The goal of semantic segmentation is to segment the input image according to semantic
information and predict the semantic category of each pixel from a given label set. With the …

Point cloud learning has lately attracted increasing attention due to its wide applications in
many areas, such as computer vision, autonomous driving, and robotics. As a dominating …

In this work, we introduce Semantic-SAM, an augmented image segmentation foundation for
segmenting and recognizing anything at desired granularities. Compared to the …

Object detection is one of the most important and challenging branches of computer vision,
which has been widely applied in people's life, such as monitoring security, autonomous …

We present Argoverse, a dataset designed to support autonomous vehicle perception tasks
including 3D tracking and motion forecasting. Argoverse includes sensor data collected by a …

Object models are gradually progressing from predicting just category labels to providing
detailed descriptions of object instances. This motivates the need for large datasets which …

Existing deep learning-based approaches for monocular 3D object detection in autonomous
driving often model the object as a rotated 3D cuboid while the object's geometric shape has …

Scene parsing aims to assign a class (semantic) label for each pixel in an image. It is a
comprehensive analysis of an image. Given the rise of autonomous driving, pixel-accurate …

Object detection is one of the most fundamental and challenging tasks to locate objects in
images and videos. Over the past, it has gained much attention to do more research on …

Concurrent perception datasets for autonomous driving are mainly limited to frontal view
with sensors mounted on the vehicle. None of them is designed for the overlooked roadside …