# OA-NBV: Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots

arXiv: https://arxiv.org/abs/2603.11072 · HTML: https://mlsanity.com/paper/2603.11072/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 37 references that are in the map.
- First posted on arXiv 10 Mar 2026.

## Builds on (most cited first, 21 of 21)

- [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024)
- [Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop](https://mlsanity.com/paper/1909.12828/) (2019)
- [RTMPose: Real-Time Multi-Person Pose Estimation based on MMPose](https://mlsanity.com/paper/2303.07399/) (2023)
- [CityPersons: A Diverse Dataset for Pedestrian Detection](https://mlsanity.com/paper/1702.05693/) (2017)
- [Instance-level Human Parsing via Part Grouping Network](https://mlsanity.com/paper/1808.00157/) (2018)
- [ActiveNeRF: Learning where to See with Uncertainty Estimation](https://mlsanity.com/paper/2209.08546/) (2022)
- [Revisiting Active Perception](https://mlsanity.com/paper/1603.02729/) (2016)
- [Uncertainty Guided Policy for Active Robotic 3D Reconstruction using Neural Radiance Fields](https://mlsanity.com/paper/2209.08409/) (2022)
- [GenNBV: Generalizable Next-Best-View Policy for Active 3D Reconstruction](https://mlsanity.com/paper/2402.16174/) (2024)
- [Neural Visibility Field for Uncertainty-Driven Active Mapping](https://mlsanity.com/paper/2406.06948/) (2024)
- [Reconstructing Humans with a Biomechanically Accurate Skeleton](https://mlsanity.com/paper/2503.21751/) (2025)
- [SAT-HMR: Real-Time Multi-Person 3D Mesh Estimation via Scale-Adaptive Tokens](https://mlsanity.com/paper/2411.19824/) (2024)
- [Where to Look Next: Learning Viewpoint Recommendations for Informative Trajectory Planning](https://mlsanity.com/paper/2203.02381/) (2022)
- [Proactive Multi-Camera Collaboration For 3D Human Pose Estimation](https://mlsanity.com/paper/2303.03767/) (2023)
- [Deep Reinforcement Learning for Active Human Pose Estimation](https://mlsanity.com/paper/2001.02024/) (2020)
- [Pred-NBV: Prediction-guided Next-Best-View for 3D Object Reconstruction](https://mlsanity.com/paper/2304.11465/) (2023)
- [Semantics-Aware Next-best-view Planning for Efficient Search and Detection of Task-relevant Plant Parts](https://mlsanity.com/paper/2306.09801/) (2023)
- [ViewActive: Active viewpoint optimization from a single image](https://mlsanity.com/paper/2409.09997/) (2024)
- [FastHMR: Accelerating Human Mesh Recovery via Token and Layer Merging with Diffusion Decoding](https://mlsanity.com/paper/2510.10868/) (2025)
- [Auto3R: Automated 3D Reconstruction and Scanning via Data-driven Uncertainty Quantification](https://mlsanity.com/paper/2512.04528/) (2025)
- [GS-NBV: a Geometry-based, Semantics-aware Viewpoint Planning Algorithm for Avocado Harvesting under Occlusions](https://mlsanity.com/paper/2506.23369/) (2025)

## Built on by (most cited first, 2 of 2)

- [INSPECT: Learning Robot View Selection from Assistant Use](https://mlsanity.com/paper/2609.20615/) (2026)
- [LEAP-NBV: Lightweight Edge Active-Perception for Foundation-Model Next-Best-View Planning](https://mlsanity.com/paper/2609.23974/) (2026)

## Abstract (arXiv)

We naturally step sideways or lean to see around the obstacle when our view is blocked, and recover a more informative observation. Enabling robots to make the same kind of viewpoint choice is critical for human-centered operations, including search, triage, and disaster response, where cluttered environments and partial visibility frequently degrade downstream perception. However, many Next-Best-View (NBV) methods primarily optimize generic exploration or long-horizon coverage, and do not explicitly target the immediate goal of obtaining a single usable observation of a partially occluded person under real motion constraints. We present Occlusion-Aware Next-Best-View Planning for Human-Centered Active Perception on Mobile Robots (OA-NBV), an occlusion-aware NBV pipeline that autonomously selects the next traversable viewpoint to obtain a more complete view of an occluded human. OA-NBV integrates perception and motion planning by scoring candidate viewpoints using a target-centric visibility model that accounts for occlusion, target scale, and target completeness, while restricting candidates to feasible robot poses. OA-NBV achieves over 90% success rate in both simulation and real-world trials, while baseline NBV methods degrade sharply under occlusion. Beyond success rate, OA-NBV improves observation quality: compared to the strongest baseline, it increases normalized target area by at least 81% and keypoint visibility by at least 58% across settings, making it a drop-in view-selection module for diverse human-centered downstream tasks.