ClementPinard/SfmLearner-Pytorch

Health v2 · maintenance only

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1031 stars · 224 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch reimplementation of SfMLearner (Zhou et al., CVPR 2017), which jointly learns monocular depth estimation and camera ego-motion from unlabeled video using photometric loss. It includes dataset preparation for KITTI and Cityscapes, training and evaluation scripts, and adds faster on-the-fly frame stacking compared to the original TensorFlow implementation.

Use cases

• estimate depth from a single camera video
• train an unsupervised depth estimation model
• recover camera pose or trajectory from video
• reproduce the SfMLearner paper results in PyTorch
• predict disparity maps from monocular images
• monocular visual odometry with deep learning
• train depth and pose networks on KITTI or Cityscapes

When to choose

• You want to reproduce, study, or build on the SfMLearner CVPR 2017 paper
• You need a self-supervised depth-plus-ego-motion baseline trainable on KITTI or Cityscapes video
• You prefer a PyTorch codebase over the original TensorFlow implementation
• You want faster training with on-the-fly frame stacking and ground-truth comparison for validation

When to avoid

• You need production-ready, state-of-the-art depth estimation rather than a research baseline
• You want an off-the-shelf pretrained model for inference without training
• You are on recent PyTorch/CUDA versions without willingness to adapt code targeting PyTorch 1.0.1 and CUDA 10
• You need stereo, multi-camera depth, or a real-time SLAM system

Facets

library · maturity maintenance

deep-learning machine-learning computer-vision computer-vision deep-learning machine-learning python depth-estimation ego-motion pose-estimation unsupervised-learning self-supervised disparity monocular-depth visual-odometry kitti cityscapes research-code cvpr-2017 pytorch-implementation reimplementation gpu linux

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