How 3D Gaussian Splatting Works: A Technical Guide

What is 3D Gaussian Splatting?

3D Gaussian Splatting enables real-time rendering and supports high-fidelity radiance field reconstruction. The core idea is to represent complex 3D scenes using a collection of 3D Gaussians, each of which is defined by its position, covariance matrix, opacity, and color parameters. This method directly renders scenes from multi-view images, preserving the continuous nature of the scene's radiance fields. Unlike traditional methods that rely on surface meshes or voxel grids, 3D Gaussian Splatting takes advantage of Gaussian functions, which can be adapted to model the complex light interactions and geometry present in real-world environments. The approach introduces anisotropic Gaussians—meaning each Gaussian can vary in shape and orientation to better represent the scene's appearance from various viewing perspectives. This flexibility allows the technique to more accurately capture the nuances of light and geometry in a scene.

At its core, 3D Gaussian Splatting leverages a mathematical foundation that decomposes each Gaussian's covariance into scaling and rotation matrices, enabling precise control over its shape and orientation. This method is both memory-efficient and computationally effective, as it focuses on sparse points initialized through camera calibration and then optimized to form a detailed 3D radiance field representation.

Want to try 3D Gaussian Splatting first?

KIRI Engine lets you create 3DGS captures from a short video or image set on iOS, Android, and web — no LiDAR required.

→ Try KIRI Engine's 3D Gaussian Splatting

How does 3D Gaussian Splatting work?

The process starts by collecting images of the scene along with camera positions, which are used to generate a sparse point cloud. Each of these points is then converted into a 3D Gaussian with defined attributes—mean position, covariance matrix (for shape and orientation), and opacity. The rendering of these Gaussians is optimized through a differentiable process, using stochastic gradient descent (SGD) to minimize the loss function, which combines L1 loss and perceptual loss (D-SSIM). This optimization helps refine the 3D model, matching the views from the available images.

To ensure the method runs in real-time, particularly on GPUs, the system employs a tile-based rasterizer that efficiently handles the visibility of each Gaussian. This visibility-aware rendering process ensures that only visible splats contribute to the final image, thus enhancing the overall rendering performance. Additionally, the optimization of each Gaussian's covariance is critical for achieving accurate representation, and the explicit gradients derived from this process avoid inefficiencies associated with traditional automatic differentiation.

In real-world tests, 3D Gaussian Splatting has been evaluated against other advanced rendering techniques, such as Neural Radiance Fields (NeRF). The results show that 3D Gaussian Splatting offers superior speed and quality in generating photorealistic renderings, making it a strong candidate for real-time applications, such as augmented reality (AR), virtual reality (VR), and interactive experiences.

Where does 3DGS perform well, and where does it struggle?

Characteristics:

3D Gaussian Splatting offers several notable advantages:

• Real-Time Performance: Its GPU-optimized, visibility-aware rendering ensures high-speed processing, which is crucial for interactive and real-time applications like AR/VR.

• High Visual Fidelity: By using anisotropic Gaussians, this technique delivers detailed, sharp images with high-quality light effects, ensuring a photorealistic rendering.

• Memory Efficiency: Compared to traditional voxel or mesh-based methods, 3D Gaussian Splatting reduces memory usage, making it suitable for real-time applications without sacrificing detail.

• Flexible Representation: The use of Gaussian functions to represent scenes means that 3D Gaussian Splatting can adapt to complex lighting conditions and geometries, capturing intricate details that other methods may miss.

Limitations:

While 3D Gaussian Splatting offers impressive capabilities, it does have certain limitations:

• Static Scenes: Currently, the method is best suited for rendering static scenes. Handling dynamic or moving objects requires additional techniques, such as temporal integration (e.g., 3D Temporal Gaussian Splatting).

• Initial Calibration: The quality of the final model heavily depends on the accuracy of initial camera calibration and the sparse point cloud. Poor calibration can lead to less accurate or distorted renderings.

• Occlusion Issues: In extremely dense or occluded scenes, some complex lighting interactions and occlusion effects may not be captured perfectly, leading to artifacts in the rendered model.

In real capture workflows, 3DGS works best when the subject stays still and the camera moves around it with enough overlap. Common failure cases include fast-moving subjects, unstable lighting, extreme transparency without surrounding visual context, and turntable-style captures where the object moves instead of the camera. Coverage also matters: if an angle is never captured, 3DGS cannot reliably reconstruct it later.

Where is 3DGS being used today?

3D Gaussian Splatting has opened up a wide range of applications across industries that require fast, high-quality, and photorealistic 3D reconstruction. In the field of augmented reality (AR) and virtual reality (VR), this technique enables real-time scene rendering from casually captured images or videos, enhancing immersive experiences with dynamic and realistic environments. It's also making significant contributions to 3D mapping, cultural heritage preservation, and digital twin creation, where high-fidelity visualization of real-world spaces is crucial.

In film and game development, 3D Gaussian Splatting offers a more efficient way to generate detailed scenes without the heavy processing demands of traditional mesh-based or neural radiance field methods. For e-commerce, it allows for quick creation of interactive 3D product models from simple image sets, streamlining digital asset pipelines.

KIRI Engine has expanded 3DGS beyond passive rendering with in-app editing tools, background removal, and mesh conversion — allowing users to take 3DGS captures directly into traditional 3D pipelines. KIRI's free and open-source Blender addon further extends this workflow, enabling recoloring, retexturing, animation, and rendering of 3DGS files inside Blender.

3D Gaussian Splatting Blender plugin developed by KIRI Engine

As research continues, we can anticipate improvements in rendering speed, model compression, and integration with AI-driven optimization, positioning 3D Gaussian Splatting as a cornerstone technology for the next generation of 3D content creation.

Related workflows:

→ 3DGS for game development: from capture to asset pipeline

→ 3D Gaussian Splatting in Godot: scene integration and hybrid workflows

How does KIRI Engine make 3DGS practical for mobile scanning?

KIRI Engine APP

KIRI Engine Web Version

KIRI Engine brings 3D Gaussian Splatting to iOS, Android, and web — processing captures in the cloud so that no specialized hardware or local compute is required. Users capture a short video or image set, upload it, and receive a 3DGS output they can view, edit, and export directly from a phone or browser.

How does KIRI Engine's 3D Gaussian Splatting work

KIRI Engine's 3DGS Scan mode transforms a short video or a series of photos taken from different angles into a dynamic 3D scene using anisotropic 3D Gaussians. These Gaussians are initialized from sparse camera-calibrated points and then optimized in the cloud to form a continuous radiance field that accurately reflects the geometry and appearance of the scene.

Key steps include:

• Capture: Users record a video or take multiple photos of the target object or scene.

• Cloud Processing: The data is uploaded to KIRI's server, where a differentiable Gaussian splatting pipeline reconstructs the 3D representation.

• Rendering: The final model supports real-time view synthesis, allowing users to preview and interact with the object from any angle.

What KIRI Engine adds to 3DGS

Mobile-first capture: record a short video or upload a photo set on iOS, Android, or web — no LiDAR or depth sensor required.

In-app editing: clean up captures before export using sphere, plane, and brush cropping tools, plus background removal.

Mesh conversion: enable 3DGS to Mesh during upload to receive both a native .PLY splat and an editable polygon mesh from the same capture.

Blender workflow: KIRI's free Blender addon supports import, editing, recoloring, animation, and rendering of 3DGS files directly inside Blender.

Export flexibility: download as .PLY for visualization, or export the converted mesh in .OBJ, .FBX, .GLTF, and other formats for use in game engines, 3D printing, or editing pipelines.

3D Gaussian Splatting is still evolving — particularly in areas like dynamic scene capture, relighting, and deeper integration with animation pipelines. But it is already practical for creators, developers, and educators who want a fast way to capture real-world scenes with a level of visual fidelity that traditional photogrammetry cannot match on difficult surfaces.

KIRI Engine focuses on making that workflow accessible: capture with a phone, process in the cloud, edit and export on mobile or web, and move into traditional 3D tools when needed.

→ Explore KIRI Engine's 3D Gaussian Splatting feature

→ Learn how 3DGS to Mesh works