no code implementations • ICCV 2023 • Enxu Li, Sergio Casas, Raquel Urtasun
To address this challenge, we propose a novel framework for semantic segmentation of a temporal sequence of LiDAR point clouds that utilizes a memory network to store, update and retrieve past information.
no code implementations • 2 Nov 2023 • Ali Athar, Enxu Li, Sergio Casas, Raquel Urtasun
4D panoptic segmentation is a challenging but practically useful task that requires every point in a LiDAR point-cloud sequence to be assigned a semantic class label, and individual objects to be segmented and tracked over time.
Ranked #1 on Panoptic Tracking on Panoptic nuScenes val
1 code implementation • 2 Mar 2023 • Chengnan Shentu, Enxu Li, Chaojun Chen, Puspita Triana Dewi, David B. Lindell, Jessica Burgner-Kahrs
A two-segment tendon-driven continuum robot is used for data collection and testing, demonstrating accurate (mean shape error of 0. 91 mm, or 0. 36% of robot length) and real-time (70 fps) shape sensing on real-world data.
no code implementations • 2 Nov 2021 • Enxu Li, Ryan Razani, YiXuan Xu, Bingbing Liu
A fast and accurate panoptic segmentation system for LiDAR point clouds is crucial for autonomous driving vehicles to understand the surrounding objects and scenes.
no code implementations • 31 Aug 2021 • Enxu Li, Ryan Razani, YiXuan Xu, Liu Bingbing
Thus, we propose to use a novel centroid-aware repel loss as an additional term to effectively supervise the network to differentiate each object cluster with its neighbours.
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 • 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