NICE-SLAM with Adaptive Feature Grids
- URL: http://arxiv.org/abs/2306.02395v2
- Date: Sat, 10 Jun 2023 06:13:54 GMT
- Title: NICE-SLAM with Adaptive Feature Grids
- Authors: Ganlin Zhang, Deheng Zhang, Feichi Lu, Anqi Li
- Abstract summary: NICE-SLAM is a dense visual SLAM system that combines neural implicit representations and hierarchical grid-based scene representation.
We present sparse NICE-SLAM, a sparse SLAM system incorporating the idea of Voxel Hashing into NICE-SLAM framework.
- Score: 1.5962515374223873
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: NICE-SLAM is a dense visual SLAM system that combines the advantages of
neural implicit representations and hierarchical grid-based scene
representation. However, the hierarchical grid features are densely stored,
leading to memory explosion problems when adapting the framework to large
scenes. In our project, we present sparse NICE-SLAM, a sparse SLAM system
incorporating the idea of Voxel Hashing into NICE-SLAM framework. Instead of
initializing feature grids in the whole space, voxel features near the surface
are adaptively added and optimized. Experiments demonstrated that compared to
NICE-SLAM algorithm, our approach takes much less memory and achieves
comparable reconstruction quality on the same datasets. Our implementation is
available at
https://github.com/zhangganlin/NICE-SLAM-with-Adaptive-Feature-Grids.
Related papers
- GlORIE-SLAM: Globally Optimized RGB-only Implicit Encoding Point Cloud SLAM [53.6402869027093]
We propose an efficient RGB-only dense SLAM system using a flexible neural point cloud representation scene.
We also introduce a novel DSPO layer for bundle adjustment which optimize the pose and depth of implicits along with the scale of the monocular depth.
arXiv Detail & Related papers (2024-03-28T16:32:06Z) - MUTE-SLAM: Real-Time Neural SLAM with Multiple Tri-Plane Hash Representations [6.266208986510979]
MUTE-SLAM is a real-time neural RGB-D SLAM system employing multiple tri-plane hash-encodings for efficient scene representation.
MUTE-SLAM effectively tracks camera positions and incrementally builds a scalable multi-map representation for both small and large indoor environments.
arXiv Detail & Related papers (2024-03-26T14:53:24Z) - DNS SLAM: Dense Neural Semantic-Informed SLAM [92.39687553022605]
DNS SLAM is a novel neural RGB-D semantic SLAM approach featuring a hybrid representation.
Our method integrates multi-view geometry constraints with image-based feature extraction to improve appearance details.
Our experimental results achieve state-of-the-art performance on both synthetic data and real-world data tracking.
arXiv Detail & Related papers (2023-11-30T21:34:44Z) - CP-SLAM: Collaborative Neural Point-based SLAM System [54.916578456416204]
This paper presents a collaborative implicit neural localization and mapping (SLAM) system with RGB-D image sequences.
In order to enable all these modules in a unified framework, we propose a novel neural point based 3D scene representation.
A distributed-to-centralized learning strategy is proposed for the collaborative implicit SLAM to improve consistency and cooperation.
arXiv Detail & Related papers (2023-11-14T09:17:15Z) - Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural
Real-Time SLAM [14.56883275492083]
Co-SLAM is an RGB-D SLAM system based on a hybrid representation.
It performs robust camera tracking and high-fidelity surface reconstruction in real time.
arXiv Detail & Related papers (2023-04-27T17:46:45Z) - Point-SLAM: Dense Neural Point Cloud-based SLAM [61.96492935210654]
We propose a dense neural simultaneous localization and mapping (SLAM) approach for monocular RGBD input.
We demonstrate that both tracking and mapping can be performed with the same point-based neural scene representation.
arXiv Detail & Related papers (2023-04-09T16:48:26Z) - NICER-SLAM: Neural Implicit Scene Encoding for RGB SLAM [111.83168930989503]
NICER-SLAM is a dense RGB SLAM system that simultaneously optimize for camera poses and a hierarchical neural implicit map representation.
We show strong performance in dense mapping, tracking, and novel view synthesis, even competitive with recent RGB-D SLAM systems.
arXiv Detail & Related papers (2023-02-07T17:06:34Z) - NICE-SLAM: Neural Implicit Scalable Encoding for SLAM [112.6093688226293]
NICE-SLAM is a dense SLAM system that incorporates multi-level local information by introducing a hierarchical scene representation.
Compared to recent neural implicit SLAM systems, our approach is more scalable, efficient, and robust.
arXiv Detail & Related papers (2021-12-22T18:45:44Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.