Marc B
Marc B
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M.Sc
Learning Correspondences For Relative Pose Estimation
We present an end-to-end learnable, differentiable method for pairwise relative pose registration of RGB-D frames. Our method is robust to big camera motions thanks to a self-supervised weighting of the predicted correspondences between the frames. Given a pair of frames, our method estimates matches of points and their visibility score. A self-supervised model predicts a confidence weight for visible matches. Finally, visible matches and their weight are fed into a differentiable weighted Procrustes aligner which estimates the rigid transformation between the input frames.
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Kinect Fusion: Dense Surface Mapping and Tracking
Implementation of the paper “Kinect Fusion: Real-Time Dense Surface Mapping and Tracking”
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Video
Original paper
SLAM for autonomous vehicles
In this project, I worked on the SLAM pipeline for an autonomous driving vehicle. The tools I used for this project are, ROS, C++, PCL library, Ceres Solver, and Google Cartographer.
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Video
Divergence-Free Shape Correspondence with Time Dependent Vector Fields
In this project, we extended the work of Eisenberger, Zorah, Cremers, “Divergence-Free Shape Interpolation and Correspondence” 1. In their work, they present a method to calculate deformation fields between shapes embedded in $\mathbb{R}^D$.
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Original paper
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