This paper studies the inference of 3D shape from a set of ? noisy photos. We derive a probabilistic framework to specify what one can infer about 3D shape for arbitrarily-shaped, ...
Rahul Bhotika, David J. Fleet, Kiriakos N. Kutulak...
Acquiring 3D models of intricate objects (like tree branches, bicycles and insects) is a hard problem due to severe self-occlusions, repeated thin structures and surface discontin...
Shuntaro Yamazaki, Srinivasa G. Narasimhan, Simon ...
Abstract: We propose a novel probabilistic method for detection of objects in noisy images. The method uses results from percolation and random graph theories. We present an algori...
This paper presents a complete modeling system that extracts complex building structures with irregular shapes and surfaces. Our modeling approach is based on the use of airborne L...
Adaptively Sampled Distance Fields (ADFs) are a unifying representation of shape that integrate numerous concepts in computer graphics including the representation of geometry and...
Sarah F. Frisken Gibson, Ronald N. Perry, Alyn P. ...