# Deep Cuboid Detection: Beyond 2D Bounding Boxes

arXiv: https://arxiv.org/abs/1611.10010 · HTML: https://mlsanity.com/paper/1611.10010/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2016.

## Built on by (most cited first, 6 of 6)

- [Cuboids Revisited: Learning Robust 3D Shape Fitting to Single RGB Images](https://mlsanity.com/paper/2105.02047/) (2021)
- [TetraPackNet: Four-Corner-Based Object Detection in Logistics Use-Cases](https://mlsanity.com/paper/2104.09123/) (2021)
- [Robust Shape Fitting for 3D Scene Abstraction](https://mlsanity.com/paper/2403.10452/) (2024)
- [6D Pose Estimation with Combined Deep Learning and 3D Vision Techniques for a Fast and Accurate Object Grasping](https://mlsanity.com/paper/2111.06276/) (2021)
- [Parcel3D: Shape Reconstruction from Single RGB Images for Applications in Transportation Logistics](https://mlsanity.com/paper/2304.08994/) (2023)
- [TAMPAR: Visual Tampering Detection for Parcel Logistics in Postal Supply Chains](https://mlsanity.com/paper/2311.03124/) (2023)

## Abstract (arXiv)

We present a Deep Cuboid Detector which takes a consumer-quality RGB image of a cluttered scene and localizes all 3D cuboids (box-like objects). Contrary to classical approaches which fit a 3D model from low-level cues like corners, edges, and vanishing points, we propose an end-to-end deep learning system to detect cuboids across many semantic categories (e.g., ovens, shipping boxes, and furniture). We localize cuboids with a 2D bounding box, and simultaneously localize the cuboid's corners, effectively producing a 3D interpretation of box-like objects. We refine keypoints by pooling convolutional features iteratively, improving the baseline method significantly. Our deep learning cuboid detector is trained in an end-to-end fashion and is suitable for real-time applications in augmented reality (AR) and robotics.