Hybrid Scene Representation Method Integrating Neural Radiation Fields and Visual Simultaneous Localization and Mapping
Currently,traditional explicit scene representation Simultaneous Localization And Mapping(SLAM)systems discretize the scene and are not suitable for continuous scene reconstruction.A RGB-D SLAM system based on hybrid scene representation of Neural Radiation Fields(NeRF)is proposed in this paper.The extended explicit octree Signed Distance Functions(SDF)prior is used to roughly represent the scene,and multi-resolution hash coding is used to represent the scene with different details levels,enabling fast initialization of scene geometry and making scene geometry easier to learn.In addition,the appearance color decomposition method is used to decompose the color into diffuse reflection color and specular reflection color based on the view direction to achieve reconstruction of lighting consistency,making the reconstruction result more realistic.Through experiments on the Replica and TUM RGB-D dataset,the scene reconstruction completion rate of the Replica dataset reaches 93.65%.Compared with the Vox-Fusion positioning accuracy,it leads on average by 87.50%on the Replica dataset and by 81.99%on the TUM RGB-D dataset.
Simultaneous Localization And Mapping(SLAM)systemNeural Radiation Fields(NeRF)Hybrid scene representationSpecular reflection
万方数据
维普
