arXiv (All)
open access
ACD-GS: Asymmetric Curvature-aware Densification for 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) has emerged as a powerful paradigm for real-time photorealistic novel view synthesis. However, its training-time densification remains largely heuristic, often resulting in redundant Gaussian proliferation and inaccurate geometric reconstruction. Existing methods predomi
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
- Diff2DGS: Reliable Reconstruction of Occluded Surgical Scenes via 2D Gaussian Splatting#820368
- Constrained Dynamic Gaussian Splatting#1248344
- SUCCESS-GS: Survey of Compactness and Compression for Efficient Static and Dynamic Gaussian Splatting#1069706
- ArtifactWorld: Scaling 3D Gaussian Splatting Artifact Restoration via Video Generation Models#1131506
- OX-NeRF: 3D X-ray Tomography Reconstruction from Sparse Views Using Implicit Neural Representation#1248827
Record · ID 1490794
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