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
SplatAnything introduces Gaussian Spacing Normalization (GSNorm) to address a limitation in feed-forward 3DGS methods where Gaussian attributes are predicted in a radial-distance-normalized space but the spacing between Gaussian centers varies across scene scales and camera configurations. By canonicalizing a robust scene-level statistic of Gaussian spacing, the method provides a more consistent spatial reference for Gaussian prediction. The authors evaluate the approach on a systematic benchmark covering diverse datasets, input sparsity levels, and challenging extrapolated viewpoints, reporting state‑of‑the‑art performance with strong gains under substantial variations in scene scale and viewpoint.
By Hao Yu, Zhenwei Wang, Yongzhen Hu, Yuning Gong, Junta Wu, Xiaowei Zhou, Hujun Bao, Tengfei Wang, Chunchao Guo, Sida Peng
DualCount introduces an instance-aware dual-decoder framework that couples density and point representations for zero‑shot object counting. By treating density estimation as a structured mass allocation over latent object instances, it applies two geometric constraints—per‑instance mass conservation and center‑of‑mass alignment—to enforce instance‑level consistency. Experiments on FSC‑147, PUCPR+, and CARPK demonstrate that this approach consistently reduces counting error and achieves new state‑of‑the‑art performance.
By Xuan Cuong Ngo
CompactSplat introduces a feedforward scene reconstruction method that adaptively allocates 3D Gaussian primitives based on texture-guided spatial partitioning and hierarchical sampling. By concentrating dense Gaussians in complex regions and using expanded primitives for flat areas, it reduces redundancy and better captures planar geometry. The framework also allows dynamic sparsity control during inference, enabling trade-offs between rendering quality and memory usage without retraining.
By Bing He, Jingnan Gao, Yunuo Chen, Zhengxue Cheng, Rong Xie, Li Song, Wenjun Zhang
The paper introduces a cluster-based method for predicting dense crowd trajectories, grouping individuals with similar attributes over time to accelerate processing. By replacing individual pedestrian inputs with cluster centroids, the approach reduces computational cost and memory usage while preserving accuracy. Extensive evaluations on challenging dense crowd scenes confirm these benefits compared to state‑of‑the‑art methods.
By Antonius Bima Murti Wijaya, Paul Henderson, Marwa Mahmoud
arXiv:2609.23509v1 Announce Type: new
Abstract: Novel view synthesis is a key task for dynamic scene reconstruction, where high rendering speed is essential for applications such as virtual reality....
By Huiwen Xue (School of Software, Northwestern Polytechnical University), Kaixing Zhao (School of Software, Northwestern Polytechnical University), Zuheng Ming (L2TI, Universit\'e Sorbonne Paris Nord, EmboMind Research), Tingcheng Li (School of Electronic Information,Engineering, Suzhou University of Science,Technology)
Merging multiple 3D Gaussian Splatting (3DGS) scenes into a single unified Gaussian representation is essential for large-scale 3D mapping and long-term map management. Despite its importance, this area remains underexplored, and existing solutions exhibit several limitations.
arXiv:2610.34344v1 Announce Type: new
Abstract: 3D Gaussian Splatting (3DGS) has become a standard representation for real-time novel-view synthesis. Yet, its quality-efficiency trade-off still relie...
By Quan Tran Hong, Long Nguyen-Chi, Binh T. Nguyen
arXiv:2602. 23172v2 Announce Type: replace-cross Abstract: Capturing 4D spatiotemporal scene structure is crucial for the safe and reliable operation of robots in dynamic environments.
By Maximilian Luz, Rohit Mohan, Thomas N\"urnberg, Yakov Miron, Daniele Cattaneo, Abhinav Valada
InceptionGS is a method that improves large‑scale Gaussian splatting for scenes captured with unstructured view sampling. It starts from an initial Gaussian splatting and then selectively repairs regions that suffer from sparse views by incorporating adaptive generative priors, while preserving quality in well‑sampled areas. Experiments on real‑world scenes show that this balanced reconstruction‑generation approach yields higher‑fidelity results and works broadly across unstructured imagery.
arXiv:2610.35152v1 Announce Type: new
Abstract: Recent studies on generalizable human Gaussian splatting have achieved the notable progress in synthesizing novel views of unseen human subjects from m...
By Gun Ryu, Gi-Mun Um, Won-Sik Cheong, Wonjun Kim
arXiv:2609.23049v1 Announce Type: new
Abstract: Large-scale scene reconstruction is a critical foundational technology in robotic autonomous systems such as 3D mapping and autonomous driving. In rece...
By Haolin Yu, Jiadong Tang, YiXian Wang, Yu Gao, Shi He, Zhilin Lai, Yi Yang, Mengyin Fu