3D Fusion of Infrared Images with Dense RGB Reconstruction from Multiple
Views -- with Application to Fire-fighting Robots
- URL: http://arxiv.org/abs/2007.14606v1
- Date: Wed, 29 Jul 2020 05:19:34 GMT
- Title: 3D Fusion of Infrared Images with Dense RGB Reconstruction from Multiple
Views -- with Application to Fire-fighting Robots
- Authors: Yuncong Chen and Will Warren
- Abstract summary: This project integrates infrared and RGB imagery to produce dense 3D environment models reconstructed from multiple views.
The resulting 3D map contains both thermal and RGB information which can be used in robotic fire-fighting applications to identify victims and active fire areas.
- Score: 1.9420928933791046
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: This project integrates infrared and RGB imagery to produce dense 3D
environment models reconstructed from multiple views. The resulting 3D map
contains both thermal and RGB information which can be used in robotic
fire-fighting applications to identify victims and active fire areas.
Related papers
- RaySt3R: Predicting Novel Depth Maps for Zero-Shot Object Completion [49.933001840775816]
RaySt3R recasts 3D shape completion as a novel view synthesis problem.<n>We train a feedforward transformer to predict depth maps, object masks, and per-pixel confidence scores for query rays.<n>RaySt3R fuses these predictions across multiple query views to reconstruct complete 3D shapes.
arXiv Detail & Related papers (2025-06-05T17:43:23Z) - MinD-3D: Reconstruct High-quality 3D objects in Human Brain [50.534007259536715]
Recon3DMind is an innovative task aimed at reconstructing 3D visuals from Functional Magnetic Resonance Imaging (fMRI) signals.
We present the fMRI-Shape dataset, which includes data from 14 participants and features 360-degree videos of 3D objects.
We propose MinD-3D, a novel and effective three-stage framework specifically designed to decode the brain's 3D visual information from fMRI signals.
arXiv Detail & Related papers (2023-12-12T18:21:36Z) - Anything-3D: Towards Single-view Anything Reconstruction in the Wild [61.090129285205805]
We introduce Anything-3D, a methodical framework that ingeniously combines a series of visual-language models and the Segment-Anything object segmentation model.
Our approach employs a BLIP model to generate textural descriptions, utilize the Segment-Anything model for the effective extraction of objects of interest, and leverages a text-to-image diffusion model to lift object into a neural radiance field.
arXiv Detail & Related papers (2023-04-19T16:39:51Z) - MobileBrick: Building LEGO for 3D Reconstruction on Mobile Devices [78.20154723650333]
High-quality 3D ground-truth shapes are critical for 3D object reconstruction evaluation.
We introduce a novel multi-view RGBD dataset captured using a mobile device.
We obtain precise 3D ground-truth shape without relying on high-end 3D scanners.
arXiv Detail & Related papers (2023-03-03T14:02:50Z) - BS3D: Building-scale 3D Reconstruction from RGB-D Images [25.604775584883413]
We propose an easy-to-use framework for acquiring building-scale 3D reconstruction using a consumer depth camera.
Unlike complex and expensive acquisition setups, our system enables crowd-sourcing, which can greatly benefit data-hungry algorithms.
arXiv Detail & Related papers (2023-01-03T11:46:14Z) - Online Localisation and Colored Mesh Reconstruction Architecture for 3D
Visual Feedback in Robotic Exploration Missions [2.8213955186000512]
This paper introduces an Online Localisation and Colored Mesh Reconstruction (OLCMR) ROS perception architecture for ground exploration robots.
It is intended to be used by a remote human operator to easily visualise the mapped environment during or after the mission.
arXiv Detail & Related papers (2022-07-21T14:09:43Z) - Beyond Visual Field of View: Perceiving 3D Environment with Echoes and
Vision [51.385731364529306]
This paper focuses on perceiving and navigating 3D environments using echoes and RGB image.
In particular, we perform depth estimation by fusing RGB image with echoes, received from multiple orientations.
We show that the echoes provide holistic and in-expensive information about the 3D structures complementing the RGB image.
arXiv Detail & Related papers (2022-07-03T22:31:47Z) - InfraredTags: Embedding Invisible AR Markers and Barcodes Using
Low-Cost, Infrared-Based 3D Printing and Imaging Tools [0.0]
We present InfraredTags, which are 2D markers and barcodes imperceptible to the naked eye that can be 3D printed as part of objects.
We achieve this by printing objects from an infrared-transmitting filament, which infrared cameras can see through.
We built a user interface that facilitates the integration of common tags with the object geometry to make them 3D printable as InfraredTags.
arXiv Detail & Related papers (2022-02-12T23:45:18Z) - Urban Radiance Fields [77.43604458481637]
We perform 3D reconstruction and novel view synthesis from data captured by scanning platforms commonly deployed for world mapping in urban outdoor environments.
Our approach extends Neural Radiance Fields, which has been demonstrated to synthesize realistic novel images for small scenes in controlled settings.
Each of these three extensions provides significant performance improvements in experiments on Street View data.
arXiv Detail & Related papers (2021-11-29T15:58:16Z) - Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD
Images [69.5662419067878]
Grounding referring expressions in RGBD image has been an emerging field.
We present a novel task of 3D visual grounding in single-view RGBD image where the referred objects are often only partially scanned due to occlusion.
Our approach first fuses the language and the visual features at the bottom level to generate a heatmap that localizes the relevant regions in the RGBD image.
Then our approach conducts an adaptive feature learning based on the heatmap and performs the object-level matching with another visio-linguistic fusion to finally ground the referred object.
arXiv Detail & Related papers (2021-03-14T11:18:50Z) - SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scans [34.397726189729994]
SPSG is a novel approach to generate high-quality, colored 3D models of scenes from RGB-D scan observations.
Our self-supervised approach learns to jointly inpaint geometry and color by correlating an incomplete RGB-D scan with a more complete version of that scan.
arXiv Detail & Related papers (2020-06-25T18:58:23Z)
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.