Real-time compression of point cloud streams

Kammerl, Julius; Blodow, Nico; Rusu, Radu Bogdan; Gedikli, Suat; Beetz, Michael; Steinbach, Eckehard

doi:10.1109/icra.2012.6224647

Cited by 295 publications

(199 citation statements)

References 9 publications

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“…Another popular use case is the compression of static point clouds (Schnabel and Klein, 2006) or point cloud streams (Kammerl et al, 2012). While our framework is general enough to also store raw point clouds, its main purpose is to integrate these point clouds into a memory-efficient, volumetric occupancy map, since point clouds as environment representation in robotics have a number of disadvantages as detailed at the beginning of this section.…”

Section: Related Workmentioning

confidence: 99%

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

et al. 2013

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Three-dimensional models provide a volumetric representation of space which is important for a variety of robotic applications including flying robots and robots that are equipped with manipulators. In this paper, we present an open-source framework to generate volumetric 3D environment models. Our mapping approach is based on octrees and uses probabilistic occupancy estimation. It explicitly represents not only occupied space, but also free and unknown areas. Furthermore, we propose an octree map compression method that keeps the 3D models compact. Our framework is available as an open-source C++ library and has already been successfully applied in several robotics projects. We present a series of experimental results carried out with real robots and on publicly available real-world datasets. The results demonstrate that our approach is able to update the representation efficiently and models the data consistently while keeping the memory requirement at a minimum.

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Section: Related Workmentioning

confidence: 99%

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

et al. 2013

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“…Filtering 3D point cloud is mainly based on mathematical morphology, such as triangulation, statistical analysis etc. Kammerl et al (2012) [16] designed a spatial and temporal lossy compression of a 3D point cloud video stream. They have used Octree to compress point clouds by performing a spatial decomposition.…”

Section: Related Workmentioning

confidence: 99%

Color Reduction in an Authenticate Live 3D Point Cloud Video Streaming System

2016

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Abstract:In this paper, an authenticate live 3D point cloud video streaming system is presented, using a low cost 3D sensor camera, the Microsoft Kinect. The proposed system is implemented on a client-server network infrastructure. The live 3D video is captured from the Kinect RGB-D sensor, then a 3D point cloud is generated and processed. Filtering and compression are used to handle the spatial and temporal redundancies. A color histogram based conditional filter is designed to reduce the color information for each frame based on the mean and standard deviation. In addition to the designed filter, a statistical outlier removal filter is used. A certificate-based authentication is used where the client will verify the identity of the server during the handshake process. The processed 3D point cloud video is live streamed over a TCP/IP protocol to the client. The system is evaluated in terms of: compression ratio, total bytes per points, peak signal to noise ratio (PSNR), and Structural Similarity (SSIM) index. The experimental results demonstrate that the proposed video streaming system have a best case with SSIM 0.859, PSNR of 26.6 dB and with average compression ratio of 8.42 while the best average compression ratio case is about 15.43 with PSNR 18.5128 dB of and SSIM 0.7936.

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“…It is the basis of many compression algorthms that are currently used, and it is further exploited to reduce the size of data needed for content reconstruction. [21][22][23][24][25] This regular representation, though, leads to visible artifacts in the form of structured loss. Octree-pruning can be obtained by setting a desirable octree LoD, which defines the size of leaf nodes.…”

Section: Octree-pruningmentioning

confidence: 99%

On the performance of metrics to predict quality in point cloud representations

Alexiou

Ebrahimi

2017

Applications of Digital Image Processing XL

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Point clouds are a promising alternative for immersive representation of visual contents. Recently, an increased interest has been observed in the acquisition, processing and rendering of this modality. Although subjective and objective evaluations are critical in order to assess the visual quality of media content, they still remain open problems for point cloud representation. In this paper we focus our efforts on subjective quality assessment of point cloud geometry, subject to typical types of impairments such as noise corruption and compression-like distortions. In particular, we propose a subjective methodology that is closer to real-life scenarios of point cloud visualization. The performance of the state-of-the-art objective metrics is assessed by considering the subjective scores as the ground truth. Moreover, we investigate the impact of adopting different test methodologies by comparing them. Advantages and drawbacks of every approach are reported, based on statistical analysis. The results and conclusions of this work provide useful insights that could be considered in future experimentation.

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Real-time compression of point cloud streams

Cited by 295 publications

References 9 publications

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

OctoMap: an efficient probabilistic 3D mapping framework based on octrees

Color Reduction in an Authenticate Live 3D Point Cloud Video Streaming System

On the performance of metrics to predict quality in point cloud representations

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