Unsupervised segmentation of indoor 3D point cloud: application to object-based classification

Paris-Lille- 3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification. ArXiv

on the Ohio dataset show that our method retrieves 99% of the objects in the detection step, 78% of connected objects are correctly segmented, and 82% of correctly segmented ones

For this, we propose to study semi-supervised Deep Learning-based methods for training automatic IOD classifiers. These methods are a mix of supervised and unsupervised approaches

The data in Figure 5-11 show the successful implementation of a device which can measure and control the phase difference between two waveguides by using a small

Importantly, this vibration corresponds to an internal rotation about the Pt–N bond, which is in agreement with the expectation that the reaction coordinate that passes through

In this study using animal models (i.e., accelerated-catheter thrombosis, arteriove- nous shunt, and CPB with cardiac surgery), we assessed the clinical applicability of Ir-CPI as

Inspired by the necessity of synthetic plant point cloud datasets for 3D plant phenotyping applications, we propose a general approach to sample points on the surface of virtual