SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering

08/01/2021
by   Yifan Zhao, et al.
0

Point clouds obtained from 3D sensors are usually sparse. Existing methods mainly focus on upsampling sparse point clouds in a supervised manner by using dense ground truth point clouds. In this paper, we propose a self-supervised point cloud upsampling network (SSPU-Net) to generate dense point clouds without using ground truth. To achieve this, we exploit the consistency between the input sparse point cloud and generated dense point cloud for the shapes and rendered images. Specifically, we first propose a neighbor expansion unit (NEU) to upsample the sparse point clouds, where the local geometric structures of the sparse point clouds are exploited to learn weights for point interpolation. Then, we develop a differentiable point cloud rendering unit (DRU) as an end-to-end module in our network to render the point cloud into multi-view images. Finally, we formulate a shape-consistent loss and an image-consistent loss to train the network so that the shapes of the sparse and dense point clouds are as consistent as possible. Extensive results on the CAD and scanned datasets demonstrate that our method can achieve impressive results in a self-supervised manner. Code is available at https://github.com/fpthink/SSPU-Net.

READ FULL TEXT

page 2

page 6

research
04/18/2022

Self-Supervised Arbitrary-Scale Point Clouds Upsampling via Implicit Neural Representation

Point clouds upsampling is a challenging issue to generate dense and uni...
research
09/09/2022

GRASP-Net: Geometric Residual Analysis and Synthesis for Point Cloud Compression

Point cloud compression (PCC) is a key enabler for various 3-D applicati...
research
07/10/2020

Progressive Point Cloud Deconvolution Generation Network

In this paper, we propose an effective point cloud generation method, wh...
research
08/08/2021

Unsupervised Learning of Fine Structure Generation for 3D Point Clouds by 2D Projection Matching

Learning to generate 3D point clouds without 3D supervision is an import...
research
05/10/2023

D-Net: Learning for Distinctive Point Clouds by Self-Attentive Point Searching and Learnable Feature Fusion

Learning and selecting important points on a point cloud is crucial for ...
research
05/05/2020

From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks

Reconstructing 3D models from 2D images is one of the fundamental proble...
research
12/08/2020

SPU-Net: Self-Supervised Point Cloud Upsampling by Coarse-to-Fine Reconstruction with Self-Projection Optimization

The task of point cloud upsampling aims to acquire dense and uniform poi...

Please sign up or login with your details

Forgot password? Click here to reset