Monocular 3D Object Detection with Auxiliary Learning

Check out the project below 🠯🠯🠯

Intro

In many variations of object detection in self-driving cars, image data is paired with additional context such as data from a depth senser or LiDAR. However, a system must also be able to make these predictions without additional assistance in situations where extra sensors aren’t usable. Specifically we wish to localize 3D bounding boxes of using only 2D images as inputs, this task is called Monocular Object Detection.

You can also put regular text between your rows of images. Say you wanted to write a little bit about your project before you posted the rest of the images. You describe how you toiled, sweated, bled for your project, and then… you reveal its glory in the next row of images.

The code is simple. Just wrap your images with <div class="col-sm"> and place them inside <div class="row"> (read more about the Bootstrap Grid system). To make images responsive, add img-fluid class to each; for rounded corners and shadows use rounded and z-depth-1 classes. Here’s the code for the last row of images above:

<div class="row justify-content-sm-center">
  <div class="col-sm-8 mt-3 mt-md-0">
    {% include figure.liquid path="assets/img/6.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
  </div>
  <div class="col-sm-4 mt-3 mt-md-0">
    {% include figure.liquid path="assets/img/11.jpg" title="example image" class="img-fluid rounded z-depth-1" %}
  </div>
</div>