We lifted 8 functions out of this paper's own repositories and ran 4 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| amirhertz/pointgmm | canonical | 4 of 8 |
| Function | Status | Where it lives |
|---|---|---|
| apply_decay | Ran | amirhertz/pointgmm/options.py code served (permissive licence) · get_code("52a2715abc17d8ce") |
| flatten | Ran | amirhertz/pointgmm/models/gm_utils.py code served (permissive licence) · get_code("5713cab2b5071792") |
| is_model_clean | Ran | amirhertz/pointgmm/models/model_factory.py code served (permissive licence) · get_code("64f469ab95c19274") |
| save_pil_image | Ran | amirhertz/pointgmm/eval_ae.py code served (permissive licence) · get_code("b99e9276405bead6") |
| collect | Not yet run | amirhertz/pointgmm/process_data/files_utils.py code served (permissive licence) · get_code("0e980c2fffa64798") |
| dkl | Not yet run | amirhertz/pointgmm/models/models_utils.py code served (permissive licence) · get_code("aaa64336b8c6ab9c") |
| do_when_its_time | Not yet run | amirhertz/pointgmm/options.py code served (permissive licence) · get_code("2592bf49e3f0cc56") |
| recursive_to | Not yet run | amirhertz/pointgmm/models/models_utils.py code served (permissive licence) · get_code("3d097ffe9ca61fdb") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Point clouds are a popular representation for 3D shapes. However, they encode a particular sampling without accounting for shape priors or non-local information. We advocate for the use of a hierarchical Gaussian mixture model (hGMM), which is a compact, adaptive and lightweight representation that probabilistically defines the underlying 3D surface. We present PointGMM, a neural network that learns to generate hGMMs which are characteristic of the shape class, and also coincide with the input point cloud. PointGMM is trained over a collection of shapes to learn a class-specific prior. The hierarchical representation has two main advantages: (i) coarse-to-fine learning, which avoids converging to poor local-minima; and (ii) (an unsupervised) consistent partitioning of the input shape. We show that as a generative model, PointGMM learns a meaningful latent space which enables generating consistent interpolations between existing shapes, as well as synthesizing novel shapes. We also present a novel framework for rigid registration using PointGMM, that learns to disentangle orientation from structure of an input shape.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2003.13326")
get_code_for_paper("2003.13326")
have("2003.13326")
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