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- W3035289617 endingPage "1627" @default.
- W3035289617 startingPage "1612" @default.
- W3035289617 abstract "Depth information is important for autonomous systems to perceive environments and estimate their own state. Traditional depth estimation methods, like structure from motion and stereo vision matching, are built on feature correspondences of multiple viewpoints. Meanwhile, the predicted depth maps are sparse. Inferring depth information from a single image (monocular depth estimation) is an ill-posed problem. With the rapid development of deep neural networks, monocular depth estimation based on deep learning has been widely studied recently and achieved promising performance in accuracy. Meanwhile, dense depth maps are estimated from single images by deep neural networks in an end-to-end manner. In order to improve the accuracy of depth estimation, different kinds of network frameworks, loss functions and training strategies are proposed subsequently. Therefore, we survey the current monocular depth estimation methods based on deep learning in this review. Initially, we conclude several widely used datasets and evaluation indicators in deep learning-based depth estimation. Furthermore, we review some representative existing methods according to different training manners: supervised, unsupervised and semi-supervised. Finally, we discuss the challenges and provide some ideas for future researches in monocular depth estimation." @default.
- W3035289617 created "2020-06-19" @default.
- W3035289617 creator A5001786564 @default.
- W3035289617 creator A5002199949 @default.
- W3035289617 creator A5005693040 @default.
- W3035289617 creator A5028570509 @default.
- W3035289617 creator A5064255140 @default.
- W3035289617 date "2020-06-10" @default.
- W3035289617 modified "2023-10-16" @default.
- W3035289617 title "Monocular depth estimation based on deep learning: An overview" @default.
- W3035289617 cites W125693051 @default.
- W3035289617 cites W1803059841 @default.
- W3035289617 cites W1833847639 @default.
- W3035289617 cites W1905829557 @default.
- W3035289617 cites W1965711210 @default.
- W3035289617 cites W1970504153 @default.
- W3035289617 cites W2029309217 @default.
- W3035289617 cites W2034392969 @default.
- W3035289617 cites W2071499765 @default.
- W3035289617 cites W2097117768 @default.
- W3035289617 cites W2120657032 @default.
- W3035289617 cites W2122765992 @default.
- W3035289617 cites W2132947399 @default.
- W3035289617 cites W2133665775 @default.
- W3035289617 cites W2135960718 @default.
- W3035289617 cites W2142284218 @default.
- W3035289617 cites W2144256191 @default.
- W3035289617 cites W2150066425 @default.
- W3035289617 cites W2151996626 @default.
- W3035289617 cites W2194775991 @default.
- W3035289617 cites W2245606284 @default.
- W3035289617 cites W2300779272 @default.
- W3035289617 cites W2313950937 @default.
- W3035289617 cites W2336968928 @default.
- W3035289617 cites W2340897893 @default.
- W3035289617 cites W2343077198 @default.
- W3035289617 cites W2436453945 @default.
- W3035289617 cites W2474281075 @default.
- W3035289617 cites W2520707372 @default.
- W3035289617 cites W2535516436 @default.
- W3035289617 cites W2555618208 @default.
- W3035289617 cites W2566832195 @default.
- W3035289617 cites W2592939477 @default.
- W3035289617 cites W2593414960 @default.
- W3035289617 cites W2604231069 @default.
- W3035289617 cites W2606794968 @default.
- W3035289617 cites W2608872540 @default.
- W3035289617 cites W2609883120 @default.
- W3035289617 cites W2622826443 @default.
- W3035289617 cites W2779522084 @default.
- W3035289617 cites W2793286375 @default.
- W3035289617 cites W2830339951 @default.
- W3035289617 cites W2860610442 @default.
- W3035289617 cites W2883362496 @default.
- W3035289617 cites W2884337350 @default.
- W3035289617 cites W2884367402 @default.
- W3035289617 cites W2887848798 @default.
- W3035289617 cites W2889061519 @default.
- W3035289617 cites W2891048446 @default.
- W3035289617 cites W2895401575 @default.
- W3035289617 cites W2897203992 @default.
- W3035289617 cites W2928601293 @default.
- W3035289617 cites W2934279571 @default.
- W3035289617 cites W2935854115 @default.
- W3035289617 cites W2936864631 @default.
- W3035289617 cites W2948647700 @default.
- W3035289617 cites W2949023359 @default.
- W3035289617 cites W2955189650 @default.
- W3035289617 cites W2958731371 @default.
- W3035289617 cites W2962721361 @default.
- W3035289617 cites W2962793481 @default.
- W3035289617 cites W2962804601 @default.
- W3035289617 cites W2962816904 @default.
- W3035289617 cites W2963359474 @default.
- W3035289617 cites W2963412495 @default.
- W3035289617 cites W2963488291 @default.
- W3035289617 cites W2963583471 @default.
- W3035289617 cites W2963591054 @default.
- W3035289617 cites W2963652981 @default.
- W3035289617 cites W2963654727 @default.
- W3035289617 cites W2963906250 @default.
- W3035289617 cites W2964014680 @default.
- W3035289617 cites W2964020152 @default.
- W3035289617 cites W2964024144 @default.
- W3035289617 cites W2964051675 @default.
- W3035289617 cites W2964062189 @default.
- W3035289617 cites W2964314455 @default.
- W3035289617 cites W2964968086 @default.
- W3035289617 cites W2967115342 @default.
- W3035289617 cites W2968014831 @default.
- W3035289617 cites W2968288611 @default.
- W3035289617 cites W2968529893 @default.
- W3035289617 cites W2970056511 @default.
- W3035289617 cites W2972093541 @default.
- W3035289617 cites W2981353488 @default.
- W3035289617 cites W2981732213 @default.
- W3035289617 cites W2982102242 @default.
- W3035289617 cites W2983393775 @default.