Three-dimensional (3D) point cloud analysis has become one of the attractive subjects in realistic imaging and machine visions due to its simplicity, flexibility and powerful capacity of visualization. Actually, the representation of scenes and buildings using 3D shapes and formats leveraged many applications among which automatic driving, scenes and objects reconstruction, etc. Nevertheless, working with this emerging type of data has been a challenging task for objects representation, scenes recognition, segmentation, and reconstruction. In this regard, a significant effort has recently been devoted to developing novel strategies, using different techniques such as deep learning models. To that end, we present in this paper a comprehensive review of existing tasks on 3D point cloud: a well-defined taxonomy of existing techniques is performed based on the nature of the adopted algorithms, application scenarios, and main objectives. Various tasks performed on 3D point could data are investigated, including objects and scenes detection, recognition, segmentation and reconstruction. In addition, we introduce a list of used datasets, we discuss respective evaluation metrics and we compare the performance of existing solutions to better inform the state-of-the-art and identify their limitations and strengths. Lastly, we elaborate on current challenges facing the subject of technology and future trends attracting considerable interest, which could be a starting point for upcoming research studies

翻译：三维点云分析因其简洁性、灵活性及强大的可视化能力，已成为实景成像与机器视觉领域的研究热点之一。实际上，利用三维形状与格式对场景和建筑进行表征已推动众多应用的发展，包括自动驾驶、场景与物体重建等。然而，处理这种新兴数据类型在物体表征、场景识别、分割与重建中仍具有挑战性。为此，近年来大量研究致力于开发基于深度学习模型等不同技术的新型策略。基于此，本文对三维点云的现有任务进行了全面综述：根据所采用算法的性质、应用场景及主要目标，构建了清晰的技术分类体系。我们系统研究了三维点云数据上的各类任务，包括物体与场景检测、识别、分割与重建。此外，本文还列举了常用数据集，讨论了相应评估指标，并对比了现有解决方案的性能，以更全面地揭示当前技术发展水平及其优势与局限性。最后，我们阐述了该技术领域面临的主要挑战及未来发展趋势，这些内容可作为后续研究的重要参考起点。