Moreover, our framework is designed to accommodate future advancements in each branch.

In this paper we present a novel method for efficient and effective 3D surface reconstruction in open scenes.

Below are the details of each step. This allows us to input any number of images as input. This split is the same split that is used by [8]. This includes many scenes with complex architecture and backgrounds. We show additional results on the BlendedMVS dataset for 3 new objects.

Several recent attempts have been made on neural reconstruction from sparse views.

We provide optimization time ( i. e ., training time in the bracket) and inference time of our method. Our method improves the state-of-the-art results while using much fewer parameters. The surfaces are reconstructed from point clouds with low noise (a) and high noise (b). Fig 2, we show the reconstructed surfaces on point clouds with different noise levels. A partially enlarged view is provided for each shape.

Normal estimation for 3D point clouds is a fundamental task in 3D geometry processing. The state-of-the-art methods rely on priors of fitting local surfaces learned from normal supervision.