The paper introduces QuADA-GS, a method for Arbitrary-Scale Super-Resolution that dynamically densifies 2D Gaussian splatting based on low‑resolution input. By allocating Gaussians adaptively to structurally complex regions and employing a sparse communication mechanism, it balances high visual fidelity with lower computational cost. Experiments show that this approach achieves a competitive trade‑off between quality and efficiency for super‑resolution tasks.
By Giulio Federico, Giuseppe Amato, Claudio Gennaro, Fabio Carrara, Marco Di Benedetto
arXiv:2606. 29400v1 Announce Type: cross Abstract: In computer graphics, visual content is continuously warped, zoomed and resampled.
By Giulio Federico, Giuseppe Amato, Claudio Gennaro, Fabio Carrara, Marco Di Benedetto
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
The paper introduces SVRecon, a generalizable neural surface reconstruction framework that uses sparse volumetric representations to achieve high-resolution 3D reconstruction. It employs a two-stage architecture: first predicting occupied voxels with an occupancy network, then rendering only within those regions using specialized sparse algorithms. This approach allows reconstruction at resolutions up to 512³ on 32 GB hardware, producing smoother and more precise surfaces, especially in sparse-view scenarios.
By Aoxiang Fan, Corentin Dumery, Nicolas Talabot, Ming Xu, Hieu Le, Pascal Fua
arXiv:2603. 02220v2 Announce Type: replace-cross Abstract: Time series forecasting remains a challenging problem due to the intricate entanglement of intra-period fluctuations and inter-period trends.
By Yixin Wang, Yifan Hu, Peiyuan Liu, Naiqi Li, Tao Dai, Shu-Tao Xia
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
GigaBench is the first benchmark designed to evaluate learnable representations (LRs) for gigapixel image compression and super‑resolution. It compiles a diverse set of gigapixel images—collected, acquired, and synthetically rendered—from various domains and tests representative LR methods (Implicit, Hybrid, and Explicit) on consumer hardware. The study systematically measures reconstruction quality, model size, compression, and inference times, revealing consistent trends and offering actionable guidance for future research.
By Matteo Fusconi, Alex Costanzino, Pierluigi Zama Ramirez, Luigi Di Stefano
KISS-GS is a modular compression pipeline for 3D Gaussian Splatting (3DGS) scenes that separates compression from training. It first compacts a vanilla 3DGS scene by 15.7× using state‑of‑the‑art pruning, then encodes the result into the SOG‑XT image‑based format, achieving an additional 6.6× reduction. Optional encoding‑aware fine‑tuning can further cut the size by 2.2×, yielding total reductions of 85× to 319× on standard benchmarks while enabling web‑native decoding.
By Wieland Morgenstern, Friedrich Elias Branschke, Florian Fleischmann, Adrian Szatmari, Paul Schlack, Florian Barthel, Peter Eisert, Anna Hilsmann
arXiv:2609.22807v1 Announce Type: new
Abstract: Implicit Neural Representation (INR) leverages neural networks to represent discrete signals such as images as continuous ones, where the network weigh...
By Jinglei Shi, Xinran Chang, Jiaqi Cui, Yingjie Xia, Zhaolin Xiao, Chongyi Li
arXiv:2605.25563v2 Announce Type: replace
Abstract: While feed-forward 3D Gaussian splatting reconstructs renderable Gaussian primitives from sparse context views without per-scene optimization, exis...
By Pengpeng Yu, Runqing Jiang, Qi Zhang, Dingquan Li, Jing Wang, Yulan Guo
arXiv:2609.23380v1 Announce Type: new
Abstract: Gaussian Splatting has enabled real-time novel view synthesis, but its tightly coupled geometry and appearance representation often require a large num...
By Zhiwei Li, Yijia Guo, Yishi Lu, Liwen Hu, Hong Rao, Shengbo Chen, Lei Ma
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