Vanilla PointNet — ModelNet10 Classifier
Simplified PointNet (no T-Nets) for 3D object classification on ModelNet10.
Architecture
| Stage | Layers |
|---|---|
| Feature Extraction | Conv1d(3→64→128→1024) + BN + ReLU |
| Global Aggregation | Global max-pool → 1024-d vector |
| Classification | FC(1024→512→256→10) + BN + Dropout |
Classes
bathtub · bed · chair · desk · dresser · monitor · night_stand · sofa · table · toilet
Usage
import torch
from model import VanillaPointNet
model = VanillaPointNet(num_classes=10)
model.load_state_dict(torch.load("pytorch_model.bin", map_location="cpu"))
model.eval()
pts = torch.randn(1, 3, 1024) # (B, 3, N)
print(model(pts).argmax(1))
Inference Providers NEW
This model isn't deployed by any Inference Provider. 🙋 Ask for provider support