We current a novel differentiable rendering framework for joint geometry, materials, and lighting estimation from multi-view pictures. In distinction to earlier strategies which assume a simplified surroundings map or co-located flashlights, on this work, we formulate the lighting of a static scene as one neural incident mild discipline (NeILF) and one outgoing neural radiance discipline (NeRF). The important thing perception of the proposed methodology is the union of the incident and outgoing mild fields by physically-based rendering and inter-reflections between surfaces, making it attainable to disentangle the scene geometry, materials, and lighting from picture observations in a physically-based method. The proposed incident mild and inter-reflection framework may be simply utilized to different NeRF programs. We present that our methodology cannot solely decompose the outgoing radiance into incident lights and floor supplies, but in addition function a floor refinement module that additional improves the reconstruction element of the neural floor. We show on a number of datasets that the proposed methodology is ready to obtain state-of-the-art outcomes by way of the geometry reconstruction high quality, materials estimation accuracy, and the constancy of novel view rendering.