论文标题
CGIS网络：室内识别的汇总颜色，几何形状和隐性语义特征
CGiS-Net: Aggregating Colour, Geometry and Implicit Semantic Features for Indoor Place Recognition
论文作者
论文摘要
我们描述了一种新的方法，可以基于具有高级隐式语义特征的低级颜色和几何特征，从RGB点云中识别室内识别。它使用了一个2阶段的深度学习框架，其中的第一阶段是针对语义分割的辅助任务进行训练的，第二阶段的第二阶段使用了第一阶段的图层中的特征来生成区分描述符以进行位置识别。辅助任务鼓励具有语义上有意义的功能，因此使用隐式语义信息汇总了RGB点云数据中的几何和颜色。我们使用从扫描仪数据集派生的室内位置识别数据集进行培训和评估，其中一个包括从100个不同房间生成的3,608点云的测试集。与传统的基于特征的方法和四种最先进的深度学习方法进行比较表明，我们的方法显着优于所有五种方法，例如，在最接近的竞争对手方法中，取得了前3名平均召回率为75％，而41％的平均召回率为41％。我们的代码可在以下网址找到：https：//github.com/yuhangming/semantic-indoor-place-recognition
We describe a novel approach to indoor place recognition from RGB point clouds based on aggregating low-level colour and geometry features with high-level implicit semantic features. It uses a 2-stage deep learning framework, in which the first stage is trained for the auxiliary task of semantic segmentation and the second stage uses features from layers in the first stage to generate discriminate descriptors for place recognition. The auxiliary task encourages the features to be semantically meaningful, hence aggregating the geometry and colour in the RGB point cloud data with implicit semantic information. We use an indoor place recognition dataset derived from the ScanNet dataset for training and evaluation, with a test set comprising 3,608 point clouds generated from 100 different rooms. Comparison with a traditional feature-based method and four state-of-the-art deep learning methods demonstrate that our approach significantly outperforms all five methods, achieving, for example, a top-3 average recall rate of 75% compared with 41% for the closest rival method. Our code is available at: https://github.com/YuhangMing/Semantic-Indoor-Place-Recognition
