L6DNet: Light 6 DoF Network for Robust and Precise Object Pose Estimation with Small Datasets
This addresses object pose estimation for augmented reality and robotics, but it appears incremental as it builds on existing hybrid and geometric methods.
The paper tackles 6 DoF object pose estimation from single RGB-D images using a hybrid two-stage pipeline, achieving more robust and accurate results than state-of-the-art methods on the LineMod dataset, with validation through visual servoing tasks.
Estimating the 3D pose of an object is a challenging task that can be considered within augmented reality or robotic applications. In this paper, we propose a novel approach to perform 6 DoF object pose estimation from a single RGB-D image. We adopt a hybrid pipeline in two stages: data-driven and geometric respectively. The data-driven step consists of a classification CNN to estimate the object 2D location in the image from local patches, followed by a regression CNN trained to predict the 3D location of a set of keypoints in the camera coordinate system. To extract the pose information, the geometric step consists in aligning the 3D points in the camera coordinate system with the corresponding 3D points in world coordinate system by minimizing a registration error, thus computing the pose. Our experiments on the standard dataset LineMod show that our approach is more robust and accurate than state-of-the-art methods. The approach is also validated to achieve a 6 DoF positioning task by visual servoing.