<title>View-dependent approach to MIP for very large data</title>

A simple and yet useful approach to visualize a variety of structures from sampled data is the Maximum Intensity Projection (MIP). Higher valued structures of interest project over occluding structures. This can make MIP images diffi- cult to interpret due to the loss of depth information. Animating about the data is one key way to try to decipher such ambiguities. The challenge is that MIP is inherently expensive and thus high frame rates are difficult to achieve. Varia- tions to the original MIP algorithm and classification can help to further alleviate ambiguities and provide improved image quality. Unfortunately, these improved techniques are even more expensive. In addition, they require substantial parameter searching and tweaking. As today's data sizes are increasingly getting larger, current methods only allow very limited interaction. We explore a view-dependent approach using concepts from image-based rendering. A novel multi- layered image representation storing scalar information is computed at a view sample and then warped to the user's view. We present algorithms using OpenGL to quickly compute MIP and its variations using commodity off-the-shelf graphics hardware to achieve near interactive rates.