This paper provides a comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation. It analyzes recent progress in deep learning methods for point cloud processing and presents challenges and potential directions to advance this field. The paper focuses on two major tasks in 3D point cloud processing: 3D shape classification and semantic segmentation. It discusses various datasets used for these tasks, including ModelNet40, ScanObjectNN, ShapeNet, S3DIS, Intra, Semantic3D, SemanticPOSS, and SydneyUrbanObjects. The paper also addresses the challenges in existing surveys and offers an immersive exploration of the latest frontiers in point cloud analysis. The primary objective is to equip readers with an extensive understanding of the diverse representations present in point clouds, with a particular emphasis on recent advancements within the field of raw point-based methodologies. The key contributions of the paper include: 1. Deep learning models for shape classification and semantic segmentation of 3D point clouds, covering the most up-to-date advancements in this domain. 2. A review that encompasses all existing methods for point cloud classification and segmentation that have not been extensively discussed before. 3. A comprehensive taxonomy that encompasses both supervised and unsupervised approaches, including previously overlooked mesh-based methods. 4. An analysis that classifies and briefly discusses the myriad models available, each leveraging distinct representations and methodologies. 5. Comprehensive comparisons of existing methods using multiple publicly available datasets and thorough explication of the inherent strengths and limitations embedded within these diverse approaches. 6. A thorough discussion of the current challenges in the field and offers insightful directions for future research. The paper's novelty is evident not only in its coverage of recent advancements but also in its meticulous attention to previously overlooked areas in the literature. The unique structure of the paper serves as a remarkable resource, catering to readers of all backgrounds—from newcomers seeking an approachable entry point to experts seeking a comprehensive taxonomy and insights into the latest deep learning methods for point cloud processing. The paper is structured as follows: Sect. 2 introduces the datasets and evaluation metrics for the respective tasks. Moving forward, Sects. 3 and 4 review the state-of-the-art methods for 3D shape classification, while Sects. 5 and 6 provide comprehensive insights into the cutting-edge methods for semantic segmentation. Section 7 contains a quantitative assessment of several indicators as well as future research directions in this field and Sect. 8 concludes the paper. The paper discusses various methods for 3D point cloud classification, including mesh-based, projection-based, volumetric-based, hybrid, and raw point-based methods. It also discusses learning strategies for point-based methods in classification, including supervised and unsupervised training. The paper presents a comprehensive comparison of these methods across various datasets, highlighting their strengths and limitations. The paper also discusses the challenges in existing surveys and offers insights into future research directions.This paper provides a comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation. It analyzes recent progress in deep learning methods for point cloud processing and presents challenges and potential directions to advance this field. The paper focuses on two major tasks in 3D point cloud processing: 3D shape classification and semantic segmentation. It discusses various datasets used for these tasks, including ModelNet40, ScanObjectNN, ShapeNet, S3DIS, Intra, Semantic3D, SemanticPOSS, and SydneyUrbanObjects. The paper also addresses the challenges in existing surveys and offers an immersive exploration of the latest frontiers in point cloud analysis. The primary objective is to equip readers with an extensive understanding of the diverse representations present in point clouds, with a particular emphasis on recent advancements within the field of raw point-based methodologies. The key contributions of the paper include: 1. Deep learning models for shape classification and semantic segmentation of 3D point clouds, covering the most up-to-date advancements in this domain. 2. A review that encompasses all existing methods for point cloud classification and segmentation that have not been extensively discussed before. 3. A comprehensive taxonomy that encompasses both supervised and unsupervised approaches, including previously overlooked mesh-based methods. 4. An analysis that classifies and briefly discusses the myriad models available, each leveraging distinct representations and methodologies. 5. Comprehensive comparisons of existing methods using multiple publicly available datasets and thorough explication of the inherent strengths and limitations embedded within these diverse approaches. 6. A thorough discussion of the current challenges in the field and offers insightful directions for future research. The paper's novelty is evident not only in its coverage of recent advancements but also in its meticulous attention to previously overlooked areas in the literature. The unique structure of the paper serves as a remarkable resource, catering to readers of all backgrounds—from newcomers seeking an approachable entry point to experts seeking a comprehensive taxonomy and insights into the latest deep learning methods for point cloud processing. The paper is structured as follows: Sect. 2 introduces the datasets and evaluation metrics for the respective tasks. Moving forward, Sects. 3 and 4 review the state-of-the-art methods for 3D shape classification, while Sects. 5 and 6 provide comprehensive insights into the cutting-edge methods for semantic segmentation. Section 7 contains a quantitative assessment of several indicators as well as future research directions in this field and Sect. 8 concludes the paper. The paper discusses various methods for 3D point cloud classification, including mesh-based, projection-based, volumetric-based, hybrid, and raw point-based methods. It also discusses learning strategies for point-based methods in classification, including supervised and unsupervised training. The paper presents a comprehensive comparison of these methods across various datasets, highlighting their strengths and limitations. The paper also discusses the challenges in existing surveys and offers insights into future research directions.