H2-Mapping: Real-time Dense Mapping Using Hierarchical Hybrid Representation (2306.03207v2)

Abstract: Constructing a high-quality dense map in real-time is essential for robotics, AR/VR, and digital twins applications. As Neural Radiance Field (NeRF) greatly improves the mapping performance, in this paper, we propose a NeRF-based mapping method that enables higher-quality reconstruction and real-time capability even on edge computers. Specifically, we propose a novel hierarchical hybrid representation that leverages implicit multiresolution hash encoding aided by explicit octree SDF priors, describing the scene at different levels of detail. This representation allows for fast scene geometry initialization and makes scene geometry easier to learn. Besides, we present a coverage-maximizing keyframe selection strategy to address the forgetting issue and enhance mapping quality, particularly in marginal areas. To the best of our knowledge, our method is the first to achieve high-quality NeRF-based mapping on edge computers of handheld devices and quadrotors in real-time. Experiments demonstrate that our method outperforms existing NeRF-based mapping methods in geometry accuracy, texture realism, and time consumption. The code will be released at: https://github.com/SYSU-STAR/H2-Mapping

• The paper introduces a hierarchical hybrid representation that fuses octree-based SDF with multiresolution hash encoding to balance detail and memory efficiency.
• It employs a keyframe selection strategy aiming to maximize coverage of unobserved regions, improving mapping in resource-constrained environments.
• Experiments demonstrate enhanced geometry accuracy, photorealistic rendering, and runtime efficiency across diverse platforms including edge devices.

H<sub\>2</sub>-Mapping: Real-time Dense Mapping Using Hierarchical Hybrid Representation

The paper "H<sub\>2</sub>-Mapping: Real-time Dense Mapping Using Hierarchical Hybrid Representation" offers an innovative approach to advancing dense mapping capabilities in scenarios with limited computational resources, such as edge computers used in handheld devices and quadrotors. This work is situated at the intersection of robotics, augmented reality/virtual reality (AR/VR), and digital twin technologies, where real-time, high-quality mapping is of paramount importance.

Methodology

The authors introduce a novel hierarchical hybrid representation framework that combines the strengths of different mapping techniques to overcome the limitations of existing Neural Radiance Field (NeRF)-based methods. The key components of their approach are:

1. Hierarchical Hybrid Representation: This consists of an explicit representation through an octree-based signed distance function (SDF) complemented by an implicit representation using multiresolution hash encoding. This dual system enables efficient and accurate scene reconstruction, balancing between memory efficiency and detail level.
2. Coverage-maximizing Keyframe Selection: To address the issue of memory efficiency and to enhance mapping in underrepresented areas, the paper presents a keyframe selection strategy. This strategy focuses on maximizing the coverage of unobserved spatial regions, particularly marginal areas.

Results

The proposed H<sub\>2</sub>-Mapping system shows significant improvement in various benchmarks over previous NeRF-based mapping methods. Key results include:

• Geometry Accuracy and Texture Realism: Experiments indicated superior performance in terms of depth L1 error and PSNR, leading to enhanced geometric accuracy and photorealistic rendering of scenes.
• Runtime Efficiency: The method demonstrates increased processing speed across various computational platforms, from RTX GPUs to edge devices like AGX Orin and Orin NX, allowing real-time operational capacity.

Implications and Future Prospects

The implications of this research are notable for applications that require high fidelity and real-time feedback in dynamic environments. The system's adaptability to scene changes without prior knowledge and efficient memory usage are particularly advantageous for autonomous navigation and digital twin applications. The ability to render novel views with high realism is also significant for AR/VR scenarios.

Potential future developments include addressing dynamic object tracking and long-term pose estimation, which could further expand the applicability of the proposed system. Moreover, enhancing the speed and optimizing resource allocation in the existing framework may open new possibilities for deployment across various low-power devices in diverse environmental conditions.

H<sub\>2</sub>-Mapping sets the stage for more advanced real-time mapping solutions, bridging the gap between computational efficiency and high-resolution output in embedded system contexts. This progress not only advances the field of robotics but also has substantial implications for immersive virtual technologies and intelligent environmental monitoring systems.