SAVMap: Structure-Aided Visual Mapping of Large-Scale 2.5D Manhattan Wireframes from Panoramic Video

• URL: http://arxiv.org/abs/2606.01939v1
• Date: Mon, 01 Jun 2026 09:04:29 GMT
• Title: SAVMap: Structure-Aided Visual Mapping of Large-Scale 2.5D Manhattan Wireframes from Panoramic Video
• Abstract summary: We propose SAVMap, a method for generating a semantic wireframe map of warehouse shelf and light structures.<n>Sequences of rectified images with shelf and ceiling-facing views are extracted from a panoramic video captured along the warehouse aisles.<n>We create wireframe maps for over 5000 shelf elements across the rows, achieving an aggregate mean absolute error of 4.8,cm with respect to ground-truth.
• Score: 6.4930367702378655
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Precise 3D representations of industrial environments enable tasks such as robot localization and digital twin generation. We propose SAVMap, a method for generating a semantic wireframe map of warehouse shelf and light structures using only a panoramic video camera as the sensor input. Sequences of rectified images with shelf and ceiling-facing views are extracted from a panoramic video captured along the warehouse aisles. Using a semantic segmentation network front end, a set of sparse, semantic structure feature points (e.g., corners of shelf structures, centers of lights) are extracted from each image and tracked across the sequences. By accounting for real-world geometric relationships among the points such as Manhattan grids, a constrained structure-from-motion algorithm yields the 3D points that form a wireframe map. We demonstrate the scalability and accuracy of our proposal in a warehouse with 46 shelving rows, each with faces spanning 55\,m by 7\,m. From an hour of panoramic video content, we create wireframe maps for over 5000 shelf elements across the rows, achieving an aggregate mean absolute error of 4.8\,cm with respect to ground-truth.

Related papers

• COMPASS: COmpact Multi-channel Prior-map And Scene Signature for Floor-Plan-Based Visual Localization [0.15635627702544694]
  We present an algorithm that exploits both geometric and semantic priors from floor plans to estimate the pose of a robot equipped with dual fisheye cameras.<n>Inspired by LiDAR-based place recognition, we design a multi-channel radial descriptor that encodes the geometric layout surrounding a position.<n>From the floor plan, rays are cast in 360 azimuth bins and the results are encoded into five channels: normalized range, structural hit type (wall, window, or opening), range gradient, inverse range, and local range variance.<n>As a first step toward full cross-modal matching, we present a window detection algorithm for fisheye
  arXiv  Detail & Related papers  (2026-04-28T08:59:44Z)
• SceneScribe-1M: A Large-Scale Video Dataset with Comprehensive Geometric and Semantic Annotations [88.8747004592363]
  SceneScribe-1M is a new large-scale, multi-temporal video dataset.<n>It comprises one million in-the-wild videos, each meticulously annotated with detailed textual descriptions, precise parameters, dense depth maps, and consistent 3D point tracks.<n>We demonstrate the versatility and value of SceneScribe-1M by establishing benchmarks across a wide array of downstream tasks, including monocular depth estimation, scene reconstruction dynamic point tracking, as well as generative tasks such as text-to-video synthesis, with or without camera control.
  arXiv  Detail & Related papers  (2026-04-09T08:59:33Z)
• S3KF: Spherical State-Space Kalman Filtering for Panoramic 3D Multi-Object Tracking [24.79542959759503]
  Panoramic multi-object tracking is important for industrial safety monitoring, wide-area robotic perception, and infrastructure-light deployment in large workspaces.<n>Existing image-plane trackers are tightly coupled to the camera projection and become unreliable in panoramic imagery.<n>We present a panoramic 3D multi-object tracking framework built on a motorized rotating LiDAR and a quad-fisheye camera rig.
  arXiv  Detail & Related papers  (2026-03-29T06:01:29Z)
• Raster2Seq: Polygon Sequence Generation for Floorplan Reconstruction [21.366278792227785]
  We propose Raster2Seq, framing floorplan reconstruction as a sequence-to-sequence task.<n>An autoregressive decoder learns to predict the next corner conditioned on image features and previously generated corners.<n>Our method achieves state-of-the-art performance on standard benchmarks.
  arXiv  Detail & Related papers  (2026-02-09T18:58:46Z)
• MV-SAM: Multi-view Promptable Segmentation using Pointmap Guidance [79.57732829495843]
  We introduce MV-SAM, a framework for multi-view segmentation that achieves 3D consistency using pointmaps.<n>MV-SAM lifts images and prompts into 3D space, eliminating the need for explicit 3D networks or annotated 3D data.
  arXiv  Detail & Related papers  (2026-01-25T15:00:37Z)
• St4RTrack: Simultaneous 4D Reconstruction and Tracking in the World [106.91539872943864]
  St4RTrack is a framework that simultaneously reconstructs and tracks dynamic video content in a world coordinate frame from RGB inputs.<n>We predict both pointmaps at the same moment, in the same world, capturing both static and dynamic scene geometry.<n>We establish a new extensive benchmark for world-frame reconstruction and tracking, demonstrating the effectiveness and efficiency of our unified, data-driven framework.
  arXiv  Detail & Related papers  (2025-04-17T17:55:58Z)
• View-Consistent Hierarchical 3D Segmentation Using Ultrametric Feature Fields [52.08335264414515]
  We learn a novel feature field within a Neural Radiance Field (NeRF) representing a 3D scene. Our method takes view-inconsistent multi-granularity 2D segmentations as input and produces a hierarchy of 3D-consistent segmentations as output. We evaluate our method and several baselines on synthetic datasets with multi-view images and multi-granular segmentation, showcasing improved accuracy and viewpoint-consistency.
  arXiv  Detail & Related papers  (2024-05-30T04:14:58Z)
• Neural Implicit Dense Semantic SLAM [83.04331351572277]
  We propose a novel RGBD vSLAM algorithm that learns a memory-efficient, dense 3D geometry, and semantic segmentation of an indoor scene in an online manner. Our pipeline combines classical 3D vision-based tracking and loop closing with neural fields-based mapping. Our proposed algorithm can greatly enhance scene perception and assist with a range of robot control problems.
  arXiv  Detail & Related papers  (2023-04-27T23:03:52Z)
• Scan2Part: Fine-grained and Hierarchical Part-level Understanding of Real-World 3D Scans [68.98085986594411]
  We propose Scan2Part, a method to segment individual parts of objects in real-world, noisy indoor RGB-D scans. We use a sparse U-Net-based architecture that captures the fine-scale detail of the underlying 3D scan geometry. As output, we are able to predict fine-grained per-object part labels, even when the geometry is coarse or partially missing.
  arXiv  Detail & Related papers  (2022-06-06T05:43:10Z)
• Monocular Road Planar Parallax Estimation [25.36368935789501]
  Estimating the 3D structure of the drivable surface and surrounding environment is a crucial task for assisted and autonomous driving. We propose Road Planar Parallax Attention Network (RPANet), a new deep neural network for 3D sensing from monocular image sequences. RPANet takes a pair of images aligned by the homography of the road plane as input and outputs a $gamma$ map for 3D reconstruction.
  arXiv  Detail & Related papers  (2021-11-22T10:03:41Z)
• Learning to Parse Wireframes in Images of Man-Made Environments [40.471924713855735]
  We propose a "wireframe"-based approach to the task of representation for manmade environments. We have built networks that are suitable for extracting two large lines and their junctions.
  arXiv  Detail & Related papers  (2020-07-15T07:54:18Z)
• 3D Crowd Counting via Geometric Attention-guided Multi-View Fusion [50.520192402702015]
  We propose to solve the multi-view crowd counting task through 3D feature fusion with 3D scene-level density maps. Compared to 2D fusion, the 3D fusion extracts more information of the people along the z-dimension (height), which helps to address the scale variations across multiple views. The 3D density maps still preserve the 2D density maps property that the sum is the count, while also providing 3D information about the crowd density.
  arXiv  Detail & Related papers  (2020-03-18T11:35:11Z)

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.