论文标题
栖息地 - 摩托车3D语义数据集
Habitat-Matterport 3D Semantics Dataset
论文作者
论文摘要
我们介绍了栖息地 - 模式3D语义（HM3SEM）数据集。 HM3DSEM是最大的3D现实空间数据集，其目前可供学术界使用密集的注释语义。它由216个3D空间的142,646个对象实例注释和这些空间内的3,100个房间组成。对象注释的规模，质量和多样性远远超过了先前数据集的规模。与其他数据集相距HM3DSEM的关键差异设置是使用纹理信息来注释像素精确的对象边界。我们使用不同的方法演示了HM3DSEM数据集在对象目标导航任务中的有效性。使用HM3DSEM训练的策略表现优于在先前数据集上训练的策略。 HM3DSEM在“栖息地” objectnav挑战中的引入导致参与的增加，从2021年的400份提交到2022年的1022次提交。
We present the Habitat-Matterport 3D Semantics (HM3DSEM) dataset. HM3DSEM is the largest dataset of 3D real-world spaces with densely annotated semantics that is currently available to the academic community. It consists of 142,646 object instance annotations across 216 3D spaces and 3,100 rooms within those spaces. The scale, quality, and diversity of object annotations far exceed those of prior datasets. A key difference setting apart HM3DSEM from other datasets is the use of texture information to annotate pixel-accurate object boundaries. We demonstrate the effectiveness of HM3DSEM dataset for the Object Goal Navigation task using different methods. Policies trained using HM3DSEM perform outperform those trained on prior datasets. Introduction of HM3DSEM in the Habitat ObjectNav Challenge lead to an increase in participation from 400 submissions in 2021 to 1022 submissions in 2022.
