Search Results for author:
Found 12 papers, 6 papers with code
DreamMover: Leveraging the Prior of Diffusion Models for Image Interpolation with Large Motion
Due to the large motion, the intermediate semantic information may be absent in input images.
Diffusion-driven lensless fiber endomicroscopic quantitative phase imaging towards digital pathology
Lensless fiber endomicroscope is an emerging tool for in-vivo microscopic imaging, where quantitative phase imaging (QPI) can be utilized as a label-free method to enhance image contrast.
MVSGaussian: Fast Generalizable Gaussian Splatting Reconstruction from Multi-View Stereo
We present MVSGaussian, a new generalizable 3D Gaussian representation approach derived from Multi-View Stereo (MVS) that can efficiently reconstruct unseen scenes.
Geometry-aware Reconstruction and Fusion-refined Rendering for Generalizable Neural Radiance Fields
We incorporate the above ACA, SVA, and CAF into a coarse-to-fine framework, termed Geometry-aware Reconstruction and Fusion-refined Rendering (GeFu).
3D Multi-frame Fusion for Video Stabilization
In this paper, we present RStab, a novel framework for video stabilization that integrates 3D multi-frame fusion through volume rendering.
DyBluRF: Dynamic Neural Radiance Fields from Blurry Monocular Video
Experimental results on our dataset demonstrate that our method outperforms existing approaches in generating sharp novel views from motion-blurred inputs while maintaining spatial-temporal consistency of the scene.
When Epipolar Constraint Meets Non-local Operators in Multi-View Stereo
This constraint reduces the 2D search space into the epipolar line in stereo matching.
Ranked #4 on
3D Reconstruction
on DTU
Fast Full-frame Video Stabilization with Iterative Optimization
Video stabilization refers to the problem of transforming a shaky video into a visually pleasing one.
Constraining Depth Map Geometry for Multi-View Stereo: A Dual-Depth Approach with Saddle-shaped Depth Cells
Learning-based multi-view stereo (MVS) methods deal with predicting accurate depth maps to achieve an accurate and complete 3D representation.
Learning Probabilistic Coordinate Fields for Robust Correspondences
We introduce Probabilistic Coordinate Fields (PCFs), a novel geometric-invariant coordinate representation for image correspondence problems.
Learning Second-Order Attentive Context for Efficient Correspondence Pruning
It is challenging because of the disorganized spatial distribution of numerous outliers, especially when putative correspondences are largely dominated by outliers.
Editable Free-viewpoint Video Using a Layered Neural Representation
Such layered representation supports fully perception and realistic manipulation of the dynamic scene whilst still supporting a free viewing experience in a wide range.
