{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/pointseg-real-time-semantic-segmentation","title":"PointSeg: Real-Time Semantic Segmentation Based on 3D LiDAR Point Cloud","arxiv_id":"1807.06288","date":"2018-07-17","proceeding":null,"authors":["Yu-An Wang","Tianyue Shi","Peng Yun","Lei Tai","Ming Liu"],"abstract":"In this paper, we propose PointSeg, a real-time end-to-end semantic\nsegmentation method for road-objects based on spherical images. We take the\nspherical image, which is transformed from the 3D LiDAR point clouds, as input\nof the convolutional neural networks (CNNs) to predict the point-wise semantic\nmap. To make PointSeg applicable on a mobile system, we build the model based\non the light-weight network, SqueezeNet, with several improvements. It\nmaintains a good balance between memory cost and prediction performance. Our\nmodel is trained on spherical images and label masks projected from the KITTI\n3D object detection dataset. Experiments show that PointSeg can achieve\ncompetitive accuracy with 90fps on a single GPU 1080ti. which makes it quite\ncompatible for autonomous driving applications.","url_abs":"http://arxiv.org/abs/1807.06288v8","url_pdf":"http://arxiv.org/pdf/1807.06288v8.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"pointseg-real-time-semantic-segmentation","repo_url":"https://github.com/ywangeq/PointSeg","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"tf","reach":null},{"paper_slug":"pointseg-real-time-semantic-segmentation","repo_url":"https://github.com/ArashJavan/PointSeg","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"pointseg-real-time-semantic-segmentation","repo_url":"https://github.com/arashjavan/deeplio","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"3d-object-detection","task_name":"3D Object Detection"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":null,"task_name":"GPU"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"real-time-semantic-segmentation","task_name":"Real-Time Semantic Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"fire-module","method_name":"Fire Module"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"},{"method_slug":"squeezenet","method_name":"SqueezeNet"},{"method_slug":"xavier-initialization","method_name":"Xavier Initialization"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.06288","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}