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Port grid voxelization from pytorch_cluster into pyg-lib. Each point is assigned a 1D cluster index based on its quantized voxel position: floor((pos - start) / size), then flattened with cumulative voxel counts.
Port CUDA grid voxelization kernel. One thread per point computes the flattened voxel index. Tests verify CPU/CUDA parity.
Port greedy farthest point sampling from pytorch_cluster. Uses at::parallel_for over batch dimension. Iteratively selects the point farthest from the already-selected set.
Port CUDA farthest point sampling kernel with shared-memory argmax reduction. Uses explicit scalar_gt/scalar_lt/scalar_min helpers to avoid NVCC operator overload ambiguity with c10::SymInt.
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…ib into port-cluster-02-grid-cuda
…g-lib into port-cluster-03-fps-dispatch
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