Point cloud scene segmentation method based on knowledge distillation and semantic fusion

A scene segmentation and point cloud technology, which is applied in the field of point cloud scene segmentation based on knowledge distillation and semantic fusion, can solve problems such as constraints, limitation of semantic segmentation effect, and loss of contextual semantic information, so as to avoid calculation and improve point cloud segmentation The effect of the result

Active Publication Date: 2020-07-28
中科人工智能创新技术研究院(青岛)有限公司
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Problems solved by technology

However, after being divided into small cube blocks, a large amount of global contextual semantic information is lost, which restricts the learning of features to the information of a larger receptive field range, and limits the effect of semantic segmentation

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  • Point cloud scene segmentation method based on knowledge distillation and semantic fusion
  • Point cloud scene segmentation method based on knowledge distillation and semantic fusion
  • Point cloud scene segmentation method based on knowledge distillation and semantic fusion

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Embodiment Construction

[0032] The present disclosure will be further described below in conjunction with the accompanying drawings and embodiments.

[0033] It should be noted that the following detailed description is exemplary and intended to provide further explanation of the present disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.

[0034] It should be noted that the terminology used herein is only for describing specific embodiments, and is not intended to limit the exemplary embodiments according to the present disclosure. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinations thereof.

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Abstract

The invention provides a point cloud scene segmentation method based on knowledge distillation and semantic fusion. The method comprises the steps: constructing a double-flow network frame which comprises dense local branches and sparse global branches; wherein the input of the dense local branch is the local region dense point cloud in the global scene, and the input of the sparse global branch is the sampled global scene point cloud. Secondly, designing a distillation module based on irregular data, performing knowledge distillation by using an Euclidean distance and an adversarial learningloss function, and transmitting local dense detail information to a sparse global branch; and finally, a dynamic graph context semantic information fusion module is designed, and the global features and the local features after detail information enhancement are fused. According to the method, rich detail information of a local scene and rich context semantic information of a global scene are fully and complementarily utilized. Meanwhile, increase of the calculation amount is avoided, and the point cloud segmentation result of a large-scale indoor scene can be effectively improved.

Description

technical field [0001] The disclosure belongs to the technical field of computer vision and pattern recognition, and relates to a point cloud scene segmentation method based on knowledge distillation and semantic fusion. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] Semantic segmentation of 3D point cloud scenes is an important and challenging classic computer vision task, which is widely used in autonomous driving, smart home, augmented reality, virtual reality and other tasks. There are problems such as how to express the characteristics of irregular data and how to deal with large-scale data when directly processing point cloud data of large-scale scenes for semantic segmentation. [0004] According to the knowledge of the inventor, the current improved method is to process large-scale point cloud data and divide the large-scale sce...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/11G06K9/62G06N3/04G06N3/08
CPCG06T7/11G06N3/084G06T2207/10028G06T2207/20081G06T2207/20084G06N3/045G06F18/25Y02T10/40
Inventor 谭铁牛王亮张彰李亚蓓单彩峰
Owner 中科人工智能创新技术研究院(青岛)有限公司
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