  We present an end-to-end joint training framework that explicitly models
6-DoF motion of multiple dynamic objects, ego-motion and depth in a monocular
camera setup without supervision. Our technical contributions are three-fold.
First, we propose a differentiable forward rigid projection module that plays a
key role in our instance-wise depth and motion learning. Second, we design an
instance-wise photometric and geometric consistency loss that effectively
decomposes background and moving object regions. Lastly, we introduce a new
auto-annotation scheme to produce video instance segmentation maps that will be
utilized as input to our training pipeline. These proposed elements are
validated in a detailed ablation study. Through extensive experiments conducted
on the KITTI dataset, our framework is shown to outperform the state-of-the-art
depth and motion estimation methods. Our code and dataset will be available at
https://github.com/SeokjuLee/Insta-DM.
