Beyond Silhouettes: Surface Reconstruction Using Multi-Flash Photography

Authors:
Daniel Crispell;Douglas Lanman;Peter G. Sibley;Yong Zhao;Gabriel Taubin
Affiliations:
Brown University, USA;Brown University, USA;Brown University, USA;Brown University, USA;Brown University, USA
Venue:
3DPVT '06 Proceedings of the Third International Symposium on 3D Data Processing, Visualization, and Transmission (3DPVT'06)
Year:
2006

Citing 0
Cited 7

Multi-flash 3D photography: capturing shape and appearance

ACM SIGGRAPH 2006 Research posters
Free-Form Object Reconstruction from Silhouettes, Occluding Edges and Texture Edges: A Unified and Robust Operator Based on Duality

IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiflash Stereopsis: Depth-Edge-Preserving Stereo with Small Baseline Illumination

IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape from Depth Discontinuities

Emerging Trends in Visual Computing
Vision-guided Robot System for Picking Objects by Casting Shadows

International Journal of Robotics Research
Detecting and segmenting un-occluded items by actively casting shadows

ACCV'07 Proceedings of the 8th Asian conference on Computer vision - Volume Part I
Rainbow flash camera: depth edge extraction using complementary colors

ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part VI

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper introduces a novel method for surface reconstruction using the depth discontinuity information captured by a multi-flash camera while the object moves along a known trajectory. Experimental results based on turntable sequences are presented. By observing the visual motion of depth discontinuities, surface points are accurately reconstructed - including many located deep inside concavities. The method extends well-established differential and global shape-from-silhouette surface reconstruction techniques by incorporating the significant additional information encoded in the depth discontinuities. The reconstruction method uses an implicit form of the epipolar parameterization and directly estimates point locations and corresponding surface normals on the surface of the object using a local temporal neighborhood of the depth discontinuities. Outliers, which correspond to the ill-conditioned cases of the reconstruction equations, are easily detected and removed by back-projection. Gaps resulting from curvaturedependent sampling and shallow concavities are filled by fitting an implicit surface to the oriented point cloud's point locations and normal vectors.