Resample and composite engine for real-time volume rendering

Abstract

The present invention is a digital electronic system for rendering a volume image in real time. The system accelerators the processing of voxels through early ray termination and space leaping techniques in the projection guided ray casting of the voxels. Predictable and regular voxel access from high-speed internal memory further accelerates the volume rendering. Through the acceleration techniques and devices of the present invention real-time rendering of parallel and perspective views, including those for stereoscopic viewing, are achieved.

Description

FIELD OF THE INVENTION [0001]The present invention is a system for providing three-dimensional computer graphics. More particularly, the present invention is a system that accelerates the processing of volume data for real-time ray casting of a three-dimensional image and a method thereof.BACKGROUND OF THE INVENTION[0002]Volume rendering projects a volume dataset onto a two-dimensional (2D) image plane or frame-buffer. Volume rendering can be used to view and analyze three-dimensional (3D) data from various disciplines, such as biomedicine, geo-physics, computational fluid dynamics, finite element models and computerized chemistry. Volume rendering is also useful in the application of 3D graphics, such as Virtual Reality (VR), Computer Aided Design (CAD), computer games, computer graphics special effects and the like. The various applications, however, may use a variety of terms, such as 3D datasets, 3D images, volume images, stacks of 2D images and the like, to describe volume data...

AI Technical Summary

Benefits of technology

[0014]The present invention is a general-purpose device that supports interactive rendering for parallel and perspective projections and stereoscopic rendering thereof. The general-purpose device is further characterized as a digital electronic system for real-time volume rendering of a 3D volume dataset. A new hybrid ray casting is used to volume render a real-time image from external memory. Volume rendering includes reconstruction, classification, shading and composition of subvolumes or voxels of a volume dataset representing the 3D image. Early ray termination and space leaping accelerate the processing of the voxels by dynamically reducing the number of voxels necessary to render the image. Furthermore, the underlying hardware of the present invention processes the remaining voxels to in an efficient manner. This allows for real-time volume imaging for stereoscopic displays.

[0015]The hardware architecture of the present invention supports projection-guided ray casting, early ray termination and space leaping for improved memory usage. The hardware architecture further accelerates the volume rendering due, in part, to regular and predictable memory accessing, fully pipelined processing and space leaping and buffering of voxels to eliminate voxel-refetch.

[0016]The incorporation of the projection guided ray casting, including early ray termination and space leaping, and the hardware architecture permit rendering of the image where the rendering is not the critical time-consuming operation. In other words, the present invention can render many volumes in a faster time period than the entire volumes can be read from external memory.

Problems solved by technology

A volume dataset may often contain more than a hundred million voxels thereby requiring a large amount of storage.

Because of the vast amount of information contained in a dataset, interactive volume rendering or real-time volume rendering defined below requires a large amount of memory bandwidth and computational throughput.

These requirements often exceed the performance provided by typical modern workstations and personal computers.

Direct volume rendering that is implemented in software, however, is typically very slow because of the vast amount of data to be processed.

These solutions are typically expensive and not widely available due to, for instance, the requirement for parallel computers.

Although accelerators have increased the availability and performance of volume rendering, a truly general-purpose RTDVR accelerator has yet to emerge.

Moreover, current accelerators also require high memory bandwidths that can often exceed 1 Gbyte per second for a 2563 dataset.

Object-order architectures, however, have not generally provided algorithmic acceleration.

This typically causes a bottleneck in achievable hardware acceleration and thereby limits the number of useful processors.

Although algorithmic acceleration for the reconstruction, classification, shading and the composition of the voxels can often increase performance, such an increase in performance is often outweighed by the voxel bottleneck in the memory system, thereby limiting the overall acceleration.

Object-order reconstruction of the dataset, however, makes it difficult, if not impossible, to implement algorithmic acceleration or support perspective projections.

Neither image-order nor object-order architectures are general-purpose techniques because of their limitations.

For example, image-order architectures only deliver interactive performance for certain types of datasets by relying heavily on algorithmic acceleration.

Performance can be extremely sensitive to viewing parameters (and dataset characteristics) potentially causing large fluctuations in performance.

On the other hand, object-order architectures yield more consistent performance but typically do not support perspective projections.

As a result, these architectures cannot be used for applications that require stereoscopic rendering, virtual reality, computer graphics, computer games and fly-throughs.

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