Summary of Pytorchgeonodes: Enabling Differentiable Shape Programs For 3d Shape Reconstruction, by Sinisa Stekovic et al.
PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape Reconstruction
by Sinisa Stekovic, Stefan Ainetter, Mattia D’Urso, Friedrich Fraundorfer, Vincent Lepetit
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary We propose PyTorchGeoNodes, a novel differentiable module for reconstructing 3D objects from images using interpretable shape programs. Unlike traditional CAD model retrieval methods, our approach enables reasoning about the semantic properties of reconstructed objects, editing, and low memory footprint. We introduce a module that translates shape programs designed in Blender into efficient PyTorch code, enabling gradient-based optimization. Additionally, we develop a method inspired by Monte Carlo Tree Search (MCTS) to jointly optimize discrete and continuous parameters of shape programs and reconstruct 3D objects for input scenes. In our experiments, we apply our algorithm to the ScanNet dataset and evaluate our results against CAD model retrieval-based reconstructions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary We’ve created a new way to use computer images to make 3D models using something called “shape programs.” This is different from how it’s usually done, where you have to use special software to create the model. Our method lets us edit and change the shape of the model easily, which can be useful in many areas like design, architecture, and even video games. We tested our method on a big dataset of images and showed that it works well and gives good results. |
Keywords
» Artificial intelligence » Optimization
