51 citations · 150 across the 26 of their papers we have counts for
5 papers · 1 filter
LCD: Learned Cross-Domain Descriptors for 2D-3D Matching
Quang-Hieu Pham, Mikaela Angelina Uy, Binh-Son Hua +3
In this work, we present a novel method to learn a local cross-domain descriptor for 2D image and 3D point cloud matching. Our proposed method is a dual auto-encoder neural network…
Revisiting Point Cloud Classification: A New Benchmark Dataset and Classification Model on Real-World Data
Mikaela Angelina Uy, Quang-Hieu Pham, Binh-Son Hua +2
Deep learning techniques for point cloud data have demonstrated great potentials in solving classical problems in 3D computer vision such as 3D object classification and segmentati…
Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
Zhiyuan Zhang, Binh-Son Hua, David W. Rosen +1
Recent progresses in 3D deep learning has shown that it is possible to design special convolution operators to consume point cloud data. However, a typical drawback is that rotatio…
ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics
Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung
Deep learning with 3D data has progressed significantly since the introduction of convolutional neural networks that can handle point order ambiguity in point cloud data. While bei…
JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields
Quang-Hieu Pham, Duc Thanh Nguyen, Binh-Son Hua +2
Deep learning techniques have become the to-go models for most vision-related tasks on 2D images. However, their power has not been fully realised on several tasks in 3D space, e.g…