|Ph.D. Candidate, School of Interactive Computing
Georgia Institute of Technology
I am a Ph.D. student at Georgia Tech, where I have the good fortune to work with Professors James Hays and Frank Dellaert. I completed my Bachelor’s and Master’s degrees in Computer Science at Stanford University in 2018, specializing in artificial intelligence.
You can reach me at johnlambert AT gatech DOT edu. Some of my code can be found here.
- April 2021: We are pleased to announce two new Argoverse competitions – Stereo and Motion Forecasting – at the CVPR 2021 Workshop on Autonomous Driving. Challenges are open through June 13th, 2021, and feature a total of $8,000 in prizes ($2000 for each first place winner, and $1000 for honorable mentions). We’ve put together a Jupyter notebook here to get started with the Stereo data with SGM.
- November 2020: I gave an invited talk at the ROS World 2020 virtual conference, discussing our MSeg work from CVPR. A recording is available here. Our lightweight 480p MSeg model can run at 25 fps in Pytorch.
- August 2020: We are the runner-up in the 2020 Robust Vision Challenge (semantic segmentation track), without training on four of the seven RVC test datasets (zero-shot, cross-dataset generalization). See our talk at the ECCV 2020 RVC workshop here.
- June 2020: Watch an excellent presentation from MachinesCanSee on our recent MSeg work, presented by Dr. Vladlen Koltun.
- June 2020: The CVPR 2020 WAD Argoverse competitions have concluded. Congratulations to the very impressive submissions from the winners. You can watch the results presentation here, or the summary presented at ICML 2020.
- April 2020: We are pleased to announce two Argoverse Competitions at the CVPR 2020 Workshop on Autonomous Driving. Argo AI is offering $5,000 in prizes for Motion Forecasting and 3D tracking methods. I’ve open-sourced my 3d tracking code that is currently 1st place on the leaderboard. Please consider participating! The competitions will remain open until June 10, 2020.
- April 2020: Our MSeg paper has been accepted to CVPR 2020 and took first place on WildDash. Pretrained models available here, data available here, and a Colab to try our demo on your own images and videos.
Humans have an amazing ability to understand the world through their visual system but designing automated systems to perform the task continues to prove difficult. We take for granted almost everything our visual system is capable of. While great progress has been made in 2D image understanding, the real world is 3D, not 2D, so reasoning in the 2D image plane is insufficient. The 3D world is high-dimensional and challenging and has a high data requirement.
My research interests revolve around geometric and semantic understanding of 3D environments. Accurate understanding of 3D environments will have enormous benefit for people all over the world, with implications for safer transportation and safer workplaces.
Aside from research, another passion of mine is teaching. I enjoy creating teaching materials for topics related to computer vision, a field which relies heavily upon numerical optimization and statistical machine learning tools. A number of teaching modules I’ve written can be found below: