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Voxelis

Tiny voxels. Huge worlds. Voxelis — a pure Rust voxel engine based on Sparse Voxel Octree DAG.

Install / Use

/learn @WildPixelGames/Voxelis

README

<h1 align="center"> <img src="https://raw.githubusercontent.com/WildPixelGames/voxelis/master/docs/voxelis_logo.png" alt="Voxelis" width="480"><br> <sub>Tiny voxels. Huge worlds. Zero hassle.</sub> </h1> <p align="center"> <a href="https://crates.io/crates/voxelis"><img src="https://img.shields.io/crates/v/voxelis.svg?style=for-the-badge&color=orange"></a> <a href="https://docs.rs/voxelis"><img src="https://img.shields.io/badge/docs‑rs-online-orange.svg?style=for-the-badge"></a> <a href="LICENSE"><img src="https://img.shields.io/badge/license-MIT%20OR%20Apache--2.0-orange?style=for-the-badge"></a> </p>

"Need voxels? Reach for Voxelis." Powered by VoxTree — a deliciously crafted SVO DAG with batching. Drop it into your Rust, C++, Godot, or Bevy project and start carving worlds down to centimetre-level detail — while your memory bill stays shockingly low.


🚀 Why Voxelis?

  • Tiny voxels (4 cm resolution) without melting your RAM.
  • Shared memory — DAG compression at 99.999% ratio.
  • Batch edits — mutate hundreds of thousands of voxels in a blink.
  • Zero garbage collection — just deterministic reference counting with generations.
  • Built for gamedev — chunk grids, paging hooks, multithread-ready.
  • Fearless Rust — no UB, no data races, only pure, undiluted speed.

This isn't just another voxel crate. It's a foundation for colossal, high-fidelity worlds.


✨ Benchmarks: "Speed so fast, it hurts."

| Operation | 32³ Voxels | Single Op | Batch Op | Notes | |----------------------------------|-------------|-------------|-------------|-------| | fill() | 32K | 9 ns | 10.6 ns | ⚡ Single leaf collapse | | set_uniform() | 32K | 5.17 ms | 23.1 μs | 🚀 ~224× faster | | set_checkerboard() | 32K | 2.52 ms | 116.6 μs | 🚀 ~22× faster | | set_sum() (high-entropy) | 32K | 5.92 ms | 194.1 μs | 🌪️ Complex pattern, ~30× faster | | perlin dunes (high-entropy) | 32K | - | ~12 μs | 🌎 ~1380 chunks/frame (60 FPS) |

Full raw results? Check benches-raw.md.

Summary tables? See benches-tables.md.

Full commentary and insights? Dive into benches.md.


🔧 Quick Start

use glam::IVec3;
use voxelis::{
    MaxDepth, VoxInterner,
    spatial::{VoxOpsBatch, VoxOpsRead, VoxOpsWrite, VoxTree},
};

fn main() {
    let mut interner = VoxInterner::<u8>::with_memory_budget(256 * 1024 * 1024);
    let mut tree = VoxTree::new(MaxDepth::new(5)); // 32³ voxels (chunk)

    let mut batch = tree.create_batch();
    batch.set(&mut interner, IVec3::new(3, 0, 4), 1); // stone

    tree.apply_batch(&mut interner, &batch);
    assert_eq!(tree.get(&interner, IVec3::new(3, 0, 4)), Some(1));
}

Add via Cargo:

cargo add voxelis glam@0.29.3 # Requires Rust 1.86+, optionally use `wide` for SIMD meshing

🔍 Under the Hood

| Concept | Purpose | |:--------|:--------| | VoxTree | The SVO-DAG — compressed octree core. | | VoxInterner | Shared memory for leaves/branches. Hash-consed. | | Batch | Bottom-up batched editing — mutate at light speed. | | VoxOps | Trait for per-voxel manipulation — set, get, fill, clear. | | BlockId | 64-bit magic to encode voxel state compactly. | | Mesher | SIMD greedy meshing (WIP) — turn voxels into worlds. |

More? Crack open The Voxelis Bible (docs/The Voxelis Bible_ From Pixels to Worlds - An In-Depth Guide v2.3.pdf) — 38 pages of dangerously concentrated nerdery.


🌏 Roadmap: Into the Voxelverse

  • Multithreaded interner with Rayon.
  • GreedyMesh v2 (distance-field LOD magic).
  • GPU frustum-culling traversals.
  • OBJ / glTF import/export.
  • True out-of-core paging (MMAP + LRU).

PRs welcome. Bonus points if your patch makes the CI bot 🐈‍🔄 purr.


🚡 Run Your Own Benches

cargo bench -p voxelis_bench

Hardware: Apple M3 Max, Rust 1.86 stable, -C target-cpu=native, final profile.

Want real numbers? We've got them — benches-tables.md and benches-raw.md await.


👍 Contributing

  1. Fork and branch (feat/my-magnificent-contribution).
  2. cargo test && cargo bench
  3. Open PR, include new benchmark delta.
  4. Bask in voxel-induced glory.

🌐 License

Dual licensed under MIT / Apache-2.0. Pick your poison, build something massive.


⚠️ Warning

Voxelis may cause extreme enthusiasm, uncontrollable world-building, and compulsive Rust evangelism. Consult your GPU before operating heavy voxel engines. 😜

Related Skills

View on GitHub
GitHub Stars84
CategoryDevelopment
Updated21h ago
Forks16

Languages

Rust

Security Score

100/100

Audited on Apr 2, 2026

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