1 code implementation • 7 Mar 2024 • Guanlin Shen, Jingwei Huang, Zhihua Hu, Bin Wang
This paper introduces CN-RMA, a novel approach for 3D indoor object detection from multi-view images.
no code implementations • 17 Dec 2023 • Yingda Yin, Yuzheng Liu, Yang Xiao, Daniel Cohen-Or, Jingwei Huang, Baoquan Chen
Advancements in 3D instance segmentation have traditionally been tethered to the availability of annotated datasets, limiting their application to a narrow spectrum of object categories.
no code implementations • 3 Jan 2023 • Jingwei Huang
Digital engineering transformation is a crucial process for the engineering paradigm shifts in the fourth industrial revolution (4IR), and artificial intelligence (AI) is a critical enabling technology in digital engineering transformation.
no code implementations • ICCV 2023 • Lei Wang, Min Dai, Jianan He, Jingwei Huang
Our key idea is using primitive graph as a unified representation of vector maps and formulating shape regularization and topology reconstruction as primitive graph reconstruction problems that can be solved in the same framework.
no code implementations • ICCV 2023 • Yunze Liu, Junyu Chen, Zekai Zhang, Jingwei Huang, Li Yi
With such frames, we can factorize geometry and motion to facilitate a feature-space geometric reconstruction for more effective 4D learning.
1 code implementation • 30 Jul 2022 • Hao Wen, Yunze Liu, Jingwei Huang, Bo Duan, Li Yi
This paper proposes a 4D backbone for long-term point cloud video understanding.
no code implementations • 28 Jun 2022 • Lei Wang, Min Dai, Jianan He, Jingwei Huang, Mingwei Sun
Then, we convert vector shape prediction, regularization, and topology reconstruction into a unique primitive graph learning problem.
1 code implementation • 19 Mar 2022 • Jie Pan, Jingwei Huang, Gengdong Cheng, Yong Zeng
This paper proposes, implements, and evaluates a reinforcement learning (RL)-based computational framework for automatic mesh generation.
no code implementations • 12 Dec 2021 • Zhihua Hu, Bo Duan, Yanfeng Zhang, Mingwei Sun, Jingwei Huang
We jointly train a layout module to produce an initial layout and a novel MVS module to obtain accurate layout geometry.
2 code implementations • CVPR 2021 • Jingwei Huang, Shan Huang, Mingwei Sun
We propose a novel approach for large-scale nonlinear least squares problems based on deep learning frameworks.
no code implementations • 14 Jun 2021 • Jingwei Huang, Wael Khallouli, Ghaith Rabadi, Mamadou Seck
This paper presents our research on leveraging social media Big Data and AI to support hurricane disaster emergency response.
6 code implementations • ICCV 2021 • Xinjun Ma, Yue Gong, Qirui Wang, Jingwei Huang, Lei Chen, Fan Yu
As a result, we achieve promising results on all datasets and the highest F-Score on the online TNT intermediate benchmark.
Ranked #8 on Point Clouds on Tanks and Temples
1 code implementation • ICCV 2021 • Jingwei Huang, Yanfeng Zhang, Mingwei Sun
We present PrimitiveNet, a novel approach for high-resolution primitive instance segmentation from point clouds on a large scale.
no code implementations • NeurIPS 2020 • Chiyu Jiang, Jingwei Huang, Andrea Tagliasacchi, Leonidas J. Guibas
Such a space naturally allows the disentanglement of geometric style (coming from the source) and structural pose (conforming to the target).
1 code implementation • 7 Aug 2020 • Yichao Zhou, Jingwei Huang, Xili Dai, Shichen Liu, Linjie Luo, Zhili Chen, Yi Ma
We present HoliCity, a city-scale 3D dataset with rich structural information.
1 code implementation • 14 Jun 2020 • Chiyu "Max" Jiang, Jingwei Huang, Andrea Tagliasacchi, Leonidas Guibas
We illustrate the effectiveness of this learned deformation space for various downstream applications, including shape generation via deformation, geometric style transfer, unsupervised learning of a consistent parameterization for entire classes of shapes, and shape interpolation.
1 code implementation • 23 May 2020 • Jingwei Huang, Chiyu Max Jiang, Baiqiang Leng, Bin Wang, Leonidas Guibas
Given a pair of shapes, our framework provides a novel shape feature-preserving mapping function that continuously deforms one model to the other by minimizing fitting and rigidity losses based on the non-rigid iterative-closest-point (ICP) algorithm.
Graphics Computational Geometry
1 code implementation • ECCV 2020 • Mikaela Angelina Uy, Jingwei Huang, Minhyuk Sung, Tolga Birdal, Leonidas Guibas
We introduce a new problem of retrieving 3D models that are deformable to a given query shape and present a novel deep deformation-aware embedding to solve this retrieval task.
1 code implementation • 19 Mar 2020 • Chiyu Max Jiang, Avneesh Sud, Ameesh Makadia, Jingwei Huang, Matthias Nießner, Thomas Funkhouser
Then, we use the decoder as a component in a shape optimization that solves for a set of latent codes on a regular grid of overlapping crops such that an interpolation of the decoded local shapes matches a partial or noisy observation.
1 code implementation • CVPR 2020 • Jingwei Huang, Justus Thies, Angela Dai, Abhijit Kundu, Chiyu Max Jiang, Leonidas Guibas, Matthias Nießner, Thomas Funkhouser
In this work, we present a novel approach for color texture generation using a conditional adversarial loss obtained from weakly-supervised views.
1 code implementation • NeurIPS 2019 • Yichao Zhou, Haozhi Qi, Jingwei Huang, Yi Ma
We present a simple yet effective end-to-end trainable deep network with geometry-inspired convolutional operators for detecting vanishing points in images.
1 code implementation • ICCV 2019 • Jingwei Huang, Yichao Zhou, Thomas Funkhouser, Leonidas Guibas
In this work, we introduce the novel problem of identifying dense canonical 3D coordinate frames from a single RGB image.
7 code implementations • CVPR 2019 • He Wang, Srinath Sridhar, Jingwei Huang, Julien Valentin, Shuran Song, Leonidas J. Guibas
The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image.
Ranked #2 on 6D Pose Estimation using RGBD on CAMERA25
1 code implementation • ICLR 2019 • Chiyu "Max" Jiang, Jingwei Huang, Karthik Kashinath, Prabhat, Philip Marcus, Matthias Niessner
We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical signals such as panorama images or planetary signals.
Ranked #24 on Semantic Segmentation on Stanford2D3D Panoramic
2 code implementations • ICLR 2019 • Chiyu "Max" Jiang, Dequan Wang, Jingwei Huang, Philip Marcus, Matthias Nießner
It has been challenging to analyze signals with mixed topologies (for example, point cloud with surface mesh).
1 code implementation • CVPR 2019 • Jingwei Huang, Haotian Zhang, Li Yi, Thomas Funkhouser, Matthias Nießner, Leonidas Guibas
We introduce, TextureNet, a neural network architecture designed to extract features from high-resolution signals associated with 3D surface meshes (e. g., color texture maps).
Ranked #22 on Semantic Segmentation on ScanNet
2 code implementations • 5 Feb 2018 • Jingwei Huang, Hao Su, Leonidas Guibas
In this paper, we describe a robust algorithm for 2-Manifold generation of various kinds of ShapeNet Models.
Computational Geometry
no code implementations • ICCV 2015 • Jingwei Huang, Huarong Chen, Bin Wang, Stephen Lin
We present an automatic thumbnail generation technique based on two essential considerations: how well they visually represent the original photograph, and how well the foreground can be recognized after the cropping and downsizing steps of thumbnailing.