Volumetric video enables immersive free viewpoint rendering of dynamic real world scenes, yet existing methods struggle with long sequences and complex motions, often leading to temporal instability and visual artifacts. To address these challenges, we propose ATGS, a Gaussian splatting based framework for volumetric video reconstruction. Our key insight is that explicitly tracking long term complex motion with individual Gaussian primitives is inherently unstable. Instead, we organize Gaussians around time conditioned anchors that localize their spatial and temporal support, thereby reducing long range motion complexity. We further introduce a temporal windowing strategy to activate only anchors relevant to the queried time, which improves scalability and temporal coherence. In addition, to ensure spatial and temporal stability, we design a compact set of multi level anchor features that encode global features, local spatial features, and local temporal features, jointly constraining Gaussian generation. Extensive experiments demonstrate that ATGS consistently outperforms prior methods on long sequence volumetric videos with complex motions. Project page: https://github.com/WuJH2001/ATGS.

While consumer displays increasingly support more than 10 stops of dynamic range, most image assets — such as internet photographs and generative AI content — remain limited to 8-bit low dynamic range (LDR), constraining their utility across high dynamic range (HDR) applications. Currently, no generative model can produce high-bit, high-dynamic range content in a generalizable way. Existing LDR-to-HDR conversion methods often struggle to produce photorealistic details and physically-plausible dynamic range in the clipped areas. We introduce LEDiff, a method that enables a generative model with HDR content generation through latent space fusion inspired by image-space exposure fusion techniques. It also functions as an LDR-to-HDR converter, expanding the dynamic range of existing low-dynamic range images. Our approach uses a small HDR dataset to enable a pretrained diffusion model to recover detail and dynamic range in clipped highlights and shadows. LEDiff brings HDR capabilities to existing generative models and converts any LDR image to HDR, creating photorealistic HDR outputs for image generation, image-based lighting (HDR environment map generation), and photographic effects such as depth of field simulation, where linear HDR data is essential for realistic quality.

Abstract Radiance field methods represent the state of the art in reconstructing complex scenes from multi‐view photos. However, these reconstructions often suffer from one or both of the following limitations: First, they typically represent scenes in low dynamic range (LDR), which restricts their use to evenly lit environments and hinders immersive viewing experiences. Secondly, their reliance on a pinhole camera model, assuming all scene elements are in focus in the input images, presents practical challenges and complicates refocusing during novel‐view synthesis. Addressing these limitations, we present a lightweight method based on 3D Gaussian Splatting that utilizes multi‐view LDR images of a scene with varying exposure times, apertures, and focus distances as input to reconstruct a high‐dynamic‐range (HDR) radiance field. By incorporating analytical convolutions of Gaussians based on a thin‐lens camera model as well as a tonemapping module, our reconstructions enable the rendering of HDR content with flexible refocusing capabilities. We demonstrate that our combined treatment of HDR and depth of field facilitates real‐time cinematic rendering, outperforming the state of the art.

Most in-the-wild images are stored in Low Dynamic Range (LDR) form, serving as a partial observation of the High Dynamic Range (HDR) visual world. Despite limited dynamic range, these LDR images are often captured with different exposures, implicitly containing information about the underlying HDR image distribution. Inspired by this intuition, in this work we present, to the best of our knowledge, the first method for learning a generative model of HDR images from in-the-wild LDR image collections in a fully unsupervised manner. The key idea is to train a generative adversarial network (GAN) to generate HDR images which, when projected to LDR under various exposures, are indistinguishable from real LDR images. The projection from HDR to LDR is achieved via a camera model that captures the stochasticity in exposure and camera response function. Experiments show that our method GlowGAN can synthesize photorealistic HDR images in many challenging cases such as landscapes, lightning, or windows, where previous supervised generative models produce overexposed images. With the assistance of GlowGAN, we showcase the novel application of unsupervised inverse tone mapping (GlowGAN-ITM) that sets a new paradigm in this field. Unlike previous methods that gradually complete information from LDR input, GlowGAN-ITM searches the entire HDR image manifold modeled by GlowGAN for the HDR images which can be mapped back to the LDR input. GlowGAN-ITM achieves more realistic reconstruction of overexposed regions compared to state-of-the-art supervised learning models, despite not requiring HDR images or paired multi-exposure images for training.

A faithful reproduction of gloss is inherently difficult because of the limited dynamic range, peak luminance, and 3D capabilities of display devices. This work investigates how the display capabilities affect gloss appearance with respect to a real-world reference object. To this end, we employ an accurate imaging pipeline to achieve a perceptual gloss match between a virtual and real object presented side-by-side on an augmented-reality high-dynamic-range (HDR) stereoscopic display, which has not been previously attained to this extent. Based on this precise gloss reproduction, we conduct a series of gloss matching experiments to study how gloss perception degrades based on individual factors: object albedo, display luminance, dynamic range, stereopsis, and tone mapping. We support the study with a detailed analysis of individual factors, followed by an in-depth discussion on the observed perceptual effects. Our experiments demonstrate that stereoscopic presentation has a limited effect on the gloss matching task on our HDR display. However, both reduced luminance and dynamic range of the display reduce the perceived gloss. This means that the visual system cannot compensate for the changes in gloss appearance across luminance (lack of gloss constancy), and the tone mapping operator should be carefully selected when reproducing gloss on a low dynamic range (LDR) display.

In everyday photography, physical limitations of camera sensors and lenses frequently lead to a variety of degradations in captured images such as saturation or defocus blur. A common approach to overcome these limitations is to resort to image stack fusion, which involves capturing multiple images with different focal distances or exposures. For instance, to obtain an all-in-focus image, a set of multi-focus images is captured. Similarly, capturing multiple exposures allows for the reconstruction of high dynamic range. In this paper, we present a novel approach that combines neural fields with an expressive camera model to achieve a unified reconstruction of an all-in-focus high-dynamic-range image from an image stack. Our approach is composed of a set of specialized implicit neural representations tailored to address specific sub-problems along our pipeline: We use neural implicits to predict flow to overcome misalignments arising from lens breathing, depth, and all-in-focus images to account for depth of field, as well as tonemapping to deal with sensor responses and saturation - all trained using a physically inspired supervision structure with a differentiable thin lens model at its core. An important benefit of our approach is its ability to handle these tasks simultaneously or independently, providing flexible post-editing capabilities such as refocusing and exposure adjustment. By sampling the three primary factors in photography within our framework (focal distance, aperture, and exposure time), we conduct a thorough exploration to gain valuable insights into their significance and impact on overall reconstruction quality. Through extensive validation, we demonstrate that our method outperforms existing approaches in both depth-from-defocus and all-in-focus image reconstruction tasks. Moreover, our approach exhibits promising results in each of these three dimensions, showcasing its potential to enhance captured image quality and provide greater control in post-processing.

A good match of material appearance between real-world objects and their digital on-screen representations is critical for many applications such as fabrication, design, and e-commerce. However, faithful appearance reproduction is challenging, especially for complex phenomena, such as gloss. In most cases, the view-dependent nature of gloss and the range of luminance values required for reproducing glossy materials exceeds the current capabilities of display devices. As a result, appearance reproduction poses significant problems even with accurately rendered images. This paper studies the gap between the gloss perceived from real-world objects and their digital counterparts. Based on our psychophysical experiments on a wide range of 3D printed samples and their corresponding photographs, we derive insights on the influence of geometry, illumination, and the display’s brightness and measure the change in gloss appearance due to the display limitations. Our evaluation experiments demonstrate that using the prediction to correct material parameters in a rendering system improves the match of gloss appearance between real objects and their visualization on a display device.

Abstract High Dynamic Range (HDR) content is becoming ubiquitous due to the rapid development of capture technologies. Nevertheless, the dynamic range of common display devices is still limited, therefore tone mapping (TM) remains a key challenge for image visualization. Recent work has demonstrated that neural networks can achieve remarkable performance in this task when compared to traditional methods, however, the quality of the results of these learning‐based methods is limited by the training data. Most existing works use as training set a curated selection of best‐performing results from existing traditional tone mapping operators (often guided by a quality metric), therefore, the quality of newly generated results is fundamentally limited by the performance of such operators. This quality might be even further limited by the pool of HDR content that is used for training. In this work we propose a learning‐based self‐supervised tone mapping operator that is trained at test time specifically for each HDR image and does not need any data labeling. The key novelty of our approach is a carefully designed loss function built upon fundamental knowledge on contrast perception that allows for directly comparing the content in the HDR and tone mapped images. We achieve this goal by reformulating classic VGG feature maps into feature contrast maps that normalize local feature differences by their average magnitude in a local neighborhood, allowing our loss to account for contrast masking effects. We perform extensive ablation studies and exploration of parameters and demonstrate that our solution outperforms existing approaches with a single set of fixed parameters, as confirmed by both objective and subjective metrics.

Tone mapping operators (TMOs) can compress the range of high dynamic range (HDR) images so that they can be displayed normally on the low dynamic range (LDR) devices. Recent TMOs based on deep neural networks can produce impressive results, but there are still some shortcomings. On the one hand, their supervised learning procedure requires a high-quality paired dataset which is hard to be accessed. On the other hand, they are too slow and heavy to meet the needs of practical applications. This paper proposes a real-time deep semi-supervised learning TMO to solve the above problems. The proposed method learns in a semi-supervised manner by combining the adversarial loss, cycle consistency loss, and the pixel-wise loss. The first two can simulate the image distributions in the real world from the unpaired LDR data and the latter can learn the guidance of paired LDR labels. In this way, the proposed method only requires HDR sources, unpaired high-quality LDR images, and a few well tone-mapped HDR-LDR pairs as training data. Furthermore, the proposed method divides tone mapping into luminance mapping and saturation adjustment and then processes them simultaneously. By this strategy, we can reconstruct each component more precisely. Based on the aforementioned improvements, we propose a lightweight tone mapping network that is efficient in tone mapping task (up to 5000x parameters-saving and 27x time-saving compared to the learning-based TMOs). Both quantitative and qualitative results demonstrate that the proposed method performs favorable against state-of-the-art TMOs.

This work studies intragroup sparsity, a fine-grained structural constraint on network weight parameters. It eliminates the computational inefficiency of fine-grained sparsity due to irregular dataflow, while at the same time achieving high inference accuracy. We present theoretical analysis on how weight group sizes affect sparsification error, and on how the performance of pruned networks changes with sparsity level. Further, we analyze inference-time I/O cost of two different strategies for achieving intragroup sparsity and how the choice of strategies affect I/O cost under mild assumptions on accelerator architecture. Moreover, we present a novel training algorithm that yield models of improved accuracies over the standard training approach under the intragroup sparsity constraint.

Material appearance hinges on material reflectance properties but also surface geometry and illumination. The unlimited number of potential combinations between these factors makes understanding and predicting material appearance a very challenging task. In this work, we collect a large-scale dataset of perceptual ratings of appearance attributes with more than 215,680 responses for 42,120 distinct combinations of material, shape, and illumination. The goal of this dataset is twofold. First, we analyze for the first time the effects of illumination and geometry in material perception across such a large collection of varied appearances. We connect our findings to those of the literature, discussing how previous knowledge generalizes across very diverse materials, shapes, and illuminations. Second, we use the collected dataset to train a deep learning architecture for predicting perceptual attributes that correlate with human judgments. We demonstrate the consistent and robust behavior of our predictor in various challenging scenarios, which, for the first time, enables estimating perceived material attributes from general 2D images. Since our predictor relies on the final appearance in an image, it can compare appearance properties across different geometries and illumination conditions. Finally, we demonstrate several applications that use our predictor, including appearance reproduction using 3D printing, BRDF editing by integrating our predictor in a differentiable renderer, illumination design, or material recommendations for scene design.

Abstract The understanding of material appearance perception is a complex problem due to interactions between material reflectance, surface geometry, and illumination. Recently, Serrano et al. collected the largest dataset to date with subjective ratings of material appearance attributes, including glossiness, metallicness, sharpness and contrast of reflections. In this work, we make use of their dataset to investigate for the first time the impact of the interactions between illumination, geometry, and eight different material categories in perceived appearance attributes. After an initial analysis, we select for further analysis the four material categories that cover the largest range for all perceptual attributes: fabric, plastic, ceramic, and metal. Using a cumulative link mixed model (CLMM) for robust regression, we discover interactions between these material categories and four representative illuminations and object geometries. We believe that our findings contribute to expanding the knowledge on material appearance perception and can be useful for many applications, such as scene design, where any particular material in a given shape can be aligned with dominant classes of illumination, so that a desired strength of appearance attributes can be achieved.

Deep neural networks are well known to achieve outstanding results in many domains. Recently, many researchers have introduced deep neural networks into remote sensing (RS) data processing. However, typical RS data usually possesses enormous scale. Processing RS data with deep neural networks requires a rather demanding computing hardware. Most high-performance deep neural networks are associated with highly complex network structures with many parameters. This restricts their deployment for real-time processing in satellites. Many researchers have attempted overcoming this obstacle by reducing network complexity. One of the promising approaches able to reduce network computational complexity and memory usage dramatically is network binarization. In this letter, through analyzing the learning behavior of binarized neural networks (BNNs), we propose several novel strategies for improving the performance of BNNs. Empirical experiments prove these strategies to be effective in improving BNN performance for image classification tasks on both small- and large-scale data sets. We also test BNN on a remote sense data set with positive results. A detailed discussion and preliminary analysis of the strategies used in the training are provided.

Tone mapping operators can convert high dynamic range (HDR) images to low dynamic range (LDR) images so that we can enjoy the informative contents of HDR images with LDR devices. However, current state-of-the-art tone mapping algorithms mainly focus on the luminance mapping while neglecting the color component. Meanwhile, they often suffer from halo artifacts and over-enhancement. In this paper, we propose a tone mapping network (TMNet) in Hue-Saturation-Value (HSV) color space to obtain better luminance and color mapping. We adopt the improved Wasserstein generative adversarial network (WGAN-GP) as the basic architecture and further introduce several improvements. A meticulously designed loss function is adopted to push tone mapped image to the natural image manifold. What’s more, we create a tone mapped image dataset in which the label images are manually adjusted by photographers. Compared with some state-of-the-art tone mapping methods, the proposed method can achieve better performance in both subjective and objective evaluations.