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
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
arXiv:2609.39553v1 Announce Type: new
Abstract: 3D Gaussian Splatting (3DGS) enables real-time novel view synthesis, but existing general-purpose acceleration methods suffer severe rendering quality...
By Changbai Li, Shuo Yang, Yichen Yang, Shuwei Shao, Huobin Tan
The paper introduces PhGS, a post‑hoc pruning and refinement pipeline for single‑view feed‑forward 3D Gaussian Splatting models. It keeps the base network frozen and applies importance‑score‑based pruning followed by a lightweight recurrent refinement module to reduce spatial redundancy while maintaining rendering quality. The method is backbone‑agnostic, integrates seamlessly with existing baselines, and allows flexible inference‑time keep ratios for different application needs.
By Rinto Yagawa, Han Cheng, Dieter Schmalstieg, Hideo Saito, Shohei Mori
The paper introduces PhGS, a post‑hoc pruning and refinement pipeline for single‑view feed‑forward 3D Gaussian Splatting models. By keeping the base network frozen, it applies importance‑score‑based pruning followed by a lightweight recurrent refinement module to reduce spatial redundancy while restoring image quality. The method is backbone‑agnostic, integrates seamlessly with existing baselines, preserves novel‑view rendering fidelity, achieves significant memory reduction, and allows flexible inference‑time keep ratios.
arXiv:2610.27712v1 Announce Type: cross
Abstract: Feed-forward 3D Gaussian Splatting (FF3DGS) reconstructs 3D scenes from images in a single forward pass, making it well-suited to applications that m...
By Injae Kim, Minseok Joo, Jihwan Park, Sehyung Kim, Minseong Bae, Hyunwoo J. Kim
3D Gaussian Splatting (3DGS) enables real-time novel view synthesis, but existing general-purpose acceleration methods suffer severe rendering quality degradation when extended to more complex, large-...
The paper presents a training‑free filtering method for feed‑forward 3D Gaussian Splatting that removes transient distractors from 3D reconstructions. By excluding each input’s per‑view Gaussians and re‑rendering the scene, the method identifies inconsistent content through feature similarity and reconstruction error reduction. The approach improves novel‑view quality across multiple models and benchmarks while preserving clean scenes.
By Kangmin Seo, Jae-Pil Heo
arXiv:2610.20590v1 Announce Type: new
Abstract: Feed-forward 3D Gaussian Splatting (3DGS) methods enable efficient reconstruction of 3D scenes from sparse multi-view images in a single forward pass,...
By Seungmi Choi, Ji-Hyoun Choi, Suhyun Jo, Tae-Gwon Park, Hui Yong Kim
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
SplatQ introduces a combinatorial pruning algorithm for 3D Gaussian Splatting that coordinates regional budget allocation and joint Gaussian selection under a fixed budget. The method partitions Gaussians into spatially and appearance‑similar blocks, smooths block retention quotas to avoid budget concentration, and builds a quadratic approximation of joint deletion effects using pixel color responses. Solving the resulting QUBO with simulated annealing or a coherent Ising machine, SplatQ achieves superior reconstruction quality, spatial balance, and rendering speed across five benchmarks compared to existing pruning techniques.
By Hongbin Ye, Haixu Liu, Shenwei Kang, Wenxiu Wei, Tinglan Wang, Hongdong Zhu, Fan Tang, Qi Gao, Yin Ma, Kai Wen, Hai Wei, Nuo Qian, Wei-Shi Zheng, Shaobo Chen, Fa-Ting Hong, Jingke Meng
PocketSplat is a feed‑forward framework that builds mobile Gaussian assets within a specified budget. It organizes dense, geometry‑aware latent candidates in world space, allocates integer capacity to local latent cells, and decodes Gaussian attributes only for retained candidates. The method uses cell‑conditioned latent fusion and spatial responsibility decoding to aggregate multi‑view evidence and adapt Gaussian support, achieving a strong quality–budget trade‑off on DL3DV and out‑of‑distribution benchmarks, and running directly on an iPhone faster than streamed MVSplat variants.
By Wenzhi Guo, Xianda Chen, Dongxuan Chen, Guangchi Fang, Bing Wang