Core Viewpoint - The article discusses the rapid development of robotics and the need for robots to understand the world similarly to humans, focusing on various scene representation methods in robotics [2][4]. Group 1: Historical Development of 3D Scene Representation - The integration of deep learning, computer graphics, and robotics has led to significant advancements, with Neural Radiance Fields (NeRF), 3D Gaussian Splatting, and Foundation Models emerging as promising innovations for achieving general embodied intelligence [8]. Group 2: Types of Scene Representation - Point Cloud: Represents scenes using discrete 3D points obtained from radar or camera sensors [10]. - Voxel: Discretizes 3D space into regular cubic grids, storing various information like density and occupancy [10]. - Mesh: Constructs continuous geometric representations of scenes through triangulated surfaces, offering higher detail [10]. - Signed Distance Function (SDF): Represents the distance from spatial points to object surfaces for continuous geometric representation [10]. Group 3: Applications in Robotics - In mapping and localization, existing methods have achieved remarkable results in SLAM, with neural scene representations enabling more precise and dense modeling, beneficial for obstacle avoidance [15]. - In the operation module, traditional methods excel in real-time performance and computational efficiency for grasping tasks, while neural network-based representations show better generalization capabilities for complex tasks [15]. - Navigation tasks benefit from neural scene representations, which provide accurate environmental reconstruction and better integration of semantic and language information for complex navigation tasks [16]. Group 4: Challenges and Future Directions - The article identifies three main challenges: 1. The need for end-to-end general networks versus modular systems, highlighting the limitations of modular intelligence in terms of generalization and transferability [19]. 2. Data scarcity in robotics compared to large language models, which hinders the development of neural scene representations and foundation models [20]. 3. Real-time performance bottlenecks in deploying neural scene representations, with a focus on cloud-based versus onboard deployment strategies [21]. Group 5: Contributions and Resources - The article provides a comprehensive and up-to-date review of various scene representation methods in robotics, detailing the advantages of different representations for each module [22]. - It highlights future research directions to address current technical limitations and encourages further advancements in this rapidly evolving field [22]. - An open-source project on GitHub has been launched to compile relevant articles and continue adding new research findings in the field of robotics [22].

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