ASIST: Automatic Semantically Invariant Scene Transformation

We present ASIST, a technique for transforming point clouds by replacing objects with their semantically equivalent counterparts. Transformations of this kind have applications in virtual reality, repair of fused scans, and robotics. ASIST is based on a unified formulation of semantic labeling and object replacement; both result from minimizing a single objective. We present numerical tools for the efficient solution of this optimization problem. The method is experimentally assessed on new datasets of both synthetic and real point clouds, and is additionally compared to two recent works on object replacement on data from the corresponding papers.

I'm a Ph.D student at the School of Electrical Engineering at Tel-Aviv University, under the supervision of Prof. Alex Bronstein. My research interests include: Computer Vision, Computational photography, Sparse Models, Machine Learning and Shape Analysis.