric encoders and attention mechanisms for point clouds. GDANet differs by assigning attention to disentangled contour and flat areas according to geometric correlations.
- Point cloud geometry: Geo-CNN aggregates edge features to capture local geometric relations, while other methods combine semantically similar neighbors in Euclidean and eigenvalue spaces.RS-CNN learns topology in local point sets, and DensePoint extracts contextual shape semantics.
- Attention-based geometry: GAC assigns attention weights to different neighbor points, whereas GDANet weights disentangled contour and flat areas based on geometric correlations.The paper further states that attention refines disentangled features.
3 Revisit Graph Signal Processing
Graph signal processing represents point-cloud features on graph nodes and analyzes them through graph shifts, eigenvalues, Fourier transforms, and polynomial filters. Ordered eigenvalues encode graph frequencies from low to high.
- Graph representation: A graph G = (V, A) uses vertices for nodes and an adjacency matrix A to encode dependencies, with node features represented as graph signals.A graph shift maps s to y = As.
- Graph frequencies: The eigenvalues of A are ordered from high to low and represent graph frequencies from low to high.The paper states λ1 ≥ λ2 ≥ · · · ≥ λN.
- Graph filtering: A graph filter is a polynomial in the graph shift that maps an input signal s to a filtered signal y = h(A)s.In the Fourier domain, filtering multiplies frequency contents by h(Λ).
- Graph filtering: The diagonal matrix h(Λ) represents the graph-frequency response of the filter h(A).Its entries specify the response associated with graph eigenvalues.
4 Method
GDANet disentangles point clouds into sharp contour and gentle flat-area components using graph-based filtering, then combines them with complementary attention to supplement local geometric context.
- Geometry-Disentangle Module: The Geometry-Disentangle Module uses graph signal processing to decompose point clouds into sharp contour and gentle flat-area components.It dynamically analyzes graph signals at different semantic levels.
- Geometry-Disentangle Module: The high-pass filter h(Ã) = I − Ã measures point variation relative to neighboring features, separating geometrically sharp and gentle points.Larger l2-norm values indicate stronger variation and select contour points; smaller values select flat-area points.
- Sharp-Gentle Complementary Attention Module: Sharp-Gentle Complementary Attention computes separate attention matrices between original features and the two disentangled components.Feature dot products measure semantic correlation or discrepancy, while separate MLPs process the components.
- Sharp-Gentle Complementary Attention Module: The module fuses each attended component with the original point features, concatenating the resulting representations to convey complementary geometry.The sharp and gentle components receive different attention weights rather than being treated equally.
- Self-Attention or Sharp-Gentle Attention: Compared with self-attention over all points, disentangled attention reduces redundancy and helps focus on geometrically correlated features.Visual comparisons report different attention distributions for the two variation components, while quantitative comparison is provided in Table 4.
5 Experiments
GDANet is evaluated on classification, segmentation, ablation, robustness, and complexity experiments across ModelNet40, ScanObjectNN, and ShapeNet Part. The results show strong accuracy, complementary benefits from disentangled components, robustness across perturbations, and substantially lower parameter count.
- Object Classification: On ScanObjectNN, GDANet obtains the highest accuracy and the lowest performance drop from OBJ ONLY to OBJ BG.These variants test noisy objects with deformed geometry and non-uniform surface density.
- Shape Part Segmentation: GDANet achieves class mIoU of 85.0% and instance mIoU of 86.5% on ShapeNet Part segmentation.It performs better on objects with obvious geometric structure such as bags, mugs, and tables.
- Ablation Study: 93.4% accuracy results when sharp and gentle features are fused with the input point cloud, while voting raises accuracy to 93.8%.The fused components outperform using only KNN by 1.2 percentage points.
- Ablation Study: Dynamic adjacency calculation reaches 93.8% accuracy versus 93.0% with precomputed adjacency, a gain of 0.8 percentage points.The module is jointly optimized during training to model different geometric structures.
- Robustness and Model Complexity: GDANet remains consistent with sparser inputs, performs best under tested rotation settings with a 2.7% gain, and reduces parameters by 84.9% versus KPConv.It also increases accuracy by 0.9 percentage points compared with KPConv.
6 Conclusion
GDANet combines geometry disentanglement with complementary attention to improve point-cloud processing, achieving state-of-the-art performance and decent robustness.
- GDANet dynamically disentangles point clouds into sharp and gentle variation components representing contours and flat areas at different semantic levels.Its Geometry-Disentangle Module performs this factorization dynamically.
- Its Sharp-Gentle Complementary Attention Module relates original points to the two variation components to provide complementary geometric information.The module uses attention to explore relations between original points and the disentangled components.
- Extensive experiments show state-of-the-art performance and decent robustness.