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
this https URL

SCALAR-NeRF 是一个用于大规模神经场景重建的新框架，它采用编码器 - 解码器架构，通过处理 3D 点坐标生成编码特征，并生成包括有符号距离和颜色的几何值。通过在整个图像数据集上训练一个粗略全局模型，将图像分割成较小的块，每个块采用专用的局部模型。使用本地模型的特征空间中的全局模型解码器来提升不同块之间的重叠区域。通过有效且高效地融合这些局部模型的输出，我们的方法在大规模场景重建方面优于现有的 NeRF 方法，并展示了可扩展性。