Comments (3)
I found that the ICP algorithm is not GPU accelerated.
What does this mean?
All registration algorithms implemented in this library use CUDA.
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Use ICP to find that it takes 10ms to construct 10,000 points of kdtree. I think this is relatively slow. Is there any way to improve it?
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You may need more points than that to benefit from the GPU.
The benchmark is tested with 1 million points, in which case it is about 100 times faster than the CPU.
https://raw.githubusercontent.com/neka-nat/cupoch/master/docs/_static/speedup.png
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Related Issues (20)
- Redefinition problem of 3rd party lib when use Cupoch side by side with Open3D
- Outlier Removal Not working HOT 1
- Poisson's surface reconstruction
- Getting cudaErrorMemoryAllocation out of memory HOT 6
- fastglobalregistration.cu have error HOT 1
- icp TransformationEstimationPointToPlane yields different result compare to open3d
- stdgpu::vector::size : Size out of bounds: -120 not in [0, 17527]. Clamping to 0 HOT 1
- Building SHARED_LIBS and run registration_cuda will crash.
- Implementation of LaserScanBuffer::CreateFromPointCloud(pointcloud_to_laserscan)
- Unable to Read Binary PLY File
- speed performace VS open3d HOT 1
- Error: OS call failed or operation not supported on this OS phong_shader.cu:330 HOT 1
- Check collision between a mesh and a set of points.
- The result of using ClusterDBSCAN() is wrong, it treats each point as a cluster HOT 4
- compile C++ static lib fail (master-v0.2.8.1), content of src/cupoch/visualization/shader/shader.h is wrong HOT 4
- registration_ransac_based_on_feature_matching
- cupoch is slower than open3d-cpu and open3d-gpu HOT 2
- How to set point cloud as numpy array HOT 2
- MemoryError cph.initialize_allocator(cph.PoolAllocation, 1000000000) HOT 5
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