Knowledge-Driven Dimension Estimation from a Single Image -3D Asset Generation Technology for Digital Twin Construction
- URL: http://arxiv.org/abs/2606.30896v1
- Date: Mon, 29 Jun 2026 20:35:54 GMT
- Title: Knowledge-Driven Dimension Estimation from a Single Image -3D Asset Generation Technology for Digital Twin Construction
- Abstract summary: This paper proposes a method for estimating the scale of an object by decomposing it into multiple structural elements.<n>It generates a 3D asset of the object by reconstructing the estimated components.<n>This method makes it possible to place 3D assets with a scale approximating the real environment within a digital twin space.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: In the verification of in-vehicle cameras, simulation technology using virtual spaces has advanced, enabling pre-evaluation of false detections and missed detections in various scenarios. However, discrepancies in the scale of the object being verified between the virtual and real environments can lead to a decrease in camera recognition performance. For traffic signs installed at high altitudes, distance measurement using LiDAR or stereo cameras is difficult, requiring size estimation from monocular images. This paper proposes a method for estimating the scale of an object by decomposing it into multiple structural elements and integrating external knowledge regarding design rules, geometric relationships, and conventional dimensions. Specifically, this method detects each component from a monocular image and estimates the size of each component by considering its structural relationships and dimensional consistency with surrounding elements. Furthermore, it generates a 3D asset of the object by reconstructing the estimated components. This method makes it possible to place 3D assets with a scale approximating the real environment within a digital twin space and is expected to contribute to improving the verification accuracy of in-vehicle cameras for autonomous driving in virtual environments.
Related papers
- Geometry-Grounded Unified 3D Perception for Autonomous Driving [36.11730832045922]
Camera-based autonomous driving perception requires a shared representation that preserves metric 3D structure across synchronized multi-camera streams.<n>We present a Geometry-grounded Unified 3D Perception framework that adapts the reconstruction-oriented latent of VGGT to calibrated, streaming driving scenes.
arXiv Detail & Related papers (2026-08-13T12:17:50Z) - Asset Harvester: Extracting 3D Assets from Autonomous Driving Logs for Simulation [63.01022057888141]
Asset Harvester is an image-to-3D model and end-to-end pipeline that converts sparse, in-thewild object observations from real driving logs into complete, simulation-ready assets.<n>SparseViewDiT is explicitly designed to address limited-angle views and other real-world data challenges.
arXiv Detail & Related papers (2026-04-20T16:20:57Z) - Pointing-Based Object Recognition [4.378919037619241]
This paper presents a comprehensive pipeline for recognizing objects targeted by human pointing gestures using RGB images.<n>Our proposed system integrates several existing state-of-the-art methods, including object detection, body pose estimation, monocular depth estimation, and vision-language models.
arXiv Detail & Related papers (2026-03-16T15:16:53Z) - Towards Scalable Spatial Intelligence via 2D-to-3D Data Lifting [64.64738535860351]
We present a scalable pipeline that converts single-view images into comprehensive, scale- and appearance-realistic 3D representations.<n>Our method bridges the gap between the vast repository of imagery and the increasing demand for spatial scene understanding.<n>By automatically generating authentic, scale-aware 3D data from images, we significantly reduce data collection costs and open new avenues for advancing spatial intelligence.
arXiv Detail & Related papers (2025-07-24T14:53:26Z) - Enhancing 3D Object Detection in Autonomous Vehicles Based on Synthetic Virtual Environment Analysis [6.109222614042277]
This study examines an AI model's ability to deduce 3D bounding boxes in the context of real-time analysis.<n>The outcomes of this work can help achieve competitive results under most variations of the tested conditions.
arXiv Detail & Related papers (2024-12-10T13:46:44Z) - Towards Flexible 3D Perception: Object-Centric Occupancy Completion Augments 3D Object Detection [54.78470057491049]
Occupancy has emerged as a promising alternative for 3D scene perception.<n>We introduce object-centric occupancy as a supplement to object bboxes.<n>We show that our occupancy features significantly enhance the detection results of state-of-the-art 3D object detectors.
arXiv Detail & Related papers (2024-12-06T16:12:38Z) - VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection [80.62052650370416]
monocular 3D object detection holds significant importance across various applications, including autonomous driving and robotics.
In this paper, we present VFMM3D, an innovative framework that leverages the capabilities of Vision Foundation Models (VFMs) to accurately transform single-view images into LiDAR point cloud representations.
arXiv Detail & Related papers (2024-04-15T03:12:12Z) - Multi-Modal Dataset Acquisition for Photometrically Challenging Object [56.30027922063559]
This paper addresses the limitations of current datasets for 3D vision tasks in terms of accuracy, size, realism, and suitable imaging modalities for photometrically challenging objects.
We propose a novel annotation and acquisition pipeline that enhances existing 3D perception and 6D object pose datasets.
arXiv Detail & Related papers (2023-08-21T10:38:32Z) - Object Detection in the Context of Mobile Augmented Reality [16.49070406578342]
We propose a novel approach that combines the geometric information from VIO with semantic information from object detectors to improve the performance of object detection on mobile devices.
Our approach includes three components: (1) an image orientation correction method, (2) a scale-based filtering approach, and (3) an online semantic map.
The results show that our approach can improve on the accuracy of generic object detectors by 12% on our dataset.
arXiv Detail & Related papers (2020-08-15T05:15:00Z) - Single View Metrology in the Wild [94.7005246862618]
We present a novel approach to single view metrology that can recover the absolute scale of a scene represented by 3D heights of objects or camera height above the ground.
Our method relies on data-driven priors learned by a deep network specifically designed to imbibe weakly supervised constraints from the interplay of the unknown camera with 3D entities such as object heights.
We demonstrate state-of-the-art qualitative and quantitative results on several datasets as well as applications including virtual object insertion.
arXiv Detail & Related papers (2020-07-18T22:31:33Z) - 3D Scene Geometry-Aware Constraint for Camera Localization with Deep
Learning [11.599633757222406]
Recently end-to-end approaches based on convolutional neural network have been much studied to achieve or even exceed 3D-geometry based traditional methods.
In this work, we propose a compact network for absolute camera pose regression.
Inspired from those traditional methods, a 3D scene geometry-aware constraint is also introduced by exploiting all available information including motion, depth and image contents.
arXiv Detail & Related papers (2020-05-13T04:15:14Z)
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.