arXiv:2505. 17338v3 Announce Type: replace-cross Abstract: Photorealistic volumetric rendering of CT scans greatly benefits clinical workflows, yet neural approaches such as Neural Radiance Fields (NeRF) and 3D Gaussian Splatting (3DGS) require prohibitive per-scan optimization (hours for NeRF, about 30 minutes for 3DGS), making them impractical in clinical settings.
By Zhongpai Gao, Benjamin Planche, Meng Zheng, Anwesa Choudhuri, Van Nguyen Nguyen, Terrence Chen, Ziyan Wu
arXiv:2610.19847v1 Announce Type: new
Abstract: We address inverse rendering with 3D Gaussian Splatting (3DGS) by introducing explicit visibility modeling for relighting as well as material and light...
By Sven Kluge, Oliver Staadt
arXiv:2603. 23297v2 Announce Type: replace-cross Abstract: Despite their output being ultimately consumed by human viewers, 3D Gaussian Splatting (3DGS) methods often rely on ad-hoc combinations of pixel-level losses, resulting in blurry renderings.
By Ezgi Ozyilkan, Zhiqi Chen, Oren Rippel, Jona Ball\'e, Kedar Tatwawadi
H-2DGS introduces a height-field 2D Gaussian Splatting pipeline that produces a sharp, deformable cortical surface reconstruction for neurosurgical navigation. The method concentrates a robust 3D Gaussian Splatting reconstruction, fuses its depth into a shared truncated signed distance field, and represents the surface with curved, surface-oriented Gaussian primitives. Across five real neurosurgical cases, H-2DGS achieves low multi-view depth inconsistency, a compact 7.97 MB representation, and interactive rendering at 29–42 ms per frame, integrating clearly with tractography and MRI anatomy.
By Zhang Zichuan, Bart Brouwers, Rembrandt Bakker, Maud Landers, Geert-Jan Rutten, Maxime Chamberland
FaCT-GS is a new framework that accelerates Gaussian Splatting (GS) for X‑ray Computed Tomography (CT) reconstruction. By optimizing voxelization and rasterization pipelines, it achieves speeds more than four times faster than the current state‑of‑the‑art GS methods on 512×512 projections and over thirteen times faster on 2k projections. The improved voxelization also allows quick fitting of Gaussians to existing volumes, providing a compressed representation or a warm‑start prior for reconstruction.
By Pawel Tomasz Pieta, Rasmus Juul Pedersen, Sina Borgi, Jakob Sauer J{\o}rgensen, Jens Wenzel Andreasen, Vedrana Andersen Dahl
Naka-GS is a low‑light 3D Gaussian Splatting framework that jointly enhances observations and improves Gaussian initialization. It introduces Naka‑Guided Chroma Correction, which uses a fixed bionic response as a photometric anchor to learn bounded, frequency‑decoupled luminance and chromatic corrections, preserving fine structures while reducing color and boundary artifacts. A lightweight, non‑learned Point‑Prior Compaction Module refines dense scene priors via voxel pooling and distance‑adaptive pruning, cutting the number of points from 1.00 M to 59.52 K and shortening optimization time by 37.7 % while achieving state‑of‑the‑art PSNR, SSIM, and LPIPS on eight RealX3D scenes.
By Runyu Zhu, SiXun Dong, Zhiqiang Zhang, Qingxia Ye, Rongjun Qin, Zhihua Xu