Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4968778 | Computer Vision and Image Understanding | 2017 | 16 Pages |
Abstract
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.
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Physical Sciences and Engineering
Computer Science
Computer Vision and Pattern Recognition
Authors
Or Litany, Tal Remez, Daniel Freedman, Lior Shapira, Alex Bronstein, Ran Gal,