We propose a practical 3D method for measuring translucent objects with high accuracy. We improve the accuracy of the measurement by a high-frequency dashed line projection to a target object, which eliminates the influence of subsurface scattering while maintaining higher image contrast. For efficient measurement, multiple dashed lines are projected at the same time and we identify them without error by a proposed complementary Gray code, thus reducing the number of projections to less than 1/10 of that by conventional light sheet scanning. With regard to performance, we show that the frequency of the dashed line largely determines the accuracy and that there is a trade off between depth offset as a systematic error and random error. We validate the proposed method by measuring both canonical translucent samples with various translucencies and actual engineering parts.

This paper describes a photometric stereo method that works with a wide range of surface reflectances. Unlike previous approaches that assume simple parametric models such as Lambertian reflectance, the only assumption that we make is that the reflectance has three properties; monotonicity, visibility, and isotropy with respect to the cosine of light direction and surface orientation. In fact, these properties are observed in many non-Lambertian diffuse reflectances. We also show that the monotonicity and isotropy properties hold specular lobes with respect to the cosine of the surface orientation and the bisector between the light direction and view direction. Each of these three properties independently gives a possible solution space of the surface orientation. By taking the intersection of the solution spaces, our method determines the surface orientation in a consensus manner. Our method naturally avoids the need for radiometrically calibrating cameras because the radiometric response function preserves these three properties. The effectiveness of the proposed method is demonstrated using various simulated and real-world scenes that contain a variety of diffuse and specular surfaces.

This paper presents a simple yet practical 3-D modeling method for recovering surface shape and reflectance from a set of images. We attach a point light source to a hand-held camera to add a photometric constraint to the multi-view stereo problem. Using the photometric constraint, we simultaneously solve for shape, surface normal, and reflectance. Unlike prior approaches, we formulate the problem using realistic assumptions of a near light source, non-Lambertian surfaces, perspective camera model, and the presence of ambient lighting. The effectiveness of the proposed method is verified using simulated and real-world scenes.

Computer-generated hologram (CGH) is expanding the application fields. However, the CGH generation requires a long calculation time. In particular, the generation of a CGH stream for three-dimensional movies takes a huge amount of calculation cost. This paper proposes a small-calculation-cost method to generate a CGH stream based on coherent neural networks that deal with complex-amplitude information with generalization ability in the frequency domain. After carrier-frequency-dependent learning, we can generate a CGH stream by sweeping the carrier frequency with neural interpolation thanks to frequency-domain generalization.