Existing inverse rendering combined with neural rendering methods~\cite{zhang2021physg, zhang2022modeling} can only perform editable novel view synthesis on object-specific scenes, while we present intrinsic neural radiance fields, dubbed IntrinsicNeRF, which introduce intrinsic decomposition into the NeRF-based~\cite{mildenhall2020nerf} neural rendering method and can extend its application to room-scale scenes. Since intrinsic decomposition is a fundamentally under-constrained inverse problem, we propose a novel distance-aware point sampling and adaptive reflectance iterative clustering optimization method, which enables IntrinsicNeRF with traditional intrinsic decomposition constraints to be trained in an unsupervised manner, resulting in temporally consistent intrinsic decomposition results. To cope with the problem that different adjacent instances of similar reflectance in a scene are incorrectly clustered together, we further propose a hierarchical clustering method with coarse-to-fine optimization to obtain a fast hierarchical indexing representation. It supports compelling real-time augmented applications such as recoloring and illumination variation. Extensive experiments and editing samples on both object-specific/room-scale scenes and synthetic/real-word data demonstrate that we can obtain consistent intrinsic decomposition results and high-fidelity novel view synthesis even for challenging sequences. Project page: https://zju3dv.github.io/intrinsic_nerf.
翻译:由于内在分解是一个根本上不受控制的问题,因此我们建议采用一种新的远程觉察点取样和适应反射迭代群集优化方法,使具有传统内在分解限制的内生神经光亮化场,即所谓的IntrinsicNeRF, 将内在分解引入基于NeRF-基础的<unk> cite{mildenhall2020nerf}神经分解法,并可以将其应用扩大到室规模的场景。由于内在分解是一个根本上不受控制的问题,我们建议采用一种新的远程点采样和适应性反射迭代群集优化方法,从而使具有传统的内在分解性约束性限制的内生神经光化场域能够以不受监督的方式接受培训,从而产生时间上一致的内在分解结果。为了应对一个场景中相类似反射的不同情况被错误地组合在一起的问题,我们进一步建议采用等级组合法,以获得快速的分级化合成分级化组合代表制。它支持实时强化的应用,例如再加色和不清晰的合成组合组合组合组合组合组合组合组合组合组合组合,3 大规模实验和不断显示高层次的模型/缩缩缩缩缩缩缩缩模型。</s>