6 papers
PlanarMesh: Building Compact 3D Meshes from LiDAR using Incremental Adaptive Resolution Reconstruction
Jiahao Wang, Nived Chebrolu, Yifu Tao +3
Building an online 3D LiDAR mapping system that produces a detailed surface reconstruction while remaining computationally efficient is a challenging task. In this paper, we presen…
Building Forest Inventories with Autonomous Legged Robots -- System, Lessons, and Challenges Ahead
Matías Mattamala, Nived Chebrolu, Jonas Frey +5
Legged robots are increasingly being adopted in industries such as oil, gas, mining, nuclear, and agriculture. However, new challenges exist when moving into natural, less-structur…
Seeing in the Dark: Benchmarking Egocentric 3D Vision with the Oxford Day-and-Night Dataset
Zirui Wang, Wenjing Bian, Xinghui Li +4
We introduce Oxford Day-and-Night, a large-scale, egocentric dataset for novel view synthesis (NVS) and visual relocalisation under challenging lighting conditions. Existing datase…
SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with Uncertainty Quantification
Yifu Tao, Maurice Fallon
We present a neural radiance field (NeRF) based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically a…
The Bare Necessities: Designing Simple, Effective Open-Vocabulary Scene Graphs
Christina Kassab, Matías Mattamala, Sacha Morin +4
3D open-vocabulary scene graph methods are a promising map representation for embodied agents, however many current approaches are computationally expensive. In this paper, we reex…
The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods
Yifu Tao, Miguel Ángel Muñoz-Bañón, Lintong Zhang +3
This paper introduces a large-scale multi-modal dataset captured in and around well-known landmarks in Oxford using a custom-built multi-sensor perception unit as well as a millime…