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Monocular Depth Estimation

Monocular Depth Estimation is the task of estimating the depth value (distance relative to the camera) of each pixel given a single (monocular) RGB image. This challenging task is a key prerequisite for determining scene understanding for applications such as 3D scene reconstruction, autonomous driving, and AR. State-of-the-art methods usually fall into one of two categories: designing a complex network that is powerful enough to directly regress the depth map, or splitting the input into bins or windows to reduce computational complexity. The most popular benchmarks are the KITTI and NYUv2 datasets. Models are typically evaluated using RMSE or absolute relative error.

Source: Defocus Deblurring Using Dual-Pixel Data

Papers

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Showing 621–630 of 876 papers

TitleStatusHype
Monocular Depth Estimation through Virtual-world Supervision and Real-world SfM Self-SupervisionCode1
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss—0
Virtual Normal: Enforcing Geometric Constraints for Accurate and Robust Depth PredictionCode2
Implicit Integration of Superpixel Segmentation into Fully Convolutional NetworksCode1
Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth EstimationCode0
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation—0
Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth PredictionCode1
Improved Point Transformation Methods For Self-Supervised Depth PredictionCode0
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance—0
Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection ConsistencyCode1
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