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Finish Hardware Accelerated Voxel Coloring Anselmo A. Montenegro , Luiz Velho , Paulo Carvalho and Marcelo Gattass [anselmo,lvelho,pcezar]@visgraf.impa.br, mgattass@tecgraf.puc-rio.br 3D object reconstruction is one of the most investigated topics in computer graphics and vision. Among different techniques, image based reconstruction is considered one of the most promising as high quality digital cameras are becoming a commodity hardware. Problems with photometric approaches: Registration and evaluation of thousands of individual elements. Solution: Registration based on projective texture mapping. Photo-consitency evaluation done by GPU programming. Volumetric carving is a very common technique use for image based reconstruction. It may use silhouette and/or photometric information. Silhouette based methods were successfully used in real- time reconstructions. This is not the case when we consider photometric approaches. Still some problems: Too much elements Memory waste Solution: Hierarchical representation of scene space Refinement approach Adaptive Carving Background estimation Camera calibration Image capture Object segmentation Reconstruction by Adaptive Space Carving Fixed pre-calibrated cameras setup Calibration by model recongnition Adaptive space carving: Works on an octree representation of the scene space.Works on an octree representation of the scene space. The reconstruction is obtained by a refinement process based on photo-consistency tests.The reconstruction is obtained by a refinement process based on photo-consistency tests. Uses photometric and silhouette information in multiresolution to detect coarse empty regions as soon as possible.Uses photometric and silhouette information in multiresolution to detect coarse empty regions as soon as possible. Classification of the cells: CONSISTENT, INCONSISTENT and UNDEFINED.Classification of the cells: CONSISTENT, INCONSISTENT and UNDEFINED. Undefined cells are subdivided and classified in later stages.Undefined cells are subdivided and classified in later stages. Level 5 Segmentation based on intervals of confidence Adaptive Space Carving Space Carving No cell subdivided ? Last registration plane of the level? Subdivide undefined cells and colorize photo-consistent cells. Update visibility maps. Test the consistency of the non- classified cells intersected by the current registration plane Project images on the current registration plane with resolution compatible to the octree level Algorithm Levels of refinement Level 6 Level7 Level 8 Zoom Fixed cameras reconstruction results Occlusion tolerant Calibration Background estimation and segmentation K 1 Rtm 1 K 2 Rtm 2 Homography H =K 1 Rtm 1 (Rtm 2 ) -1 (K 2 ) -1 Final considerations Hand-held camera setup Problems: Calibration Background estimation Solution: Insert model in the scene Background estimation by warping images of the scene without the object Hand-held camera reconstruction results In this work we only explored convencional GPU hardware accelerated operations, as in the registration step by projective texture mapping. The mechanism of copying framebuffer information to main memory introduces significant overhead to the overall processing time. We believe that by combining our adaptive approach with photo-consistency test done by GPU programming we can obtain considerable gains in efficiency. =0 YES NO YES NO Process next registration plane Process next octree refinement level Determine the registration planes at the current level Initialize the octree root cell with the bounding box of the scene Segmentation problem at the pattern lines due to alignement errors (a). Solution: Solution: the interval of confidence for a pixel p(i,j) in the target image is calculated by sampling the pixels from the registered images at a neighborhood of (i,j) whose color is the closest to p(i,j) (b). wrong (a) correct(a) Registered backgrounds Input image Background image Images and segmentation Visibility and noise maps H Z=0