DB-TSDF: Directional Bitmask-based Truncated Signed Distance Fields for Efficient Volumetric Mapping
This paper presents a high-efficiency, CPU-only volumetric mapping framework based on a Truncated Signed Distance Field (TSDF). The system incrementally fuses raw LiDAR point-cloud data into a voxel grid using a directional bitmask-based integration scheme, producing dense and consistent TSDF representations suitable for real-time 3D reconstruction. A key feature of the approach is that the processing time per point-cloud remains constant, regardless of the voxel grid resolution, enabling high resolution mapping without sacrificing runtime performance. In contrast to most recent TSDF/ESDF methods that rely on GPU acceleration, our method operates entirely on CPU, achieving competitive results in speed. Experiments on real-world open datasets demonstrate that the generated maps attain accuracy on par with contemporary mapping techniques.
Code (0)
등록된 구현이 없습니다.
Tasks
3D ReconstructionSimilar Papers 제목 키워드 기반
Rendering and Tracking the Directional TSDF: Modeling Surface Orientation for Coherent Maps
Dense real-time tracking and mapping from RGB-D images is an important tool for many robotic applications, such as navigation or grasping. The recently presented Directional Truncated Signed Distance Function (DTSDF) is …
Directional TSDF: Modeling Surface Orientation for Coherent Meshes
Real-time 3D reconstruction from RGB-D sensor data plays an important role in many robotic applications, such as object modeling and mapping. The popular method of fusing depth information into a truncated signed distanc…
3D ReconstructionRendering the Directional TSDF for Tracking and Multi-Sensor Registration with Point-To-Plane Scale ICP
Dense real-time tracking and mapping from RGB-D images is an important tool for many robotic applications, such as navigation and manipulation. The recently presented Directional Truncated Signed Distance Function (DTSDF…
TSDF-Sampling: Efficient Sampling for Neural Surface Field using Truncated Signed Distance Field
Multi-view neural surface reconstruction has exhibited impressive results. However, a notable limitation is the prohibitively slow inference time when compared to traditional techniques, primarily attributed to the dense…
Surface ReconstructionOmniNeRF: Hybriding Omnidirectional Distance and Radiance fields for Neural Surface Reconstruction
3D reconstruction from images has wide applications in Virtual Reality and Automatic Driving, where the precision requirement is very high. Ground-breaking research in the neural radiance field (NeRF) by utilizing Multi-…
3D Reconstruction3D Scene Reconstruction3D Shape RepresentationNeRF+1