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Point Cloud Completion by Skip-attention Network with Hierarchical Folding
Xin Wen, Tianyang Li, Zhizhong Han, Yu-Shen Liu
TL;DR
Point cloud completion must infer missing geometry while retaining local details that global representations may lose. SA-Net uses skip-attention with a hierarchical, structure-preserving decoder, and the authors report state-of-the-art completion results on ShapeNet and KITTI.
Problem
Existing point cloud completion methods often rely on global representations that lose local structural details needed to infer missing regions.
Method
SA-Net selectively conveys local-region geometry from encoder features to decoder features at multiple resolutions and generates shapes with hierarchical folding.
Results
SA-Net achieves state-of-the-art results for point cloud completion, with completion experiments on ShapeNet and KITTI supporting its effectiveness.
Takeaways & Limitations
The method uses local-region information across resolutions while preserving complete-shape structure during point-cloud generation.
Abstract
from arXiv · showhide
Point cloud completion aims to infer the complete geometries for missing regions of 3D objects from incomplete ones. Previous methods usually predict the complete point cloud based on the global shape representation extracted from the incomplete input. However, the global representation often suffers from the information loss of structure details on local regions of incomplete point cloud. To address this problem, we propose Skip-Attention Network (SA-Net) for 3D point cloud completion. Our main contributions lie in the following two-folds. First, we propose a skip-attention mechanism to effectively exploit the local structure details of incomplete point clouds during the inference of missing parts. The skip-attention mechanism selectively conveys geometric information from the local regions of incomplete point clouds for the generation of complete ones at different resolutions, where the skip-attention reveals the completion process in an interpretable way. Second, in order to fully utilize the selected geometric information encoded by skip-attention mechanism at different resolutions, we propose a novel structure-preserving decoder with hierarchical folding for complete shape generation. The hierarchical folding preserves the structure of complete point cloud generated in upper layer by progressively detailing the local regions, using the skip-attentioned geometry at the same resolution. We conduct comprehensive experiments on ShapeNet and KITTI datasets, which demonstrate that the proposed SA-Net outperforms the state-of-the-art point cloud completion methods.
1. Introduction
Point cloud completion must preserve existing local structure while inferring missing regions, but global representations can lose these details. SA-Net addresses this with selective skip-attention and hierarchical folding for multi-resolution completion.
- Motivation: Point cloud completion preserves input geometry and recovers missing regions, but global representations can lose local structural details needed for inference.For example, an existing airplane wing can guide reconstruction of a missing similar wing.
- Motivation: Directly applying image-style skip connections is unsuitable because point clouds are unordered and indiscriminately revisiting all local features can introduce redundancy.The limitation concerns both feature ordering and the usefulness of local features at each resolution.
- Structure-Preserving Decoder: Its structure-preserving decoder uses hierarchical folding to progressively detail local regions while preserving complete-shape structure across resolutions.The decoder connects corresponding encoder and decoder levels and aims to maintain global consistency alongside local information.
- Reported Contributions: SA-Net is presented as achieving state-of-the-art results for point cloud completion and extending to shape segmentation and unsupervised shape classification.The supplied contribution states these outcomes without reporting numerical values.
- Skip-Attention Network: SA-Net selectively fuses informative local-region features from the encoder into decoder point features at different resolutions.The attention mechanism supports geometry-aware completion and provides an interpretable view of the completion process.
2. Related Work
Point cloud completion methods include volumetric, geometric, alignment-based, and deep-learning approaches. Within point-cloud methods, SA-Net targets the local-detail information loss of global-feature-based completion.
- Completion Approaches: 3D shape completion research includes geometry-based, alignment-based, volumetric, and deep-learning-based approaches.The paper places its method in the deep-learning branch of 3D computer vision.
- Point Cloud Completion: Point clouds compactly represent arbitrary detailed 3D structures with lower storage cost than volumetric data.This motivates point-cloud-based completion as a distinct research direction.
- Point Cloud Completion: Recent point-cloud methods commonly learn a global representation from partial input and generate complete shapes from that feature.Examples include PCN, FoldingNet, AtlasNet, and TopNet, while other methods use reinforcement learning or adversarial networks.
- Point Cloud Completion: The paper identifies information loss of local structure details as a limitation of methods that predict the whole point cloud from a single global representation.SA-Net is introduced to address this limitation through skip-attention and structure-preserving decoding.
3. The Architecture of SA-Net
SA-Net connects encoder local-region features to decoder point features across matching resolutions, then progressively expands and refines geometry through hierarchical folding and attention.
- Encoder: The encoder extracts a global representation and local-region features at multiple resolution levels from incomplete point clouds.PointNet++ is used as the feature-encoder backbone, with local features retained for decoder connections.
- Folding Block: Each folding block uses a down-module and two up-modules with self-attention to expand point features and refine their geometric information.The down-module aggregates expanded features into higher-quality local-region features, while the up-module uses copied features and 2D grids before MLP transformation.
- Structure-Preserving Decoder: The structure-preserving decoder mirrors encoder resolutions and combines same-level skip-attention with folding blocks to progressively generate complete point clouds.Each decoder level conveys encoder local features and increases the number of point features.
- Folding Block: Hierarchical folding samples increasingly dense 2D grids across resolution levels, preserving upper-layer structure while progressively detailing local regions.The decoder generates point clouds at multiple resolutions rather than folding the entire point set at one resolution.
- Skip-Attention: Skip-attention matches decoder point features with encoder local-region features and selectively fuses weighted feature sums into the decoder.It is intended to reconstruct consistent details in existing regions and use incomplete-input geometry when generating missing regions.
- Training: SA-Net trains with a weighted sum of Chamfer distance and Earth Mover distance, using λ=10 in the reported experiments.Chamfer distance is also the main evaluation measurement.
4. Experiments
Experiments on ShapeNet and KITTI evaluate SA-Net’s completion quality, while ablations and application studies examine its attention, folding, loss, and transfer to segmentation and classification. SA-Net shows strong completion results and broader utility, with qualitative evidence on KITTI and ShapeNet application benchmarks.
- Completion performance: SA-Net achieves the best average Chamfer distance on ShapeNet and outperforms other methods in 6 of 8 categories.The comparison includes PCN, TopNet, FoldingNet, and AtlasNet.
- Completion performance: SA-Net predicts more reasonable missing shapes while preserving more consistent geometry in existing regions than the compared methods.Qualitative examples include realistic lamp holders and table legs, plus more consistent wings and beams.
- Completion performance: SA-Net produces more structural details and higher-quality shapes on KITTI, including car tiers and trunks.KITTI evaluation is qualitative because the dataset lacks ground truth for incomplete car shapes.
- Model analysis: The original SA-Net achieves the best performance among the tested attention variants, including No-skip, Skip-L, and Fold-C.Replacing attention in skip-attention or the folding self-attention reduces performance.
- Model analysis: Both EMD and CD contribute to SA-Net’s performance, as shown by comparisons with SA-Net-EMD and SA-Net-CD.The study evaluates versions trained using only EMD or only CD against the full model.
- Model analysis on applications: Skip-attention improves PointNet++ segmentation by 0.6% in mIoUs, while SA-Net-cls achieves the best performance among unsupervised classification methods.SA-Net-seg also produces more precise semantic labels, and SA-Net-cls is only 0.1% below supervised PointNet++.
5. Conclusion
The conclusion presents SA-Net as a point cloud completion network that uses local-region features through skip-attention and progressively generates shapes with a structure-preserving decoder. Experiments support its effectiveness for completion and demonstrate applications to segmentation and classification.
- SA-Net uses skip-attention to incorporate local-region features from input point clouds during completion.
- Its structure-preserving decoder progressively generates point clouds while incorporating local-region features at different resolutions.
- Completion experiments on ShapeNet and KITTI support the effectiveness of SA-Net.
- ShapeNet segmentation and ModelNet40 classification experiments further demonstrate the effectiveness of skip-attention and the structure-preserving decoder, respectively.