Computer Vision News Computer Vision News 26 Congrats, Doctor Erik! Erik Sandström recently obtained his PhD from the Computer Vision Lab at ETH Zürich, working with Luc Van Gool and Martin R. Oswald. In his thesis, he focused on dense SLAM with monocular RGB or RGB-D input. He is now looking for industry positions in 3D Vision - feel free to reach out, he’s a catch! Congrats, Doctor Erik! Visual Simultaneous Localization and Mapping (VSLAM) is a longstanding problem in computer vision. The input is a video from an RGB or an RGB-D camera and the output is generated in real-time in the form of camera poses per frame and the geometry of the surroundings. Traditionally, sparse SLAM was the dominating solution, where only a subset of the map is reconstructed. On the other hand, dense SLAM, where we seek to reconstruct geometry for every pixel input, lends itself for a multitude of downstream tasks like path planning, perception and various AR solutions. Traditional dense SLAM used either voxel grids storing signed distances or surfels. Since the introduction of neural implicit representations, neural radiance fields and lately, 3D Gaussian splats, new attention has been shed to dense SLAM, with the aim to leverage the benefits of these representations for improved accuracy and robustness. In my PhD, I focused on three main problems related to dense SLAM: handling
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