LCP-Fusion: A Neural Implicit SLAM with Enhanced Local Constraints and Computable Prior

• URL: http://arxiv.org/abs/2411.03610v1
• Date: Wed, 06 Nov 2024 02:05:44 GMT
• Title: LCP-Fusion: A Neural Implicit SLAM with Enhanced Local Constraints and Computable Prior
• Authors: Jiahui Wang, Yinan Deng, Yi Yang, Yufeng Yue,
• Abstract summary: LCP-Fusion is a neural implicit SLAM system with enhanced local constraints and computable prior. We show that our method achieve better localization accuracy and reconstruction consistency than existing RGB-D implicit SLAM.
• Score: 22.948360879064758
• License:
• Abstract: Recently the dense Simultaneous Localization and Mapping (SLAM) based on neural implicit representation has shown impressive progress in hole filling and high-fidelity mapping. Nevertheless, existing methods either heavily rely on known scene bounds or suffer inconsistent reconstruction due to drift in potential loop-closure regions, or both, which can be attributed to the inflexible representation and lack of local constraints. In this paper, we present LCP-Fusion, a neural implicit SLAM system with enhanced local constraints and computable prior, which takes the sparse voxel octree structure containing feature grids and SDF priors as hybrid scene representation, enabling the scalability and robustness during mapping and tracking. To enhance the local constraints, we propose a novel sliding window selection strategy based on visual overlap to address the loop-closure, and a practical warping loss to constrain relative poses. Moreover, we estimate SDF priors as coarse initialization for implicit features, which brings additional explicit constraints and robustness, especially when a light but efficient adaptive early ending is adopted. Experiments demonstrate that our method achieve better localization accuracy and reconstruction consistency than existing RGB-D implicit SLAM, especially in challenging real scenes (ScanNet) as well as self-captured scenes with unknown scene bounds. The code is available at https://github.com/laliwang/LCP-Fusion.

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)
• PNeRFLoc: Visual Localization with Point-based Neural Radiance Fields [54.8553158441296]
  We propose a novel visual localization framework, ie, PNeRFLoc, based on a unified point-based representation. On the one hand, PNeRFLoc supports the initial pose estimation by matching 2D and 3D feature points. On the other hand, it also enables pose refinement with novel view synthesis using rendering-based optimization.
  arXiv  Detail & Related papers  (2023-12-17T08:30:00Z)
• HI-SLAM: Monocular Real-time Dense Mapping with Hybrid Implicit Fields [11.627951040865568]
  Recent neural mapping frameworks show promising results, but rely on RGB-D or pose inputs, or cannot run in real-time. Our approach integrates dense-SLAM with neural implicit fields. For efficient construction of neural fields, we employ multi-resolution grid encoding and signed distance function.
  arXiv  Detail & Related papers  (2023-10-07T12:26:56Z)
• Decoupled Local Aggregation for Point Cloud Learning [12.810517967372043]
  We propose to decouple the explicit modelling of spatial relations from local aggregation. We present DeLA, a lightweight point network, where in each learning stage relative spatial encodings are first formed. DeLA achieves over 90% overall accuracy on ScanObjectNN and 74% mIoU on S3DIS Area 5.
  arXiv  Detail & Related papers  (2023-08-31T08:21:29Z)
• GIFD: A Generative Gradient Inversion Method with Feature Domain Optimization [52.55628139825667]
  Federated Learning (FL) has emerged as a promising distributed machine learning framework to preserve clients' privacy. Recent studies find that an attacker can invert the shared gradients and recover sensitive data against an FL system by leveraging pre-trained generative adversarial networks (GAN) as prior knowledge. We propose textbfGradient textbfInversion over textbfFeature textbfDomains (GIFD), which disassembles the GAN model and searches the feature domains of the intermediate layers.
  arXiv  Detail & Related papers  (2023-08-09T04:34:21Z)
• FMapping: Factorized Efficient Neural Field Mapping for Real-Time Dense RGB SLAM [3.6985351289638957]
  We introduce FMapping, an efficient neural field mapping framework that facilitates the continuous estimation of a colorized point cloud map in real-time dense RGB SLAM. We propose an effective factorization scheme for scene representation and introduce a sliding window strategy to reduce the uncertainty for scene reconstruction.
  arXiv  Detail & Related papers  (2023-06-01T11:51:46Z)
• Fast Monocular Scene Reconstruction with Global-Sparse Local-Dense Grids [84.90863397388776]
  We propose to directly use signed distance function (SDF) in sparse voxel block grids for fast and accurate scene reconstruction without distances. Our globally sparse and locally dense data structure exploits surfaces' spatial sparsity, enables cache-friendly queries, and allows direct extensions to multi-modal data. Experiments show that our approach is 10x faster in training and 100x faster in rendering while achieving comparable accuracy to state-of-the-art neural implicit methods.
  arXiv  Detail & Related papers  (2023-05-22T16:50:19Z)
• 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)
• Sign-Agnostic CONet: Learning Implicit Surface Reconstructions by Sign-Agnostic Optimization of Convolutional Occupancy Networks [39.65056638604885]
  We learn implicit surface reconstruction by sign-agnostic optimization of convolutional occupancy networks. We show this goal can be effectively achieved by a simple yet effective design.
  arXiv  Detail & Related papers  (2021-05-08T03:35:32Z)
• Local Propagation in Constraint-based Neural Network [77.37829055999238]
  We study a constraint-based representation of neural network architectures. We investigate a simple optimization procedure that is well suited to fulfil the so-called architectural constraints.
  arXiv  Detail & Related papers  (2020-02-18T16:47:38Z)

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.