Omnidirectional-Sensor-System-Based Texture Noise Correction in Large-Scale 3D Reconstruction
The evolution of cameras and LiDAR has propelled the techniques and applications of three-dimensional (3D) reconstruction. However, due to inherent sensor limitations and environmental interference, the reconstruction process often entails significant texture noise, such as specular highlight, color...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-12-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/24/1/78 |
| _version_ | 1797358196403208192 |
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| author | Wenya Xie Xiaoping Hong |
| author_facet | Wenya Xie Xiaoping Hong |
| author_sort | Wenya Xie |
| collection | DOAJ |
| description | The evolution of cameras and LiDAR has propelled the techniques and applications of three-dimensional (3D) reconstruction. However, due to inherent sensor limitations and environmental interference, the reconstruction process often entails significant texture noise, such as specular highlight, color inconsistency, and object occlusion. Traditional methodologies grapple to mitigate such noise, particularly in large-scale scenes, due to the voluminous data produced by imaging sensors. In response, this paper introduces an omnidirectional-sensor-system-based texture noise correction framework for large-scale scenes, which consists of three parts. Initially, we obtain a colored point cloud with luminance value through LiDAR points and RGB images organization. Next, we apply a voxel hashing algorithm during the geometry reconstruction to accelerate the computation speed and save the computer memory. Finally, we propose the key innovation of our paper, the frame-voting rendering and the neighbor-aided rendering mechanisms, which effectively eliminates the aforementioned texture noise. From the experimental results, the processing rate of one million points per second shows its real-time applicability, and the output figures of texture optimization exhibit a significant reduction in texture noise. These results indicate that our framework has advanced performance in correcting multiple texture noise in large-scale 3D reconstruction. |
| first_indexed | 2024-03-08T14:57:21Z |
| format | Article |
| id | doaj.art-62d03bf86da54ebb830c3db126005196 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-08T14:57:21Z |
| publishDate | 2023-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-62d03bf86da54ebb830c3db1260051962024-01-10T15:08:29ZengMDPI AGSensors1424-82202023-12-012417810.3390/s24010078Omnidirectional-Sensor-System-Based Texture Noise Correction in Large-Scale 3D ReconstructionWenya Xie0Xiaoping Hong1The School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, ChinaThe School of System Design and Intelligent Manufacturing, Southern University of Science and Technology, Shenzhen 518055, ChinaThe evolution of cameras and LiDAR has propelled the techniques and applications of three-dimensional (3D) reconstruction. However, due to inherent sensor limitations and environmental interference, the reconstruction process often entails significant texture noise, such as specular highlight, color inconsistency, and object occlusion. Traditional methodologies grapple to mitigate such noise, particularly in large-scale scenes, due to the voluminous data produced by imaging sensors. In response, this paper introduces an omnidirectional-sensor-system-based texture noise correction framework for large-scale scenes, which consists of three parts. Initially, we obtain a colored point cloud with luminance value through LiDAR points and RGB images organization. Next, we apply a voxel hashing algorithm during the geometry reconstruction to accelerate the computation speed and save the computer memory. Finally, we propose the key innovation of our paper, the frame-voting rendering and the neighbor-aided rendering mechanisms, which effectively eliminates the aforementioned texture noise. From the experimental results, the processing rate of one million points per second shows its real-time applicability, and the output figures of texture optimization exhibit a significant reduction in texture noise. These results indicate that our framework has advanced performance in correcting multiple texture noise in large-scale 3D reconstruction.https://www.mdpi.com/1424-8220/24/1/78imaging sensortexture noise correctionframe fusionvoxel hashing3D reconstruction |
| spellingShingle | Wenya Xie Xiaoping Hong Omnidirectional-Sensor-System-Based Texture Noise Correction in Large-Scale 3D Reconstruction Sensors imaging sensor texture noise correction frame fusion voxel hashing 3D reconstruction |
| title | Omnidirectional-Sensor-System-Based Texture Noise Correction in Large-Scale 3D Reconstruction |
| title_full | Omnidirectional-Sensor-System-Based Texture Noise Correction in Large-Scale 3D Reconstruction |
| title_fullStr | Omnidirectional-Sensor-System-Based Texture Noise Correction in Large-Scale 3D Reconstruction |
| title_full_unstemmed | Omnidirectional-Sensor-System-Based Texture Noise Correction in Large-Scale 3D Reconstruction |
| title_short | Omnidirectional-Sensor-System-Based Texture Noise Correction in Large-Scale 3D Reconstruction |
| title_sort | omnidirectional sensor system based texture noise correction in large scale 3d reconstruction |
| topic | imaging sensor texture noise correction frame fusion voxel hashing 3D reconstruction |
| url | https://www.mdpi.com/1424-8220/24/1/78 |
| work_keys_str_mv | AT wenyaxie omnidirectionalsensorsystembasedtexturenoisecorrectioninlargescale3dreconstruction AT xiaopinghong omnidirectionalsensorsystembasedtexturenoisecorrectioninlargescale3dreconstruction |