Shape from Texture using Locally Scaled Point Processes
This addresses shape reconstruction for computer vision applications, but appears incremental as it builds on existing point process methods.
The paper tackles the problem of extracting 3D shape from 2D images with irregular texture by introducing a statistical framework that uses a locally scaled point process model to infer 3D information from texture elements.
Shape from texture refers to the extraction of 3D information from 2D images with irregular texture. This paper introduces a statistical framework to learn shape from texture where convex texture elements in a 2D image are represented through a point process. In a first step, the 2D image is preprocessed to generate a probability map corresponding to an estimate of the unnormalized intensity of the latent point process underlying the texture elements. The latent point process is subsequently inferred from the probability map in a non-parametric, model free manner. Finally, the 3D information is extracted from the point pattern by applying a locally scaled point process model where the local scaling function represents the deformation caused by the projection of a 3D surface onto a 2D image.