79 citations · 312 across the 16 of their papers we have counts for
18 papers
Point Primitive Transformer for Long-Term 4D Point Cloud Video Understanding
Hao Wen, Yunze Liu, Jingwei Huang +2
This paper proposes a 4D backbone for long-term point cloud video understanding. A typical way to capture spatial-temporal context is using 4Dconv or transformer without hierarchy.…
Primitive Graph Learning for Unified Vector Mapping
Lei Wang, Min Dai, Jianan He +2
Large-scale vector mapping is important for transportation, city planning, and survey and census. We propose GraphMapper, a unified framework for end-to-end vector map extraction f…
Reinforcement learning for automatic quadrilateral mesh generation: a soft actor-critic approach
Jie Pan, Jingwei Huang, Gengdong Cheng +1
This paper proposes, implements, and evaluates a reinforcement learning (RL)-based computational framework for automatic mesh generation. Mesh generation plays a fundamental role i…
MVLayoutNet:3D layout reconstruction with multi-view panoramas
Zhihua Hu, Bo Duan, Yanfeng Zhang +2
We present MVLayoutNet, an end-to-end network for holistic 3D reconstruction from multi-view panoramas. Our core contribution is to seamlessly combine learned monocular layout esti…
HoliCity: A City-Scale Data Platform for Learning Holistic 3D Structures
Yichao Zhou, Jingwei Huang, Xili Dai +4
We present HoliCity, a city-scale 3D dataset with rich structural information. Currently, this dataset has 6,300 real-world panoramas of resolution that are acc…
ShapeFlow: Learnable Deformations Among 3D Shapes
Chiyu "Max" Jiang, Jingwei Huang, Andrea Tagliasacchi +1
We present ShapeFlow, a flow-based model for learning a deformation space for entire classes of 3D shapes with large intra-class variations. ShapeFlow allows learning a multi-templ…
ManifoldPlus: A Robust and Scalable Watertight Manifold Surface Generation Method for Triangle Soups
Jingwei Huang, Yichao Zhou, Leonidas Guibas
We present ManifoldPlus, a method for robust and scalable conversion of triangle soups to watertight manifolds. While many algorithms in computer graphics require the input mesh to…
MeshODE: A Robust and Scalable Framework for Mesh Deformation
Jingwei Huang, Chiyu Max Jiang, Baiqiang Leng +2
We present MeshODE, a scalable and robust framework for pairwise CAD model deformation without prespecified correspondences. Given a pair of shapes, our framework provides a novel…
Deformation-Aware 3D Model Embedding and Retrieval
Mikaela Angelina Uy, Jingwei Huang, Minhyuk Sung +2
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. 3D…
Local Implicit Grid Representations for 3D Scenes
Chiyu Max Jiang, Avneesh Sud, Ameesh Makadia +3
Shape priors learned from data are commonly used to reconstruct 3D objects from partial or noisy data. Yet no such shape priors are available for indoor scenes, since typical 3D au…
Adversarial Texture Optimization from RGB-D Scans
Jingwei Huang, Justus Thies, Angela Dai +5
Realistic color texture generation is an important step in RGB-D surface reconstruction, but remains challenging in practice due to inaccuracies in reconstructed geometry, misalign…
NeurVPS: Neural Vanishing Point Scanning via Conic Convolution
Yichao Zhou, Haozhi Qi, Jingwei Huang +1
We present a simple yet effective end-to-end trainable deep network with geometry-inspired convolutional operators for detecting vanishing points in images. Traditional convolution…
FrameNet: Learning Local Canonical Frames of 3D Surfaces from a Single RGB Image
Jingwei Huang, Yichao Zhou, Thomas Funkhouser +1
In this work, we introduce the novel problem of identifying dense canonical 3D coordinate frames from a single RGB image. We observe that each pixel in an image corresponds to a su…
Convolutional Neural Networks on non-uniform geometrical signals using Euclidean spectral transformation
Chiyu "Max" Jiang, Dequan Wang, Jingwei Huang +2
Convolutional Neural Networks (CNN) have been successful in processing data signals that are uniformly sampled in the spatial domain (e.g., images). However, most data signals do n…
Spherical CNNs on Unstructured Grids
Chiyu "Max" Jiang, Jingwei Huang, Karthik Kashinath +3
We present an efficient convolution kernel for Convolutional Neural Networks (CNNs) on unstructured grids using parameterized differential operators while focusing on spherical sig…
Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation
He Wang, Srinath Sridhar, Jingwei Huang +3
The goal of this paper is to estimate the 6D pose and dimensions of unseen object instances in an RGB-D image. Contrary to "instance-level" 6D pose estimation tasks, our problem as…
TextureNet: Consistent Local Parametrizations for Learning from High-Resolution Signals on Meshes
Jingwei Huang, Haotian Zhang, Li Yi +3
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). The…
Robust Watertight Manifold Surface Generation Method for ShapeNet Models
Jingwei Huang, Hao Su, Leonidas Guibas
In this paper, we describe a robust algorithm for 2-Manifold generation of various kinds of ShapeNet Models. The input of our pipeline is a triangle mesh, with a set of vertices an…