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