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Multi-Object Discovery by Low-Dimensional Object Motion
We achieve state-of-the-art results in unsupervised multi-object segmentation on synthetic and real-world datasets by modeling the scene structure and object motion.
DepthP+P: Metric Accurate Monocular Depth Estimation using Planar and Parallax
We perform experiments on the KITTI driving dataset and show that the planar parallax approach, which only needs to predict camera translation, can be a metrically accurate alternative to the current methods that rely on estimating 6DoF camera motion.
Stochastic Video Prediction with Structure and Motion
The existing methods fail to fully capture the dynamics of the structured world by only focusing on changes in pixels.
Self-Supervised Monocular Scene Decomposition and Depth Estimation
Self-supervised monocular depth estimation approaches either ignore independently moving objects in the scene or need a separate segmentation step to identify them.
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