6 papers
SpatioTemporal Difference Network for Video Depth Super-Resolution
Zhengxue Wang, Yuan Wu, Xiang Li +2
Depth super-resolution has achieved impressive performance, and the incorporation of multi-frame information further enhances reconstruction quality. Nevertheless, statistical anal…
See through the Dark: Learning Illumination-affined Representations for Nighttime Occupancy Prediction
Yuan Wu, Zhiqiang Yan, Yigong Zhang +2
Occupancy prediction aims to estimate the 3D spatial distribution of occupied regions along with their corresponding semantic labels. Existing vision-based methods perform well on…
DuCos: Duality Constrained Depth Super-Resolution via Foundation Model
Zhiqiang Yan, Zhengxue Wang, Haoye Dong +3
We introduce DuCos, a novel depth super-resolution framework grounded in Lagrangian duality theory, offering a flexible integration of multiple constraints and reconstruction objec…
Learning Inverse Laplacian Pyramid for Progressive Depth Completion
Kun Wang, Zhiqiang Yan, Junkai Fan +2
Depth completion endeavors to reconstruct a dense depth map from sparse depth measurements, leveraging the information provided by a corresponding color image. Existing approaches…
Depth-Centric Dehazing and Depth-Estimation from Real-World Hazy Driving Video
Junkai Fan, Kun Wang, Zhiqiang Yan +4
In this paper, we study the challenging problem of simultaneously removing haze and estimating depth from real monocular hazy videos. These tasks are inherently complementary: enha…
Completion as Enhancement: A Degradation-Aware Selective Image Guided Network for Depth Completion
Zhiqiang Yan, Zhengxue Wang, Kun Wang +2
In this paper, we introduce the Selective Image Guided Network (SigNet), a novel degradation-aware framework that transforms depth completion into depth enhancement for the first t…