⚡ Conquer3D

High-Performance GPU-Accelerated Differentiable Geometry, Spatial Computing & Neural Rendering Toolbox

🌐 Website • Guide • API Reference • Benchmarks • Results • Installation

[!NOTE] The API documentation and the documentation website were written with Claude. The library itself — every CUDA kernel, data structure and operator — is the author's own work.

🌟 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

Every figure below is real library output on the bundled benchmark assets, regenerated by docs/_figures/make_figures.py — nothing is mocked or hand-drawn. More, at full size, on the showcase.

Isosurface extraction

Five extractors on one signed distance field

One signed distance field on a 64³ narrow-band grid of 19,329 cells, meshed by five extractors, with the source mesh on the left. The lower row crops the crease where Marching Cubes and Dual Contouring disagree most.

Sharp features from Hermite data

Dual methods with and without Hermite data

Dual Contouring and Dual Marching Cubes run twice each: once with normals interpolated from the grid, once with exact Hermite data from compute_hermite_from_mesh, which ray-casts all 25,832 sign-crossing edges for the true intersection and face normal. The crease is reconstructed rather than rounded.

Detail is a resolution dial

Dual Marching Cubes on the Armadillo at seven grid resolutions from 64³ to 2048³. Face count runs from 12,076 to 13,294,500, with Chamfer and Hausdorff distance to the source measured at every step.

From mesh to surface, step by step

The input mesh, the narrow-band cells allocated around it (7,863 of a possible 64,000 at 40³), the signed distance shown as a cutaway, the 2,646 bipolar cells, and the extracted surface. No dense volume is ever held.

Six ways to decide inside

One axial slice signed by each of the six sign modes and contoured at zero — ray parity, pseudonormal, winding number, flood fill, hybrid consensus, and coarse-fine fill.

Ray queries against the hierarchies

MeshBVH.get_ray_intersection returns the triangles a ray pierces; BVH.query_ray returns the narrow-band cells it crosses. Five rays from five origins, each drawn as far as its own first hit.

⚡ 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 1,020-symbol API reference are on the documentation site.

📄 License

Conquer3D is licensed under the MIT License.

_{Built with CUDA and PyTorch · khoidoo.github.io/conquer3d}

Release files for conquer3d 0.7.7

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Source distribution (sdist)

Source distribution for conquer3d 0.7.7
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