arXiv (All)
open access
A Vision-Language Model (VLM)-based Pipeline for End-to-End Procedural Modeling of Field-Grown Maize from Point Clouds
Editable 3D models of field-grown crops support high-throughput phenotyping and in silico breeding trials, but building them from scanned point clouds requires organ-level segmentation and fitting. Procedural generators can turn an organ-level parameter set into an analysis-suitable 3D model, but ob
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
- MaizeField3D: A curated 3D point cloud and procedural model dataset of field-grown maize from a diversity panel.#138633
- 3D point cloud driven organ semantic segmentation to assess maize structural responses along the planting-density gradient.#330957
- Mira-Scene: Pixel-Aligned Layouts for Generative 3D Scene#1054722
- PRUE: A Practical Recipe for Field Boundary Segmentation at Scale#1218356
- Toward Comprehensive 3D Grounding: Orientation Grounding through Vision-Language Models#1117821
Record · ID 1188146
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