Camera Models and Optical Systems Used in Computer Graphics: Part II, Image-Based Techniques

Barsky, Brian A.; Horn, Daniel Reiter; Klein, Stanley A.; Pang, Jeffrey A.; Yu, Meng

doi:10.1007/3-540-44842-x_27

Cited by 22 publications

(20 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Rendering techniques that use these models trade off complexity and efficiency for accuracy and realism. In our companion paper [3], published later in these same proceedings, we survey several image space techniques to simulate these models, and address topics including linear filtering, ray distribution buffers, light fields, and simulation techniques for interactive applications.…”

Section: Discussionmentioning

confidence: 99%

“…They range from object-based modifications to ray tracing and scanline rendering to image-based techniques that convolve and otherwise distort an image after it has been rendered. We discuss object based techniques in this paper, and image based techniques in our companion paper [3], published later in these proceedings. Currently, these techniques offer varying realism and speed in rendering images with camera models.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Camera Models and Optical Systems Used in Computer Graphics: Part I, Object-Based Techniques

Barsky

Horn

Klein

et al. 2003

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Images rendered with traditional computer graphics techniques, such as scanline rendering and ray tracing, appear focused at all depths. However, there are advantages to having blur, such as adding realism to a scene or drawing attention to a particular place in a scene. In this paper we describe the optics underlying camera models that have been used in computer graphics, and present object space techniques for rendering with those models. In our companion paper [3], we survey image space techniques to simulate these models. These techniques vary in both speed and accuracy.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Camera Models and Optical Systems Used in Computer Graphics: Part I, Object-Based Techniques

Barsky

Horn

Klein

et al. 2003

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…While we admit that various improvements over this naive approach may increase the quality of the output [Potmesil and Chakravarty 1981;Kraus and Strengert 2007;Barsky et al 2003;Zhou et al 2007;Kolb et al 1995], image based algorithms always lack visibility information and are therefore necessarily biased. These methods produce approximations which are acceptable when real-time images are required, while ours produces an unbiased result.…”

Section: Comparison With Image-based Methodsmentioning

confidence: 97%

“…A number of approaches, in particular in real-time rendering, start from a pinhole image together with a depth map and post-process it using various forms of spatially-varying blur, e.g. [Potmesil and Chakravarty 1981;Kraus and Strengert 2007;Barsky et al 2003;Zhou et al 2007;Kolb et al 1995]. In this paper, we focus on high-quality offline image synthesis that resolves visibility based on a full thin-lens model, not an input pinhole image.…”

Section: Related Workmentioning

confidence: 99%

Fourier depth of field

Soler

Subr

Durand³

et al. 2009

ACM Trans. Graph.

View full text Add to dashboard Cite

Optical systems used in photography and cinema produce depth of field effects, that is, variations of focus with depth. These effects are simulated in image synthesis by integrating incoming radiance at each pixel over the lense aperture. Unfortunately, aperture integration is extremely costly for defocused areas where the incoming radiance has high variance, since many samples are then required for a noise-free Monte Carlo integration. On the other hand, using many aperture samples is wasteful in focused areas where the integrand varies little. Similarly, image sampling in defocused areas should be adapted to the very smooth appearance variations due to blurring. This paper introduces an analysis of focusing and depth of field in the frequency domain, allowing a practical characterization of a light field's frequency content both for image and aperture sampling. Based on this analysis we propose an adaptive depth of field rendering algorithm which optimizes sampling in two important ways. First, image sampling is based on conservative bandwidth prediction and a splatting reconstruction technique ensures correct image reconstruction. Second, at each pixel the variance in the radiance over the aperture is estimated, and used to govern sampling. This technique is easily integrated in any sampling-based renderer, and vastly improves performance.

show abstract

“…For each pixel, a number of rays are chosen to sample the aperture. Accumulation buffer methods [6] provide essentially the same results of distributed ray tracing, but render entire images per aperture sample, in order to utilize graphics hardware Both distributed ray tracing and accumulation buffer methods are quite expensive, so a variety of faster post-process methods have been created [12,15,2]. Post-process methods use image filters to blur images originally rendered with everything in perfect focus.…”

mentioning

confidence: 99%

Three Techniques for Rendering Generalized Depth of Field Effects

Kosloff¹,

Barsky²

2010

Proceedings of the 2009 SIAM Conference on “Mathematics for Industry”

Self Cite

View full text Add to dashboard Cite

Depth of field refers to the swath that is imaged in sufficient focus through an optics system, such as a camera lens. Control over depth of field is an important artistic tool that can be used to emphasize the subject of a photograph. In a real camera, the control over depth of field is limited by the laws of physics and by physical constraints. Depth of field has been rendered in computer graphics, but usually with the same limited control as found in real camera lenses. In this paper, we generalize depth of field in computer graphics by allowing the user to specify the distribution of blur throughout a scene in a more flexible manner. Generalized depth of field provides a novel tool to emphasize an area of interest within a 3D scene, to select objects from a crowd, and to render a busy, complex picture more understandable by focusing only on relevant details that may be scattered throughout the scene. We present three approaches for rendering generalized depth of field based on nonlinear distributed ray tracing, compositing, and simulated heat diffusion. Each of these methods has a different set of strengths and weaknesses, so it is useful to have all three available. The ray tracing approach allows the amount of blur to vary with depth in an arbitrary way. The compositing method creates a synthetic image with focus and aperture settings that vary per-pixel. The diffusion approach provides full generality by allowing each point in 3D space to have an arbitrary amount of blur.

show abstract

Camera Models and Optical Systems Used in Computer Graphics: Part II, Image-Based Techniques

Cited by 22 publications

References 12 publications

Camera Models and Optical Systems Used in Computer Graphics: Part I, Object-Based Techniques

Camera Models and Optical Systems Used in Computer Graphics: Part I, Object-Based Techniques

Fourier depth of field

Three Techniques for Rendering Generalized Depth of Field Effects

Contact Info

Product

Resources

About