Koc University Digital Collections
metadata only
Correspondence-free alignment of 3D object models
In this work, we present a pose-invariant shape matching methodology for complete 3D object models. Our approach is based on first describing the objects with shape descriptors and then minimizing the distance between descriptors over an appropriate set of geometric transformations. Our chosen shape
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
- FAST: Flow Any Scene Transformer#1144915
- 3d-OT: a deep geometry-aware framework for heterogeneous slices alignment of spatial multi-omics.#13580
- PAOLI: Pose-free Articulated Object Learning from Sparse-view Images#1036573
- STA-TFM: Spatio-Temporal Aggregation Across Views TransForMer for Pose Estimation#1050656
- SAGE-Flow: A Decoupled Framework for Geometry Alignment and Stateless Real-Time Multi-Camera 3D Reconstruction#1142706
Record · ID 954458
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