2 citations · 3 across the 11 of their papers we have counts for
14 papers
WaveFreqAnchor: Wave-Structural Anchoring and Frequency Correction Diffusion for Training-Free Face Restoration
Zelin Du, Wenjie Li, Zhengxue Wang +3
Diffusion-based face restoration that adjusts the sampling trajectory of pre-trained diffusion models has achieved remarkable progress. However, existing approaches provide insuffi…
URNet: A Unified Reparameterized Network for Efficient RGB-D Semantic Segmentation
Guoan Xu, Zhengxue Wang, Yang Xiao +3
Previous RGB-D semantic segmentation methods commonly employ dual encoders to separately process RGB and depth inputs, followed by dedicated modules for cross-modal feature fusion.…
Height-Guided Projection Reparameterization for Camera-LiDAR Occupancy
Yuan Wu, Zhiqiang Yan, Jiawei Lian +2
3D occupancy prediction aims to infer dense, voxel-wise scene semantics from sensor observations, where the 2D-to-3D view transformation serves as a crucial step in bridging image…
DegBins: Degradation-Driven Binning for Depth Super-Resolution
Zhiqiang Yan, Zhengxue Wang, Jian Yang +1
Depth super-resolution (DSR) aims to recover a high-resolution (HR) depth map from its low-resolution (LR) counterpart. With color image guidance, this task is typically formulated…
PASDiff: Physics-Aware Semantic Guidance for Joint Real-World Low-Light Face Enhancement and Restoration
Yilin Ni, Wenjie Li, Zhengxue Wang +3
Face images captured in real-world low light suffer multiple degradations-low illumination, blur, noise, and low visibility, etc. Existing cascaded solutions often suffer from seve…
Multi-Order Matching Network for Alignment-Free Depth Super-Resolution
Zhengxue Wang, Zhiqiang Yan, Yuan Wu +3
Recent guided depth super-resolution methods are premised on the assumption of strict spatial alignment between depth and RGB, achieving high-quality depth reconstruction. However,…
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…
Event-Driven Dynamic Scene Depth Completion
Zhiqiang Yan, Jianhao Jiao, Zhengxue Wang +1
Depth completion in dynamic scenes poses significant challenges due to rapid ego-motion and object motion, which can severely degrade the quality of input modalities such as RGB im…
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…
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…
DORNet: A Degradation Oriented and Regularized Network for Blind Depth Super-Resolution
Zhengxue Wang, Zhiqiang Yan, Jinshan Pan +3
Recent RGB-guided depth super-resolution methods have achieved impressive performance under the assumption of fixed and known degradation (e.g., bicubic downsampling). However, in…
Deep Height Decoupling for Precise Vision-based 3D Occupancy Prediction
Yuan Wu, Zhiqiang Yan, Zhengxue Wang +3
The task of vision-based 3D occupancy prediction aims to reconstruct 3D geometry and estimate its semantic classes from 2D color images, where the 2D-to-3D view transformation is a…
Scene Prior Filtering for Depth Super-Resolution
Zhengxue Wang, Zhiqiang Yan, Ming-Hsuan Yang +4
Multi-modal fusion serves as a cornerstone for successful depth map super-resolution. However, commonly used fusion strategies, such as addition and concatenation, fall short of ef…
SGNet: Structure Guided Network via Gradient-Frequency Awareness for Depth Map Super-Resolution
Zhengxue Wang, Zhiqiang Yan, Jian Yang
Depth super-resolution (DSR) aims to restore high-resolution (HR) depth from low-resolution (LR) one, where RGB image is often used to promote this task. Recent image guided DSR ap…