We present SLIDE, a modular and unified system for single image 3D photography that uses a simple yet effective soft layering strategy to better preserve appearance details in novel views.
We present a practical and robust deep learning solution for capturing and rendering novel views of complex real world scenes for virtual exploration.
Unlike prior learning based work which has focused on predicting dense pixel-wise optical flow field and/or a depth map for each image, we propose to predict object instance specific 3D scene flow maps and instance masks from which we are able to derive the motion direction and speed for each object instance.
Actions as simple as grasping an object or navigating around it require a rich understanding of that object's 3D shape from a given viewpoint.
We consider the problem of enriching current object detection systems with veridical object sizes and relative depth estimates from a single image.
Object reconstruction from a single image -- in the wild -- is a problem where we can make progress and get meaningful results today.