Direct Analytical Methods for Solving Poisson Equations in Computer Vision Problems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dense Shape Reconstruction of a Moving Object under Arbitrary, Unknown Lighting
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Efficiently combining positions and normals for precise 3D geometry
ACM SIGGRAPH 2005 Papers
Performance relighting and reflectance transformation with time-multiplexed illumination
ACM SIGGRAPH 2005 Papers
Dynamic shape capture using multi-view photometric stereo
ACM SIGGRAPH Asia 2009 papers
3D shape recovery of smooth surfaces: dropping the fixed viewpoint assumption
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part I
Self-calibrated, Multi-spectral Photometric Stereo for 3D Face Capture
International Journal of Computer Vision
Towards optimal design of time and color multiplexing codes
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI
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We present a photometric stereo method for non-rigid objects of unknown and spatially varying materials. The prior art uses time-multiplexed illumination but assumes constant surface normals across several frames, fundamentally limiting the accuracy of the estimated normals. We explicitly account for time-varying surface orientations, and show that for unknown Lambertian materials, five images are sufficient to recover surface orientation in one frame. Our optimized system implementation exploits the physical properties of typical cameras and LEDs to reduce the required number of images to just three, and also facilitates frame-to-frame image alignment using standard optical flow methods, despite varying illumination. We demonstrate the system's performance by computing surface orientations for several different moving, deforming objects.