Hi there, let’s break down your problem and the options you’re considering step by step—this is a common pain point when mixing geometric processing with differentiable ML pipelines, and your observations about Open3D and PyTorch3D are spot-on.

Open3D vs PyTorch3D: Quick Recap #

First, to align on what you’ve already noted:

• Open3D is a powerhouse for traditional 3D geometry: it has out-of-the-box alpha shape mesh generation (create_from_point_cloud_alpha_shape), watertightness checks (is_watertight), and volume calculation (get_volume). Its ML add-on is great for batch data handling, but it doesn’t support backpropagation natively—so you’d have to treat mesh generation as a one-off step or hack around with numerical gradients if you need to update points.
• PyTorch3D is built for differentiable workflows, integrating seamlessly with PyTorch’s autograd. But as you’ve seen, it lacks the geometric utility belt that Open3D provides, forcing you to implement tools like alpha shapes or watertightness checks yourself.

Answers to Your Key Questions #

1. How hard is it to integrate Open3D into a differentiable pipeline? #

It’s doable, but it depends on whether you need dynamic mesh topology updates during optimization:

• If your point position changes are small enough that the mesh topology (faces/edges) doesn’t need to update, you can split your pipeline: use Open3D to generate a watertight mesh from your initial point cloud, then export the vertices/faces to PyTorch3D for differentiable optimization. This is low-effort and works well for many use cases where you’re refining point positions rather than completely reshaping the object.
• If you need to update the mesh topology as points move, you’ll have to use numerical gradients (e.g., finite differences) to approximate the derivative of your loss with respect to point positions. This is slower and noisier than native autograd, but it’s a workaround for Open3D’s non-differentiable operations. Alternatively, you could wrap Open3D’s functions as custom PyTorch ops with hand-written backward passes, but that’s a significant engineering lift.

2. Are there workarounds for PyTorch3D’s missing geometry tools? #

Absolutely—here’s how to fill the gaps:

• Alpha shapes: You can implement a differentiable version using PyTorch’s tensor operations, or leverage community-contributed implementations. A simpler alternative is to first run a Delaunay triangulation (you can use PyTorch-based Delaunay libraries or even a lightweight custom implementation) and then filter out faces that don’t fit your alpha radius criteria.
• Watertightness checks: While PyTorch3D doesn’t have a native is_watertight function, you can build one using its mesh structure utilities. Check that every edge is shared by exactly two faces—this is the core requirement for watertightness, and you can implement this with tensor operations that are compatible with autograd.
• Volume calculation: The 3D shoelace formula (summing tetrahedral volumes) is straightforward to implement in PyTorch. For each face, compute the volume of the tetrahedron formed by the face’s vertices and the origin, then sum signed volumes (watertight meshes will have consistent signs, so the total will be accurate). This is fully differentiable and only takes a few lines of code.
• Self-intersection prevention: PyTorch3D’s mesh_intersection module can detect self-intersections. You can convert the intersection results into a loss term (e.g., penalize overlapping face areas) and add it to your pipeline—this lets the model learn to avoid self-intersections during optimization.

3. Are there better libraries that combine both strengths? #

Yes, a few options might be a better fit than picking between Open3D and PyTorch3D:

• Kaolin: NVIDIA’s 3D deep learning library is built specifically for this kind of workflow. It supports differentiable alpha shape generation, watertightness checks, volume calculation, and integrates natively with PyTorch. It’s designed to bridge geometric processing and differentiable ML, so it might check all your boxes.
• Hybrid Open3D + PyTorch3D pipeline: Don’t sleep on this! Use Open3D for the heavy geometric lifting (initial mesh generation, watertightness validation, one-off volume calculations) and PyTorch3D for the differentiable parts (point optimization, 2D projection, loss calculation). This combines the best of both worlds with minimal engineering effort.
• Implicit mesh libraries: If you’re open to using implicit representations (instead of explicit triangular meshes), libraries like DeepSDF or Neuralangelo offer end-to-end differentiable mesh generation and optimization. But this is a bigger shift if you strictly need explicit meshes.

Final Advice #

If you need end-to-end differentiability with dynamic mesh updates, Kaolin is your best bet. If your optimization doesn’t require frequent topology changes, the hybrid Open3D + PyTorch3D approach is the most pragmatic and low-effort solution. Your concerns about the gaps in each library are valid—they’re not overemphasized—but there are solid workarounds or alternative tools to address them.

备注：内容来源于stack exchange，提问作者jliang