arXiv Computer Vision 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)

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

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arXiv Computer Vision
Sep 4

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.

By Injae Kim, Chaehyeon Kim, Minseong Bae, Minseok Joo, Hyunwoo J. Kim
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
Hugging Face Trending Papers
Oct 29

ACD-GS: Asymmetric Curvature-aware Densification for 3D Gaussian Splatting

ACD-GS introduces an Asymmetric Curvature-aware Densification framework for 3D Gaussian Splatting that uses second-order depth responses to guide cloning and splitting, thereby better capturing geometric discontinuities. It incorporates a multi-view photometric consistency constraint and a capacity-aware adaptive regulation strategy to balance densification and pruning during training. The approach reduces Gaussian count and storage by nearly 60% while maintaining competitive rendering fidelity, outperforming existing methods across multiple datasets.