Good and fast 3D object detection by using simulated depth data
Good and fast 3D object detection by using simulated depth data
Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth
arXiv paper abstract https://arxiv.org/abs/2107.13269v1
arXiv PDF paper https://arxiv.org/pdf/2107.13269v1.pdf
Current geometry-based monocular 3D object detection models can efficiently detect objects by leveraging perspective geometry, but their performance is limited due to the absence of accurate depth information.
... propose a rendering module to augment the training data by synthesizing images with virtual-depths.
The rendering module takes as input the RGB image and its corresponding sparse depth image, outputs a variety of photo-realistic synthetic images, from which the detection model can learn more discriminative features to adapt to the depth changes of the objects.
... Both modules are working in the training time and no extra computation will be introduced to the detection model.
... leading accuracy on the KITTI 3D detection benchmark.
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