# Photorealistic Image Synthesis for Object Instance Detection

## TL;DR

This paper introduces a photorealistic image synthesis method for training object detectors, significantly improving detection accuracy without using real images, by rendering realistic scenes with physics-based techniques.

## Contribution

The authors develop a novel synthesis approach combining realistic rendering, physics-based scene configuration, and scene-level rendering to enhance object detection training.

## Key findings

- 24% improvement in mAP@.75IoU on Rutgers APC dataset
- 11% improvement in mAP@.75IoU on LineMod-Occluded dataset
- Synthetic images outperform baseline in object detection accuracy

## Abstract

We present an approach to synthesize highly photorealistic images of 3D object models, which we use to train a convolutional neural network for detecting the objects in real images. The proposed approach has three key ingredients: (1) 3D object models are rendered in 3D models of complete scenes with realistic materials and lighting, (2) plausible geometric configuration of objects and cameras in a scene is generated using physics simulations, and (3) high photorealism of the synthesized images achieved by physically based rendering. When trained on images synthesized by the proposed approach, the Faster R-CNN object detector achieves a 24% absolute improvement of mAP@.75IoU on Rutgers APC and 11% on LineMod-Occluded datasets, compared to a baseline where the training images are synthesized by rendering object models on top of random photographs. This work is a step towards being able to effectively train object detectors without capturing or annotating any real images. A dataset of 600K synthetic images with ground truth annotations for various computer vision tasks will be released on the project website: thodan.github.io/objectsynth.