Object-Centric 2D Gaussian Splatting: Background Removal and Occlusion-Aware Pruning for Compact Object Models

📅 2025-01-14
📈 Citations: 0
✨ Influential: 0
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🤖 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.

Technology Category

Computer Vision: Low Level & Physics-based VisionSearch and Optimization: Sampling/Simulation-based SearchPlanning, Routing, and Scheduling: Replanning and Plan Repair

Application Category

User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendationGraph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphsSearch and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
📝 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.
Problem

Research questions and friction points this paper is trying to address.

Gaussian Splattering
Efficient Reconstruction
Occluded Objects
Innovation

Methods, ideas, or system contributions that make the work stand out.

Efficient Object Reconstruction
Occlusion Handling
Contour-based Modeling
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