Recent advances in Intrinsic Image Decomposition (IID) have increasingly relied on generative models. However, progress remains limited by three key challenges: (a) insufficient physical consistency,...
arXiv:2512.23667v3 Announce Type: replace Abstract: Intrinsic image decomposition aims to recover material and illumination factors from RGB observations, but real-world images entangle reflectance w...
arXiv:2609.00901v1 Announce Type: new Abstract: Modifying the illumination of driving images is a fundamental challenge, as most datasets are captured at specific times of day. Existing methods rely...
The paper introduces a benchmark called Shedding Light to evaluate how well generative image models understand and reproduce lighting. The benchmark tests models by asking them to inpaint a simple object, called a light probe, into real photographs and then compares the generated probe to the ground truth to assess lighting direction, colour, and radiance. The authors provide a scalable protocol and open-source code and data for systematic assessment of photometric accuracy in future models.
The paper introduces a prior-guided material reparameterization for physically based inverse rendering, leveraging learned material predictions to restrict the optimization space without altering the photometric objective. By fusing multi-view predictions onto a mesh and constructing a permutohedral lattice in prior feature space, surface points with similar predictions share material variables, which are jointly optimized with geometry and illumination. Experiments on synthetic benchmarks and Stanford-ORB show improved material decomposition and relighting, with PSNR gains of 1.5–2.5 dB over strong baselines and a faster optimization schedule that maintains high relighting quality.
The paper introduces a real‑time technique for reconstructing fully integrated pixel radiance in volumetric media under both direct and ambient illumination. It bypasses expensive path‑traced radiance estimates by inferring the final light‑transport solution from compact scene features and statistics, using an attention‑augmented image‑to‑image network. The method achieves high reconstruction accuracy in challenging volumetric cloud scenarios while maintaining stable computational cost regardless of camera zoom or volume density.
arXiv:2606.26738v3 Announce Type: replace Abstract: While recent advancements in generative image editing models have achieved stunning visual fidelity, it remains an open question whether these syst...
arXiv:2407.00500v2 Announce Type: replace-cross Abstract: Recent point-based intrinsic decomposition and inverse rendering methods have advanced the modelling of the shading and albedo of 3D scenes....
arXiv:2606. 31637v1 Announce Type: cross Abstract: Intrinsic decomposition which expresses image colors as the product of diffuse albedo and shading, possibly augmented with view-dependent residuals has a long history in image editing as it enables the modification of object colors and textures without altering lighting.
arXiv:2610.17610v2 Announce Type: new Abstract: Recently, the integration of generative priors has revolutionized Monocular Depth Estimation (MDE), achieving state-of-the-art (SOTA) performance with...
arXiv:2611.05969v1 Announce Type: new Abstract: Single Image Reflection Removal (SIRR) seeks to recover the transmitted scene from a photograph captured through glass. Recent latent restoration metho...
PAM-ToD is a lightweight plug‑in that adapts a pre‑trained 3D Gaussian Splatting road scene to a new time of day by learning color corrections while keeping the original 3DGS parameters fixed. It scales each Gaussian’s existing color to model illumination changes and adds a brightness term for effects such as street lamps, learning these corrections from a few anchor images without separately estimating albedo and illumination. The method includes a spatial regularization to avoid abrupt changes and is evaluated on the CARLA-ToD benchmark, achieving higher PSNR and lower LPIPS than baselines in both static and dynamic settings.