Improving 3D Reconstruction Through RGB-D Sensor Noise Modeling
High-resolution RGB-D sensors are widely used in computer vision, manufacturing, and robotics. The depth maps from these sensors have inherently high measurement uncertainty that includes both systematic and non-systematic noise. These noisy depth estimates degrade the quality of scans, resulting in...
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| Language: | English |
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MDPI AG
2025-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/3/950 |
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| author | Fahira Afzal Maken Sundaram Muthu Chuong Nguyen Changming Sun Jinguang Tong Shan Wang Russell Tsuchida David Howard Simon Dunstall Lars Petersson |
| author_facet | Fahira Afzal Maken Sundaram Muthu Chuong Nguyen Changming Sun Jinguang Tong Shan Wang Russell Tsuchida David Howard Simon Dunstall Lars Petersson |
| author_sort | Fahira Afzal Maken |
| collection | DOAJ |
| description | High-resolution RGB-D sensors are widely used in computer vision, manufacturing, and robotics. The depth maps from these sensors have inherently high measurement uncertainty that includes both systematic and non-systematic noise. These noisy depth estimates degrade the quality of scans, resulting in less accurate 3D reconstruction, making them unsuitable for some high-precision applications. In this paper, we focus on quantifying the uncertainty in the depth maps of high-resolution RGB-D sensors for the purpose of improving 3D reconstruction accuracy. To this end, we estimate the noise model for a recent high-precision RGB-D structured light sensor called Zivid when mounted on a robot arm. Our proposed noise model takes into account the measurement distance and angle between the sensor and the measured surface. We additionally analyze the effect of background light, exposure time, and the number of captures on the quality of the depth maps obtained. Our noise model seamlessly integrates with well-known classical and modern neural rendering-based algorithms, from KinectFusion to Point-SLAM methods using bilinear interpolation as well as 3D analytical functions. We collect a high-resolution RGB-D dataset and apply our noise model to improve tracking and produce higher-resolution 3D models. |
| format | Article |
| id | doaj-art-720dbe35c6324937aeb8dbc3935c18c7 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-720dbe35c6324937aeb8dbc3935c18c72025-08-20T02:48:05ZengMDPI AGSensors1424-82202025-02-0125395010.3390/s25030950Improving 3D Reconstruction Through RGB-D Sensor Noise ModelingFahira Afzal Maken0Sundaram Muthu1Chuong Nguyen2Changming Sun3Jinguang Tong4Shan Wang5Russell Tsuchida6David Howard7Simon Dunstall8Lars Petersson9Data61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, AustraliaData61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, AustraliaData61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, AustraliaData61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, AustraliaData61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, AustraliaData61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, AustraliaData61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, AustraliaData61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, AustraliaData61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, AustraliaData61, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Canberra, ACT 2601, AustraliaHigh-resolution RGB-D sensors are widely used in computer vision, manufacturing, and robotics. The depth maps from these sensors have inherently high measurement uncertainty that includes both systematic and non-systematic noise. These noisy depth estimates degrade the quality of scans, resulting in less accurate 3D reconstruction, making them unsuitable for some high-precision applications. In this paper, we focus on quantifying the uncertainty in the depth maps of high-resolution RGB-D sensors for the purpose of improving 3D reconstruction accuracy. To this end, we estimate the noise model for a recent high-precision RGB-D structured light sensor called Zivid when mounted on a robot arm. Our proposed noise model takes into account the measurement distance and angle between the sensor and the measured surface. We additionally analyze the effect of background light, exposure time, and the number of captures on the quality of the depth maps obtained. Our noise model seamlessly integrates with well-known classical and modern neural rendering-based algorithms, from KinectFusion to Point-SLAM methods using bilinear interpolation as well as 3D analytical functions. We collect a high-resolution RGB-D dataset and apply our noise model to improve tracking and produce higher-resolution 3D models.https://www.mdpi.com/1424-8220/25/3/950sensor noise modeling3D reconstructionRGB-D fusion |
| spellingShingle | Fahira Afzal Maken Sundaram Muthu Chuong Nguyen Changming Sun Jinguang Tong Shan Wang Russell Tsuchida David Howard Simon Dunstall Lars Petersson Improving 3D Reconstruction Through RGB-D Sensor Noise Modeling Sensors sensor noise modeling 3D reconstruction RGB-D fusion |
| title | Improving 3D Reconstruction Through RGB-D Sensor Noise Modeling |
| title_full | Improving 3D Reconstruction Through RGB-D Sensor Noise Modeling |
| title_fullStr | Improving 3D Reconstruction Through RGB-D Sensor Noise Modeling |
| title_full_unstemmed | Improving 3D Reconstruction Through RGB-D Sensor Noise Modeling |
| title_short | Improving 3D Reconstruction Through RGB-D Sensor Noise Modeling |
| title_sort | improving 3d reconstruction through rgb d sensor noise modeling |
| topic | sensor noise modeling 3D reconstruction RGB-D fusion |
| url | https://www.mdpi.com/1424-8220/25/3/950 |
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