Computer Vision is a branch of AI that allows computers to represent the visual environment. Thanks to neural networks that can learn from data how to generate correct predictions, Deep Learning has transformed this sector. Recent advancements promise to make cars safer, boost mobility through automated vehicles, and eventually provide robotic support to people with impairments and the world’s rapidly ageing population.

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There is, however, a catch. Apart from privacy and other ethical considerations to address when creating machine learning systems, all state-of-the-art computer vision models rely on millions (or more!) of labels to achieve the high level of accuracy required for real-world safety-critical applications. Manual labelling is time-consuming and costly, requiring several hours and costing tens of dollars per image. And, in some cases, it’s simply impossible.

This is the case with monocular depth estimation, which aims to aid the computer in comprehending image depth and predicting how far scene items are for each pixel in a single image.

The purpose of monocular depth estimation is to generate pixel-wise estimations of how far each scene piece is from the camera (a.k.a. a depth map).

Although several sensor rigs can directly (e.g., LiDAR) or indirectly (e.g., stereo systems) detect depth, single cameras are inexpensive and widely used. They’re in your phone, car dashboard cameras, online videos, and so on. As a result, being able to extract useful depth information from videos is not only a fun scientific problem, but it also has a lot of practical applications. We also know that humans are capable of doing so without having to measure everything. Instead, we rely on strong inductive priors and our ability to reason about 3D interactions, whether consciously or unconsciously. Close one eye and grab for something with the other. You should have little trouble determining the depth.

This is precisely the strategy we take at TRI. Instead of training deep neural networks by telling them the exact solution (a.k.a. supervised learning), we’re attempting to use projective geometry as a teacher to encourage self-supervised learning! This training paradigm allows us to employ an infinite number of unlabeled films to enhance our models as they are exposed to additional data.

We’ll go over our recent research on how to create and train deep neural networks for depth estimation in this two-part blog post series (and beyond). We’ll explore self-supervised learning with projective geometry across a range of camera combinations in this first post. We’ll talk about the practical constraints of self-supervised learning in the second piece, as well as how to get around them by employing weak supervision or transfer learning.

Part 1: Self-Supervised Depth Estimation Learning

Deep Roots: Stereovision and Supervision

Eigen et al. were the first to demonstrate that using a calibrated camera and LiDAR sensor rig, monocular depth estimation can be turned into a supervised learning problem. The concept is simple: we use a neural network to turn an input image into per-pixel distance estimations (a depth map). Then, using correct LiDAR measurements reprojected onto the camera picture, we use deep learning tools like PyTorch to monitor the learning of the depth network weights using standard backpropagation of prediction errors.

We investigated a self-supervised learning strategy that employed solely images collected by a stereo pair (two cameras next to each other) instead of LiDAR, building on the work of Godard et al. The geometric consistency of images collected from different viewpoints of the same scene is true, and we can exploit this fact to learn a depth network. Simple geometric equations explain how to reconstruct the left picture from pixels in the right image only if it makes a correct prediction on the left image of the stereo pair, a job known as vision synthesis. If the depth prediction is incorrect, the reconstruction will be poor, resulting in a photometric loss that must be minimised through backpropagation.

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It’s worth noting that the network is still monocular: it’s only trained on left images, and past knowledge of projective geometry is only employed to self-supervise the network.

the process of learning This also differs from most self-supervised learning research in Computer Vision, which just learn representations: here, we learn the entire model for the job of depth estimation without using any labels!

The devil is in the details when it comes to self-supervised depth.

We identified that low image resolution is a substantial obstacle to monocular depth performance in our SuperDepth ICRA’19 study. The devil is in the details, and it’s difficult to get an accurate self-supervised error signal if they get lost in the standard downsampling procedures used by most deep convolutional networks. We discovered that sub-pixel convolutions on intermediate depth estimations, inspired by super-resolution approaches, can recover some of those fine-grained information to improve prediction accuracy, especially at the high resolutions required for self-driving cars (2 megapixels and above).

While stereo cameras can help with self-supervised learning, Zhou et al. have proven that this method can also be used with footage captured by a single moving monocular camera! Instead of left-right stereo pictures, identical geometric ideas can be applied to temporally consecutive frames. This greatly expanded the range of applications for self-supervised learning, but it also made the process considerably more difficult. Indeed, the spatial relationship between successive frames, often known as the camera’s ego-motion, is unknown and must be calculated. Fortunately, there is a large body of research (including our own) on the ego-motion estimation problem, which may be effortlessly incorporated with self-supervised depth estimation frameworks, for example, via collaborative learning of a pose network. Self-supervised depth estimation frameworks, such as cooperative learning of a pose network, can be used.

Self-supervised learning makes use of depth and posture networks to synthesise the current frame using data from a previous frame. During training, the photometric loss between original and synthetic images should be reduced.

We discovered that high-resolution details are important in this setting as well, just as they are in SuperDepth, but we went a step farther this time. Rather than attempting to retrieve lost information through super-resolution, we set out to maintain them as effectively as possible across the whole deep network. As a result, we proposed PackNet, a neural network architecture specifically designed for self-supervised monocular depth estimation, in our CVPR’20 publication. We created new packing and unpacking layers that keep spatial resolution intact.