Point Projection Network: A Multi-View-Based Point Completion Network with Encoder-Decoder Architecture

Weichao Wu,Jie Wan,Yongyang Xu,Ziyin Zeng,Zhong Xie

doi:10.3390/rs13234917

Weichao Wu, Jie Wan + Show 3 more

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https://doi.org/10.3390/rs13234917

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Abstract

Recently, unstructured 3D point clouds have been widely used in remote sensing application. However, inevitable is the appearance of an incomplete point cloud, primarily due to the angle of view and blocking limitations. Therefore, point cloud completion is an urgent problem in point cloud data applications. Most existing deep learning methods first generate rough frameworks through the global characteristics of incomplete point clouds, and then generate complete point clouds by refining the framework. However, such point clouds are undesirably biased toward average existing objects, meaning that the completion results lack local details. Thus, we propose a multi-view-based shape-preserving point completion network with an encoder–decoder architecture, termed a point projection network (PP-Net). PP-Net completes and optimizes the defective point cloud in a projection-to-shape manner in two stages. First, a new feature point extraction method is applied to the projection of a point cloud, to extract feature points in multiple directions. Second, more realistic complete point clouds with finer profiles are yielded by encoding and decoding the feature points from the first stage. Meanwhile, the projection loss in multiple directions and adversarial loss are combined to optimize the model parameters. Qualitative and quantitative experiments on the ShapeNet dataset indicate that our method achieves good results in learning-based point cloud shape completion methods in terms of chamfer distance (CD) error. Furthermore, PP-Net is robust to the deletion of multiple parts and different levels of incomplete data.

Highlights

With the rapid development of 3D scanning technology, point clouds, as an irregular set of points that represent 3D geometry, have been widely used in various modern vision tasks, such as remote sensing application [1,2,3], robot navigation [4,5,6], autonomous driving [7,8,9], and object pose estimation [10,11,12]
Given that it can be challenging for networks to directly exploit edge features in irregularly distributed incomplete point clouds, this study introduces a multi-view-based method for point cloud completion, and designs a convolutional neural network with an encoder–decoder architecture, comprising (1) multi-view-based boundary feature point extraction and (2) point cloud generation based on the encoder–decoder structure
To fully extract the input data information, this study introduces combined multi-layer perceptron (CMLP) in the model coding stage

Summary

Introduction

With the rapid development of 3D scanning technology, point clouds, as an irregular set of points that represent 3D geometry, have been widely used in various modern vision tasks, such as remote sensing application [1,2,3], robot navigation [4,5,6], autonomous driving [7,8,9], and object pose estimation [10,11,12]. Most traditional methods of shape completion are based on the geometric assumption [13,14,15] that the incomplete area and some parts of the input are geometrically symmetric. These assumptions significantly limit the real-world applications of these methods. Poisson surface reconstruction [16,17,18] can usually repair the holes in 3D model surfaces, but discard fine-scale structures Another geometry-based shape completion method is retrieval matching or shape similarity [19,20,21]. Such methods are time consuming when applied to the matching process according to the database size, and cannot tolerate noise in the input 3D shape

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