arXiv (All)
open access
Contrastive Metric Learning for Point Cloud Segmentation in Highly Granular Detectors
We propose a novel clustering approach for point-cloud segmentation based on supervised contrastive metric learning (CML). Rather than predicting cluster assignments or object-centric variables, the method learns a latent representation in which points belonging to the same object are embedded nearb
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
- Latent-to-Latent Flow for Volumetric Stochastic Segmentation#984381
- MarsRecon: Self-Supervised and Multimodal Surface Representations for Mars#1037062
- Acquiring Weak Annotations for Tumor Localization in Temporal and Volumetric Data.#345235
- Semi-Supervised Biomedical Image Segmentation via Diffusion Models and Teacher-Student Co-Training#820192
- SegCol Challenge: Semantic Segmentation for Tools and Fold Edges in Colonoscopy data#1122903
Record · ID 1343539
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