SDFMAP: Neural Signed Distance Fields for Mapping and Positioning in Real-Time

Abstract

Neural surface reconstruction has recently gained a bit attention due to the promising result on scene rendering. Nevertheless, most of existing approaches either treat the camera parameters as the prior during training or indirectly estimate them through structure-from-motion. To tap the potential of implicit neural networks, we present a novel end-to-end neural network, termed SDFMAP, without any prior knowledge of the scene, like pre-computed camera parameters and pretrained geometric priors. Specifically, our method adopts a single multilayer perceptron to achieve simultaneously pose estimation and indoor scene reconstruction in real-time through learning the truncated signed distance function. Comparing to the recent neural implicit vSLAM systems, our approach achieves higher tracking speed via a lightweight network. Experiments on several challenging benchmark datasets show that our SDFMAP method achieves the state-of-the-art results on camera tracking and scene reconstruction.