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Found 14 papers, 7 papers with code
Consensus-based construction of high-dimensional free energy surface
One essential problem in quantifying the collective behaviors of molecular systems lies in the accurate construction of free energy surfaces (FESs).
Training with Product Digital Twins for AutoRetail Checkout
Because the digital twins individually mimic user bias, the resulting DT training set better reflects the characteristics of the target scenario and allows us to train more effective product detection and tracking models.
Large-scale Training Data Search for Object Re-identification
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 set from a large-scale data pool such that a competitive model can be obtained.
CircNet: Meshing 3D Point Clouds with Circumcenter Detection
In this paper, we leverage the duality between a triangle and its circumcenter, and introduce a deep neural network that detects the circumcenters to achieve point cloud triangulation.
DeePN$^2$: A deep learning-based non-Newtonian hydrodynamic model
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 encode the underlying micro-scale polymer dynamics.
Mesh Convolution with Continuous Filters for 3D Surface Parsing
In this paper, we propose a series of modular operations for effective geometric feature learning from 3D triangle meshes.
Picasso: A CUDA-based Library for Deep Learning over 3D Meshes
We present Picasso, a CUDA-based library comprising novel modules for deep learning over complex real-world 3D meshes.
Potential Escalator-related Injury Identification and Prevention Based on Multi-module Integrated System for Public Health
Escalator-related injuries threaten public health with the widespread use of escalators.
SegGCN: Efficient 3D Point Cloud Segmentation With Fuzzy Spherical Kernel
Our second major contribution comes as the proposal of an efficient graph convolutional network, SegGCN for segmenting point clouds.
Machine learning based non-Newtonian fluid model with molecular fidelity
We introduce a machine-learning-based framework for constructing continuum non-Newtonian fluid dynamics model directly from a micro-scale description.
Spherical Kernel for Efficient Graph Convolution on 3D Point Clouds
We propose a spherical kernel for efficient graph convolution of 3D point clouds.
Ranked #5 on
3D Object Classification
on ModelNet40
Octree guided CNN with Spherical Kernels for 3D Point Clouds
We propose an octree guided neural network architecture and spherical convolutional kernel for machine learning from arbitrary 3D point clouds.
Ranked #10 on
3D Part Segmentation
on ShapeNet-Part
Spherical Convolutional Neural Network for 3D Point Clouds
We propose a neural network for 3D point cloud processing that exploits `spherical' convolution kernels and octree partitioning of space.
Color Constancy with Derivative Colors
While extracting derivative colors from achromatic regions to approximate the illuminant color well is basically straightforward, the success of our extraction in highlight regions is attributed to the different rates of variation of the diffuse and specular magnitudes in the dichromatic reflection model.
