Publications (25)
Deep Ordinal Regression Network for Monocular Depth Estimation
Huan Fu, Mingming Gong, Chaohui Wang +2
Monocular depth estimation, which plays a crucial role in understanding 3D scene geometry, is an ill-posed problem. Recent methods have gained significant improvement by exploring…
Adaptive Context-Aware Multi-Modal Network for Depth Completion
Shanshan Zhao, Mingming Gong, Huan Fu +1
Depth completion aims to recover a dense depth map from the sparse depth data and the corresponding single RGB image. The observed pixels provide the significant guidance for the r…
Ray Priors through Reprojection: Improving Neural Radiance Fields for Novel View Extrapolation
Jian Zhang, Yuanqing Zhang, Huan Fu +6
Neural Radiance Fields (NeRF) have emerged as a potent paradigm for representing scenes and synthesizing photo-realistic images. A main limitation of conventional NeRFs is that the…
Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Spyridon Bakas, Mauricio Reyes, Andras Jakab +421
Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritum…
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
Shanshan Zhao, Huan Fu, Mingming Gong +1
Supervised depth estimation has achieved high accuracy due to the advanced deep network architectures. Since the groundtruth depth labels are hard to obtain, recent methods try to…
Exploiting Diverse Characteristics and Adversarial Ambivalence for Domain Adaptive Segmentation
Bowen Cai, Huan Fu, Rongfei Jia +3
Adapting semantic segmentation models to new domains is an important but challenging problem. Recently enlightening progress has been made, but the performance of existing methods…