3D model retrieval using the 2D Poisson equation

3D Model Retrieval is one of the most popular topics in computer vision and huge efforts are dedicated to finding a way to improve retrieval accuracy. Defining a new efficient and effective way to describe 3D models plays a critical role in the retrieval process. In this paper we propose a view-based shape signature to search and retrieve 3D objects using the 2D Poisson equation. Our proposed method uses 60 different 2D silhouettes, which are automatically extracted from different view-angles of 3D models. Solving the Poisson equation for each Silhouette assigns a number to each pixel as the pixel's signature. Counting and accumulating these pixel signatures generates a histogram-based signature for each silhouette (Silhouette Poisson Histogram or simply SilPH). By doing some preprocessing steps one can see that the signature is insensitive to rotation, scaling and translation. The results show a high power of discrimination on the McGill dataset and demonstrate that the proposed method outperforms other existing methods.