6 citations · 13 across the 10 of their papers we have counts for
10 papers
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…
ROA-BEV: 2D Region-Oriented Attention for BEV-based 3D Object Detection
Jiwei Chen, Yubao Sun, Laiyan Ding +1
Vision-based Bird's-Eye-View (BEV) 3D object detection has recently become popular in autonomous driving. However, objects with a high similarity to the background from a camera pe…
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…
Towards Balanced RGB-TSDF Fusion for Consistent Semantic Scene Completion by 3D RGB Feature Completion and a Classwise Entropy Loss Function
Laiyan Ding, Panwen Hu, Jie Li +1
Semantic Scene Completion (SSC) aims to jointly infer semantics and occupancies of 3D scenes. Truncated Signed Distance Function (TSDF), a 3D encoding of depth, has been a common i…
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…
IMENet: Joint 3D Semantic Scene Completion and 2D Semantic Segmentation through Iterative Mutual Enhancement
Jie Li, Laiyan Ding, Rui Huang
3D semantic scene completion and 2D semantic segmentation are two tightly correlated tasks that are both essential for indoor scene understanding, because they predict the same sem…
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…