optical flow perceiver

Introduction

The Perceiver IO model by DeepMind is designed to predict optical flow between image pairs. It leverages a general architecture for structured inputs and outputs, utilizing a transformer encoder model adaptable to multiple modalities, including text, images, audio, and video. This model is particularly effective in applications such as navigation and visual odometry in robotics and estimating 3D geometry.

Architecture

Perceiver IO employs self-attention on a limited set of latent vectors, allowing the model's time and memory requirements to remain independent of input size. It features decoder queries for flexibly interpreting latent states to produce outputs in various sizes and semantics. When applied to optical flow, it outputs a tensor predicting flow with dimensions corresponding to the batch size, image height, width, and two flow values.

Training

The model was trained using the AutoFlow dataset, consisting of 400,000 annotated image pairs. Preprocessing involves resizing frames to 368x496 pixels, concatenating them along the channel dimension, and extracting 3x3 patches around each pixel. The model achieves superior performance on benchmarks like Sintel and KITTI, with detailed hyperparameters available in the paper's Appendix E.

Guide: Running Locally

To run the Perceiver IO model locally, follow these steps:

1. Install Dependencies: Ensure you have Python and PyTorch installed. Use pip to install Hugging Face's Transformers library.
2. Clone the Repository: Retrieve the model code from DeepMind's repository.
3. Load the Model: Use the Transformers library to load the Perceiver IO model and associated weights.
4. Prepare Input Data: Preprocess your image pairs as described, resizing and concatenating them adequately.
5. Run Inference: Use the model to predict optical flow on preprocessed image pairs.

For enhanced performance, especially with large datasets or real-time processing, consider utilizing cloud GPUs from providers like AWS, Google Cloud, or Azure.

License

The Perceiver IO model is distributed under the Apache-2.0 License, permitting wide use, modification, and distribution.