3D Pose Regression using Convolutional Neural Networks

TL;DR

This paper introduces a CNN-based regression method for 3D pose estimation that models the continuous pose space, demonstrating competitive results on PASCAL3D+ by leveraging geometry-aware representations and data augmentation.

Contribution

It proposes a novel CNN regression framework for 3D pose estimation that better captures the continuous nature of pose space, improving upon discretization-based methods.

Findings

Achieves competitive accuracy on PASCAL3D+ dataset.

Outperforms pose classification approaches in certain metrics.

Utilizes geometry-aware loss functions and data augmentation.

Abstract

3D pose estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding. Most state-of-the-art approaches to 3D pose estimation solve this problem as a pose-classification problem in which the pose space is discretized into bins and a CNN classifier is used to predict a pose bin. We argue that the 3D pose space is continuous and propose to solve the pose estimation problem in a CNN regression framework with a suitable representation, data augmentation and loss function that captures the geometry of the pose space. Experiments on PASCAL3D+ show that the proposed 3D pose regression approach achieves competitive performance compared to the state-of-the-art.