How can we generate high-quality auto-labels for self-driving LiDAR data without a human in the loop? 🚗
Check our @corl_conf paper LabelFormer, a simple, efficient, effective transformer-based model to refine object trajectories for auto-labelling.
More: https://t.co/U9e4KlTGZR
I am excited to share that our paper "Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss" has been accepted to #CVPR2023!
@TRoboAIL
Paper: https://t.co/8vI7OQXyCD
Our paper DVF "Dense Voxel Fusion for 3D Object Detection" was accepted at #WACV2023 !
DVF densely fuses image predictions and LiDAR voxel features improving expressiveness in low point density regions. @TRoboAIL
https://t.co/7oPeQ73v0u
Congrats to @Jordan_Hu on successfully finishing his DMS and graduating with a MASc! Jordan's work on Point Density-Aware LiDAR 3D Object Detection for Autonomous Vehicles was accepted to #CVPR2022: https://t.co/c72VuGBDdY.
We are happy to share that our Lab’s Director @stevewaslander has been promoted to Full Professor at @UofT !
If you’re interested in M.S., Ph.D., post-doctoral, or research associate positions, learn more about how you can get involved at TRAIL: https://t.co/pjjFKmNz1Y
Our students @Veronica_Chat and Joe have touched down in NYC for #RSS2022! Check out their spotlight talk and poster session on Tuesday about POCD: Probabilistic Object-Level Change Detection and Volumetric Mapping in Semi-Static Scenes https://t.co/zaQnC6q0q7.
@RoboticsSciSys
Come to Poster 52b at #CVPR2022 to see our MASc candidate @Jordan_Hu talk about his work! PDV is a LiDAR 3D object detection network that explicitly encodes point density variations across distance in LiDAR point clouds.
Paper: https://t.co/B0K04bu21n
Our MASc candidate @Jordan_Hu is jazzed to be at #CVPR2022 in New Orleans this week!
Watch his poster presentation on PDV, a LiDAR 3D object detection network that explicitly encodes point density variations across distance in LiDAR point clouds.
Paper: https://t.co/B0K04bu21n