# Synthesizing Training Data for Object Detection in Indoor Scenes

## TL;DR

This paper investigates using synthetic composite images to train deep CNN-based object detectors for indoor scenes, reducing the need for extensive manual annotation while maintaining high detection performance.

## Contribution

It introduces a method for generating synthetic training data by superimposing object models into real images, enhancing detector training with minimal manual labeling.

## Key findings

- Synthetic data can match performance of large labeled datasets.
- Combining synthetic and real data improves detection accuracy.
- Depth and semantics informed placement enhances training effectiveness.

## Abstract

Detection of objects in cluttered indoor environments is one of the key enabling functionalities for service robots. The best performing object detection approaches in computer vision exploit deep Convolutional Neural Networks (CNN) to simultaneously detect and categorize the objects of interest in cluttered scenes. Training of such models typically requires large amounts of annotated training data which is time consuming and costly to obtain. In this work we explore the ability of using synthetically generated composite images for training state-of-the-art object detectors, especially for object instance detection. We superimpose 2D images of textured object models into images of real environments at variety of locations and scales. Our experiments evaluate different superimposition strategies ranging from purely image-based blending all the way to depth and semantics informed positioning of the object models into real scenes. We demonstrate the effectiveness of these object detector training strategies on two publicly available datasets, the GMU-Kitchens and the Washington RGB-D Scenes v2. As one observation, augmenting some hand-labeled training data with synthetic examples carefully composed onto scenes yields object detectors with comparable performance to using much more hand-labeled data. Broadly, this work charts new opportunities for training detectors for new objects by exploiting existing object model repositories in either a purely automatic fashion or with only a very small number of human-annotated examples.