 | Erik Sandström*, Ganlin Zhang*, Keisuke Tateno, Michael Oechsle, Youmin Zhang, Manthan Patel, Luc Van Gool, Martin R. Oswald, Federico Tombari Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), 2025 Github Repo | Paper | Supp We use a keyframe based frame to frame tracker based on dense optical flow connected to a pose graph for global consistency. For dense mapping, we resort to a 3DGS representation, suitable for extracting both dense geometry and rendering from. |
 | Weirong Chen, Ganlin Zhang, Felix Wimbauer, Rui Wang, Nikita Araslanov, Andrea Vedaldi, Daniel Cremers Preprint on ArXiv, 2025 Github Repo (Coming Soon) | ArXiv | Project website A method for consistent dynamic scene reconstruction via motion decoupling, bundle adjustment, and global refinement. |
 | Ganlin Zhang*, Erik Sandström*, Youmin Zhang, Manthan Patel, Luc Van Gool, Martin R. Oswald Preprint on ArXiv, 2024 Github Repo | ArXiv | Project website 1. A monocular SLAM pipeline with deformalbe neural pointcloud scene representation. 2. Novel DSPO layer for BA, which can jointly optimize depth map, depth scale and camera pose. |
 | Ganlin Zhang, Viktor Larsson, Daniel Barath Conference on Computer Vision and Pattern Recognition (CVPR), 2023 Github Repo | ArXiv 1. Better model the underlying noise distributions by directly propagating the uncertainty from the point correspondences into the rotation averaging. 2. Integrate a variant of the MAGSAC++ loss into the rotation averaging, instead of using the classical robust losses. |