Object detection is one of the areas of computer vision that has matured very rapidly. Nowadays, developments in this research area have been playing special attention to the detection of objects in point clouds due to the emerging of high-resolution LiDAR sensors. However, data from from a Light Detection and Ranging (LiDAR) sensor is not characterised by having consistency in relative pixel densities and introduces a third dimension, raising a set of drawbacks. The following paper presents a study on the requirements of 3D object detection for autonomous vehicles; presents an overview of the 3D object detection pipeline that generalises the operation principle of models based on point clouds; and categorises the recent works on methods to extract features and summarise their performance.

In increasingly demanding environments, organizations need to manage their human resources effectively, as these are fundamental to their success. Therefore, it is important to improve the performance of these resources by conducting systematic evaluations and by collecting information on their productivity, training needs, and individual and team performance. In the context of Information Technologies/Information Systems (IT/IS) projects, there is not much research work focused on human resources performance evaluation. This paper aims to fill this gap by reviewing approaches and methods for performance appraisal, which can be successfully applied in IT/IS projects. Are presented approaches and methods focused on: personality; behaviors; comparison; and outcomes/results.