Search Results for author:
Found 8 papers, 7 papers with code
NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields
We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-time scene reconstruction from monocular images.
Probabilistic Volumetric Fusion for Dense Monocular SLAM
We present a novel method to reconstruct 3D scenes from images by leveraging deep dense monocular SLAM and fast uncertainty propagation.
Smooth Mesh Estimation from Depth Data using Non-Smooth Convex Optimization
In this paper, we leapfrog these intermediate representations and build a 3D mesh directly from a depth map and the sparse landmarks triangulated with visual odometry.
Kimera: from SLAM to Spatial Perception with 3D Dynamic Scene Graphs
This mental model captures geometric and semantic aspects of the scene, describes the environment at multiple levels of abstractions (e. g., objects, rooms, buildings), includes static and dynamic entities and their relations (e. g., a person is in a room at a given time).
Primal-Dual Mesh Convolutional Neural Networks
Recent works in geometric deep learning have introduced neural networks that allow performing inference tasks on three-dimensional geometric data by defining convolution, and sometimes pooling, operations on triangle meshes.
3D Dynamic Scene Graphs: Actionable Spatial Perception with Places, Objects, and Humans
Our second contribution is to provide the first fully automatic Spatial PerceptIon eNgine(SPIN) to build a DSG from visual-inertial data.
Kimera: an Open-Source Library for Real-Time Metric-Semantic Localization and Mapping
We provide an open-source C++ library for real-time metric-semantic visual-inertial Simultaneous Localization And Mapping (SLAM).
Incremental Visual-Inertial 3D Mesh Generation with Structural Regularities
We propose instead to tightly couple mesh regularization and state estimation by detecting and enforcing structural regularities in a novel factor-graph formulation.
