2.4k citations · 2.9k across the 53 of their papers we have counts for
54 papers
A Survey on Computational Solutions for Reconstructing Complete Objects by Reassembling Their Fractured Parts
Jiaxin Lu, Yongqing Liang, Huijun Han +4
Reconstructing a complete object from its parts is a fundamental problem in many scientific domains. The purpose of this article is to provide a systematic survey on this topic. Th…
MoGenTS: Motion Generation based on Spatial-Temporal Joint Modeling
Weihao Yuan, Weichao Shen, Yisheng He +5
Motion generation from discrete quantization offers many advantages over continuous regression, but at the cost of inevitable approximation errors. Previous methods usually quantiz…
Atlas Gaussians Diffusion for 3D Generation
Haitao Yang, Yuan Dong, Hanwen Jiang +3
Using the latent diffusion model has proven effective in developing novel 3D generation techniques. To harness the latent diffusion model, a key challenge is designing a high-fidel…
Real3D: Scaling Up Large Reconstruction Models with Real-World Images
Hanwen Jiang, Qixing Huang, Georgios Pavlakos
The default strategy for training single-view Large Reconstruction Models (LRMs) follows the fully supervised route using large-scale datasets of synthetic 3D assets or multi-view…
OmniGlue: Generalizable Feature Matching with Foundation Model Guidance
Hanwen Jiang, Arjun Karpur, Bingyi Cao +2
The image matching field has been witnessing a continuous emergence of novel learnable feature matching techniques, with ever-improving performance on conventional benchmarks. Howe…
Freditor: High-Fidelity and Transferable NeRF Editing by Frequency Decomposition
Yisheng He, Weihao Yuan, Siyu Zhu +3
This paper enables high-fidelity, transferable NeRF editing by frequency decomposition. Recent NeRF editing pipelines lift 2D stylization results to 3D scenes while suffering from…
VideoMV: Consistent Multi-View Generation Based on Large Video Generative Model
Qi Zuo, Xiaodong Gu, Lingteng Qiu +8
Generating multi-view images based on text or single-image prompts is a critical capability for the creation of 3D content. Two fundamental questions on this topic are what data we…
An Optimization Framework to Enforce Multi-View Consistency for Texturing 3D Meshes
Zhengyi Zhao, Chen Song, Xiaodong Gu +6
A fundamental problem in the texturing of 3D meshes using pre-trained text-to-image models is to ensure multi-view consistency. State-of-the-art approaches typically use diffusion…
LEAP: Liberate Sparse-view 3D Modeling from Camera Poses
Hanwen Jiang, Zhenyu Jiang, Yue Zhao +1
Are camera poses necessary for multi-view 3D modeling? Existing approaches predominantly assume access to accurate camera poses. While this assumption might hold for dense views, a…
Cloth2Tex: A Customized Cloth Texture Generation Pipeline for 3D Virtual Try-On
Daiheng Gao, Xu Chen, Xindi Zhang +5
Fabricating and designing 3D garments has become extremely demanding with the increasing need for synthesizing realistic dressed persons for a variety of applications, e.g. 3D virt…
Detector-Free Structure from Motion
Xingyi He, Jiaming Sun, Yifan Wang +4
We propose a new structure-from-motion framework to recover accurate camera poses and point clouds from unordered images. Traditional SfM systems typically rely on the successful d…
Multi-View Representation is What You Need for Point-Cloud Pre-Training
Siming Yan, Chen Song, Youkang Kong +1
A promising direction for pre-training 3D point clouds is to leverage the massive amount of data in 2D, whereas the domain gap between 2D and 3D creates a fundamental challenge. Th…
Jigsaw: Learning to Assemble Multiple Fractured Objects
Jiaxin Lu, Yifan Sun, Qixing Huang
Automated assembly of 3D fractures is essential in orthopedics, archaeology, and our daily life. This paper presents Jigsaw, a novel framework for assembling physically broken 3D o…
LiDAR-Based 3D Object Detection via Hybrid 2D Semantic Scene Generation
Haitao Yang, Zaiwei Zhang, Xiangru Huang +5
Bird's-Eye View (BEV) features are popular intermediate scene representations shared by the 3D backbone and the detector head in LiDAR-based object detectors. However, little resea…
GenCorres: Consistent Shape Matching via Coupled Implicit-Explicit Shape Generative Models
Haitao Yang, Xiangru Huang, Bo Sun +2
This paper introduces GenCorres, a novel unsupervised joint shape matching (JSM) approach. Our key idea is to learn a mesh generator to fit an unorganized deformable shape collecti…
3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining
Siming Yan, Yuqi Yang, Yuxiao Guo +5
Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision. Unlike MAEs used i…
DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation
Chen Song, Chandrajit Bajaj, Qixing Huang
We present DeblurSR, a novel motion deblurring approach that converts a blurry image into a sharp video. DeblurSR utilizes event data to compensate for motion ambiguities and explo…
Neural Volumetric Mesh Generator
Yan Zheng, Lemeng Wu, Xingchao Liu +3
Deep generative models have shown success in generating 3D shapes with different representations. In this work, we propose Neural Volumetric Mesh Generator(NVMG) which can generate…
PatchRD: Detail-Preserving Shape Completion by Learning Patch Retrieval and Deformation
Bo Sun, Vladimir G. Kim, Noam Aigerman +2
This paper introduces a data-driven shape completion approach that focuses on completing geometric details of missing regions of 3D shapes. We observe that existing generative meth…
HM3D-ABO: A Photo-realistic Dataset for Object-centric Multi-view 3D Reconstruction
Zhenpei Yang, Zaiwei Zhang, Qixing Huang
Reconstructing 3D objects is an important computer vision task that has wide application in AR/VR. Deep learning algorithm developed for this task usually relies on an unrealistic…
FvOR: Robust Joint Shape and Pose Optimization for Few-view Object Reconstruction
Zhenpei Yang, Zhile Ren, Miguel Angel Bautista +3
Reconstructing an accurate 3D object model from a few image observations remains a challenging problem in computer vision. State-of-the-art approaches typically assume accurate cam…
E-CIR: Event-Enhanced Continuous Intensity Recovery
Chen Song, Qixing Huang, Chandrajit Bajaj
A camera begins to sense light the moment we press the shutter button. During the exposure interval, relative motion between the scene and the camera causes motion blur, a common u…
Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning
Siming Yan, Zhenpei Yang, Haoxiang Li +5
This paper advocates the use of implicit surface representation in autoencoder-based self-supervised 3D representation learning. The most popular and accessible 3D representation,…
ARAPReg: An As-Rigid-As Possible Regularization Loss for Learning Deformable Shape Generators
Qixing Huang, Xiangru Huang, Bo Sun +3
This paper introduces an unsupervised loss for training parametric deformation shape generators. The key idea is to enforce the preservation of local rigidity among the generated s…