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Found 8 papers, 0 papers with code
3DGS-Calib: 3D Gaussian Splatting for Multimodal SpatioTemporal Calibration
We introduce 3DGS-Calib, a new calibration method that relies on the speed and rendering accuracy of 3D Gaussian Splatting to achieve multimodal spatiotemporal calibration that is accurate, robust, and with a substantial speed-up compared to methods relying on implicit neural representations.
SWAG: Splatting in the Wild images with Appearance-conditioned Gaussians
To tackle this, we extend over 3D Gaussian Splatting to handle unstructured image collections.
SCILLA: SurfaCe Implicit Learning for Large Urban Area, a volumetric hybrid solution
SCILLA's hybrid architecture models two separate implicit fields: one for the volumetric density and another for the signed distance to the surface.
RoDUS: Robust Decomposition of Static and Dynamic Elements in Urban Scenes
The task of separating dynamic objects from static environments using NeRFs has been widely studied in recent years.
SOAC: Spatio-Temporal Overlap-Aware Multi-Sensor Calibration using Neural Radiance Fields
In this paper, we leverage the ability of Neural Radiance Fields (NeRF) to represent different sensors modalities in a common volumetric representation to achieve robust and accurate spatio-temporal sensor calibration.
PlaNeRF: SVD Unsupervised 3D Plane Regularization for NeRF Large-Scale Scene Reconstruction
Neural Radiance Fields (NeRF) enable 3D scene reconstruction from 2D images and camera poses for Novel View Synthesis (NVS).
CROSSFIRE: Camera Relocalization On Self-Supervised Features from an Implicit Representation
Beyond novel view synthesis, Neural Radiance Fields are useful for applications that interact with the real world.
MOISST: Multimodal Optimization of Implicit Scene for SpatioTemporal calibration
With the recent advances in autonomous driving and the decreasing cost of LiDARs, the use of multimodal sensor systems is on the rise.
