nvTorchCam: An Open-source Library for Camera-Agnostic Differentiable Geometric Vision (2410.12074v1)

Abstract: We introduce nvTorchCam, an open-source library under the Apache 2.0 license, designed to make deep learning algorithms camera model-independent. nvTorchCam abstracts critical camera operations such as projection and unprojection, allowing developers to implement algorithms once and apply them across diverse camera models--including pinhole, fisheye, and 360 equirectangular panoramas, which are commonly used in automotive and real estate capture applications. Built on PyTorch, nvTorchCam is fully differentiable and supports GPU acceleration and batching for efficient computation. Furthermore, deep learning models trained for one camera type can be directly transferred to other camera types without requiring additional modification. In this paper, we provide an overview of nvTorchCam, its functionality, and present various code examples and diagrams to demonstrate its usage. Source code and installation instructions can be found on the nvTorchCam GitHub page at https://github.com/NVlabs/nvTorchCam.

• The paper introduces a unified camera abstraction model that supports various camera setups in a differentiable framework.
• It extends backward warping operations to accommodate fisheye and ERP models while effectively tracking invalid points.
• The library integrates batch processing and differentiable operations, enabling multiview stereo depth estimation for deep learning tasks.

Overview of nvTorchCam: An Open-source Library for Camera-Agnostic Differentiable Geometric Vision

The paper presents nvTorchCam, an open-source library aimed at facilitating the use of diverse camera models in deep learning applications. It addresses the complexity inherent in dealing with various camera setups, such as pinhole, fisheye, and equirectangular panorama (ERP) models, especially in fields like automotive and real estate imaging.

nvTorchCam enables developers to implement camera-dependent algorithms without the need to consider specific camera models, thus promoting code simplicity and reusability. Leveraging PyTorch for its fully differentiable operations, the library offers GPU acceleration and batching, facilitating efficient processing across different camera types.

Key Contributions

1. Camera Abstraction: nvTorchCam introduces a unified camera model by abstracting critical operations like pixel projection and ray conversion through a base class, CameraBase. This abstraction allows algorithms designed for one camera type to be seamlessly applied to others, significantly reducing the technical overhead involved in camera-specific implementations.
2. Backward Warping: The library extends backward warping operations to accommodate various camera models. Existing solutions like Kornia are limited to pinhole models, whereas nvTorchCam provides support for fisheye and ERP formats. The handling of complex camera setups is further enhanced by a mechanism to track invalid points, crucial for effective warping.
3. Batch and Differentiable Operations: nvTorchCam supports operations on entire batches of camera data, thereby enhancing computational efficiency. It also ensures that operations remain differentiable, which is beneficial for deep learning tasks that require gradient-based optimization.
4. Multi-view Stereo Depth Estimation: The library's backward warping functionalities have been effectively integrated into the FoV-Depth project, supporting multiview stereo depth estimation. This capability underscores nvTorchCam's application potential in areas requiring depth information from large-FoV datasets.

Practical and Theoretical Implications

Practically, nvTorchCam simplifies the integration of new camera models into existing systems, reduces the need for custom development, and facilitates the sharing and reuse of code across projects. Theoretical implications suggest that nvTorchCam could advance the development of novel computer vision algorithms that are robust to variations in camera configurations.

Speculations on Future Developments

Possible future enhancements of nvTorchCam include the integration of more non-central camera models, such as rolling shutter cameras, to further extend its applicability. The development of a unified calibration framework in PyTorch could also enrich the library, allowing for easier parameter optimization.

nvTorchCam stands as a promising tool for the computer vision community, offering a flexible and efficient solution to the challenges posed by diverse camera models. While the library’s current focus is on central cameras, there is substantial scope for expanding its capabilities, suggesting a fertile area for future research and community collaboration.