Raycasting

Résumé

thumb|right|350px|Capture d’écran de Freedoom : reproduction libre de Doom utilisant également le raycasting. Le raycasting est une technique de calcul d'images de synthèse 3D, facilement accélérée matériellement par une carte graphique dédiée. L'accélération peut se faire en utilisant l'API OpenGL, Glide (tombé en désuétude), ou Direct3D. Elle a été utilisée avec succès au début des années 1990 dans les jeux vidéo comme Wolfenstein 3D ou Doom. L'implémentation était alors entièrement logicielle et ne faisait pas appel à du matériel spécifique. Différence entre le raycasting et le raytracing (lancer de rayons) Certains ouvrages utilisent les deux termes comme des synonymes ; toutefois, le raycasting a historiquement une autre définition puisqu'il est intimement lié aux premiers jeux vidéo en 3D. Contrairement au lancer de rayons, le raycasting est nettement plus rapide. Il tire parti de certaines caractéristiques du monde virtuel :

les murs sont des plans, en général

Source officielle

https://fr.wikipedia.org/wiki/Raycasting

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Evaluation of the ray-casting analytical algorithm for pencil beam scanning proton therapy

Sojin Shim, Ye Zhang

For pencil beam scanned (PBS) proton therapy, analytical dose calculation engines are still typically used for the optimisation process, and often for the final evaluation of the plan. Recently however, the suitability of analytical calculations for planning PBS treatments has been questioned. Conceptually, the two main approaches for these analytical dose calculations are the ray-casting (RC) and the pencil-beam (PB) method. In this study, we compare dose distributions and dosimetric indices, calculated on both the clinical dose calculation grid and as a function of dose grid resolution, to Monte Carlo (MC) calculations. The analysis is done using a comprehensive set of clinical plans which represent a wide choice of treatment sites. When analysing dose difference histograms for relative treatment plans, pencil beam calculations with double grid resolution perform best, with on average 97.7%/91.9% (RC), 97.9%/92.7% (RC, double grid resolution), 97.6%/91.0% (PB) and 98.6%/94.0% (PB, double grid resolution) of voxels agreeing within +/- 5%/+/- 3% between the analytical and the MC calculations. Even though these point-to-point dose comparison shows differences between analytical and MC calculations, for all algorithms, clinically relevant dosimetric indices agree within +/- 4% for the PTV and within +/- 5% for critical organs. While the clinical agreement depends on the treatment site, there is no substantial difference of indices between the different algorithms. The pencil-beam approach however comes at a higher computational cost than the ray-casting calculation. In conclusion, we would recommend using the ray-casting algorithm for fast dose optimization and subsequently combine it with one MC calculation to scale the absolute dose and assure the quality of the treatment plan.

2019

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We present the disambiguation canvas, a technique developed for easy, accurate and fast selection of small objects and objects inside cluttered virtual environments. Disambiguation canvas rely on selection by progressive refinement, it uses a mobile device and consists of two steps. During the first, the user defines a subset of objects by means of the orientation sensors of the device and a volume casting pointing technique. The subsequent step consists of the disambiguation of the desired target among the previously defined subset of objects, and is accomplished using the mobile device touchscreen. By relying on the touchscreen for the last step, the user can disambiguate among hundreds of objects at once. User tests show that our technique performs faster than ray-casting for targets with approximately 0.53 degrees of angular size, and is also much more accurate for all the tested target sizes.

Springer Berlin Heidelberg2013

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Towards a Disambiguation Canvas

Ronan Boulic, Anderson Maciel

We present Disambiguation canvas, a technique for selection by progressive refinement using a mobile device and consisting of two steps. During the first, the user defines a subset of objects through the orientation sensors of the device and a volume-casting pointing technique. The subsequent step consists of the disambiguation of the desired target among the previously-defined subset of objects, and is accomplished using the mobile device touchscreen. By relying on the touchscreen for the last step the user can disambiguate among hundreds of objects at once, previous progressive refinement techniques do not scale as well as ours. Disambiguation canvas is mainly developed for easy, accurate and fast selection of small objects, or objects inside cluttered virtual environments. User tests show that our technique performs faster than ray-casting for targets with approximate to 0 : 53 degrees of angular size, and is also much more accurate for all the tested target sizes.

Ieee2013

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