Below, we show a video of predicted navigability maps while traversing four held-out test set environments from the Gibson dataset. We compare two versions of our model, denoted as 'Noisy Training' and 'Noiseless Training'. In the noisy case (our primary setting), we show results from a model trained using dead-reckoning with noisy estimates of the agent's egomotion. We show the second, noiseless, case as a visualization of what the method could achieve with access to more accurate egomotion.
Explore our results! Our model can estimate the 2D distance function of new scenes, that is, the distance of each point to the closest obstacle. Examples of this distance prediction are shown on the left, and can be viewed for many example images. Clicking on one of the points reveals a plot of the underlying 'hitting time distribution' for that point, which indicates how many steps the model estimates an agent could travel in any given direction before colliding with the environment.
@article{raistrick2021collision
author = {Raistrick, Alexander and Kulkarni, Nilesh and Fouhey, David F.},
title = {Collision Replay: What Does Bumping Into Things Tell You About Scene Geometry?},
journal = {BMVC},
year = {2021},
}