3 citations · 12 across the 13 of their papers we have counts for
15 papers
GemDepth: Geometry-Embedded Features for 3D-Consistent Video Depth
Yuecheng Liu, Junda Cheng, Longliang Liu +4
Video depth estimation extends monocular prediction into the temporal domain to ensure coherence. However, existing methods often suffer from spatial blurring in fine-detail region…
PointForward: Feedforward Driving Reconstruction through Point-Aligned Representations
Cheng Chi, Xianqi Wang, Hongcheng Luo +9
High-fidelity reconstruction of driving scenes is crucial for autonomous driving. While recent feedforward 3D Gaussian Splatting (3DGS) methods enable fast reconstruction, their pe…
PromptStereo: Zero-Shot Stereo Matching via Structure and Motion Prompts
Xianqi Wang, Hao Yang, Hangtian Wang +4
Modern stereo matching methods have leveraged monocular depth foundation models to achieve superior zero-shot generalization performance. However, most existing methods primarily f…
Generalized Geometry Encoding Volume for Real-time Stereo Matching
Jiaxin Liu, Gangwei Xu, Xianqi Wang +2
Real-time stereo matching methods primarily focus on enhancing in-domain performance but often overlook the critical importance of generalization in real-world applications. In con…
Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers
Gangwei Xu, Haotong Lin, Hongcheng Luo +11
This paper presents Pixel-Perfect Depth, a monocular depth estimation model based on pixel-space diffusion generation that produces high-quality, flying-pixel-free point clouds fro…
BANet: Bilateral Aggregation Network for Mobile Stereo Matching
Gangwei Xu, Jiaxin Liu, Xianqi Wang +5
State-of-the-art stereo matching methods typically use costly 3D convolutions to aggregate a full cost volume, but their computational demands make mobile deployment challenging. D…
ZeroStereo: Zero-shot Stereo Matching from Single Images
Xianqi Wang, Hao Yang, Gangwei Xu +6
State-of-the-art supervised stereo matching methods have achieved remarkable performance on various benchmarks. However, their generalization to real-world scenarios remains challe…
MonSter++: Unified Stereo Matching, Multi-view Stereo, and Real-time Stereo with Monodepth Priors
Junda Cheng, Wenjing Liao, Zhipeng Cai +10
We introduce MonSter++, a geometric foundation model for multi-view depth estimation, unifying rectified stereo matching and unrectified multi-view stereo. Both tasks fundamentally…
IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching
Gangwei Xu, Xianqi Wang, Zhaoxing Zhang +3
Stereo matching is a core component in many computer vision and robotics systems. Despite significant advances over the last decade, handling matching ambiguities in ill-posed regi…
Adaptive Fusion of Single-View and Multi-View Depth for Autonomous Driving
JunDa Cheng, Wei Yin, Kaixuan Wang +3
Multi-view depth estimation has achieved impressive performance over various benchmarks. However, almost all current multi-view systems rely on given ideal camera poses, which are…
HDRFlow: Real-Time HDR Video Reconstruction with Large Motions
Gangwei Xu, Yujin Wang, Jinwei Gu +2
Reconstructing High Dynamic Range (HDR) video from image sequences captured with alternating exposures is challenging, especially in the presence of large camera or object motion.…
Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching
Xianqi Wang, Gangwei Xu, Hao Jia +1
Stereo matching methods based on iterative optimization, like RAFT-Stereo and IGEV-Stereo, have evolved into a cornerstone in the field of stereo matching. However, these methods s…
FlowDA: Unsupervised Domain Adaptive Framework for Optical Flow Estimation
Miaojie Feng, Longliang Liu, Hao Jia +2
Collecting real-world optical flow datasets is a formidable challenge due to the high cost of labeling. A shortage of datasets significantly constrains the real-world performance o…
Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume
Gangwei Xu, Shujun Chen, Hao Jia +2
The full 4D cost volume in Recurrent All-Pairs Field Transforms (RAFT) or global matching by Transformer achieves impressive performance for optical flow estimation. However, their…
MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo Matching
Miaojie Feng, Junda Cheng, Hao Jia +4
Stereo matching is a fundamental task in scene comprehension. In recent years, the method based on iterative optimization has shown promise in stereo matching. However, the current…