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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 201–250 of 876 papers

TitleStatusHype
Deeper Depth Prediction with Fully Convolutional Residual NetworksCode1
OceanLens: An Adaptive Backscatter and Edge Correction using Deep Learning Model for Enhanced Underwater ImagingCode1
On the uncertainty of self-supervised monocular depth estimationCode1
Image Masking for Robust Self-Supervised Monocular Depth EstimationCode1
EndoDepth: A Benchmark for Assessing Robustness in Endoscopic Depth PredictionCode1
IEBins: Iterative Elastic Bins for Monocular Depth EstimationCode1
An intelligent modular real-time vision-based system for environment perceptionCode1
HSPFormer: Hierarchical Spatial Perception Transformer for Semantic SegmentationCode1
HR-Depth: High Resolution Self-Supervised Monocular Depth EstimationCode1
Detecting Invisible PeopleCode1
A benchmark with decomposed distribution shifts for 360 monocular depth estimationCode1
Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that MatterCode1
A geometry-aware deep network for depth estimation in monocular endoscopyCode1
Implicit Integration of Superpixel Segmentation into Fully Convolutional NetworksCode1
Latent Discriminant deterministic UncertaintyCode1
Advancing Self-supervised Monocular Depth Learning with Sparse LiDARCode1
Digging Into Uncertainty-based Pseudo-label for Robust Stereo MatchingCode1
GroCo: Ground Constraint for Metric Self-Supervised Monocular DepthCode1
Guiding Monocular Depth Estimation Using Depth-Attention VolumeCode1
Prompt Guided Transformer for Multi-Task Dense PredictionCode1
RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth EstimationCode1
RCDPT: Radar-Camera fusion Dense Prediction TransformerCode1
DCDepth: Progressive Monocular Depth Estimation in Discrete Cosine DomainCode1
Aerial Single-View Depth Completion with Image-Guided Uncertainty EstimationCode1
Gradient-based Uncertainty for Monocular Depth EstimationCode1
Distilled Semantics for Comprehensive Scene Understanding from VideosCode1
Harnessing Diffusion Models for Visual Perception with Meta PromptsCode1
Excavating the Potential Capacity of Self-Supervised Monocular Depth EstimationCode1
A Practical Stereo Depth System for Smart GlassesCode1
Revealing the Dark Secrets of Masked Image ModelingCode1
DaRF: Boosting Radiance Fields from Sparse Inputs with Monocular Depth AdaptationCode1
Global and Hierarchical Geometry Consistency Priors for Few-shot NeRFs in Indoor ScenesCode1
S2R-DepthNet: Learning a Generalizable Depth-specific Structural RepresentationCode1
Scalable Vision-Based 3D Object Detection and Monocular Depth Estimation for Autonomous DrivingCode1
DARES: Depth Anything in Robotic Endoscopic Surgery with Self-supervised Vector-LoRA of the Foundation ModelCode1
Self-distilled Feature Aggregation for Self-supervised Monocular Depth EstimationCode1
GCNDepth: Self-supervised Monocular Depth Estimation based on Graph Convolutional NetworkCode1
Self-supervised Adversarial Training of Monocular Depth Estimation against Physical-World AttacksCode1
GEDepth: Ground Embedding for Monocular Depth EstimationCode1
A Study on Self-Supervised Pretraining for Vision Problems in Gastrointestinal EndoscopyCode1
Global-Local Path Networks for Monocular Depth Estimation with Vertical CutDepthCode1
Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth PredictionCode1
Dyna-DM: Dynamic Object-aware Self-supervised Monocular Depth MapsCode1
A Study on the Generality of Neural Network Structures for Monocular Depth EstimationCode1
EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenesCode1
High Quality Monocular Depth Estimation via Transfer LearningCode1
Feature-metric Loss for Self-supervised Learning of Depth and EgomotionCode1
Self-supervised Monocular Trained Depth Estimation using Self-attention and Discrete Disparity VolumeCode1
FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier ConvolutionsCode1
Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World AttacksCode1
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