The proliferation of technologies, such as extended reality (XR), has increased the demand
for high-quality three-dimensional (3D) graphical representations. Industrial 3D applications …

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 …

Abstract Neural Radiance Fields (NeRFs) have shown promise in applications like view
synthesis and depth estimation but learning from multiview images faces inherent …

Neural implicit surface learning has shown significant progress in multi-view 3D
reconstruction, where an object is represented by multilayer perceptrons that provide …

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 …

In recent years, the neural implicit surface has emerged as a powerful representation for
multi-view surface reconstruction due to its simplicity and State-of-the-Art performance …

This letter proposes LONER, the first real-time LiDAR SLAM algorithm that uses a neural
implicit scene representation. Existing implicit mapping methods for LiDAR show promising …