PointAttN: You Only Need Attention for Point Cloud Completion

• URL: http://arxiv.org/abs/2203.08485v1
• Date: Wed, 16 Mar 2022 09:20:01 GMT
• Title: PointAttN: You Only Need Attention for Point Cloud Completion
• Authors: Jun Wang, Ying Cui, Dongyan Guo, Junxia Li, Qingshan Liu, Chunhua Shen
• Abstract summary: Point cloud completion refers to completing 3D shapes from partial 3D point clouds. We propose a novel neural network for processing point cloud in a per-point manner to eliminate kNNs. The proposed framework, namely PointAttN, is simple, neat and effective, which can precisely capture the structural information of 3D shapes.
• Score: 89.88766317412052
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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

Related papers

• Shrinking unit: a Graph Convolution-Based Unit for CNN-like 3D Point Cloud Feature Extractors [0.0]
  We argue that a lack of inspiration from the image domain might be the primary cause of such a gap. We propose a graph convolution-based unit, dubbed Shrinking unit, that can be stacked vertically and horizontally for the design of CNN-like 3D point cloud feature extractors.
  arXiv  Detail & Related papers  (2022-09-26T15:28:31Z)
• A Conditional Point Diffusion-Refinement Paradigm for 3D Point Cloud Completion [69.32451612060214]
  Real-scanned 3D point clouds are often incomplete, and it is important to recover complete point clouds for downstream applications. Most existing point cloud completion methods use Chamfer Distance (CD) loss for training. We propose a novel Point Diffusion-Refinement (PDR) paradigm for point cloud completion.
  arXiv  Detail & Related papers  (2021-12-07T06:59:06Z)
• TreeGCN-ED: Encoding Point Cloud using a Tree-Structured Graph Network [24.299931323012757]
  This work proposes an autoencoder based framework to generate robust embeddings for point clouds. We demonstrate the applicability of the proposed framework in applications like: 3D point cloud completion and Single image based 3D reconstruction.
  arXiv  Detail & Related papers  (2021-10-07T03:52:56Z)
• PnP-3D: A Plug-and-Play for 3D Point Clouds [38.05362492645094]
  We propose a plug-and-play module, -3D, to improve the effectiveness of existing networks in analyzing point cloud data. To thoroughly evaluate our approach, we conduct experiments on three standard point cloud analysis tasks. In addition to achieving state-of-the-art results, we present comprehensive studies to demonstrate our approach's advantages.
  arXiv  Detail & Related papers  (2021-08-16T23:59:43Z)
• Refinement of Predicted Missing Parts Enhance Point Cloud Completion [62.997667081978825]
  Point cloud completion is the task of predicting complete geometry from partial observations using a point set representation for a 3D shape. Previous approaches propose neural networks to directly estimate the whole point cloud through encoder-decoder models fed by the incomplete point set. This paper proposes an end-to-end neural network architecture that focuses on computing the missing geometry and merging the known input and the predicted point cloud.
  arXiv  Detail & Related papers  (2020-10-08T22:01:23Z)
• SoftPoolNet: Shape Descriptor for Point Cloud Completion and Classification [93.54286830844134]
  We propose a method for 3D object completion and classification based on point clouds. For the decoder stage, we propose regional convolutions, a novel operator aimed at maximizing the global activation entropy. We evaluate our approach on different 3D tasks such as object completion and classification, achieving state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2020-08-17T14:32:35Z)
• GRNet: Gridding Residual Network for Dense Point Cloud Completion [54.43648460932248]
  Estimating the complete 3D point cloud from an incomplete one is a key problem in many vision and robotics applications. We propose a novel Gridding Residual Network (GRNet) for point cloud completion. Experimental results indicate that the proposed GRNet performs favorably against state-of-the-art methods on the ShapeNet, Completion3D, and KITTI benchmarks.
  arXiv  Detail & Related papers  (2020-06-06T02:46:39Z)
• Point Cloud Completion by Skip-attention Network with Hierarchical Folding [61.59710288271434]
  We propose Skip-Attention Network (SA-Net) for 3D point cloud completion. First, we propose a skip-attention mechanism to effectively exploit the local structure details of incomplete point clouds. 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.
  arXiv  Detail & Related papers  (2020-05-08T06:23:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.