4D reconstruction

In computer vision and computer graphics, 4D reconstruction is the process of capturing the shape and appearance of real objects along a temporal dimension. In other words, it adds the concept of time to a 3D representation to show the scene changes over time. This process can be accomplished by methods such as depth camera imaging, photometric stereo, or structure from motion, and is also referred to as spatio-temporal reconstruction.

Methods

There are many methods for 4D reconstruction. They can mainly be differentiated in three ways: how to capture data, how to handle changes over time and how to represent the scene.

For capturing data, the input can come from a single moving camera, several synchronized cameras or a sensor that measures depth directly. There are so many methods that use structure from motion to estimate the camera's location at first. And next step is to reconstruct the scene.

There are mainly two approaches to handle change over time. One is keeping a single canonical model and changing it each frame. The other is reconstructing the scene at each time step.

For scene representation, early methods used templates or used depth cameras to build it in real time. And then neural network methods gave higher quality, but that were slower than before. Recently, we use 3D Gaussians and can render dynamic scenes in real time. It is shown in the next section (4D Gaussian splatting).

4D Gaussian splatting

This section is an excerpt from Gaussian splatting § 3D temporal Gaussian splatting.[edit]

Extending 3D Gaussian splatting to dynamic scenes, 3D temporal Gaussian splatting incorporates a time component, allowing for real-time rendering of dynamic scenes with high resolutions. It represents and renders dynamic scenes by modeling complex motions while maintaining efficiency. The method uses a HexPlane to connect adjacent Gaussians, providing an accurate representation of position and shape deformations. By utilizing only a single set of canonical 3D Gaussians and predictive analytics, it models how they move over different timestamps.

It is sometimes referred to as "4D Gaussian splatting"; however, this naming convention implies the use of 4D Gaussian primitives (parameterized by a 4×4 mean and a 4×4 covariance matrix). Most work in this area still employs 3D Gaussian primitives, applying temporal constraints as an extra parameter of optimization.[citation needed]

Achievements of this technique include real-time rendering on dynamic scenes with high resolutions, while maintaining quality. It showcases potential applications for future developments in film and other media, although there are current limitations regarding the length of motion captured.

Application

In immersive media, 4D reconstruction is used for free viewpoint video. That means a performance can be recorded by many cameras at the same time. It will generate a 3D video that can be compressed and played online. So viewers can choose their own viewing angle for free. It can also be used for telepresence. That means a person is reconstructed as a 3D model in real time and send it to a far-away place. And someone wearing a mixed reality headset can see this person like they are standing in front of them and can interact with them. In virtual reality, a musician's performance can be recorded and compressed into a very small file, and then viewers wearing a VR headset can watch it from any angle. Researchers were developing 3D live streaming for sports. The players in the basketball game are reconstructed, so viewers can move around in the 3D scene when they are watching the live streaming game.

In medical imaging

Besides computer graphics, the field of medical images also has a similar requirement about 4D reconstruction. The article in computer graphics focuses on the shape of object surfaces, as it was mainly used in film and extended reality. In medical imaging, it focuses on how organs change with human breathing and heartbeat because it needs to be used to evaluate how organs work.

See also

References

  1. 1 2 Dou, Mingsong, et al. "Fusion4d: Real-time performance capture of challenging scenes." ACM Transactions on Graphics (TOG) 35.4 (2016): 1-13.
  2. ↑ Mustafa, Armin, et al. "Temporally coherent 4d reconstruction of complex dynamic scenes." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
  3. ↑ Oswald, Martin Ralf, Jan Stühmer, and Daniel Cremers. "Generalized connectivity constraints for spatio-temporal 3d reconstruction." European Conference on Computer Vision. Springer, Cham, 2014.
  4. 1 2 Dong, Jing, et al. "4D crop monitoring: Spatio-temporal reconstruction for agriculture." 2017 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2017.
  5. ↑ Bansal, Aayush; Vo, Minh; Sheikh, Yaser; Ramanan, Deva; Narasimhan, Srinivasa (June 2020). "4D Visualization of Dynamic Events From Unconstrained Multi-View Videos". 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR): 5365–5374. doi:10.1109/CVPR42600.2020.00541. ISSN 2575-7075.
  6. ↑ Kyriakaki, Georgia, et al. "4D reconstruction of tangible cultural heritage objects from web-retrieved images." International Journal of Heritage in the Digital Era 3.2 (2014): 431-451.
  7. 1 2 3 4 5 6 Yunus, Raza; Lenssen, Jan Eric; Niemeyer, Michael; Liao, Yiyi; Rupprecht, Christian; Theobalt, Christian; Pons-Moll, Gerard; Huang, Jia-Bin; Golyanik, Vladislav; Ilg, Eddy (2024). "Recent Trends in 3D Reconstruction of General Non-Rigid Scenes". Computer Graphics Forum. 43 (2): e15062. doi:10.1111/cgf.15062. ISSN 1467-8659.{{cite journal}}: CS1 maint: article number as page number (link)
  8. ↑ Newcombe, Richard A.; Fox, Dieter; Seitz, Steven M. (2015-06). "DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time". 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR): 343–352. doi:10.1109/CVPR.2015.7298631. ISSN 1063-6919. {{cite journal}}: Check date values in: |date= (help)
  9. ↑ Wu, Guanjun; Yi, Taoran; Fang, Jiemin; Xie, Lingxi; Zhang, Xiaopeng; Wei, Wei; Liu, Wenyu; Tian, Qi; Wang, Xinggang (June 2024). 4D Gaussian Splatting for Real-Time Dynamic Scene Rendering. 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 20310–20320. arXiv:2310.08528. doi:10.1109/CVPR52733.2024.01920.
  10. 1 2 Franzen, Carl (16 October 2023). "Actors' worst fears come true? New 3D Temporal Gaussian Splatting method captures human motion". venturebeat.com. VentureBeat. Retrieved October 18, 2023.
  11. ↑ Collet, Alvaro; Chuang, Ming; Sweeney, Pat; Gillett, Don; Evseev, Dennis; Calabrese, David; Hoppe, Hugues; Kirk, Adam; Sullivan, Steve (2015-07-27). "High-quality streamable free-viewpoint video". ACM Transactions on Graphics (TOG). 34 (4). New York, NY, USA: Association for Computing Machinery: 69:1–69:13. doi:10.1145/2766945. ISSN 0730-0301.
  12. ↑ Orts-Escolano, Sergio; Rhemann, Christoph; Fanello, Sean; Chang, Wayne; Kowdle, Adarsh; Degtyarev, Yury; Kim, David; Davidson, Philip L.; Khamis, Sameh; Dou, Mingsong; Tankovich, Vladimir; Loop, Charles; Cai, Qin; Chou, Philip A.; Mennicken, Sarah (2016-10-16). "Holoportation: Virtual 3D Teleportation in Real-time". Proceedings of the 29th Annual Symposium on User Interface Software and Technology. UIST '16. New York, NY, USA: Association for Computing Machinery: 741–754. doi:10.1145/2984511.2984517. ISBN 978-1-4503-4189-9.{{cite journal}}: CS1 maint: periodical has ISBN (link)
  13. ↑ Jiang, Yuheng; Shen, Zhehao; Hong, Yu; Guo, Chengcheng; Wu, Yize; Zhang, Yingliang; Yu, Jingyi; Xu, Lan (2024-11-19). "Robust Dual Gaussian Splatting for Immersive Human-centric Volumetric Videos". ACM Transactions on Graphics (TOG). 43 (6). New York, NY, USA: Association for Computing Machinery: 265:1–265:15. doi:10.1145/3687926. ISSN 0730-0301.
  14. ↑ "Echoes of the Coliseum: Towards 3D Live streaming of Sports Events - Robotics Institute Carnegie Mellon University". publications.ri.cmu.edu. Retrieved 2026-10-31.
  15. 1 2 Hoffman, Eric A (2022-04-01). "Origins of and lessons from quantitative functional X-ray computed tomography of the lung". British Journal of Radiology. 95 (1132): 20211364. doi:10.1259/bjr.20211364. ISSN 1748-880X. PMC 9153696. PMID 35193364.{{cite journal}}: CS1 maint: article number as page number (link)
  16. 1 2 Zhang, Yawei; Jiang, Zhuoran; Zhang, You; Ren, Lei (2024). "A review on 4D cone-beam CT (4D-CBCT) in radiation therapy: Technical advances and clinical applications". Medical Physics. 51 (8): 5164–5180. doi:10.1002/mp.17269. ISSN 2473-4209. PMC 11321939. PMID 38922912.