Statistical Inverse Ray Tracing for Image-Based 3D Modeling
By Shubao Liu, David B. Cooper. 2014
22 citations recorded by the source corpus.
Subfields: Computer Graphics and Computer-Aided Design, Computer Vision and Pattern Recognition
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Graphics and Computer-Aided Design
- Andreas Geiger
- Ali O. Ulusoy
- Michael J. Black
- Marc Pollefeys
- Christian Häne
- Lubor Ladicky
- Gernot Riegler
- Luc Van Gool
In Computer Vision and Pattern Recognition
- Luc Van Gool
- Andreas Geiger
- Jitendra Malik
- Ming-Hsuan Yang
- Xiaogang Wang
- Dacheng Tao
- Andrea Vedaldi
- Andrew Zisserman
Citations in the corpus
- RayNet: Learning Volumetric 3D Reconstruction with Ray Potentials (2018, methodology): “Occlusion constraints are encoded using high-order ray potentials [19, 28, 35].”
- OctNetFusion: Learning Depth Fusion from Data (2017, methodology): “Liu and Cooper [24] formulate the task as MAP inference in a MRF.”
- Towards Probabilistic Volumetric Reconstruction Using Ray Potentials (2015, methodology): “While previous works on ray potentials aim at the maximum-a-posteriori (MAP) solution of the occupancy variables [20, 28], our goal is to infer the marginal distributions of occupancy and appearance at each voxel.”
- Patches, Planes and Probabilities: A Non-Local Prior for Volumetric 3D Reconstruction (2016, methodology): “To address this, a number of recent approaches have phrased 3D volumetric reconstruction as MRF inference, exploiting the special characteristics of high-order ray potentials to accurately model the image formation process [18, 31, 40, 50].”
- Semantic 3D Reconstruction with Continuous Regularization and Ray Potentials Using a Visibility Consistency Constraint (2016, methodology): “This was formulated in [25, 9, 21, 22, 31] as a problem of finding a voxel labeling in terms of color and occupancy such that the first occupied voxel along a ray has a similar color as the pixel it projects to.”
- Semantic Multi-view Stereo: Jointly Estimating Objects and Voxels (2017, methodology): “Existing works that consider low-level geometric priors such as spatial smoothness [6, 22, 38], piecewise planarity [16, 33] or Manhattan-world constraints [12, 31] cannot complete large occluded regions, especially for objects with complex geometry.”