PMP-Net++: Point Cloud Completion by Transformer-Enhanced Multi-step Point Moving Paths

• URL: http://arxiv.org/abs/2202.09507v2
• Date: Tue, 22 Feb 2022 03:03:08 GMT
• Title: PMP-Net++: Point Cloud Completion by Transformer-Enhanced 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 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.
• Score: 60.32185890237936
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud completion concerns to predict missing part for incomplete 3D shapes. A common strategy is to generate complete shape according to incomplete input. However, unordered nature of point clouds will degrade generation of high-quality 3D shapes, as detailed topology and structure of unordered points are hard to be captured during the generative process using an extracted latent code. We address this problem by formulating completion as point cloud deformation process. Specifically, 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. Therefore, PMP-Net++ predicts unique PMP for each point according to constraint of point moving distances. The network learns a strict and unique correspondence on point-level, and thus improves quality of predicted complete shape. Moreover, since moving points heavily relies on per-point features learned by network, we further introduce a transformer-enhanced representation learning network, which significantly improves completion performance of PMP-Net++. We conduct comprehensive experiments in shape completion, and further explore application on point cloud up-sampling, which demonstrate non-trivial improvement of PMP-Net++ over state-of-the-art point cloud completion/up-sampling methods.

Related papers

• 3DMambaComplete: Exploring Structured State Space Model for Point Cloud Completion [19.60626235337542]
  3DMambaComplete is a point cloud completion network built on the novel Mamba framework. It encodes point cloud features using Mamba's selection mechanism and predicts a set of Hyperpoints. A deformation method transforms the 2D mesh representation of HyperPoints into a fine-grained 3D structure for point cloud reconstruction.
  arXiv  Detail & Related papers  (2024-04-10T15:45:03Z)
• Snowflake Point Deconvolution for Point Cloud Completion and Generation with Skip-Transformer [60.32185890237936]
  We propose SnowflakeNet with Snowflake Point Deconvolution (SPD) to generate the complete point clouds. SPD models the generation of complete point clouds as the snowflake-like growth of points, where the child points are progressively generated by splitting their parent points after each SPD. Our insight of revealing detailed geometry is to introduce skip-transformer in SPD to learn point splitting patterns which can fit local regions the best.
  arXiv  Detail & Related papers  (2022-02-18T17:09:49Z)
• 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)
• CpT: Convolutional Point Transformer for 3D Point Cloud Processing [10.389972581905]
  We present CpT: Convolutional point Transformer - a novel deep learning architecture for dealing with the unstructured nature of 3D point cloud data. CpT is an improvement over existing attention-based Convolutions Neural Networks as well as previous 3D point cloud processing transformers. Our model can serve as an effective backbone for various point cloud processing tasks when compared to the existing state-of-the-art approaches.
  arXiv  Detail & Related papers  (2021-11-21T17:45:55Z)
• SnowflakeNet: Point Cloud Completion by Snowflake Point Deconvolution with Skip-Transformer [54.45987960347304]
  We propose SnowflakeNet with Snowflake Point Deconvolution (SPD) to generate the complete point clouds. SPD learns point splitting patterns which can fit local regions the best. Our experimental results outperform the state-of-the-art point cloud completion methods under widely used benchmarks.
  arXiv  Detail & Related papers  (2021-08-10T04:33:37Z)
• 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)
• 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)
• 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.