Method For The Generation Of Depth Images For View-based Shape Retrieval Of 3D CAD Model From Partial Point Cloud

Abstract

A laser scanner can easily acquire the geometric data of physical environments in the form of a point cloud. Recognizing objects from a point cloud is often required for industrial 3D reconstruction, which should include not only geometry information but also semantic information. However, recognition process is often a bottleneck in 3D reconstruction because it requires expertise on domain knowledge and intensive labor. To address this problem, various methods have been developed to recognize objects by retrieving the corresponding model in the database from an input geometry query. In recent years, the technique of converting geometric data into an image and applying view-based 3D shape retrieval has demonstrated high accuracy. Depth image which encodes depth value as intensity of pixel is frequently used for view-based 3D shape retrieval. However, geometric data collected from objects is often incomplete due to the occlusions and the limit of line of sight. Image generated by occlud

Stats

• citations17
• S2 citations—
• github stars0
• HF likes0
• heat score9.41
• arxiv keykim2020method

Related papers

• U-RED: Unsupervised 3D Shape Retrieval And Deformation For Partial Point Clouds (2023)8.09
• Weakly-supervised End-to-end CAD Retrieval To Scan Objects (2022)0.00
• Mask2cad: 3D Shape Prediction By Learning To Segment And Retrieve (2020)12.87
• Geometric Deep Learning For Computer-aided Design: A Survey (2024)8.60
• Generalizing Single-view 3D Shape Retrieval To Occlusions And Unseen Objects (2023)5.24
• Fastcad: Real-time CAD Retrieval And Alignment From Scans And Videos (2024)6.34
• Sketchcleannet -- A Deep Learning Approach To The Enhancement And Correction Of Query Sketches For A 3D CAD Model Retrieval System (2022)9.03
• Purely Geometric Scene Association And Retrieval - A Case For Macro Scale 3D Geometry (2018)0.00