arXiv (OAI Expanded)
open access
Illusion of Depth: Revealing Hidden Stereo Vision Vulnerabilities in Depth Estimation
Stereo cameras are integrated into autonomous systems such as self-driving cars, drones, and robots to offer precise depth estimation in a cost-effective manner compared to LiDAR technology. In this work, we reveal an intrinsic vulnerability in stereo cameras that stems from their pixel sampling and
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
- Illusion of Depth: Revealing Hidden Stereo Vision Vulnerabilities in Depth Estimation#919264
- Robust Depth Estimation using Auto-Exposure Bracketing#374906
- Judging to Improve: A De-biased VLM-as-3D-Judge Protocol for Single-Image 3D Generation#290589
- DepthArb: Training-Free Depth-Arbitrated Generation for Occlusion-Robust Image Synthesis#609889
- Research trends in deep learning and machine learning for cloud computing security#339532
Record · ID 1110700
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