Instance-level perception tasks like object detection, instance segmentation, and 3D
detection require many training samples to achieve satisfactory performance. The …

Segmenting and determining the 3D bounding boxes of objects of interest in RGB videos is
an important task for a variety of applications such as augmented reality, navigation, and …

Augmented Reality has been subject to various integration efforts within industries due to its
ability to enhance human machine interaction and understanding. Neural networks have …

We introduce H3DNet, which takes a colorless 3D point cloud as input and outputs a
collection of oriented object bounding boxes (or BB) and their semantic labels. The critical …

In dynamic 3D environments the ability to recognize a diverse range of objects without the
constraints of predefined categories is indispensable for real-world applications. In response …

For the last few decades, several major subfields of artificial intelligence including computer
vision, graphics, and robotics have progressed largely independently from each other …

Recently, progress in acquisition equipment such as LiDAR sensors has enabled sensing
increasingly spacious outdoor 3D environments. Making sense of such 3D acquisitions …

To be useful in everyday environments, robots must be able to observe and learn about
objects. Recent datasets enable progress for classifying data into known object categories; …

The rapid progress in 3D scene understanding has come with growing demand for data;
however, collecting and annotating 3D scenes (eg point clouds) are notoriously hard. For …

Deep neural networks have revolutionized many areas of computer vision, but they require
notoriously large amounts of labeled training data. For tasks such as semantic segmentation …