  We present CIRCLE, a framework for large-scale scene completion and geometric
refinement based on local implicit signed distance functions. It is based on an
end-to-end sparse convolutional network, CircNet, that jointly models local
geometric details and global scene structural contexts, allowing it to preserve
fine-grained object detail while recovering missing regions commonly arising in
traditional 3D scene data. A novel differentiable rendering module enables
test-time refinement for better reconstruction quality. Extensive experiments
on both real-world and synthetic datasets show that our concise framework is
efficient and effective, achieving better reconstruction quality than the
closest competitor while being 10-50x faster.
