3D object detection model based on VoteNet
Chenyang Wan, Shijie Guan · 2023
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 (+[email protected]) over VoteNet.