We propose a method for 3D point cloud surface fitting. This method, called DeepFit, incorporates a neural network to learn point-wise weights for weighted least squares polynomial surface fitting.
This post will show you a good way to visualize normal vectors on 3D point clouds. At the beginning of my master’s degree, I was working on a project where I used normal vectors on 3D point clouds to perform
This was my first visit to IMVC which just celebrated 10 years of activity. I found it to be a very interesting combination of academia and industry work which provides a great platform for disseminating knowledge and fostering future collaborations.
Important links: [paper], [code], [video],[poster] Abstract We propose a normal estimation method for unstructured 3D point clouds. This method, called Nesti-Net, builds on a new local point cloud representation which consists of multi-scale point statistics (MuPS), estimated on a local
I recently accepted a research fellow position at the ANU node of the Australian Centre for Robotic Vision (ACRV) under the supervision of Prof. Stephen Gould (starting July 2019). Shortly after, he invited me to attend the Robotic Vision
When I started writing this post I thought it would be a chronologically ordered technical documentation of my research visit. Eventually, I found it much more coherent when divided into major events and it came out much less technical
This post aims to gather all of the useful links spread across multiple posts on the topic of 3D point cloud classification. Recently we published a paper about 3D point cloud classification using our 3D modified Fisher Vector (3DmFV) representation
Recently we published a paper on 3D point cloud classification (and segmentation) using our proposed 3D modified Fisher Vector (3DmFV) representation and convolutional neural networks (CNNs). The preprint is available on ArXiv and the final version is available in Robotics and Automation