The Flow has not summarised this story yet — read it at Hugging Face Trending Papers.
The paper introduces 2D GauSS-MI, an active scene reconstruction framework that uses 2D Gaussian Splatting (2DGS) to efficiently process incremental RGB‑D data. It presents an online 2DGS mapping pipeline and a probabilistic reliability model to assess view‑dependent reconstruction quality. Leveraging this model, the authors define a Shannon Mutual Information metric that guides active view selection, balancing visual and geometric quality while reducing computational cost and storage compared to state‑of‑the‑art baselines.
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....
arXiv:2512.07197v2 Announce Type: replace Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful explicit representation enabling real-time, high-fidelity 3D reconstruction and novel view s...
arXiv:2603. 21933v2 Announce Type: replace-cross Abstract: The pruning of 3D Gaussian splats is essential for reducing their complexity to enable efficient storage, transmission, and downstream processing.
EliGSiR is a continual RGB‑D mapping method that extends 3D Gaussian Splatting to online settings by adaptively allocating optimization resources. It introduces Map‑Guided View Scheduling to filter redundant views, Load‑Adaptive Fidelity to adjust supervision resolution, and Targeted Geometry Growth to add geometric capacity only where needed. The approach is evaluated on Replica, TUM RGB‑D, ScanNet++, and real sensor sequences, achieving higher reconstruction quality and faster performance than several baselines.
arXiv:2608.22465v1 Announce Type: new Abstract: High-fidelity free-viewpoint video (FVV) and interactive rendering increasingly rely on explicit Gaussian representations, yet practical deployment rem...