RefracGS is a novel framework for generating novel views through refractive water surfaces. It jointly reconstructs the water surface using a neural height field and the underlying scene with a 3D Gaussian field, employing refraction‑aware Gaussian ray tracing based on Snell’s law. The method achieves high‑fidelity view synthesis, outperforms prior refractive approaches, and offers 15× faster training with real‑time rendering at 200 FPS.
By Yiming Shao, Qiyu Dai, Chong Gao, Guanbin Li, Yequan Wang, He Sun, Qiong Zeng, Baoquan Chen, Wenzheng Chen
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.
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:2603. 07664v3 Announce Type: replace-cross Abstract: The reflective appearance, especially strong and typically near-field specular reflections, poses a fundamental challenge for accurate surface reconstruction and novel view synthesis.
By Ningjing Fan, Yiqun Wang, Dongming Yan, Peter Wonka
The paper proposes an all‑reflective two‑mirror projection system for EUV lithography that achieves a 4× demagnification at a numerical aperture close to unity (NA≈0.993). Unlike conventional EUV objectives that use 6–10 aspheric mirrors and have <15 % throughput, the design uses a fixed two‑reflection path for each accepted diffraction order, retaining 50–60 % of the power and eliminating order‑dependent phase shifts. The authors optimize 30‑bilayer Bragg coatings for each mirror facet, formulate a 3‑D vector model for a two‑dimensionally periodic mask, and use inverse lithography with a differentiable modal solver to demonstrate simulated sub‑10‑nm aerial images with resolved peaks up to 5 nm defocus.
By Vasiliy A. Es'kin, Egor V. Ivanov, Olga V. Martynova
The paper presents a method for training single‑step neural surrogates that can handle wave‑scattering problems with tens of thousands of controllable variables. By dynamically generating training examples that highlight surrogate errors and using a replay dataset with normalization, the authors achieve a surrogate that accurately simulates two‑dimensional wave scattering for up to 41,772 variables and generalizes to over 3 million variables without retraining. The surrogate is applied to forward simulations and inverse design of freeform beam splitters and gradient‑index lenses, achieving speedups up to 26.5× compared to traditional FDTD methods.
By Charles Dove, Laura Waller