Optical Flow Perceiver
| Property | Value |
|---|---|
| Parameters | 41.1M |
| License | Apache 2.0 |
| Framework | PyTorch |
| Paper | Perceiver IO Paper |
| Training Data | AutoFlow Dataset |
What is optical-flow-perceiver?
The optical-flow-perceiver is a specialized implementation of the Perceiver IO architecture designed to tackle the challenging computer vision task of optical flow estimation. This model represents a significant advancement in processing structured visual inputs by using a transformer-based architecture that efficiently handles high-dimensional data through a unique latent space approach.
Implementation Details
The model employs a sophisticated architecture that processes image pairs through cross-attention mechanisms, working with latent vectors rather than direct input processing. It operates on raw pixel values, analyzing 3x3 patches around each pixel in concatenated image pairs, resulting in 54-dimensional feature vectors per pixel location.
- Input Processing: Images are resized to 368x496 resolution
- Attention Mechanism: Uses cross-attention with latent vectors to reduce computational complexity
- Output Format: Produces flow predictions of shape (batch_size, height, width, 2)
- Training Dataset: 400,000 annotated image pairs from AutoFlow
Core Capabilities
- State-of-the-art performance on Sintel and KITTI benchmarks
- Efficient processing of high-dimensional visual data
- Flexible output generation through decoder queries
- Memory-efficient attention mechanism independent of input size
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its ability to process optical flow estimation using a transformer-based architecture that maintains constant memory requirements regardless of input size, achieving state-of-the-art results while being computationally efficient.
Q: What are the recommended use cases?
The model is ideal for applications requiring motion estimation between image pairs, including robot navigation, visual odometry, 3D geometry estimation, and synthetic-to-real transfer learning for 3D human pose estimation.
