Comments (1)
You're right that if we use CUDA KNN libraries, this operation may run faster. We're currently using CPU implementation is because we have a convenient implementation in Open3D. In the future, we're planning GPU acceleration to Open3D, which could further accelerate this implementation.
from open3d-pointnet2-semantic3d.
Related Issues (20)
- test_tf_ops.py failed. libtf_grouping.so: undefined symbol: _ZN10tensorflow12OpDefBuilder4AttrESs HOT 1
- Vulkan directory and The RandR headers Error HOT 2
- Error: macro "fmt" passed 2 arguments, but takes just 1 fmt(out, std::get<I>(tup)); HOT 3
- After training the model, how should I predict my point cloud, for example, input a point cloud and output the result after semantic segmentation
- cmake build error HOT 2
- Cmake :Open3D build was not successful! HOT 6
- Free(): invalid pointer
- open3d version HOT 1
- can i build this item with windows HOT 4
- how can i replace this code “open3d.crop_point_cloud” to version 0.10.0 code
- can i have the pre trained model?
- about partition for the training and validation sets on the Semantic3d
- How to do online testing for Semantic3D?
- Error about libraries HOT 1
- problem when classes are not presented in the val.set
- interpolate.py rise Segmentation fault (core dumped) HOT 1
- Downloaded set vs. Pre-processed set
- Down sampling error HOT 2
- segmentation_fault while running test_tf_ops.py
- interpolate.py rise Segmentation fault (core dumped)
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