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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 51–75 of 876 papers

TitleStatusHype
Semantically-Guided Representation Learning for Self-Supervised Monocular DepthCode2
Towards Scale-Aware, Robust, and Generalizable Unsupervised Monocular Depth Estimation by Integrating IMU Motion DynamicsCode2
DDP: Diffusion Model for Dense Visual PredictionCode2
Mono-ViFI: A Unified Learning Framework for Self-supervised Single- and Multi-frame Monocular Depth EstimationCode2
Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth EstimationCode2
BinsFormer: Revisiting Adaptive Bins for Monocular Depth EstimationCode2
A Simple Framework for 3D Occupancy Estimation in Autonomous DrivingCode2
Joint 2D-3D Multi-Task Learning on Cityscapes-3D: 3D Detection, Segmentation, and Depth EstimationCode2
InvPT: Inverted Pyramid Multi-task Transformer for Dense Scene UnderstandingCode2
Kick Back & Relax++: Scaling Beyond Ground-Truth Depth with SlowTV & CribsTVCode2
HybridDepth: Robust Metric Depth Fusion by Leveraging Depth from Focus and Single-Image PriorsCode2
A Survey on RGB-D DatasetsCode2
Boosting Monocular Depth Estimation Models to High-Resolution via Content-Adaptive Multi-Resolution MergingCode2
IDOL: Unified Dual-Modal Latent Diffusion for Human-Centric Joint Video-Depth GenerationCode2
DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving ApplicationsCode2
Enforcing geometric constraints of virtual normal for depth predictionCode2
ImOV3D: Learning Open-Vocabulary Point Clouds 3D Object Detection from Only 2D ImagesCode2
NeW CRFs: Neural Window Fully-connected CRFs for Monocular Depth EstimationCode2
Single-View View Synthesis in the Wild with Learned Adaptive Multiplane ImagesCode2
Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth EstimationCode1
Detecting Invisible PeopleCode1
A Study on the Generality of Neural Network Structures for Monocular Depth EstimationCode1
A Study on Self-Supervised Pretraining for Vision Problems in Gastrointestinal EndoscopyCode1
Absolute distance prediction based on deep learning object detection and monocular depth estimation modelsCode1
DEPTHOR: Depth Enhancement from a Practical Light-Weight dToF Sensor and RGB ImageCode1
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