348 citations · 414 across the 22 of their papers we have counts for
23 papers
SeamFlow: Structure-Aware Flow Matching on Edge Probabilities for Artist-Like UV Unwrapping
Yuming Zhao, Zangyueyang Xian, Qijian Zhang +4
3D surface cutting and UV unwrapping are fundamental problems in computer graphics. Traditional geometric optimization methods mainly focus on reducing parameterization distortion,…
From Extrinsic to Intrinsic: Geodesic-Guided Representation Learning for 3D Geometric Data
Yuming Zhao, Junhui Hou, Qijian Zhang +2
Geometric analysis fundamentally distinguishes between \textit{extrinsic} and \textit{intrinsic} perspectives. The dominant paradigm in current 3D representation learning relies on…
Joint Geometry-Appearance Human Reconstruction in a Unified Latent Space via Bridge Diffusion
Yingzhi Tang, Qijian Zhang, Junhui Hou
Achieving consistent and high-fidelity geometry and appearance reconstruction of 3D digital humans from a single RGB image is inherently a challenging task. Existing studies typica…
FlexPara: Flexible Neural Surface Parameterization
Yuming Zhao, Qijian Zhang, Junhui Hou +3
Surface parameterization is a fundamental geometry processing task, laying the foundations for the visual presentation of 3D assets and numerous downstream shape analysis scenarios…
SuperCarver: Texture-Consistent 3D Geometry Super-Resolution for High-Fidelity Surface Detail Generation
Qijian Zhang, Xiaozheng Jian, Xuan Zhang +2
Conventional production workflow of high-precision mesh assets necessitates a cumbersome and laborious process of manual sculpting by specialized 3D artists/modelers. The recent ye…
HuGDiffusion: Generalizable Single-Image Human Rendering via 3D Gaussian Diffusion
Yingzhi Tang, Qijian Zhang, Junhui Hou
We present HuGDiffusion, a generalizable 3D Gaussian splatting (3DGS) learning pipeline to achieve novel view synthesis (NVS) of human characters from single-view input images. Exi…
Flatten Anything: Unsupervised Neural Surface Parameterization
Qijian Zhang, Junhui Hou, Wenping Wang +1
Surface parameterization plays an essential role in numerous computer graphics and geometry processing applications. Traditional parameterization approaches are designed for high-q…
ParaPoint: Learning Global Free-Boundary Surface Parameterization of 3D Point Clouds
Qijian Zhang, Junhui Hou, Ying He
Surface parameterization is a fundamental geometry processing problem with rich downstream applications. Traditional approaches are designed to operate on well-behaved mesh models…
Dynamic 3D Point Cloud Sequences as 2D Videos
Yiming Zeng, Junhui Hou, Qijian Zhang +2
Dynamic 3D point cloud sequences serve as one of the most common and practical representation modalities of dynamic real-world environments. However, their unstructured nature in b…
Human as Points: Explicit Point-based 3D Human Reconstruction from Single-view RGB Images
Yingzhi Tang, Qijian Zhang, Junhui Hou +1
The latest trends in the research field of single-view human reconstruction devote to learning deep implicit functions constrained by explicit body shape priors. Despite the remark…
NeuroGF: A Neural Representation for Fast Geodesic Distance and Path Queries
Qijian Zhang, Junhui Hou, Yohanes Yudhi Adikusuma +2
Geodesics are essential in many geometry processing applications. However, traditional algorithms for computing geodesic distances and paths on 3D mesh models are often inefficient…
Unleash the Potential of Image Branch for Cross-modal 3D Object Detection
Yifan Zhang, Qijian Zhang, Junhui Hou +2
To achieve reliable and precise scene understanding, autonomous vehicles typically incorporate multiple sensing modalities to capitalize on their complementary attributes. However,…
PointVST: Self-Supervised Pre-training for 3D Point Clouds via View-Specific Point-to-Image Translation
Qijian Zhang, Junhui Hou
The past few years have witnessed the great success and prevalence of self-supervised representation learning within the language and 2D vision communities. However, such advanceme…
Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis
Qijian Zhang, Junhui Hou, Yue Qian +3
Point clouds are characterized by irregularity and unstructuredness, which pose challenges in efficient data exploitation and discriminative feature extraction. In this paper, we p…
Leveraging Single-View Images for Unsupervised 3D Point Cloud Completion
Lintai Wu, Qijian Zhang, Junhui Hou +1
Point clouds captured by scanning devices are often incomplete due to occlusion. To overcome this limitation, point cloud completion methods have been developed to predict the comp…
PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition
Qijian Zhang, Junhui Hou, Yue Qian
As two fundamental representation modalities of 3D objects, 3D point clouds and multi-view 2D images record shape information from different domains of geometric structures and vis…
GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation
Yifan Zhang, Qijian Zhang, Zhiyu Zhu +2
The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by occlusions, signal missing, or manual annotation errors, can confuse deep 3D object detectors dur…
WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation
Yingzhi Tang, Yue Qian, Qijian Zhang +3
We propose WarpingGAN, an effective and efficient 3D point cloud generation network. Unlike existing methods that generate point clouds by directly learning the mapping functions b…
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment
Yiming Zeng, Yue Qian, Qijian Zhang +3
This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation. We formulate the problem as estimation of point-wise traje…
ParaNet: Deep Regular Representation for 3D Point Clouds
Qijian Zhang, Junhui Hou, Yue Qian +2
Although convolutional neural networks have achieved remarkable success in analyzing 2D images/videos, it is still non-trivial to apply the well-developed 2D techniques in regular…
Dense Attention Fluid Network for Salient Object Detection in Optical Remote Sensing Images
Qijian Zhang, Runmin Cong, Chongyi Li +5
Despite the remarkable advances in visual saliency analysis for natural scene images (NSIs), salient object detection (SOD) for optical remote sensing images (RSIs) still remains a…
CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection
Qijian Zhang, Runmin Cong, Junhui Hou +2
Co-Salient Object Detection (CoSOD) aims at discovering salient objects that repeatedly appear in a given query group containing two or more relevant images. One challenging issue…
MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling
Yue Qian, Junhui Hou, Qijian Zhang +3
This paper explores the problem of task-oriented downsampling over 3D point clouds, which aims to downsample a point cloud while maintaining the performance of subsequent applicati…