DeepAI AI Chat
Log In Sign Up

Consistent Two-Flow Network for Tele-Registration of Point Clouds

by   Zihao Yan, et al.

Rigid registration of partial observations is a fundamental problem in various applied fields. In computer graphics, special attention has been given to the registration between two partial point clouds generated by scanning devices. State-of-the-art registration techniques still struggle when the overlap region between the two point clouds is small, and completely fail if there is no overlap between the scan pairs. In this paper, we present a learning-based technique that alleviates this problem, and allows registration between point clouds, presented in arbitrary poses, and having little or even no overlap, a setting that has been referred to as tele-registration. Our technique is based on a novel neural network design that learns a prior of a class of shapes and can complete a partial shape. The key idea is combining the registration and completion tasks in a way that reinforces each other. In particular, we simultaneously train the registration network and completion network using two coupled flows, one that register-and-complete, and one that complete-and-register, and encourage the two flows to produce a consistent result. We show that, compared with each separate flow, this two-flow training leads to robust and reliable tele-registration, and hence to a better point cloud prediction that completes the registered scans. It is also worth mentioning that each of the components in our neural network outperforms state-of-the-art methods in both completion and registration. We further analyze our network with several ablation studies and demonstrate its performance on a large number of partial point clouds, both synthetic and real-world, that have only small or no overlap.


page 6

page 7

page 8

page 10

page 15


Multi-View Partial (MVP) Point Cloud Challenge 2021 on Completion and Registration: Methods and Results

As real-scanned point clouds are mostly partial due to occlusions and vi...

Globally optimal registration of noisy point clouds

Registration of 3D point clouds is a fundamental task in several applica...

RANSIP : From noisy point clouds to complete ear models, unsupervised

Ears are a particularly difficult region of the human face to model, not...

Non-rigid Point Cloud Registration for Middle Ear Diagnostics with Endoscopic Optical Coherence Tomography

Purpose: Middle ear infection is the most prevalent inflammatory disease...

Adversarial point set registration

We present a novel approach to point set registration which is based on ...

Deep Confidence Guided Distance for 3D Partial Shape Registration

We present a novel non-iterative learnable method for partial-to-partial...