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20232026
most citedCFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation

1 citations · 1 across the 8 of their papers we have counts for

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cs.CV2026

StereoSplat+: Feed-Forward Stereo Gaussian Splatting with Diffusion-Assisted Progressive Inference

Zihua Liu, Masatoshi Okutomi

Recent advances in 3D Gaussian Splatting (3DGS) have enabled high-quality, render-ready scene representations for novel-view synthesis. However, most existing 3DGS pipelines rely o…

cs.CV2025

VSRD++: Autolabeling for 3D Object Detection via Instance-Aware Volumetric Silhouette Rendering

Zihua Liu, Hiroki Sakuma, Masatoshi Okutomi

Monocular 3D object detection is a fundamental yet challenging task in 3D scene understanding. Existing approaches heavily depend on supervised learning with extensive 3D annotatio…

cs.CV2025

DMS:Diffusion-Based Multi-Baseline Stereo Generation for Improving Self-Supervised Depth Estimation

Zihua Liu, Yizhou Li, Songyan Zhang +1

While supervised stereo matching and monocular depth estimation have advanced significantly with learning-based algorithms, self-supervised methods using stereo images as supervisi…

cs.CV2025

TDM: Temporally-Consistent Diffusion Model for All-in-One Real-World Video Restoration

Yizhou Li, Zihua Liu, Yusuke Monno +1

In this paper, we propose the first diffusion-based all-in-one video restoration method that utilizes the power of a pre-trained Stable Diffusion and a fine-tuned ControlNet. Our m…

cs.CV2024

Disparity Estimation Using a Quad-Pixel Sensor

Zhuofeng Wu, Doehyung Lee, Zihua Liu +3

A quad-pixel (QP) sensor is increasingly integrated into commercial mobile cameras. The QP sensor has a unit of 22 four photodiodes under a single microlens, generating mul…

cs.CV2024

VSRD: Instance-Aware Volumetric Silhouette Rendering for Weakly Supervised 3D Object Detection

Zihua Liu, Hiroki Sakuma, Masatoshi Okutomi

Monocular 3D object detection poses a significant challenge in 3D scene understanding due to its inherently ill-posed nature in monocular depth estimation. Existing methods heavily…

cs.CV2024★ 1 cited

CFDNet: A Generalizable Foggy Stereo Matching Network with Contrastive Feature Distillation

Zihua Liu, Yizhou Li, Masatoshi Okutomi

Stereo matching under foggy scenes remains a challenging task since the scattering effect degrades the visibility and results in less distinctive features for dense correspondence…

cs.CV2023

Global Occlusion-Aware Transformer for Robust Stereo Matching

Zihua Liu, Yizhou Li, Masatoshi Okutomi

Despite the remarkable progress facilitated by learning-based stereo-matching algorithms, the performance in the ill-conditioned regions, such as the occluded regions, remains a bo…