# MonoVisual3DFilter: 3D tomatoes' localisation with monocular cameras using histogram filters

arXiv: https://arxiv.org/abs/2310.05762 · HTML: https://mlsanity.com/paper/2310.05762/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 51 references that are in the map.
- First posted on arXiv 9 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfer](https://mlsanity.com/paper/1907.01341/) (2019)
- [3D Bounding Box Estimation Using Deep Learning and Geometry](https://mlsanity.com/paper/1612.00496/) (2016)
- [MiDaS v3.1 -- A Model Zoo for Robust Monocular Relative Depth Estimation](https://mlsanity.com/paper/2307.14460/) (2023)
- [Accurate Monocular Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving](https://mlsanity.com/paper/1903.11444/) (2019)
- [Rethinking Pseudo-LiDAR Representation](https://mlsanity.com/paper/2008.04582/) (2020)
- [NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation](https://mlsanity.com/paper/2203.04802/) (2022)
- [SilhoNet: An RGB Method for 6D Object Pose Estimation](https://mlsanity.com/paper/1809.06893/) (2018)
- [Peduncle Detection of Sweet Pepper for Autonomous Crop Harvesting - Combined Colour and 3D Information](https://mlsanity.com/paper/1701.08608/) (2017)
- [Evaluating the Single-Shot MultiBox Detector and YOLO Deep Learning Models for the Detection of Tomatoes in a Greenhouse](https://mlsanity.com/paper/2109.00810/) (2021)
- [MORE: Simultaneous Multi-View 3D Object Recognition and Pose Estimation](https://mlsanity.com/paper/2103.09863/) (2021)
- [Imitrob: Imitation Learning Dataset for Training and Evaluating 6D Object Pose Estimators](https://mlsanity.com/paper/2209.07976/) (2022)
- [Active localization of multiple targets using noisy relative measurements](https://mlsanity.com/paper/2002.09850/) (2020)

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

- [BVE + EKF: A viewpoint estimator for the estimation of the object's position in the 3D task space using Extended Kalman Filters](https://mlsanity.com/paper/2406.03591/) (2024)

## Abstract (arXiv)

Performing tasks in agriculture, such as fruit monitoring or harvesting, requires perceiving the objects' spatial position. RGB-D cameras are limited under open-field environments due to lightning interferences. So, in this study, we state to answer the research question: "How can we use and control monocular sensors to perceive objects' position in the 3D task space?" Towards this aim, we approached histogram filters (Bayesian discrete filters) to estimate the position of tomatoes in the tomato plant through the algorithm MonoVisual3DFilter. Two kernel filters were studied: the square kernel and the Gaussian kernel. The implemented algorithm was essayed in simulation, with and without Gaussian noise and random noise, and in a testbed at laboratory conditions. The algorithm reported a mean absolute error lower than 10 mm in simulation and 20 mm in the testbed at laboratory conditions with an assessing distance of about 0.5 m. So, the results are viable for real environments and should be improved at closer distances.