Point Cloud Completion by Learning Shape Priors

• URL: http://arxiv.org/abs/2008.00394v2
• Date: Thu, 15 Jul 2021 08:07:03 GMT
• Title: Point Cloud Completion by Learning Shape Priors
• Authors: Xiaogang Wang, Marcelo H Ang Jr and Gim Hee Lee
• Abstract summary: shape priors include geometric information in both complete and partial point clouds. We design a feature alignment strategy to learn the shape prior from complete points, and a coarse to fine strategy to incorporate partial prior in the fine stage. We achieve state-of-the-art performances on the point cloud completion task.
• Score: 74.80746431691938
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In view of the difficulty in reconstructing object details in point cloud completion, we propose a shape prior learning method for object completion. The shape priors include geometric information in both complete and the partial point clouds. We design a feature alignment strategy to learn the shape prior from complete points, and a coarse to fine strategy to incorporate partial prior in the fine stage. To learn the complete objects prior, we first train a point cloud auto-encoder to extract the latent embeddings from complete points. Then we learn a mapping to transfer the point features from partial points to that of the complete points by optimizing feature alignment losses. The feature alignment losses consist of a L2 distance and an adversarial loss obtained by Maximum Mean Discrepancy Generative Adversarial Network (MMD-GAN). The L2 distance optimizes the partial features towards the complete ones in the feature space, and MMD-GAN decreases the statistical distance of two point features in a Reproducing Kernel Hilbert Space. We achieve state-of-the-art performances on the point cloud completion task. Our code is available at https://github.com/xiaogangw/point-cloud-completion-shape-prior.

Related papers

• Self-supervised 3D Point Cloud Completion via Multi-view Adversarial Learning [61.14132533712537]
  We propose MAL-SPC, a framework that effectively leverages both object-level and category-specific geometric similarities to complete missing structures. Our MAL-SPC does not require any 3D complete supervision and only necessitates a single partial point cloud for each object.
  arXiv  Detail & Related papers  (2024-07-13T06:53:39Z)
• P2C: Self-Supervised Point Cloud Completion from Single Partial Clouds [44.02541315496045]
  Point cloud completion aims to recover the complete shape based on a partial observation. Existing methods require either complete point clouds or multiple partial observations of the same object for learning. We present Partial2Complete, the first self-supervised framework that completes point cloud objects.
  arXiv  Detail & Related papers  (2023-07-27T09:31:01Z)
• Point Cloud Completion Guided by Prior Knowledge via Causal Inference [19.935868881427226]
  We propose a novel approach to point cloud completion task called Point-PC. Point-PC uses a memory network to retrieve shape priors and designs a causal inference model to filter missing shape information. Experimental results on the ShapeNet-55, PCN, and KITTI datasets demonstrate that Point-PC outperforms the state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-05-28T16:33:35Z)
• Shape Completion with Points in the Shadow [13.608498759468024]
  Single-view point cloud completion aims to recover the full geometry of an object based on only limited observation. Inspired by the classic shadow volume technique in computer graphics, we propose a new method to reduce the solution space effectively.
  arXiv  Detail & Related papers  (2022-09-17T14:58:56Z)
• PMP-Net: Point Cloud Completion by Learning Multi-step Point Moving Paths [54.459879603473034]
  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.
  arXiv  Detail & Related papers  (2020-12-07T01:34:38Z)
• Cascaded Refinement Network for Point Cloud Completion with Self-supervision [74.80746431691938]
  We introduce a two-branch network for shape completion. The first branch is a cascaded shape completion sub-network to synthesize complete objects. The second branch is an auto-encoder to reconstruct the original partial input.
  arXiv  Detail & Related papers  (2020-10-17T04:56:22Z)
• 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)
• 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.