3D Pose Estimation for Vehicles

Introduction

This work generates 4 key-points and 2 key-edges from vertices and edges of vehicles as ground truth. The brightness of each generated heatmap is normal distribution. The model is combined with Resnet-50 for feature generation and three transpose-convolution layers for generation of heatmaps.

This code was adapted from an official pytorch implementation of Simple Baselines for Human Pose Estimation and Tracking. This work provides baseline methods that are surprisingly simple and effective, thus helpful for inspiring and evaluating new ideas for the field. State-of-the-art results are achieved on challenging benchmarks. On COCO keypoints valid dataset, their best single model achieves 74.3 of mAP.

Results

Training after 4 epochs (27000+ samples per epoch)
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GitHub

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