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...
By Tim K\"uchler, Johann-Friedrich Feiden, Matthias Nie{\ss}ner, Carsten Rother
arXiv:2609.28300v1 Announce Type: new
Abstract: Ill-posed inverse problems require priors to constrain the solution space toward plausible outcomes. In inverse rendering, learned priors modeling the...
By Andreea Ardelean, Bernhard Egger
PureLight introduces a neural approach to estimate the appearance of complex luminaires that are difficult for traditional path tracing, such as small emitters surrounded by multiple specular layers. The method uses light tracing to build paths from emitters to exit surfaces and learns the probability density function of outgoing radiance with a large normalizing flow network, then distills this into a lightweight MLP for efficient inference. Additionally, a sampling network and a blending network are trained to compute direct illumination and composite the luminaire into arbitrary scenes, enabling low‑sample rendering of challenging luminaires.
By Pedro Figueiredo, Zixuan Li, Beibei Wang, Milo\v{s} Ha\v{s}an, Nima Khademi Kalantari
arXiv:2610.32075v1 Announce Type: new
Abstract: Feed-forward neural renderers synthesize global illumination across novel 3D scenes without per-scene optimization or runtime ray tracing. Existing tra...
By Sarah Zhang, Zhiyang Dou, Minghao Guo, Benjamin Tod Jones, Wojciech Matusik
Recent advances in neural scene representations enable photorealistic novel-view synthesis, yet most methods remain tightly coupled to a single rendering paradigm, limiting their versatility and integration with conventional graphics workflows. We introduce Floating Radiance Networks (FlaRe), a neural scene representation combining explicit ray-traceable geometry with continuous neural radiance functions.
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...
By Kang Du, Duotun Wang, Wanling Li, Yirui Guan, Zeyu Wang