swMATH ID: 18889
Software Authors: R. A. Newcombe, S. J. Lovegrove, A. J. Davison
Description: DTAM: Dense tracking and mapping in real-time. DTAM is a system for real-time camera tracking and reconstruction which relies not on feature extraction but dense, every pixel methods. As a single hand-held RGB camera flies over a static scene, we estimate detailed textured depth maps at selected keyframes to produce a surface patchwork with millions of vertices. We use the hundreds of images available in a video stream to improve the quality of a simple photometric data term, and minimise a global spatially regularised energy functional in a novel non-convex optimisation framework. Interleaved, we track the camera’s 6DOF motion precisely by frame-rate whole image alignment against the entire dense model. Our algorithms are highly parallelisable throughout and DTAM achieves real-time performance using current commodity GPU hardware. We demonstrate that a dense model permits superior tracking performance under rapid motion compared to a state of the art method using features; and also show the additional usefulness of the dense model for real-time scene interaction in a physics-enhanced augmented reality application.
Homepage: http://ieeexplore.ieee.org/abstract/document/6126513/
Related Software: ORB-SLAM2; LSD-SLAM; ORB-SLAM; KITTI; MonoSLAM; PyTorch; KinectFusion; FlowNet; Adam; Caffe; OpenCV; SBA; Key.Net; SVO; FastSLAM; SynSin; Make3D; PIFuHD; ViLBERT; BRISK
Cited in: 8 Publications

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