mlsanityData to 14 Oct 2026

ActiveGAMER: Active GAussian Mapping through Efficient Rendering

arXiv 2501.06897 ↗First posted 12 Jan 2025cs.CV cs.ROcited by 19

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Abstract (arXiv)

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.

Key facts. Cited by 19 papers in the map (data through 14 Oct 2026). Builds on 44 of its 75 references that are in the map. First posted on arXiv 12 Jan 2025.

Builds on

44 of 75 references.

References · 44

  1. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  2. 2022Instant Neural Graphics Primitives with a Multiresolution Hash Encodingcited by 3,723
  3. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,689
  4. 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,584
  5. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,465
  6. 2017Matterport3D: Learning from RGB-D Data in Indoor Environmentscited by 1,376
  7. 2014Depth Map Prediction from a Single Image using a Multi-Scale Deep Networkcited by 1,274
  8. 2019Habitat: A Platform for Embodied AI Researchcited by 1,191
  9. 20242D Gaussian Splatting for Geometrically Accurate Radiance Fieldscited by 1,076
  10. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 964
  11. 2019The Replica Dataset: A Digital Replica of Indoor Spacescited by 904
  12. 2020NeRF++: Analyzing and Improving Neural Radiance Fieldscited by 861
32 more
  1. 2018MVSNet: Depth Inference for Unstructured Multi-view Stereocited by 788
  2. 2020GIRAFFE: Representing Scenes as Compositional Generative Neural Feature Fieldscited by 785
  3. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 689
  4. 2023Mip-Splatting: Alias-free 3D Gaussian Splattingcited by 667
  5. 2021BARF: Bundle-Adjusting Neural Radiance Fieldscited by 642
  6. 2021iMAP: Implicit Mapping and Positioning in Real-Timecited by 476
  7. 2023Gaussian Splatting SLAMcited by 415
  8. 2016Past, Present, and Future of Simultaneous Localization And Mapping: Towards the Robust-Perception Agecited by 411
  9. 2023SplaTAM: Splat, Track & Map 3D Gaussians for Dense RGB-D SLAMcited by 388
  10. 2020Learning to Explore using Active Neural SLAMcited by 316
  11. 2021Neural RGB-D Surface Reconstructioncited by 313
  12. 2019Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Videocited by 244
  13. 2018Unsupervised Learning of Monocular Depth Estimation and Visual Odometry with Deep Feature Reconstructioncited by 159
  14. 2021Vision-Only Robot Navigation in a Neural Radiance Worldcited by 155
  15. 2022ActiveNeRF: Learning where to See with Uncertainty Estimationcited by 127
  16. 2020Occupancy Anticipation for Efficient Exploration and Navigationcited by 106
  17. 2022GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimationcited by 86
  18. 2022A Survey on Active Simultaneous Localization and Mapping: State of the Art and New Frontierscited by 66
  19. 2022NeurAR: Neural Uncertainty for Autonomous 3D Reconstruction with Implicit Neural Representationscited by 55
  20. 2022Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fieldscited by 54
  21. 2020Next-Best View Policy for 3D Reconstructioncited by 35
  22. 2022Uncertainty-driven Planner for Exploration and Navigationcited by 33
  23. 2024ActiveSplat: High-Fidelity Scene Reconstruction through Active Gaussian Splattingcited by 32
  24. 2023Active Neural Mappingcited by 31
  25. 2022ActiveRMAP: Radiance Field for Active Mapping And Planningcited by 30
  26. 2022PlaneMVS: 3D Plane Reconstruction from Multi-View Stereocited by 29
  27. 2022BNV-Fusion: Dense 3D Reconstruction using Bi-level Neural Volume Fusioncited by 29
  28. 2024NARUTO: Neural Active Reconstruction from Uncertain Target Observationscited by 28
  29. 2023Learning the Distribution of Errors in Stereo Matching for Joint Disparity and Uncertainty Estimationcited by 23
  30. 2022RIAV-MVS: Recurrent-Indexing an Asymmetric Volume for Multi-View Stereocited by 12
  31. 2024Active Neural Mapping at Scalecited by 8
  32. 2023EDI: ESKF-based Disjoint Initialization for Visual-Inertial SLAM Systemscited by 4

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 5; 2026: 14.

19 papers.

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  1. 2025GauSS-MI: Gaussian Splatting Shannon Mutual Information for Active 3D Reconstructioncited by 17
  2. 2025ActiveInitSplat: How Active Image Selection Helps Gaussian Splattingcited by 7
  3. 2025Understanding while Exploring: Semantics-driven Active Mappingcited by 5
  4. 2025Peering into the Unknown: Active View Selection with Neural Uncertainty Maps for 3D Reconstructioncited by 5
  5. 2026MAGICIAN: Efficient Long-Term Planning with Imagined Gaussians for Active Mappingcited by 5
  6. 2026Uncertainty-driven 3D Gaussian Splatting Active Mapping via Anisotropic Visibility Fieldcited by 5
  7. 2025JointSplat: Probabilistic Joint Flow-Depth Optimization for Sparse-View Gaussian Splattingcited by 3
  8. 2026Dream-SLAM: Dreaming the Unseen for Active SLAM in Dynamic Environmentscited by 3
  9. 2026SA-ResGS: Self-Augmented Residual 3D Gaussian Splatting for Next Best View Selectioncited by 2
  10. 2026B3-Seg: Camera-Free, Training-Free 3DGS Segmentation via Analytic EIG and Beta-Bernoulli Bayesian Updatescited by 2
  11. 2026Active View Selection with Perturbed Gaussian Ensemble for Tomographic Reconstructioncited by 2
  12. 2026Informative Object-centric Next Best View for Object-aware 3D Gaussian Splatting in Cluttered Scenescited by 1
7 more
  1. 2026GO-PRE: Goal-Oriented Next-Best-View Selection via Predictive Rendering Entropy for Active 3D Reconstructioncited by 1
  2. 2026R2S-EGO: Dual-Proxy Refinement for Sparse-Capture Real-to-Simcited by 1
  3. 2026Remember to be Curious: Episodic Context and Persistent Worlds for 3D Explorationcited by 0
  4. 2026TRACE: Ergodic Trajectory Optimization for Active Scene Reconstructioncited by 0
  5. 2026AGILE-GS: Anchor-Guided Fast Next-Best-View Selection for Active 3D Gaussian Splattingcited by 0
  6. 2026Multi-Scale Semantic Mapping in Urban Environments via Observation Calibration and Policy Dependence Regularizationcited by 0
  7. 2026Matisse: Evidence-Space Reasoning for Active 3D Reconstructioncited by 0