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

12/08/2015
by   Francisco Massa, et al.
0

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.

READ FULL TEXT

page 2

page 6

page 7

research
02/20/2018

Scale Optimization for Full-Image-CNN Vehicle Detection

Many state-of-the-art general object detection methods make use of share...
research
04/22/2016

Synthetic Data for Text Localisation in Natural Images

In this paper we introduce a new method for text detection in natural im...
research
11/05/2017

Strategies for Conceptual Change in Convolutional Neural Networks

A remarkable feature of human beings is their capacity for creative beha...
research
12/06/2017

Deep Regionlets for Object Detection

A key challenge in generic object detection is being to handle large var...
research
11/24/2020

Insights From A Large-Scale Database of Material Depictions In Paintings

Deep learning has paved the way for strong recognition systems which are...
research
07/20/2017

An All-in-One Network for Dehazing and Beyond

This paper proposes an image dehazing model built with a convolutional n...
research
09/27/2018

Compressing the Input for CNNs with the First-Order Scattering Transform

We study the first-order scattering transform as a candidate for reducin...

Please sign up or login with your details

Forgot password? Click here to reset