19 citations · 70 across the 12 of their papers we have counts for
12 papers
OffsetOPT: Explicit Surface Reconstruction without Normals
Huan Lei
Neural surface reconstruction has been dominated by implicit representations with marching cubes for explicit surface extraction. However, those methods typically require high-qual…
Level-Set Parameters: Novel Representation for 3D Shape Analysis
Huan Lei, Hongdong Li, Andreas Geiger +1
3D shape analysis has been largely focused on traditional 3D representations of point clouds and meshes, but the discrete nature of these data makes the analysis susceptible to var…
Training with Product Digital Twins for AutoRetail Checkout
Yue Yao, Xinyu Tian, Zheng Tang +4
Automating the checkout process is important in smart retail, where users effortlessly pass products by hand through a camera, triggering automatic product detection, tracking, and…
Large-scale Training Data Search for Object Re-identification
Yue Yao, Huan Lei, Tom Gedeon +1
We consider a scenario where we have access to the target domain, but cannot afford on-the-fly training data annotation, and instead would like to construct an alternative training…
CircNet: Meshing 3D Point Clouds with Circumcenter Detection
Huan Lei, Ruitao Leng, Liang Zheng +1
Reconstructing 3D point clouds into triangle meshes is a key problem in computational geometry and surface reconstruction. Point cloud triangulation solves this problem by providin…
DeePN: A deep learning-based non-Newtonian hydrodynamic model
Lidong Fang, Pei Ge, Lei Zhang +2
A long standing problem in the modeling of non-Newtonian hydrodynamics of polymeric flows is the availability of reliable and interpretable hydrodynamic models that faithfully enco…
Mesh Convolution with Continuous Filters for 3D Surface Parsing
Huan Lei, Naveed Akhtar, Mubarak Shah +1
Geometric feature learning for 3D surfaces is critical for many applications in computer graphics and 3D vision. However, deep learning currently lags in hierarchical modeling of 3…
Picasso: A CUDA-based Library for Deep Learning over 3D Meshes
Huan Lei, Naveed Akhtar, Ajmal Mian
We present Picasso, a CUDA-based library comprising novel modules for deep learning over complex real-world 3D meshes. Hierarchical neural architectures have proved effective in mu…
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
Huan Lei, Naveed Akhtar, Ajmal Mian
We propose a spherical kernel for efficient graph convolution of 3D point clouds. Our metric-based kernels systematically quantize the local 3D space to identify distinctive geomet…
Octree guided CNN with Spherical Kernels for 3D Point Clouds
Huan Lei, Naveed Akhtar, Ajmal Mian
We propose an octree guided neural network architecture and spherical convolutional kernel for machine learning from arbitrary 3D point clouds. The network architecture capitalizes…
Spherical Convolutional Neural Network for 3D Point Clouds
Huan Lei, Naveed Akhtar, Ajmal Mian
We propose a neural network for 3D point cloud processing that exploits `spherical' convolution kernels and octree partitioning of space. The proposed metric-based spherical kernel…
Color Constancy with Derivative Colors
Huan Lei, Guang Jiang, Long Quan
Information about the illuminant color is well contained in both achromatic regions and the specular components of highlight regions. In this paper, we propose a novel way to achie…