arXiv (All)
open access
BRICE: Brain-inspired Recurrent Illusory Contour Encoder for Point Cloud Completion
Mainstream point cloud completion methods are unanimously based on deep learning architectures. On one hand, these methods tend to repetitively stacking attention modules to enhance network performance, which drifts away from the neat design of unified and interpret-able networks; on the other hand,
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
- Template-Based Deployment for Real-Time Deep Learning on Point Clouds / Marc Neu ; Jürgen Becker, Torben Ferber#669207
- PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric\n Space#7143
- Muon Learns More Robust and Transferable Features than Adam#267662
- CARVE: Content-Aware Recurrent with Value Efficiency for Chunk-Parallel Linear Attention#310816
- HLA: Expressive Hybrid Linear Attention via Chunk-Wise Dynamic Mixing#1202261
Record · ID 1387290
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