Search Results for author:
Found 27 papers, 11 papers with code
Sparse-to-Dense Depth Completion Revisited: Sampling Strategy and Graph Construction
Depth completion is a widely studied problem of predicting a dense depth map from a sparse set of measurements and a single RGB image.
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.
S-DyRF: Reference-Based Stylized Radiance Fields for Dynamic Scenes
To address this limitation, we present S-DyRF, a reference-based spatio-temporal stylization method for dynamic neural radiance fields.
Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix
To improve the quality of pseudo labels, we propose a novel motion selection and re-generation module.
Make-It-4D: Synthesizing a Consistent Long-Term Dynamic Scene Video from a Single Image
To animate the visual content, the feature point cloud is displaced based on the scene flow derived from motion estimation and the corresponding camera pose.
Diffusion-Augmented Depth Prediction with Sparse Annotations
We leverage the structural characteristics of diffusion model to enforce depth structures of depth models in a plug-and-play manner.
Neural Video Depth Stabilizer
Video depth estimation aims to infer temporally consistent depth.
Ranked #15 on
Monocular Depth Estimation
on NYU-Depth V2
(using extra training data)
Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priors
To this end, we present a Defocus to Focus (D2F) framework to learn realistic bokeh rendering by fusing defocus priors with the all-in-focus image and by implementing radiance priors in layered fusion.
Lens-to-lens bokeh effect transformation. NTIRE 2023 challenge report
We present the new Bokeh Effect Transformation Dataset (BETD), and review the proposed solutions for this novel task at the NTIRE 2023 Bokeh Effect Transformation Challenge.
Point-and-Shoot All-in-Focus Photo Synthesis from Smartphone Camera Pair
Benefiting from the multi-camera module in modern smartphones, we introduce a new task of AIF synthesis from main (wide) and ultra-wide cameras.
3D Cinemagraphy from a Single Image
To animate the scene, we perform motion estimation and lift the 2D motion into the 3D scene flow.
SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis
We study the problem of novel view synthesis of objects from a single image.
Less is More: Consistent Video Depth Estimation with Masked Frames Modeling
Temporal consistency is the key challenge of video depth estimation.
MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects
Partial occlusion effects are a phenomenon that blurry objects near a camera are semi-transparent, resulting in partial appearance of occluded background.
BokehMe: When Neural Rendering Meets Classical Rendering
Based on this formulation, we implement the classical renderer by a scattering-based method and propose a two-stage neural renderer to fix the erroneous areas from the classical renderer.
Composing Photos Like a Photographer
To this end, we introduce the concept of the key composition map (KCM) to encode the composition rules.
Ranked #1 on
Image Cropping
on FLMS
Fast and Accurate Single-Image Depth Estimation on Mobile Devices, Mobile AI 2021 Challenge: Report
While many solutions have been proposed for this task, they are usually very computationally expensive and thus are not applicable for on-device inference.
AIM 2020 Challenge on Rendering Realistic Bokeh
This paper reviews the second AIM realistic bokeh effect rendering challenge and provides the description of the proposed solutions and results.
Structure-Guided Ranking Loss for Single Image Depth Prediction
Large-scale disparity data generated from stereo photos and 3D videos is a promising source of supervision, however, such disparity data can only approximate the inverse ground truth depth up to an affine transformation.
Iterative Clustering with Game-Theoretic Matching for Robust Multi-consistency Correspondence
Matching corresponding features between two images is a fundamental task to computer vision with numerous applications in object recognition, robotics, and 3D reconstruction.
A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching
Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision.
Learning to Fuse Local Geometric Features for 3D Rigid Data Matching
This paper presents a simple yet very effective data-driven approach to fuse both low-level and high-level local geometric features for 3D rigid data matching.
Deep attention-based classification network for robust depth prediction
However, robust depth prediction suffers from two challenging problems: a) How to extract more discriminative features for different scenes (compared to a single scene)?
Monocular Depth Estimation with Augmented Ordinal Depth Relationships
In this paper, we propose to improve the performance of metric depth estimation with relative depths collected from stereo movie videos using existing stereo matching algorithm.
Monocular Relative Depth Perception With Web Stereo Data Supervision
In this paper we study the problem of monocular relative depth perception in the wild.
Performance Evaluation of 3D Correspondence Grouping Algorithms
This paper presents a thorough evaluation of several widely-used 3D correspondence grouping algorithms, motived by their significance in vision tasks relying on correct feature correspondences.
When Unsupervised Domain Adaptation Meets Tensor Representations
Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another.
