This paper presents a comprehensive survey on advancing 3D point cloud (3DPC) understanding through deep transfer learning (DTL) and domain adaptation (DA). The authors analyze the challenges in 3DPC processing, including data scarcity, distribution shifts, and computational demands, and propose DTL as a solution to improve performance. They review existing DTL frameworks for 3DPC tasks, such as segmentation, classification, and registration, and discuss their effectiveness in addressing these challenges. The paper introduces a well-defined taxonomy of DTL methods, categorizing them based on knowledge transfer strategies, data annotation, and task similarity. It also identifies current challenges in DTL for 3DPC and suggests future research directions. The authors evaluate various DTL-based models, including pre-trained 2D and 3D models, and discuss their performance on benchmark datasets. They highlight the importance of transfer learning in reducing data requirements and improving generalization across different environments. The paper also covers evaluation metrics, such as F1 score, overall accuracy (OA), and intersection over union (IoU), for assessing the performance of DTL-based 3DPC methods. The study emphasizes the potential of DTL in enhancing 3DPC understanding and its applications in various domains, including autonomous driving, robotics, and medical imaging. The authors conclude that DTL offers a promising approach for improving the efficiency and effectiveness of 3DPC processing, with significant implications for future research and development.This paper presents a comprehensive survey on advancing 3D point cloud (3DPC) understanding through deep transfer learning (DTL) and domain adaptation (DA). The authors analyze the challenges in 3DPC processing, including data scarcity, distribution shifts, and computational demands, and propose DTL as a solution to improve performance. They review existing DTL frameworks for 3DPC tasks, such as segmentation, classification, and registration, and discuss their effectiveness in addressing these challenges. The paper introduces a well-defined taxonomy of DTL methods, categorizing them based on knowledge transfer strategies, data annotation, and task similarity. It also identifies current challenges in DTL for 3DPC and suggests future research directions. The authors evaluate various DTL-based models, including pre-trained 2D and 3D models, and discuss their performance on benchmark datasets. They highlight the importance of transfer learning in reducing data requirements and improving generalization across different environments. The paper also covers evaluation metrics, such as F1 score, overall accuracy (OA), and intersection over union (IoU), for assessing the performance of DTL-based 3DPC methods. The study emphasizes the potential of DTL in enhancing 3DPC understanding and its applications in various domains, including autonomous driving, robotics, and medical imaging. The authors conclude that DTL offers a promising approach for improving the efficiency and effectiveness of 3DPC processing, with significant implications for future research and development.