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in this video i will present the paper deep sdf learning continuous sign distance function for shape representation 3d reconstruction also commonly addressed as the inverse graphic problem is about recovery of 3d object from 2d or partial 3d observations the research interest in this field has been rising rapidly due to its wide application in robotics simulation and content creation in contrast to the learning in 2d domain there is an ongoing dispute on which 3d representation is the best for learning the desired representation should be compact expressive and efficient before the deep sdf and other implicit representations came out the dominant representations could be categorized into three categories their voxels point cloud and match representations voxel can represent arbitrary typology but it has a cubically growing compute and memory requirements more compact representation point cloud do not describe surface methods that produce triangular mesh directly at that time are limit
