Risk assessment for RGBD scans in real time

In this paper we address the notion of risk assessment of three dimensional scenes. Furthermore through the use of local feature recognition techniques and machine learning we perform this analysis on real time point cloud recordings. We provide a definition of risk and potential hazards that incorporates different elements but mainly focuses on intrinsic risk related properties of an object (e.g sharpness). A 3D Voxel HOG descriptor is utilised that aims to classify and recognise the presence of hazardous characteristics and features of objects present in a given scene. Additionally we utilise and extend the 3D Risk Scenes Dataset (3DRS) designed for risk evaluation in scene analysis. The effectiveness of our method is tested on captured point cloud sequences containing hazardous and non hazardous data with a high degree of accuracy across all tested data.