Removing Camera Placement Constraints in Shape from Silhouette on Large Acquisition Volumes

Shape from Silhouette (SFS) is a technique used to estimate the 3D shape of objects from their silhouette images. SFS uses the intersection of the visual cones of the silhouettes seen by many cameras to estimate a 3D volume that is guaranteed to contain the object. Unfortunately, if one arbitrarily adds a camera whose visual cone does not intersect this volume, the classical algorithm breaks down. We propose modifications to SFS extend the capture volume with the addition of cameras. In this paper, we define different coherency concepts to relax the camera placement constraints, without adding ghost objects when there is only one object in the scene. Finally, we present a real-time system that captures a person moving through many cameras to demonstrate the application and robustness of our method. Figure 1: A person traversing different camera’s field of view. The colour indicate the number of cameras that see a voxel. There is seven cameras, some which see a fullsilhouette (red), some partial-silhouettes, and some nothing at all; The system does not place any constraints in camera positions.