# Intel RealSense Stereoscopic Depth Cameras

arXiv: https://arxiv.org/abs/1705.05548 · HTML: https://mlsanity.com/paper/1705.05548/

## Key facts

- Cited by 83 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 May 2017.

## Built on by (most cited first, 30 of 83)

- [Learning High-Speed Flight in the Wild](https://mlsanity.com/paper/2110.05113/) (2021)
- [Track2Act: Predicting Point Tracks from Internet Videos enables Generalizable Robot Manipulation](https://mlsanity.com/paper/2405.01527/) (2024)
- [CompletionFormer: Depth Completion with Convolutions and Vision Transformers](https://mlsanity.com/paper/2304.13030/) (2023)
- [ALOHA 2: An Enhanced Low-Cost Hardware for Bimanual Teleoperation](https://mlsanity.com/paper/2405.02292/) (2024)
- [NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representations](https://mlsanity.com/paper/2207.10985/) (2022)
- [Coupling Vision and Proprioception for Navigation of Legged Robots](https://mlsanity.com/paper/2112.02094/) (2021)
- [Approximate Differentiable Rendering with Algebraic Surfaces](https://mlsanity.com/paper/2207.10606/) (2022)
- [Generative Action Description Prompts for Skeleton-based Action Recognition](https://mlsanity.com/paper/2208.05318/) (2022)
- [I2-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs](https://mlsanity.com/paper/2303.07634/) (2023)
- [Stubborn: A Strong Baseline for Indoor Object Navigation](https://mlsanity.com/paper/2203.07359/) (2022)
- [Frame Mining: a Free Lunch for Learning Robotic Manipulation from 3D Point Clouds](https://mlsanity.com/paper/2210.07442/) (2022)
- [Bridging the Human to Robot Dexterity Gap through Object-Oriented Rewards](https://mlsanity.com/paper/2410.23289/) (2024)
- [Depth Prompting for Sensor-Agnostic Depth Estimation](https://mlsanity.com/paper/2405.11867/) (2024)
- [Trans4Trans: Efficient Transformer for Transparent Object and Semantic Scene Segmentation in Real-World Navigation Assistance](https://mlsanity.com/paper/2108.09174/) (2021)
- [SA-DVAE: Improving Zero-Shot Skeleton-Based Action Recognition by Disentangled Variational Autoencoders](https://mlsanity.com/paper/2407.13460/) (2024)
- [RGB-Depth Fusion GAN for Indoor Depth Completion](https://mlsanity.com/paper/2203.10856/) (2022)
- [Transformers are Adaptable Task Planners](https://mlsanity.com/paper/2207.02442/) (2022)
- [TODE-Trans: Transparent Object Depth Estimation with Transformer](https://mlsanity.com/paper/2209.08455/) (2022)
- [Multimodal Transformer for Nursing Activity Recognition](https://mlsanity.com/paper/2204.04564/) (2022)
- [Facial Depth and Normal Estimation using Single Dual-Pixel Camera](https://mlsanity.com/paper/2111.12928/) (2021)
- [ReorientBot: Learning Object Reorientation for Specific-Posed Placement](https://mlsanity.com/paper/2202.11092/) (2022)
- [ASGrasp: Generalizable Transparent Object Reconstruction and 6-DoF Grasp Detection from RGB-D Active Stereo Camera](https://mlsanity.com/paper/2405.05648/) (2024)
- [Single-Photon Structured Light](https://mlsanity.com/paper/2204.05300/) (2022)
- [Active Stereo Without Pattern Projector](https://mlsanity.com/paper/2309.12315/) (2023)
- [SparseDC: Depth Completion from sparse and non-uniform inputs](https://mlsanity.com/paper/2312.00097/) (2023)
- [ActiveZero: Mixed Domain Learning for Active Stereovision with Zero Annotation](https://mlsanity.com/paper/2112.02772/) (2021)
- [USA-Net: Unified Semantic and Affordance Representations for Robot Memory](https://mlsanity.com/paper/2304.12164/) (2023)
- [RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion](https://mlsanity.com/paper/2306.03584/) (2023)
- [Robust Event Detection based on Spatio-Temporal Latent Action Unit using Skeletal Information](https://mlsanity.com/paper/2109.02376/) (2021)
- [Visual Language Models as Operator Agents in the Space Domain](https://mlsanity.com/paper/2501.07802/) (2025)

## Abstract (arXiv)

We present a comprehensive overview of the stereoscopic Intel RealSense RGBD imaging systems. We discuss these systems' mode-of-operation, functional behavior and include models of their expected performance, shortcomings, and limitations. We provide information about the systems' optical characteristics, their correlation algorithms, and how these properties can affect different applications, including 3D reconstruction and gesture recognition. Our discussion covers the Intel RealSense R200 and the Intel RealSense D400 (formally RS400).