arXiv (All)
open access
Prior-Driven Enhancements in 3D Gaussian Splatting: Normals and Depths Regularization
3D Gaussian Splatting (3DGS) is a state-of-the-art technique for 3D scene rendering, offering high efficiency and excellent visual quality. However, because 3DGS relies on an initial sparse point set from Structure-from-Motion (SfM) and view-dependent properties, it can suffer from geometric inaccur
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
- Learn2Splat: Extending the Horizon of Learned 3DGS Optimization#1063913
- 3D Gaussian Splatting with Fisheye Images: Field of View Analysis and Depth-Based Initialization#609811
- RGS: Reflection-aware Gaussian Splatting via Learning Geometry Continuity for Reflective Objects#1014015
- AdaGScale: Viewpoint-Adaptive Gaussian Scaling in 3D Gaussian Splatting to Reduce Gaussian-Tile Pairs#1062891
- A Survey on 3D Gaussian Splatting#128272
Record · ID 1132170
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