Robust multi-camera calibration is a fundamental task for all multi-view camera systems, leveraging discreet camera model fitting from sparse target observations. Stereo systems, photogrammetry and light-field arrays have all demonstrated the need for geometrically consistent calibrations to achieve higherlevels of sub-pixel localization accuracy for improved depth estimation. This work presents a calibration target that leverages multi-directional features to achieve improved dense calibrations of camera systems. We begin by presenting a 2D target that uses an encoded feature set, each with 12 bits of uniqueness for flexible patterning and easy identification. These features combine orthogonal sets of straight and circular binary edges, along with Gaussian peaks. Our proposed feature extraction algorithm uses steerable filters for edge localization, and an ellipsoidal peak fitting for the circle center estimation. Feature uniqueness is used for associativity across views, which is combined into a 3D pose graph for nonlinear optimization. Existing camera models are leveraged for intrinsic and extrinsic estimates, demonstrating a reduction in mean re-projection error of for stereo calibration from 0.2 pixels to 0.01 pixels when using a traditional checkerboard and the proposed target respectively.
The most common sensor arrangement of 360 panoramic video cameras is a radial design where a number of sensors are outward looking as in spokes on a wheel. The cameras are typically spaced at approximately human interocular distance with high overlap. We present a novel method of leveraging small form-factor camera units arranged in stereo pairs and interleaved to achieve a fully panoramic view with fully parallel sensor pairs. This arrangement requires less keystone correction to get depth information and the discontinuity between images that have to be stitched together is smaller than in the radial design. The primary benefit for this arrangement is the small form factor of the system with the large number of sensors enabling a high resolving power. We highlight mechanical considerations, system performance and software capabilities of these manufactured and tested imaging units. One is based on the Raspberry Pi cameras and a second based on a 16 camera system leveraging 8 pairs of 13 megapixel AR1335 cell phone sensors. In addition several different variations on the conceptual design were simulated with synthetic projections to compare stitching difficulty of the rendered scenes.
The stereoscopic rendering of rain has been previously studied. We extend the behavior and distribution of rainfall to include photorealistic stereo rendering of rain and snow precipitation at video frame rates. We ignore stereo rendering optimization and concentrate on the visual factors necessary to produce photorealistic output. The experimental method uses a series of controlled human experiments where participants are presented with video clips and still photos of real precipitation. The stimuli vary along three visual factors: particle numbers, particle size, and motion. The goal is to determine the statistical ranking and importance of these visual factors for producing a photorealistic output. The experiments are extended to investigate if stereo improves photorealism. Additionally, experimental stimuli include post-processing on rendered output to produce variable lighting, glow, and fog effects to study their impact on photorealism as the stereo camera moves in the scene. The results demonstrate that the visual factors for photorealism can be ranked as more sensitive to particle numbers and motion than to particle size. Varying light, glow, and fog effects contribute towards photorealism independent of stereo. Future research will exploit the geometric symmetry of the stereoscopic image pairs to render precipitation while maintaining realtime frame rates.