Omnidirectional Perception via Perspective Knowledge Distillation
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Solution Overview
Problem
The limited availability of suitably labeled omnidirectional images hinders the application of learning-based techniques for machine perception tasks, and converting omnidirectional images to perspective projection images increases computation costs.
Innovation Solution
A method is developed to train a model for performing perception tasks directly in the omnidirectional image domain by generating perspective projection images from omnidirectional images, using a teacher model to produce perception outputs, and training a student model to adapt these outputs for the omnidirectional domain.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If omnidirectional images are converted to multiple perspective projection images to utilize existing models, then machine perception tasks can be performed using existing resources, but computation cost during inference increases significantly
Solution Approach 1:
The patent introduces an intermediary module that projects omnidirectional images to perspective projection images. This intermediary serves as a bridge between the omnidirectional image domain and existing perspective-based models, enabling knowledge transfer without requiring direct adaptation of all existing models to omnidirectional inputs, thus reducing computation cost while maintaining adaptability.
Solution Approach 2:
The patent changes the parameter space by projecting omnidirectional images (different parameter domain) to perspective projection images (target parameter domain). This parameter transformation allows existing models trained on perspective images to be applied to omnidirectional inputs without requiring retraining on large omnidirectional datasets, reducing computational requirements while maintaining model versatility.
2Ease of operation
If learning-based techniques are applied to omnidirectional images, then machine perception tasks can be performed directly in the omnidirectional domain, but the limited amount of suitably labeled omnidirectional images hinders model training
Solution Approach 1:
The patent performs preliminary action by pre-processing omnidirectional images through projection to perspective projection images before feeding them to existing models. This preliminary transformation enables the system to leverage existing perspective-based models and their associated labeled datasets, effectively working around the limitation of scarce labeled omnidirectional training data while maintaining direct omnidirectional domain processing capability.
Solution Approach 2:
The patent creates a copy of the omnidirectional image in the perspective projection domain. By generating perspective projections from the omnidirectional image, the system can use existing perspective-based models and their training data, effectively copying the problem into a domain where sufficient labeled data exists, thereby enabling direct omnidirectional processing despite data limitations.
3Speed
If a model is trained to perform perception tasks directly on omnidirectional images, then inference can be performed efficiently in the omnidirectional domain, but sufficient labeled training data is unavailable
Solution Approach 1:
The patent uses perspective projection images as an intermediary to transfer knowledge from perspective-based models to omnidirectional image processing. This intermediary approach enables the system to achieve efficient omnidirectional inference by leveraging pre-trained perspective models, avoiding the need for extensive labeled omnidirectional training data while maintaining fast inference performance.
Solution Approach 2:
The system performs preliminary projection of omnidirectional images to perspective projection images before inference. This preliminary action allows the use of existing pre-trained perspective models for efficient inference in the omnidirectional domain, circumventing the need for extensive omnidirectional training data while maintaining inference speed.
Data Source
AI summary
A system and method are disclosed herein for developing a machine perception model in the omnidirectional image domain. The system and method utilize the knowledge distillation process to transfer and adapt knowledge from the perspective projection image domain to the omnidirectional image domain. A teacher model is pre-trained to perform the machine perception task in the perspective projection image. A student model is trained by adapting the pre-existing knowledge of the teacher model from the perspective projection image domain to the omnidirectional image domain. By way of this training, the student model learns to perform the same machine perception task, except in the omnidirectional image domain, using limited or no suitably labeled training data in the omnidirectional image domain.