Valuable insights
1.Gaussian Splatting is a Trending Immersive Technology: Gaussian Splatting has emerged as a highly discussed technology in immersive fields over the last two years, noted for delivering near-magical rendering quality in captured environments.
2.GS Solves NeRF Computation Issues: Unlike Neural Radiance Fields (NeRFs), Gaussian Splatting significantly reduces computational load and file size, making photorealistic environment reconstruction faster and more accessible to a wider range of hardware.
3.Scene Representation via Gaussian Points: The technology represents scenes using a cloud of points, each defined by spatial coordinates, color, opacity, radius, and spherical harmonics to accurately model light interaction.
4.View-Dependent Real-Time Recalculation: A key differentiator is that the scene is not a static 3D model; visibility is calculated based on the current field of vision, recalculating in real-time as the observer moves.
5.Significant Speed Improvements in Rendering: Processing times have drastically decreased; complex rendering tasks that previously took several minutes can now approach real-time speeds, making interactive previews feasible.
6.Superior Handling of Reflective Materials: GS demonstrates a notable advantage over LiDAR and photogrammetry in accurately rendering challenging surfaces like glass, mirrors, and transparent materials without creating distortions or holes.
7.Accessible Data Capture Methods: Capture is accessible using common devices like smartphones or standard DSLRs, provided the capture process ensures comprehensive angular coverage of the subject or environment.
8.Post-Processing Cleanup is Necessary: Although automated tools exist, manual or automated cleanup is often required post-capture to remove floating artifacts (splats) and crop extraneous data for optimized final output.
9.Broad Industry Adoption Potential: Sectors such as architecture, product visualization, real estate walkthroughs, and special effects (VFX) stand to benefit greatly from the speed and visual fidelity offered by GS.
10.Limitations in Precision Measurement: For applications demanding exact point-to-point measurement and high geometric precision, traditional tools like laser scanning (LiDAR) remain the more appropriate solution.
Introduction to Gaussian Splatting and Guest Profile
Gaussian Splatting (GS) has rapidly become a prominent subject within immersive technologies over the last two years, generating significant interest due to the seemingly magical quality of its rendered outputs. The objective of this discussion is to move beyond the initial awe to explore the underlying technologies and concepts that constitute GS, including discussions on 3D and 4D video applications. The goal is to provide necessary technical context without delving into overly complex details, while ensuring resources are available for deeper exploration.
Guest Introduction and Expertise
Grégory Duvalet, the guest expert, serves as a technological advisor at Clarté. His primary focus involves raising corporate awareness regarding immersive technologies and industrial applications of Artificial Intelligence, recently including Gaussian Splatting and augmented reality. Clarté itself is a state-labeled technological resource center, operating for nearly 30 years, specializing in consulting, support, and innovative custom development for businesses adopting these advanced technologies.
Understanding the Core Technology of Gaussian Splatting
To understand Gaussian Splatting, one must first consider the historical context, moving past simple rasterization. A significant precursor emerged in 2020: Neural Radiance Fields (NeRFs). NeRF technology utilizes AI and neural networks to reconstruct photorealistic 3D environments from multi-angle photographs. However, this process demands substantial computational power, requiring high-end graphics cards and resulting in very large file sizes, which limits accessibility.
The Arrival of Gaussian Splatting
In 2023, a joint paper from INRIA introduced Gaussian Splatting, a novel technology that effectively addresses many of the performance bottlenecks associated with NeRFs. This new method involves significantly less calculation, leading to smaller file weights and greater accessibility for users without top-tier hardware. The term itself translates roughly to 'Gaussian splash,' hinting at its foundational structure.
Deconstructing the Gaussian Splat
Gaussian Splatting operates on a point cloud foundation, similar to a LiDAR scan. Each point possesses essential parameters: XYZ position in space and RGB color. Crucially, GS adds supplementary parameters: a radius, an opacity value, and spherical harmonics. These harmonics form a mathematical function that helps represent a sphere based on the viewer's position.
One must imagine a snowball, you know, that one turns over.
When all these elements are combined, they allow for the representation of complex 3D scenes. While it may not perfectly match the photorealism achievable by a NeRF, the result is highly satisfactory and visually appealing, creating detailed 3D environments.
Performance Comparison and Handling Complex Light Interactions
A fundamental characteristic distinguishing GS from traditional 3D modeling is that the scene representation is inherently view-dependent. The scene does not exist as a fixed 3D model; rather, what is seen is determined by the current field of vision. When the observer moves, the field of vision shifts, necessitating a real-time recalculation, which represents a significant shift in rendering methodology.
Performance Metrics Comparison
The primary differences between GS, NeRFs, photogrammetry, and point clouds center on file size, calculation time, and rendering speed. If optimized lightly, Gaussian Splatting can approach real-time calculation speeds. This improvement is substantial; for instance, a photoboost setup that previously required 4 to 5 minutes of rendering time using 18 webcams and a standard graphics card now approaches 60 seconds.
Managing Light and Reflective Surfaces
A major hurdle for older capture methods, including LiDAR and photogrammetry, involves handling light interactions such as reflections, refractions, and mirror effects. These phenomena often cause issues on surfaces like glass or very dark, matte objects, resulting in holes or deformations in the final output because the algorithm cannot properly catch the light.
This management, we would never have had it with photogrammetry.
While not entirely perfect, GS approaches these challenging scenarios with interesting results, as demonstrated by successfully rendering the transparency of a bus windshield in a visualization platform called Super Splat. Such accurate handling of transparent or reflective materials was previously unachievable with standard photogrammetry techniques.
The Gaussian Splatting Workflow and Sector Applications
The initial step in utilizing Gaussian Splatting involves data capture. This can be accomplished using smartphone applications available on both iPhone and Android devices. Historically, the iPad Pro was favored due to its integrated LiDAR capabilities, which many dedicated 3D rendering apps leverage. Applications such as Kiri Engine, Scaniverse by Nantic, and Polycam now offer solutions incorporating GS alongside LiDAR and photogrammetry features.
- Smartphone apps leveraging built-in LiDAR or standard photo/video capture.
- Classic DSLR cameras injecting a set of photos into the processing pipeline.
- 360-degree cameras, though the processing workflow for these is less standardized.
Capture Best Practices
To ensure maximum precision, capture best practices align closely with those used in photogrammetry. It is essential to cover the entire model or environment, capturing all angles and different levels of proximity (near and far). Furthermore, optimizing incidence angles is important for capturing the maximum amount of light information, especially if the resulting data is intended for advanced lighting exploitation.
Processing and Rendering Phase
The rendering calculation is often offloaded, especially when using mobile applications, relying on remote cloud processing. However, local processing benefits significantly from powerful hardware, such as high-end gaming or 3D specialist graphics cards, or powerful mobile chips like the M2 in iPads. Once rendered, the output, typically in the PLY format, can be hosted on online platforms, unlike NeRFs, facilitating easy sharing and integration into other tools.
Post-Processing and Optimization
A necessary phase involves cleaning up imperfections, such as floating Gaussian splats or extraneous captured data surrounding the main subject. This step allows for cropping and selection of only the relevant information, similar to masking in traditional photography, which helps significantly reduce the final file size and processing load.
Key Industry Use Cases
The technology presents numerous perspectives across various sectors. Architecture benefits from contextualizing building models within real environments at a reasonable computational cost. The gaming industry finds it naturally compatible, and VFX benefits from quick visualization. Real estate platforms, like Matterport, can evolve beyond station-to-station navigation to provide seamless spatial transitions.
Limitations and Future Evolution of GS
Despite its advantages, Gaussian Splatting has inherent limitations. It is clearly not the optimal tool when high geometric precision or point-to-point measurement is required; in those specific cases, LiDAR scanning remains the superior instrument for accurate geospatial data collection.
Clearly, it is not adapted if one wants to have measurement side by side, if one wants to have precision.
Rendering Quality Trade-offs
While GS handles light dynamics effectively, NeRFs and photogrammetric rendering methods may still produce more performant results in specific lighting conditions. Currently, GS is generally utilized for its lightweight web portability rather than as a replacement for traditional 3D meshes.
Bridging the Gap to Classic 3D
A significant industry trend involves creating bridges between GS data and traditional XYZ texture-based 3D workflows. Solution providers, including Nvidia, are actively developing tools to leverage the easy capture inherent in GS data while converting it into formats compatible with established 3D pipelines, thereby easing content creation.
Future Trajectory and Standardization
The technology is rapidly professionalizing, with major platforms like Unity and Unreal Engine beginning to integrate support. Future developments are focused heavily on 4D kinematics (capturing movement over time), automatic capture systems using drones, and enhanced web visualization tools like Spatial or Revile Space.
- Implementing 4D capture for dynamic scenes, currently requiring extensive calculation time.
- Automating capture processes, especially using drone data.
- Integrating GS visualization into VR headsets, as seen with tools like Gracia VR.
Advice for Professionals and Conclusion
For XR professionals interested in integrating Gaussian Splatting into their content production workflows, the primary recommendation is to maintain a rigorous technological watch. Since the sector is evolving extremely quickly, staying informed about new tools and research is vital to avoid pitfalls and leverage emerging capabilities effectively.
- Following specialized portals like radiancefields.com for aggregated data.
- Connecting with and following subject matter experts publishing on professional networks like LinkedIn.
- Monitoring dedicated company news, patents, and scientific papers, often synthesized by experts.
The Importance of Hybridization
A critical area to track is the hybridization of GS with other technologies, such as AI. For example, using AI for pattern detection within a GS capture allows for the isolation of specific assets, such as extracting a single product from a complex factory environment. This integration of disparate technologies promises to deliver increasingly comprehensive tools that will fundamentally reshape immersive content creation methods.
Useful links
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