17964827. ADVERSARIAL OBJECT-AWARE NEURAL SCENE RENDERING FOR 3D OBJECT DETECTION simplified abstract (TOYOTA JIDOSHA KABUSHIKI KAISHA)
Contents
ADVERSARIAL OBJECT-AWARE NEURAL SCENE RENDERING FOR 3D OBJECT DETECTION
Organization Name
TOYOTA JIDOSHA KABUSHIKI KAISHA
Inventor(s)
Rares Andrei Ambrus of San Francisco CA (US)
Sergey Zakharov of San Francisco CA (US)
Vitor Guizilini of Santa Clara CA (US)
Adrien David Gaidon of Mountain View CA (US)
ADVERSARIAL OBJECT-AWARE NEURAL SCENE RENDERING FOR 3D OBJECT DETECTION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17964827 titled 'ADVERSARIAL OBJECT-AWARE NEURAL SCENE RENDERING FOR 3D OBJECT DETECTION
Simplified Explanation
The method described in the patent application involves improving 3D object detection through object-level augmentations. Here is a simplified explanation of the abstract:
- Recognizing an object in an image using an image recognition model.
- Generating a 3D reconstruction of the scene from the image, including the recognized object, using a 3D reconstruction model.
- Manipulating a random property of the object at an object level using an object level augmentation model to maximize a loss function of the image recognition model.
- Training a downstream task network based on the set of properties and magnitudes of the object manipulation to minimize the loss function.
- Potential Applications
This technology could be applied in autonomous driving systems, robotics, surveillance systems, and augmented reality applications.
- Problems Solved
This technology addresses the challenge of accurately detecting and recognizing objects in 3D space, which is crucial for various computer vision applications.
- Benefits
The method improves the accuracy and robustness of 3D object detection systems, leading to better performance in real-world scenarios.
- Potential Commercial Applications
Commercial applications of this technology include autonomous vehicles, security systems, industrial automation, and virtual reality experiences.
- Possible Prior Art
One possible prior art could be the use of data augmentation techniques in computer vision models to improve object detection accuracy.
- Unanswered Questions
- How does this method compare to existing object-level augmentation techniques in terms of performance and efficiency?
The article does not provide a direct comparison with existing object-level augmentation techniques, so it is unclear how this method stacks up against them.
- What are the computational requirements for implementing this method in real-time applications?
The article does not delve into the computational resources needed to implement this method in real-time scenarios, leaving a gap in understanding the practical feasibility of the technology.
Original Abstract Submitted
A method for improving 3D object detection via object-level augmentations is described. The method includes recognizing, using an image recognition model of a differentiable data generation pipeline, an object in an image of a scene. The method also includes generating, using a 3D reconstruction model, a 3D reconstruction of the scene from the image including the recognized object. The method further includes manipulating, using an object level augmentation model, a random property of the object by a random magnitude at an object level to determine a set of properties and a set of magnitudes of an object manipulation that maximizes a loss function of the image recognition model. The method also includes training a downstream task network based on a set of training data generated based on the set of properties and the set of magnitudes of the object manipulation, such that the loss function is minimized.
