Translucent Objects Research Articles

NeuPreSS: Compact Neural Precomputed Subsurface Scattering for Distant Lighting of Heterogeneous Translucent Objects

AbstractMonte Carlo rendering of translucent objects with heterogeneous scattering properties is often expensive both in terms of memory and computation. If the scattering properties are described by a 3D texture, memory consumption is high. If we do path tracing and use a high dynamic range lighting environment, the computational cost of the rendering can easily become significant. We propose a compact and efficient neural method for representing and rendering the appearance of heterogeneous translucent objects. Instead of assuming only surface variation of optical properties, our method represents the appearance of a full object taking its geometry and volumetric heterogeneities into account. This is similar to a neural radiance field, but our representation works for an arbitrary distant lighting environment. In a sense, we present a version of neural precomputed radiance transfer that captures relighting of heterogeneous translucent objects. We use a multi‐layer perceptron (MLP) with skip connections to represent the appearance of an object as a function of spatial position, direction of observation, and direction of incidence. The latter is considered a directional light incident across the entire non‐self‐shadowed part of the object. We demonstrate the ability of our method to compactly store highly complex materials while having high accuracy when comparing to reference images of the represented object in unseen lighting environments. As compared with path tracing of a heterogeneous light scattering volume behind a refractive interface, our method more easily enables importance sampling of the directions of incidence and can be integrated into existing rendering frameworks while achieving interactive frame rates.

Automated phase-visibility modulating interferometry

This paper presents the Phase-Visibility Modulating Interferometry (PVMI) method automated by a microcontroller and three servomotors, under inhomogeneous out-of-phase amplitude modulation (OPAM) in the On-Off Modulation (OOM) mode. PVMI is implemented in a triple-aperture common-path interferometer (TACPI), based on a 4f optical imaging system. The high mechanical stability of the TACPI and the properties of the PVMI technique make the present method immune to noise, robust, fast, and convenient for measuring complex amplitude with high resolution and accuracy. PVMI does not preprocess and post-process the experimental irradiances and does not need a phase step shifter or a carrier frequency. Therefore, a calibration is unnecessary, there are no detuning errors, and no bandwidth reduction. The great advantage of automated PVMI is explained and demonstrated experimentally for three translucent objects. The focal length estimation of a lens with a percentual deviation of 0.14 to validate the automated PVMI, and the evaluation of two arbitrary translucent objects with high resolution and accuracy.

Neural SSS: Lightweight Object Appearance Representation

AbstractWe present a method for capturing the BSSRDF (bidirectional scattering‐surface reflectance distribution function) of arbitrary geometry with a neural network. We demonstrate how a compact neural network can represent the full 8‐dimensional light transport within an object including heterogeneous scattering. We develop an efficient rendering method using importance sampling that is able to render complex translucent objects under arbitrary lighting. Our method can also leverage the common planar half‐space assumption, which allows it to represent one BSSRDF model that can be used across a variety of geometries. Our results demonstrate that we can render heterogeneous translucent objects under arbitrary lighting and obtain results that match the reference rendered using volumetric path tracing.

NeuralTO: Neural Reconstruction and View Synthesis of Translucent Objects

Learning from multi-view images using neural implicit signed distance functions shows impressive performance on 3D Reconstruction of opaque objects. However, existing methods struggle to reconstruct accurate geometry when applied to translucent objects due to the non-negligible bias in their rendering function. To address the inaccuracies in the existing model, we have reparameterized the density function of the neural radiance field by incorporating an estimated constant extinction coefficient. This modification forms the basis of our innovative framework, which is geared towards highfidelity surface reconstruction and the novel-view synthesis of translucent objects. Our framework contains two stages. In the reconstruction stage, we introduce a novel weight function to achieve accurate surface geometry reconstruction. Following the recovery of geometry, the second phase involves learning the distinct scattering properties of the participating media to enhance rendering. A comprehensive dataset, comprising both synthetic and real translucent objects, has been built for conducting extensive experiments. Experiments reveal that our method outperforms existing approaches in terms of reconstruction and novel-view synthesis.

Optimal multiple fringe pattern composition by weighted complex amplitude fusion in fringe projection profilometry

This paper presents an optimal multiple fringe pattern composition method for 3D shape measurement of high-dynamic-range (HDR) objects using fringe projection profilometry (FPP). With the inverse variance weighting theory, we take the square of the modulation intensities of the fringe pattern images with different intensity levels as the weights to obtain the composited phase of fringe patterns by weighted complex amplitude fusion, which improves the measurement precision of HDR objects. Additionally, we integrate HDR 3D shape measurement and temporal noise reduction into a unified framework by utilizing weighted complex amplitude fusion to completely measure translucent objects with specular reflections. Simulations and experiments demonstrate that our method can achieve higher measurement precision and is resistant to the time-varying ambient light.

Navigating the Manifold of Translucent Appearance

AbstractWe present a perceptually‐motivated manifold for translucent appearance, designed for intuitive editing of translucent materials by navigating through the manifold. Classic tools for editing translucent appearance, based on the use of sliders to tune a number of parameters, are challenging for non‐expert users: These parameters have a highly non‐linear effect on appearance, and exhibit complex interplay and similarity relations between them. Instead, we pose editing as a navigation task in a low‐dimensional space of appearances, which abstracts the user from the underlying optical parameters. To achieve this, we build a low‐dimensional continuous manifold of translucent appearance that correlates with how humans perceive this type of materials. We first analyze the correlation of different distance metrics in image space with human perception. We select the best‐performing metric to build a low‐dimensional manifold, which can be used to navigate the space of translucent appearance. To evaluate the validity of our proposed manifold within its intended application scenario, we build an editing interface that leverages the manifold, and relies on image navigation plus a fine‐tuning step to edit appearance. We compare our intuitive interface to a traditional, slider‐based one in a user study, demonstrating its effectiveness and superior performance when editing translucent objects.

Reconstruction of transparent objects using phase shifting profilometry based on diffusion models

Phase shifting profilometry is an important technique for reconstructing the three-dimensional (3D) geometry of objects with purely diffuse surfaces. However, it is challenging to measure the transparent objects due to the pattern aliasing caused by light refraction and multiple reflections inside the object. In this work, we analyze the aliasing fringe pattern formation for transparent objects and then, propose to learn the front surface light intensity distribution based on the formation principle by using the diffusion models for generating the non-aliased fringe patterns reflected from the front surface only. With the generated fringe patterns, the 3D shape of the transparent objects can be reconstructed via the conventional structured light. We show the feasibility and performance of the proposed method on the data of purely transparent objects that are not seen in the training stage. Moreover, we found it could be generalized to other cases with local-transparent and translucent objects, showing the potential capability of the diffusion based learnable framework in tackling the problems of transparent object reconstruction.

On the validity of two-flux and four-flux models for light scattering in translucent layers: angular distribution of internally reflected light at the interfaces.

Optical characterization and appearance prediction of translucent materials are required in many fields of engineering such as computer graphics, dental restorations or 3D printing technologies. In the case of strongly scattering materials, flux transfer models like the Kubelka-Munk model (2-flux) or the Maheu's 4-flux model have been successfully used to this aim for decades. However, they lead to inaccurate prediction of the color variations of translucent objects of different thicknesses. Indeed, as they rely on the assumption of lambertian fluxes at any depth within the material, they fail to model the internal reflectance at the interfaces, penalizing the accuracy of the optical parameter extraction. The aim of this paper is to investigate the impact of translucency on light angular distribution and corresponding internal reflectances by the mean of the radiative transfer equation, which describes more rigorously the impact of scattering on light propagation. It turns out that the light angular distribution at the bordering interfaces is often far from being lambertian, and that the internal reflectance may vary significantly according to the layer's thickness, refractive index, scattering and absorption coefficients and scattering anisotropy. This work enables to better understand the impact of scattering within a translucent layer and also invites to revisit the well-known Saunderson correction used in 2- or 4-flux models.

Development and comparison of primary facilities for traceable BSSRDF measurements

The bidirectional scattering-surface reflectance distribution function (BSSRDF) describes the radiance originated by the volume scattering and the surface reflectance of any position on the surface of a sample, when it is directionally irradiated at any other position. The present state of the technology for appearance rendering requires a more detailed physical description of translucent objects by traceable measurements of the BSSRDF. This work presents two primary facilities developed for traceable BSSRDF measurements by different measuring approaches. Their results on the same translucent samples have been compared and the achieved BSSRDF scale has been transferred to a commercial measuring system. This study highlights the problems of the different measuring instrument designs and provides the scientific community with more knowledge on the measurement of the BSSRDF, which will be crucial for future works on material appearance.

3-D Shape Measurement of Translucent Objects Based on Fringe Projection

3-D Shape Measurement of Translucent Objects Based on Fringe Projection

Error Model and Concise Temporal Network for Indirect Illumination in 3D Reconstruction.

3D reconstruction is a fundamental task in robotics and AI, providing a prerequisite for many related applications. Fringe projection profilometry is an efficient and non-contact method for generating 3D point clouds out of 2D images. However, during the actual measurement, it is inevitable to experiment with translucent objects, such as skin, marble, and fruit. Indirect illumination from these objects has substantially compromised the precision of 3D reconstruction via the contamination of 2D images. This paper presents a fast and accurate approach to correct for indirect illumination. The essential idea is to design a highly suitable network architecture founded on a precise error model that facilitates accurate error rectification. Initially, our method transforms the error generated by indirect illumination into a sine series. Based on this error model, the multilayer perceptron is more effective in error correction than traditional methods and convolutional neural networks. Our network was trained solely on simulated data but was tested on authentic images. Three sets of experiments, including two sets of comparison experiments, indicate that the designed network can efficiently rectify the error induced by indirect illumination.

Poster Session II: Image features involved in translucency enhancement by chromaticity information.

Previous studies have reported that chromaticity information in object images enhances perceived translucency. The aim of this study, was to elucidate how color forms image features that contribute to translucency in psychophysical experiments. The stimuli were computer-graphics images of translucent objects with different spectral scattering coefficients (i.e., hues) and various optical and geometrical parameters. Achromatic images with the same luminance as the chromatic images were also created. Perceived translucency was measured using Thurston's pairwise comparison, and the effect of chromaticity or translucency was evaluated by comparing translucency in achromatic and chromatic images. The results showed higher perceived translucency for the chromatic object images than the achromatic ones. Subsequently, we analyzed how different image features correlate with the effects of color on translucency using multiple regression analysis. The result indicated that the luminance- chromaticity correlation, which has been proposed as a potential cue for translucency could hardly explain the color effects. Rather, we found that the change in brightness contrast in the diffuse components modulated by the Helmholtz-Kohlrausch (H-K) effect was strongly and negatively correlated with the color effects. These results suggest that some image features, which covary with brightness contrast brightness contrast, contribute to the color effects on translucency.

Towards Understanding and Editing the Appearance of Translucent Materials

In computer graphics, the ability to edit and manipulate material is crucial for creating realistic and believable images. While this ability has always been related to the film and video-games industries, lately more and more product companies want to let their users customize their own products, before buying. Moreover, the recent advances in 3D printing technologies follow this trend. In this context, the usual tools for editing complex materials need to be revised, to simplify the editing phase for novice users. Therefore, we focus on translucent materials, a class of materials that is widely present in the physical world that surrounds us and encompasses both natural substances like food or human skin, as well as synthetic materials such as plastic or jade. While multiple models for translucent materials exist, understanding how we perceive translucent appearance, and how it is affected by illumination and geometry, remains an open problem. In this study, we investigate how accurately human observers can estimate the density of translucent objects under static and dynamic illumination conditions. Surprisingly, our findings suggest that dynamic illumination may not be essential for accurately assessing the nature of translucent materials.

Efficient Path‐Space Differentiable Volume Rendering With Respect To Shapes

AbstractDifferentiable rendering of translucent objects with respect to their shapes has been a long‐standing problem. State‐of‐the‐art methods require detecting object silhouettes or specifying change rates inside translucent objects—both of which can be expensive for translucent objects with complex shapes.In this paper, we address this problem for translucent objects with no refractive or reflective boundaries. By reparameterizing interior components of differential path integrals, our new formulation does not require change rates to be specified in the interior of objects. Further, we introduce new Monte Carlo estimators based on this formulation that do not require explicit detection of object silhouettes.

Practical Acquisition of Shape and Plausible Appearance of Reflective and Translucent Objects

AbstractWe present a practical method for acquisition of shape and plausible appearance of reflective and translucent objects for realistic rendering and relighting applications. Such objects are extremely challenging to scan with existing capture setups, and have previously required complex lightstage hardware emitting continuous illumination. We instead employ a practical capture setup consisting of a set of desktop LCD screens to illuminate such objects with piece‐wise continuous illumination for acquisition. We employ phase‐shifted sinusoidal illumination for novel estimation of high quality photometric normals and transmission vector along with diffuse‐specular separated reflectance/transmission maps for realistic relighting. We further employ neural in‐painting to fill gaps in our measurements caused by gaps in screen illumination, and a novel NeuS‐based neural rendering that combines these shape and reflectance maps acquired from multiple viewpoints for high‐quality 3D surface geometry reconstruction along with plausible realistic rendering of complex light transport in such objects.

Can image cues explain the impact of translucency on perceived gloss?

Gloss perception is a complex psychovisual phenomenon, whose mechanisms are not yet fully explained. Instrumentally measured surface reflectance is usually poor predictor of human perception of gloss. The state-of-the-art studies demonstrate that, in addition to surface reflectance, object's shape and illumination geometry also affect the magnitude of gloss perceived by the human visual system (HVS). Recent studies attribute this to image cues – the specific regularities in image statistics that are generated by a combination of these physical properties, and that, in their part, are proposedly used by the HVS for assessing gloss. Another study has recently demonstrated that subsurface scattering of light is an additional factor that can play the role in perceived gloss, but the study provides limited explanation of this phenomenon. In this work, we aimed to shed more light to this observation and explain why translucency impacts perceived gloss, and why this impact varies among shapes. We conducted four psychophysical experiments in order to explore whether image cues typical for opaque objects also explain the variation of perceived gloss in translucent objects and to quantify how these cues are modulated by the subsurface scattering properties. We found that perceived contrast, coverage area, and sharpness of the highlights can be combined to reliably predict perceived gloss. While sharpness is the most significant cue for assessing glossiness of spherical objects, coverage is more important for a complex Lucy shape. Both of these observations propose an explanation why subsurface scattering albedo impacts perceived gloss.

Quantitative phase contrast imaging with a nonlocal angle-selective metasurface

Phase contrast microscopy has played a central role in the development of modern biology, geology, and nanotechnology. It can visualize the structure of translucent objects that remains hidden in regular optical microscopes. The optical layout of a phase contrast microscope is based on a 4 f image processing setup and has essentially remained unchanged since its invention by Zernike in the early 1930s. Here, we propose a conceptually new approach to phase contrast imaging that harnesses the non-local optical response of a guided-mode-resonator metasurface. We highlight its benefits and demonstrate the imaging of various phase objects, including biological cells, polymeric nanostructures, and transparent metasurfaces. Our results showcase that the addition of this non-local metasurface to a conventional microscope enables quantitative phase contrast imaging with a 0.02π phase accuracy. At a high level, this work adds to the growing body of research aimed at the use of metasurfaces for analog optical computing.

Replaceability of two deep generative models trained with a pair of translucent objects with different geometries

Replaceability of two deep generative models trained with a pair of translucent objects with different geometries

The perception of translucency from surface gloss

Translucent objects (like fruit and wax) reflect and transmit incident light to generate complex retinal image structure. Understanding how we visually perceive translucency from these images is challenging, but previous studies have demonstrated that perceived shape and shading is important for perceiving translucency. We considered the possibility that perceived translucency might also depend on 3D shape inferred from surface gloss (i.e., shape from specular highlights). Here, we performed experiments to test whether interactions between specular and non-specular image properties generated by different 3D shape information influences perceived translucency. Results revealed that perceived translucency could be explained by incongruence in 3D shape used to generate specular and non-specular image components. We proposed a new computational model based on measurable image features informative of shading relative to specular highlights that accounted for 59% of the variability in judgments of perceived translucency from the result of 10-fold cross validation. This model was found to outperform other models based on explicit subjective measures of perceived surface shape, suggesting it implicitly taps much of the relevant geometric information necessary for predicting observer judgments of translucency for glossy materials. These results provide new insight into how the visual system might infer translucency from the structure of specular and non-specular shading generated by glossy semi-opaque materials.

Automatic recognition of internal structures in translucent objects based on hologram-moire interferometry

A new principal optical scheme for automatic recognition of the shape and relative position of inclusions in moving translucent objects is presented. A new criterion for automatic identification of structures (their localization) based on the analysis of the interference pattern projected on the surface of a CCD matrix, which is an element of the proposed scheme of an optical correlator based on the confocal laser tomograph, has been introduced. The results of this work may be of interest to the specialists in the field of non-destructive control; it can find application in the relevant fields