Jinliang Zang — accepted AI-conference papers

2025

BANet: Bilateral Aggregation Network for Mobile Stereo Matching

Gangwei Xu, Jiaxin Liu, Xianqi Wang, Junda Cheng, Yong Deng, Jinliang Zang +2

ICCV 2025poster

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. Directly applying 2D convolutions for cost aggregation often results in edge blurring, detail loss, and mismatches in tex…

Cited by 0 Source ↗PDF ↗code ↗Scholar ↗

2025

MonSter: Marry Monodepth to Stereo Unleashes Power

Junda Cheng, Longliang Liu, Gangwei Xu, Xianqi Wang, Zhaoxing Zhang, Yong Deng +4

CVPR 2025highlight

Stereo matching recovers depth from image correspondences. Existing methods struggle to handle ill-posed regions with limited matching cues, such as occlusions and textureless areas. To address this, we propose MonSter, a novel method that leverages the complementary strengths of monocular depth est…

Cited by 10 Source ↗PDF ↗code ↗Scholar ↗