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Found 4 papers, 0 papers with code
SiLVR: Scalable Lidar-Visual Radiance Field Reconstruction with Uncertainty Quantification
Furthermore, we estimate the epistemic uncertainty of the reconstruction as the spatial variance of each point location in the radiance field given the sensor observations from camera and lidar.
The Oxford Spires Dataset: Benchmarking Large-Scale LiDAR-Visual Localisation, Reconstruction and Radiance Field Methods
Our evaluation demonstrates a key limitation of state-of-the-art radiance field methods: we show that they tend to overfit to the training poses/images and do not generalise well to out-of-sequence poses.
Visual Localization in 3D Maps: Comparing Point Cloud, Mesh, and NeRF Representations
This paper presents and evaluates a global visual localization system capable of localizing a single camera image across various 3D map representations built using both visual and lidar sensing.
SiLVR: Scalable Lidar-Visual Reconstruction with Neural Radiance Fields for Robotic Inspection
We present a neural-field-based large-scale reconstruction system that fuses lidar and vision data to generate high-quality reconstructions that are geometrically accurate and capture photo-realistic textures.
