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:2605.15760v2 Announce Type: replace
Abstract: 3D Gaussian Splatting (3DGS) optimization is most commonly performed using general-purpose first-order optimizers such as Adam or SGD. Although rob...
By Naama Pearl, Stefano Esposito, Haofei Xu, Amit Peleg, Patricia Gschossmann, Lorenzo Porzi, Peter Kontschieder, Gerard Pons-Moll, Andreas Geiger
The paper introduces VISTA-GS, a visibility‑guided structured measure flow framework for generating class‑conditioned 3D Gaussian Splatting (3DGS) objects. It treats a 3DGS object as a structured Gaussian measure weighted by opacity, anisotropic covariance, and multi‑view visibility, and employs a visibility‑aware VAE to learn permutation‑invariant, variable‑size, rendering‑aware latent representations. The method includes a renderer‑consistent measure flow and structure‑preserving patch transport, achieving 60–72% improvements over the strongest baseline on the VISTA‑Obj30 dataset in geometry, appearance, view‑consistency, and speed.
By Yizhao Wang
Spackle is a lightweight residual learning framework designed to improve large-view single-image novel view synthesis (NVS) by mitigating capacity competition in hybrid decoupled systems that combine 3D Gaussian Splatting (3DGS) and diffusion models. It operates in three stages: predicting base 3DGS attributes, automatically identifying poorly reconstructed regions, and learning a residual 3DGS focused on those areas. During inference, Spackle merges the baseline and augmented Gaussians to produce high-fidelity novel views, achieving state‑of‑the‑art performance on large-view-deviation cases.
By Xuanzhi Liu, Yuhe Zhou, Xinyi Wu, Zhenyao Wu, Jinghao Chen, Ruize Han, Song Wang
GS‑VLA introduces a lightweight, plug‑and‑play framework that uses a 4 M‑parameter 3D‑Gaussian canonicalizer to adapt frozen Vision‑Language‑Action (VLA) policies to viewpoint shifts without retraining the policy. By treating viewpoint changes as a localized novel‑view synthesis problem under a locality assumption, the method normalizes observations through a scene‑ and policy‑independent disocclusion task. Experiments on the LIBERO benchmark demonstrate that GS‑VLA recovers a large portion of performance lost due to camera displacement, improving results across different policy architectures, unseen task suites, and perturbation scales.
whyItMatters":"The approach offers a computationally efficient alternative to costly fine‑tuning or generative augmentation, enabling robust VLA deployment in real‑world settings where camera configurations may vary."
By Yechan Park, HyunJin Kim
arXiv:2609.17230v1 Announce Type: new
Abstract: Streaming 3D reconstruction demands both speed and temporal fidelity, goals that existing methods undermine by updating every Gaussian every frame, eve...
By Idil Sulo, Alexey Supikov, Ilke Demir, Sainan Liu