25 citations · 146 across the 27 of their papers we have counts for
24 papers · 1 filter
Dynamic View Synthesis from Small Camera Motion Videos
Huiqiang Sun, Xingyi Li, Juewen Peng +4
Novel view synthesis for dynamic D scenes poses a significant challenge. Many notable efforts use NeRF-based approaches to address this task and yield impressive results. Howeve…
Self-Distilled Depth Refinement with Noisy Poisson Fusion
Jiaqi Li, Yiran Wang, Jinghong Zheng +4
Depth refinement aims to infer high-resolution depth with fine-grained edges and details, refining low-resolution results of depth estimation models. The prevailing methods adopt t…
iControl3D: An Interactive System for Controllable 3D Scene Generation
Xingyi Li, Yizheng Wu, Jun Cen +6
3D content creation has long been a complex and time-consuming process, often requiring specialized skills and resources. While recent advancements have allowed for text-guided 3D…
S-DyRF: Reference-Based Stylized Radiance Fields for Dynamic Scenes
Xingyi Li, Zhiguo Cao, Yizheng Wu +4
Current 3D stylization methods often assume static scenes, which violates the dynamic nature of our real world. To address this limitation, we present S-DyRF, a reference-based spa…
DyBluRF: Dynamic Neural Radiance Fields from Blurry Monocular Video
Huiqiang Sun, Xingyi Li, Liao Shen +3
Recent advancements in dynamic neural radiance field methods have yielded remarkable outcomes. However, these approaches rely on the assumption of sharp input images. When faced wi…
Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix
Kewei Wang, Yizheng Wu, Zhiyu Pan +5
Class-agnostic motion prediction methods aim to comprehend motion within open-world scenarios, holding significance for autonomous driving systems. However, training a high-perform…
Make-It-4D: Synthesizing a Consistent Long-Term Dynamic Scene Video from a Single Image
Liao Shen, Xingyi Li, Huiqiang Sun +4
We study the problem of synthesizing a long-term dynamic video from only a single image. This is challenging since it requires consistent visual content movements given large camer…
Diffusion-Augmented Depth Prediction with Sparse Annotations
Jiaqi Li, Yiran Wang, Zihao Huang +4
Depth estimation aims to predict dense depth maps. In autonomous driving scenes, sparsity of annotations makes the task challenging. Supervised models produce concave objects due t…
NVDS+: Towards Efficient and Versatile Neural Stabilizer for Video Depth Estimation
Yiran Wang, Min Shi, Jiaqi Li +7
Video depth estimation aims to infer temporally consistent depth. One approach is to finetune a single-image model on each video with geometry constraints, which proves inefficient…
Defocus to focus: Photo-realistic bokeh rendering by fusing defocus and radiance priors
Xianrui Luo, Juewen Peng, Ke Xian +2
We consider the problem of realistic bokeh rendering from a single all-in-focus image. Bokeh rendering mimics aesthetic shallow depth-of-field (DoF) in professional photography, bu…
Point-and-Shoot All-in-Focus Photo Synthesis from Smartphone Camera Pair
Xianrui Luo, Juewen Peng, Weiyue Zhao +3
All-in-Focus (AIF) photography is expected to be a commercial selling point for modern smartphones. Standard AIF synthesis requires manual, time-consuming operations such as focal…
3D Cinemagraphy from a Single Image
Xingyi Li, Zhiguo Cao, Huiqiang Sun +3
We present 3D Cinemagraphy, a new technique that marries 2D image animation with 3D photography. Given a single still image as input, our goal is to generate a video that contains…
SymmNeRF: Learning to Explore Symmetry Prior for Single-View View Synthesis
Xingyi Li, Chaoyi Hong, Yiran Wang +3
We study the problem of novel view synthesis of objects from a single image. Existing methods have demonstrated the potential in single-view view synthesis. However, they still fai…
Less is More: Consistent Video Depth Estimation with Masked Frames Modeling
Yiran Wang, Zhiyu Pan, Xingyi Li +3
Temporal consistency is the key challenge of video depth estimation. Previous works are based on additional optical flow or camera poses, which is time-consuming. By contrast, we d…
MPIB: An MPI-Based Bokeh Rendering Framework for Realistic Partial Occlusion Effects
Juewen Peng, Jianming Zhang, Xianrui Luo +3
Partial occlusion effects are a phenomenon that blurry objects near a camera are semi-transparent, resulting in partial appearance of occluded background. However, it is challengin…
BokehMe: When Neural Rendering Meets Classical Rendering
Juewen Peng, Zhiguo Cao, Xianrui Luo +3
We propose BokehMe, a hybrid bokeh rendering framework that marries a neural renderer with a classical physically motivated renderer. Given a single image and a potentially imperfe…
LRF-Net: Learning Local Reference Frames for 3D Local Shape Description and Matching
Angfan Zhu, Jiaqi Yang, Weiyue Zhao +1
The local reference frame (LRF) acts as a critical role in 3D local shape description and matching. However, most of existing LRFs are hand-crafted and suffer from limited repeatab…
Iterative Clustering with Game-Theoretic Matching for Robust Multi-consistency Correspondence
Chen Zhao, Jiaqi Yang, Ke Xian +2
Matching corresponding features between two images is a fundamental task to computer vision with numerous applications in object recognition, robotics, and 3D reconstruction. Curre…
A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching
Jiaqi Yang, Ke Xian, Peng Wang +1
Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision. While a number of corr…
Learning to Fuse Local Geometric Features for 3D Rigid Data Matching
Jiaqi Yang, Chen Zhao, Ke Xian +2
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. It is a common pract…
Deep attention-based classification network for robust depth prediction
Ruibo Li, Ke Xian, Chunhua Shen +3
In this paper, we present our deep attention-based classification (DABC) network for robust single image depth prediction, in the context of the Robust Vision Challenge 2018 (ROB 2…
Monocular Depth Estimation with Augmented Ordinal Depth Relationships
Yuanzhouhan Cao, Tianqi Zhao, Ke Xian +3
Most existing algorithms for depth estimation from single monocular images need large quantities of metric groundtruth depths for supervised learning. We show that relative depth c…
Performance Evaluation of 3D Correspondence Grouping Algorithms
Jiaqi Yang, Ke Xian, Yang Xiao +1
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 cor…
When Unsupervised Domain Adaptation Meets Tensor Representations
Hao Lu, Lei Zhang, Zhiguo Cao +4
Domain adaption (DA) allows machine learning methods trained on data sampled from one distribution to be applied to data sampled from another. It is thus of great practical importa…