Search Results for author:
Found 12 papers, 2 papers with code
Semi-Supervised Representative Region Texture Extraction of Façade
Our method can extract texture from any repetitive images, not just fa\c{c}ade, which is not capable in an end-to-end model as it relies on the distribution of training set.
Multi-scale Network with Attentional Multi-resolution Fusion for Point Cloud Semantic Segmentation
In this paper, we present a comprehensive point cloud semantic segmentation network that aggregates both local and global multi-scale information.
A Region-Based Deep Learning Approach to Automated Retail Checkout
Automating the product checkout process at conventional retail stores is a task poised to have large impacts on society generally speaking.
OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion
In this paper, we propose a 360 monocular depth estimation pipeline, OmniFusion, to tackle the spherical distortion issue.
Ranked #6 on
Depth Estimation
on Stanford2D3D Panoramic
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation
In this paper, we propose a novel, model-agnostic, two-stage pipeline for omnidirectional monocular depth estimation.
Ranked #13 on
Depth Estimation
on Stanford2D3D Panoramic
MedNet: Pre-trained Convolutional Neural Network Model for the Medical Imaging Tasks
Nowadays, multiple classification methods from medical imaging make use of TL from general-purpose pre-trained models, e. g., ImageNet, which has been proven to be ineffective due to the mismatch between the features learned from natural images (ImageNet) and those more specific from medical images especially medical gray images such as X-rays.
Fast Point Voxel Convolution Neural Network with Selective Feature Fusion for Point Cloud Semantic Segmentation
Each layer has two parallel branches, namely the voxel branch and the point branch.
SPNet: Multi-Shell Kernel Convolution for Point Cloud Semantic Segmentation
Experimental results show that SPConv is effective in local shape encoding, and our SPNet is able to achieve top-ranking performances in semantic segmentation tasks.
RED-NET: A Recursive Encoder-Decoder Network for Edge Detection
In this paper, we introduce RED-NET: A Recursive Encoder-Decoder Network with Skip-Connections for edge detection in natural images.
A Fast, Semi-Automatic Brain Structure Segmentation Algorithm for Magnetic Resonance Imaging
Medical image segmentation has become an essential technique in clinical and research-oriented applications.
Web Based Brain Volume Calculation for Magnetic Resonance Images
Brain volume calculations are crucial in modern medical research, especially in the study of neurodevelopmental disorders.
PointGrid: A Deep Network for 3D Shape Understanding
This paper presents a new deep learning architecture called PointGrid that is designed for 3D model recognition from unorganized point clouds.
Ranked #22 on
3D Part Segmentation
on ShapeNet-Part
