In this paper, we explore how a wide field-of-view imaging system that consists of a number of cameras in a network arranged to approximate a spherical eye can reduce the complexity of estimating camera motion. Depth map of the imaged scene can be reconstructed once the camera motion is there. We present a direct method to recover camera motion from video data, which neither requires establishment of feature correspondences nor recovery of optical flow, but from normal flow which is directly observable. With a wide visual field, the inherent ambiguities between translation and rotation disappear. Several subsets of normal flow pairs and triplets can be utilized to constraint the directions of translation and rotation separately. The intersection of solution spaces arising from normal flow pairs or triplets yields the estimate on the direction of motion. In addition, the larger number of normal flow measurements so resulted can be used to combat the local flow extraction error. Rotational magnitude is recovered in a subsequent stage. This article details how motion recovery can be improved with the use of such an approximate spherical imaging system. Experimental results on synthetic and real image data are provided. The results show that the accuracy of motion estimation is comparable to those of the state-of-the-art methods that require to use explicit feature correspondences or full optical flows, and our method has a much faster computational speed. 
Determining shape and motion from non-overlapping multi-camera rig: A direct approach using normal flows