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ADATEX4D: adaptive texture capacity allocation for 4D gaussian splatting

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ADATEX4D introduces an adaptive texture-capacity module for 4D Gaussian Splatting, allowing each Gaussian to carry packed RGBA triplanes whose axes grow independently based on visibility-normalized screen-space gradients and deformed local scales. This approach reduces texture storage by more than half while maintaining reconstruction quality, as shown in experiments on N3DV and PanopticSports. Under fixed memory budgets, adaptive allocation improves quality over uniform texture assignment and lowers overall model and peak memory usage.

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arXiv AI
Sep 25

ADATEX4D: adaptive texture capacity allocation for 4D gaussian splatting

ADATEX4D introduces an adaptive texture-capacity module for deformation-based 4D Gaussian Splatting, allowing each Gaussian to carry packed RGBA triplanes whose axes grow independently based on visibility-normalized screen-space gradients and deformed local scales. The method reduces texture storage by more than half on datasets such as N3DV and PanopticSports while preserving reconstruction quality. Under fixed memory budgets, adaptive allocation improves quality over uniform texture assignment and lowers overall model and peak memory usage.

By De Jiang, Peiqiang Wang, Kehong Yuan, Shaohua Ma
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 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