# Empirical Comparison of Four Stereoscopic Depth Sensing Cameras for Robotics Applications

arXiv: https://arxiv.org/abs/2501.07421 · HTML: https://mlsanity.com/paper/2501.07421/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2025.

## Built on by (most cited first, 2 of 2)

- [SpatialPoint: Spatial-aware Point Prediction for Embodied Localization](https://mlsanity.com/paper/2603.26690/) (2026)
- [Depth from Dual Differential Defocus and Stereo Consensus](https://mlsanity.com/paper/2606.02906/) (2026)

## Abstract (arXiv)

Depth sensing is an essential technology in robotics and many other fields. Many depth sensing (or RGB-D) cameras are available on the market and selecting the best one for your application can be challenging. In this work, we tested four stereoscopic RGB-D cameras that sense the distance by using two images from slightly different views. We empirically compared four cameras (Intel RealSense D435, Intel RealSense D455, StereoLabs ZED 2, and Luxonis OAK-D Pro) in three scenarios: (i) planar surface perception, (ii) plastic doll perception, (iii) household object perception (YCB dataset). We recorded and evaluated more than 3,000 RGB-D frames for each camera. For table-top robotics scenarios with distance to objects up to one meter, the best performance is provided by the D435 camera that is able to perceive with an error under 1 cm in all of the tested scenarios. For longer distances, the other three models perform better, making them more suitable for some mobile robotics applications. OAK-D Pro additionally offers integrated AI modules (e.g., object and human keypoint detection). ZED 2 is overall the best camera which is able to keep the error under 3 cm even at 4 meters. However, it is not a standalone device and requires a computer with a GPU for depth data acquisition. All data (more than 12,000 RGB-D frames) are publicly available at https://rustlluk.github.io/rgbd-comparison.