216 citations · 299 across the 6 of their papers we have counts for
9 papers
DuNet: Learning Depth Estimation from Dual-Cameras and Dual-Pixels
Yinda Zhang, Neal Wadhwa, Sergio Orts-Escolano +3
Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges. We present a novel…
Discrete Optimization of Ray Potentials for Semantic 3D Reconstruction
Nikolay Savinov, Lubor Ladicky, Christian Haene +1
Dense semantic 3D reconstruction is typically formulated as a discrete or continuous problem over label assignments in a voxel grid, combining semantic and depth likelihoods in a M…
Learning Independent Object Motion from Unlabelled Stereoscopic Videos
Zhe Cao, Abhishek Kar, Christian Haene +1
We present a system for learning motion of independently moving objects from stereo videos. The only human annotation used in our system are 2D object bounding boxes which introduc…
Cost-Sensitive Active Learning for Intracranial Hemorrhage Detection
Weicheng Kuo, Christian Häne, Esther Yuh +2
Deep learning for clinical applications is subject to stringent performance requirements, which raises a need for large labeled datasets. However, the enormous cost of labeling med…
PatchFCN for Intracranial Hemorrhage Detection
Weicheng Kuo, Christian Häne, Esther Yuh +2
This paper studies the problem of detecting and segmenting acute intracranial hemorrhage on head computed tomography (CT) scans. We propose to solve both tasks as a semantic segmen…
Large-Scale 3D Shape Reconstruction and Segmentation from ShapeNet Core55
Li Yi, Lin Shao, Manolis Savva +47
We introduce a large-scale 3D shape understanding benchmark using data and annotation from ShapeNet 3D object database. The benchmark consists of two tasks: part-level segmentation…