# BVE + EKF: A viewpoint estimator for the estimation of the object's position in the 3D task space using Extended Kalman Filters

arXiv: https://arxiv.org/abs/2406.03591 · HTML: https://mlsanity.com/paper/2406.03591/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 30 references that are in the map.
- First posted on arXiv 5 Jun 2024.

## Builds on (most cited first, 5 of 5)

- [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)
- [NeRF-Pose: A First-Reconstruct-Then-Regress Approach for Weakly-supervised 6D Object Pose Estimation](https://mlsanity.com/paper/2203.04802/) (2022)
- [MORE: Simultaneous Multi-View 3D Object Recognition and Pose Estimation](https://mlsanity.com/paper/2103.09863/) (2021)
- [MonoVisual3DFilter: 3D tomatoes' localisation with monocular cameras using histogram filters](https://mlsanity.com/paper/2310.05762/) (2023)

## Abstract (arXiv)

RGB-D sensors face multiple challenges operating under open-field environments because of their sensitivity to external perturbations such as radiation or rain. Multiple works are approaching the challenge of perceiving the 3D position of objects using monocular cameras. However, most of these works focus mainly on deep learning-based solutions, which are complex, data-driven, and difficult to predict. So, we aim to approach the problem of predicting the 3D objects' position using a Gaussian viewpoint estimator named best viewpoint estimator (BVE) powered by an extended Kalman filter (EKF). The algorithm proved efficient on the tasks and reached a maximum average Euclidean error of about 32 mm. The experiments were deployed and evaluated in MATLAB using artificial Gaussian noise. Future work aims to implement the system in a robotic system.