  We present a novel framework named NeuralRecon for real-time 3D scene
reconstruction from a monocular video. Unlike previous methods that estimate
single-view depth maps separately on each key-frame and fuse them later, we
propose to directly reconstruct local surfaces represented as sparse TSDF
volumes for each video fragment sequentially by a neural network. A
learning-based TSDF fusion module based on gated recurrent units is used to
guide the network to fuse features from previous fragments. This design allows
the network to capture local smoothness prior and global shape prior of 3D
surfaces when sequentially reconstructing the surfaces, resulting in accurate,
coherent, and real-time surface reconstruction. The experiments on ScanNet and
7-Scenes datasets show that our system outperforms state-of-the-art methods in
terms of both accuracy and speed. To the best of our knowledge, this is the
first learning-based system that is able to reconstruct dense coherent 3D
geometry in real-time.
