Oct 21, 2019 · We propose a novel end-to-end convolutional neural network (CNN) that encapsulates adaptive attention information, and achieve instance segmentation by fusing ...

We present a robust real-time LiDAR 3D object detector that leverages heteroscedastic aleatoric uncertainties to significantly improve its detection performance ...

Specifically, we propose a novel end-to-end convolutional neural network (CNN) that encapsulates adaptive attention information, and achieve instance ...

Adaptive attention model for LiDAR instance segmentation. P Xiong, X Hao, Y Shao, J Yu. Advances in Visual Computing: 14th International Symposium on Visual ...

Mar 29, 2024 · Abstract—Domain adaptive LiDAR point cloud segmentation aims to learn an effective target segmentation model from labelled.

Abstract—We propose a robust baseline method for in- stance segmentation which are specially designed for large-scale outdoor LiDAR point clouds.

Aug 10, 2021 · Especially, the FusionPainting framework consists of three main modules: a multi-modal semantic segmentation module, an adaptive attention-based ...

Jul 12, 2024 · In this module, an adaptive mechanism enables the model to autonomously learn the weights between features extracted by global max pooling and ...

Semantic segmentation of point clouds provided by airborne LiDAR survey in urban scenes is a great challenge.

In this study, two instance segmentation methods, Mask R–CNN and DETR, were applied to precisely delineate single tree crowns using multispectral images.