PointVoteNet: Accurate Object Detection and 6 DoF Pose Estimation in Point Clouds
This addresses the challenge of object detection and pose estimation in 3D point clouds for applications like robotics and augmented reality, though it appears incremental as it builds on existing learning-based approaches.
The paper tackles the problem of 6 DoF pose estimation of rigid objects in point clouds by proposing a learning-based method that processes unordered point sets directly from detection to transformation, achieving accurate results that sometimes surpass state-of-the-art methods on the same data.
We present a learning-based method for 6 DoF pose estimation of rigid objects in point cloud data. Many recent learning-based approaches use primarily RGB information for detecting objects, in some cases with an added refinement step using depth data. Our method consumes unordered point sets with/without RGB information, from initial detection to the final transformation estimation stage. This allows us to achieve accurate pose estimates, in some cases surpassing state of the art methods trained on the same data.