3D Object Positioning Using Differentiable Multimodal Learning
Sean Zanyk-McLean Krishna Kumar Paul Navratil
This article describes a multi-modal method using simulated Lidar data via
ray tracing and image pixel loss with differentiable rendering to optimize an
object's position with respect to an observer or some referential objects in a
computer graphics scene. Object position optimization is completed using
gradient descent with the loss function being influenced by both modalities.
Typical object placement optimization is done using image pixel loss with
differentiable rendering only, this work shows the use of a second modality
(Lidar) leads to faster convergence. This method of fusing sensor input
presents a potential usefulness for autonomous vehicles, as these methods can
be used to establish the locations of multiple actors in a scene. This article
also presents a method for the simulation of multiple types of data to be used
in the training of autonomous vehicles.