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PyTorch implementation of Deep Ordinal Regression Network for Monocular Depth Estimation

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DORN

Update

The entire codebase has been updated, and some layers and loss functions have been reimplemented to make it running fast and using less memory. This respository only contains the core code of DORN model. The whole code will be saved in SupervisedDepthPrediction.

Introduction

This is a PyTorch implementation of Deep Ordinal Regression Network for Monocular Depth Estimation.

Pretrained Model

The resnet backbone of DORN, which has three conv in first conv layer, is different from original resnet. The pretrained model of the resnet backbone can download from MIT imagenet pretrained resnet101.

Datasets

NYU Depth V2

Not Implemented.

KITTI

According to the pull request, we should move away from eigen split and switch to kiiti depth benchmark. More details, please see SupervisedDepthPrediction.

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PyTorch implementation of Deep Ordinal Regression Network for Monocular Depth Estimation

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