A widely used technique to recover a 3D surface from photographs is patch-based (multi-view) stereo reconstruction. Current methods are able to reproduce fine surface details, they are however limited by the sampling density and the patch size used for reconstruction. We show that there is a systematic error in the reconstruction depending on the details in the unknown surface (frequencies) and the reconstruction resolution. For this purpose we present a theoretical analysis of patch-based depth reconstruction. We prove that our model of the reconstruction process yields a linear system, allowing us to apply the transfer (or system) function concept. We derive the modulation transfer function theoretically and validate it experimentally on synthetic examples using rendered images as well as on photographs of a 3D test target. Our analysis proves that there is a significant but predictable amplitude loss in reconstructions of fine scale details. In a first experiment on real-world data we show how this can be compensated for within the limits of noise and reconstruction accuracy by an inverse transfer function in frequency space.

View interpolation and image-based rendering algorithms often produce visual artifacts in regions where the 3D scene geometry is erroneous, uncertain, or incomplete. We introduce ambient point clouds constructed from colored pixels with uncertain depth, which help reduce these artifacts while providing non-photorealistic background coloring and emphasizing reconstructed 3D geometry. Ambient point clouds are created by randomly sampling colored points along the viewing rays associated with uncertain pixels. Our real-time rendering system combines these with more traditional rigid 3D point clouds and colored surface meshes obtained using multiview stereo. Our resulting system can handle larger-range view transitions with fewer visible artifacts than previous approaches.