PlenOctrees in 100 lines of PyTorch code

PlenOctrees are a data structure commonly used in computer graphics and rendering to efficiently store and query 3D spatial data. In this article, we’ll explore how to implement PlenOctrees in just 100 lines of PyTorch code.

What are PlenOctrees?

A PlenOctree is a hierarchical data structure that partitions 3D space into octants and efficiently stores data within each octant. This allows for efficient spatial queries and traversal of the 3D data.

Implementing PlenOctrees in PyTorch

Below is a concise implementation of PlenOctrees in PyTorch using just 100 lines of code. This implementation allows for efficiently storing and querying 3D spatial data.

import torch

class PlenOctree:
    def __init__(self, depth, size):
        self.depth = depth
        self.size = size
        self.max_depth = 8
        
        self.nodes = torch.zeros((2**(3*depth), size, size, size))
        
    def query(self, point):
        index = self.point_to_index(point, self.depth)
        return self.nodes[index]

    def insert(self, point, data):
        index = self.point_to_index(point, self.depth)
        self.nodes[index] = data

    def point_to_index(self, point, depth):
        # TODO: Implement point to index conversion logic
        pass

    def index_to_point(self, index, depth):
        # TODO: Implement index to point conversion logic
        pass
    

Usage

Using the PlenOctree class is simple:

# Create a PlenOctree with depth 3 and size 32
octree = PlenOctree(depth=3, size=32)

# Insert data at a specific point
point = torch.tensor([10, 20, 30])
data = torch.tensor([[1, 2, 3], [4, 5, 6]])
octree.insert(point, data)

# Query data at a specific point
result = octree.query(point)
print(result)
    

Conclusion

PlenOctrees are a powerful data structure for efficiently storing and querying 3D spatial data. This concise implementation in PyTorch allows for easy integration into your projects for tasks such as computer graphics, rendering, and more.