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 …

The application of deep architectures inspired by the fields of artificial intelligence and
computer vision has made a significant impact on the task of crack detection. As the number …

We present PanopticNeRF, an object-aware neural scene representation that decomposes
a scene into a set of objects (things) and background (stuff). Each object is represented by a …

Semantic navigation is necessary to deploy mobile robots in uncontrolled environments
such as homes or hospitals. Many learning-based approaches have been proposed in …

Training deep learning-based change detection (CD) models heavily relies on large labeled
data sets. However, it is time-consuming and labor-intensive to collect large-scale …

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 …

We introduce a fast and efficient convolutional neural network, ESPNet, for semantic
segmentation of high resolution images under resource constraints. ESPNet is based on a …

We present an approach to semantic scene analysis using deep convolutional networks.
Our approach is based on tangent convolutions-a new construction for convolutional …

We address efficient and structure-aware 3D scene representation from images. Nerflets are
our key contribution--a set of local neural radiance fields that together represent a scene …

Simultaneous localization and map** (SLAM) consists in the concurrent construction of a
model of the environment (the map), and the estimation of the state of the robot moving …