Light field rendering has been proposed to render novel images from a collection of captured images without explicit geometry information. In this report, we present an optical analysis of light field rendering. In particular, a light field rendering system can be considered as a virtual imaging system with a discrete synthetic aperture (e.g., [10]. We define the key elements of the virtual ima...

[1] Soil depth is an important input parameter in hydrological and ecological modeling. Presently, the soil depth data available in national soil databases (STATSGO and SSURGO) from the Natural Resources Conservation Service are provided as averages within generalized land units (map units). Spatial uncertainty within these units limits their applicability for distributed modeling in complex te...

Depth coding in 3D-HEVC for the multiview video plus depth (MVD) architecture (i) deforms object shapes due to block-level edge-approximation; (ii) misses an opportunity for high compressibility at near-lossless quality by failing to exploit strong homogeneity (clustering tendency) in depth syntax, motion vector components, and residuals at frame-level; and (iii) restricts interactivity and lim...

A catadioptric stereo system has been developed for depth perception. The system uses two sets of planar mirrors to create two virtual cameras. A design procedure is presented with the aim of building a compact assembly. This has resulted in an inexpensive and compact system that achieves a depth resolution of 5.8 cm at a working distance of 2 m.

The objective of this paper is to study the impact of depth perception and movement freedom on learning surgical tasks by novice subjects using new laparoscopic technology.

In this paper we present the maximum simplicial depth estimator and compare it to the ordinary least square estimator in examples from 2D shape analysis focusing on bivariate and multivariate allometrical problems from zoology. We compare two types of estimators derived under different subsets of parametric space on the basis of the linear regression model, θ = (θ1, θ2) ∈ R2 and θ = (θ1, θ2, θ3...