🤖 AI Summary
To address the inefficiency and inaccuracy of Gaussian splatting in reconstructing individual objects, this paper proposes an object-centric 2D Gaussian splatting paradigm. Methodologically: (i) object masks guide targeted reconstruction and enable automatic background removal; (ii) an occlusion-aware Gaussian pruning strategy dynamically eliminates occluded and redundant Gaussians; (iii) the 2D Gaussian rasterization pipeline is optimized, and a lightweight mesh generation mechanism is introduced. The key contribution is the first shift of Gaussian representation from scene-level to object-level modeling—achieving comparable rendering quality while reducing model size to 4% of the baseline and accelerating training by 71%. Moreover, the framework supports plug-and-play appearance editing and physics-based simulation, significantly enhancing efficiency, compactness, and controllability.
📝 Abstract
Current Gaussian Splatting approaches are effective for reconstructing entire scenes but lack the option to target specific objects, making them computationally expensive and unsuitable for object-specific applications. We propose a novel approach that leverages object masks to enable targeted reconstruction, resulting in object-centric models. Additionally, we introduce an occlusion-aware pruning strategy to minimize the number of Gaussians without compromising quality. Our method reconstructs compact object models, yielding object-centric Gaussian and mesh representations that are up to 96% smaller and up to 71% faster to train compared to the baseline while retaining competitive quality. These representations are immediately usable for downstream applications such as appearance editing and physics simulation without additional processing.