Articles by Tobias Cornille

A picture of Tobias, with glasses, short brown hair, and a light blue button-up shirt.
Tobias Cornille is a creative computer scientist with a passion for machine learning, photography, and video editing. He was the first engineer at Segments.ai, where he developed key features such as the 3D labeling suite and machine learning-powered image segmentation tools. Tobias now works as a software engineer at Monumental, where he helps automate on-site construction through robotics and software, contributing to making construction autonomous and software-defined.
Tobias holds a Master’s degree in Computer Science from KU Leuven, specializing in artificial intelligence, and has experience across various roles, including full-stack and front-end engineering. His expertise spans building intuitive tools for AI and robotics, with a focus on scalable and efficient annotation platforms.

[Guide] Label Gaussian Splats with Segments.ai

May 7th, 2024|

Gaussian splatting is a technique to render detailed 3D scenes. Instead of representing a scene as a set of meshes, a Gaussian splat represents the scene as a sort of point cloud, where each point is a 3D Gaussian. In 2023, Kerbl, Kopanas, et al. published a paper titled “3D [...]

Software Tools For Robotics Landscape (2024)

March 21st, 2024|

In today's workforce landscape, where labor shortages and aging populations pose significant challenges, automation has become an urgent necessity. Thanks to advancements in artificial intelligence, robots could form a solution for these pressing issues. With crucial components like batteries and processors becoming more affordable, the potential for widespread robotic adoption [...]

Simulating Real Cameras using Three.js

August 21st, 2023|

Ever wondered how you can simulate a physical camera in a 3D web app? In this blog post, I’ll show you how it can be done using Three.js, a popular JavaScript library for building 3D web applications, and OpenCV. We’ll start by simulating the pinhole camera model and then [...]

Improved image viewer for 3D point cloud labeling

March 27th, 2023|

At Segments.ai, we’re constantly striving to improve our platform for labeling 3D point clouds. One of the ways we’re doing this, is by enabling you to upload camera images alongside your point clouds, providing important context for labelers. To view these images, we recently shipped an improved image viewer [...]

Fast point cloud labeling with model predictions

August 4th, 2022|

Modern machine learning models can be used to detect and track objects in 3D point clouds, or to classify every single point (3D point cloud segmentation). To create such a model, you need a lot of data, i.e., labeled point clouds. Labeling point clouds manually is typically very difficult [...]

10 Lidar Datasets for Autonomous Driving

April 25th, 2022|

Lidar sensors use laser beams to capture the world in 3D. The sensors output 3D point clouds, which are simply collections of points in 3D. Machine learning models can be used to detect and track objects in these point clouds, or even to classify every single point (segmentation). This [...]

Introducing Autosegment

February 21st, 2022|

Segments.ai has always been a great tool for creating image segmentation labels; it’s in the name! Our superpixel technology allows you to segment an image in just a few clicks. However, superpixels aren’t well-suited for segmenting fine/small objects, or for segmenting very high-resolution images. That’s why we’re happy to [...]

Panoptic Segmentation: Introduction and Datasets

December 16th, 2021|

In this article, we’ll look at what panoptic segmentation is, which public datasets exist, and how you can create your own panoptic segmentation dataset. What is panoptic segmentation? Panoptic segmentation is the combination of instance segmentation and semantic segmentation. It was first introduced in a 2018 paper [...]

Create a Data-Centric ML Pipeline with W&B

December 2nd, 2021|

Most machine learning researchers focus on improving models and creating new kinds of models. However, when you’re building an ML system for real-world use, the data you use for training your model is often more important than the specifics of the model itself. That’s why a new paradigm has [...]

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