1 citations · 1 across the 7 of their papers we have counts for
7 papers
CCF: Complementary Collaborative Fusion for Domain Generalized Multi-Modal 3D Object Detection
Yuchen Wu, Kun Wang, Yining Pan +1
Multi-modal fusion has emerged as a promising paradigm for accurate 3D object detection. However, performance degrades substantially when deployed in target domains different from…
Metric Depth Estimation from Arbitrarily Degraded Low-Resolution Depth Prompts
Kun Wang, Yun Zhu, Pan Zhou +1
We propose AdaDS, an Adaptive Diffusion Sampling framework for prompted metric depth estimation, which estimates high-resolution metric depth from an image and an arbitrarily degra…
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…
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine Domain
Kun Wang, Zhiqiang Yan, Junkai Fan +4
In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial…
Driving-Video Dehazing with Non-Aligned Regularization for Safety Assistance
Junkai Fan, Jiangwei Weng, Kun Wang +4
Real driving-video dehazing poses a significant challenge due to the inherent difficulty in acquiring precisely aligned hazy/clear video pairs for effective model training, especia…