PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths

• URL: http://arxiv.org/abs/2012.03408v2
• Date: Mon, 8 Mar 2021 02:32:52 GMT
• Title: PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths
• Authors: Xin Wen, Peng Xiang, Zhizhong Han, Yan-Pei Cao, Pengfei Wan, Wen Zheng, Yu-Shen Liu
• Abstract summary: We design a novel neural network, named PMP-Net, to mimic the behavior of an earth mover. It moves each point of the incomplete input to complete the point cloud, where the total distance of point moving paths should be shortest. It learns a strict and unique correspondence on point-level, which can capture the detailed topology and structure relationships between the incomplete shape and the complete target.
• Score: 54.459879603473034
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The task of point cloud completion aims to predict the missing part for an incomplete 3D shape. A widely used strategy is to generate a complete point cloud from the incomplete one. However, the unordered nature of point clouds will degrade the generation of high-quality 3D shapes, as the detailed topology and structure of discrete points are hard to be captured by the generative process only using a latent code. In this paper, we address the above problem by reconsidering the completion task from a new perspective, where we formulate the prediction as a point cloud deformation process. Specifically, we design a novel neural network, named PMP-Net, to mimic the behavior of an earth mover. It moves move each point of the incomplete input to complete the point cloud, where the total distance of point moving paths (PMP) should be shortest. Therefore, PMP-Net predicts a unique point moving path for each point according to the constraint of total point moving distances. As a result, the network learns a strict and unique correspondence on point-level, which can capture the detailed topology and structure relationships between the incomplete shape and the complete target, and thus improves the quality of the predicted complete shape. We conduct comprehensive experiments on Completion3D and PCN datasets, which demonstrate our advantages over the state-of-the-art point cloud completion methods.

Related papers

• PointAttN: You Only Need Attention for Point Cloud Completion [89.88766317412052]
  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.
  arXiv  Detail & Related papers  (2022-03-16T09:20:01Z)
• PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths [60.32185890237936]
  We design a novel neural network, named PMP-Net++, to mimic behavior of an earth mover. It moves each point of incomplete input to obtain a complete point cloud, where total distance of point moving paths (PMPs) should be the shortest. The network learns a strict and unique correspondence on point-level, and thus improves quality of predicted complete shape.
  arXiv  Detail & Related papers  (2022-02-19T03:00:40Z)
• 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)
• Skeleton-bridged Point Completion: From Global Inference to Local Adjustment [48.2757171993437]
  We propose a skeleton-bridged point completion network (SK-PCN) for shape completion. Given a partial scan, our method first predicts its 3D skeleton to obtain the global structure. We decouple the shape completion into structure estimation and surface reconstruction, which eases the learning difficulty.
  arXiv  Detail & Related papers  (2020-10-14T22:49:30Z)
• 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)
• Cascaded Refinement Network for Point Cloud Completion [74.80746431691938]
  We propose a cascaded refinement network together with a coarse-to-fine strategy to synthesize the detailed object shapes. Considering the local details of partial input with the global shape information together, we can preserve the existing details in the incomplete point set. We also design a patch discriminator that guarantees every local area has the same pattern with the ground truth to learn the complicated point distribution.
  arXiv  Detail & Related papers  (2020-04-07T13:03:29Z)
• PF-Net: Point Fractal Network for 3D Point Cloud Completion [6.504317278066694]
  Point Fractal Network (PF-Net) is a novel learning-based approach for precise and high-fidelity point cloud completion. PF-Net preserves the spatial arrangements of the incomplete point cloud and can figure out the detailed geometrical structure of the missing region(s) in the prediction. Our experiments demonstrate the effectiveness of our method for several challenging point cloud completion tasks.
  arXiv  Detail & Related papers  (2020-03-01T05:40:21Z)

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.