1 code implementation • ICCV 2023 • Ziyu Li, Jingming Guo, Tongtong Cao, Liu Bingbing, Wankou Yang
LiDAR-based 3D detection has made great progress in recent years.
no code implementations • 14 Jan 2022 • Eduardo R. Corral-Soto, Mrigank Rochan, Yannis Y. He, Shubhra Aich, Yang Liu, Liu Bingbing
We consider the setting where we have a fully-labeled data set from source domain and a target domain with a few labeled and many unlabeled examples.
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 • 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 • 15 Mar 2021 • Ran Cheng, Ryan Razani, Yuan Ren, Liu Bingbing
In literature, several approaches are introduced to attempt LiDAR semantic segmentation task, such as projection-based (range-view or birds-eye-view), and voxel-based approaches.
no code implementations • 16 Dec 2020 • Ran Cheng, Christopher Agia, Yuan Ren, Xinhai Li, Liu Bingbing
With the increasing reliance of self-driving and similar robotic systems on robust 3D vision, the processing of LiDAR scans with deep convolutional neural networks has become a trend in academia and industry alike.
Ranked #2 on 3D Semantic Scene Completion on SemanticKITTI