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Found 10 papers, 6 papers with code
Vox-Fusion++: Voxel-based Neural Implicit Dense Tracking and Mapping with Multi-maps
In this paper, we introduce Vox-Fusion++, a multi-maps-based robust dense tracking and mapping system that seamlessly fuses neural implicit representations with traditional volumetric fusion techniques.
AEGIS-Net: Attention-guided Multi-Level Feature Aggregation for Indoor Place Recognition
We evaluate our AEGIS-Net on the ScanNetPR dataset and compare its performance with a pre-deep-learning feature-based method and five state-of-the-art deep-learning-based methods.
EDI: ESKF-based Disjoint Initialization for Visual-Inertial SLAM Systems
Our method incorporates an Error-state Kalman Filter (ESKF) to estimate gyroscope bias and correct rotation estimates from monocular SLAM, overcoming dependence on pure monocular SLAM for rotation estimation.
Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation
In this work, we present a dense tracking and mapping system named Vox-Fusion, which seamlessly fuses neural implicit representations with traditional volumetric fusion methods.
iDF-SLAM: End-to-End RGB-D SLAM with Neural Implicit Mapping and Deep Feature Tracking
The neural implicit mapper is trained on-the-fly, while though the neural tracker is pretrained on the ScanNet dataset, it is also finetuned along with the training of the neural implicit mapper.
DeFlowSLAM: Self-Supervised Scene Motion Decomposition for Dynamic Dense SLAM
We present a novel dual-flow representation of scene motion that decomposes the optical flow into a static flow field caused by the camera motion and another dynamic flow field caused by the objects' movements in the scene.
PVO: Panoptic Visual Odometry
We present PVO, a novel panoptic visual odometry framework to achieve more comprehensive modeling of the scene motion, geometry, and panoptic segmentation information.
FD-SLAM: 3-D Reconstruction Using Features and Dense Matching
It is well known that visual SLAM systems based on dense matching are locally accurate but are also susceptible to long-term drift and map corruption.
CGiS-Net: Aggregating Colour, Geometry and Implicit Semantic Features for Indoor Place Recognition
We describe a novel approach to indoor place recognition from RGB point clouds based on aggregating low-level colour and geometry features with high-level implicit semantic features.
Object-Augmented RGB-D SLAM for Wide-Disparity Relocalisation
During the map construction, we use a pre-trained neural network to detect objects and estimate 6D poses from RGB-D data.
