DeepAI
Log In Sign Up

StablePose: Learning 6D Object Poses from Geometrically Stable Patches

02/18/2021
by   Junwen Huang, et al.
0

We introduce the concept of geometric stability to the problem of 6D object pose estimation and propose to learn pose inference based on geometrically stable patches extracted from observed 3D point clouds. According to the theory of geometric stability analysis, a minimal set of three planar/cylindrical patches are geometrically stable and determine the full 6DoFs of the object pose. We train a deep neural network to regress 6D object pose based on geometrically stable patch groups via learning both intra-patch geometric features and inter-patch contextual features. A subnetwork is jointly trained to predict per-patch poses. This auxiliary task is a relaxation of the group pose prediction: A single patch cannot determine the full 6DoFs but is able to improve pose accuracy in its corresponding DoFs. Working with patch groups makes our method generalize well for random occlusion and unseen instances. The method is easily amenable to resolve symmetry ambiguities. Our method achieves the state-of-the-art results on public benchmarks compared not only to depth-only but also to RGBD methods. It also performs well in category-level pose estimation.

READ FULL TEXT

page 1

page 4

page 7

09/15/2020

3DPVNet: Patch-level 3D Hough Voting Network for 6D Pose Estimation

In this paper, we focus on estimating the 6D pose of objects in point cl...
01/03/2020

HandAugment: A Simple Data Augmentation for HANDS19 Challenge Task 1 – Depth-Based 3D Hand Pose Estimation

Hand pose estimation from 3D depth images, has been explored widely usin...
03/15/2022

GPV-Pose: Category-level Object Pose Estimation via Geometry-guided Point-wise Voting

While 6D object pose estimation has recently made a huge leap forward, m...
12/19/2019

Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis

In this paper, we work on intra-variable handwriting, where the writing ...
09/25/2021

Learning Stereopsis from Geometric Synthesis for 6D Object Pose Estimation

Current monocular-based 6D object pose estimation methods generally achi...
07/19/2017

STag: A Stable Fiducial Marker System

In this paper, we propose STag, a fiducial marker system that provides s...
03/26/2021

OmniHang: Learning to Hang Arbitrary Objects using Contact Point Correspondences and Neural Collision Estimation

In this paper, we explore whether a robot can learn to hang arbitrary ob...