The paper introduces a geometry‑driven shadow harmonisation pipeline for composited faces that preserves albedo by applying a per‑pixel gain field derived from a rasterised 3D face proxy. The method estimates key‑light direction from host cues, computes surface normals and cast shadows, and applies a controlled, monotonic darkening operation that never alters chromaticity or identity. Experiments on analytic face heightfields show that the default parameters modify over half the face pixels while maintaining hue invariance and avoiding repainting the donor face.
By Vijesh KP
The study audits the AION-1 foundation model, a 39‑modality transformer trained on over 200 million astronomical objects, and finds that its reliance on a survey detection channel—specifically the segmentation map—introduces a severe systematic bias. By keeping image tokens unchanged and editing only the segmentation map, all model outputs (flux, size, ellipticity, redshift) shift by factors of 110–4400 compared to a placebo, revealing that the model’s predictions are driven more by detection gating than by the actual light distribution. This bias propagates into cosmological analyses, shifting tomographic mean redshifts by a median 0.71 × the LSST DESC requirement and exceeding it in multiple assignments, while removing the detection channel eliminates the effect without measurable cost.
whyItMatters":"The bias in the detection channel directly inflates errors in key astronomical measurements, potentially compromising the precision of cosmological studies that rely on accurate redshift estimates."
By Ihor Kendiukhov
arXiv:2602.18741v3 Announce Type: replace-cross
Abstract: Spectral rendering reproduces the wavelength-dependent appearance that RGB rendering cannot: metamerism, colour shifts under spectrally rich...
By Jiaqi Yu (University of York), Dar'ya Guarnera (University of York), Giuseppe Claudio Guarnera (University of York, Lumirithmic Ltd)
arXiv:2609.18302v1 Announce Type: new
Abstract: RAW-to-sRGB image signal processing (ISP) must recover perceptually faithful colors and fine details from sensor measurements, often under imperfect sp...
By Tailai Chen, Xiaotong Luo, Yuan Gao, Xin Jin, Wenjun Zeng
The paper introduces a new hyperspectral image dataset for benchmarking salient object detection, comprising 60 hyperspectral images, their ground‑truth binary masks, and corresponding sRGB renderings. The dataset was curated to include diverse object sizes, counts, contrasts, and positions, addressing the lack of dedicated hyperspectral data for this task. The authors also evaluate existing hyperspectral saliency models using the AUC metric and provide the dataset on GitHub and Hugging Face.
By Nevrez Imamoglu, Yu Oishi, Xiaoqiang Zhang, Guanqun Ding, Yuming Fang, Toru Kouyama, Ryosuke Nakamura
The SEE Challenge 2026 invites participants to restore RGB images using synchronized event camera data and a target brightness statistic across a wide illumination range. Using the SEE-600K dataset of 610,126 image‑event pairs from 202 real‑world scenes, teams compete under an open‑system protocol, with PSNR as the primary ranking metric and SSIM as a secondary measure. Fifteen valid submissions were evaluated, revealing closely spaced top scores and consistent local errors under severe underexposure, while the report also examines exposure subsets, semantic test cases, shared failure patterns, and system design choices.
By Yunfan Lu, Mingchao Xu, Hanyu Zhou, Shaoyu Liu, Haoyue Liu, Peiqi Duan, Shihan Peng, Yinqiang Zheng, Boxin Shi, Gim Hee Lee, Hui Xiong, Davide Scaramuzza
The paper introduces the skin‑restricted Reinhard transform, a diagonal affine mapping in CIE Lab that preserves lightness while adjusting chromaticity for skin recolouring. It fixes the lightness gain to +1 and the shift to the mean difference, then optimises chromatic gains within [0.72, 1.18] using quadratic transport and projection. Experiments on nine images show the method maintains a lightness contrast ratio of 0.974 ± 0.029 with only 0.77 CIE Lab chromatic error, outperforming traditional Reinhard, Monge, and histogram matching approaches that reduce lightness contrast.
By Vijesh KP
Editable 3D scene creation requires object instances and lights that can be inspected, moved, and imported into standard engines, yet existing single-image methods largely stop at room-scale geometry, baked/global illumination, or text-driven generation. We introduce Lumera (Light-aware Unified Engine-native Reconstruction and Assembly), a benchmark and reference pipeline for engine-native, light-aware 3D scene parsing from a single image.
arXiv:2606.25483v2 Announce Type: replace
Abstract: Path-traced synthetic stereo is a primary training substrate for disparity networks, and the pipelines that consume it assume Monte~Carlo (MC) rend...
By Po-Ting Lin
arXiv:2608.21847v1 Announce Type: new
Abstract: Low-light image enhancement (LLIE) must correct ambiguous exposure without overwriting structure already supported by the input. Generative transport c...
By Yi Ai, Zheng Chen, Yuanhao Cai, Yulun Zhang, Xiaokang Yang
arXiv:2609.28283v1 Announce Type: new
Abstract: Several foundation models dedicated to hyperspectral images have recently been made available. These models are trained on large unlabeled datasets and...
By Edgard Dabier, Christophe Kervazo, Pietro Gori, Florence Tupin
arXiv:2608.28341v1 Announce Type: new
Abstract: Precise dense correspondence is a fundamental prerequisite for multimodal spectral imaging systems that fuse disparate wavelength ranges for subsequent...
By Eric L. Wisotzky, Jost Triller, Simon W. H\"artl, Oliver T. Bruns, Peter Eisert, Anna Hilsmann