aiqbench analysis
This research introduces 2D GauSS-MI, a novel metric leveraging 2D Gaussian Splatting to optimize active view selection for scene reconstruction. By explicitly accounting for both visual fidelity and geometric accuracy, the method achieves high-quality results with significantly reduced computational overhead compared to existing baselines. The work advances real-time 3D mapping capabilities by efficiently balancing resource constraints with reconstruction quality in dynamic environments.
2D GauSS-MI: Efficient Active Scene Reconstruction with Balanced Visual and Geometric Quality
arXiv:2609.21516v1 Announce Type: cross Abstract: Active reconstruction requires efficient active view selection to achieve high-quality reconstruction within limited onboard computational resources. Existing methods face challenges in adequately balancing visual and geometric quality with the computational efficiency required for real-time operation. In this work, we present an active reconstruction framework based on 2D Gaussian Splatting (2DGS). We develop an efficient online 2DGS mapping pipeline for incremental RGB-D observations and introduce a probabilistic reliability model that characterizes the view-dependent reconstruction quality of individual 2D Gaussian splats. Building on this model, we formulate 2D Gaussian Splatting Shannon Mutual Information (2D GauSS-MI), a mutual-information-based metric that exploits the explicit surface orientation of 2DGS to evaluate the expected information gain of candidate views. The proposed metric enables active view selection to account for both visual and geometric reconstruction quality. We evaluate the proposed system against three state-of-the-art baselines on eight Replica scenes. Experimental results demonstrate that our method achieves a favorable balance between visual and geometric reconstruction quality with substantially lower computational cost and competitive model storage.
Abstract:Active reconstruction requires efficient active view selection to achieve high-quality reconstruction within limited onboard computational resources. Existing methods face challenges in adequately balancing visual and geometric quality with the computational efficiency required for real-time operation. In this work, we present an active reconstruction framework based on 2D Gaussian Splatting (2DGS). We develop an efficient online 2DGS mapping pipeline for incremental RGB-D observations and introduce a probabilistic reliability model that characterizes the view-dependent reconstruction quality of individual 2D Gaussian splats. Building on this model, we formulate 2D Gaussian Splatting Shannon Mutual Information (2D GauSS-MI), a mutual-information-based metric that exploits the explicit surface orientation of 2DGS to evaluate the expected information gain of candidate views. The proposed metric enables active view selection to account for both visual and geometric reconstruction quality. We evaluate the proposed system against three state-of-the-art baselines on eight Replica scenes. Experimental results demonstrate that our method achieves a favorable balance between visual and geometric reconstruction quality with substantially lower computational cost and competitive model storage.
Computer Vision and Pattern Recognition (cs.CV); Robotics (cs.RO)
arXiv:2609.21516 [cs.CV]
(or arXiv:2609.21516v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2609.21516 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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