  In this paper, we propose a novel method for joint recovery of camera pose,
object geometry and spatially-varying Bidirectional Reflectance Distribution
Function (svBRDF) of 3D scenes that exceed object-scale and hence cannot be
captured with stationary light stages. The input are high-resolution RGB-D
images captured by a mobile, hand-held capture system with point lights for
active illumination. Compared to previous works that jointly estimate geometry
and materials from a hand-held scanner, we formulate this problem using a
single objective function that can be minimized using off-the-shelf
gradient-based solvers. To facilitate scalability to large numbers of
observation views and optimization variables, we introduce a distributed
optimization algorithm that reconstructs 2.5D keyframe-based representations of
the scene. A novel multi-view consistency regularizer effectively synchronizes
neighboring keyframes such that the local optimization results allow for
seamless integration into a globally consistent 3D model. We provide a study on
the importance of each component in our formulation and show that our method
compares favorably to baselines. We further demonstrate that our method
accurately reconstructs various objects and materials and allows for expansion
to spatially larger scenes. We believe that this work represents a significant
step towards making geometry and material estimation from hand-held scanners
scalable.
