Search Results for author: Liu Bingbing

Found 7 papers, 1 papers with code

SMAC-Seg: LiDAR Panoptic Segmentation via Sparse Multi-directional Attention Clustering

no code implementations31 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.

Autonomous Driving Clustering +4

GP-S3Net: Graph-based Panoptic Sparse Semantic Segmentation Network

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.

Panoptic Segmentation Segmentation

S3Net: 3D LiDAR Sparse Semantic Segmentation Network

no code implementations15 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.

Autonomous Driving LIDAR Semantic Segmentation +2

S3CNet: A Sparse Semantic Scene Completion Network for LiDAR Point Clouds

no code implementations16 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.

3D Semantic Scene Completion Segmentation +1

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