Search Results for author:
Found 7 papers, 2 papers with code
NeuraLoc: Visual Localization in Neural Implicit Map with Dual Complementary Features
Specifically, to enforce geometric constraints and reduce storage requirements, we implicitly learn a 3D keypoint descriptor field, avoiding the need to explicitly store point-wise features.
SplatLoc: 3D Gaussian Splatting-based Visual Localization for Augmented Reality
Visual localization plays an important role in the applications of Augmented Reality (AR), which enable AR devices to obtain their 6-DoF pose in the pre-build map in order to render virtual content in real scenes.
NIS-SLAM: Neural Implicit Semantic RGB-D SLAM for 3D Consistent Scene Understanding
However, a notable gap exists in the existing approaches when it comes to scene understanding.
Scene Understanding
Simultaneous Localization and Mapping
+1
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.
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.
Vox-Surf: Voxel-based Implicit Surface Representation
Virtual content creation and interaction play an important role in modern 3D applications such as AR and VR.
