تقرير
Multiview Based 3D Scene Understanding On Partial Point Sets
| العنوان: | Multiview Based 3D Scene Understanding On Partial Point Sets |
|---|---|
| المؤلفون: | Zhu, Ye, Shepstone, Sven Ewan, Martínez-Nuevo, Pablo, Kristoffersen, Miklas Strøm, Moutarde, Fabien, Fu, Zhuang |
| سنة النشر: | 2018 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | Deep learning within the context of point clouds has gained much research interest in recent years mostly due to the promising results that have been achieved on a number of challenging benchmarks, such as 3D shape recognition and scene semantic segmentation. In many realistic settings however, snapshots of the environment are often taken from a single view, which only contains a partial set of the scene due to the field of view restriction of commodity cameras. 3D scene semantic understanding on partial point clouds is considered as a challenging task. In this work, we propose a processing approach for 3D point cloud data based on a multiview representation of the existing 360{\deg} point clouds. By fusing the original 360{\deg} point clouds and their corresponding 3D multiview representations as input data, a neural network is able to recognize partial point sets while improving the general performance on complete point sets, resulting in an overall increase of 31.9% and 4.3% in segmentation accuracy for partial and complete scene semantic understanding, respectively. This method can also be applied in a wider 3D recognition context such as 3D part segmentation. Comment: This paper has been submitted to IEEE Transactions on Neural Networks and Learning Systems |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1812.01712 |
| رقم الأكسشن: | edsarx.1812.01712 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |