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Found 17 papers, 1 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.
Exploiting map information for self-supervised learning in motion forecasting
Inspired by recent developments regarding the application of self-supervised learning (SSL), we devise an auxiliary task for trajectory prediction that takes advantage of map-only information such as graph connectivity with the intent of improving map comprehension and generalization.
Exploring the trade off between human driving imitation and safety for traffic simulation
In this work, we show that a trade-off exists between imitating human driving and maintaining safety when learning driving policies.
Uncertainty estimation for Cross-dataset performance in Trajectory prediction
While a lot of work has been carried on developing trajectory prediction methods, and various datasets have been proposed for benchmarking this task, little study has been done so far on the generalizability and the transferability of these methods across dataset.
ImPosing: Implicit Pose Encoding for Efficient Visual Localization
We propose a novel learning-based formulation for visual localization of vehicles that can operate in real-time in city-scale environments.
LENS: Localization enhanced by NeRF synthesis
Neural Radiance Fields (NeRF) have recently demonstrated photo-realistic results for the task of novel view synthesis.
THOMAS: Trajectory Heatmap Output with learned Multi-Agent Sampling
In this paper, we propose THOMAS, a joint multi-agent trajectory prediction framework allowing for an efficient and consistent prediction of multi-agent multi-modal trajectories.
Ranked #6 on
Trajectory Prediction
on nuScenes
GOHOME: Graph-Oriented Heatmap Output for future Motion Estimation
In this paper, we propose GOHOME, a method leveraging graph representations of the High Definition Map and sparse projections to generate a heatmap output representing the future position probability distribution for a given agent in a traffic scene.
Ranked #1 on
Trajectory Prediction
on INTERACTION Dataset - Validation
(minFDE6 metric)
HOME: Heatmap Output for future Motion Estimation
In this paper, we propose HOME, a framework tackling the motion forecasting problem with an image output representing the probability distribution of the agent's future location.
Ranked #32 on
Motion Forecasting
on Argoverse CVPR 2020
CoordiNet: uncertainty-aware pose regressor for reliable vehicle localization
In this setup, structure-based methods require a large database, and we show that our proposal is a reliable alternative, achieving 29cm median error in a 1. 9km loop in a busy urban area
Ranked #2 on
Camera Localization
on Oxford RobotCar Full
