serial-neighbor 0.1.1

Serial order neighbor search module using spatial encoding.

serial-neighbor

A lightweight and fast implementation of spatial neighbor search using serialization encoding from space filling curves (e.g., Z-order, Hilbert curves).
Designed for 3D point cloud applications, fully compatible with PyTorch.

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🚀 Installation

pip install serial-neighbor

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🧠 Function Description

serial_neighbor(...)

Finds k nearest neighbors for each query point using serial encoding. We propose to retrieve a neighborhood from a 1-D ordered list, by serializing points along a space-filling curve, and excluding the impact of points distant from the query (i.e. remove false positives).

Arguments:

• points (Tensor):
  Tensor of shape (M, 3) giving the source point cloud with M points in 3D space.

• query_xyz (Tensor):
  Tensor of shape (N, 3) giving the query points (N points in 3D space).

• serial_orders (List[str]):
  Serialization orders to use, e.g., ["z"], ["z", "hilbert"], etc.
  Using multiple orders can improve neighbor search accuracy.

• k_neighbors (int):
  Number of nearest neighbors to find for each query point.

• grid_size (float, optional):
  Grid cell size for point discretization.
  Smaller values give more accurate results but slower performance. Default: 0.01

• mask_threshold (float, optional):
  Maximum distance threshold. Neighbors farther than this will be masked with -1.
  Default: None

Returns:

• combined_idx (LongTensor):
  Shape: (N, K * O)
  Indices of the nearest neighbors for each query point.
  O is the number of serial orders used. Invalid neighbors are marked as -1.

• neighbor_dists (Tensor):
  Shape: (N, K * O)
  Euclidean distances to the neighbors.

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🧪 Usage Example

import torch
from serial_neighbor import serial_neighbor

points = torch.rand(1000, 3).cuda()
query_xyz = torch.rand(100, 3).cuda()
idx, dists = serial_neighbor(points, query_xyz, ["z"], k_neighbors=8)

print("Neighbor indices:", idx.shape)
print("Neighbor distances:", dists.shape)

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📄 Based on NoKSR

This module is part of the work described in the paper:

NoKSR: Kernel-Free Neural Surface Reconstruction via Point Cloud Serialization
Zhen Li, Weiwei Sun †, Shrisudhan Govindarajan, Shaobo Xia, Daniel Rebain, Kwang Moo Yi, Andrea Tagliasacchi
📄 Paper | 🔗 Project Page

We present a novel approach to large-scale point cloud surface reconstruction by converting an irregular point cloud into a signed distance field (SDF) through serialization-based neighbor search. This framework achieves state-of-the-art performance in both accuracy and efficiency, particularly on large-scale outdoor datasets.

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📰 News

• [2025/03/22] The package serial-neighbor is released.
• [2025/02/21] Code released!
• [2025/02/19] ArXiv version is released.

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📬 Contact

For questions, comments, or bug reports, please contact:

Zhen Li (SFU) – [e-mail]

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🛡 License

This project is licensed under the Apache License 2.0 – see the LICENSE file for details.