OctreeSplat introduces a hierarchical octree-guided feed‑forward Gaussian splatting method that extracts multi‑view features and geometric priors from a frozen visual foundation model. By constructing a hierarchical octree and predicting densification scores, it allocates Gaussian primitives at spatially varying densities, improving depth rendering and surface reconstruction. The approach requires only 76 million additional parameters and 35 GPU‑hours to train, and demonstrates competitive RGB view synthesis, depth synthesis, and surface reconstruction on Replica, TUM, Tanks and Temples, and DTU datasets while reducing the number of Gaussian primitives needed.
arXiv:2609.12343v1 Announce Type: new
Abstract: Feed-forward Gaussian splatting models have demonstrated remarkable effectiveness in reconstructing three-dimensional (3D) objects from a few two-dimen...
By Yunsu Jeong, Hyuk Heo, Youngsang Kwak, Jaehwa Kwak, Il Yong Chun
GeoGSplat introduces a geometry‑grounded, feed‑forward Gaussian splatting framework that avoids the pixel‑aligned prediction bottleneck by using a fixed number of Gaussians. A dual‑branch encoder builds coarse 3D anchors and fine triplane features, while a hierarchical decoder progressively generates dense 3D points and Gaussian primitives for reconstruction and novel view synthesis. The method outperforms state‑of‑the‑art pixel‑aligned and non‑pixel‑aligned splatting techniques on DTU and NRGBD datasets, especially when rendering views far from the input cameras.
By Jiale Wang, Chen Zhao, Haoyang Zhang, Yanhao Wu, Fei He, Yanhu Shan, Rui Wu, Wei Ke, Tong Zhang
LonGaussian is a streaming feed‑forward framework that reconstructs compact, globally renderable Gaussian scenes from long image sequences with known intrinsics and unknown camera poses. It decodes long‑context geometric features into dense Gaussians, links repeated observations via multi‑frame point tracks, and consolidates predictions with a learned merger. Rendered‑depth consistency filtering removes incorrectly occluding Gaussians, leading to improved novel‑view rendering quality on DL3DV and RealEstate10K benchmarks while using fewer Gaussians than baseline methods.
By Zhoufeng Xie, Guanjun Wu, Shuai Zhang, Xinggang Wang, Wenyu Liu
arXiv:2610.07958v1 Announce Type: new
Abstract: Feed-forward 3D Gaussian Splatting (3DGS) reconstructs a scene in a single forward pass, replacing per-scene optimization with a network trained across...
By Minhyeok Lee, Jungho Lee, Minseok Kang, Heeseung Choi, Ig-Jae Kim, Sangyoun Lee
The paper addresses the lack of standardized evaluation for single‑view novel view synthesis by highlighting the importance of aligning a method’s reference frame with the ground‑truth camera pose. It proposes using traditional 2D‑to‑2D and 2D‑to‑3D image matching to align 3D reconstructions and restricts evaluation to co‑visible regions where a correct ground truth exists. Experiments show that with proper alignment, 3D Gaussian‑based methods reach state‑of‑the‑art performance, and a simple baseline using a video generator and geometry estimator matches specialized novel‑view synthesis models without task‑specific training.
By Yang-Che Tseng, Erqun Dong, Jeong-gi Kwak, Gopal Sharma, Shakiba Kheradmand, Stephan Garbin, Hossam Isack, Kwang Moo Yi
Anchor3DGS introduces a pose‑free, feed‑forward 3D Gaussian Splatting framework that replaces pixel‑aligned Gaussian prediction with a compact anchor‑based representation. By placing 3D anchors guided by information entropy, the method concentrates primitives in visually complex regions while ensuring broad spatial coverage, and each anchor aggregates multi‑view features to generate local Gaussians. A lightweight feature enhancement module refines the coarse RGB output, resulting in state‑of‑the‑art novel view synthesis quality with an order of magnitude fewer Gaussian primitives than prior approaches.
By Sheng Ye, Jiangke Lin, Fudong Wang, Yi Yuan, Zhen-Hui Dong, Yong-Jin Liu
arXiv:2610.35361v1 Announce Type: new
Abstract: 3D Gaussian Splatting (3DGS) has become a promising representation for real-time novel view synthesis and 3D scene reconstruction. For practical deploy...
By Xiaohan Lu, Jiaqi Zhang, Weijia Jiang, Siwei Ma
F4Splat introduces a feed‑forward predictive densification strategy for 3D Gaussian splatting that allocates Gaussians based on a densification‑score guided by spatial complexity and multi‑view overlap. The method predicts per‑region scores to estimate required Gaussian density, enabling explicit control over the total Gaussian budget without retraining. This adaptive allocation reduces redundancy in simple regions and minimizes duplicate Gaussians across overlapping views, yielding compact yet high‑quality 3D representations and superior novel‑view synthesis performance with fewer Gaussians.
By Injae Kim, Chaehyeon Kim, Minseong Bae, Minseok Joo, Hyunwoo J. Kim
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
GenIA is a framework that aligns generative 3D foundation models with test‑time observations, improving pose estimation and reconstruction from monocular, multi‑view, and dynamic inputs. It derives translation and scale from geometry, retains learned rotation priors, and aligns appearance using visibility‑biased attention, cross‑observation fusion, and differentiable rendering guidance during denoising. An optional post‑denoising refinement further adapts appearance latents and object placement, and the method also supports externally supplied geometry for dynamic objects, achieving better results than recent optimization‑based and image‑to‑3D methods on synthetic and real benchmarks.
By Stefano Esposito, Naama Pearl, Polina Karpikova, Samuel Rota Bul\`o, Lorenzo Porzi, Peter Kontschieder, Andreas Geiger, Jonathon Luiten
arXiv:2609.15032v1 Announce Type: new
Abstract: Live free-viewpoint visualization of real humans is critical for immersive communication and interactive digital experiences. Existing methods either r...
By Hanzhang Tu, Zhanfeng Liao, Wei Min, Jiajun Zhang, Yebin Liu