综述动物3D形貌与运动重建的最新技术进展。
Advances and Trends in the 3D Reconstruction of the Shape and Motion of Animals
- 基于深度学习从图像/视频非侵入式重建动物3D姿态与动作
- 系统梳理了不同输入模态与表示方法的性能表现
- 适合关注生物研究、VR/AR内容生成的读者
动物3D形貌与运动重建是长期存在的难题，广泛应用于生物学、畜牧管理、动物保护与福利，以及数字娱乐和虚拟/增强现实（VR/AR）。传统方法依赖3D扫描仪，但存在侵入性强、成本高、难部署于自然环境等问题。近年来，基于深度学习的方法显著发展，仅需RGB图像或视频即可实现非侵入式3D重建。本文综述该新兴领域的最新进展，按输入模态、3D几何与运动表示方式、重建技术及训练机制分类讨论前沿方法，分析关键模型性能，探讨其优缺点，并指出当前挑战与未来研究方向。
原文摘要 · Abstract (English)
Reconstructing the 3D geometry, pose, and motion of animals is a long-standing problem, which has a wide range of applications, from biology, livestock management, and animal conservation and welfare to content creation in digital entertainment and Virtual/Augmented Reality (VR/AR). Traditionally, 3D models of real animals are obtained using 3D scanners. These, however, are intrusive, often prohibitively expensive, and difficult to deploy in the natural environment of the animals. In recent years, we have seen a significant surge in deep learning-based techniques that enable the 3D reconstruction, in a non-intrusive manner, of the shape and motion of dynamic objects just from their RGB image and/or video observations. Several papers have explored their application and extension to various types of animals. This paper surveys the latest developments in this emerging and growing field of research. It categorizes and discusses the state-of-the-art methods based on their input modalities, the way the 3D geometry and motion of animals are represented, the type of reconstruction techniques they use, and the training mechanisms they adopt. It also analyzes the performance of some key methods, discusses their strengths and limitations, and identifies current challenges and directions for future research.
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