CNS Core: Medium: Characterization, Mitigation, and Management of Active 3D Camera Interference

CNS 核心：中：主动 3D 相机干扰的表征、缓解和管理

• 批准号: 2107060
• 负责人: Suman Banerjee
• 金额: $ 100万
• 依托单位: University of Wisconsin-Madison
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2025-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2107060&HistoricalAwards=false
• 关键词: CNS; Core; Medium; Characterization; Mitigation

This project focuses on interference encountered in active 3D camera networks. Active 3D cameras are gaining widespread use in many scenarios, primarily to understand location and presence of objects in vicinity of the camera. While expensive forms of it is common in autonomous vehicles for understanding the environment, technological improvements have led to these devices to be inexpensive, and suitable for integration in common mobile devices, such as smartphones and tablets. In such mobile devices, active 3D cameras enable many new ways that users can interact with the physical world. Active 3D cameras operate by sending light pulses in different directions, and by observing the reflections of these light pulses, can understand the properties of its environment. As more and more devices are equipped with active 3D cameras, chances of interference between multiple active 3D cameras go up. As the light pulses from different active 3D cameras "collide" in space and time, the inference about the environment is going to be inaccurate. In the case of autonomous vehicles this can lead to incorrect estimates of objects in the physical world leading to disastrous consequences. In the case of common mobile devices and their applications, such errors will lead to poor user experience and overall performance. While this problem has not been a significant issue thus far, due to limited number of active 3D cameras in our environment, the significant reduction in its cost will likely lead to an explosion of these devices. This project is designing methods by which such interference between active 3D cameras can be detected, mitigated, thereby preserving their usefulness in diverse scenarios. Beyond the core research activities, the project also involves significant educational components which include setup of a laboratory-oriented curriculum around 3D camera and wireless systems for students at both the undergraduate and graduate levels; creation of interactive, children-focused workshops on “fun with cameras” at public science events in the University Wisconsin-Madison campus; and engagement of undergraduates in research activities. The outreach plan involves engagement with industrial partners, public dissemination of results through project website, through open-sourced artifacts, and through conferences and journals, and in creating specific forums that encourage students from underrepresented groups towards STEM careers.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该项目重点是在活动3D摄像机网络中遇到的干扰。在许多情况下，主动3D摄像机正在获得广泛使用，主要了解相机附近物体的位置和存在。虽然昂贵的形式在自动驾驶汽车中很常见以了解环境，但技术的改进导致这些设备便宜，并且适合在通用移动设备（例如智能手机和平板电脑）中集成。在这样的移动设备中，主动的3D摄像机可以使用户可以与物理世界互动的许多新方法。主动3D摄像机通过向不同方向发送光脉冲并观察这些光脉冲的反射可以理解其环境的特性来运行。随着越来越多的设备配备有效的3D摄像头，多个主动3D摄像机之间的干扰可能会增加。随着来自不同活性3D摄像机“碰撞”时空的光脉冲，对环境的推断将不准确。就自动驾驶汽车而言，这可能会导致物理世界中物体的错误估计，从而导致灾难后果。对于常见的移动设备及其应用程序，此类错误将导致用户体验和整体性能差。虽然到目前为止，由于我们环境中的活跃3D摄像机数量有限，因此该问题的大幅降低可能会导致这些设备的爆炸式增长。该项目正在设计方法，通过该方法可以检测到活跃的3D摄像机之间的这种干扰，从而在潜水员场景中保留其有用性。除核心研究活动外，该项目还涉及重要的教育组成部分，包括在3D摄像头周围设置面向实验室的课程和本科和研究生水平的学生的无线系统；在威斯康星州麦迪逊校园的公共科学活动中，创建了以儿童为中心的互动，专注于“与摄像机”的研讨会；和本科生参与研究活动。 The outreach plan involves engagement with industrial partners, public dissemination of results through project website, through open-sourced artifacts, and through conferences and journals, and in creating specific forums that encourage students from underrepresented groups towards STEM careers.This award reflects NSF's statutory mission and has been deemed honestly of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

项目成果

3D Scene Inference from Transient Histograms

• DOI: 10.48550/arxiv.2211.05094
• 发表时间: 2022-11
• 作者: Sacha Jungerman;A. Ingle;Yin Li;Mohit Gupta

When Two Cameras Are a Crowd

当两台相机拥挤时

• DOI: 10.1145/3588998
• 发表时间: 2023
• 期刊: Communications of the ACM
• 影响因子: 22.7
• 作者: Lee, Jongho;Gupta, Mohit;Krishnaswamy, Bhuvana;Banerjee, Suman
• 通讯作者: Banerjee, Suman

Mitigating AC and DC Interference in Multi-ToF-Camera Environments

• DOI: 10.1109/tpami.2023.3307564
• 发表时间: 2023-08
• 期刊: IEEE Transactions on Pattern Analysis and Machine Intelligence
• 影响因子: 23.6
• 作者: Jongho Lee;Mohit Gupta

Unlocking the Performance of Proximity Sensors by Transient Histograms

通过瞬态直方图释放接近传感器的性能

• 发表时间: 2023
• 期刊: IEEE robotics automation letters
• 作者: Carter Sifferman;Yeping Wang;Mohit Gupta;Michael Gleicher
• 通讯作者: Michael Gleicher

Compressive Single-Photon 3D Cameras

压缩式单光子 3D 相机

• DOI: 10.1109/cvpr52688.2022.01733
• 发表时间: 2022
• 期刊: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR
• 作者: Gutierrez-Barragan, Felipe;Ingle, Atul;Seets, Trevor;Gupta, Mohit;Velten, Andreas
• 通讯作者: Velten, Andreas