![]() A car on the left side of the image activates features the same way, using the same network weights, as a car on the right side of the image. This translation invariance is a prior that provides an inductive bias and plays a significant role in the incredible success of deep learning for computer vision. As the network becomes deeper and the feature channels become spatially smaller due to strided convolutions or max-pooling, the receptive field becomes larger, but maintains the translation invariance property. Each feature has no access to its spatial coordinate, i.e., it does not “know” where it is in the image. $$ P=\left[\begin\right)$ and the class (e.g., car, bicycle, pedestrian).Ĭonvolutional neural networks (CNNs) have a translation invariance property: if the image size is, say, $1000\times1000$, and the convolution kernel size is $3\times3$, and the stride is 1, then the output of the convolution layer is features of the same spatial size $1000\times1000$ where each feature has a receptive field of $3\times3$ with respect to the input. The camera model defines the mapping between a point in 3D space, Perspective images and the pinhole camera model The perspective projectionĪn image is a two-dimensional array of pixels, with size $h\times\ w$, where each pixel is a three-dimensional array for a color image. We will focus on two computer vision tasks: 2D object detection and monocular 3D object detection (the task of detecting 3D bounding boxes around objects from a single camera, with no additional sensors). The second part of the tutorial will apply similar principles to fisheye cameras. We cannot truly understand fisheye cameras without digging into the way computer vision works with perspective images, and the first part of this tutorial will deal only with perspective images. This tutorial shows how to handle fisheye cameras much like perspective cameras, making many existing computer vision models that were developed for perspective images applicable to fisheye images directly or with minimal modifications. Yet, the computer vision community has neglected to develop a methodical approach to applying the immense progress which has been made since the deep learning revolution to fisheye cameras, leading some authors and practitioners to solutions that are unnecessarily inferior, overcomplicated, or misguided. Nevertheless, the ability of a single camera to capture a wide FoV makes fisheye cameras extremely valuable in applications such as video surveillance, aerial photography, and autonomous vehicles. Fisheye images appear deformed compared to standard perspective images, which we tend to regard as a natural view of the world. Instead of producing rectilinear images, fisheye cameras map straight lines in the 3D world to curved lines in the image according to some known mapping.
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