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Found 13 papers, 12 papers with code
Mesh-Learner: Texturing Mesh with Spherical Harmonics
Conversely, gradients from each pixel are back-propagated to the related SH Texels in SH textures.
GS-SDF: LiDAR-Augmented Gaussian Splatting and Neural SDF for Geometrically Consistent Rendering and Reconstruction
The accurate LiDAR point clouds enable a trained neural signed distance field to offer a manifold geometry field, This motivates us to offer an SDF-based Gaussian initialization for physically grounded primitive placement and a comprehensive geometric regularization for geometrically consistent rendering and reconstruction.
FAST-LIVO2: Fast, Direct LiDAR-Inertial-Visual Odometry
The fusion of both visual and LiDAR measurements is based on a single unified voxel map where the LiDAR module constructs the geometric structure for registering new LiDAR scans and the visual module attaches image patches to the LiDAR points.
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.
ImMesh: An Immediate LiDAR Localization and Meshing Framework
This voxel-wise meshing operation is delicately designed for the purpose of efficiency; it first performs a dimension reduction by projecting 3D points to a 2D local plane contained in the voxel, and then executes the meshing operation with pull, commit and push steps for incremental reconstruction of triangle facets.
STD: Stable Triangle Descriptor for 3D place recognition
For a triangle, its shape is uniquely determined by the length of the sides or included angles.
R$^3$LIVE++: A Robust, Real-time, Radiance reconstruction package with a tightly-coupled LiDAR-Inertial-Visual state Estimator
The LIO subsystem utilizes the measurements from a LiDAR for reconstructing the geometric structure (i. e., the positions of 3D points), while the VIO subsystem simultaneously recovers the radiance information of the geometric structure from the input images.
R3LIVE: A Robust, Real-time, RGB-colored, LiDAR-Inertial-Visual tightly-coupled state Estimation and mapping package
Moreover, to make R3LIVE more extensible, we develop a series of offline utilities for reconstructing and texturing meshes, which further minimizes the gap between R3LIVE and various of 3D applications such as simulators, video games and etc (see our demos video).
R2LIVE: A Robust, Real-time, LiDAR-Inertial-Visual tightly-coupled state Estimator and mapping
Our proposed framework is composed of two parts: the filter-based odometry and factor graph optimization.
Robotics
A decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs
To improve the system robustness and performance in self-localization and mapping, we develop a decentralized framework for simultaneous calibration, localization and mapping with multiple LiDARs.
Robotics
A fast, complete, point cloud based loop closure for LiDAR odometry and mapping
This paper presents a loop closure method to correct the long-term drift in LiDAR odometry and mapping (LOAM).
Loam_livox: A fast, robust, high-precision LiDAR odometry and mapping package for LiDARs of small FoV
LiDAR odometry and mapping (LOAM) has been playing an important role in autonomous vehicles, due to its ability to simultaneously localize the robot's pose and build high-precision, high-resolution maps of the surrounding environment.
Flying through a narrow gap using neural network: an end-to-end planning and control approach
To this end, we propose an end-to-end policy network, which imitates from the traditional pipeline and is fine-tuned using reinforcement learning.
Robotics
