We introduce Implicit Autoencoder(IAE), a simple yet effective method that addresses this challenge by replacing the point cloud decoder with an implicit decoder.
This task is challenging because 3D scenes exhibit diverse patterns, ranging from continuous ones, such as object sizes and the relative poses between pairs of shapes, to discrete patterns, such as occurrence and co-occurrence of objects with symmetrical relationships.
Our approach builds on an approximation of the as-rigid-as possible (or ARAP) deformation energy.
Experimental results show that our method can generate high-quality alpha mattes for various videos featuring appearance change, occlusion, and fast motion.
This paper introduces HPNet, a novel deep-learning approach for segmenting a 3D shape represented as a point cloud into primitive patches.
We introduce an approach for establishing dense correspondences between partial scans of human models and a complete template model.
Maximum a posteriori (MAP) inference over discrete Markov random fields is a fundamental task spanning a wide spectrum of real-world applications, which is known to be NP-hard for general graphs.