Reflectance and Texture of Real-World Surfaces KRISTIN J. DANA Columbia University BRAM VAN GINNEKEN Utrecht University SHREE K. NAYAR Columbia University JAN J. KOENDERINK Utrecht University ACM Transactions on Graphics, Vol. 18, No. 1, January 1999

Overview Introduce BRDF and BTF BTF Texture Gathering Technique CUReT Database BTF Applications Future Work Pretty Pictures

Bidirectional Reflectance Distribution Function (BRDF) Nicodemus [1970] and Nicodemus et al. [1977] Coarse scale level –local surface variations are subpixel –local intensity is uniform Bidirectional: 1.Camera Angle 2.Light Angle “Objects look differently when viewed from different angles, and when illuminated from different directions”

Bidirectional Texture Function (BTF) Fine scale level –Surface variations give rise to local intensity variations Bidirectional: 1.Camera Angle 2.Light Angle “Objects look differently when viewed from different angles, and when illuminated from different directions”

BRDF vs. BTF

Why do we need BTFs? Traditional 2-D texture synthesis and texture-mapping do not take into account the change in texture appearance as the viewing and illumination directions change –A single digital image of a rough surface is mapped onto a 3-D object and the appearance of roughness is usually lost or distorted Bump-mapping [Blinn 1977, 1978] preserves some of the appearance of roughness –knowledge of the surface shape is required –shadows cast from the local surface relief are not rendered ray tracing can be used –exact geometry of the surface must be known –high computational cost solid texturing: combine a volumetric texture synthesis with volume rendering techniques –computationally intensive –applicable for a limited variety of textures. BTF database –“potential exists for 3-D texturing algorithms using images, without the need for a volumetric texture model or surface synthesis procedure”

BTF: Where do we start? Already BRDF databases Employ new techniques to create BTF database Pull Together: –Robot –Lamp –PC –Photometer –Video camera

Texture Gathering Technique Fixed light source –Halogen bulb with a Fresnel lens (single- beam focusing) Camera moves through 7 positions –22.5°, 45°, 67.5°, 90°, 112.5°, 135°, 157.5° from light source Texture sample moves through multiple orientations –Robot arm orients sample normal along vertices of quarter-sphere facing the light source

Texture Gathering Technique At each camera position, texture is captured with its normal along quarter- sphere vertices Not all vertices captured at each position –At position 7, only a few normals are actually visible to the camera Quarter-Sphere Orientations: Camera Positions

Texture Gathering Technique Sample lies in xs–ys plane with its global normal pointing in the direction of zs Each circular marker represents a distinct illumination direction For each of these illumination directions, the sample is imaged from seven viewing directions Quarter-Sphere Orientations: Illumination Directions

Texture Gathering Technique Textures that have grids or grains Measurements are repeated rotating sample about zs by either 45° or 90° depending on the structure of the anisotropy Examples: –Linen (square grid) rotated 45° –Corduroy (vertical lines) rotated 90 ° Special Case: Anisotropic Textures

Texture Gathering Technique Relate radiance to pixel values Use Kodak standard card image for every sample measured. Letting r denote the total radiance and p denote the average pixel value, a linear relationship was found Data with significant pixel underflow (pixel values near 0) or overflow (pixel values near 255) were not used. Control Considerations

End Product 205 images for each sample 640 x 480 pixels 24 bits per pixel (8 bits per RGB channel). Database total: over 14,000 images (61 samples, 205 measurements per sample, plus 205 additional measurements for anisotropic samples) CUReT Database: Camera PositionImages Total205

Columbia-Utrecht Reflectance and Texture Database (CUReT)

BTF Applications Top row –Two images of “plaster_b” with different illumination and viewing directions Bottom row –Spatial spectrum of “plaster_b” with zero frequency at the center and brighter regions corresponding to higher magnitudes –Notice orientation change due to change of illumination direction causing change in shadow direction. Computer vision: –Texture recognition algorithms often based on spectral content of image textures –BTF should be considered for recognition of real-world surfaces Sample 11: “plaster_b”

BTF Applications BTF texture gathering technique allows easy gathering of BRDF data –Pros: Simple system Simultaneously gather BRDF and BTF measurements –Cons: Not as accurate as traditional BRDF measurement systems

Future Work Synthesizing Bidirectional Texture Functions for Real-World Surfaces Xinguo Liu, Yizhou Yu, Heung-Yeung Shum 3 Step approach to synthetically generate BTFs 1.Recovers approximate 3D geometry of surface details using a shape-from-shading approach 2.Generates a novel version of the geometric details with the same statistical properties as the sample surface 3.Uses an “appearance preserving procedure” to synthesize novel images under various viewing/lighting settings, defining a novel BTF

Show me some BTF pictures!!! 13 images per sample used from database collection of 205 –1 image of frontal view –12 oblique views Use averaging of three pixels at the section borders to reduce the appearance of seams

Pretty Pictures Traditional 2-D texture-mapping BTF 3-D texture-mapping Sample 11 (plaster)

Pretty Pictures Traditional 2-D texture-mapping BTF 3-D texture-mapping Sample 8 (pebbles)

Pretty Pictures Traditional 2-D texture-mapping BTF 3-D texture-mapping Sample 45 (concrete)

Pretty Pictures Traditional 2-D texture-mapping BTF 3-D texture-mapping Sample 28 (crumpled paper)

Pretty Pictures Traditional 2-D texture-mapping BTF 3-D texture-mapping Sample 19 (plush rug)

Pretty Pictures Traditional 2-D texture-mapping BTF 3-D texture-mapping Sample 56 (wood) (anisotropic)

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