For CS291 J00 These slides are slightly edited versions of those available at http grail cs washington edu projects slf Surface Light Fields for 3D Photography Daniel Wood Daniel Azuma Wyvern Aldinger Brian Curless Tom Duchamp David Salesin Werner Stuetzle 3D Photography Goals Rendering and editing Inputs Photographs and geometry Requirements Estimation and compression 1 Surface light fields Walter et al 1997 Miller et al 1998 Nishino et al 1999 Lumisphere valued texture maps For one point on the surface Lumisphere NOTE Lighting remains fixed and isn t contrllable 2 Overview Data acquisition Estimation and compression Rendering Editing Scan and reconstruct geometry Range scans only a few shown Reconstructed geometry 3 Take photographs Camera positions Photographs Register photographs to geometry Geometry Photographs 4 Register photographs to geometry User selected correspondences rays Parameterizing the geometry Atlas of Charts Map Base mesh Scanned geometry L K 0 M 3 5 Assembling data lumispheres L K 0 S 2 RGB Data lumisphere Overview Data acquisition Estimation and compression Rendering Editing 6 Pointwise fairing Interpolation filling in missing data Data lumisphere Faired lumisphere Pointwise fairing results Input photograph Pointwise faired 177 MB 7 Pointwise fairing Many input data lumispheres Many faired lumispheres Compression Two approaches based on 1 Vector quantization VQ 2 Singular value decomposition SVD Preprocessing to improve coherence Many input data lumispheres Small set of prototypes 8 Reflected reparameterization Reflected reparameterization 9 Reflection reparameterization Reflect the lumispheres through their normals The specular lobes point in approximately the same direction back towards the light source Reflected reparameterization Before After 10 Median removal Reflected Median diffuse Median removed specular Median removal Median values Residual Specular Result 11 Function quantization based on vector quantization Input data lumisphere Codebook of lumispheres Construct codebook using Lloyd iteration Iterate until convergence 1 Assign all data lumispheres to closest codeword forming clusters 2 Compute new codeword for each cluster by cluster wise fairing Then split all codewords and start over 12 Lloyd iteration Input data lumispheres Lloyd iteration Codeword 13 Lloyd iteration Perturb codewords to create larger codebook Lloyd iteration Form clusters around each codeword 14 Lloyd iteration Optimize codewords based on clusters Lloyd iteration Create new clusters 15 Function quantization results Input photograph Function quantized 1010 codewords 2 6 MB Principal function analysis Input data lumisphere Prototype lumisphere Subspace of lumispheres 16 Principal function analysis results Input photograph PFA compressed Order 5 2 5 MB Compression comparison Pointwise fairing 177 MB Function quantization 2 6 MB Principal function analysis 2 5 MB 17 Qualitative comparison PCA leads to smoother images Function quantization introduces artifacts such as jaggies on tail Function quantizatino better preserves colors in highlights and effects of interreflections Comparison with 2 plane light field uncompressed Pointwise faired surface light field 177 MB Uncompressed lumigraph light field 177 MB 18 Comparison with 2 plane light field compressed Compressed PFA surface light field 2 5 MB Vector quantized lumigraph light field 8 1 MB Overview Data acquisition Estimation and compression Rendering Editing 19 Interactive renderer screen capture Overview Data acquisition Estimation and compression Rendering Editing 20 Lumisphere filtering Simple bias function to the values in the lumisphere making the specular lobes taller and narrower Original surface light field Glossier coat Rotating the Lighting by rotating the lumispheres Original surface light field Rotated environment 21 Deformation Original Deformed Deformation 22
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