SplatR : Experience Goal Visual Rearrangement with 3D Gaussian Splatting and Dense Feature Matching

Experience Goal Visual Rearrangement task stands as a However, these methods have disadvantages: 2D and 3D foundational challenge within Embodied AI, requiring an semantic maps store object pose and semantic information agent to construct a robust world model that accurately in a grid; this approach provides limited resolution, does captures the goal state. The agent uses this world model to not inherently capture interactions between objects and is restore a shuffled scene to its original configuration, making prone to sensitivity issues and quantization errors. Although an accurate representation of the world essential for pointcloud based representation can provide more robustness successfully completing the task. In this work, we present to sensitivity, it lacks structural semantics: identifying a novel framework that leverages on 3D Gaussian Splatting objects and their interactions with the world in a noisy as a 3D scene representation for experience goal visual pointcloud is challenging. Scene graph based methods often rearrangement task. Recent advances in volumetric assume a clear and well defined relationship between scene representation like 3D Gaussian Splatting, offer fast objects, which often limits the granularity of scene understanding, rendering of high quality and photo-realistic novel views.