Seeking 3D Neural Network Architecture Suggestions for ModelNet Dataset
Published:Dec 27, 2025 19:18
•1 min read
•r/deeplearning
Analysis
This post from r/deeplearning highlights a common challenge in applying neural networks to 3D data: overfitting or underfitting. The user has experimented with CNNs and ResNets on ModelNet datasets (10 and 40) but struggles to achieve satisfactory accuracy despite data augmentation and hyperparameter tuning. The problem likely stems from the inherent complexity of 3D data and the limitations of directly applying 2D-based architectures. The user's mention of a linear head and ReLU/FC layers suggests a standard classification approach, which might not be optimal for capturing the intricate geometric features of 3D models. Exploring alternative architectures specifically designed for 3D data, such as PointNets or graph neural networks, could be beneficial.
Key Takeaways
Reference
“"tried out cnns and resnets, for 3d models they underfit significantly. Any suggestions for NN architectures."”