arXiv Computer Vision

F4Splat: Feed-Forward Predictive Densification for Feed-Forward 3D Gaussian Splatting

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.

arXiv Computer Vision
Oct 12

CompactSplat: Spatially Adaptive Gaussian Distribution for Feedforward Scene Reconstruction

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
arXiv AI
Sep 28

Spackle: Completing Large View Single Image NVS with Adaptive Gaussians

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 Computer Vision
Oct 16

Anchor3DGS: Feed-Forward 3D Gaussian Splatting with Compact Anchor-Based Representation

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 Computer Vision
Aug 28

ReSplat: Learning Recurrent Gaussian Splatting

ReSplat introduces a recurrent Gaussian splatting model that iteratively refines 3D Gaussians using the rendering error as a feedback signal, avoiding explicit gradient computation. The method starts from a compact reconstruction in a subsampled space, producing far fewer Gaussians than prior per‑pixel models, which reduces computational cost. Experiments on multiple datasets, view counts, and resolutions show state‑of‑the‑art performance with faster rendering speeds.

By Haofei Xu, Daniel Barath, Andreas Geiger, Marc Pollefeys
arXiv Computer Vision
Oct 22

Unfolding 3D Gaussian Splatting via Iterative Gaussian Synopsis

The paper introduces Iterative Gaussian Synopsis, a top‑down unfolding framework that generates compact, progressive levels of detail for 3D Gaussian Splatting (3DGS). Starting from a full‑resolution 3DGS model, it iteratively prunes Gaussian primitives using a learnable mask, building a multi‑level hierarchy that preserves visual quality while reducing storage. The method combines hierarchical spatial grids with a shared Anchor Codebook to capture global structure and local details, enabling efficient, level‑specific rendering with minimal data overhead.

By Yuqin Lu, Yihua Dai, Yang Zhou, Guiqing Li, Shengfeng He
arXiv Computer Vision
Sep 22

GARO: Geometry-Aware Redundancy Optimization for Real-Time and High-Fidelity Dynamic Gaussian Splatting

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)
arXiv Computer Vision
Sep 22

D3GS: Depth, DINO, and RGB Diffusion Co-Guided 3D Gaussian Splatting for Sparse-View Reconstruction

arXiv:2609.22941v1 Announce Type: new Abstract: Novel view synthesis from sparse inputs remains challenging for 3D Gaussian Splatting (3DGS) due to ambiguous geometry, cross-view inconsistency, and m...

By Yunqi Gao, Zhanfeng Liao, Hanzhang Tu, Zhaoqi Su, Guoqing Zheng, Songtao Wang, Hongwen Zhang, Zhou Xue, Leyuan Liu, Yebin Liu