  We propose VQ-NeRF, a two-branch neural network model that incorporates
Vector Quantization (VQ) to decompose and edit reflectance fields in 3D scenes.
Conventional neural reflectance fields use only continuous representations to
model 3D scenes, despite the fact that objects are typically composed of
discrete materials in reality. This lack of discretization can result in noisy
material decomposition and complicated material editing. To address these
limitations, our model consists of a continuous branch and a discrete branch.
The continuous branch follows the conventional pipeline to predict decomposed
materials, while the discrete branch uses the VQ mechanism to quantize
continuous materials into individual ones. By discretizing the materials, our
model can reduce noise in the decomposition process and generate a segmentation
map of discrete materials. Specific materials can be easily selected for
further editing by clicking on the corresponding area of the segmentation
outcomes. Additionally, we propose a dropout-based VQ codeword ranking strategy
to predict the number of materials in a scene, which reduces redundancy in the
material segmentation process. To improve usability, we also develop an
interactive interface to further assist material editing. We evaluate our model
on both computer-generated and real-world scenes, demonstrating its superior
performance. To the best of our knowledge, our model is the first to enable
discrete material editing in 3D scenes.
