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 …

Accurate detection of objects in 3D point clouds is a central problem in many applications,
such as autonomous navigation, housekee** robots, and augmented/virtual reality. To …

Many recent works on 3D object detection have focused on designing neural network
architectures that can consume point cloud data. While these approaches demonstrate …

With the increased availability of very high-resolution satellite imagery, terrain based
imaging and participatory sensing, inexpensive platforms, and advanced information and …

This paper aims at high-accuracy 3D object detection in autonomous driving scenario. We
propose Multi-View 3D networks (MV3D), a sensory-fusion framework that takes both LIDAR …

We present an efficient 3D object detection framework based on a single RGB image in the
scenario of autonomous driving. Our efforts are put on extracting the underlying 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 …

This paper presents a novel approach to estimating the continuous six degree of freedom (6-
DoF) pose (3D translation and rotation) of an object from a single RGB image. The approach …

This paper proposes a deep neural architecture, PlaneRCNN, that detects and reconstructs
piecewise planar regions from a single RGB image. PlaneRCNN employs a variant of Mask …

This paper poses object category detection in images as a type of 2D-to-3D alignment
problem, utilizing the large quantities of 3D CAD models that have been made publicly …