  We present a novel 3D mapping method leveraging the recent progress in neural
implicit representation for 3D reconstruction. Most existing state-of-the-art
neural implicit representation methods are limited to object-level
reconstructions and can not incrementally perform updates given new data. In
this work, we propose a fusion strategy and training pipeline to incrementally
build and update neural implicit representations that enable the reconstruction
of large scenes from sequential partial observations. By representing an
arbitrarily sized scene as a grid of latent codes and performing updates
directly in latent space, we show that incrementally built occupancy maps can
be obtained in real-time even on a CPU. Compared to traditional approaches such
as Truncated Signed Distance Fields (TSDFs), our map representation is
significantly more robust in yielding a better scene completeness given noisy
inputs. We demonstrate the performance of our approach in thorough experimental
validation on real-world datasets with varying degrees of added pose noise.
