Comments (3)
Hello,
No in this repo, we only reimplemented the network architecture of KPConv for K-nearest neighbors conv. layers, not the convolutional layer described in KPConv.
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I am writing a paper comparing different algorithm. The Lightconv and Conv is the fastest algo among all models. I intent to compare KPconv as well. However, i am not a coder. Do you know how use implement also Kpconv for the original layer, in your code style. is it possible that you show some guidance and add the layer in your repo if there is not?
# Network
network:
model: KPConvCls
backend_conv:
layer: FKAConv(KPConv)
kernel_separation: false
backend_search: SearchQuantized
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Hello,
You cannot do for now. I have coded only the spatial search using K-nearest neighbors (all search algorithm are based on KNN not radius). I do not know if I will have time to do so.
from lightconvpoint.
Related Issues (15)
- I am very interested in your work, but A problem accured when install the setup.py. Do you kown why? HOT 1
- No 'libtorch_cpu.so' for knn
- Install ERROR: Command errored out with exit status 1 HOT 9
- Install Error HOT 1
- error about installing package in project HOT 1
- Space quantization
- question
- time consuming in test.py
- SemanticKITTI benchmark HOT 1
- NPM3D Benchmark HOT 2
- implement your model for cloudpoint HOT 5
- If LightConvPoint is faster than Convpoint for training Modelnet40
- Setup.py HOT 4
- validation
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