Considering this, this paper conducts a comprehensive survey of precise boundary recovery for semantic segmentation, focusing mainly on 2D images and 3D point clouds. Firstly, we formulate the problem of potential boundary recovery for semantic segmentation based on DCNNs, elaborate on the terminology ...
GitHub - VinAIResearch/MagNet: Progressive Semantic Segmentation (CVPR-2021) 一、要解决的问题(Why) 现有语义分割方法基本都是基于深度学习,假定输入的图片通过一次前向传播来得到分割结果。但这种方式由于显存和计算量的限制,难以应用在高分辨率图像上。一种简单的方式是对图像进行下采样,但毫无疑问这样会导致细节丢...
title={3D Semantic Scene Completion: a Survey}, author={Roldao, Luis and de Charette, Raoul and Verroust-Blondet, Anne}, journal={arXiv preprint arXiv:2103.07466}, year={2021} } 摘要 语义场景补全SSC旨在联合估计出一个场景完整的几何和语义信息,假设只需要部分稀疏输入数据。在过去几年,随着大规...
Rethinking BiSeNet For Real-time Semantic Segmentation 一言以蔽之:提模块,弃支路,想法和效果都能打 1.Abstract BiSeNet是实时分割领域主流的双流(two-stream)网络。然而BiSeNet增加支路去编码空间信息的做法是耗时的,而且直接使用诸如分类等预训练任务的骨干网络进行分割任务是不合理的。为了解决以上问题,我们移除了冗余...
Instance segmentation:- Instance segmentation differs from semantic segmentation in the sense that it gives a unique label to every instance of a particular object in the image. As can be seen in the image above all 3 dogs are assigned different colours i.e different labels. With semantic segme...
Recently, many semantic segmentation methods based on fully supervised learning are leading the way in the computer vision field. In particular, deep neura
Bidirectional Learning for Domain Adaptation of Semantic Segmentation 简介 具体方法 简介 语义分割在深度网络的驱使下发展迅速,但是大规模的数据标注太过昂贵。虽然,近期的工作可以通过生成式网络使计算机生成带标注的比较逼真的图像,但是这种方式生成的图像存在域不匹配的问题,即计算机生成的图像(源域)和测试时使用的图...
Abstract 基本任务:大规模点云上的语义分割 一方面,为了减少邻近点的歧义,通过充分利用双边结构中的几何和语义特征来增加它们的局部上下文。 另一方面,全面地从多个分辨率中提取点的存在性,并在点级按照自适应融合方法表示特征图,以实现精确的语义分割。 Introduction
Semantic segmentationContext modelingMulti-modal distributionsCovariance poolingRecently most of the state-of-the-art semantic segmentation methods have focused on context modeling for more accurate prediction. As real-world images often contain multiple objects and stuff, image features may have complex ...
Recently, a number of approaches based on fully convolutional networks (FCN) [14] have been proposed for semantic segmentation. Due to the fixed geometric structure and the limited receptive field, however, these methods expose a common drawback in contextual information modeling. ...