Aron Monszpart

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Missing data due to occlusion is a key challenge in 3D acquisition, particularly in cluttered man-made scenes. Such partial information about the scenes limits our ability to analyze and understand them. In this work we abstract such environments as collections of cuboids and hallucinate geometry in the occluded regions by globally analyzing the physical(More)
With the proliferation of acquisition devices, gathering massive volumes of 3D data is now easy. Processing such large masses of pointclouds, however, remains a challenge. This is particularly a problem for raw scans with missing data, noise, and varying sampling density. In this work, we present a simple, scalable, yet powerful data reconstruction(More)
The field of scene understanding endeavours to extract a broad range of information from 3D scenes. Current approaches exploit one or at most a few different criteria (e.g., spatial, semantic, functional information) simultaneously for analysis. We argue that to take scene understanding to the next level of performance, we need to take into account many(More)
Collision sequences are commonly used in games and entertainment to add drama and excitement. Authoring even two body collisions in real world can be difficult as one has to get timing and the object trajectories to be correctly synchronized. After trial-and-error iterations, when objects can actually be made to collide, then they are difficult to acquire(More)
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