WiNeRF is a neural field framework that learns a spatially continuous, complex-valued wireless channel representation from sparse channel state information collected by commodity WiFi devices. It incorporates system constraints such as antenna geometry, limited spatial resolution, and phase uncertainty through a 3D conical wave sampling model, a multi-resolution implicit scene representation, and a differentiable optimization framework. In diverse indoor environments with non‑line‑of‑sight regions, WiNeRF achieves a median prediction SNR of 5.3 dB, outperforming prior neural baselines by 4.9 dB on average, and produces a task‑agnostic channel representation that can be reused in standard signal‑processing pipelines without hardware or protocol changes.
By Saif Ur Rahman, Rafid Umayer Murshed, Anton Dmitriev, Cagri Tanriover, Rahul C. Shah, Elah\'e Soltanaghai
arXiv:2608. 05896v1 Announce Type: new Abstract: Beamforming plays a key role in multiple-input-multiple-output (MIMO) communication systems.
By Yijie Bian, Wei Guo, Zixin Wang, Shenghui Song, Jun Zhang, Khaled B. Letaief
Beamforming plays a key role in multiple-input-multiple-output (MIMO) communication systems. However, conventional beamforming design normally requires accurate instantaneous channel state information (CSI) and iterative optimization, which incur substantial pilot overhead and computational complexity.
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
WaterClear-GS introduces a physics-informed Gaussian splatting method tailored for underwater 3D reconstruction and appearance restoration. It models underwater degradation as intrinsic Gaussian attributes and employs a dual-branch optimization that separates clean appearance from degradation while preserving photometric consistency. The approach incorporates depth-guided geometry regularization, perception-driven supervision, exposure constraints, adaptive regularization, and spectral regularization, achieving strong novel view synthesis and image restoration performance at over 160 FPS.
By Xinrui Zhang, Yufeng Wang, Zesheng Wang, Dacheng Qi, Wenrui Ding, Shuangkang Fang
arXiv:2606. 18734v1 Announce Type: cross Abstract: Accurate, site-specific channel information is crucial for optimizing next-generation wireless networks.
By Ye Xue, Yiheng Wang, Xinhua Shao, Qi Yan, Shutao Zhang, Tsung-Hui Chang
arXiv:2609.37115v1 Announce Type: new
Abstract: We revisit the role of appearance modeling in 3D Gaussian Splatting (3DGS) and show that limited expressiveness in view-dependent reflectance is a key...
By Pratik Singh Bisht, Andreas Kolb
Recent advances in 3D Gaussian Splatting have demonstrated unprecedented success in novel view synthesis. However, the substantial inference and storage overhead driven by high-order Spherical Harmonics (SH) are primary bottlenecks for mobile platforms.
arXiv:2511. 15022v2 Announce Type: replace-cross Abstract: Complex-valued Gaussian primitives have recently been explored for representing holographic radiance fields in 3D novel view synthesis.
By Yicheng Zhan, Xiangjun Gao, Long Quan, Kaan Ak\c{s}it
SpectralCTGaussians introduces a novel spectral CT reconstruction and basis material decomposition technique that employs 3D Gaussian splatting with per‑Gaussian material fractions and energy‑dependent basis functions. By jointly optimizing these parameters across all energy channels via a differentiable polychromatic forward model, the method achieves superior novel view synthesis and higher PSNR for spectral CT volume reconstruction compared to traditional and learning‑based baselines. It also provides one‑step material decomposition with direct RGB segmentation and recovers the photoelectric basis more accurately than existing pipelines.
By Reinout Vos, Saptarshi Neil Sinha, Michael Weinmann
arXiv:2609. 03334v1 Announce Type: new Abstract: A key bottleneck in 3D Gaussian Splatting training is the continual growth of Gaussian primitives, which increases optimization cost and slows convergence, especially at high resolutions.
By Yixiong Yang, Sisheng Zhang, Qingsong Yan, Shaohuai Shi, Qiang Wang
arXiv:2607. 28994v1 Announce Type: cross Abstract: High-fidelity radio fields are typically simulated for every scene--transmitter configuration or fitted separately to each scene, failing to exploit propagation structures shared across environments.
By Chaozheng Wen, Chenghong Bian, Hongze Chen, Jun Zhang