Efficient, high-quality, GPU-based visualization of voxelized surface data with fine and complicated structures

Sven Forstmann*, Jun Ohya


研究成果: Article査読

5 被引用数 (Scopus)


This paper proposes a GPU-based method that can visualize voxelized surface data with fine and complicated features, has high rendering quality at interactive frame rates, and provides low memory consumption. The surface data is compressed using run-length encoding (RLE) for each level of detail (LOD). Then, the loop for the rendering process is performed on the GPU for the position of the viewpoint at each time instant. The scene is raycasted in planes, where each plane is perpendicular to the horizontal plane in the world coordinate system and passes through the viewpoint. For each plane, one ray is cast to rasterize all RLE elements intersecting this plane, starting from the viewpoint and ranging up to the maximum view distance. This rasterization process projects each RLE element passing the occlusion test onto the screen at a LOD that decreases with the distance of the RLE element from the viewpoint. Finally, the smoothing of voxels in screen space and full screen anti-aliasing is performed. To provide lighting calculations without storing the normal vector inside the RLE data structure, our algorithm recovers the normal vectors from the rendered scene's depth buffer. After the viewpoint changes, the same process is re-executed for the new viewpoint. Experiments using different scenes have shown that the proposed algorithm is faster than the equivalent CPU implementation and other related methods. Our experiments further prove that this method is memory efficient and achieves high quality results.

ジャーナルIEICE Transactions on Information and Systems
出版ステータスPublished - 2010 11月

ASJC Scopus subject areas

  • ソフトウェア
  • ハードウェアとアーキテクチャ
  • コンピュータ ビジョンおよびパターン認識
  • 電子工学および電気工学
  • 人工知能


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