SPVLoc: Semantic Panoramic Viewport Matching for 6D Camera Localization in Unseen Environments

• URL: http://arxiv.org/abs/2404.10527v2
• Date: Mon, 22 Jul 2024 11:26:57 GMT
• Title: SPVLoc: Semantic Panoramic Viewport Matching for 6D Camera Localization in Unseen Environments
• Authors: Niklas Gard, Anna Hilsmann, Peter Eisert,
• Abstract summary: SPVLoc is a global indoor localization method that accurately determines the 6D camera pose of a query image. Our approach employs a novel matching procedure to localize the perspective camera's viewport. It achieves superior localization accuracy compared to the state of the art methods and also estimates more degrees of freedom of the camera pose.
• Score: 4.2603120588176635
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present SPVLoc, a global indoor localization method that accurately determines the six-dimensional (6D) camera pose of a query image and requires minimal scene-specific prior knowledge and no scene-specific training. Our approach employs a novel matching procedure to localize the perspective camera's viewport, given as an RGB image, within a set of panoramic semantic layout representations of the indoor environment. The panoramas are rendered from an untextured 3D reference model, which only comprises approximate structural information about room shapes, along with door and window annotations. We demonstrate that a straightforward convolutional network structure can successfully achieve image-to-panorama and ultimately image-to-model matching. Through a viewport classification score, we rank reference panoramas and select the best match for the query image. Then, a 6D relative pose is estimated between the chosen panorama and query image. Our experiments demonstrate that this approach not only efficiently bridges the domain gap but also generalizes well to previously unseen scenes that are not part of the training data. Moreover, it achieves superior localization accuracy compared to the state of the art methods and also estimates more degrees of freedom of the camera pose. Our source code is publicly available at https://fraunhoferhhi.github.io/spvloc .

Related papers

• Global Localization: Utilizing Relative Spatio-Temporal Geometric Constraints from Adjacent and Distant Cameras [7.836516315882875]
  Re-localizing a camera from a single image in a previously mapped area is vital for many computer vision applications in robotics and augmented/virtual reality. We propose to leverage a novel network of relative spatial and temporal geometric constraints to guide the training of a Deep Network for localization. We show that our method, through these constraints, is capable of learning to localize when little or very sparse ground-truth 3D coordinates are available.
  arXiv  Detail & Related papers  (2023-12-01T11:03:07Z)
• Anyview: Generalizable Indoor 3D Object Detection with Variable Frames [63.51422844333147]
  We present a novel 3D detection framework named AnyView for our practical applications. Our method achieves both great generalizability and high detection accuracy with a simple and clean architecture.
  arXiv  Detail & Related papers  (2023-10-09T02:15:45Z)
• TopNet: Transformer-based Object Placement Network for Image Compositing [43.14411954867784]
  Local clues in background images are important to determine the compatibility of placing objects with certain locations/scales. We propose to learn the correlation between object features and all local background features with a transformer module. Our new formulation generates a 3D heatmap indicating the plausibility of all location/scale combinations in one network forward pass.
  arXiv  Detail & Related papers  (2023-04-06T20:58:49Z)
• MeshLoc: Mesh-Based Visual Localization [54.731309449883284]
  We explore a more flexible alternative based on dense 3D meshes that does not require features matching between database images to build the scene representation. Surprisingly competitive results can be obtained when extracting features on renderings of these meshes, without any neural rendering stage. Our results show that dense 3D model-based representations are a promising alternative to existing representations and point to interesting and challenging directions for future research.
  arXiv  Detail & Related papers  (2022-07-21T21:21:10Z)
• Semantic keypoint-based pose estimation from single RGB frames [64.80395521735463]
  We present an approach to estimating the continuous 6-DoF pose of an object from a single RGB image. The approach combines semantic keypoints predicted by a convolutional network (convnet) with a deformable shape model. We show that our approach can accurately recover the 6-DoF object pose for both instance- and class-based scenarios.
  arXiv  Detail & Related papers  (2022-04-12T15:03:51Z)
• DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based Optimization [66.25948693095604]
  We propose a novel method for panoramic 3D scene understanding which recovers the 3D room layout and the shape, pose, position, and semantic category for each object from a single full-view panorama image. Experiments demonstrate that our method outperforms existing methods on panoramic scene understanding in terms of both geometry accuracy and object arrangement.
  arXiv  Detail & Related papers  (2021-08-24T13:55:29Z)
• Layout-Guided Novel View Synthesis from a Single Indoor Panorama [41.627708450356614]
  We make the first attempt to generate novel views from a single indoor panorama. CNNs are used to extract the deep features and estimate the depth map from the source-view image. We also constrain the room layout of the generated target-view images to enforce geometric consistency.
  arXiv  Detail & Related papers  (2021-03-31T12:12:22Z)
• Shape and Viewpoint without Keypoints [63.26977130704171]
  We present a learning framework that learns to recover the 3D shape, pose and texture from a single image. We trained on an image collection without any ground truth 3D shape, multi-view, camera viewpoints or keypoint supervision. We obtain state-of-the-art camera prediction results and show that we can learn to predict diverse shapes and textures across objects.
  arXiv  Detail & Related papers  (2020-07-21T17:58:28Z)
• Perspective Plane Program Induction from a Single Image [85.28956922100305]
  We study the inverse graphics problem of inferring a holistic representation for natural images. We formulate this problem as jointly finding the camera pose and scene structure that best describe the input image. Our proposed framework, Perspective Plane Program Induction (P3I), combines search-based and gradient-based algorithms to efficiently solve the problem.
  arXiv  Detail & Related papers  (2020-06-25T21:18:58Z)

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.