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PointAttN: You Only Need Attention for Point Cloud Completion

Jun Wang, Ying Cui, Dongyan Guo, Junxia Li, Qingshan Liu, Chunhua Shen

arXiv:2203.08485v1cs.CV

TL;DR

Point-cloud completion estimates a complete object shape from an incomplete observation, but existing methods' explicit local neighborhoods are sensitive to point density and can limit receptive fields. PointAttN uses cross-attention and self-attention in an encoder-decoder framework without explicit local-region partitioning. It outperforms state-of-the-art methods on Completion3D and PCN.

  • Problem

    Existing completion methods use explicit local-region partitions such as kNNs, which are sensitive to point-density variation and offer limited receptive fields for long-range context.

    Source: §1 · p. 1

  • Method

    PointAttN combines cross-attention and self-attention in an encoder-decoder framework, using GDP for adaptive local structure and SFA for point relationships without explicit local-region partitioning.

    Source: §1 · p. 2

  • Results

    PointAttN outperforms state-of-the-art methods on Completion3D and PCN; on Completion3D it reduces average CD by 0.97 versus SnowflakeNet (6.63 versus 7.60).

    Source: §4.1 · p. 9, §5 · p. 10

  • Takeaways & Limitations

    The authors report that attention-based local and global structural modeling produces complete point clouds with detailed geometry while avoiding explicit local-region operations.

    Source: §1 · p. 2

Abstract

from arXiv · show

Point cloud completion referring to completing 3D shapes from partial 3D point clouds is a fundamental problem for 3D point cloud analysis tasks. Benefiting from the development of deep neural networks, researches on point cloud completion have made great progress in recent years. However, the explicit local region partition like kNNs involved in existing methods makes them sensitive to the density distribution of point clouds. Moreover, it serves limited receptive fields that prevent capturing features from long-range context information. To solve the problems, we leverage the cross-attention and self-attention mechanisms to design novel neural network for processing point cloud in a per-point manner to eliminate kNNs. Two essential blocks Geometric Details Perception (GDP) and Self-Feature Augment (SFA) are proposed to establish the short-range and long-range structural relationships directly among points in a simple yet effective way via attention mechanism. Then based on GDP and SFA, we construct a new framework with popular encoder-decoder architecture for point cloud completion. The proposed framework, namely PointAttN, is simple, neat and effective, which can precisely capture the structural information of 3D shapes and predict complete point clouds with highly detailed geometries. Experimental results demonstrate that our PointAttN outperforms state-of-the-art methods by a large margin on popular benchmarks like Completion3D and PCN. Code is available at: https://github.com/ohhhyeahhh/PointAttN

1 Introduction

Existing point-cloud completion methods use explicit local neighborhoods that are sensitive to uneven point density, while limited receptive fields can restrict long-range context. PointAttN uses cross-attention and self-attention in an encoder-decoder framework to capture local and global structure without explicit local-region partitioning.

1 Introduction

  • Fixed-k neighborhood operations are sensitive to point-density variation and provide limited receptive fields for learning local geometry and long-range context.Density can vary across different point clouds and across regions within the same cloud.Source: §1 · p. 1
  • GDP adaptively captures local structure, while SFA models point relationships and progressively reveals shape structure during coarse-to-fine decoding.SFA can also be used in encoding to enhance global-structure perception.Source: §1 · p. 2
  • PointAttN uses cross-attention and self-attention to learn short- and long-range structural relationships without explicit local-region partitioning.The authors describe this design as alleviating density-distribution effects and helping recover fine-grained information.Source: §1 · p. 2
  • The authors present PointAttN as the first Transformer-based method to eliminate explicit local-region operations such as kNNs.They also state that GDP and SFA can be incorporated into other networks for large-scale point-cloud analysis.Source: §1 · p. 2
  • PointAttN achieves state-of-the-art performance on Completion3D and PCN with an architecture the authors describe as simple and neat.The framework is presented as using farthest point sampling, MLPs, and attention layers.Source: §1 · p. 2

2 Related Work

Prior completion approaches use structured shape priors, voxel-based processing, point-based encoders and decoders, or Transformers. The section describes trade-offs involving coverage, computational cost, local geometry, receptive fields, and continued reliance on kNNs.

2 Related Work

  • Traditional methods reconstruct from predefined smooth surfaces or structured shape priors, whose data distributions may not cover enough cases.The cited approaches include smooth-surface reconstruction and structural local priors from collections of 3D shapes [21, 22].Source: §2 · p. 3
  • Voxel-based methods use 3D convolutions to extract features, but incur memory and computation costs and may represent point-cloud surfaces poorly.GRNet maps voxels back to points through a gridding reverse module, while VE-PCN adds a shape-edge branch.Source: §2 · p. 3
  • Point-based completion methods refine decoding and local features, but max-pooled PointNet features miss local structure, while kNN-based neighborhoods remain density-sensitive and receptive-field-limited.Examples include TopNet, PFNet, CDN, SpareNet, and PointNet++ [13].Source: §2 · p. 3
  • Transformer-based methods capture long-range interactions, yet existing completion approaches still use kNNs because of memory constraints or explicit local-part requirements.The section discusses SA-Net, SnowflakeNet [5], and PointTr [15] among these methods.Source: §2 · p. 3

3 Proposed Method

PointAttN is an encoder-decoder framework built from GDP and SFA blocks, using attention to process point features and generate complete shapes. Its feature extractor captures local and global information, while cascaded SFA blocks support coarse-to-fine decoding.

3 Proposed Method

  • PointAttN uses a feature extractor, seed generator, and point generator to encode shape features and generate a complete point cloud from coarse to fine.The framework is built on GDP and SFA blocks and uses an encoder-decoder architecture.Source: §3 · p. 3, Figure 2 · p. 4, §3.3 · p. 5

3.1 Geometric Details Perception

  • GDP downsamples point features with farthest point sampling, then uses residual multi-head cross-attention to aggregate input features and perceive local structure.Each sampled point can aggregate similar features from the input; the output has shape (n/d) × 2c.Source: §3.1 · p. 4

3.2 Self-Feature Augment

  • SFA applies residual multi-head self-attention to integrate information across points, expanding feature dimensions by the up-sampling ratio u.Its output has size n × uc.Source: §3.2 · p. 5

3.3 PointAttN for Point Cloud Completion

  • The feature extractor alternates three GDP and three SFA blocks, the seed generator produces a sparse complete cloud, and two cascaded point generators refine it.The point generator combines seed-point and shape-code features before progressively upsampling them into dense point coordinates.Source: §3.3 · pp. 5–6

3.4 Training Loss

  • Training sums Chamfer-distance losses for the seed cloud and both generator outputs against ground-truth subclouds of matching density, with every weight λ_i set to 1.Ground-truth subclouds are produced by farthest point sampling.Source: §3.4 · p. 6

4 Experiments

PointAttN is evaluated on Completion3D and PCN, with comparisons against state-of-the-art methods and ablations of its components. It reports leading benchmark results and examines how the feature extractor and decoding modules contribute.

4 Experiments

  • The evaluation uses Completion3D and PCN, both derived from ShapeNet, with L2 CD for Completion3D and L1 CD for PCN.The PCN setup uses the same split settings as PCN; Completion3D follows its specified training, validation, and test split.Source: §4 · pp. 6–7

4.1 Results on Completion3D; 4.2 Results on PCN

  • PointAttN reduces Completion3D average CD 12.8% vs SnowflakeNet (6.63 versus 7.60) and PCN average CD 4.9% vs SnowflakeNet (6.86 versus 7.21).It ranks first on all Completion3D categories in CD; on PCN, it surpasses SnowflakeNet and PointTr on average and all categories.Source: Table 1 · p. 7, Table 2 · p. 8, §4.1 · p. 9

4.1 Results on Completion3D

  • On Completion3D, PointAttN produces smoother surfaces and more precise geometries, especially for lampshades and complex boats.The visual comparison covers all eight categories; smoother surfaces are noted for planes, cars, chairs, and tables, and detailed geometry for chairs, lamps, couches, and boats.Source: Figure 4 · p. 7, §4.1 · p. 9

4.2 Results on PCN

  • On PCN, visual comparisons highlight finer plane-wing, car-trunk, and lamp-shape details than SnowflakeNet and PointTr.The authors attribute the advantage to avoiding the limited receptive field of explicit local partitions, allowing local details and global structures to be perceived.Source: Figure 5 · p. 8, §4.2 · p. 9

4.3 Ablation study

  • Using GDPs lowers average CD by 10.6% versus FE w/o GDP (6.63 versus 7.42), while SFA is also reported to improve feature extraction.The ablation study evaluates the component variations on Completion3D against the default PointAttN and listed baselines.Source: §4.3 · p. 10, Table 3 · p. 10
  • FE+FoldingNet lowers average CD by 56.6% versus FoldingNet (8.28 versus 19.07), supporting the feature extractor as an alternative backbone.FE+SG+SPD lowers average CD by 3.8% versus SnowflakeNet (7.31 versus 7.60); PN+SG+PG lowers it by 35.6% versus SA-Net (7.23 versus 11.22).Source: §4.3 · pp. 9–10
  • Across the five ablation variations, the authors report that each part contributes to PointAttN’s overall performance.The variants replace or remove GDP, SFA, the feature extractor, or point-generation modules while leaving other experiment settings unchanged.Source: §4.3 · pp. 9–10

5 Conclusions

PointAttN applies attention to point-cloud completion to enlarge receptive fields and preserve geometric structure and context. The paper reports that it outperforms state-of-the-art methods on Completion3D and PCN.

  • The paper’s motivation is to enlarge receptive fields so completion better preserves geometric structure and context.Source: §5 · p. 10
  • Cross-attention and self-attention are used to perceive local details and global context while eliminating explicit local partitions such as kNNs.Source: §5 · p. 10
  • PointAttN outperforms state-of-the-art methods on Completion3D and PCN, according to the reported comparisons and ablations.Source: §5 · p. 10
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