monodepth2 and monodepth_benchmark
The benchmark tool, B, complements the monocular depth estimation tool, A, by providing an evaluation framework to assess and compare the performance of different design decisions, including those implemented in models like A, within the broader context of monocular depth reconstruction.
About monodepth2
nianticlabs/monodepth2
[ICCV 2019] Monocular depth estimation from a single image
This project helps computer vision and robotics engineers accurately estimate the distance of objects in a scene using only a single 2D image. It takes a standard image as input and outputs a 'depth map' or 'metric depth' that shows how far away each point in the image is. This is useful for tasks like autonomous navigation, 3D reconstruction, and augmented reality, where understanding spatial relationships from camera feeds is crucial.
About monodepth_benchmark
jspenmar/monodepth_benchmark
Code for "Deconstructing Monocular Depth Reconstruction: The Design Decisions that Matter" (https://arxiv.org/abs/2208.01489)
This project helps researchers and engineers analyze and understand how different design choices impact the performance of self-supervised monocular depth estimation models. By taking raw image sequences and various model configurations as input, it produces depth maps and performance metrics. This is useful for computer vision researchers, autonomous driving engineers, and anyone working on 3D reconstruction from 2D images.
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