Our goal is to synthesize a novel view I t , given target camera parameters K t , R t , T t , from a set of input images, I s i , i = 1, 2, ..., N . We assume there is sufficient overlap between the source views such that correspondences can be established. We estimate source view camera intrinsic and extrinsic by a well-established structure-from-motion (SfM) pipeline, e.g.COLMAP [26]. Fig. 2 illustrates the situation. Mathematically, we formulate this problem as:
I t * = argmax I t p I t |I s 1 , I s 2 , ..., I s N ,(1)
where p(·) is a probability function. Due to the expensive accessibility of 3D **data** (e.g., depths) and a limited number of input views, it is hard to compute accurate 3D geometry from input source views. Therefore, our intuition is to develop an end-to-end framework that combines geometry estimation and image synthesis, to eliminate the error propagation issue. We achieve this goal by estimating target-view depth and source-view visibility for target pixels directly under the target view.