マルチ深度カメラシステムを用いたスキャン動物のセマンティックキーポイント抽出
Semantic keypoint extraction for scanned animals using multi-depth-camera systems
複数のRGB-Dカメラで撮影した移動中の牛の点群から、キーポイントと他の点との距離を回帰するエンコーダ・デコーダでセマンティックキーポイントを抽出する手法を提案。
著者: Raphael Falque, Teresa Vidal-Calleja, Alen Alempijevic
分類: cs.RO, cs.CV
原文アブストラクト
Keypoint annotation in point clouds is an important task for 3D reconstruction, object tracking and alignment, in particular in deformable or moving scenes. In the context of agriculture robotics, it is a critical task for livestock automation to work toward condition assessment or behaviour recognition. In this work, we propose a novel approach for semantic keypoint annotation in point clouds, by reformulating the keypoint extraction as a regression problem of the distance between the keypoints and the rest of the point cloud. We use the distance on the point cloud manifold mapped into a radial basis function (RBF), which is then learned using an encoder-decoder architecture. Special consideration is given to the data augmentation specific to multi-depth-camera systems by considering noise over the extrinsic calibration and camera frame dropout. Additionally, we investigate computationally efficient non-rigid deformation methods that can be applied to animal point clouds. Our method is tested on data collected in the field, on moving beef cattle, with a calibrated system of multiple hardware-synchronised RGB-D cameras.
