Abstract
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Self-supervised pretraining and finetuning for monocular depth and visual odometry
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| Boris Chidlovskii, Leonid Antsfeld |
| The IEEE International Conference on Robotics and Automation (ICRA), Yokohama, Japan, 13-17 May, 2024 |
For the task of simultaneous monocular depth and visual odometry estimation, we propose learning self-supervised transformer-based models in two steps. Our first step consists in a generic pretraining to learn 3D geometry, using cross-view completion objective (CroCo), followed by self-supervised finetuning on non-annotated videos.
We show that our self-supervised models can reach state-of-the-art performance ‘without bells and whistles’ using the standard components such as visual transformers, dense prediction transformers and adapters. We demonstrate the effectiveness of our proposed method by running evaluations on six benchmark datasets, both static and dynamic, indoor and outdoor, with synthetic and real images. For all datasets, our method outperforms state-of-the-art methods, in particular for depth prediction task.
We propose a new down-stream task for CroCo pretraining task. We show how to efficiently finetune the CroCo pre-trained model on monocular depth and visual odometry, in self-supervised way. This is, to our best knowledge, the first task where the model pre-training and fine-tuning are both totally self-supervised, with supervision signals coming from image completion at pretraining step, and from the geometric constraints at finetuning step. We benefit from the generic pretrained models oriented towards understanding 3D geometry of a scene, and finetune them on non-annotated videos to reach optimal downstream task performance.
To make robots autonomous in real-world everyday spaces, they should be able to learn from their interactions within these spaces, how to best execute tasks specified by non-expert users in a safe and reliable way. To do so requires sequential decision-making skills that combine machine learning, adaptive planning and control in uncertain environments as well as solving hard combinatorial optimisation problems. Our research combines expertise in reinforcement learning, computer vision, robotic control, sim2real transfer, large multimodal foundation models and neural combinatorial optimisation to build AI-based architectures and algorithms to improve robot autonomy and robustness when completing everyday complex tasks in constantly changing environments.
VISION
The research we conduct on expressive visual representations is applicable to visual search, object detection, image classification and the automatic extraction of 3D human poses and shapes that can be used for human behavior understanding and prediction, human-robot interaction or even avatar animation. We also extract 3D information from images that can be used for intelligent robot navigation, augmented reality and the 3D reconstruction of objects, buildings or even entire cities.
Our work covers the spectrum from unsupervised to supervised approaches, and from very deep architectures to very compact ones. We’re excited about the promise of big data to bring big performance gains to our algorithms but also passionate about the challenge of working in data-scarce and low-power scenarios.
Furthermore, we believe that a modern computer vision system needs to be able to continuously adapt itself to its environment and to improve itself via lifelong learning. Our driving goal is to use our research to deliver embodied intelligence to our users in robotics, autonomous driving, via phone cameras and any other visual means to reach people wherever they may be.
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