Dense interpolation of 3D points based on surface and color
A laser scan is useful in building the 3D model, and in one run it can capture thousands of 3D points. However these 3D points are sparse compared to a normal image, which can easily have millions of pixels. To achieve a denser 3D map, 3D-interpolation is applied to each pixel in the image. In this work we propose an algorithm to combine the 3D geometry and the color for 3D-interpolation. We segment the 3D points based on their latent surfaces, and combine the surfaces with color through Markov Random Field. We find that the 3D geometry provides rich information for interpolation: 3D points with similar colors can be robustly clustered where not possible in the color space, and the interpolation can be performed on a better fitting surface rather than on the locally linear ones. Our experiments show that the proposed algorithm outperforms the baselines.