Decomposing scene radiance into physically meaningful components, including direct reflection, interreflection, and scattering, enables a deeper understanding of scene appearance. In this paper, we propose the first method to perform multi-range radiance component separation using only events captured by an event camera, without requiring any additional frame-based measurements. Our approach scans the scene by swiping line-shaped illumination across it, while exploiting the event camera's high temporal resolution and wide dynamic range to recover both direct and multiple global components corresponding to different light propagation distances. To address the noise inherent in event-integration-based radiance recovery, we present a pixel-wise calibration strategy that leverages the reproducibility of per-pixel noise patterns. We demonstrate that this calibration is highly effective in suppressing noise, enabling stable recovery from subtle signals. Moreover, we show that by detecting the timing at which the scanning line passes each pixel, the same line-scan event data can be exploited for coarse 3D reconstruction. Experimental results on real scenes show that our event-based approach achieves faster and finer component separation, while also enabling coarse depth estimation without the exposure control required by frame-based cameras.

Universal Photometric Stereo is a promising approach for recovering surface normals without strict lighting assumptions. However, it struggles when multi-illumination cues are unreliable, such as under biased lighting or in shadows or self-occluded regions of complex in-the-wild scenes. We propose GeoUniPS, a universal photometric stereo network that integrates synthetic supervision with high-level geometric priors from large-scale 3D reconstruction models pretrained on massive in-the-wild data. Our key insight is that these 3D reconstruction models serve as visual-geometry foundation models, inherently encoding rich geometric knowledge of real scenes. To leverage this, we design a Light-Geometry Dual-Branch Encoder that extracts both multi-illumination cues and geometric priors from the frozen 3D reconstruction model. We also address the limitations of the conventional orthographic projection assumption by introducing the PS-Perp dataset with realistic perspective projection to enable learning of spatially varying view directions. Extensive experiments demonstrate that GeoUniPS delivers state-of-the-arts performance across multiple datasets, both quantitatively and qualitatively, especially in the complex in-the-wild scenes.

The perception of visual content in optical-see-through augmented reality (AR) devices is affected by the light coming from the environment. This additional light interacts with the content in a non-trivial manner because of the illusion of transparency, different focal depths, and motion parallax. To investigate the impact of environment light on display artifact visibility (such as blur or color fringes), we created the first subjective quality dataset targeted toward augmented reality displays. Our study consisted of 6 scenes, each affected by one of 6 distortions at two strength levels, seen against one of 3 background patterns shown at 2 luminance levels: 432 conditions in total. Our dataset shows that environment light has a much smaller masking effect than expected. Further, we show that this effect cannot be explained by compositing of the AR-content with the background using optical blending models. As a consequence, we demonstrate that existing video quality metrics perform worse than expected when predicting the perceived magnitude of degradation in AR displays, motivating further research.

ColorVideoVDP is a video and image quality metric that models spatial and temporal aspects of vision for both luminance and color. The metric is built on novel psychophysical models of chromatic spatiotemporal contrast sensitivity and cross-channel contrast masking. It accounts for the viewing conditions, geometric, and photometric characteristics of the display. It was trained to predict common video-streaming distortions (e.g., video compression, rescaling, and transmission errors) and also 8 new distortion types related to AR/VR displays (e.g., light source and waveguide non-uniformities). To address the latter application, we collected our novel XR-Display-Artifact-Video quality dataset (XR-DAVID), comprised of 336 distorted videos. Extensive testing on XR-DAVID, as well as several datasets from the literature, indicate a significant gain in prediction performance compared to existing metrics. ColorVideoVDP opens the doors to many novel applications that require the joint automated spatiotemporal assessment of luminance and color distortions, including video streaming, display specification, and design, visual comparison of results, and perceptually-guided quality optimization. The code for the metric can be found at https://github.com/gfxdisp/ColorVideoVDP.

High-fidelity radiance recovery plays a crucial role in scene information reconstruction and understanding. Conventional cameras suffer from limited sensitivity in dynamic range, bit depth, and spectral response, etc. In this paper, we propose to use event cameras with bio-inspired silicon sensors, which are sensitive to radiance changes, to recover precise radiance values. We reveal that, under active lighting conditions, the transient frequency of event signals triggering linearly reflects the radiance value. We propose an innovative method to convert the high temporal resolution of event signals into precise radiance values. The precise radiance values yields several capabilities in image analysis. We demonstrate the feasibility of recovering radiance values solely from the transient event frequency (TEF) through multiple experiments.

Spectral photoacoustic imaging (PAI) is a new technology that is able to provide 3D geometric structure associated with 1D wavelength-dependent absorption information of the interior of a target in a non-invasive manner. It has potentially broad applications in clinical and medical diagnosis. Unfortunately, the usability of spectral PAI is severely affected by a time-consuming data scanning process and complex noise. Therefore in this study, we propose a reliability-aware restoration framework to recover clean 4D data from incomplete and noisy observations. To the best of our knowledge, this is the first attempt for the 4D spectral PA data restoration problem that solves data completion and denoising simultaneously. We first present a sequence of analyses, including modeling of data reliability in the depth and spectral domains, developing an adaptive correlation graph, and analyzing local patch orientation. On the basis of these analyses, we explore global sparsity and local self-similarity for restoration. We demonstrated the effectiveness of our proposed approach through experiments on real data captured from patients, where our approach outperformed the state-of-the-art methods in both objective evaluation and subjective assessment.

Recognizing wet surfaces and their degrees of wetness is essential for many computer vision applications. Surface wetness can inform us slippery spots on a road to autonomous vehicles, muddy areas of a trail to humanoid robots, and the freshness of groceries to us. The fact that surfaces darken when wet, i.e., monochromatic appearance change, has been modeled to recognize wet surfaces in the past. In this paper, we show that color change, particularly in its spectral behavior, carries rich information about surface wetness. We first derive an analytical spectral appearance model of wet surfaces that expresses the characteristic spectral sharpening due to multiple scattering and absorption in the surface. We present a novel method for estimating key parameters of this spectral appearance model, which enables the recovery of the original surface color and the degree of wetness from a single multispectral image. Applied to a multispectral image, the method estimates the spatial map of wetness together with the dry spectral distribution of the surface. To our knowledge, this is the first work to model and leverage the spectral characteristics of wet surfaces to decipher its appearance. We conduct comprehensive experimental validation with a number of wet real surfaces. The results demonstrate the accuracy of our model and the effectiveness of our method for surface wetness and color estimation.

Recovering the 3D geometry of a purely texture-less object with generally unknown surface reflectance (e.g. non-Lambertian) is regarded as a challenging task in multi-view reconstruction. The major obstacle revolves around establishing cross-view correspondences where photometric constancy is violated. This paper proposes a simple and practical solution to overcome this challenge based on a co-located camera-light scanner device. Unlike existing solutions, we do not explicitly solve for correspondence. Instead, we argue the problem is generally well-posed by multi-view geometrical and photometric constraints, and can be solved from a small number of input views. We formulate the reconstruction task as a joint energy minimization over the surface geometry and reflectance. Despite this energy is highly non-convex, we develop an optimization algorithm that robustly recovers globally optimal shape and reflectance even from a random initialization. Extensive experiments on both simulated and real data have validated our method, and possible future extensions are discussed.

This paper introduces a novel depth recovery method based on light absorption in water. Water absorbs light at almost all wavelengths whose absorption coefficient is related to the wavelength. Based on the Beer-Lambert model, we introduce a bispectral depth recovery method that leverages the light absorption difference between two near-infrared wavelengths captured with a distant point source and orthographic cameras. Through extensive analysis, we show that accurate depth can be recovered irrespective of the surface texture and reflectance, and introduce algorithms to correct for nonidealities of a practical implementation including tilted light source and camera placement, nonideal bandpass filters and the perspective effect of the camera with a diverging point light source. We construct a coaxial bispectral depth imaging system using low-cost off-the-shelf hardware and demonstrate its use for recovering the shapes of complex and dynamic objects in water. We also present a trispectral variant to further improve robustness to extremely challenging surface reflectance. Experimental results validate the theory and practical implementation of this novel depth recovery paradigm, which we refer to as shape from water.

This paper proposes a method of underwater depth estimation from an orthographic multispectral image. In accordance with Snell's law, incoming light is refracted when it enters the water surface, and its directions are determined by the refractive index and the normals of the water surface. The refractive index is wavelength-dependent, and this leads to some disparity between images taken at different wavelengths. Given the camera orientation and the refractive index of a medium such as water, our approach can reconstruct the underwater scene with unknown water surface from the disparity observed in images taken at different wavelengths. We verified the effectiveness of our method through simulations and real experiments on various scenes.

Visual recognition of wet surfaces and their degrees of wetness is important for many computer vision applications. It can inform slippery spots on a road to autonomous vehicles, muddy areas of a trail to humanoid robots, and the freshness of groceries to us. In the past, monochromatic appearance change, the fact that surfaces darken when wet, has been modeled to recognize wet surfaces. In this paper, we show that color change, particularly in its spectral behavior, carries rich information about a wet surface. We derive an analytical spectral appearance model of wet surfaces that expresses the characteristic spectral sharpening due to multiple scattering and absorption in the surface. We derive a novel method for estimating key parameters of this spectral appearance model, which enables the recovery of the original surface color and the degree of wetness from a single observation. Applied to a multispectral image, the method estimates the spatial map of wetness together with the dry spectral distribution of the surface. To our knowledge, this work is the first to model and leverage the spectral characteristics of wet surfaces to revert its appearance. We conduct comprehensive experimental validation with a number of wet real surfaces. The results demonstrate the accuracy of our model and the effectiveness of our method for surface wetness and color estimation.

Many natural and manmade objects contain fluorescent substance. To visualize the distribution of fluorescence emitting substance is of great importance for food freshness examination, molecular dynamics analysis and so on. Unfortunately, the presence of fluorescent substance is usually imperceptible under strong ambient illumination, since fluorescent emission is relatively weak compared with surface reflectance. Even assuming that surface reflectance could be somehow blocked out, shading effect on fluorescent emission that relates to surface geometry would still interfere with visibility of fluorescent substance in the scene. In this paper, we propose a visibility enhancement method to better visualize the distribution of fluorescent substance under unknown and uncontrolled ambient illumination. By using an image pair captured with UV and visible flash illumination, we obtain a shading-free luminance image that visualizes the distribution of fluorescent emission. We further replace the luminance of the RGB image under ambient illumination by using this fluorescent emission luminance, so as to obtain a full colored image. The effectiveness of our method has been verified when used to visualize weak fluorescence from bacteria on rotting cheese and meat.