NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising

• URL: http://arxiv.org/abs/2403.20034v1
• Date: Fri, 29 Mar 2024 07:59:37 GMT
• Title: NeSLAM: Neural Implicit Mapping and Self-Supervised Feature Tracking With Depth Completion and Denoising
• Authors: Tianchen Deng, Yanbo Wang, Hongle Xie, Hesheng Wang, Jingchuan Wang, Danwei Wang, Weidong Chen,
• Abstract summary: We present NeSLAM, a framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views. Experiments on various indoor datasets demonstrate the effectiveness and accuracy of the system in reconstruction, tracking quality, and novel view synthesis.
• Score: 23.876281686625134
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: In recent years, there have been significant advancements in 3D reconstruction and dense RGB-D SLAM systems. One notable development is the application of Neural Radiance Fields (NeRF) in these systems, which utilizes implicit neural representation to encode 3D scenes. This extension of NeRF to SLAM has shown promising results. However, the depth images obtained from consumer-grade RGB-D sensors are often sparse and noisy, which poses significant challenges for 3D reconstruction and affects the accuracy of the representation of the scene geometry. Moreover, the original hierarchical feature grid with occupancy value is inaccurate for scene geometry representation. Furthermore, the existing methods select random pixels for camera tracking, which leads to inaccurate localization and is not robust in real-world indoor environments. To this end, we present NeSLAM, an advanced framework that achieves accurate and dense depth estimation, robust camera tracking, and realistic synthesis of novel views. First, a depth completion and denoising network is designed to provide dense geometry prior and guide the neural implicit representation optimization. Second, the occupancy scene representation is replaced with Signed Distance Field (SDF) hierarchical scene representation for high-quality reconstruction and view synthesis. Furthermore, we also propose a NeRF-based self-supervised feature tracking algorithm for robust real-time tracking. Experiments on various indoor datasets demonstrate the effectiveness and accuracy of the system in reconstruction, tracking quality, and novel view synthesis.

Related papers

• SG-NeRF: Neural Surface Reconstruction with Scene Graph Optimization [16.460851701725392]
  We present a novel approach that optimize radiance fields with scene graphs to mitigate the influence of outlier poses. Our method incorporates an adaptive inlier-outlier confidence estimation scheme based on scene graphs. We also introduce an effective intersection-over-union (IoU) loss to optimize the camera pose and surface geometry.
  arXiv  Detail & Related papers  (2024-07-17T15:50:17Z)
• MM3DGS SLAM: Multi-modal 3D Gaussian Splatting for SLAM Using Vision, Depth, and Inertial Measurements [59.70107451308687]
  We show for the first time that using 3D Gaussians for map representation with unposed camera images and inertial measurements can enable accurate SLAM. Our method, MM3DGS, addresses the limitations of prior rendering by enabling faster scale awareness, and improved trajectory tracking. We also release a multi-modal dataset, UT-MM, collected from a mobile robot equipped with a camera and an inertial measurement unit.
  arXiv  Detail & Related papers  (2024-04-01T04:57:41Z)
• NeRF-VO: Real-Time Sparse Visual Odometry with Neural Radiance Fields [13.178099653374945]
  NeRF-VO integrates learning-based sparse visual odometry for low-latency camera tracking and a neural radiance scene representation. We surpass SOTA methods in pose estimation accuracy, novel view fidelity, and dense reconstruction quality across a variety of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2023-12-20T22:42:17Z)
• 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)
• LiveNVS: Neural View Synthesis on Live RGB-D Streams [4.717325308876748]
  We present LiveNVS, a system that allows for neural novel view synthesis on a live RGB-D input stream. LiveNVS achieves state-of-the-art neural rendering quality of unknown scenes during capturing.
  arXiv  Detail & Related papers  (2023-11-28T10:29:39Z)
• Spatiotemporally Consistent HDR Indoor Lighting Estimation [66.26786775252592]
  We propose a physically-motivated deep learning framework to solve the indoor lighting estimation problem. Given a single LDR image with a depth map, our method predicts spatially consistent lighting at any given image position. Our framework achieves photorealistic lighting prediction with higher quality compared to state-of-the-art single-image or video-based methods.
  arXiv  Detail & Related papers  (2023-05-07T20:36:29Z)
• SurfelNeRF: Neural Surfel Radiance Fields for Online Photorealistic Reconstruction of Indoor Scenes [17.711755550841385]
  SLAM-based methods can reconstruct 3D scene geometry progressively in real time but can not render photorealistic results. NeRF-based methods produce promising novel view synthesis results, their long offline optimization time and lack of geometric constraints pose challenges to efficiently handling online input. We introduce SurfelNeRF, a variant of neural radiance field which employs a flexible and scalable neural surfel representation to store geometric attributes and extracted appearance features from input images.
  arXiv  Detail & Related papers  (2023-04-18T13:11:49Z)
• 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)
• Learning Neural Radiance Fields from Multi-View Geometry [1.1011268090482573]
  We present a framework, called MVG-NeRF, that combines Multi-View Geometry algorithms and Neural Radiance Fields (NeRF) for image-based 3D reconstruction. NeRF has revolutionized the field of implicit 3D representations, mainly due to a differentiable rendering formulation that enables high-quality and geometry-aware novel view synthesis.
  arXiv  Detail & Related papers  (2022-10-24T08:53:35Z)
• CLONeR: Camera-Lidar Fusion for Occupancy Grid-aided Neural Representations [77.90883737693325]
  This paper proposes CLONeR, which significantly improves upon NeRF by allowing it to model large outdoor driving scenes observed from sparse input sensor views. This is achieved by decoupling occupancy and color learning within the NeRF framework into separate Multi-Layer Perceptrons (MLPs) trained using LiDAR and camera data, respectively. In addition, this paper proposes a novel method to build differentiable 3D Occupancy Grid Maps (OGM) alongside the NeRF model, and leverage this occupancy grid for improved sampling of points along a ray for rendering in metric space.
  arXiv  Detail & Related papers  (2022-09-02T17:44:50Z)
• SCFusion: Real-time Incremental Scene Reconstruction with Semantic Completion [86.77318031029404]
  We propose a framework that performs scene reconstruction and semantic scene completion jointly in an incremental and real-time manner. Our framework relies on a novel neural architecture designed to process occupancy maps and leverages voxel states to accurately and efficiently fuse semantic completion with the 3D global model.
  arXiv  Detail & Related papers  (2020-10-26T15:31:52Z)

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.