Ray Tracing from the Ground Up

Suffern, Kevin

doi:10.1201/b10675

Cited by 30 publications

(37 citation statements)

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“…Widlowski et al [20] have demonstrated the use of ray tracing for scenes consisting of several millions of elements and for scenes comprising a stand of individual trees at needle-level resolution. For cases where the data size exceeds computer memory and the scenes exhibit some repetition in structure (e.g., scenes that represent fractal geometries or a collection of identical objects), techniques known as instantiation exist to reduce memory requirements (e.g., [35]). To ensure that absorption probabilities at the level of individual scene elements are accurately sampled, a higher structural fidelity of scenes requires the simulation of a larger numbers of photons and requires the use of advanced, optimized ray tracing algorithms.…”

Section: Discussionmentioning

confidence: 99%

“…A constant Lambertian scattering probability was used across all scene elements representing foliage, p l , ρ l " 0.05, τ c,l " 0.02˘and a constant Lambertian reflectance for all soil and bark elements, p s and p b respectively pρ s " ρ b " 0.1q. For a Lambertian surface, the direction of scattering (i.e., reflection or transmission) is independent from the photon incidence angle and follows a cosine-weighted hemispherical probability distribution where the reflected intensity drops with the cosine angle [35,50]. Earlier work [18,51] showed the use of ray tracers for the simulation of transmittance, reflectance, and absorptance by virtual plant canopies.…”

Section: Canopy Radiation Modelingmentioning

confidence: 99%

“…Ray tracing simulations are computationally expensive and require the use of accelerating structures that group scene elements into hierarchical bounding volumes or regular grids to speed up ray-intersection tests [34,35]. These accelerating structures provide performance gains for fixed illumination conditions; however, they do not provide additional gains for cases where illumination conditions are dynamic, i.e., when the distribution of radiation across leaves is to be studied over time and for different sun angles and cloudiness conditions.…”

Section: Introductionmentioning

confidence: 99%

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Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity

2015

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Eddy-covariance carbon dioxide flux measurement is an established method to estimate primary productivity at the forest stand level (typically 10 ha). To validate eddy-covariance estimates, researchers rely on extensive time-series analysis and an assessment of flux contributions made by various ecosystem components at spatial scales much finer than the eddy-covariance footprint. Scaling these contributions to the stand level requires a consideration of the heterogeneity in the canopy radiation field. This paper presents a stochastic ray tracing approach to predict the probabilities of light absorption from over a thousand hemispherical directions by thousands of individual scene elements. Once a look-up table of absorption probabilities is computed, dynamic illumination conditions can be simulated in a computationally realistic time, from which stand-level gross primary productivity can be obtained by integrating photosynthetic assimilation over the scene. We demonstrate the method by inverting a leaf-level photosynthesis model with eddy-covariance and meteorological data. Optimized leaf photosynthesis parameters and canopy structure were able to explain 75% of variation in eddy-covariance gross primary productivity estimates, and commonly used parameters, including photosynthetic capacity and quantum yield, fell within reported ranges. Remaining challenges are discussed including the need to address the distribution of radiation within shoots and needles.

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Section: Discussionmentioning

confidence: 99%

Section: Canopy Radiation Modelingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity

2015

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“…Apart from the 18 face glyphs, all other glyphs are randomly sampled from the entire structure's volume. Thus, there are no guarantees that the structure glyphs will be evenly spread out inside the volume of the box, such as could be achieved using sampling strategies such as n-rooks or Hammersley sampling [Suffern 2007]. The random sampling approach was chosen to best approximate the most likely conditions if the glyph positions had been mapped from an arbitrary real-world database.…”

Section: Task and Responsementioning

confidence: 99%

On the Benefits of Using Constant Visual Angle Glyphs in Interactive Exploration of 3D Scatterplots

Stenholt

2014

ACM Trans. Appl. Percept.

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Visual exploration of clouds of data points is an important application of virtual environments. The common goal of this activity is to use the strengths of human perception to identify interesting structures in data, which are often not detected using traditional, computational analysis methods. In this article, we seek to identify some of the parameters that affect how well structures in visualized data clouds can be identified by a human observer. Two of the primary factors tested are the volumetric densities of the visualized structures and the presence/absence of clutter around the displayed structures. Furthermore, we introduce a new approach to glyph visualization-constant visual angle (CVA) glyphs-which has the potential to mitigate the effect of clutter at the cost of dispensing with the common real-world depth cue of relative size. In a controlled experiment where test subjects had to locate and select visualized structures in an immersive virtual environment, we identified several significant results. One result is that CVA glyphs ease perception of structures in cluttered environments while not deteriorating it when clutter is absent. Another is the existence of threshold densities, above which perception of structures becomes easier and more precise. ACM Reference Format:Rasmus Stenholt. 2014. On the benefits of using constant visual angle glyphs in interactive exploration of 3D scatterplots. ACM Trans. Appl.

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“…The multiple sampling (MS) technique is a classic topic in computer graphics and is used for the screen antialiasing of display devices to smooth the edges of primitives (Suffern ). It is worth noting that MS is too burdensome to be handled within an extremely short interval of frame buffer refreshing (at least 20 fps for 3D interaction, which means 0.05 second as one interval to run the callback functions).…”

Section: Our Approachmentioning

confidence: 99%

Rendering 2D Lines on 3D Terrain Model with Optimization in Visual Quality and Running Performance

She¹,

Tan²,

Guo³

et al. 2016

Transactions in GIS

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With the gradual shift from 2D maps to a 3D virtual environment, various visual artifacts were generated by overlaying 2D map symbols on 3D terrain models. This work proposes a novel screen-based method for rendering 2D vector lines with the accuracy of more than one pixel on the screen in real time. First, screen pixels are inversely projected onto a 3D terrain surface, and then onto the 2D vector plane. Next, these pixels are classified into three categories in terms of their intersection situation with the 2D lines. After that, a multiple sampling process is applied to the pixels that intersect with the 2D lines in order to eliminate visual artifacts, such as intermittence and aliasing (in pixel scale). Finally, a suitable point-inpolygon judgment is implemented to color each sample point quickly. The algorithm is realized in a heterogeneously parallel model so that the performance is improved and becomes acceptable.

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Ray Tracing from the Ground Up

Cited by 30 publications

References 0 publications

Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity

Using Stochastic Ray Tracing to Simulate a Dense Time Series of Gross Primary Productivity

On the Benefits of Using Constant Visual Angle Glyphs in Interactive Exploration of 3D Scatterplots

Rendering 2D Lines on 3D Terrain Model with Optimization in Visual Quality and Running Performance

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