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ActiveGAMER: Active GAussian Mapping through Efficient Rendering

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arxiv 2501.06897 v3 pith:QGJWDWXB submitted 2025-01-12 cs.CV cs.RO

classification cs.CVcs.RO
keywords mappingactiveactivegamerefficientexplorationsystemaccuracycompleteness
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We introduce ActiveGAMER, an active mapping system that utilizes 3D Gaussian Splatting (3DGS) to achieve high-quality, real-time scene mapping and exploration. Unlike traditional NeRF-based methods, which are computationally demanding and restrict active mapping performance, our approach leverages the efficient rendering capabilities of 3DGS, allowing effective and efficient exploration in complex environments. The core of our system is a rendering-based information gain module that dynamically identifies the most informative viewpoints for next-best-view planning, enhancing both geometric and photometric reconstruction accuracy. ActiveGAMER also integrates a carefully balanced framework, combining coarse-to-fine exploration, post-refinement, and a global-local keyframe selection strategy to maximize reconstruction completeness and fidelity. Our system autonomously explores and reconstructs environments with state-of-the-art geometric and photometric accuracy and completeness, significantly surpassing existing approaches in both aspects. Extensive evaluations on benchmark datasets such as Replica and MP3D highlight ActiveGAMER's effectiveness in active mapping tasks.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. JointSplat: Probabilistic Joint Flow-Depth Optimization for Sparse-View Gaussian Splatting

    cs.CV 2025-06 conditional novelty 6.0 of 10

    A feed-forward 3D Gaussian splatting method fuses depth and optical flow via a learned reliability mask, improving novel-view PSNR on RealEstate10K by 0.19 dB over its backbone.

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