59 research outputs found
NeFII: Inverse Rendering for Reflectance Decomposition with Near-Field Indirect Illumination
Inverse rendering methods aim to estimate geometry, materials and
illumination from multi-view RGB images. In order to achieve better
decomposition, recent approaches attempt to model indirect illuminations
reflected from different materials via Spherical Gaussians (SG), which,
however, tends to blur the high-frequency reflection details. In this paper, we
propose an end-to-end inverse rendering pipeline that decomposes materials and
illumination from multi-view images, while considering near-field indirect
illumination. In a nutshell, we introduce the Monte Carlo sampling based path
tracing and cache the indirect illumination as neural radiance, enabling a
physics-faithful and easy-to-optimize inverse rendering method. To enhance
efficiency and practicality, we leverage SG to represent the smooth environment
illuminations and apply importance sampling techniques. To supervise indirect
illuminations from unobserved directions, we develop a novel radiance
consistency constraint between implicit neural radiance and path tracing
results of unobserved rays along with the joint optimization of materials and
illuminations, thus significantly improving the decomposition performance.
Extensive experiments demonstrate that our method outperforms the
state-of-the-art on multiple synthetic and real datasets, especially in terms
of inter-reflection decomposition.Comment: Accepted in CVPR 202
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