SCALAR-NeRF: SCAlable LARge-scale Neural Radiance Fields for Scene
  Reconstruction
Yu Chen Gim Hee Lee
In this work, we introduce SCALAR-NeRF, a novel framework tailored for
scalable large-scale neural scene reconstruction. We structure the neural
representation as an encoder-decoder architecture, where the encoder processes
3D point coordinates to produce encoded features, and the decoder generates
geometric values that include volume densities of signed distances and colors.
Our approach first trains a coarse global model on the entire image dataset.
Subsequently, we partition the images into smaller blocks using KMeans with
each block being modeled by a dedicated local model. We enhance the overlapping
regions across different blocks by scaling up the bounding boxes of each local
block. Notably, the decoder from the global model is shared across distinct
blocks and therefore promoting alignment in the feature space of local
encoders. We propose an effective and efficient methodology to fuse the outputs
from these local models to attain the final reconstruction. Employing this
refined coarse-to-fine strategy, our method outperforms state-of-the-art NeRF
methods and demonstrates scalability for large-scale scene reconstruction. The
code will be available on our project page at
https://aibluefisher.github.io/SCALAR-NeRF/