⚡ Conquer3D
High-Performance GPU-Accelerated Differentiable Geometry, Spatial Computing & Neural Rendering Toolbox
🌐 Website • Guide • API Reference • Benchmarks • Results • Installation
🌟 Overview
Conquer3D is an ultra-fast, GPU-native computational geometry and differentiable spatial computing library engineered in PyTorch and CUDA. Designed from the ground up for 3D computer vision, generative AI, neural surface reconstruction, and differentiable rendering, Conquer3D delivers up to ~1.3 Billion faces/second isosurface extraction, exact CAD sharp crease preservation, and memory-efficient spatial acceleration structures.
Every operator consumes and produces PyTorch tensors in place — no host round-trip, no format conversion — so meshing a field is an operation inside a training step rather than a preprocessing stage around it.
pip install -U conquer3d
import torch
from conquer3d.data_structure import create_voxel_grid
from conquer3d.ops import dmc
grid_vertices, voxels, _ = create_voxel_grid(
grid_min=[-1.0] * 3, grid_max=[1.0] * 3, res=[64, 64, 64], device="cuda"
)
sdf = (torch.norm(grid_vertices, dim=-1) - 0.6).requires_grad_(True)
verts, faces = dmc(grid_vertices, voxels, sdf, iso=0.0)
verts.sum().backward() # gradients flow back into the field
🔬 Qualitative Results
Isosurface extraction
One signed distance field meshed by four different extractors.
Sharp features
Exact Hermite data lets the dual methods reconstruct a crease instead of rounding it.
Grid resolution
The same model extracted from 64³ up to 2048³, with the error measured at each step.
Extraction pipeline
Every stage of one extraction, from input mesh to extracted surface.
Sign modes
One slice through each of two meshes, signed by all seven ways of deciding inside.
Ray queries
Which triangles and which voxels a ray hits, found through the BVH.
⚡ Benchmarks
RTX 4090, torch 2.8.0+cu128, CUDA 12.8. Fandisk at $1024^3$ (5.15M active cells). CUDA events around the operator alone, median of 7 runs after 2 warm-ups.
| Algorithm | Output | Vertices | Faces | Latency | Throughput |
|---|---|---|---|---|---|
| Dual Marching Cubes | Triangles | 1,716,384 | 3,432,764 | 2.62 ms | 1,311M faces/s |
| DMC (pure quads) | Quads | 1,716,384 | 1,716,382 | 2.51 ms | 684M quads/s |
| MC Asymptotic | Triangles | 1,716,382 | 3,432,760 | 2.71 ms | 1,267M faces/s |
| Dual Contouring | Triangles | 1,716,384 | 3,432,764 | 3.88 ms | 884M faces/s |
| Marching Cubes | Triangles | 1,716,382 | 3,432,760 | 7.90 ms | 434M faces/s |
| Marching Tetrahedra | Triangles | 6,113,918 | 12,227,832 | 44.90 ms | 272M faces/s |
Sign-mode costs, distance-operator throughput, pipeline breakdown and memory scaling are on the benchmarks page.
📦 Installation
pip install -U conquer3d
Building from source, the full feature list, worked pipelines and the complete API reference are on the documentation site.
📄 License
Conquer3D is licensed under the MIT License.
1 maintainer
Release files for conquer3d 0.8.7
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| conquer3d-0.8.7.tar.gz | 3.0 MB | Details |
Release files / conquer3d-0.8.7.tar.gz
| Download URL | conquer3d-0.8.7.tar.gz |
|---|---|
| Size | 3.0 MB |
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