aiqbench analysis
This benchmark study evaluates four commercial depth sensors—stereo, structured-light, and time-of-flight—against complex medical phantoms to determine their accuracy in surgical contexts. The findings highlight that while the Zivid 2M+ 60 excelled across all metrics, performance varied significantly depending on surface properties like subsurface scattering and specular reflection. For AI developers integrating vision systems into robotic surgery, this data is critical for selecting hardware capable of handling real-world tissue challenges rather than idealized lab conditions.
Comparing Commercial Depth Sensor Accuracy for Medical Applications
arXiv:2606.13028v2 Announce Type: replace Abstract: Depth estimation has numerous medical and surgical applications. We benchmark four depth sensors on a porcine bone specimen, a porcine belly specimen, and a silicone kidney phantom using stylus-sampled references. These objects contain several real-world challenges, including homogeneous surfaces, specular surfaces, and subsurface scattering. The comparison includes stereo, structured-light, and time-of-flight sensors at a distance of approximately 50 cm. Specifically, the Intel RealSense D405 (Intel RealSense, United States), PMD Flexx2 (pmdtechnologies, Germany), Stereolabs ZED 2i (Stereolabs, France), and Zivid 2M+ 60 (Zivid, Norway) are compared. The Zivid 2M+ 60 performed best across all objects and metrics considered in this work. The ZED ranked second for real tissue, but last on the phantom.
Abstract:Depth estimation has numerous medical and surgical applications. We benchmark four depth sensors on a porcine bone specimen, a porcine belly specimen, and a silicone kidney phantom using stylus-sampled references. These objects contain several real-world challenges, including homogeneous surfaces, specular surfaces, and subsurface scattering. The comparison includes stereo, structured-light, and time-of-flight sensors at a distance of approximately 50 cm. Specifically, the Intel RealSense D405 (Intel RealSense, United States), PMD Flexx2 (pmdtechnologies, Germany), Stereolabs ZED 2i (Stereolabs, France), and Zivid 2M+ 60 (Zivid, Norway) are compared. The Zivid 2M+ 60 performed best across all objects and metrics considered in this work. The ZED ranked second for real tissue, but last on the phantom.
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
arXiv:2606.13028 [cs.RO]
(or arXiv:2606.13028v2 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2606.13028 Focus to learn more arXiv-issued DOI via DataCite
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.
Full text under the publisher's open licence · 209 words
Related coverage
Comparing Commercial Depth Sensor Accuracy for Medical Applications
arXiv · Jun 12, 2026

News
Quantum Computing Report · Jun 7, 2026

NVIDIA GTC Taipei at COMPUTEX: Live Updates on What’s Next in AI
NVIDIA · May 21, 2026
How bad would it be if GPS satellites were shot down?
LessWrong · Jun 12, 2026