2D Gaussian Splatting is an attractive direction for image representation due to its explicit formulation, fast rasterization, and favorable decoding efficiency. The representation quality of this paradigm depends on the proper allocation of Gaussian capacity to the demanding regions.
arXiv:2607. 02721v1 Announce Type: cross Abstract: 3D Gaussian Splatting (3DGS) enables high-quality real-time novel-view synthesis, but practical scenes often contain millions of Gaussians, making compression essential for deployment on limited hardware.
By Waseem Mousa, Alaa Maalouf
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
Budgeted-GS is a post‑hoc technique that transforms any trained 3D Gaussian Splatting model into a factoring tree—a multi‑resolution hierarchy of moment‑matched aggregates. After a brief construction pass, a single quality parameter selects the appropriate level of detail for each view, allowing the same city‑scale model to run in real time on GPUs with varying memory capacities. The method also informs budget‑centered training, enabling models to be built directly at the necessary size and avoiding wasted optimization of later discarded primitives.
By Haipeng Wang
Constrained Dynamic Gaussian Splatting (CDGS) addresses the memory challenges of Dynamic Gaussian Splatting by framing reconstruction as a budget‑constrained optimization problem. It introduces a differentiable budget controller that fuses geometric, motion, and perceptual cues to enforce a strict, user‑defined Gaussian budget during training. CDGS further splits the representation into static and dynamic branches, allocates capacity based on motion complexity, and employs a three‑phase training strategy and hybrid compression to achieve high‑quality rendering with over 3× model compression compared to Ex4DGS.
By Zihan Zheng, Zhenlong Wu, Xuanxuan Wang, Houqiang Zhong, Xiaoyun Zhang, Qiang Hu, Guangtao Zhai, Wenjun Zhang
Gauss What You Need: Compact Gaussian Splatting Across Scene Scales introduces TangoGS, a method that automatically selects the number of Gaussian primitives for 3D Gaussian Splatting by combining capture-derived model sizing with training-based adaptation. The approach first estimates a learning allowance based on the capture’s total pixels, then adjusts the number of Gaussians during training according to reconstruction quality. On standard benchmarks, TangoGS matches the best baseline’s PSNR while using 48% fewer Gaussians, and on larger captures it scales automatically to achieve the highest mean PSNR with 2.3× more Gaussians.
By Afif Boudaoud, Jiayi Liu, Alexandru Calotoiu, Torsten Hoefler