24 citations · 29 across the 8 of their papers we have counts for
8 papers · 1 filter
CMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo Matching
Zhelun Shen, Zhuo Li, Chenming Wu +4
Recently, learning-based stereo matching methods have achieved great improvement in public benchmarks, where soft argmin and smooth L1 loss play a core contribution to their succes…
Self-supervised Event-based Monocular Depth Estimation using Cross-modal Consistency
Junyu Zhu, Lina Liu, Bofeng Jiang +4
An event camera is a novel vision sensor that can capture per-pixel brightness changes and output a stream of asynchronous ``events''. It has advantages over conventional cameras i…
Digging into Depth Priors for Outdoor Neural Radiance Fields
Chen Wang, Jiadai Sun, Lina Liu +5
Neural Radiance Fields (NeRF) have demonstrated impressive performance in vision and graphics tasks, such as novel view synthesis and immersive reality. However, the shape-radiance…
Semi-Supervised Learning for Visual Bird's Eye View Semantic Segmentation
Junyu Zhu, Lina Liu, Yu Tang +3
Visual bird's eye view (BEV) semantic segmentation helps autonomous vehicles understand the surrounding environment only from images, including static elements (e.g., roads) and dy…
FG-Depth: Flow-Guided Unsupervised Monocular Depth Estimation
Junyu Zhu, Lina Liu, Yong Liu +3
The great potential of unsupervised monocular depth estimation has been demonstrated by many works due to low annotation cost and impressive accuracy comparable to supervised metho…
Self-supervised Monocular Depth Estimation for All Day Images using Domain Separation
Lina Liu, Xibin Song, Mengmeng Wang +2
Remarkable results have been achieved by DCNN based self-supervised depth estimation approaches. However, most of these approaches can only handle either day-time or night-time ima…
HR-Depth: High Resolution Self-Supervised Monocular Depth Estimation
Xiaoyang Lyu, Liang Liu, Mengmeng Wang +5
Self-supervised learning shows great potential in monoculardepth estimation, using image sequences as the only source ofsupervision. Although people try to use the high-resolutioni…
FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion
Lina Liu, Xibin Song, Xiaoyang Lyu +4
Depth completion aims to recover a dense depth map from a sparse depth map with the corresponding color image as input. Recent approaches mainly formulate depth completion as a one…