# Deep Exemplar 2D-3D Detection by Adapting from Real to Rendered Views

arXiv: https://arxiv.org/abs/1512.02497 · HTML: https://mlsanity.com/paper/1512.02497/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Dec 2015.

## Built on by (most cited first, 7 of 7)

- [Patch2CAD: Patchwise Embedding Learning for In-the-Wild Shape Retrieval from a Single Image](https://mlsanity.com/paper/2108.09368/) (2021)
- [PoseContrast: Class-Agnostic Object Viewpoint Estimation in the Wild with Pose-Aware Contrastive Learning](https://mlsanity.com/paper/2105.05643/) (2021)
- [Generalizing Single-View 3D Shape Retrieval to Occlusions and Unseen Objects](https://mlsanity.com/paper/2401.00405/) (2023)
- [PyTorchGeoNodes: Enabling Differentiable Shape Programs for 3D Shape Reconstruction](https://mlsanity.com/paper/2404.10620/) (2024)
- [HOC-Search: Efficient CAD Model and Pose Retrieval from RGB-D Scans](https://mlsanity.com/paper/2309.06107/) (2023)
- [Zero in on Shape: A Generic 2D-3D Instance Similarity Metric learned from Synthetic Data](https://mlsanity.com/paper/2108.04091/) (2021)
- [A Novel Convolution and Attention Mechanism-based Model for 6D Object Pose Estimation](https://mlsanity.com/paper/2501.01993/) (2024)

## Abstract (arXiv)

This paper presents an end-to-end convolutional neural network (CNN) for 2D-3D exemplar detection. We demonstrate that the ability to adapt the features of natural images to better align with those of CAD rendered views is critical to the success of our technique. We show that the adaptation can be learned by compositing rendered views of textured object models on natural images. Our approach can be naturally incorporated into a CNN detection pipeline and extends the accuracy and speed benefits from recent advances in deep learning to 2D-3D exemplar detection. We applied our method to two tasks: instance detection, where we evaluated on the IKEA dataset, and object category detection, where we out-perform Aubry et al. for "chair" detection on a subset of the Pascal VOC dataset.