An Efficient Background Term for 3D Reconstruction
and Tracking with Smooth Surface Models
Abstract
We present a novel strategy to shrink and constrain a 3D
model, represented as a smooth spline-like surface, within
the visual hull of an object observed from one or multiple
views. This new ‘background’ or ‘silhouette’ term combines
the efficiency of previous approaches based on an imageplane distance transform with the accuracy of formulations
based on raycasting or ray potentials. The overall formulation is solved by alternating an inner nonlinear minimization
(raycasting) with a joint optimization of the surface geometry, the camera poses and the data correspondences. Experiments on 3D reconstruction and object tracking show that
the new formulation corrects several deficiencies of existing
approaches, for instance when modelling non-convex shapes.
Moreover, our proposal is more robust against defects in the
object segmentation and inherently handles the presence of
uncertainty in the measurements (e.g. null depth values in
images provided by RGB-D cameras)