# Diver Interest via Pointing: Human-Directed Object Inspection for AUVs

arXiv: https://arxiv.org/abs/2212.01205 · HTML: https://mlsanity.com/paper/2212.01205/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2022.

## Built on by (most cited first, 4 of 4)

- [GesVLA: Gesture-Aware Vision-Language-Action Model Embedded Representations](https://mlsanity.com/paper/2605.22812/) (2026)
- [GIVE: Grounding Human Gestures in Vision-Language-Action Models](https://mlsanity.com/paper/2606.13435/) (2026)
- [A comparison of RL-based and PID controllers for 6-DOF swimming robots: hybrid underwater object tracking](https://mlsanity.com/paper/2401.16618/) (2024)
- [SafeDiver: Cooperative AUV-USV Assisted Diver Communication via Multi-agent Reinforcement Learning Approach](https://mlsanity.com/paper/2509.11508/) (2025)

## Abstract (arXiv)

In this paper, we present the Diver Interest via Pointing (DIP) algorithm, a highly modular method for conveying a diver's area of interest to an autonomous underwater vehicle (AUV) using pointing gestures for underwater human-robot collaborative tasks. DIP uses a single monocular camera and exploits human body pose, even with complete dive gear, to extract underwater human pointing gesture poses and their directions. By extracting 2D scene geometry based on the human body pose and density of salient feature points along the direction of pointing, using a low-level feature detector, the DIP algorithm is able to locate objects of interest as indicated by the diver.