Sunday, March 12, 2006
SDG Cut: 3D Reconstruction of Non-lambertian Objects Using Graph Cuts on Surface Distance Grid
Tianli Yu (UIUC), Narendra Ahuja (UIUC) and Wei-Chao Chen (Nvidia Corp.)
Part of my thesis work in 3D reconstruction of non-lambertian (specular) objects using graph cuts on a new non-uniform grid is published in CVPR 2006.
Abstract
We show that the approaches to 3D reconstruction that use volumetric graph cuts to minimize a cost function over the object surface have two types of biases, the minimal surface bias and the discretization bias. These biases make it difficult to recover surface extrusions and other details, especially when a non-lambertian photo consistency measure is used. To reduce these biases, we propose a new iterative graph cuts based algorithm that operates on the Surface Distance Grid (SDG), which is a special discretization of the 3D space, constructed using a signed distance transform of the current surface estimate. It can be shown that SDG significantly reduces the minimal surface bias, and transforms the discretization bias into a controllable degree of surface smoothness. Experiments on 3D reconstruction of non-lambertian objects confirm the effectiveness of our algorithm over previous methods.
Paper
Data Set
We are using Univ. of Washington's Fish data set and Intel lab's Van Gogh data set for the experiments.
The original fish data has some large calibration errors which make it difficult to match the surface texture. We manually segment the silhouettes in 30 images of the fish data set, and use these silhouettes and the scanned object shape to improve the camera calibration. Here are the manually segmented silhouette masks. Calibration refinement routines and the results will be posted later.
Part of my thesis work in 3D reconstruction of non-lambertian (specular) objects using graph cuts on a new non-uniform grid is published in CVPR 2006.
Abstract
We show that the approaches to 3D reconstruction that use volumetric graph cuts to minimize a cost function over the object surface have two types of biases, the minimal surface bias and the discretization bias. These biases make it difficult to recover surface extrusions and other details, especially when a non-lambertian photo consistency measure is used. To reduce these biases, we propose a new iterative graph cuts based algorithm that operates on the Surface Distance Grid (SDG), which is a special discretization of the 3D space, constructed using a signed distance transform of the current surface estimate. It can be shown that SDG significantly reduces the minimal surface bias, and transforms the discretization bias into a controllable degree of surface smoothness. Experiments on 3D reconstruction of non-lambertian objects confirm the effectiveness of our algorithm over previous methods.
Paper
- Tianli Yu, Narendra Ahuja, and Wei-Chao Chen, SDG Cut: 3D Reconstruction for Non-lambertian Objects Using Graph Cuts on Surface Distance Grid, accepted by IEEE Conference on Computer Vision and Pattern Recognition 2006, New York, June 2006. [Download from IEEE Xplore]
Data Set
We are using Univ. of Washington's Fish data set and Intel lab's Van Gogh data set for the experiments.
The original fish data has some large calibration errors which make it difficult to match the surface texture. We manually segment the silhouettes in 30 images of the fish data set, and use these silhouettes and the scanned object shape to improve the camera calibration. Here are the manually segmented silhouette masks. Calibration refinement routines and the results will be posted later.
