OctaIndex3D

A 3D Spatial Indexing and Routing System based on BCC Lattice

What's New
Overview
Why BCC Lattice?
Interactive 3D Maze Game
Installation
Quick Start
ID System Architecture
Examples
Streaming Container Format
Performance
Use Cases
Comparison with Alternatives
Platform Support
FAQ
Troubleshooting
Contributing
Research and Citation

What's New

Version 0.4.4 (Latest)

Dependency Updates: Updated to Rust 1.91.1 and latest ecosystem dependencies
Enhanced Compatibility: Updated 8 major dependencies (lz4_flex, cudarc, clap, metal, glam, pollster, crossterm, zerocopy)
Improved Code Quality: Fixed clippy lints for Rust 1.91.1
CUDA Support: Fixed CUDA backend for cudarc API changes
Maintenance: Resolved advisory for unmaintained paste crate dependency

See the full Changelog for detailed release history.

Overview

OctaIndex3D is a high-performance 3D spatial indexing and routing library based on a Body-Centered Cubic (BCC) lattice with truncated octahedral cells.

30-Second Quick Start

# Try the interactive 3D maze game (fastest way to experience BCC lattice!)
cargo install octaindex3d --features cli
octaindex3d play --difficulty medium

# Or use as a library
cargo add octaindex3d

use octaindex3d::{Route64, neighbors::neighbors_route64};

// Create a BCC lattice point
let point = Route64::new(0, 10, 20, 30)?;

// Get all 14 neighbors
let neighbors = neighbors_route64(point);
assert_eq!(neighbors.len(), 14);

Key Features

🎮 Interactive 3D Maze Game: Play through procedurally-generated octahedral mazes with BCC lattice pathfinding
Three ID Types: Galactic128 (global), Index64 (Morton), Route64 (local routing)
High Performance: Cross-platform optimizations for modern CPU architectures
14-Neighbor Connectivity: More isotropic than cubic grids (6 neighbors)
Space-Filling Curves: Morton and Hilbert encoding for spatial locality
Hierarchical Refinement: 8:1 parent-child relationships across resolutions
Bech32m Encoding: Human-readable IDs with checksums
Compression: LZ4 (default) and optional Zstd support
Frame Registry: Coordinate reference system management
Streaming Container Format: Append-friendly compressed spatial data storage (v2)
GeoJSON Export: WGS84 coordinate export for GIS integration

Why BCC Lattice?

Our system is built on a Body-Centered Cubic (BCC) lattice, which offers fundamental advantages over traditional grid-based systems for 3D spatial analysis.

1. Superior Efficiency and Fidelity

The BCC lattice is the optimal structure for sampling three-dimensional signals. It achieves the same level of analytical fidelity with approximately 29% fewer data points than a standard cubic grid. This translates to significant reductions in memory usage, storage costs, and processing time for large-scale datasets, without sacrificing precision.

2. Enhanced Isotropy for Realistic Analysis

Spatial relationships in the real world are continuous, not confined to rigid, 90-degree angles. Our system's cells have 14 neighbors, a significant increase from the 6 offered by cubic cells. This near-uniform connectivity in all directions results in:

More realistic pathfinding: Routes are not biased along cardinal axes
Smoother data interpolation: Gradients and fields are represented more naturally
Unbiased neighborhood analysis: Operations like k-rings and spatial statistics are not distorted by grid orientation

3. Consistent and Unambiguous Topology

Every cell in our system is a truncated octahedron, a shape that tiles 3D space perfectly without gaps or overlaps. This guarantees a consistent and unambiguous topology, which is critical for:

Reliable data aggregation: No double-counting or missed regions
Simplified hierarchical models: Parent-child relationships (8:1 refinement) are clear and consistent across all resolutions
Robust algorithms: Eliminates the need for complex edge cases to handle topological inconsistencies found in other tiling systems

🎮 Interactive 3D Maze Game

Experience the power of BCC lattice pathfinding with our interactive 3D octahedral maze game! Navigate through procedurally-generated mazes using 14-neighbor connectivity and compete against optimal A* pathfinding.

Features

Three difficulty levels: Easy (8³), Medium (20³), Hard (40³)
Procedural generation: Randomized Prim's algorithm creates unique mazes every time
Deterministic seeds: Replay specific mazes or share challenges with friends
Competitive stats: Track your performance against optimal A* solutions
Real-time feedback: See your efficiency compared to the theoretical minimum path
BCC lattice navigation: Experience true 3D movement with 14-neighbor connectivity

Quick Start

# Install the CLI (requires 'cli' feature)
cargo install octaindex3d --features cli

# Play on medium difficulty
octaindex3d play --difficulty medium

# Try a specific seed (reproducible maze)
octaindex3d play --seed 42 --size 20

# View your competitive stats
octaindex3d stats

Game Controls

Arrow keys: Navigate in X/Y plane
W/S: Move up/down in Z axis
Q: Quit game
Goal: Reach the target coordinates in as few moves as possible

Example Session

🎮 3D Octahedral Maze Game - BCC Lattice Edition
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Maze: 20×20×20 | Seed: 42
Start: (0, 0, 0) → Goal: (18, 18, 18)
Optimal moves: 18 | Your moves: 19 | Efficiency: 94.7%

Position: (10, 10, 10) | Distance to goal: 13.9
Available moves: 14 (full BCC connectivity)

[Navigate with arrow keys, W/S for Z-axis, Q to quit]

Performance Metrics

The game demonstrates real-world BCC lattice performance:

Maze generation: <200ms for 8,000 cells using Prim's algorithm
A pathfinding*: <5ms for optimal path computation
Memory efficient: <10MB for medium-sized mazes

Try the BCC-14 Demo

For a comprehensive demonstration of the algorithms powering the game:

# Run the BCC-14 Prim's → A* showcase
cargo run --release --example bcc14_prim_astar_demo

# Features:
# - Builds spanning tree on 549K valid BCC nodes in 131ms
# - Solves optimal path with A* in 1ms
# - Comprehensive validation (5/5 checks)
# - Dynamic seeding with reproducible results

Installation

As a Library

Add to your Cargo.toml:

[dependencies]
# Minimal installation
octaindex3d = "0.4"

# Recommended (includes common features)
octaindex3d = { version = "0.4", features = ["hilbert", "parallel", "container_v2"] }

# Full-featured (for advanced use cases)
octaindex3d = { version = "0.4", features = ["hilbert", "parallel", "container_v2", "gis_geojson", "zstd"] }

As a CLI Tool

# Install the interactive maze game and utilities
cargo install octaindex3d --features cli

# Run the maze game
octaindex3d play --difficulty medium

# Explore other CLI commands
octaindex3d --help

Available Features

Feature	Default	Description	When to Use
`serde`	✅ Yes	Serialization support	Data persistence, JSON export
`parallel`	✅ Yes	Multi-threaded batch operations (Rayon)	Processing 1000+ items
`simd`	✅ Yes	SIMD acceleration (BMI2, AVX2, NEON)	Performance optimization
`lz4`	✅ Yes	LZ4 compression	Container storage
`hilbert`	❌ No	Hilbert64 space-filling curve	Better spatial locality than Morton
`container_v2`	❌ No	Streaming container format	Append-friendly storage, large datasets
`gis_geojson`	❌ No	GeoJSON export (WGS84)	GIS integration (QGIS, ArcGIS)
`cli`	❌ No	Interactive maze game & CLI utilities	Interactive use, demos
`zstd`	❌ No	Zstd compression (slower, better ratio)	High compression needs
`pathfinding`	❌ No	Legacy pathfinding APIs	Compatibility with v0.2.x
`gpu-metal`	❌ No	Metal GPU acceleration (macOS)	Massive batch operations (millions)
`gpu-cuda`	❌ No	CUDA GPU acceleration (Linux)	Massive batch operations (millions)
`gpu-vulkan`	❌ No	Vulkan GPU acceleration (experimental)	Experimental GPU support

Recommended combinations:

# For general use
octaindex3d = { version = "0.4", features = ["hilbert", "parallel"] }

# For GIS applications
octaindex3d = { version = "0.4", features = ["hilbert", "parallel", "gis_geojson"] }

# For data storage systems
octaindex3d = { version = "0.4", features = ["hilbert", "parallel", "container_v2", "zstd"] }

# For interactive development
octaindex3d = { version = "0.4", features = ["cli"] }

Build from Source

git clone https://github.com/FunKite/OctaIndex3D
cd OctaIndex3D
cargo build --release

# Run tests
cargo test

# Run benchmarks
cargo bench

# Run the maze game
cargo run --release --features cli --bin octaindex3d -- play

Quick Start

Basic Usage

use octaindex3d::{Galactic128, Index64, Route64, Result};

fn main() -> Result<()> {
    // Create a global ID (128-bit)
    let galactic = Galactic128::new(0, 5, 1, 10, 0, 2, 4, 6)?;
    println!("Galactic ID: {}", galactic.to_bech32m()?);

    // Create a Morton-encoded index (64-bit)
    let index = Index64::new(0, 0, 5, 100, 200, 300)?;
    println!("Morton coordinates: {:?}", index.decode_coords());

    // Create a local routing coordinate (64-bit)
    let route = Route64::new(0, 100, 200, 300)?;
    println!("Route: ({}, {}, {})", route.x(), route.y(), route.z());

    // Get 14 neighbors
    let neighbors = octaindex3d::neighbors::neighbors_route64(route);
    assert_eq!(neighbors.len(), 14);

    Ok(())
}

Working with Hilbert Curves

use octaindex3d::{Hilbert64, Index64};

// Create Hilbert-encoded ID (better spatial locality than Morton)
let hilbert = Hilbert64::new(0, 0, 5, 100, 200, 300)?;

// Hierarchical operations
let parent = hilbert.parent().unwrap();
let children = hilbert.children();

// Convert between Morton and Hilbert
let index: Index64 = hilbert.into();
let hilbert2: Hilbert64 = index.try_into()?;

// Batch encoding
let coords = vec![(0, 0, 0), (1, 1, 1), (2, 2, 2)];
let hilbert_ids = Hilbert64::encode_batch(&coords, 0, 0, 5)?;

Streaming Container Storage

use octaindex3d::container_v2::{ContainerWriterV2, StreamConfig};
use std::fs::File;

// Create streaming container with append support
let file = File::create("data.octa3d")?;
let config = StreamConfig {
    checkpoint_frames: 1000,
    checkpoint_bytes: 64 * 1024 * 1024,
    enable_sha256: false,
};

let mut writer = ContainerWriterV2::new(file, config)?;

// Write spatial data frames
for data in spatial_data {
    writer.write_frame(&data)?;
}

writer.finish()?; // Writes final TOC and footer

GeoJSON Export

use octaindex3d::geojson::{to_geojson_points, write_geojson_linestring, GeoJsonOptions};
use std::path::Path;

// Export points to GeoJSON
let ids = vec![
    Galactic128::new(0, 0, 0, 0, 0, 0, 0, 0)?,
    Galactic128::new(0, 0, 0, 0, 0, 1000, 1000, 0)?,
];

let opts = GeoJsonOptions {
    include_properties: true,
    precision: 7, // ~1cm precision
};

let geojson = to_geojson_points(&ids, &opts);
println!("{}", serde_json::to_string_pretty(&geojson)?);

// Export path as LineString
write_geojson_linestring(Path::new("path.geojson"), &path_ids, &opts)?;

ID System Architecture (v0.3.0+)

Three Interoperable ID Types

┌─────────────────────────────────────────────────────────────┐
│                       Galactic128                           │
│  128-bit global ID with frame, tier, LOD, and coordinates   │
│  ┌────────┬──────┬─────┬──────┬──────────────────────────┐  │
│  │ Frame  │ Tier │ LOD │ Attr │    Coordinates (90b)     │  │
│  │ 8 bits │ 2b   │ 4b  │ 24b  │    X, Y, Z (30b each)    │  │
│  └────────┴──────┴─────┴──────┴──────────────────────────┘  │
│  HRP: g3d1                                                  │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│                        Index64                              │
│  64-bit Morton-encoded spatial index (Z-order curve)        │
│  ┌────┬────────┬──────┬─────┬──────────────────────────┐    │
│  │ Hdr│ Frame  │ Tier │ LOD │  Morton Code (48 bits )  │    │
│  │ 2b │ 8 bits │ 2b   │ 4b  │  16b/axis interleaved    │    │
│  └────┴────────┴──────┴─────┴──────────────────────────┘    │
│  HRP: i3d1  |  BMI2 PDEP/PEXT optimized                     │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│                        Route64                              │
│  64-bit signed local routing coordinates                    │
│  ┌────┬────────┬──────────────────────────────────────┐     │
│  │ Hdr│ Parity │    X, Y, Z (20 bits each, signed)    │     │
│  │ 2b │  2b    │    ±524k range per axis              │     │
│  └────┴────────┴──────────────────────────────────────┘     │
│  HRP: r3d1  |  Fast neighbor lookup                         │
└─────────────────────────────────────────────────────────────┘

┌─────────────────────────────────────────────────────────────┐
│                       Hilbert64                             │
│  64-bit Hilbert curve spatial index (Gray code)             │
│  ┌────┬────────┬──────┬─────┬──────────────────────────┐    │
│  │ Hdr│ Frame  │ Tier │ LOD │  Hilbert Code (48 bits)  │    │
│  │ 2b │ 8 bits │ 2b   │ 4b  │  Better locality         │    │
│  └────┴────────┴──────┴─────┴──────────────────────────┘    │
│  HRP: h3d1  |  Requires 'hilbert' feature                   │
└─────────────────────────────────────────────────────────────┘

BCC Lattice Properties

Parity Constraint: (x + y + z) % 2 == 0 for all lattice points
14 Neighbors: 8 opposite-parity (distance √3) + 6 same-parity (distance 2)
Hierarchical: 8:1 refinement, each parent has 8 children
Voronoi Cell: Truncated octahedron (14 faces: 6 squares + 8 hexagons)

Examples

🎮 Interactive Maze Game

The fastest way to experience BCC lattice pathfinding:

# Play the interactive 3D maze game
cargo run --release --features cli --bin octaindex3d -- play --difficulty medium

# Try specific challenges
cargo run --release --features cli --bin octaindex3d -- play --seed 42 --size 30

# View your stats
cargo run --release --features cli --bin octaindex3d -- stats

🚀 BCC-14 Prim's Algorithm → A* Demo

Run the comprehensive showcase example demonstrating the algorithms behind the game:

cargo run --release --example bcc14_prim_astar_demo

What it demonstrates:

Prim's Algorithm: Generate spanning tree on 549,450 valid BCC nodes
14-Neighbor Connectivity: All edges preserve BCC lattice parity
A Pathfinding*: Heuristic-guided search with Euclidean distance
Performance: 131ms tree generation, 1ms pathfinding on Apple M1 Max
Validation: 5 comprehensive checks ensuring correctness

Sample output:

🚀 BCC-14 3D Graph: Randomized Prim's Algorithm → A* Pathfinding
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Configuration
  Extent: 130×130×130 (2,197,000 total, 549,450 valid BCC)
  Seed: 42 🍀
  Start: (0, 0, 0) → Goal: (128, 128, 128)

BUILD: Prim's Algorithm
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  ✓ Carved 549,450 nodes (100.0% coverage) in 131 ms
  Performance: 4,194,656 nodes/sec | 11 MB memory
  Validation: ✓ Tree structure valid (E = N-1)

SOLVE: A* Pathfinding
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  ✓ Found path: 129 hops in 1 ms
  Performance: 200,000 nodes/sec
  Validation: ✓ All edges verified on spanning tree

Pathfinding with A*

use octaindex3d::{Route64, path::{astar, EuclideanCost}};

let start = Route64::new(0, 0, 0, 0)?;
let goal = Route64::new(0, 10, 10, 10)?;

// Use legacy pathfinding (from v0.2.0)
use octaindex3d::CellID;
let start_cell = CellID::from_coords(0, 5, 0, 0, 0)?;
let goal_cell = CellID::from_coords(0, 5, 10, 10, 10)?;
let path = astar(start_cell, goal_cell, &EuclideanCost)?;

println!("Path length: {} cells", path.len());

Data Layers and Aggregation

use octaindex3d::layer::{Layer, Aggregation};

// Create data layer (legacy API from v0.2.0)
let mut layer = Layer::new("temperature");

for cell in cells {
    layer.set(cell, temperature_value);
}

// Aggregate over region
let mean_temp = layer.aggregate(&region_cells, Aggregation::Mean)?;

// Roll up to coarser resolution
let coarse_layer = layer.rollup(Aggregation::Mean)?;

Frame Registry

use octaindex3d::frame::{FrameDescriptor, register_frame};

// Register custom coordinate system
let frame = FrameDescriptor {
    id: 1,
    name: "LocalENU".to_string(),
    description: "East-North-Up local frame".to_string(),
    base_unit: 1.0, // meters
    origin: [0.0, 0.0, 0.0],
    srid: None,
};

register_frame(frame)?;

Streaming Container Format

The container format provides efficient storage for spatial data with streaming support:

[Header] [Frame 1] [Frame 2] ... [TOC] [Footer]

Features:

Append-friendly: Add data without full rewrite
Fast loading: Footer + TOC enables <50ms open time for 100k frames
Crash recovery: Checkpoint-based resilience
Compression: LZ4 (default) or Zstd per-frame compression
Integrity: Optional SHA-256 checksums
Configurable: Adjust checkpoint intervals (frames/bytes)

Use Cases:

Real-time sensor data streaming
Incremental dataset updates
Long-running data collection

Performance

OctaIndex3D is optimized for modern CPU architectures with support for:

BMI2 hardware acceleration (x86_64 Intel/AMD)
NEON SIMD (Apple Silicon, ARM)
AVX2 vectorization (x86_64)
Adaptive batch processing with automatic threshold selection

For detailed performance analysis and benchmarks, see:

Performance Guide - Usage examples and optimization tips
CPU Comparison - Cross-platform performance analysis
Benchmark Suite - Criterion benchmarks and profiling tools

Use Cases

🎮 Gaming & Interactive: 3D maze games, spatial partitioning, NPC navigation with 14-neighbor pathfinding, procedural generation, voxel worlds
Robotics: 3D occupancy grids, UAV path planning, obstacle avoidance
Geospatial: Volumetric environmental data, atmospheric modeling, ocean data
Scientific: Crystallography, molecular modeling, particle simulations
Urban Planning: 3D city models, airspace management, building information
GIS Integration: Export to WGS84 for visualization in QGIS, ArcGIS, etc.

Comparison with Alternatives

Feature	OctaIndex3D (BCC)	H3 (Hexagonal)	S2 (Spherical)	Octree
Dimensionality	3D	2D (Earth surface)	2D (Sphere)	3D
Cell Shape	Truncated Octahedron	Hexagon	Spherical quad	Cube
Neighbors	14 (uniform)	6	4-8 (variable)	6-26
Isotropy	Excellent	Good	Excellent	Poor
Hierarchical	Yes (8:1)	Yes (7:1)	Yes (4:1)	Yes (8:1)
Space-Filling Curve	Morton/Hilbert	H3	S2 Cell	Z-order
Efficiency vs Cubic	+29%	N/A	N/A	Baseline
Best For	3D volumes	Geospatial 2D	Global spherical	Adaptive 3D
Rust Native	Yes	No (C bindings)	No (C++)	Various

When to choose OctaIndex3D:

You need true 3D volumetric indexing (not just surface)
You want optimal sampling efficiency (29% fewer points than cubic)
You need isotropic neighbor relationships for pathfinding or analysis
You're working with atmospheric, oceanic, geological, or urban 3D data
You want a pure Rust implementation with modern performance features

Platform Support

Supported Platforms

Platform	Architecture	Status	SIMD	GPU
Linux	x86_64	✅ Full	BMI2, AVX2, AVX-512	CUDA, Vulkan
Linux	aarch64	✅ Full	NEON	-
macOS	Apple Silicon (M1+)	✅ Full	NEON	Metal
macOS	x86_64	✅ Full	BMI2, AVX2	-
Windows	x86_64	✅ Full	BMI2, AVX2	-
Windows	aarch64	⚠️ Tier 2	NEON	-

Minimum Requirements

Rust: 1.77+ (MSRV)
CPU: Any 64-bit processor
Memory: 100MB+ recommended for typical workloads
Optional: BMI2 support for hardware-accelerated Morton encoding (Intel Haswell+, AMD Zen+)

GPU Acceleration (Optional)

Metal: macOS with Metal-capable GPU (M1+ or Intel with Metal support)
CUDA: NVIDIA GPU with CUDA 12.0+ and compute capability 5.0+
Vulkan: Linux with Vulkan-capable GPU (experimental)

FAQ

General Questions

Q: What is a BCC lattice? A: A Body-Centered Cubic lattice is a 3D crystal structure where each point has one point at the center of each cube. It's the optimal structure for sampling 3D space, requiring 29% fewer points than a cubic grid for the same fidelity.

Q: How does this compare to octrees? A: While octrees partition space hierarchically, OctaIndex3D uses a regular BCC lattice with truncated octahedral cells. This provides consistent topology, isotropic neighbor relationships, and efficient space-filling curves, making it better for uniform spatial indexing and pathfinding.

Q: Can I use this for 2D applications? A: While optimized for 3D, you can use OctaIndex3D for 2D by fixing one coordinate (e.g., z=0). However, dedicated 2D libraries like H3 may be more efficient for purely 2D use cases.

Q: What are the ID types used for? A:

Galactic128: Global unique IDs with frame/tier/LOD hierarchy (128-bit)
Index64: Morton-encoded IDs for spatial locality and range queries (64-bit)
Hilbert64: Hilbert curve IDs with better locality than Morton (64-bit, requires hilbert feature)
Route64: Local routing coordinates for neighbor traversal (64-bit, signed)

Q: Is this suitable for real-time applications? A: Yes! OctaIndex3D is designed for high performance with SIMD acceleration, hardware Morton encoding (BMI2), and efficient neighbor lookups. The maze game demonstrates real-time pathfinding on large graphs.

Performance Questions

Q: Do I need a special CPU for good performance? A: No. OctaIndex3D works on any 64-bit CPU. However, modern CPUs with BMI2 (Intel Haswell 2013+, AMD Zen 2017+) get hardware-accelerated Morton encoding for 5-10x faster performance on encoding operations.

Q: Should I enable the parallel feature? A: Yes, for batch operations on datasets with 1000+ items. The parallel feature (enabled by default) uses Rayon for multi-threaded processing.

Q: What about GPU acceleration? A: GPU features are optional and experimental. They're useful for massive batch operations (millions of points) but add complexity. Start with CPU features first.

Usage Questions

Q: How do I convert between ID types? A: Use the From/Into traits:

let index: Index64 = galactic128.try_into()?;
let hilbert: Hilbert64 = index.try_into()?;
let route: Route64 = index.try_into()?;

Q: How do I get a cell's neighbors? A: Use the neighbor functions:

use octaindex3d::neighbors::neighbors_route64;
let neighbors = neighbors_route64(route); // Returns Vec<Route64> with 14 neighbors

Q: Can I store custom data with cells? A: Yes, use your own HashMap or spatial data structure with IDs as keys. For legacy code, see the Layer API in the documentation.

Troubleshooting

Build Issues

Issue: Build fails with "feature xyz not found" Solution: Update your Cargo.toml to use the correct feature names. See Installation for available features.

Issue: CUDA build fails Solution: CUDA support requires CUDA 12.0+ and is only available on non-Windows platforms. Ensure you have CUDA toolkit installed:

# Ubuntu/Debian
sudo apt-get install nvidia-cuda-toolkit

# Verify
nvcc --version

Issue: Metal build fails on macOS Solution: Ensure you're using a Metal-capable macOS version (10.11+). Update Xcode command-line tools:

xcode-select --install

Runtime Issues

Issue: "Parity violation" error when creating coordinates Solution: BCC lattice points must satisfy (x + y + z) % 2 == 0. Ensure your coordinates follow this constraint:

// Valid BCC points (even sum)
Route64::new(0, 0, 0, 0)?;  // 0+0+0 = 0 ✓
Route64::new(0, 1, 1, 0)?;  // 1+1+0 = 2 ✓
Route64::new(0, 2, 3, 1)?;  // 2+3+1 = 6 ✓

// Invalid (odd sum)
Route64::new(0, 1, 0, 0)?;  // 1+0+0 = 1 ✗ Error!

Issue: Morton encoding seems slow Solution: If you have a modern CPU (Intel Haswell 2013+ or AMD Zen 2017+), ensure the simd feature is enabled (it's on by default). Check if BMI2 is being used:

# Linux
lscpu | grep bmi2

# macOS
sysctl machdep.cpu.features | grep BMI2

Issue: Container v2 files won't open Solution: Ensure you're using the container_v2 feature. V2 containers are incompatible with v0.2.x readers:

octaindex3d = { version = "0.4", features = ["container_v2"] }

Getting Help

Documentation: Check docs.rs/octaindex3d
Examples: See the examples/ directory
Issues: Open an issue for bugs or feature requests
Discussions: GitHub Discussions for questions
Security: See SECURITY.md for reporting vulnerabilities

Contributing

Contributions are welcome! Please see our Contributing Guide for details on:

Code of conduct and community guidelines
How to submit bug reports and feature requests
Development setup and coding standards
Pull request process and review guidelines

Feel free to:

Open an issue for bugs or feature requests
Submit a pull request with improvements
Start a discussion for questions or ideas
Improve documentation or examples

License

Licensed under the MIT License. See LICENSE for details.

Research and Citation

For an in-depth technical analysis, see the OctaIndex3D Whitepaper, which covers:

Mathematical foundations of BCC lattice geometry
Detailed architecture and implementation
Performance benchmarks and analysis
Applications across multiple domains
Future research directions

If you use OctaIndex3D in academic work, please cite:

@techreport{mclarney2025octaindex3d,
  title={OctaIndex3D: A High-Performance 3D Spatial Indexing System Based on Body-Centered Cubic Lattice},
  author={McLarney, Michael A. and Claude},
  year={2025},
  institution={GitHub},
  url={https://github.com/FunKite/OctaIndex3D}
}

References

Made with ❤️ and Rust

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octaindex3d 0.4.4

OctaIndex3D

Table of Contents

What's New

Version 0.4.4 (Latest)

Overview

30-Second Quick Start

Key Features

Why BCC Lattice?

1. Superior Efficiency and Fidelity

2. Enhanced Isotropy for Realistic Analysis

3. Consistent and Unambiguous Topology

🎮 Interactive 3D Maze Game

Features

Quick Start

Game Controls

Example Session

Performance Metrics

Try the BCC-14 Demo

Installation

As a Library

As a CLI Tool

Available Features

Build from Source

Quick Start

Basic Usage

Working with Hilbert Curves

Streaming Container Storage

GeoJSON Export

ID System Architecture (v0.3.0+)

Three Interoperable ID Types

BCC Lattice Properties

Examples

🎮 Interactive Maze Game

🚀 BCC-14 Prim's Algorithm → A* Demo

Pathfinding with A*

Data Layers and Aggregation

Frame Registry

Streaming Container Format

Performance

Use Cases

Comparison with Alternatives

Platform Support

Supported Platforms

Minimum Requirements

GPU Acceleration (Optional)

FAQ

General Questions

Performance Questions

Usage Questions

Troubleshooting

Build Issues

Runtime Issues

Getting Help

Contributing

License

Research and Citation

References