Comments (2)
antao97
commented on August 19, 2024
DGCNN follows the setting of PointNet for semantic segmentation. In the 5.1. Applications of the PointNet paper, the authors introduce the 9-dim vector for semantic segmentation.
The description is presented as follows:
To prepare training data, we firstly split points by room, and then sample rooms into blocks with area 1m by 1m. We train our segmentation version of PointNet to predict per point class in each block. Each point is represented by a 9-dim vector of XYZ, RGB and normalized location as to the room (from 0 to 1).
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LeopoldACC
commented on August 19, 2024
thanks a lot!
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Related Issues (20)
- how to set the value of K with different points for other datasets HOT 1
- How long you take to train a epoch in S3DIS? I want to know it. Thank you! HOT 1
- 代码运行结果，比原来的要高很多 HOT 7
- Hello, I would like to ask, how do you test the miou of each class_names in the semantic segmentation content? HOT 4
- 请问model的get_graph_feature中这一段代码是不是没有什么作用 HOT 2
- How to visualize a category or an area in an S3DIS room HOT 7
- 作者您好，请问是否可以看一下您在ScanNet数据集中的data目录？ HOT 2
- 3d coordinates + extra feature HOT 1
- problems about transfer learning HOT 4
- Fine Tuning partseg DGCNN
- Problem with visualization(semseg)
- Can DGCNN be converted to ONNX model? HOT 2
- 引入更多的采样点以增加模型的细节 HOT 3
- Why the implemented network architecture is different with the architecture in the paper? HOT 1
- about dataset
- ValueError: setting an array element with a sequence. The requested array has an inhomogeneous shape after 1 dimensions. The detected shape was (1201,) + inhomogeneous part.
- Don't find the file `from config import CLASS_NAMES`.
- No data inside raw_data3d
- the error results for Visualization(比较严重的可视化结果)
- problems with Semantic Segmentation on the S3DIS Dataset
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