arXiv (All)
open access
GlassFormer: Learning Real-time Glass Segmentation using Radar-Depth Fusion
Transparent surfaces are ubiquitous in built environments, yet they remain a persistent failure case for robotic perception. RGB cameras perceive the background behind glass rather than the surface itself, while depth sensors such as LiDAR, time-of-flight, and RGB-D often return invalid or backgroun
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
- CRAFT: Camera-Radar 3D Object Detection with Spatio-Contextual Fusion Transformer#464518
- ShelfChange3D: Object-Level 3D Change Detection for Retail Shelf Monitoring#1156555
- The EventCV Library for Event-Based Robotic Vision#1035737
- Pano3D: Unified 3D Reconstruction and Panoptic Segmentation#820508
- Geometry-Grounded Unified 3D Perception for Autonomous Driving#800135
Record · ID 1132045
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