3D object detection model based on VoteNet

Chenyang Wan; Shijie Guan

doi:10.1117/12.2675250

25 May 2023 3D object detection model based on VoteNet

Chenyang Wan, Shijie Guan

Proceedings Volume 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022); 126361X (2023) https://doi.org/10.1117/12.2675250
Event: Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 2022, Shenyang, China

Abstract

By using point clouds as the only input, 3D object detection has made significant progress. However, point clouds often suffer from incomplete geometry and lack of semantic information, which makes it difficult for detectors to accurately classify and locate detected objects, in order to effectively use the rich semantic information in images to improve the performance of point-based 3D detectors, we propose a three-dimensional object detection method based on VoteNet multimodal self-attention mechanism. Firstly, the feature extraction of the data of the two modes of point cloud and image is carried out. Sencondly the three-dimensional point cloud features are mapped to the image, pseudo-3D votes are generated on the image, and then stitched with the point cloud features. Finally the stitched features are deeply fused through the self-attention mechanism. We validated our method on a challenging SUN RGB-D dataset. The results show that our model provides a significant gain (+4.8mAP@0.25) over VoteNet.

Citation Download Citation

Chenyang Wan and Shijie Guan "3D object detection model based on VoteNet", Proc. SPIE 12636, Third International Conference on Machine Learning and Computer Application (ICMLCA 2022), 126361X (25 May 2023); https://doi.org/10.1117/12.2675250

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KEYWORDS

Point clouds

3D image processing

Object detection

3D modeling

RGB color model

Semantics

Feature extraction

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