Research Article
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Year 2025, Volume: 8 Issue: 2, 260 - 272

Cemil Zeyveli , Ali Furkan Kamanlı

https://doi.org/10.35377/saucis...1637290

Abstract

References

  • H. Durrant-Whyte, D. Rye, and E. Nebot, “Localization of Autonomous Guided Vehicles,” Robotics Research, pp. 613–625, 1996, doi: 10.1007/978-1-4471-1021-7_69.
  • H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: Part I,” IEEE Robotics and Automation Magazine, vol. 13, no. 2, pp. 99–108, Jun. 2006, doi: 10.1109/MRA.2006.1638022.
  • R. C. Smith and P. Cheeseman, “On the Representation and Estimation of Spatial Uncertainty,” The international journal of Robotics Research, vol. 5, no. 4, pp. 56–68, Dec. 1986, doi: 10.1177/027836498600500404.
  • H. Taheri and Z. C. Xia, “SLAM; definition and evolution,” Engineering Applications of Artificial Intelligence, vol. 97, p. 104032, Jan. 2021, doi: 10.1016/J.ENGAPPAI.2020.104032.
  • T. J. Chong, X. J. Tang, C. H. Leng, M. Yogeswaran, O. E. Ng, and Y. Z. Chong, “Sensor Technologies and Simultaneous Localization and Mapping (SLAM),” Procedia Computer Science, vol. 76, pp. 174–179, Jan. 2015, doi: 10.1016/J.PROCS.2015.12.336.
  • W. Chen et al., “An Overview on Visual SLAM: From Tradition to Semantic,” Remote Sensing 2022, Vol. 14, Page 3010, vol. 14, no. 13, p. 3010, Jun. 2022, doi: 10.3390/RS14133010.
  • A. R. Sahili et al., “A Survey of Visual SLAM Methods,” IEEE Access, vol. 11, pp. 139643–139677, 2023, doi: 10.1109/ACCESS.2023.3341489.
  • A. Macario Barros, M. Michel, Y. Moline, G. Corre, and F. Carrel, “A Comprehensive Survey of Visual SLAM Algorithms,” Robotics 2022, Vol. 11, Page 24, vol. 11, no. 1, p. 24, Feb. 2022, doi: 10.3390/ROBOTICS11010024.
  • E. Sandström, Y. Li, L. van Gool, M. R. Oswald, E. Zürich, and K. Leuven, “Point-SLAM: Dense Neural Point Cloud-based SLAM.” pp. 18433–18444, 2023.
  • N. Keetha et al., “SplaTAM: Splat Track & Map 3D Gaussians for Dense RGB-D SLAM.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 21357–21366, 2024.
  • C. Yan et al., “GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19595–19604, 2024.
  • B. Kerbl, G. Kopanas, T. Leimkuehler, and G. Drettakis, “3D Gaussian Splatting for Real-Time Radiance Field Rendering,” ACM Transactions on Graphics, vol. 42, no. 4, p. 14, Aug. 2023, doi: 10.1145/3592433.
  • R. A. Newcombe, S. J. Lovegrove, and A. J. Davison, “DTAM: Dense tracking and mapping in real-time,” Proceedings of the IEEE International Conference on Computer Vision, pp. 2320–2327, 2011, doi: 10.1109/ICCV.2011.6126513.
  • R. A. Newcombe et al., “KinectFusion: Real-time dense surface mapping and tracking,” 2011 10th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2011, pp. 127–136, 2011, doi: 10.1109/ISMAR.2011.6092378.
  • T. Whelan, R. F. Salas-Moreno, B. Glocker, A. J. Davison, and S. Leutenegger, “ElasticFusion: Real-time dense SLAM and light source estimation,” The International Journal of Robotics Research, vol. 35, no. 14, pp. 1697–1716, Sep. 2016, doi: 10.1177/0278364916669237.
  • T. Schops, T. Sattler, and M. Pollefeys, “BAD SLAM: Bundle Adjusted Direct RGB-D SLAM.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 134–144, 2019.
  • Z. Teed and J. Deng, “DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras,” Advances in Neural Information Processing Systems, vol. 20, pp. 16558–16569, Aug. 2021.
  • E. Sucar, S. Liu, J. Ortiz, and A. J. Davison, “iMAP: Implicit Mapping and Positioning in Real-Time,” Proceedings of the IEEE International Conference on Computer Vision, pp. 6209–6218, 2021, doi: 10.1109/ICCV48922.2021.00617.
  • Z. Zhu et al., “NICE-SLAM: Neural Implicit Scalable Encoding for SLAM,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2022-June, pp. 12776–12786, 2022, doi: 10.1109/CVPR52688.2022.01245.
  • X. Yang, H. Li, H. Zhai, Y. Ming, Y. Liu, and G. Zhang, “Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation,” Proceedings - 2022 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2022, pp. 499–507, Oct. 2022, doi: 10.1109/ISMAR55827.2022.00066.
  • H. Wang, J. Wang, and L. Agapito, “Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2023-June, pp. 13293–13302, Apr. 2023, doi: 10.1109/CVPR52729.2023.01277.
  • H. Matsuki, R. Murai, P. H. J. Kelly, and A. J. Davison, “Gaussian Splatting SLAM.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18039–18048, 2024.
  • C. Yan et al., “GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19595–19604, 2024.
  • J. Straub et al., “The Replica Dataset: A Digital Replica of Indoor Spaces,” Jun. 2019, Accessed: Jan. 24, 2025. [Online]. Available: https://arxiv.org/abs/1906.05797v1
  • A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Niessner, “ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5828–5839, 2017.
  • C. Yeshwanth, Y.-C. Liu, M. Nießner, and A. Dai, “ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes,” Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12–22, 2023.
  • J. Sturm, W. Burgard, and D. Cremers, “Evaluating Egomotion and Structure-from-Motion Approaches Using the TUM RGB-D Benchmark,” Proc. of the Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RJS International Conference on Intelligent Robot Systems (IROS), vol. 13, 2012.
  • Q. Yang, R. Yang, J. Davis, and D. Nistér, “Spatial-depth super resolution for range images,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007, doi: 10.1109/CVPR.2007.383211.
  • G. Deng and L. W. Cahill, “Adaptive Gaussian filter for noise reduction and edge detection,” IEEE Nuclear Science Symposium & Medical Imaging Conference, no. pt 3, pp. 1615–1619, 1994, doi: 10.1109/NSSMIC.1993.373563.
  • I. Pitas and A. N. Venetsanopoulos, “Nonlinear Mean Filters in Image Processing,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 34, no. 3, pp. 573–584, Jun. 1986, doi: 10.1109/TASSP.1986.1164857.
  • M. Kazubek, “Wavelet domain image denoising by thresholding and Wiener filtering,” IEEE Signal Processing Letters, vol. 10, no. 11, pp. 324–326, Nov. 2003, doi: 10.1109/LSP.2003.818225.
  • P. Jain and V. Tyagi, “A survey of edge-preserving image denoising methods,” Information Systems Frontiers, vol. 18, no. 1, pp. 159–170, Feb. 2016, doi: 10.1007/S10796-014-9527-0/TABLES/1.
  • T. S. Huang, G. J. Yang, and G. Y. Tang, “A Fast Two-Dimensional Median Filtering Algorithm,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 27, no. 1, pp. 13–18, 1979, doi: 10.1109/TASSP.1979.1163188.
  • A. Ravishankar, S. Anusha, H. K. Akshatha, A. Raj, S. Jahnavi, and J. Madhura, “A survey on noise reduction techniques in medical images,” Proceedings of the International Conference on Electronics, Communication and Aerospace Technology, ICECA 2017, vol. 2017-January, pp. 385–389, 2017, doi: 10.1109/ICECA.2017.8203711.
  • F. Artuğer and F. Özkaynak, “Görüntü Sıkıştırma Algoritmalarının Performans Analizi İçin Değerlendirme Rehberi,” International Journal of Pure and Applied Sciences, vol. 8, no. 1, pp. 102–110, Jun. 2022, doi: 10.29132/IJPAS.1012013.
  • S. Ghazanfari, S. Garg, P. Krishnamurthy, F. Khorrami, and A. Araujo, “R-LPIPS: An Adversarially Robust Perceptual Similarity Metric,” Jul. 2023.

Feature Enhancement of TUM-RGBD Depth Images and Performance Evaluation of Gaussian Splatting-Based SplaTAM Method

Year 2025, Volume: 8 Issue: 2, 260 - 272

Cemil Zeyveli , Ali Furkan Kamanlı

https://doi.org/10.35377/saucis...1637290

Abstract

Simultaneous Localization and Mapping (SLAM) methods are used in autonomous systems to determine their locations in unknown environments and map these environments. Autonomous systems need to act autonomously without external intervention. These methods are widely used in robotics and AR/VR applications. Gaussian Splatting SLAM is a Visual SLAM method that performs mapping and localization using depth and RGB images and uses Gaussian structures for scene representation. Popular datasets such as TUM-RGBD, Replica, and Scannet++ are used in the performance evaluation and testing of the visual SLAM methods. However, the depth images in the TUM-RGBD dataset are of lower quality than other datasets. This problem negatively affects the depth data's accuracy and reduces the quality of mapping results. In this study, to increase the quality of depth images, the features of depth images were corrected using the median filter, which is the depth smoothing method, and a cleaner depth dataset was obtained. The new dataset obtained was processed using the Gaussian Splatting SLAM method, and better metric results (PSNR, SSIM, and LPIPS) were obtained compared to the original dataset. As a result, in the dataset with corrected features, an improvement of 8.08% in the first scene and 4.69% in the second scene was achieved according to metric values compared to the original dataset.

Keywords

SLAM Gaussian Splatting Median Filter

Ethical Statement

It is declared that during the preparation process of this study, scientific and ethical principles were followed, and all the studies benefited from are stated in the bibliography.

References

  • H. Durrant-Whyte, D. Rye, and E. Nebot, “Localization of Autonomous Guided Vehicles,” Robotics Research, pp. 613–625, 1996, doi: 10.1007/978-1-4471-1021-7_69.
  • H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: Part I,” IEEE Robotics and Automation Magazine, vol. 13, no. 2, pp. 99–108, Jun. 2006, doi: 10.1109/MRA.2006.1638022.
  • R. C. Smith and P. Cheeseman, “On the Representation and Estimation of Spatial Uncertainty,” The international journal of Robotics Research, vol. 5, no. 4, pp. 56–68, Dec. 1986, doi: 10.1177/027836498600500404.
  • H. Taheri and Z. C. Xia, “SLAM; definition and evolution,” Engineering Applications of Artificial Intelligence, vol. 97, p. 104032, Jan. 2021, doi: 10.1016/J.ENGAPPAI.2020.104032.
  • T. J. Chong, X. J. Tang, C. H. Leng, M. Yogeswaran, O. E. Ng, and Y. Z. Chong, “Sensor Technologies and Simultaneous Localization and Mapping (SLAM),” Procedia Computer Science, vol. 76, pp. 174–179, Jan. 2015, doi: 10.1016/J.PROCS.2015.12.336.
  • W. Chen et al., “An Overview on Visual SLAM: From Tradition to Semantic,” Remote Sensing 2022, Vol. 14, Page 3010, vol. 14, no. 13, p. 3010, Jun. 2022, doi: 10.3390/RS14133010.
  • A. R. Sahili et al., “A Survey of Visual SLAM Methods,” IEEE Access, vol. 11, pp. 139643–139677, 2023, doi: 10.1109/ACCESS.2023.3341489.
  • A. Macario Barros, M. Michel, Y. Moline, G. Corre, and F. Carrel, “A Comprehensive Survey of Visual SLAM Algorithms,” Robotics 2022, Vol. 11, Page 24, vol. 11, no. 1, p. 24, Feb. 2022, doi: 10.3390/ROBOTICS11010024.
  • E. Sandström, Y. Li, L. van Gool, M. R. Oswald, E. Zürich, and K. Leuven, “Point-SLAM: Dense Neural Point Cloud-based SLAM.” pp. 18433–18444, 2023.
  • N. Keetha et al., “SplaTAM: Splat Track & Map 3D Gaussians for Dense RGB-D SLAM.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 21357–21366, 2024.
  • C. Yan et al., “GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19595–19604, 2024.
  • B. Kerbl, G. Kopanas, T. Leimkuehler, and G. Drettakis, “3D Gaussian Splatting for Real-Time Radiance Field Rendering,” ACM Transactions on Graphics, vol. 42, no. 4, p. 14, Aug. 2023, doi: 10.1145/3592433.
  • R. A. Newcombe, S. J. Lovegrove, and A. J. Davison, “DTAM: Dense tracking and mapping in real-time,” Proceedings of the IEEE International Conference on Computer Vision, pp. 2320–2327, 2011, doi: 10.1109/ICCV.2011.6126513.
  • R. A. Newcombe et al., “KinectFusion: Real-time dense surface mapping and tracking,” 2011 10th IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2011, pp. 127–136, 2011, doi: 10.1109/ISMAR.2011.6092378.
  • T. Whelan, R. F. Salas-Moreno, B. Glocker, A. J. Davison, and S. Leutenegger, “ElasticFusion: Real-time dense SLAM and light source estimation,” The International Journal of Robotics Research, vol. 35, no. 14, pp. 1697–1716, Sep. 2016, doi: 10.1177/0278364916669237.
  • T. Schops, T. Sattler, and M. Pollefeys, “BAD SLAM: Bundle Adjusted Direct RGB-D SLAM.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 134–144, 2019.
  • Z. Teed and J. Deng, “DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras,” Advances in Neural Information Processing Systems, vol. 20, pp. 16558–16569, Aug. 2021.
  • E. Sucar, S. Liu, J. Ortiz, and A. J. Davison, “iMAP: Implicit Mapping and Positioning in Real-Time,” Proceedings of the IEEE International Conference on Computer Vision, pp. 6209–6218, 2021, doi: 10.1109/ICCV48922.2021.00617.
  • Z. Zhu et al., “NICE-SLAM: Neural Implicit Scalable Encoding for SLAM,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2022-June, pp. 12776–12786, 2022, doi: 10.1109/CVPR52688.2022.01245.
  • X. Yang, H. Li, H. Zhai, Y. Ming, Y. Liu, and G. Zhang, “Vox-Fusion: Dense Tracking and Mapping with Voxel-based Neural Implicit Representation,” Proceedings - 2022 IEEE International Symposium on Mixed and Augmented Reality, ISMAR 2022, pp. 499–507, Oct. 2022, doi: 10.1109/ISMAR55827.2022.00066.
  • H. Wang, J. Wang, and L. Agapito, “Co-SLAM: Joint Coordinate and Sparse Parametric Encodings for Neural Real-Time SLAM,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2023-June, pp. 13293–13302, Apr. 2023, doi: 10.1109/CVPR52729.2023.01277.
  • H. Matsuki, R. Murai, P. H. J. Kelly, and A. J. Davison, “Gaussian Splatting SLAM.” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 18039–18048, 2024.
  • C. Yan et al., “GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting,” Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 19595–19604, 2024.
  • J. Straub et al., “The Replica Dataset: A Digital Replica of Indoor Spaces,” Jun. 2019, Accessed: Jan. 24, 2025. [Online]. Available: https://arxiv.org/abs/1906.05797v1
  • A. Dai, A. X. Chang, M. Savva, M. Halber, T. Funkhouser, and M. Niessner, “ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes,” Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 5828–5839, 2017.
  • C. Yeshwanth, Y.-C. Liu, M. Nießner, and A. Dai, “ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes,” Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 12–22, 2023.
  • J. Sturm, W. Burgard, and D. Cremers, “Evaluating Egomotion and Structure-from-Motion Approaches Using the TUM RGB-D Benchmark,” Proc. of the Workshop on Color-Depth Camera Fusion in Robotics at the IEEE/RJS International Conference on Intelligent Robot Systems (IROS), vol. 13, 2012.
  • Q. Yang, R. Yang, J. Davis, and D. Nistér, “Spatial-depth super resolution for range images,” Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007, doi: 10.1109/CVPR.2007.383211.
  • G. Deng and L. W. Cahill, “Adaptive Gaussian filter for noise reduction and edge detection,” IEEE Nuclear Science Symposium & Medical Imaging Conference, no. pt 3, pp. 1615–1619, 1994, doi: 10.1109/NSSMIC.1993.373563.
  • I. Pitas and A. N. Venetsanopoulos, “Nonlinear Mean Filters in Image Processing,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 34, no. 3, pp. 573–584, Jun. 1986, doi: 10.1109/TASSP.1986.1164857.
  • M. Kazubek, “Wavelet domain image denoising by thresholding and Wiener filtering,” IEEE Signal Processing Letters, vol. 10, no. 11, pp. 324–326, Nov. 2003, doi: 10.1109/LSP.2003.818225.
  • P. Jain and V. Tyagi, “A survey of edge-preserving image denoising methods,” Information Systems Frontiers, vol. 18, no. 1, pp. 159–170, Feb. 2016, doi: 10.1007/S10796-014-9527-0/TABLES/1.
  • T. S. Huang, G. J. Yang, and G. Y. Tang, “A Fast Two-Dimensional Median Filtering Algorithm,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 27, no. 1, pp. 13–18, 1979, doi: 10.1109/TASSP.1979.1163188.
  • A. Ravishankar, S. Anusha, H. K. Akshatha, A. Raj, S. Jahnavi, and J. Madhura, “A survey on noise reduction techniques in medical images,” Proceedings of the International Conference on Electronics, Communication and Aerospace Technology, ICECA 2017, vol. 2017-January, pp. 385–389, 2017, doi: 10.1109/ICECA.2017.8203711.
  • F. Artuğer and F. Özkaynak, “Görüntü Sıkıştırma Algoritmalarının Performans Analizi İçin Değerlendirme Rehberi,” International Journal of Pure and Applied Sciences, vol. 8, no. 1, pp. 102–110, Jun. 2022, doi: 10.29132/IJPAS.1012013.
  • S. Ghazanfari, S. Garg, P. Krishnamurthy, F. Khorrami, and A. Araujo, “R-LPIPS: An Adversarially Robust Perceptual Similarity Metric,” Jul. 2023.
There are 36 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Cemil Zeyveli 0009-0000-8316-450X

Ali Furkan Kamanlı 0000-0002-4155-5956

Early Pub Date June 16, 2025
Publication Date
Submission Date February 11, 2025
Acceptance Date June 5, 2025
Published in Issue Year 2025Volume: 8 Issue: 2

Cite

APA Zeyveli, C., & Kamanlı, A. F. (2025). Feature Enhancement of TUM-RGBD Depth Images and Performance Evaluation of Gaussian Splatting-Based SplaTAM Method. Sakarya University Journal of Computer and Information Sciences, 8(2), 260-272. https://doi.org/10.35377/saucis...1637290
AMA Zeyveli C, Kamanlı AF. Feature Enhancement of TUM-RGBD Depth Images and Performance Evaluation of Gaussian Splatting-Based SplaTAM Method. SAUCIS. June 2025;8(2):260-272. doi:10.35377/saucis.1637290
Chicago Zeyveli, Cemil, and Ali Furkan Kamanlı. “Feature Enhancement of TUM-RGBD Depth Images and Performance Evaluation of Gaussian Splatting-Based SplaTAM Method”. Sakarya University Journal of Computer and Information Sciences 8, no. 2 (June 2025): 260-72. https://doi.org/10.35377/saucis. 1637290.
EndNote Zeyveli C, Kamanlı AF (June 1, 2025) Feature Enhancement of TUM-RGBD Depth Images and Performance Evaluation of Gaussian Splatting-Based SplaTAM Method. Sakarya University Journal of Computer and Information Sciences 8 2 260–272.
IEEE C. Zeyveli and A. F. Kamanlı, “Feature Enhancement of TUM-RGBD Depth Images and Performance Evaluation of Gaussian Splatting-Based SplaTAM Method”, SAUCIS, vol. 8, no. 2, pp. 260–272, 2025, doi: 10.35377/saucis...1637290.
ISNAD Zeyveli, Cemil - Kamanlı, Ali Furkan. “Feature Enhancement of TUM-RGBD Depth Images and Performance Evaluation of Gaussian Splatting-Based SplaTAM Method”. Sakarya University Journal of Computer and Information Sciences 8/2 (June 2025), 260-272. https://doi.org/10.35377/saucis. 1637290.
JAMA Zeyveli C, Kamanlı AF. Feature Enhancement of TUM-RGBD Depth Images and Performance Evaluation of Gaussian Splatting-Based SplaTAM Method. SAUCIS. 2025;8:260–272.
MLA Zeyveli, Cemil and Ali Furkan Kamanlı. “Feature Enhancement of TUM-RGBD Depth Images and Performance Evaluation of Gaussian Splatting-Based SplaTAM Method”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 2, 2025, pp. 260-72, doi:10.35377/saucis. 1637290.
Vancouver Zeyveli C, Kamanlı AF. Feature Enhancement of TUM-RGBD Depth Images and Performance Evaluation of Gaussian Splatting-Based SplaTAM Method. SAUCIS. 2025;8(2):260-72.
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