LitePT Combines Convolutions and Attention to Process 3D Point Clouds
In the field of deep learning for 3D, mixing convolutional layers and attention blocks is common, but the ideal way to integrate them was not evident. A recent study discovers a clear pattern: each operator has an optimal moment to act within the neural network. 🧠
The Role of Each Operator in the Feature Hierarchy
The study reveals that convolutions work best in the early high-resolution layers. Here, they efficiently extract basic geometric details, while attention mechanisms would be computationally expensive without providing benefits. Conversely, in the deep layers where the data has lower resolution, attention mechanisms excel at capturing semantic context and long-range relationships.
Key Principles of Efficient Design:- Convolutions handle low-level geometry in early stages.
- Attention handles high-level semantics in later stages.
- Forcing both to work together from the start is not the optimal strategy.
The most elegant solution is to let each block do what it does best at the right time, like in a good team.
LitePT is Born: A Practical Hybrid Architecture
Guided by these findings, LitePT is presented, a novel model that implements this principle. It employs convolutional layers in the early stages and makes a progressive shift toward attention blocks in the deeper layers. To preserve crucial spatial information when reducing convolutional layers, PointROPE is introduced, an innovative trainable-free 3D positional encoding. 🚀
LitePT Performance Advantages:- Works with 3.6 times fewer parameters than the reference model.
- Is approximately 2 times faster when running.
- Consumes nearly 2 times less memory.
- The comparison model is Point Transformer V3, the current state of the art.
Results that Validate the Approach
Despite its great efficiency, LitePT does not sacrifice accuracy. In multiple tasks and public datasets, its performance matches or even surpasses that of Point Transformer V3. This proves that understanding the role of each operator in the feature hierarchy allows creating lighter and faster networks. The code and models are available to the community, thus promoting its development and application. ✅