This project addresses the problem of reconstructing the surface shape of transparent objects. The difficulty of this problem originates from the viewpoint dependent appearance of a transparent object, which quickly makes reconstruction methods tailored for diffuse surfaces fail disgracefully. In this project, we introduce a fixed viewpoint approach to dense surface reconstruction of transparent objects based on refraction of light. We present a simple setup that allows us to alter the incident light paths before light rays enter the object by immersing the object partially in a liquid, and develop a method for recovering the object surface through reconstructing and triangulating such incident light paths. Our proposed approach does not need to model the complex interactions of light as it travels through the object, neither does it assume any parametric form for the object shape nor the exact number of refractions and reflections taken place along the light paths. It can therefore handle transparent objects with a relatively complex shape and structure, with unknown and inhomogeneous refractive index. We also show that for thin transparent objects, our proposed acquisition setup can be further simplified by adopting a single refraction approximation. Experimental results on both synthetic and real data demonstrate the feasibility and accuracy of our proposed approach.
Publications
[1] Kai Han, Kwan-Yee K. Wong, Miaomiao Liu
Dense Reconstruction of Transparent Objects by Altering Incident Light Paths Through Refraction
International Journal of Computer Vision (IJCV), 2018. [DOI]
[2] Kai Han, Kwan-Yee K. Wong, Miaomiao Liu
A Fixed Viewpoint Approach for Dense Reconstruction of Transparent Objects
IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015. [PDF]