arXiv (All)
open access
Efficient Confidence-Aware Densification for 3DGS
3DGS-MCMC has replaced the gradient-driven density control of 3D Gaussian Splatting with opacity-based sampling and relocation, and is now the de facto standard for prior-free densification under a fixed primitive budget. Because opacity stays high where the scene is already well reconstructed, this
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Full-Model Optimality for Tunable Linear Generative Priors in Compressed Sensing#924919
- Full-Model Optimality for Tunable Linear Generative Priors in Compressed Sensing#920685
- PhysForge: Material-Controllable 3D Reconstruction with Separable Components#1298177
- Robust Langevin Sampling under State-Dependent Heavy-Tailed Noise with Applications to LLM Fine-Tuning#1296872
- Robust diagnostics and estimation for dual response surface function with eteroscedastic errors using new optimization technique in the presence of outliers#428817
Record · ID 1297893
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive