Robust View Synthesis In Wide-Baseline Complex Geometric Environments

One of the most challenging problems of novel view synthesis is to predict the scene in complex geometric environments. Existing methods depend on either homography optimization or deep neural networks. In this paper, we provide a framework of view synthesis, which includes grid-based warp, depth refinement and ghost artifacts removal. The depth refinement method is our main contribution, which can be combined with any other warp operation to generate high quality images. To achieve it, the depth refinement method is combined with a shape-preserving warp operation together based on reliable, half-reliable and unreliable superpixel dis-crimination. We remove outliers in half-reliable superpixels by considering their neighboring reliable superpixels and distinguish half-reliable ones into reliable and unreliable parts. This step helps us to get more accurate depth information. Experimental results show that our view synthesis system has nearly 0.7dB gains in PSNR and 0.03 gains in SSIM compared with the state-of-the-art view synthesis algorithm.