LAUSR

Recent years have witnessed remarkable success of Graph Fourier Transform (GFT) in point cloud attribute compression. Existing researches mainly utilize geometry distance to define graph structure for coding attribute (e.g., color), which may distribute high weights to the edges connecting points across texture boundaries. In this case, these geometry-based graphs cannot model attribute differences between points adequately, thus limiting the compression efficiency of GFT. Hence, we firstly utilize attribute itself to refine the distance-based weight values by setting penalty function, which smoothens signal variations on graph and concentrates more energies in the low frequencies. Then, adjacency matrices acting as penalty function variables are transmitted to decoder with extra bit overheads. To balance the attribute smoothness on graph and the cost of coding adjacency matrices, we finally propose the graph based on Rate-Distortion (RD) optimization and find the optimal adjacency matrix. Experimental results show that our algorithm improves RD performance compared with competitive platforms. Moreover, additional experiments also analyze the gain source by evaluating the effectiveness of RD optimized graphs.