no code implementations • ICCV 2021 • Ryan Razani, Ran Cheng, Enxu Li, Ehsan Taghavi, Yuan Ren, Liu Bingbing
GP-S3Net is a proposal-free approach in which no object proposals are needed to identify the objects in contrast to conventional two-stage panoptic systems, where a detection network is incorporated for capturing instance information.
no code implementations • 16 Mar 2021 • Ryan Razani, Ran Cheng, Ehsan Taghavi, Liu Bingbing
Autonomous driving vehicles and robotic systems rely on accurate perception of their surroundings.
no code implementations • 8 Feb 2021 • Ran Cheng, Ryan Razani, Ehsan Taghavi, Enxu Li, Bingbing Liu
Autonomous robotic systems and self driving cars rely on accurate perception of their surroundings as the safety of the passengers and pedestrians is the top priority.
Ranked #3 on 3D Semantic Segmentation on nuScenes
no code implementations • 24 Aug 2020 • Martin Gerdzhev, Ryan Razani, Ehsan Taghavi, Bingbing Liu
Semantic segmentation of point clouds is a key component of scene understanding for robotics and autonomous driving.
Ranked #15 on 3D Semantic Segmentation on SemanticKITTI
no code implementations • CVPR 2020 • Ehsan Nezhadarya, Ehsan Taghavi, Ryan Razani, Bingbing Liu, Jun Luo
While several convolution-like operators have recently been proposed for extracting features out of point clouds, down-sampling an unordered point cloud in a deep neural network has not been rigorously studied.