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Computer Vision Group
TUM School of Computation, Information and Technology
Technical University of Munich

Technical University of Munich



TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo

Authors: Lukas Koestler*, Nan Yang*, Niclas Zeller, Daniel Cremers

For up-to-date information, code, and data please visit our GitHub repository.


In this paper, we present TANDEM a real-time monocular tracking and dense mapping framework. For pose estimation, TANDEM performs photometric bundle adjustment based on a sliding window of keyframes. To increase the robustness, we propose a novel tracking front-end that performs dense direct image alignment using depth maps rendered from a global model that is built incrementally from dense depth predictions. To predict the dense depth maps, we propose Cascade View-Aggregation MVSNet (CVA-MVSNet) that utilizes the entire active keyframe window by hierarchically constructing 3D cost volumes with adaptive view aggregation to balance the different stereo baselines between the keyframes. Finally, the predicted depth maps are fused into a consistent global map represented as a truncated signed distance function (TSDF) voxel grid. Our experimental results show that TANDEM outperforms other state-of-the-art traditional and learning-based monocular visual odometry (VO) methods in terms of camera tracking. Moreover, TANDEM shows state-of-the-art real-time 3D reconstruction performance.



3DV Demo Model

Model created as a test run for the 3DV demo of TANDEM.


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Conference and Workshop Papers
[]TANDEM: Tracking and Dense Mapping in Real-time using Deep Multi-view Stereo (L Koestler, N Yang, N Zeller and D Cremers), In Conference on Robot Learning (CoRL), 2021. ([GitHub][video][project page]) [bibtex] [arXiv:2111.07418] [pdf]3DV'21 Best Demo Award
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