Search Results for author:
Found 9 papers, 8 papers with code
LoopSplat: Loop Closure by Registering 3D Gaussian Splats
However, existing 3DGS-based methods fail to address the global consistency of the scene via loop closure and/or global bundle adjustment.
Point Cloud Registration
Simultaneous Localization and Mapping
VF-NeRF: Learning Neural Vector Fields for Indoor Scene Reconstruction
Concretely, we develop a novel density-VF relationship and a training scheme that allows us to learn VF via volume rendering By doing this, VF-NeRF can model large planar surfaces and sharp corners accurately.
Splat-SLAM: Globally Optimized RGB-only SLAM with 3D Gaussians
In response, we propose the first RGB-only SLAM system with a dense 3D Gaussian map representation that utilizes all benefits of globally optimized tracking by adapting dynamically to keyframe pose and depth updates by actively deforming the 3D Gaussian map.
GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM
To alleviate this issue, with the aid of a monocular depth estimator, we introduce a novel DSPO layer for bundle adjustment which optimizes the pose and depth of keyframes along with the scale of the monocular depth.
How NeRFs and 3D Gaussian Splatting are Reshaping SLAM: a Survey
Over the past two decades, research in the field of Simultaneous Localization and Mapping (SLAM) has undergone a significant evolution, highlighting its critical role in enabling autonomous exploration of unknown environments.
Loopy-SLAM: Dense Neural SLAM with Loop Closures
Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization And Mapping (SLAM), yet face challenges such as error accumulation during camera tracking resulting in distorted maps.
UncLe-SLAM: Uncertainty Learning for Dense Neural SLAM
We present an uncertainty learning framework for dense neural simultaneous localization and mapping (SLAM).
Point-SLAM: Dense Neural Point Cloud-based SLAM
We propose a dense neural simultaneous localization and mapping (SLAM) approach for monocular RGBD input which anchors the features of a neural scene representation in a point cloud that is iteratively generated in an input-dependent data-driven manner.
Learning Online Multi-Sensor Depth Fusion
Multi-sensor depth fusion is able to substantially improve the robustness and accuracy of 3D reconstruction methods, but existing techniques are not robust enough to handle sensors which operate with diverse value ranges as well as noise and outlier statistics.
