chrischoy/3D-R2N2

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1410 stars · 291 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

3D-R2N2 is a PyTorch-based implementation of a recurrent neural network that reconstructs voxelized 3D models of objects from one or multiple 2D images. It accompanies the ECCV 2016 paper and introduced the 3D Convolutional LSTM for order-invariant multi-view reconstruction.

Use cases

• reconstruct a 3D voxel model from a single photo
• generate 3D object reconstructions from multiple unordered views
• reproduce the 3D-R2N2 ECCV 2016 paper results
• train a 3D convolutional LSTM on ShapeNet data
• convert e-commerce product images into voxelized 3D shapes

When to choose

• you need a research baseline for learning-based single/multi-view 3D reconstruction
• you want voxelized outputs from arbitrary numbers of input images
• you are studying 3D convolutional LSTM/GRU architectures

When to avoid

• you need high-resolution meshes rather than coarse voxel grids
• you want actively maintained code with modern framework support
• you need production-ready 3D reconstruction rather than a research prototype

Facets

library · maturity maintenance

machine-learning deep-learning computer-vision image-processing computer-vision deep-learning machine-learning python 3d-reconstruction voxel rnn lstm single-view-reconstruction multi-view-reconstruction shapenet eccv-2016 research linux gpu

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