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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 751–775 of 876 papers

TitleStatusHype
On the Synergies between Machine Learning and Binocular Stereo for Depth Estimation from Images: a Survey—0
Optical Lens Attack on Deep Learning Based Monocular Depth Estimation—0
Optical Lens Attack on Monocular Depth Estimation for Autonomous Driving—0
OrchardDepth: Precise Metric Depth Estimation of Orchard Scene from Monocular Camera Images—0
Outdoor Monocular Depth Estimation: A Research Review—0
P2D: a self-supervised method for depth estimation from polarimetry—0
PanoDepth: A Two-Stage Approach for Monocular Omnidirectional Depth Estimation—0
Parameter-efficient Bayesian Neural Networks for Uncertainty-aware Depth Estimation—0
Pattern-Affinitive Propagation across Depth, Surface Normal and Semantic Segmentation—0
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds—0
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey—0
Photo-realistic Neural Domain Randomization—0
Physical Adversarial Attack on Monocular Depth Estimation via Shape-Varying Patches—0
Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transport—0
Pixel-Pair Occlusion Relationship Map (P2ORM): Formulation, Inference & Application—0
Playing for Depth—0
PLG-IN: Pluggable Geometric Consistency Loss with Wasserstein Distance in Monocular Depth Estimation—0
PMPNet: Pixel Movement Prediction Network for Monocular Depth Estimation in Dynamic Scenes—0
Polarimetric Imaging for Perception—0
PolyMaX: General Dense Prediction with Mask Transformer—0
PPEA-Depth: Progressive Parameter-Efficient Adaptation for Self-Supervised Monocular Depth Estimation—0
Pre-training Auto-regressive Robotic Models with 4D Representations—0
PriorDiffusion: Leverage Language Prior in Diffusion Models for Monocular Depth Estimation—0
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction—0
Promoting CNNs with Cross-Architecture Knowledge Distillation for Efficient Monocular Depth Estimation—0
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