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Found 8 papers, 1 papers with code
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).
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.
LMSCNet: Lightweight Multiscale 3D Semantic Completion
We introduce a new approach for multiscale 3Dsemantic scene completion from voxelized sparse 3D LiDAR scans.
Ranked #4 on
3D Semantic Scene Completion
on KITTI-360
3D Surface Reconstruction from Voxel-based Lidar Data
To achieve fully autonomous navigation, vehicles need to compute an accurate model of their direct surrounding.
Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps
In this paper, we discuss real-time dynamic object detection algorithms which leverages previously mapped Lidar point clouds to reduce processing.
