1 code implementation • 2 Nov 2022 • Qiangqiang Huang, Joseph DeGol, Victor Fragoso, Sudipta N. Sinha, John J. Leonard
Our main contribution is a novel framework for modeling camera localizability that incorporates both natural scene features and artificial fiducial markers added to the scene.
no code implementations • CVPR 2022 • Tien Do, Ondrej Miksik, Joseph DeGol, Hyun Soo Park, Sudipta N. Sinha
Our key idea is to implicitly encode the appearance of a sparse yet salient set of 3D scene points into a convolutional neural network (CNN) that can detect these scene points in query images whenever they are visible.
1 code implementation • ICCV 2021 • Jae Yong Lee, Joseph DeGol, Chuhang Zou, Derek Hoiem
To overcome the challenge of the non-differentiable PatchMatch optimization that involves iterative sampling and hard decisions, we use reinforcement learning to minimize expected photometric cost and maximize likelihood of ground truth depth and normals.
1 code implementation • CVPR 2021 • Jae Yong Lee, Joseph DeGol, Victor Fragoso, Sudipta N. Sinha
We address estimating dense correspondences between two images depicting different but semantically related scenes.
no code implementations • CVPR 2020 • Victor Fragoso, Joseph DeGol, Gang Hua
Many real-world applications in augmented reality (AR), 3D mapping, and robotics require both fast and accurate estimation of camera poses and scales from multiple images captured by multiple cameras or a single moving camera.
no code implementations • ECCV 2018 • Joseph DeGol, Timothy Bretl, Derek Hoiem
In this paper, we present an incremental structure from motion (SfM) algorithm that signiï¬cantly outperforms existing algorithms when ï¬ducial markers are present in the scene, and that matches the performance of existing algorithms when no markers are present.
no code implementations • ICCV 2017 • Joseph DeGol, Timothy Bretl, Derek Hoiem
Current fiducial marker detection algorithms rely on marker IDs for false positive rejection.
no code implementations • CVPR 2016 • Joseph DeGol, Mani Golparvar-Fard, Derek Hoiem
Our goal is to recognize material categories using images and geometry information.