HAL (France)
open access
Massive parallelization of projection-based depths
A novel methodology for the massive parallelization of projection-based depths is proposed, addressing the computational challenges of data depth in high-dimensional spaces. Using this approach, an algorithmic framework based on Refined Random Search (RRS) is built, resulting in significant speedups
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
- Systems and methods for depth estimation by learning triangulation and … — Magic Leap, Inc. (US11948320B2)#607312
- Feed-Forward 3D Scene Modeling: A Problem-Driven Perspective#14056
- Generative Models as Adaptive Trust Regions for Discrete Bayesian Optimisation#1341873
- InfiniSplat v2: Analyzing and Improving Feed-Forward 3DGS from Any Views#1355268
- Generating Heterogeneous 3D Geological Microstructures from 2D Images via a Stable Diffusion-Adversarial Model#1034122
Record · ID 1203689
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