A dynamic compression technique for streaming kinect-based Point Cloud data

Moreno, Carlos; Chen, Yilin; Li, Ming

doi:10.1109/iccnc.2017.7876188

Cited by 11 publications

(6 citation statements)

References 15 publications

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“…In [4], a frame rate of 5.86 Hz was achieved with a powerful CPU (Intel i7), while 20 Hz was achieved in this paper using only a much less powerful ARM processor. The reason for the improved performance achieved in our work results from (1) a dedicated local network with no background transmission and (2) all the data points inside one voxel are in our work filtered and described by only one coordinate and one intensity value.…”

Section: Discussionmentioning

confidence: 99%

“…A dynamic compression scheme based on [2] was developed in [4]. Using a single Kinect depth sensor, the authors were able to achieve an average frame rate of 5.86 Hz on a network subjected to different levels of background transmissions.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike in most literature where single sensors often are considered [4,9,10], an industrial application will typically need multiple sensors to cover relatively large areas. To ensure scalability, a new approach is presented, where each sensor is connected to a local computer.…”

Section: Introductionmentioning

confidence: 99%

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Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments

Dybedal

Aalerud

Hovland

2019

Sensors

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This paper presents a scalable embedded solution for processing and transferring 3D point cloud data. Sensors based on the time-of-flight principle generate data which are processed on a local embedded computer and compressed using an octree-based scheme. The compressed data is transferred to a central node where the individual point clouds from several nodes are decompressed and filtered based on a novel method for generating intensity values for sensors which do not natively produce such a value. The paper presents experimental results from a relatively large industrial robot cell with an approximate size of 10 m × 10 m × 4 m. The main advantage of processing point cloud data locally on the nodes is scalability. The proposed solution could, with a dedicated Gigabit Ethernet local network, be scaled up to approximately 440 sensor nodes, only limited by the processing power of the central node that is receiving the compressed data from the local nodes. A compression ratio of 40.5 was obtained when compressing a point cloud stream from a single Microsoft Kinect V2 sensor using an octree resolution of 4 cm.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments

Dybedal

Aalerud

Hovland

2019

Sensors

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“…The authors of [4] and [5] try to live stream point clouds captured by a Microsoft Kinect in real-time at 30 frames per second (FPS). In [4], they adapt the voxel length of the doublebuffered octree [3] to the measured available network data rate in an interval of 15 frames and achieved an average of 5.86 FPS. In [5], they propose a new distributed octree data structure to increase the performance.…”

Section: Related Workmentioning

confidence: 99%

On the Potential of Rate Adaptive Point Cloud Streaming on the Point Level

Laniewski

Aschenbruck

2021

2021 IEEE 46th Conference on Local Computer Networks (LCN)

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In many robotic applications as well as in the area of immersive multimedia, large point clouds need to be transmitted over a network preferably in real-time. Typically, the point cloud is first compressed to a significantly smaller size, before it is transmitted as a simple file transfer. A drawback of this method is that it requires a point cloud to be fully available for the compression process to begin, inducing additional delay. In this paper, we explore the potential of streaming incoming points directly from an ongoing laser scan. Therefore, we propose a compression algorithm that operates on an ongoing point stream. Furthermore, we propose and compare different rate adaptation methods that dynamically adapt the compression rate to the currently available network data rate. Our evaluations show promising results in terms of rate fluctuations around the available network data rate and the achieved quality of our solution.

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“…Schnabel et al discussed an octree-based point cloud compression approach combined with a specialized prediction technique for point-sampled geometry and surface approximation [15]. By using exclusive disjunction operation (XOR) on the octree byte stream, a point cloud data stream can be compressed in real-time as the XOR prediction is relatively simple and can be performed quickly [23]- [25]. However, the approach can only be applied to scenarios with limited movement, which is not the case for the envisioned application with moving people.…”

Section: Related Workmentioning

confidence: 99%

Prioritized Transmission Control of Point Cloud Data Obtained by LIDAR Devices

et al. 2020

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Smart monitoring, particularly at intersections, is a promising service that is being considered for the concept of smart cities. A network of light detection and ranging (LIDAR) sensors, which generates point cloud data in real time, can be used to detect people's mobility in smart monitoring. Due to the sheer volume of point cloud data, data transmission requires a significant amount of communication resources. In order to monitor people's mobility in real time, it is necessary to reduce the amount of transmission data to shorten delay. Point cloud compression is one method for reducing the amount of data. However, prior works addressing point cloud compression mainly focused on accuracy for the compression of an entire point cloud without considering its spatial characteristics. The more dynamically a spatial region changes, the more important it is when detecting moving objects such as cars, trucks, pedestrians, and bikes in smart monitoring. This paper proposes a prioritized transmission scheme that applies multiple point cloud compression methods to point cloud data according to the spatial importance of the data, i.e., how dynamically spatial regions change. This paper assumes data transmission of point cloud data from multiple LIDAR devices to an edge server and addresses the intra-frame geometry compression of point cloud data. The proposed scheme splits the point cloud into multiple classes according to the spatial importance and applies multiple point cloud compression methods to each class. A numerical study using a real point cloud dataset obtained at an intersection demonstrates the dependencies of quality, volume, and processing time on possible compression format options. The results verify that the proposed scheme reduces the amount of point cloud data drastically while satisfying the quality and processing time requirements.

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A dynamic compression technique for streaming kinect-based Point Cloud data

Cited by 11 publications

References 15 publications

Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments

Embedded Processing and Compression of 3D Sensor Data for Large Scale Industrial Environments

On the Potential of Rate Adaptive Point Cloud Streaming on the Point Level

Prioritized Transmission Control of Point Cloud Data Obtained by LIDAR Devices

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