We introduce GRM, a large-scale reconstructor capable of recovering a 3D asset from
sparse-view images in around 0.1 s. GRM is a feed-forward transformer-based model that …

We introduce a novel monocular visual odometry (VO) system, NeRF-VO, that integrates
learning-based sparse visual odometry for low-latency camera tracking and a neural …

Neural Radiance Fields (NeRF) have emerged as a powerful paradigm for 3D scene
representation, offering high-fidelity renderings and reconstructions from a set of sparse and …

3D Gaussian Splatting (3DGS) has emerged as a prominent technique with the potential to
become a mainstream method for 3D representations. It can effectively transform multi-view …

Motivated by the need to leverage technologies to enhance the preservation, accessibility,
quantitative data analysis, and valorization of Dance Heritage, this work introduces DanXe …

The attempt to accurately reproduce real-world objects and environments in three-
dimensional digital form has always been a significant challenge in the field of Digital …

We propose NEDS-SLAM, a dense semantic SLAM system based on 3D Gaussian
representation, that enables robust 3D semantic mapping, accurate camera tracking, and …

Indoor scene reconstruction plays a pivotal role in the fields of computer graphics and vision,
significantly impacting areas such as robotics and augmented reality. Recently …

Simultaneous localization and mapping (SLAM) is fundamental for intelligent mobile units to
perform diverse tasks. Recent work has shown that integrating neural rendering and SLAM …

Given the image collection of an object, we aim at building a real-time image-based pose
estimation method, which requires neither its CAD model nor hours of object-specific …