Indirect time-of-flight (iToF) imaging allows us to capture dense depth information at a low cost. However, iToF imaging often suffers from multipath interference (MPI) artifacts in the presence of scattering media, resulting in severe depth-accuracy degradation. For instance, iToF cameras cannot measure depth accurately through fog because ToF active illumination scatters back to the sensor before reaching the farther target surface. In this work, we propose a polarimetric iToF imaging method that can capture depth information robustly through scattering media. Our observations on the principle of indirect ToF imaging and polarization of light allow us to formulate a novel computational model of scattering-aware polarimetric phase measurements that enables us to correct MPI errors. We first devise a scattering-aware polarimetric iToF model that can estimate the phase of unpolarized backscattered light. We then combine the optical filtering of polarization and our computational modeling of unpolarized backscattered light via scattering analysis of phase and amplitude. This allows us to tackle the MPI problem by estimating the scattering energy through the participating media. We validate our method on an experimental setup using a customized off-the-shelf iToF camera. Our method outperforms baseline methods by a significant margin by means of our scattering model and polarimetric phase measurements.

Abstract Despite advances in display technology, many existing applications rely on psychophysical datasets of human perception gathered using older, sometimes outdated displays. As a result, there exists the underlying assumption that such measurements can be carried over to the new viewing conditions of more modern technology. We have conducted a series of psychophysical experiments to explore contrast sensitivity using a state‐of‐the‐art HDR display, taking into account not only the spatial frequency and luminance of the stimuli but also their surrounding luminance levels. From our data, we have derived a novel surround‐aware contrast sensitivity function (CSF), which predicts human contrast sensitivity more accurately. We additionally provide a practical version that retains the benefits of our full model, while enabling easy backward compatibility and consistently producing good results across many existing applications that make use of CSF models. We show examples of effective HDR video compression using a transfer function derived from our CSF, tone‐mapping and improved accuracy in visual difference prediction.

Ellipsometry techniques allow to measure polarization information of materials, requiring precise rotations of optical components with different configurations of lights and sensors. This results in cumbersome capture devices, carefully calibrated in lab conditions, and in very long acquisition times, usually in the order of a few days per object. Recent techniques allow to capture polarimetric spatially-varying reflectance information, but limited to a single view, or to cover all view directions, but limited to spherical objects made of a single homogeneous material. We present sparse ellipsometry , a portable polarimetric acquisition method that captures both polarimetric SVBRDF and 3D shape simultaneously. Our handheld device consists of off-the-shelf, fixed optical components. Instead of days, the total acquisition time varies between twenty and thirty minutes per object. We develop a complete polarimetric SVBRDF model that includes diffuse and specular components, as well as single scattering, and devise a novel polarimetric inverse rendering algorithm with data augmentation of specular reflection samples via generative modeling. Our results show a strong agreement with a recent ground-truth dataset of captured polarimetric BRDFs of real-world objects.

Single-shot monocular birefractive stereo methods have been used for estimating sparse depth from double refraction over edges. They also obtain an ordinary-ray (oray) image concurrently or subsequently through additional post-processing of depth densification and deconvolution. However, when an extraordinary-ray (e-ray) image is restored to acquire stereo images, the existing methods suffer from very severe restoration artifacts due to a low signal-to-noise ratio of input e-ray image or depth/deconvolution errors. In this work, we present a novel stereo image restoration network that can restore stereo images directly from a double-refraction image. First, we built a physically faithful birefractive stereo imaging dataset by simulating the double refraction phenomenon with existing RGB-D datasets. Second, we formulated a joint stereo restoration problem that accounts for not only geometric relation between o/e-ray images but also joint optimization of restoring both stereo images. We trained our model with our birefractive image dataset in an end-to-end manner. Our model restores high-quality stereo images directly from double refraction in real-time, enabling high-quality stereo video using a monocular camera. Our method also allows us to estimate dense depth maps from stereo images using a conventional stereo method. We evaluate the performance of our method experimentally and synthetically with the ground truth. Results validate that our stereo image restoration network outperforms the existing methods with high accuracy. We demonstrate several image-editing applications using our high-quality stereo images and dense depth maps.

A set of cameras with fisheye lenses have been used to capture a wide field of view. The traditional scan-line stereo algorithms based on epipolar geometry are directly inapplicable to this non-pinhole camera setup due to optical characteristics of fisheye lenses; hence, existing complete 360° RGB-D imaging systems have rarely achieved realtime performance yet. In this paper, we introduce an efficient sphere-sweeping stereo that can run directly on multiview fisheye images without requiring additional spherical rectification. Our main contributions are: First, we introduce an adaptive spherical matching method that accounts for each input fisheye camera’s resolving power concerning spherical distortion. Second, we propose a fast inter-scale bilateral cost volume filtering method that refines distance in noisy and textureless regions with optimal complexity of O(n). It enables real-time dense distance estimation while preserving edges. Lastly, the fisheye color and distance images are seamlessly combined into a complete 360° RGB-D image via fast inpainting of the dense distance map. We demonstrate an embedded 360° RGB-D imaging prototype composed of a mobile GPU and four fisheye cameras. Our prototype is capable of capturing complete 360° RGB-D videos with a resolution of two megapixels at 29 fps. Results demonstrate that our real-time method outperforms traditional omnidirectional stereo and learning-based omnidirectional stereo in terms of accuracy and performance.

In order to enable view-dependent appearance synthesis from the light fields of a scene, it is critical to evaluate the geometric relationships between light and view over surfaces in the scene with high accuracy. Perfect diffuse reflectance is commonly assumed to estimate geometry from light fields via multiview stereo. However, this diffuse surface assumption is invalid with real-world objects. Geometry estimated from light fields is severely degraded over specular surfaces. Additional scene-scale 3D scanning based on active illumination could provide reliable geometry, but it is sparse and thus still insufficient to calculate view-dependent appearance, such as specular reflection, in geometry-based view synthesis. In this work, we present a practical solution of inverse rendering to enable view-dependent appearance synthesis, particularly of scene scale. We enhance the scene geometry by eliminating the specular component, thus enforcing photometric consistency. We then estimate spatially-varying parameters of diffuse, specular, and normal components from wide-baseline light fields. To validate our method, we built a wide-baseline light field imaging prototype that consists of 32 machine vision cameras with fisheye lenses of 185 degrees that cover the forward hemispherical appearance of scenes. We captured various indoor scenes, and results validate that our method can estimate scene geometry and reflectance parameters with high accuracy, enabling view-dependent appearance synthesis at scene scale with high fidelity, i.e., specular reflection changes according to a virtual viewpoint.

Imaging depth and spectrum have been extensively studied in isolation from each other for decades. Recently, hyperspectral-depth (HS-D) imaging emerges to capture both information simultaneously by combining two different imaging systems; one for depth, the other for spectrum. While being accurate, this combinational approach induces increased form factor, cost, capture time, and alignment/registration problems. In this work, departing from the combinational principle, we propose a compact single-shot monocular HS-D imaging method. Our method uses a diffractive optical element (DOE), the point spread function of which changes with respect to both depth and spectrum. This enables us to reconstruct spectrum and depth from a single captured image. To this end, we develop a differentiable simulator and a neural-network-based reconstruction method that are jointly optimized via automatic differentiation. To facilitate learning the DOE, we present a first HS-D dataset by building a benchtop HS-D imager that acquires high-quality ground truth. We evaluate our method with synthetic and real experiments by building an experimental prototype and achieve state-of-the-art HS-D imaging results.

Traditional snapshot hyperspectral imaging systems include various optical elements: a dispersive optical element (prism), a coded aperture, several relay lenses, and an imaging lens, resulting in an impractically large form factor. We seek an alternative, minimal form factor of snapshot spectral imaging based on recent advances in diffractive optical technology. We thereupon present a compact, diffraction-based snapshot hyperspectral imaging method, using only a novel diffractive optical element (DOE) in front of a conventional, bare image sensor. Our diffractive imaging method replaces the common optical elements in hyperspectral imaging with a single optical element. To this end, we tackle two main challenges: First, the traditional diffractive lenses are not suitable for color imaging under incoherent illumination due to severe chromatic aberration because the size of the point spread function (PSF) changes depending on the wavelength. By leveraging this wavelength-dependent property alternatively for hyperspectral imaging, we introduce a novel DOE design that generates an anisotropic shape of the spectrally-varying PSF. The PSF size remains virtually unchanged, but instead the PSF shape rotates as the wavelength of light changes. Second, since there is no dispersive element and no coded aperture mask, the ill-posedness of spectral reconstruction increases significantly. Thus, we propose an end-to-end network solution based on the unrolled architecture of an optimization procedure with a spatial-spectral prior, specifically designed for deconvolution-based spectral reconstruction. Finally, we demonstrate hyperspectral imaging with a fabricated DOE attached to a conventional DSLR sensor. Results show that our method compares well with other state-of-the-art hyperspectral imaging methods in terms of spectral accuracy and spatial resolution, while our compact, diffraction-based spectral imaging method uses only a single optical element on a bare image sensor.

We present a novel method that can enhance the spatial resolution of stereo images using a parallax prior. While traditional stereo imaging has focused on estimating depth from stereo images, our method utilizes stereo images to enhance spatial resolution instead of estimating disparity. The critical challenge for enhancing spatial resolution from stereo images: how to register corresponding pixels with subpixel accuracy. Since disparity in traditional stereo imaging is calculated per pixel, it is directly inappropriate for enhancing spatial resolution. We, therefore, learn a parallax prior from stereo image datasets by jointly training two-stage networks. The first network learns how to enhance the spatial resolution of stereo images in luminance, and the second network learns how to reconstruct a high-resolution color image from high-resolution luminance and chrominance of the input image. Our two-stage joint network enhances the spatial resolution of stereo images significantly more than single-image super-resolution methods. The proposed method is directly applicable to any stereo depth imaging methods, enabling us to enhance the spatial resolution of stereo images.

Capturing appearance often requires dense sampling in light-view space, which is often achieved in specialized, expensive hardware setups. With the aim of realizing a compact acquisition setup without multiple angular samples of light and view, we sought to leverage an alternative optical property of light, polarization. To this end, we capture a set of polarimetric images with linear polarizers in front of a single projector and camera to obtain the appearance and normals of real-world objects. We encountered two technical challenges: First, no complete polarimetric BRDF model is available for modeling mixed polarization of both specular and diffuse reflection. Second, existing polarization-based inverse rendering methods are not applicable to a single local illumination setup since they are formulated with the assumption of spherical illumination. To this end, we first present a complete polarimetric BRDF (pBRDF) model that can define mixed polarization of both specular and diffuse reflection. Second, by leveraging our pBRDF model, we propose a novel inverse-rendering method with joint optimization of pBRDF and normals to capture spatially-varying material appearance: per-material specular properties (including the refractive index, specular roughness and specular coefficient), per-pixel diffuse albedo and normals. Our method can solve the severely ill-posed inverse-rendering problem by carefully accounting for the physical relationship between polarimetric appearance and geometric properties. We demonstrate how our method overcomes limited sampling in light-view space for inverse rendering by means of polarization.

Microfacet theory concisely models light transport over rough surfaces. Specular reflection is the result of single mirror reflections on each facet, while exact computation of multiple scattering is either neglected, or modeled using costly importance sampling techniques. Practical but accurate simulation of multiple scattering in microfacet theory thus remains an open challenge. In this work, we revisit the traditional V-groove cavity model and derive an analytical, cost-effective solution for multiple scattering in rough surfaces. Our kaleidoscopic model is made up of both real and virtual V-grooves, and allows us to calculate higher-order scattering in the microfacets in an analytical fashion. We then extend our model to include nonsymmetric grooves, allowing for additional degrees of freedom on the surface geometry, improving multiple reflections at grazing angles with backward compatibility to traditional normal distribution functions. We validate the accuracy of our model against ground-truth Monte Carlo simulations, and demonstrate its flexibility on anisotropic and textured materials. Our model is analytical, does not introduce significant cost and variance, can be seamless integrated in any rendering engine, preserves reciprocity and energy conservation, and is suitable for bidirectional methods.

We present a novel hyperspectral image reconstruction algorithm, which overcomes the long-standing tradeoff between spectral accuracy and spatial resolution in existing compressive imaging approaches. Our method consists of two steps: First, we learn nonlinear spectral representations from real-world hyperspectral datasets; for this, we build a convolutional autoencoder which allows reconstructing its own input through its encoder and decoder networks. Second, we introduce a novel optimization method, which jointly regularizes the fidelity of the learned nonlinear spectral representations and the sparsity of gradients in the spatial domain, by means of our new fidelity prior. Our technique can be applied to any existing compressive imaging architecture, and has been thoroughly tested both in simulation, and by building a prototype hyperspectral imaging system. It outperforms the state-of-the-art methods from each architecture, both in terms of spectral accuracy and spatial resolution, while its computational complexity is reduced by two orders of magnitude with respect to sparse coding techniques. Moreover, we present two additional applications of our method: hyperspectral interpolation and demosaicing. Last, we have created a new high-resolution hyperspectral dataset containing sharper images of more spectral variety than existing ones, available through our project website.

Abstract The coded aperture snapshot spectral imaging (CASSI) architecture has been employed widely for capturing hyperspectral video. Despite allowing concurrent capture of hyperspectral video, spatial modulation in CASSI sacrifices image resolution significantly while reconstructing spectral projection via sparse sampling. Several multiview alternatives have been proposed to handle this low spatial resolution problem and improve measurement accuracy, for instance, by adding a translation stage for the coded aperture or changing the static coded aperture with a digital micromirror device for dynamic modulation. State‐of‐the‐art solutions enhance spatial resolution significantly but are incapable of capturing video using CASSI. In this paper, we present a novel compressive coded aperture imaging design that increases spatial resolution while capturing 4D hyperspectral video of dynamic scenes. We revise the traditional CASSI design to allow for multiple sampling of the randomness of spatial modulation in a single frame. We demonstrate that our compressive video spectroscopy approach yields enhanced spatial resolution and consistent measurements, compared with the traditional CASSI design.