Webmaster's Home (ChinaZ.com) December 19 News: Gaussian-SLAM is an emerging technology that can create realistic 3D models by analyzing images in video streams to reconstruct real-world scenes.
By watching a video, Gaussian-SLAM can analyze the images in the video and understand the layout of the environment and the location of objects in the video. These image data are then used to create and restore 3D models that can be observed from various angles to reconstruct real-world scenes. This process is rendered in real time and helps users view and explore 3D virtual environments on their computers.
Paper address: https://ivi.fnwi.uva.nl/cv/paper/GaussianSLAM.pdf
Project address: https://github.com/VladimirYugay/Gaussian-SLAM
Demonstration address: https://vladimiryugay.github. io/gaussian_slam/
For example, if you have a video shot in a park that includes objects such as trees, benches, paths, and pedestrians. Traditional videos can only provide a two-dimensional perspective, but using Gaussian-SLAM technology, we can analyze individual objects in the video and understand their relative positions in space.
By analyzing the movement and perspective changes of objects in the video, Gaussian-SLAM can calculate the position and shape of these objects in three-dimensional space. Ultimately, the technology could create a three-dimensional model of a digital replica of a park, allowing users to view every corner of the park from any angle, including trees, benches and people's activities. The main functional features and working principles of
Gaussian-SLAM are as follows:
main functional features:
1. Optically realistic rendering: capable of reconstructing and rendering real-world and synthetic scenes in a highly realistic manner.
2, Gaussian blob scene representation: Using Gaussian blobs as the main representation unit of the scene is a novel method that is different from traditional point cloud or grid representation.
3, interactive time reconstruction: allows the scene to be reconstructed in interactive time, that is, the reconstruction process is fast enough to be rendered in real time or near real time.
4, suitable for monocular RGBD input: optimized for monocular RGBD (red, green and blue depth) input data, suitable for a variety of scenarios.
Gaussian-SLAM is specifically optimized for the input data of RGBD cameras. In addition to capturing ordinary color images, this camera can also provide depth information for each pixel, which is crucial for creating accurate three-dimensional scene models. Working principle of
: The working principle of
Gaussian-SLAM mainly includes data processing, 3D Gaussian initialization, scene construction, keyframe storage and rendering, and optimization and updating. By receiving RGBD keyframe input, subsampling and taking into account color gradients, the sampling points are projected into 3D space, new Gaussians are initialized at these sampling positions, and the new 3D Gaussians are added to the current active part of the global map to form the scene. part. The input RGBD keyframes are temporarily stored, together with other keyframes that contribute to the active subgraph, and all keyframes that contribute to the active subgraph are rendered, and finally the depth and color losses associated with the subgraph input keyframes are calculated, and then Update the parameters of the 3D Gaussian in the active subgraph.
application scenarios:
Gaussian-SLAM is suitable for SLAM applications that require a high degree of realism and accuracy, such as autonomous driving, robot navigation, augmented reality and virtual reality, etc. The emergence of this technology offers new possibilities for simulating the real world and creating realistic virtual environments.
