0:00 Introduction
0:20 Limitations of traditional SfM and SLAM techniques.
0:57 Shortcomings of existing neural network methods.
1:07 MegaSaM's approach: balance of accuracy, speed, and robustness.
1:31 Differentiable bundle adjustment (BA) layer.
2:03 Integration of monocular depth priors and motion probability maps.
2:37 Uncertainty-aware global BA scheme.
3:14 Two-stage training scheme.
3:45 Consistent video depth estimation without test-time fine-tuning.
4:16 Key quantitative and qualitative improvements.
4:49 Limitations of MegaSaM and future research avenues.
5:15 Synthetic data for training and generalization to real-world videos.
5:49 Datasets used for evaluation.
6:26 DepthAnything and UniDepth for monocular depth estimation.
7:02 Summary of MegaSaM's advancements.
Authors: Zhengqi Li, Richard Tucker, Forrester Cole, Qianqian Wang, Linyi Jin, Vickie Ye, Angjoo Kanazawa, Aleksander Holynski, Noah Snavely
Affiliations: Google DeepMind, UC Berkeley, University of Michigan
Abstract: We present a system that allows for accurate, fast, and robust estimation of camera parameters and depth maps from casual monocular videos of dynamic scenes. Most conventional structure from motion and monocular SLAM techniques assume input videos that feature predominantly static scenes with large amounts of parallax. Such methods tend to produce erroneous estimates in the absence of these conditions. Recent neural network-based approaches attempt to overcome these challenges; however, such methods are either computationally expensive or brittle when run on dynamic videos with uncontrolled camera motion or unknown field of view. We demonstrate the surprising effectiveness of a deep visual SLAM framework: with careful modifications to its training and inference schemes, this system can scale to real-world videos of complex dynamic scenes with unconstrained camera paths, including videos with little camera parallax. Extensive experiments on both synthetic and real videos demonstrate that our system is significantly more accurate and robust at camera pose and depth estimation when compared with prior and concurrent work, with faster or comparable running times. See interactive results on our project page: this https URL
Link: https://mega-sam.github.io/
