自己運動推定のための自己教師あり3Dキーポイント学習
Self-Supervised 3D Keypoint Learning for Ego-motion Estimation
ラベルなし動画から深度を考慮したキーポイントを自己教師ありで学習し、視覚オドメトリに統合して車両の自己運動を高精度に推定する手法を提案した。
著者: Jiexiong Tang, Rares Ambrus, Vitor Guizilini, Sudeep Pillai, Hanme Kim, Patric Jensfelt, Adrien Gaidon
分類: cs.CV, cs.LG, cs.RO
原文アブストラクト
Detecting and matching robust viewpoint-invariant keypoints is critical for visual SLAM and Structure-from-Motion. State-of-the-art learning-based methods generate training samples via homography adaptation to create 2D synthetic views with known keypoint matches from a single image. This approach, however, does not generalize to non-planar 3D scenes with illumination variations commonly seen in real-world videos. In this work, we propose self-supervised learning of depth-aware keypoints directly from unlabeled videos. We jointly learn keypoint and depth estimation networks by combining appearance and geometric matching via a differentiable structure-from-motion module based on Procrustean residual pose correction. We describe how our self-supervised keypoints can be integrated into state-of-the-art visual odometry frameworks for robust and accurate ego-motion estimation of autonomous vehicles in real-world conditions.
