Object-Centric 2D Gaussian Splatting: Background Removal and Occlusion-Aware Pruning for Compact Object Models
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.
当前的高斯散点方法在重建整个场景方面表现出色，但缺乏针对特定对象的选项，导致计算成本高昂，不适用于以对象为中心的应用。我们提出了一种新方法，利用对象掩码实现目标重建，从而生成以对象为中心的模型。 此外，我们引入了一种感知遮挡的剪枝策略，以在不降低质量的情况下最小化高斯数量。我们的方法能够重建紧凑的对象模型，生成的以对象为中心的高斯和网格表示相比基线模型减少了高达 96% 的存储需求，并且训练速度提升高达 71%，同时保留了具有竞争力的质量。 这些表示可以直接应用于下游任务，例如外观编辑和物理模拟，而无需额外处理。