A-Scan2BIM: Assistive Scan to Building Information Modeling
- URL: http://arxiv.org/abs/2311.18166v1
- Date: Thu, 30 Nov 2023 01:07:14 GMT
- Title: A-Scan2BIM: Assistive Scan to Building Information Modeling
- Authors: Weilian Song, Jieliang Luo, Dale Zhao, Yan Fu, Chin-Yi Cheng, Yasutaka
Furukawa
- Abstract summary: This paper proposes an assistive system for architects that converts a large-scale point cloud into a standardized digital representation of a building for Building Information Modeling applications.
We report our system's reconstruction quality with standard metrics, and we introduce a novel metric that measures how natural the order of reconstructed operations is.
- Score: 24.128574719493365
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: This paper proposes an assistive system for architects that converts a
large-scale point cloud into a standardized digital representation of a
building for Building Information Modeling (BIM) applications. The process is
known as Scan-to-BIM, which requires many hours of manual work even for a
single building floor by a professional architect. Given its challenging
nature, the paper focuses on helping architects on the Scan-to-BIM process,
instead of replacing them. Concretely, we propose an assistive Scan-to-BIM
system that takes the raw sensor data and edit history (including the current
BIM model), then auto-regressively predicts a sequence of model editing
operations as APIs of a professional BIM software (i.e., Autodesk Revit). The
paper also presents the first building-scale Scan2BIM dataset that contains a
sequence of model editing operations as the APIs of Autodesk Revit. The dataset
contains 89 hours of Scan2BIM modeling processes by professional architects
over 16 scenes, spanning over 35,000 m^2. We report our system's reconstruction
quality with standard metrics, and we introduce a novel metric that measures
how natural the order of reconstructed operations is. A simple modification to
the reconstruction module helps improve performance, and our method is far
superior to two other baselines in the order metric. We will release data,
code, and models at a-scan2bim.github.io.
Related papers
- Instruct-ReID++: Towards Universal Purpose Instruction-Guided Person Re-identification [62.894790379098005]
We propose a novel instruct-ReID task that requires the model to retrieve images according to the given image or language instructions.
Instruct-ReID is the first exploration of a general ReID setting, where existing 6 ReID tasks can be viewed as special cases by assigning different instructions.
We propose a novel baseline model, IRM, with an adaptive triplet loss to handle various retrieval tasks within a unified framework.
arXiv Detail & Related papers (2024-05-28T03:35:46Z) - FollowIR: Evaluating and Teaching Information Retrieval Models to Follow Instructions [71.5977045423177]
We study the use of instructions in Information Retrieval systems.
We introduce our dataset FollowIR, which contains a rigorous instruction evaluation benchmark.
We show that it is possible for IR models to learn to follow complex instructions.
arXiv Detail & Related papers (2024-03-22T14:42:29Z) - MTP: Advancing Remote Sensing Foundation Model via Multi-Task Pretraining [73.81862342673894]
Foundation models have reshaped the landscape of Remote Sensing (RS) by enhancing various image interpretation tasks.
transferring the pretrained models to downstream tasks may encounter task discrepancy due to their formulation of pretraining as image classification or object discrimination tasks.
We conduct multi-task supervised pretraining on the SAMRS dataset, encompassing semantic segmentation, instance segmentation, and rotated object detection.
Our models are finetuned on various RS downstream tasks, such as scene classification, horizontal and rotated object detection, semantic segmentation, and change detection.
arXiv Detail & Related papers (2024-03-20T09:17:22Z) - Machine Learning-Enabled Software and System Architecture Frameworks [48.87872564630711]
The stakeholders with data science and Machine Learning related concerns, such as data scientists and data engineers, are yet to be included in existing architecture frameworks.
We surveyed 61 subject matter experts from over 25 organizations in 10 countries.
arXiv Detail & Related papers (2023-08-09T21:54:34Z) - Automatic Reconstruction of Semantic 3D Models from 2D Floor Plans [1.8581514902689347]
We present a pipeline for reconstruction of vectorized 3D models from scanned 2D plans.
The method presented state-of-the-art results in the public dataset CubiCasa5k.
arXiv Detail & Related papers (2023-06-02T16:06:42Z) - Learning to Learn from APIs: Black-Box Data-Free Meta-Learning [95.41441357931397]
Data-free meta-learning (DFML) aims to enable efficient learning of new tasks by meta-learning from a collection of pre-trained models without access to the training data.
Existing DFML work can only meta-learn from (i) white-box and (ii) small-scale pre-trained models.
We propose a Bi-level Data-free Meta Knowledge Distillation (BiDf-MKD) framework to transfer more general meta knowledge from a collection of black-box APIs to one single model.
arXiv Detail & Related papers (2023-05-28T18:00:12Z) - Prompter: Utilizing Large Language Model Prompting for a Data Efficient
Embodied Instruction Following [4.532517021515834]
Embodied Instruction Following studies how autonomous mobile manipulation robots should be controlled to accomplish long-horizon tasks.
We show that embedding the physical constraints of the deployed robots into the module design is highly effective.
Our design also allows the same modular system to work across robots of different configurations with minimal modifications.
arXiv Detail & Related papers (2022-11-07T02:24:39Z) - Multi-Stage Progressive Image Restoration [167.6852235432918]
We propose a novel synergistic design that can optimally balance these competing goals.
Our main proposal is a multi-stage architecture, that progressively learns restoration functions for the degraded inputs.
The resulting tightly interlinked multi-stage architecture, named as MPRNet, delivers strong performance gains on ten datasets.
arXiv Detail & Related papers (2021-02-04T18:57:07Z) - Segmentation and Analysis of a Sketched Truss Frame Using Morphological
Image Processing Techniques [0.0]
Development of computational tools to analyze and assess the building capacities has had a major impact in civil engineering.
One of the difficulties and the most time consuming steps involved in the structural modeling is defining the geometry of the structure to provide the analysis.
This paper is dedicated to the development of a methodology to automate analysis of a hand sketched or computer generated truss frame drawn on a piece of paper.
arXiv Detail & Related papers (2020-09-28T08:50:18Z)
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.