Graph-Based Point Cloud Denoising Using Shape-Aware Consistency For Free-Viewpoint Video

We propose a novel graph-based denoising method to correct the quantization error (step noise) arising in the process of generating the visual hull, a commonly used technique to synthesize free-viewpoint video. To reduce this step noise effectively, we propose two new notions of consistency, pixel value consistency and normal vector consistency. The resulting denoising method involves a first step of graph construction using the proposed consistency metrics, followed by graph filtering of the 3D point cloud coordinates. Our experiments show that our approach provides visually and quantitatively better performance than state-of-the-art methods.