Three-dimensional semantic segmentation method based on channel attention and multi-scale fusion
A multi-scale fusion and semantic segmentation technology, which is applied in neural learning methods, instruments, biological neural network models, etc., can solve problems such as information loss, inability to make full use of 3D data, and result impact, and achieve the effect of improving segmentation results
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[0085] The dataset used in this example is the S3DIS dataset, which is collected from the indoor environments of three different buildings and contains 271 rooms in 6 areas. It has a total of 695,878,620 point clouds, each of which has corresponding coordinates and color information, as well as semantic labels such as chairs, tables, floors, walls, etc., with a total of 13 categories. In this embodiment, regions 1, 2, 3, 4, and 6 are selected for training, and region 5 is selected for testing. During training, this embodiment samples the input points into a uniform number of 4096 points, and uses all the points during testing.
[0086] This example trains 150 epochs on two GeForce RTX 2080Ti GPUs with a batch size of 16, using an SGD optimizer with an initial learning rate of 0.05, momentum of 0.9, and weight decay rate of 10 -4 , and implemented on the Pytorch platform using Linux. After using the training set to train the network to obtain the model, the model performance ...
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