Neural radiance fields (NeRFs) refer to a suit of deep neural networks that are used to learn
and represent objects or scenes. Generally speaking, NeRFs have five main characters …

Rotated object detection aims to identify and locate objects in images with arbitrary
orientation. In this scenario, the oriented directions of objects vary considerably across …

Abstract Neural Radiance Field (NeRF) has emerged as a powerful paradigm for scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …

Autonomous robotic tasks require actively perceiving the environment to achieve application-
specific goals. In this paper, we address the problem of positioning an RGB camera to …

We address the problem of active mapping with a continually-learned neural scene
representation, namely Active Neural Mapping. The key lies in actively finding the target …

A high-quality 3D reconstruction of a scene from a collection of 2D images can be achieved
through offline/online mapping methods. In this paper, we explore active mapping from the …

We present NARUTO a neural active reconstruction system that combines a hybrid neural
representation with uncertainty learning enabling high-fidelity surface reconstruction. Our …

Multi-view photometric stereo (MVPS) is a preferred method for detailed and precise 3D
acquisition of an object from images. Although popular methods for MVPS can provide …

As a promising fashion for visual localization, scene coordinate regression (SCR) has seen
tremendous progress in the past decade. Most recent methods usually adopt neural …

Feature extraction and matching are the basic parts of many robotic vision tasks, such as 2D
or 3D object detection, recognition, and registration. As is known, 2D feature extraction and …