  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.
