We describe an adaptive display algorithm for interactive frame rates during visualization of very complex virtual environments. The algorithm relies upon a hierarchical model representation in which objects are described at multiple levels of detail and can be drawn with various rendering

images of real caustics on the water surface. Using efficient approximations and employing the specialized 3-D and CPU features of modern PCs, this simulation runs at rates of several frames per second. Part of this solution is the emulation of a hardware accumulation buffer on consumer 3-D graphics

We describe an adaptive display algorithm for interactive frame rates during visualization of very complex virtual environments. The algorithm relies upon a hierarchical model representation in which objects are described at multiple levels of detail and can be drawn with various rendering

be computed quickly, making it suitable for large data sets. Our implementation, written for use in a large-scale 3D digitization project, launches quickly, maintains a user-settable interactive frame rate regardless of object complexity or camera position, yields reasonable image quality during motion

rates. Our system achieves high frame rates working only with the information present in a single grey scale image. These alternative sources of information can also be integrated with our system to achieve even higher frame rates.

quadtree decomposition and a view-independent visible priority. Our experiments have shown that the morphing can be performed at interactive rates on today’s high-end personal computers. Potential applications of the method include virtual holograms, a walkthrough in a virtual environment, image

frames/second, detects pedestrians at very small scales (as small as 20x15 pixels), and has a very low false positive rate

Building animation tools for fluid-like motions is an important and challenging problem with many applications in computer graphics. The use of physics-based models for fluid flow can greatly assist in creating such tools. Physical models, unlike key frame or procedural based techniques, permit

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This paper describes a machine learning approach for visual object detection which is capable of processing images extremely rapidly and achieving high detection rates. This work is distinguished by three key contributions. The first is the introduction of a new image representation called