Monocular Depth Estimation
339 papers with code • 17 benchmarks • 27 datasets
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.
Benchmarks
These leaderboards are used to track progress in Monocular Depth Estimation
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Libraries
Use these libraries to find Monocular Depth Estimation models and implementationsDatasets
Cityscapes
KITTI
NYUv2
SUN RGB-D
Matterport3D
Make3D
DIODE
DDAD
Middlebury 2014
MannequinChallenge
Latest papers with no code
FutureDepth: Learning to Predict the Future Improves Video Depth Estimation
In this paper, we propose a novel video depth estimation approach, FutureDepth, which enables the model to implicitly leverage multi-frame and motion cues to improve depth estimation by making it learn to predict the future at training.
SSAP: A Shape-Sensitive Adversarial Patch for Comprehensive Disruption of Monocular Depth Estimation in Autonomous Navigation Applications
In this paper, we introduce SSAP (Shape-Sensitive Adversarial Patch), a novel approach designed to comprehensively disrupt monocular depth estimation (MDE) in autonomous navigation applications.
Touch-GS: Visual-Tactile Supervised 3D Gaussian Splatting
Optical tactile sensors have become widespread in their use in robotics for manipulation and object representation; however, raw optical tactile sensor data is unsuitable to directly supervise a 3DGS scene.
DD-VNB: A Depth-based Dual-Loop Framework for Real-time Visually Navigated Bronchoscopy
Specifically, the relative pose changes are fed into the registration process as the initial guess to boost its accuracy and speed.
Pyramid Feature Attention Network for Monocular Depth Prediction
Deep convolutional neural networks (DCNNs) have achieved great success in monocular depth estimation (MDE).
PCDepth: Pattern-based Complementary Learning for Monocular Depth Estimation by Best of Both Worlds
Therefore, existing complementary learning approaches for MDE fuse intensity information from images and scene details from event data for better scene understanding.
Zero-BEV: Zero-shot Projection of Any First-Person Modality to BEV Maps
Bird's-eye view (BEV) maps are an important geometrically structured representation widely used in robotics, in particular self-driving vehicles and terrestrial robots.
An Endoscopic Chisel: Intraoperative Imaging Carves 3D Anatomical Models
Purpose: Preoperative imaging plays a pivotal role in sinus surgery where CTs offer patient-specific insights of complex anatomy, enabling real-time intraoperative navigation to complement endoscopy imaging.
Unveiling the Depths: A Multi-Modal Fusion Framework for Challenging Scenarios
Monocular depth estimation from RGB images plays a pivotal role in 3D vision.
MAL: Motion-Aware Loss with Temporal and Distillation Hints for Self-Supervised Depth Estimation
To address this issue, we present Motion-Aware Loss, which leverages the temporal relation among consecutive input frames and a novel distillation scheme between the teacher and student networks in the multi-frame self-supervised depth estimation methods.
