Tri‑DehazeGS is a Gaussian Splatting framework that decouples clean scene reconstruction from atmospheric haze by representing the scene with Gaussian primitives and the haze medium with an independent view‑shared tri‑plane field. It uses a physical scattering model to compose hazy observations and introduces Medium‑Decoupled Transmittance Gradient Compensation (MD‑TGC) to re‑balance gradients in low‑transmittance regions without altering forward rendering. Experiments on real and synthetic haze benchmarks demonstrate that this approach improves clean novel‑view reconstruction.
By Kui Jiang, Yang Gu, Jiacheng Liu, Shiyu Liu, Youyu Chen, Hui Liu
Manifold4D introduces a new denoising strategy for video re‑shooting that injects a rendered point‑cloud directly into the initial noise manifold, eliminating the need for the render to be an explicit conditioning stream during denoising. This approach allows the network to rely solely on the source video as a visual condition, improving camera‑control accuracy on the DAVIS‑Traj benchmark and Vista4D set, with significant reductions in rotation and translation errors while maintaining video fidelity. User studies confirm enhanced trajectory following and dynamic consistency, especially for large yaw amplitudes and even when the render is corrupted.
By Yongqi Mao, Zijia Dai, Zhishuo Liu, Wei Xu, Kaiwei Wang, Guotao Meng
arXiv:2607.05598v2 Announce Type: replace-cross
Abstract: Novel View Synthesis (NVS) methods, such as 3D Gaussian Splatting (3DGS), rely on the assumption of clean, multi-view consistent, posed input...
By Kristof Overdulve, Lode Jorissen, Nick Michiels
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
Tri-DehazeGS tackles the problem of reconstructing clean 3D scenes from hazy multi‑view images by decoupling the scene and the haze medium. It represents the scene with Gaussian primitives while modeling the haze as an independent view‑shared tri‑plane field, and composes hazy observations through a physical scattering model. The method introduces Medium‑Decoupled Transmittance Gradient Compensation (MD‑TGC) to balance gradients in low‑transmittance regions, leading to improved novel‑view reconstruction on real and synthetic haze benchmarks.
arXiv:2601.00913v2 Announce Type: replace
Abstract: A pretrained 3D Gaussian Splatting (3DGS) reconstruction can reproduce its capture views well while retaining off-object structure that becomes vis...
By Jyotirmaya Shivottam, Om Kumar, Subhankar Mishra
Gauss What You Need: Compact Gaussian Splatting Across Scene Scales introduces TangoGS, a method that automatically selects the number of Gaussian primitives for 3D Gaussian Splatting by combining capture-derived model sizing with training-based adaptation. The approach first estimates a learning allowance based on the capture’s total pixels, then adjusts the number of Gaussians during training according to reconstruction quality. On standard benchmarks, TangoGS matches the best baseline’s PSNR while using 48% fewer Gaussians, and on larger captures it scales automatically to achieve the highest mean PSNR with 2.3× more Gaussians.
By Afif Boudaoud, Jiayi Liu, Alexandru Calotoiu, Torsten Hoefler
Camera intrinsics are vital for recovering 3D structure from 2D video. However, most 3D algorithms assume fixed intrinsics throughout a video, an assumption that often fails for real-world in-the-wild videos.
arXiv:2609.23182v1 Announce Type: new
Abstract: Feed-forward 3D Gaussian Splatting now reconstructs renderable scenes from unposed, uncalibrated images. Yet, most models supervise only photometric co...
By Si-Yu Lu, Yung-Yao Chen, Yi Jan Chen, Shang-Lin Li, Ching-Chan Liao, Wen-Huang Cheng
TileSkipper introduces a region‑adaptive tile pruning strategy for 3D Gaussian splatting rasterizers, assigning a static per‑Gaussian contribution cutoff policy that is calibrated on a frozen checkpoint. The method partitions Gaussians into 64 groups, uses a single‑byte policy per Gaussian, and requires no runtime updates or additional kernels. Across 13 benchmark scenes, TileSkipper achieves up to 1.238× speedups with negligible PSNR loss, and integrates with existing opacity‑aware bounds for further compiler‑only gains.
By Jingxing Li, Yongjae Lee, Deliang Fan, Abhay Kumar Yadav, Cheng Peng, Rama Chellappa
The paper introduces GPERT, a framework that separates event-based 3D Gaussian Splatting into two rendering branches: event-by-event geometry rendering and snapshot-based radiance rendering. By employing ray-tracing and warped event images, GPERT balances accuracy and temporal resolution, achieving state‑of‑the‑art results on real‑world datasets and competitive performance on synthetic data. The method operates without pretrained models or COLMAP initialization, offers flexible event selection, and produces sharp reconstructions of scene edges with rapid training.
By Kai Kohyama, Yoshimitsu Aoki, Guillermo Gallego, Shintaro Shiba
arXiv:2608.22465v1 Announce Type: new
Abstract: High-fidelity free-viewpoint video (FVV) and interactive rendering increasingly rely on explicit Gaussian representations, yet practical deployment rem...
By Xinhui Liu, Lei Liu, Zhenghao Chen, Lebin Zhou, Wei Wang, Wei Jiang