単眼画像からの自己教師あり学習に基づく深度推定
Self-Supervised Learning based Depth Estimation from Monocular Images
単眼RGB画像から深度マップを予測するため、ポーズ推定や効率的サブピクセル畳み込み補間、セマンティックセグメンテーションを組み合わせた自己教師あり学習ベースの深度推定モデルを提案し、カメラ内部パラメータを不要にしつつ天候拡張で汎化性能を高める。
著者: Mayank Poddar, Akash Mishra, Mohit Kewlani, Haoyang Pei
分類: cs.CV, cs.AI, cs.LG, cs.RO
原文アブストラクト
Depth Estimation has wide reaching applications in the field of Computer vision such as target tracking, augmented reality, and self-driving cars. The goal of Monocular Depth Estimation is to predict the depth map, given a 2D monocular RGB image as input. The traditional depth estimation methods are based on depth cues and used concepts like epipolar geometry. With the evolution of Convolutional Neural Networks, depth estimation has undergone tremendous strides. In this project, our aim is to explore possible extensions to existing SoTA Deep Learning based Depth Estimation Models and to see whether performance metrics could be further improved. In a broader sense, we are looking at the possibility of implementing Pose Estimation, Efficient Sub-Pixel Convolution Interpolation, Semantic Segmentation Estimation techniques to further enhance our proposed architecture and to provide fine-grained and more globally coherent depth map predictions. We also plan to do away with camera intrinsic parameters during training and apply weather augmentations to further generalize our model.