Figure 1: Our method achieves DOF blur effects comparable to accurate solutions in real time and avoids postprocessing artifacts. We present a GPU-based real-time rendering method that simulates high-quality depth-of-field effects, similar in quality to multiview accumulation methods. Most real-time approaches have difficulties to obtain good approximations of visibility and view-dependent shading due to the use of a single view image. Our method also avoids the multiple rendering of a scene, but can approximate different views by relying on a layered image-based scene representation. We present several performance and quality improvements, such as early culling, approximate cone tracing, and jittered sampling. Our method achieves artifact-free results for complex scenes and reasonable depth-of-field blur in real time. This is the authors’ version of the paper. The ultimate version was published at SIGGRAPH Asia 09 1

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. 1 Background and Previous Work

Abstract. Depth of field refers to the swath that is imaged in sharp focus through an optics system, such as a camera lens. Control over depth of field is an important artistic tool, which can be used, for example, to emphasize the subject of a photograph. The most efficient algorithms for simulating depth of field are post-processing methods. Post-processing can be made more efficient by making various approximations. We start with the assumption that the point spread function (PSF) is Gaussian. This assumption introduces structure into the problem which we exploit to achieve speed. Two methods will be presented. In our first approach, which we call pyramid spreading, PSFs are spread into a pyramid. By writing larger PSFs to coarser levels of the pyramid, the performance remains constant, independent of the size of the PSFs. After spreading all the PSFs, the pyramid is then collapsed to yield the final blurred image. Our second approach, called the tensor method, exploits the fact that blurring is a linear operator. The operator is treated as a large tensor which is compressed by finding structure in it. The compressed representation is then used to directly blur the image. Both methods present new perspectives on the problem of efficiently blurring an image. 1

(d) Scene depth map (dark means close) (e) Defocus maps used to generate the images in (b) and (c), respectively (orange means blurry) Figure 1: Manipulating depth of field using a focal stack. (a) A single slice from a focal stack of 32 photographs, captured with a Canon 7D and a 28mm lens at f/4.5. The slice shown is focused 64cm away. (b) A simulated f/2.0 composite, focused at the same depth. To simulate the additional blur, objects closer to the camera are rendered from a slice focused afar, and objects far from the camera are rendered from a slice focused near. (c) An extended depth of field composite that blurs the foreground flower and is sharp for all depths beyond it. (d) A depth map for the scene, representing depth as image intensity (dark means close.) (e) A pair of defocus maps that encapsulate the requested amount of per-pixel defocus blur used to generate the composites above. Its magnitude is encoded with saturation. Many cameras provide insufficient control over depth of field. Some have a fixed aperture; others have a variable aperture that is either too small or too large to produce the desired amount of blur. To overcome this limitation, one can capture a focal stack, which is a collection of images each focused at a different depth, then combine these slices to form a single composite that exhibits the desired depth of field. In this paper, we present a theory of focal stack compositing, and algorithms for computing images with extended depth of field, shallower depth of field than the lens aperture naturally provides, or even freeform (non-physical) depth of field. We show that while these composites are subject to halo artifacts, there is a principled methodology for avoiding these artifacts—by feathering a slice selection map according to certain rules before computing the composite image.