3D Gaussian Splatting in Robotics: A Survey (2410.12262v2)

Abstract: Dense 3D representations of the environment have been a long-term goal in the robotics field. While previous Neural Radiance Fields (NeRF) representation have been prevalent for its implicit, coordinate-based model, the recent emergence of 3D Gaussian Splatting (3DGS) has demonstrated remarkable potential in its explicit radiance field representation. By leveraging 3D Gaussian primitives for explicit scene representation and enabling differentiable rendering, 3DGS has shown significant advantages over other radiance fields in real-time rendering and photo-realistic performance, which is beneficial for robotic applications. In this survey, we provide a comprehensive understanding of 3DGS in the field of robotics. We divide our discussion of the related works into two main categories: the application of 3DGS and the advancements in 3DGS techniques. In the application section, we explore how 3DGS has been utilized in various robotics tasks from scene understanding and interaction perspectives. The advance of 3DGS section focuses on the improvements of 3DGS own properties in its adaptability and efficiency, aiming to enhance its performance in robotics. We then summarize the most commonly used datasets and evaluation metrics in robotics. Finally, we identify the challenges and limitations of current 3DGS methods and discuss the future development of 3DGS in robotics.

A Survey of 3D Gaussian Splatting in Robotics

The paper "3D Gaussian Splatting in Robotics: A Survey" offers an extensive exploration of the integration of 3D Gaussian Splatting (3DGS) within the robotics domain. The survey is aimed at researchers familiar with neural radiance fields, SLAM, and related technologies.

Overview of 3D Gaussian Splatting

3D Gaussian Splatting represents a significant development in dense 3D scene representation, leveraging explicit radiance fields for efficient rendering and photo-realistic quality. Unlike implicit methods such as NeRF, 3DGS utilizes Gaussian primitives with learnable parameters to explicitly model the environment. This promotes real-time rendering capabilities and adaptability in robotic applications, where precision and speed are of essence.

Applications in Robotics

The paper systematically categorizes applications of 3DGS into two principal domains: scene understanding and scene interaction.

1. Scene Understanding:
   • Reconstruction: 3DGS has been applied to both static and dynamic scene reconstruction in robotics. For static scenes, methods are divided into indoor and outdoor applications, each adapting 3DGS to unique challenges such as environmental scale and lighting conditions. Dynamic reconstruction tackles the complexities of scene changes over time.

• Segmentation and Editing: 3DGS supports semantic segmentation by integrating semantic information into its Gaussian representation, offering improved segmentation accuracy over multiple views. Scene editing extends to object modification and style adaptation, utilizing 3D Gaussian properties for efficient scene alteration.
• SLAM: Visual SLAM with 3DGS addresses indoor mapping using RGB-D data, while multi-sensor fusion SLAM incorporates additional sensor information for robust mapping in outdoor environments. 3DGS-based SLAM yields high-density maps, improving trajectory estimation and map accuracy.

1. Scene Interaction:
   • Manipulation: For robotic grasping tasks, 3DGS provides explicit object positions, enhancing manipulative precision without additional pose estimation layers. The survey indicates potential within multi-stage manipulations involving dynamic environments.

• Navigation: Path planning leverages 3DGS's spatial understanding to calculate optimal navigation paths. Hierarchical Gaussian structures support efficient route identification and real-time decision-making, especially in systems requiring risk-aware path planning.

Advances in 3D Gaussian Splatting

The adaptability of 3DGS is augmented by improvements in handling motion blur, large-scale environments, and data efficiency:

• Motion Blur: 3DGS approaches deblurring with both physical and implicit modeling techniques, offering robust methods to counteract the degradation caused by rapid movements.
• Large-Scale Challenges: Partitioning strategies and level-of-detail encoding enable 3DGS to scale effectively, meeting the computational demands of larger environments with high efficiency.
• Data Efficiency: Memory-efficient algorithms and few-shot learning adaptations reduce storage needs and enhance data utilization, significant for resource-constrained robotics applications.

Implications and Future Directions

The survey identifies key performance metrics for evaluating 3DGS in practice, including reconstruction accuracy, rendering quality, and tracking stability. The robust and explicit nature of 3DGS offers promising enhancements in robotics, positioning it as a versatile tool capable of improving the performance across various applications, from SLAM to real-time manipulation.

Future research should address challenges such as robust tracking under uncertain conditions, lifelong mapping in dynamic environments, and the potential for large-scale scene localization without prior data. These advancements could foster deeper integration of 3DGS within robotic systems, facilitating innovative automation capabilities.

Overall, the comprehensive survey provided in this paper lays a solid foundation for future exploration and development within the field of 3D Gaussian Splatting in robotics, forecasting its potential to play a transformative role in robotic perception and interaction.