Robust and high-speed watertight manifold surface generation for complex 3D meshes.
ManifoldPlus is a high-performance geometric processing library designed to solve the 'watertightness' problem in 3D meshes, a critical requirement for 3D printing, physics simulations, and neural network training. Unlike traditional methods that rely on simple voxelization—which often results in a loss of sharp features—or local mesh repair—which fails on complex self-intersecting geometry—ManifoldPlus utilizes a robust projection-based algorithm. It operates by generating an initial coarse manifold and iteratively refining it against the original input using Intel Embree-accelerated ray-casting. By 2026, ManifoldPlus has solidified its position as the foundational preprocessing step for 3D Generative AI pipelines, enabling the conversion of 'soup' meshes (unstructured triangles) into valid Signed Distance Functions (SDFs) and Occupancy Fields. The tool is technically optimized for speed, handling millions of facets in seconds, and ensures that the resulting mesh is perfectly closed, manifold, and free of self-intersections. Its architecture is primarily C++ based, offering low-level memory management that is essential for high-throughput industrial applications and synthetic data generation for spatial computing.
Uses Intel Embree kernels to perform high-speed intersection tests that resolve self-intersecting faces and overlapping shells.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Employs an octree-based refinement process that projects manifold vertices back to the original surface.
Optimized C++ structures allow for the processing of dense meshes (5M+ triangles) on standard consumer hardware.
Specifically designed to handle 'triangle soup' where faces cross through each other without proper edge sharing.
Multi-threaded ray casting and octree construction take full advantage of multi-core CPU architectures.
Automatically re-orienting face normals to ensure a consistent outward-facing orientation.
User-defined depth parameters allow for a trade-off between geometric accuracy and computational time.
Non-manifold models cause printer software to fail or create hollow internal structures.
Registry Updated:2/7/2026
Neural networks like Occupancy Networks require perfectly watertight meshes to generate ground truth data.
Complex architecture models often have internal geometry that wastes rendering resources.