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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 511–520 of 876 papers

TitleStatusHype
Robust Geometry-Preserving Depth Estimation Using Differentiable Rendering—0
Deep Neighbor Layer Aggregation for Lightweight Self-Supervised Monocular Depth EstimationCode0
Real-time Monocular Depth Estimation on Embedded Systems—0
AltNeRF: Learning Robust Neural Radiance Field via Alternating Depth-Pose Optimization—0
Robust Monocular Depth Estimation under Challenging Conditions—0
Discretization-Induced Dirichlet Posterior for Robust Uncertainty Quantification on RegressionCode0
Improving Depth Gradient Continuity in Transformers: A Comparative Study on Monocular Depth Estimation with CNN—0
FrozenRecon: Pose-free 3D Scene Reconstruction with Frozen Depth Models—0
SAAM: Stealthy Adversarial Attack on Monocular Depth Estimation—0
Robust Self-Supervised Extrinsic Self-Calibration—0
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