53 citations · 106 across the 24 of their papers we have counts for
23 papers · 1 filter
Fresco++: Frequency-Guided and Canonical-Consistent Optimization for Fine-Grained Head Avatar Modeling
Shikun Zhang, Yong Li, Yiqun Wang +2
We propose Fresco++, a unified optimization framework for fine-grained and view-consistent head avatar reconstruction. Head avatar optimization is typically driven by per-view imag…
Pose-Free Omnidirectional Gaussian Splatting for 360-Degree Videos with Consistent Depth Priors
Chuanqing Zhuang, Xin Lu, Zehui Deng +4
Omnidirectional 3D Gaussian Splatting with panoramas is a key technique for 3D scene representation, and existing methods typically rely on slow SfM to provide camera poses and spa…
SSR-GS: Separating Specular Reflection in Gaussian Splatting for Glossy Surface Reconstruction
Ningjing Fan, Yiqun Wang
In recent years, 3D Gaussian splatting (3DGS) has achieved remarkable progress in novel view synthesis. However, accurately reconstructing glossy surfaces under complex illuminatio…
HDINO: A Concise and Efficient Open-Vocabulary Detector
Hao Zhang, Yiqun Wang, Qinran Lin +2
Despite the growing interest in open-vocabulary object detection in recent years, most existing methods rely heavily on manually curated fine-grained training datasets as well as r…
Ref-DGS: Reflective Dual Gaussian Splatting
Ningjing Fan, Yiqun Wang, Dong-Ming Yan +1
The reflective appearance, especially strong and typically near-field specular reflections, poses a fundamental challenge for accurate surface reconstruction and novel view synthes…
LSF-Animation: Label-Free Speech-Driven Facial Animation via Implicit Feature Representation
Xin Lu, Chuanqing Zhuang, Chenxi Jin +4
Speech-driven 3D facial animation has attracted increasing interest since its potential to generate expressive and temporally synchronized digital humans. While recent works have b…
EAvatar: Expression-Aware Head Avatar Reconstruction with Generative Geometry Priors
Shikun Zhang, Cunjian Chen, Yiqun Wang +2
High-fidelity head avatar reconstruction plays a crucial role in AR/VR, gaming, and multimedia content creation. Recent advances in 3D Gaussian Splatting (3DGS) have demonstrated e…
MT-PCR: Hybrid Mamba-Transformer Network with Spatial Serialization for Point Cloud Registration
Bingxi Liu, An Liu, Hao Chen +4
Point cloud registration (PCR) is a fundamental task in 3D computer vision and robotics. Most learning-based PCR methods rely on Transformer architectures, which suffer from quadra…
Empowering Vector Graphics with Consistently Arbitrary Viewing and View-dependent Visibility
Yidi Li, Jun Xiao, Zhengda Lu +2
This work presents a novel text-to-vector graphics generation approach, Dream3DVG, allowing for arbitrary viewpoint viewing, progressive detail optimization, and view-dependent occ…
3D Surface Reconstruction with Enhanced High-Frequency Details
Shikun Zhang, Yiqun Wang, Cunjian Chen +2
Neural implicit 3D reconstruction can reproduce shapes without 3D supervision, and it learns the 3D scene through volume rendering methods and neural implicit representations. Curr…
DehazeGS: Seeing Through Fog with 3D Gaussian Splatting
Jinze Yu, Yiqun Wang, Aiheng Jiang +5
Current novel view synthesis methods are typically designed for high-quality and clean input images. However, in foggy scenes, scattering and attenuation can significantly degrade…
SpikeGS: Learning 3D Gaussian Fields from Continuous Spike Stream
Jinze Yu, Xin Peng, Zhengda Lu +2
A spike camera is a specialized high-speed visual sensor that offers advantages such as high temporal resolution and high dynamic range compared to conventional frame cameras. Thes…
NocPlace: Nocturnal Visual Place Recognition via Generative and Inherited Knowledge Transfer
Bingxi Liu, Yiqun Wang, Huaqi Tao +5
Visual Place Recognition (VPR) is crucial in computer vision, aiming to retrieve database images similar to a query image from an extensive collection of known images. However, lik…
NeuSD: Surface Completion with Multi-View Text-to-Image Diffusion
Savva Ignatyev, Daniil Selikhanovych, Oleg Voynov +4
We present a novel method for 3D surface reconstruction from multiple images where only a part of the object of interest is captured. Our approach builds on two recent developments…
PET-NeuS: Positional Encoding Tri-Planes for Neural Surfaces
Yiqun Wang, Ivan Skorokhodov, Peter Wonka
A signed distance function (SDF) parametrized by an MLP is a common ingredient of neural surface reconstruction. We build on the successful recent method NeuS to extend it by three…
Factored-NeuS: Reconstructing Surfaces, Illumination, and Materials of Possibly Glossy Objects
Yue Fan, Ningjing Fan, Ivan Skorokhodov +5
We develop a method that recovers the surface, materials, and illumination of a scene from its posed multi-view images. In contrast to prior work, it does not require any additiona…
SDFReg: Learning Signed Distance Functions for Point Cloud Registration
Leida Zhang, Zhengda Lu, Kai Liu +1
Learning-based point cloud registration methods can handle clean point clouds well, while it is still challenging to generalize to noisy, partial, and density-varying point clouds.…
BlobGAN-3D: A Spatially-Disentangled 3D-Aware Generative Model for Indoor Scenes
Qian Wang, Yiqun Wang, Michael Birsak +1
3D-aware image synthesis has attracted increasing interest as it models the 3D nature of our real world. However, performing realistic object-level editing of the generated images…
DS-MVSNet: Unsupervised Multi-view Stereo via Depth Synthesis
Jingliang Li, Zhengda Lu, Yiqun Wang +2
In recent years, supervised or unsupervised learning-based MVS methods achieved excellent performance compared with traditional methods. However, these methods only use the probabi…
EpiGRAF: Rethinking training of 3D GANs
Ivan Skorokhodov, Sergey Tulyakov, Yiqun Wang +1
A very recent trend in generative modeling is building 3D-aware generators from 2D image collections. To induce the 3D bias, such models typically rely on volumetric rendering, whi…
HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details
Yiqun Wang, Ivan Skorokhodov, Peter Wonka
Neural rendering can be used to reconstruct implicit representations of shapes without 3D supervision. However, current neural surface reconstruction methods have difficulty learni…
DepthGAN: GAN-based Depth Generation of Indoor Scenes from Semantic Layouts
Yidi Li, Yiqun Wang, Zhengda Lu +1
Limited by the computational efficiency and accuracy, generating complex 3D scenes remains a challenging problem for existing generation networks. In this work, we propose DepthGAN…
ACDNet: Adaptively Combined Dilated Convolution for Monocular Panorama Depth Estimation
Chuanqing Zhuang, Zhengda Lu, Yiqun Wang +2
Depth estimation is a crucial step for 3D reconstruction with panorama images in recent years. Panorama images maintain the complete spatial information but introduce distortion wi…