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Meet ActiveSplat, "an autonomous high-fidelity reconstruction system leveraging Gaussian Splatting." Simply put, it walks around the building/apartment/office and produces a realistic 3D representation of the space.
3D Gaussian Splatting is a rendering technique that leverages 3D Gaussians to represent the scene, thus allowing one to synthesize 3D scenes out of 2D footage. Simply put, it takes samples of images and turns them into 3D scenes without creating meshes by converting a point cloud to Gaussians using machine learning. You can learn more about it here.
Yuetao Li, Zijia Kuang et al.
According to researchers from Beijing Institute of Technology and AIR, Tsinghua University, ActiveSplat "enables the agent to explore the environment autonomously to build a 3D map on the fly." The system uses a Gaussian map and a Voronoi graph, assuring "efficient and complete exploration with high-fidelity reconstruction results."
"The key to ActiveSplat is a hybrid map representation that integrates both dense information about the environment and a sparse abstraction of the workspace. Therefore, the system leverages sparse topology for efficient viewpoint sampling and path planning, while exploiting view-dependent dense prediction for viewpoint selection, facilitating efficient decision-making with promising accuracy and completeness."
Yuetao Li, Zijia Kuang et al.
To mitigate repetitive trajectories and improve local granularity, the team adopted a hierarchical planning strategy based on the topological map, which ensures high-fidelity reconstruction with photorealistic view synthesis.
The creators say their method is highly efficient and accurate.
If you wish to know about its technical details, check out the paper here.
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