3D cameras can transform autonomous driving and health technologies by scanning the geometry of a scene and forming a so-called 3D picture. New 3D camera research from the Media Lab’s Camera Culture group appears in the Best Papers Special Issue of the International Conference on Computer Vision (ICCV) and published in the December 2017 issue of  the International Journal of Computer Vision (volume 125). Only nine out of 1700 paper submissions to ICCV were accepted for the issue, based on their expected impact to the field.

Above: Starting from a coarse depth map, is it possible to achieve laser scan quality? By combining the information from the Kinect depth frame in (a) with information in 3 polarized photographs (b) , we reconstruct the 3D surface shown in (c). The subtle change between polarization images provides additional information about surface orientation. 

The paper that appears in the best papers issue is titled “Depth Sensing using Geometrically Constrained Polarization Normals” (doi:10.1007/s11263-017-1025-7). Achuta Kadambi was the first author and was joined by Vage Taamazyan (from Russia), Boxin Shi (now faculty at Peking University), and Ramesh Raskar. The authors address the problem of recovering 3D geometry from polarimetric properties of reflected light. In particular, the authors use prior geometric information from coarse depth maps to constrain shape information extracted from polarization cues. This new paper builds upon earlier work from the Camera Culture group, by now introducing mathematical guarantees as to when 3D shape can be accurately recovered.

With an improved understanding of 3D shape recovery, the authors are currently exploring how the technique can impact a range of Media Lab research, from the scanning of prosthetic devices, to photorealistic virtual reality experiences, to safer forms of autonomous driving.