# OctoSplat: Hybrid OctoMap-Gaussian Splatting for Active Semantic Mapping and Phenotyping with Horticultural Robots

arXiv: https://arxiv.org/abs/2601.12122 · HTML: https://mlsanity.com/paper/2601.12122/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 23 of its 43 references that are in the map.
- First posted on arXiv 17 Jan 2026.

## Builds on (most cited first, 23 of 23)

- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection](https://mlsanity.com/paper/2303.05499/) (2023)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [Detecting Twenty-thousand Classes using Image-level Supervision](https://mlsanity.com/paper/2201.02605/) (2022)
- [SGS-SLAM: Semantic Gaussian Splatting For Neural Dense SLAM](https://mlsanity.com/paper/2402.03246/) (2024)
- [GS-Planner: A Gaussian-Splatting-based Planning Framework for Active High-Fidelity Reconstruction](https://mlsanity.com/paper/2405.10142/) (2024)
- [ActiveGS: Active Scene Reconstruction Using Gaussian Splatting](https://mlsanity.com/paper/2412.17769/) (2024)
- [ActiveSplat: High-Fidelity Scene Reconstruction through Active Gaussian Splatting](https://mlsanity.com/paper/2410.21955/) (2024)
- [RT-GuIDE: Real-Time Gaussian Splatting for Information-Driven Exploration](https://mlsanity.com/paper/2409.18122/) (2024)
- [Semantic OcTree Mapping and Shannon Mutual Information Computation for Robot Exploration](https://mlsanity.com/paper/2112.04063/) (2021)
- [HGS-Planner: Hierarchical Planning Framework for Active Scene Reconstruction Using 3D Gaussian Splatting](https://mlsanity.com/paper/2409.17624/) (2024)
- [Panoptic Mapping with Fruit Completion and Pose Estimation for Horticultural Robots](https://mlsanity.com/paper/2303.08923/) (2023)
- [Wheat3DGS: In-field 3D Reconstruction, Instance Segmentation and Phenotyping of Wheat Heads with Gaussian Splatting](https://mlsanity.com/paper/2504.06978/) (2025)
- [Viewpoint Planning for Fruit Size and Position Estimation](https://mlsanity.com/paper/2011.00275/) (2020)
- [NBV-SC: Next Best View Planning based on Shape Completion for Fruit Mapping and Reconstruction](https://mlsanity.com/paper/2209.15376/) (2022)
- [4D Crop Monitoring: Spatio-Temporal Reconstruction for Agriculture](https://mlsanity.com/paper/1610.02482/) (2016)
- [ActiveInitSplat: How Active Image Selection Helps Gaussian Splatting](https://mlsanity.com/paper/2503.06859/) (2025)
- [Graph-based View Motion Planning for Fruit Detection](https://mlsanity.com/paper/2303.03048/) (2023)
- [Semantic Mapping for Orchard Environments by Merging Two-Sides Reconstructions of Tree Rows](https://mlsanity.com/paper/1809.00075/) (2018)
- [Autonomous Apple Fruitlet Sizing with Next Best View Planning](https://mlsanity.com/paper/2309.13669/) (2023)
- [Object-Centric 3D Gaussian Splatting for Strawberry Plant Reconstruction and Phenotyping](https://mlsanity.com/paper/2511.02207/) (2025)
- [Active Semantic Mapping with Mobile Manipulator in Horticultural Environments](https://mlsanity.com/paper/2412.10515/) (2024)

## Built on by (most cited first, 1 of 1)

- [ArborSplat: Online Semantic Gaussian Splatting SLAM for Orchards](https://mlsanity.com/paper/2609.26315/) (2026)

## Abstract (arXiv)

Semantic reconstruction of agricultural scenes plays a vital role in tasks such as phenotyping and yield estimation. However, traditional approaches based on manual scanning or fixed camera setups remain a major bottleneck, while active-mapping methods based solely on occupancy grids are too coarse for accurate trait estimation. To address this gap, we propose an active 3D reconstruction framework for horticultural environments using a mobile manipulator. The system integrates OctoMap with 3D Gaussian Splatting to enable accurate and efficient target-aware mapping. A low-resolution OctoMap provides probabilistic occupancy information for informative viewpoint selection and collision-free planning, while 3D Gaussian Splatting leverages geometric, photometric, and semantic information to optimize 3D Gaussians for high-fidelity scene reconstruction. We further introduce a robust mapping strategy that mitigates semantic segmentation and depth noise, together with a background pruning method that reduces memory and computational cost. We validate our framework across simulated, laboratory, and real greenhouse scenes, showing consistent improvements across three state-of-the-art Gaussian Splatting backbones. In simulation, where ground-truth geometry is available, our approach outperforms occupancy-based mapping in both reconstruction accuracy and runtime efficiency: compared with a 0.01m-resolution OctoMap, it doubles the fruit-level F1 score under noisy conditions while achieving up to a threefold reduction in runtime. Beyond simulation, novel-view synthesis quality also improves consistently in laboratory and real greenhouse environments, with PSNR and mIoU improving by up to 1.5 dB and 18%, respectively. Finally, the reconstructed semantic maps enable fruit counting and volume estimation with accuracies approaching 80%.