Renderable Neural Radiance Map (RNRM): A Novel Visual Navigation Method

This paper introduces a novel visual navigation method called 'Renderable Neural Radiance Map' (RNRM). RNRM generates high-quality 3D scene reconstructions, enabling more accurate visual navigation.

RNRM is based on the radiative transfer equation to infer lighting and object surfaces within a scene. The method utilizes neural networks to learn the radiative transfer equation of a scene and generate a renderable neural radiance map. This image representation captures the scene's radiance, allowing for the generation of views from all directions.

The key to RNRM lies in mapping latent variables within the neural network to the scene's surface. These latent variables represent different directions and viewpoints within the scene. By learning these latent variables, RNRM can generate high-quality 3D scene reconstructions.

The paper also details the implementation of RNRM. This involves converting the scene into a radiative volume and employing neural networks to learn the radiative transfer equation. Subsequently, RNRM generates a renderable neural radiance map, which is then used for visual navigation.

Experimental results demonstrate that RNRM surpasses other methods in accuracy and reliability. RNRM can generate high-quality 3D scene reconstructions in complex environments, leading to more precise visual navigation.

In conclusion, this paper presents a novel visual navigation method, RNRM, which generates high-quality 3D scene reconstructions for more accurate visual navigation. This method holds significant potential for applications in robotics navigation, virtual reality, and augmented reality.