# Comparing Commercial Depth Sensor Accuracy for Medical Applications

arXiv: https://arxiv.org/abs/2606.13028 · HTML: https://mlsanity.com/paper/2606.13028/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 9 references that are in the map.
- First posted on arXiv 11 Jun 2026.

## Builds on (most cited first, 5 of 5)

- [FoundationStereo: Zero-Shot Stereo Matching](https://mlsanity.com/paper/2501.09898/) (2025)
- [LOOC: Localizing Organs using Occupancy Networks and Body Surface Depth Images](https://mlsanity.com/paper/2406.12407/) (2024)
- [LUDO: Low-Latency Understanding of Deformable Objects using Point Cloud Occupancy Functions](https://mlsanity.com/paper/2411.08777/) (2024)
- [iRBSM: A Deep Implicit 3D Breast Shape Model](https://mlsanity.com/paper/2412.13244/) (2024)
- [Point Cloud Segmentation for Autonomous Clip Positioning in Laparoscopic Cholecystectomy on a Phantom](https://mlsanity.com/paper/2606.12048/) (2026)

## Abstract (arXiv)

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.