7 citations · 11 across the 2 of their papers we have counts for
3 papers
cs.CV2021★ 4 cited
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation
Qi Tang, Runmin Cong, Ronghui Sheng +4
Depth map super-resolution is a task with high practical application requirements in the industry. Existing color-guided depth map super-resolution methods usually necessitate an e…
cs.CV2021★ 7 cited
Towards Fast and Accurate Real-World Depth Super-Resolution: Benchmark Dataset and Baseline
Lingzhi He, Hongguang Zhu, Feng Li +6
Depth maps obtained by commercial depth sensors are always in low-resolution, making it difficult to be used in various computer vision tasks. Thus, depth map super-resolution (SR)…
cs.CV2019
UG Track 2: A Collective Benchmark Effort for Evaluating and Advancing Image Understanding in Poor Visibility Environments
Ye Yuan, Wenhan Yang, Wenqi Ren +3
The UG challenge in IEEE CVPR 2019 aims to evoke a comprehensive discussion and exploration about how low-level vision techniques can benefit the high-level automatic visual…