One-day outdoor photometric stereo via skylight estimation

Abstract

We present an outdoor photometric stereo method using images captured in a single day. We simulate a sky hemisphere for each image according to its GPS and timestamp, and parameterize the obtained sky hemisphere into a quadratic skylight and a Gaussian sunlight distribution. Unlike previous works which usually model outdoor illumination as a sum of constant ambient light and a distant point light, our method models natural illumination according to a popular sky model and thus provides sufficient constraints for shape reconstruction from one day images. We generate pixel profiles of uniformly sampled unit vectors for the corresponding time of captures and evaluate them using correlation with the actual pixel profiles. The estimated surface normal is refined by MRF optimization. We have tested our method to recover objects and scenes of various sizes in real-world outdoor daylight.

Photometric stereo with only two images: A theoretical study and numerical resolution

“…However, he essentially considers the second image as a means to more accurately solve the SFS problem. More recently, the problem of outdoor PS has been reexplored in [8,21,22]. Finally, let us quote a preliminary version [11] of our work.…”

“…Let us first solve this PS2 example using the non-differential formulation (8), which is written as:…”

Real-time rendering of aerial perspective effect based on turbidity estimation

“…In this way, we approximated the spectral sensitivity in the direct transmission and airlight by Dirac's delta function. Generalizing this approximation for any observer's viewing direction V (θ, φ), we obtain c (T, s)) , (10) where I 0 c is the intensity value of a pixel at the target object, at distance s, and viewing direction V, without any aerial perspective effect. I ∞ c is the sky intensity value at an infinite distance in the same viewing direction V, and c is the attenuation factor approximated as …”

“…Such scattering models can be analyzed from captured skies using sky illumination models [1][2][3][4][5][6][7]. Due to its simplicity and accuracy for scattering modeling, a heuristic parameter called turbidity (T) has been used to categorize atmospheric conditions [8][9][10][11][12]. Following that approach, we propose a full-spectrum turbidity-based aerial perspective model that enables us to render realistic aerial perspective effects in real time.…”

Simple and effective calculations about spectral power distributions of outdoor light sources for computer vision

“…This analytic model of spectral sky-dome radiance is widely-used. Using images captured during a single day, Jung et al [18] presented an outdoor photometric stereo method via skylight estimation according to the Preetham sky model. Given sky images and viewing geometry as the input, Kawakami et al [19] proposed a method to estimate the turbidity of the sky by fitting image intensities to the Preetham sky model.…”