Learning Implicit Probability Distribution Functions for Symmetric Orientation Estimation from RGB Images Without Pose Labels

TL;DR

This paper introduces a novel method for symmetric object orientation estimation from RGB images without requiring pose labels, using an automatic labeling scheme and a neural implicit probability distribution model on SO(3).

Contribution

It presents an automatic pose labeling pipeline and a neural ImplicitPDF model for symmetry-aware orientation estimation without pose annotations.

Findings

Effective automatic pose labeling scheme generated multiple pseudo-ground-truth poses.

ImplicitPDF model accurately estimates object orientations on benchmark datasets.

Hierarchical sampling enables efficient exploration of SO(3) for symmetry considerations.

Abstract

Object pose estimation is a necessary prerequisite for autonomous robotic manipulation, but the presence of symmetry increases the complexity of the pose estimation task. Existing methods for object pose estimation output a single 6D pose. Thus, they lack the ability to reason about symmetries. Lately, modeling object orientation as a non-parametric probability distribution on the SO(3) manifold by neural networks has shown impressive results. However, acquiring large-scale datasets to train pose estimation models remains a bottleneck. To address this limitation, we introduce an automatic pose labeling scheme. Given RGB-D images without object pose annotations and 3D object models, we design a two-stage pipeline consisting of point cloud registration and render-and-compare validation to generate multiple symmetrical pseudo-ground-truth pose labels for each image. Using the generated pose labels, we train an ImplicitPDF model to estimate the likelihood of an orientation hypothesis given an RGB image. An efficient hierarchical sampling of the SO(3) manifold enables tractable generation of the complete set of symmetries at multiple resolutions. During inference, the most likely orientation of the target object is estimated using gradient ascent. We evaluate the proposed automatic pose labeling scheme and the ImplicitPDF model on a photorealistic dataset and the T-Less dataset, demonstrating the advantages of the proposed method.