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.
@article{arxiv.1512.01515,
title = {ASIST: Automatic Semantically Invariant Scene Transformation},
author = {Or Litany and Tal Remez and Daniel Freedman and Lior Shapira and Alex Bronstein and Ran Gal},
journal= {arXiv preprint arXiv:1512.01515},
year = {2016}
}