Light fields (LFs) are image-based representation that records the radiance along all rays along every direction through every point in space. Traditionally LFs are acquired by using a 2D grid of evenly spaced pinhole cameras or by translating a pinhole camera along the 2D grid using a robot arm. In this paper, we present a novel LF sampling scheme by exploiting a special non-centric camera called the crossed-slit or XSlit camera. An XSlit camera acquires rays that simultaneously pass through two oblique slits. We show that, instead of translating the camera as in the pinhole case, we can effectively sample the LF by rotating individual or both slits while keeping the camera fixed. This leads a "fixed-location" LF acquisition scheme. We further show through theoretical analysis and experiments that the resulting XSlit LFs provide several advantages: they provide more dense spatial-angular sampling, are amenable multi-view stereo matching and volumetric reconstruction, and can synthesize unique refocusing effects.

There is an emerging interest on using high-dimensional datasets beyond 2D images in saliency detection. Examples include 3D data based on stereo matching and Kinect sensors and more recently 4D light field data. However, these techniques adopt very different solution frameworks, in both type of features and procedures on using them. In this paper, we present a unified saliency detection framework for handling heterogenous types of input data. Our approach builds dictionaries using data-specific features. Specifically, we first select a group of potential foreground superpixels to build a primitive saliency dictionary. We then prune the outliers in the dictionary and test on the remaining superpixels to iteratively refine the dictionary. Comprehensive experiments show that our approach universally outperforms the state-of-the-art solution on all 2D, 3D and 4D data.