I have a large dataset of vehicles with the ground truth of their lengths (Over 100k samples). Is it possible to train a deep network to measure/estimate vehicle length ? I haven't seen any papers related to estimating object size using deep neural network.
Yes! This most certainly can be done. Since you have a labeled dataset, that makes it all the more simple!
I would take a look at this project and that should get you where you need to go.
The implementation details should be pretty straightforward. Let me know if I can help further.
Yes it's possible, but first you'll have to recognize some object in the image, either 1) the vehicle itself, and then report that vehicle's known size, or 2) a known object that's the same distance from the camera as the car (a curb, a stop sign, the driver's head, a shetland pony... whatever), and then use that object to calibrate the size of the car that's very close to it.
Any car in an image will be an unknown distance from the camera, making the car object appear larger or smaller from photo to photo. If you don't recognize the car or at least a referent object that has a known size, the physical size of the car will be uncalibrated -- you'll have no basis for your size estimate.
If the car is unknown, then even if you do have visual clues (there is a referent object present or the distance from camera to car is known), the unknown extent of wide angle-ness of the camera's lens may distort an unknown car's shape (height vs width), further complicating your ability to estimate its apparent dimensions.
I think this paper can help you out: 3D Bounding Box Estimation Using Deep Learning and Geometry
He used 1 VGG-19 (pretrained on ImageNet) to learn the size of cars