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arxiv: 2606.13028 · v1 · pith:6FZDT3C4new · submitted 2026-06-11 · 💻 cs.RO · cs.CV

Comparing Commercial Depth Sensor Accuracy for Medical Applications

Pit Henrich , Maximilian Weiherer , Franziska Hansen , Bernhard Egger , Franziska Mathis-Ullrich This is my paper

Pith reviewed 2026-06-27 06:27 UTC · model grok-4.3

classification 💻 cs.RO cs.CV
keywords depth sensorsmedical applicationsaccuracy comparisonporcine tissuesilicone phantomstylus referencestructured lighttime of flight
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The pith

The Zivid 2M+ 60 sensor delivered the highest depth accuracy on porcine tissue and a kidney phantom among four commercial devices tested.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper compares four off-the-shelf depth sensors on objects that present real surgical difficulties: a porcine bone, a porcine belly specimen, and a silicone kidney phantom. Measurements were taken at roughly 50 cm using stylus-sampled points as the reference standard. The evaluation covered stereo, structured-light, and time-of-flight technologies and recorded performance on surfaces that are homogeneous, specular, or subject to subsurface scattering. The work aims to guide hardware selection for medical applications that require reliable depth data.

Core claim

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, PMD Flexx2, Stereolabs ZED 2i, and Zivid 2M+ 60 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.

What carries the argument

Stylus-sampled reference points serving as ground-truth depth values on the three test objects.

If this is right

  • Medical systems needing depth at close range can prioritize the Zivid 2M+ 60 over the other three sensors on similar surfaces.
  • The ZED 2i may still be usable on real tissue but should be avoided on phantoms with homogeneous or scattering properties.
  • Structured-light or time-of-flight designs appear more robust than stereo under the tested conditions at 50 cm.
  • Sensor choice for surgical navigation or robotic assistance can be narrowed using these concrete rankings rather than manufacturer specifications alone.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The ranking may shift if the working distance changes substantially from 50 cm or if different reference measurement methods replace the stylus.
  • Results on porcine tissue suggest that performance on living human tissue could follow similar patterns, though vascular or motion effects remain untested.
  • Hardware selection for depth-based medical imaging pipelines can now incorporate these empirical differences when trading off cost, size, and accuracy.

Load-bearing premise

Stylus-sampled points provide accurate ground-truth depth values on the porcine specimens and phantom despite homogeneous surfaces, specular reflections, and subsurface scattering.

What would settle it

Independent verification that the stylus points deviate from the true surface geometry on the porcine specimens by more than the reported sensor errors would reverse the accuracy ranking.

Figures

Figures reproduced from arXiv: 2606.13028 by Bernhard Egger, Franziska Hansen, Franziska Mathis-Ullrich, Maximilian Weiherer, Pit Henrich.

Figure 2
Figure 2. Figure 2: Evaluated objects: porcine bone specimen, porcine belly specimen, and [PITH_FULL_IMAGE:figures/full_fig_p001_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Stylus reference acquisition, depth capture, and point cloud registra [PITH_FULL_IMAGE:figures/full_fig_p002_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative comparison of point clouds obtained from each depth sensor (structure only). All images were captured at approximately [PITH_FULL_IMAGE:figures/full_fig_p003_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative comparison of the 50 cm and the 33 cm close-up D405 point clouds (structure only) on the porcine belly. The backgrounds were trimmed away for visualization purposes. all error metrics noticeably and results in a clearer overall point cloud, see [PITH_FULL_IMAGE:figures/full_fig_p004_5.png] view at source ↗
Figure 4
Figure 4. Figure 4: Moreover, and despite the Zivid having faster modes, [PITH_FULL_IMAGE:figures/full_fig_p004_4.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The manuscript presents an empirical benchmark of four commercial depth sensors (Intel RealSense D405, PMD Flexx2, Stereolabs ZED 2i, Zivid 2M+ 60) on a porcine bone specimen, porcine belly specimen, and silicone kidney phantom at ~50 cm distance. Using stylus-sampled points as ground truth, the authors evaluate performance on objects exhibiting homogeneous surfaces, specular reflections, and subsurface scattering. They conclude that the Zivid 2M+ 60 performed best across all objects and metrics, while the ZED 2i ranked second on real tissue but last on the phantom.

Significance. If the ground-truth method is validated, the work supplies practical guidance for depth-sensor selection in medical and surgical applications involving real tissue and phantoms. The inclusion of both biological specimens and multiple sensing modalities (stereo, structured light, ToF) adds relevance. However, the absence of any quantitative error values or statistical details in the abstract limits immediate utility, and the unvalidated reference method reduces the strength of the reported rankings.

major comments (2)
  1. [Abstract] Abstract: the central ranking claims (Zivid best overall; ZED second on tissue, last on phantom) are asserted without any quantitative error values, statistical tests, sample sizes, or protocol details, preventing verification of the performance claims.
  2. [Methods (ground-truth acquisition)] Methods (stylus ground-truth acquisition): stylus-sampled points are treated as accurate references for all error metrics despite the explicit presence of specular reflections and subsurface scattering on the tested surfaces; no repeatability tests, CMM comparison, or error-propagation analysis is reported. This assumption is load-bearing for the sensor rankings.
minor comments (2)
  1. [Abstract] Abstract: the specific error metrics (e.g., RMSE, MAE) and number of sampled points per object are not named.
  2. [Abstract] Abstract: the exact sensor-to-object distance variation and calibration procedure are described only as 'approximately 50 cm'.

Simulated Author's Rebuttal

2 responses · 1 unresolved

We appreciate the referee's thorough review and constructive feedback on our manuscript comparing commercial depth sensors for medical applications. Below, we provide point-by-point responses to the major comments.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the central ranking claims (Zivid best overall; ZED second on tissue, last on phantom) are asserted without any quantitative error values, statistical tests, sample sizes, or protocol details, preventing verification of the performance claims.

    Authors: We concur that the abstract lacks quantitative details to support the stated rankings. We will revise the abstract to incorporate key quantitative results, including mean absolute errors and standard deviations for each sensor across specimens, the number of stylus-sampled points, and a note on statistical significance of differences. This will allow readers to verify the claims while remaining within abstract length limits. revision: yes

  2. Referee: [Methods (ground-truth acquisition)] Methods (stylus ground-truth acquisition): stylus-sampled points are treated as accurate references for all error metrics despite the explicit presence of specular reflections and subsurface scattering on the tested surfaces; no repeatability tests, CMM comparison, or error-propagation analysis is reported. This assumption is load-bearing for the sensor rankings.

    Authors: This is a fair critique. The manuscript does not include repeatability tests, CMM comparisons, or error-propagation analysis for the stylus method. We will revise the Methods and add a limitations discussion to explicitly acknowledge potential inaccuracies from surface properties (specular reflections, subsurface scattering) and note that the same reference was applied uniformly to enable relative comparisons. We cannot conduct or report new validation experiments in this revision cycle. revision: partial

standing simulated objections not resolved
  • Full empirical validation of the stylus ground-truth method (repeatability tests, CMM comparison, or error-propagation analysis)

Circularity Check

0 steps flagged

Pure empirical benchmark with no derivations or self-referential steps

full rationale

The paper reports direct measurements of depth sensor errors on porcine specimens and a phantom, using stylus-sampled points as external ground-truth references. No equations, fitted parameters, predictions, or theoretical derivations are present. The sensor ranking follows immediately from the computed error metrics (e.g., mean absolute error) against the stylus data. No self-citations of theorems, ansatzes, or uniqueness results are invoked. The central claim is therefore a straightforward empirical comparison that does not reduce to its own inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Empirical benchmark study; no mathematical derivations, fitted parameters, or postulated entities are described in the abstract.

pith-pipeline@v0.9.1-grok · 5688 in / 1041 out tokens · 16814 ms · 2026-06-27T06:27:16.496874+00:00 · methodology

discussion (0)

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