Globally Consistent Video Depth and Pose Estimation with Efficient Test-Time Training

• URL: http://arxiv.org/abs/2208.02709v1
• Date: Thu, 4 Aug 2022 15:12:03 GMT
• Title: Globally Consistent Video Depth and Pose Estimation with Efficient Test-Time Training
• Authors: Yao-Chih Lee, Kuan-Wei Tseng, Guan-Sheng Chen and Chu-Song Chen
• Abstract summary: We present GCVD, a globally consistent method for learning-based video structure from motion (SfM) GCVD integrates a compact pose graph into the CNN-based optimization to achieve globally consistent from an effective selection mechanism. Experimental results show that GCVD outperforms the state-of-the-art methods on both depth and pose estimation.
• Score: 15.46056322267856
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dense depth and pose estimation is a vital prerequisite for various video applications. Traditional solutions suffer from the robustness of sparse feature tracking and insufficient camera baselines in videos. Therefore, recent methods utilize learning-based optical flow and depth prior to estimate dense depth. However, previous works require heavy computation time or yield sub-optimal depth results. We present GCVD, a globally consistent method for learning-based video structure from motion (SfM) in this paper. GCVD integrates a compact pose graph into the CNN-based optimization to achieve globally consistent estimation from an effective keyframe selection mechanism. It can improve the robustness of learning-based methods with flow-guided keyframes and well-established depth prior. Experimental results show that GCVD outperforms the state-of-the-art methods on both depth and pose estimation. Besides, the runtime experiments reveal that it provides strong efficiency in both short- and long-term videos with global consistency provided.

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