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 …

With the availability of low-cost and compact 2.5/3D visual sensing devices, computer vision
community is experiencing a growing interest in visual scene understanding of indoor …

The goal of this paper is to perform 3D object detection in single monocular images in the
domain of autonomous driving. Our method first aims to generate a set of candidate class …

Monocular 3D scene understanding tasks, such as object size estimation, heading angle
estimation and 3D localization, is challenging. Successful modern-day methods for 3D …

We present a fast inverse-graphics framework for instance-level 3D scene understanding.
We train a deep convolutional network that learns to map image regions to the full 3D shape …

The goal of this paper is to generate high-quality 3D object proposals in the context of
autonomous driving. Our method exploits stereo imagery to place proposals in the form of …

The goal of this paper is to perform 3D object detection in the context of autonomous driving.
Our method aims at generating a set of high-quality 3D object proposals by exploiting stereo …

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 …

Autonomous driving has attracted remarkable attention from both industry and academia. An
important task is to estimate 3D properties (eg translation, rotation and shape) of a moving or …

In this paper, we propose to learn a deep fitting degree scoring network for monocular 3D
object detection, which aims to score fitting degree between proposals and object …