Self-Supervised 3D Keypoint Learning for Ego-motion Estimation

Generating reliable illumination and viewpoint invariant keypoints is critical for feature-based SLAM and SfM. State-of-the-art learning-based methods often rely on generating training samples by employing homography adaptation to create 2D synthetic views. While such approaches trivially solve data association between views, they cannot effectively learn from real illumination and non-planar 3D scenes. In this work, we propose a fully self-supervised approach towards learning depth-aware keypoints \textit{purely} from unlabeled videos by incorporating a differentiable pose estimation module that jointly optimizes the keypoints and their depths in a Structure-from-Motion setting. We introduce 3D Multi-View Adaptation, a technique that exploits the temporal context in videos to self-supervise keypoint detection and matching in an end-to-end differentiable manner. Finally, we show how a fully self-supervised keypoint detection and description network can be trivially incorporated as a front-end into a state-of-the-art visual odometry framework that is robust and accurate.