64 citations · 110 across the 16 of their papers we have counts for
17 papers
Global Graph-Validated Optimization for VLM-based 3D Indoor Scene Generation
Jialu Huang, Yingxuan You, Fei Wang +1
We study open-vocabulary 3D indoor layout generation, which synthesizes diverse and physically plausible scenes from unlabeled 3D assets using free-form language instructions. Rece…
MixRI: Mixing Features of Reference Images for Novel Object Pose Estimation
Xinhang Liu, Jiawei Shi, Zheng Dang +1
We present MixRI, a lightweight network that solves the CAD-based novel object pose estimation problem in RGB images. It can be instantly applied to a novel object at test time wit…
OpenMaterial: A Large-scale Dataset of Complex Materials for 3D Reconstruction
Zheng Dang, Jialu Huang, Fei Wang +1
Recent advances in deep learning, such as neural radiance fields and implicit neural representations, have significantly advanced 3D reconstruction. However, accurately reconstruct…
DVMNet++: Rethinking Relative Pose Estimation for Unseen Objects
Chen Zhao, Tong Zhang, Zheng Dang +1
Determining the relative pose of a previously unseen object between two images is pivotal to the success of generalizable object pose estimation. Existing approaches typically pred…
Adaptive Multi-step Refinement Network for Robust Point Cloud Registration
Zhi Chen, Yufan Ren, Tong Zhang +4
Point Cloud Registration (PCR) estimates the relative rigid transformation between two point clouds of the same scene. Despite significant progress with learning-based approaches,…
SE(3) Diffusion Model-based Point Cloud Registration for Robust 6D Object Pose Estimation
Haobo Jiang, Mathieu Salzmann, Zheng Dang +2
In this paper, we introduce an SE(3) diffusion model-based point cloud registration framework for 6D object pose estimation in real-world scenarios. Our approach formulates the 3D…
AutoSynth: Learning to Generate 3D Training Data for Object Point Cloud Registration
Zheng Dang, Mathieu Salzmann
In the current deep learning paradigm, the amount and quality of training data are as critical as the network architecture and its training details. However, collecting, processing…
Robust Outlier Rejection for 3D Registration with Variational Bayes
Haobo Jiang, Zheng Dang, Zhen Wei +3
Learning-based outlier (mismatched correspondence) rejection for robust 3D registration generally formulates the outlier removal as an inlier/outlier classification problem. The co…
MatchNorm: Learning-based Point Cloud Registration for 6D Object Pose Estimation in the Real World
Zheng Dang, Lizhou Wang, Yu Guo +1
In this work, we tackle the task of estimating the 6D pose of an object from point cloud data. While recent learning-based approaches to addressing this task have shown great succe…
What Stops Learning-based 3D Registration from Working in the Real World?
Zheng Dang, Lizhou Wang, Junning Qiu +2
Much progress has been made on the task of learning-based 3D point cloud registration, with existing methods yielding outstanding results on standard benchmarks, such as ModelNet40…
Robust Differentiable SVD
Wei Wang, Zheng Dang, Yinlin Hu +2
Eigendecomposition of symmetric matrices is at the heart of many computer vision algorithms. However, the derivatives of the eigenvectors tend to be numerically unstable, whether u…
3D Registration for Self-Occluded Objects in Context
Zheng Dang, Fei Wang, Mathieu Salzmann
While much progress has been made on the task of 3D point cloud registration, there still exists no learning-based method able to estimate the 6D pose of an object observed by a 2.…
A Method to Generate High Precision Mesh Model and RGB-D Datasetfor 6D Pose Estimation Task
Minglei Lu, Yu Guo, Fei Wang +1
Recently, 3D version has been improved greatly due to the development of deep neural networks. A high quality dataset is important to the deep learning method. Existing datasets fo…
Learning 3D-3D Correspondences for One-shot Partial-to-partial Registration
Zheng Dang, Fei Wang, Mathieu Salzmann
While 3D-3D registration is traditionally tacked by optimization-based methods, recent work has shown that learning-based techniques could achieve faster and more robust results. I…
Eigendecomposition-Free Training of Deep Networks for Linear Least-Square Problems
Zheng Dang, Kwang Moo Yi, Yinlin Hu +3
Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be tackled by solving a linear least-square pro…
Backpropagation-Friendly Eigendecomposition
Wei Wang, Zheng Dang, Yinlin Hu +2
Eigendecomposition (ED) is widely used in deep networks. However, the backpropagation of its results tends to be numerically unstable, whether using ED directly or approximating it…
Eigendecomposition-free Training of Deep Networks with Zero Eigenvalue-based Losses
Zheng Dang, Kwang Moo Yi, Yinlin Hu +3
Many classical Computer Vision problems, such as essential matrix computation and pose estimation from 3D to 2D correspondences, can be solved by finding the eigenvector correspond…