RGBD-Net: Predicting color and depth images for novel views synthesis

Phong Nguyen, Animesh Karnewar, Lam Huynh, Esa Rahtu, Jiri Matas, Janne Heikkila

We address the problem of novel view synthesis from an unstructured set of reference images. A new method called RGBD-Net is proposed to predict the depth map and the color images at the target pose in a multi-scale manner. The reference views are warped to the target pose to obtain multi-scale plane sweep volumes, which are then passed to our first module, a hierarchical depth regression network which predicts the depth map of the novel view. Second, a depth-aware generator network refines the warped novel views and renders the final target image. These two networks can be trained with or without depth supervision. In experimental evaluation, RGBD-Net not only produces novel views with higher quality than the previous state-of-the-art methods, but also the obtained depth maps enable reconstruction of more accurate 3D point clouds than the existing multi-view stereo methods. The results indicate that RGBD-Net generalizes well to previously unseen data.

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