Gen6D: Generalizable Model-Free 6-DoF Object Pose Estimation from RGB Images

TL;DR

Gen6D is a novel model-free 6-DoF object pose estimation method that generalizes to unseen objects using only posed images, without requiring 3D models or additional sensor data, achieving state-of-the-art results.

Contribution

It introduces a fully generalizable, model-free pose estimator that operates without 3D models or depth data, expanding application scope to unseen objects.

Findings

Achieves state-of-the-art results on MOPED and GenMOP datasets.

Performs competitively on LINEMOD dataset.

Does not require 3D models or depth maps during testing.

Abstract

In this paper, we present a generalizable model-free 6-DoF object pose estimator called Gen6D. Existing generalizable pose estimators either need high-quality object models or require additional depth maps or object masks in test time, which significantly limits their application scope. In contrast, our pose estimator only requires some posed images of the unseen object and is able to accurately predict the poses of the object in arbitrary environments. Gen6D consists of an object detector, a viewpoint selector and a pose refiner, all of which do not require the 3D object model and can generalize to unseen objects. Experiments show that Gen6D achieves state-of-the-art results on two model-free datasets: the MOPED dataset and a new GenMOP dataset collected by us. In addition, on the LINEMOD dataset, Gen6D achieves competitive results compared with instance-specific pose estimators. Project page: https://liuyuan-pal.github.io/Gen6D/.