Indoor Image Surface Normal Estimation Using Convolutional Neural Network

Abstract

A surface normal vector is related to material of object, shape of geometry, re- flection of light. To estimate other features, the surface normal vector is necessary. Therefore, the surface normal estimation problem is one of the most important topic in inverse graphics. Previously, computing surface normal image from single RGB-D image has been stud- ied since estimating surface normal vector from single RGB image is extremely ill- posed problem. Recently, deep learning based approach has been presented for the task. However, it is not well-known that which deep learning model is efficiently solve the problem. Also there are limitations in dataset computed from real RGB-D images. In this thesis, first we describes the problems in previous works

the dataset. Sec- ond, we suggest a way to overcome the limitation of previous work

synthetic dataset. Lastly, we test several neural net model with set of experiments to find the most proper model. After that we train the neural network model with synthetic dataset. The models prediction of surface normal image looks plausible. Numerically the trained model achieve lower error compared to previous works.