S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Electrical and Computer Engineering (전기·정보공학부) Theses (Master's Degree_전기·정보공학부)
Indoor Image Surface Normal Estimation Using Convolutional Neural Network : 합성곱 신경망을 이용한 실내 이미지에서의 표면 노말 추정
- 공과대학 전기·정보공학부
- Issue Date
- 서울대학교 대학원
- 학위논문 (석사)-- 서울대학교 대학원 : 공과대학 전기·정보공학부, 2018. 2. 고형석.
- 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
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.