The story behind the “IKEA Assembly dataset” paper
The story behind the creation of the IKEA assembly Dataset. The tale of 4 postdocs, a major screw-up, and how they emerged victoriously.
RESEARCH BLOG
Surface fitting for 3D Point Cloud : DeepFit
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.
How to stand out (in academia) during COVID-19?
I am now in the middle of my first year of post-doc at ANU. COVID-19 has changed the world as we knew it. Even more so the academic world – no classes, no conferences and no research (for some. luckily,
CVPR 2019
About three months ago, I hit one of my first career goals – have a paper accepted to CVPR (achievement unlocked ! ). Last week I had the privilege to present a poster of our work on normal estimation for
How to Visualize Normal Vectors on 3D Point Clouds
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
Israel Machine Vision Conference (IMVC) 2019
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.
Nesti-Net: Normal estimation for 3D Point Clouds
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
Robotic Vision Summer School (RVSS) 2019
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
Reasearch visit in Germany – Prof. Matthias Niessner’s Visual Computing Group (TUM)
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
3D point cloud classification – a summary of useful links
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