120 citations · 140 across the 13 of their papers we have counts for
13 papers · 1 filter
Depth Anything in : Towards Scale Invariance in the Wild
Hualie Jiang, Ziyang Song, Zhiqiang Lou +2
Panoramic depth estimation captures the complete 360 scene geometry, being essential for robotics and AR/VR applications. While perspective depth models have achieved remar…
Self-Supervised Enhancement for Depth from a Lightweight ToF Sensor with Monocular Images
Laiyan Ding, Hualie Jiang, Jiwei Chen +1
Depth map enhancement using paired high-resolution RGB images offers a cost-effective solution for improving low-resolution depth data from lightweight ToF sensors. Nevertheless, n…
DEFOM-Stereo: Depth Foundation Model Based Stereo Matching
Hualie Jiang, Zhiqiang Lou, Laiyan Ding +4
Stereo matching is a key technique for metric depth estimation in computer vision and robotics. Real-world challenges like occlusion and non-texture hinder accurate disparity estim…
CFPNet: Improving Lightweight ToF Depth Completion via Cross-zone Feature Propagation
Laiyan Ding, Hualie Jiang, Rui Xu +1
Depth completion using lightweight time-of-flight (ToF) depth sensors is attractive due to their low cost. However, lightweight ToF sensors usually have a limited field of view (FO…
Towards Cross-View-Consistent Self-Supervised Surround Depth Estimation
Laiyan Ding, Hualie Jiang, Jie Li +2
Depth estimation is a cornerstone for autonomous driving, yet acquiring per-pixel depth ground truth for supervised learning is challenging. Self-Supervised Surround Depth Estimati…
RomniStereo: Recurrent Omnidirectional Stereo Matching
Hualie Jiang, Rui Xu, Minglang Tan +1
Omnidirectional stereo matching (OSM) is an essential and reliable means for depth sensing. However, following earlier works on conventional stereo matching, prior st…
An Improved RaftStereo Trained with A Mixed Dataset for the Robust Vision Challenge 2022
Hualie Jiang, Rui Xu, Wenjie Jiang
Stereo-matching is a fundamental problem in computer vision. Despite recent progress by deep learning, improving the robustness is ineluctable when deploying stereo-matching models…
Dense Depth Distillation with Out-of-Distribution Simulated Images
Junjie Hu, Chenyou Fan, Mete Ozay +2
We study data-free knowledge distillation (KD) for monocular depth estimation (MDE), which learns a lightweight model for real-world depth perception tasks by compressing it from a…
PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimation
Hualie Jiang, Laiyan Ding, Junjie Hu +1
Unsupervised learning of depth from indoor monocular videos is challenging as the artificial environment contains many textureless regions. Fortunately, the indoor scenes are full…
Unsupervised Monocular Depth Perception: Focusing on Moving Objects
Hualie Jiang, Laiyan Ding, Zhenglong Sun +1
As a flexible passive 3D sensing means, unsupervised learning of depth from monocular videos is becoming an important research topic. It utilizes the photometric errors between the…
Boosting Light-Weight Depth Estimation Via Knowledge Distillation
Junjie Hu, Chenyou Fan, Hualie Jiang +4
Monocular depth estimation (MDE) methods are often either too computationally expensive or not accurate enough due to the trade-off between model complexity and inference performan…
UniFuse: Unidirectional Fusion for 360 Panorama Depth Estimation
Hualie Jiang, Zhe Sheng, Siyu Zhu +2
Learning depth from spherical panoramas is becoming a popular research topic because a panorama has a full field-of-view of the environment and provides a relatively complete descr…
DiPE: Deeper into Photometric Errors for Unsupervised Learning of Depth and Ego-motion from Monocular Videos
Hualie Jiang, Laiyan Ding, Zhenglong Sun +1
Unsupervised learning of depth and ego-motion from unlabelled monocular videos has recently drawn great attention, which avoids the use of expensive ground truth in the supervised…