A Field Model for Repairing 3D Shapes

Duc Thanh Nguyen

Binh Son Hua

Minh Khoi Tran

Quang Hieu Pham

Sai-Kit Yeung

Singapore University of Technology and Design

Proposed MRF model


This paper proposes a field model for repairing 3D shapes constructed from multi-view RGB data. Specifically, we represent a 3D shape in a Markov random field (MRF) in which the geometric information is encoded by random binary variables and the appearance information is retrieved from a set of RGB images captured at multiple viewpoints. The local priors in the MRF model capture the local structures of object shapes and are learnt from 3D shape templates using a convolutional deep belief network. Repairing a 3D shape is formulated as the maximum a posteriori MAP) estimation in the corresponding MRF. Variational mean field approximation technique is adopted for the MAP estimation. The proposed method was evaluated on both artificial data and real data obtained from reconstruction of practical scenes. Experimental results have shown the robustness and efficiency of the proposed method in repairing noisy and incomplete 3D shapes.



Sai-Kit Yeung is supported by Singapore MOE Academic Research Fund MOE2013-T2-1-159 and SUTD-MIT International Design Center Grant IDG31300106. We acknowledge the support of the SUTD Digital Manufacturing and Design (DManD) Centre which is supported by the Singapore National Research Foundation (NRF). This research is also supported by the National Research Foundation, Prime Minister’s Office, Singapore under its IDM Futures Funding Initiative.


Some samples of our dataset: Complete 3D reconstructed model (left) and RGB images captured at different viewpoints:

Some completion results of our proposed method under different levels of degradation: