Recent years have witnessed enormous progress in AI-related fields such as computer
vision, machine learning, and autonomous vehicles. As with any rapidly growing field, it …

Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames
by watching unlabeled videos via deep convolutional network has made significant progress …

This work investigates the estimation of dense three-dimensional motion fields, commonly
referred to as scene flow. While great progress has been made in recent years, large …

In this paper we tackle the problem of scene flow estimation in the context of self-driving. We
leverage deep learning techniques as well as strong priors as in our application domain the …

Existing methods for 3D scene flow estimation often fail in the presence of large
displacement or local ambiguities, eg, at texture-less or reflective surfaces. However, these …

Despite significant progress in image-based 3D scene flow estimation, the performance of
such approaches has not yet reached the fidelity required by many applications …

Learning to estimate 3D geometry in a single image by watching unlabeled videos via deep
convolutional network has made significant process recently. Current state-of-the-art (SOTA) …

Estimation of 3D motion in a dynamic scene from a temporal pair of images is a core task in
many scene understanding problems. In real world applications, a dynamic scene is …

We introduce a compact network for holistic scene flow estimation, called SENSE, which
shares common encoder features among four closely-related tasks: optical flow estimation …

In the last few years, convolutional neural networks (CNNs) have demonstrated increasing
success at learning many computer vision tasks including dense estimation problems such …