This paper surveys the literature on content-based 3D shape retrieval methods, focusing on their applicability to both surface models and volume models. The evaluation criteria include shape representation requirements, properties of dissimilarity measures, efficiency, discrimination abilities, partial matching capabilities, robustness, and the necessity of pose normalization. The paper discusses various aspects of 3D shape retrieval, such as the retrieval framework, shape representations, measuring similarity, efficiency, discriminative power, partial matching, robustness, and pose normalization. It also categorizes shape matching methods into feature-based, graph-based, and other methods, detailing their strengths and limitations. The paper concludes by identifying research issues, including the need for benchmark comparisons, efficient indexing, partial matching, and combining different shape matching methods.This paper surveys the literature on content-based 3D shape retrieval methods, focusing on their applicability to both surface models and volume models. The evaluation criteria include shape representation requirements, properties of dissimilarity measures, efficiency, discrimination abilities, partial matching capabilities, robustness, and the necessity of pose normalization. The paper discusses various aspects of 3D shape retrieval, such as the retrieval framework, shape representations, measuring similarity, efficiency, discriminative power, partial matching, robustness, and pose normalization. It also categorizes shape matching methods into feature-based, graph-based, and other methods, detailing their strengths and limitations. The paper concludes by identifying research issues, including the need for benchmark comparisons, efficient indexing, partial matching, and combining different shape matching methods.