(AF) 2 -S3Net: Attentive Feature Fusion with Adaptive Feature Selection for Sparse Semantic Segmentation Network
Ran Cheng,Ryan Razani,Ehsan Taghavi,Enxu Li,Bingbing Liu +4 more
- 20 Jun 2021
- pp 12547-12556
TL;DR: In this paper, an end-to-end encoder-decoder CNN network for 3D LiDAR semantic segmentation is proposed, where a multibranch attentive feature fusion module in the encoder and a unique adaptive feature selection module with feature map re-weighting in the decoder are introduced.
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Abstract: 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. Semantic segmentation is one of the essential components of road scene perception that provides semantic information of the surrounding environment. Recently, several methods have been introduced for 3D LiDAR semantic segmentation. While they can lead to improved performance, they are either afflicted by high computational complexity, therefore are inefficient, or they lack fine details of smaller object instances. To alleviate these problems, we propose (AF)2-S3Net, an end-to-end encoder-decoder CNN network for 3D LiDAR semantic segmentation. We present a novel multibranch attentive feature fusion module in the encoder and a unique adaptive feature selection module with feature map re-weighting in the decoder. Our (AF)2-S3Net fuses the voxel-based learning and point-based learning methods into a unified framework to effectively process the potentially large 3D scene. Our experimental results show that the proposed method outperforms the state-of-the-art approaches on the large-scale nuScenes-lidarseg and SemanticKITTI benchmark, ranking 1st on both competitive public leaderboard competitions upon publication.
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Citations
CPGNet: Cascade Point-Grid Fusion Network for Real-Time LiDAR Semantic Segmentation
23 May 2022
TL;DR: Cascade Point-Grid Fusion Network (CPGNet) as discussed by the authors proposes a point-grid fusion block to extract semantic features mainly on the 2D projected grid for efficiency, while summarizes both 2D and 3D features on 3D point for minimal information loss.
PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation
Yuqi Wang,Yuntao Chen,Xingyu Liao,Lue Fan,Zhaoxiang Zhang +4 more
- 16 Jun 2024
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GRAB-Net: Graph-based Boundary-aware Network for Medical Point Cloud Segmentation
01 Jan 2023
TL;DR: Zhang et al. as discussed by the authors proposed a graph-based boundary-aware network (GRAB-Net) with three paradigms, Graph-based Boundary-perception Module (GBM), Outer-boundary Context-assignment Module (OCM), and Inner-Boundary Feature Rectification Module (IFM), for medical point cloud segmentation.
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Spatiotemporal Self-Supervised Learning for Point Clouds in the Wild
Yanhao Wu,Tong Zhang,Wei Ke,Sabine Süsstrunk,Mathieu Salzmann +4 more
- 01 Jun 2023
TL;DR: Spatiotemporal self-supervised learning (STSSL) for point clouds in the wild achieves superior performance by leveraging positive pairs in both spatial and temporal domains.
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Object Insertion Based Data Augmentation for Semantic Segmentation
Yuan Zeng Ren,Siyan Zhao,Bingbing Liu +2 more
- 23 May 2022
TL;DR: An object insertion based data augmentation method is proposed which can increase the performance of the semantic segmentation network remarkably and an object library is created by using the labeled LiDAR point clouds.
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