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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 651–700 of 876 papers

TitleStatusHype
X-Distill: Improving Self-Supervised Monocular Depth via Cross-Task Distillation—0
Pseudo Supervised Monocular Depth Estimation with Teacher-Student Network—0
Self-Supervised Monocular Scene Decomposition and Depth Estimation—0
Depth360: Self-supervised Learning for Monocular Depth Estimation using Learnable Camera Distortion Model—0
Attention meets Geometry: Geometry Guided Spatial-Temporal Attention for Consistent Self-Supervised Monocular Depth Estimation—0
Plugging Self-Supervised Monocular Depth into Unsupervised Domain Adaptation for Semantic SegmentationCode0
Attentive and Contrastive Learning for Joint Depth and Motion Field Estimation—0
Monocular Depth Estimation with Sharp Boundary—0
D-Net: A Generalised and Optimised Deep Network for Monocular Depth EstimationCode0
f-Cal: Calibrated aleatoric uncertainty estimation from neural networks for robot perception—0
Weakly-Supervised Monocular Depth Estimationwith Resolution-Mismatched Data—0
Multi-task learning from fixed-wing UAV images for 2D/3D city modeling—0
Monocular Depth Estimation Primed by Salient Point Detection and Normalized Hessian Loss—0
Lightweight Monocular Depth with a Novel Neural Architecture Search Method—0
Panoramic Depth Estimation via Supervised and Unsupervised Learning in Indoor ScenesCode0
UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial AttacksCode0
R4Dyn: Exploring Radar for Self-Supervised Monocular Depth Estimation of Dynamic Scenes—0
Visual Domain Adaptation for Monocular Depth Estimation on Resource-Constrained HardwareCode0
Pix2Point: Learning Outdoor 3D Using Sparse Point Clouds and Optimal Transport—0
CI-Net: Contextual Information for Joint Semantic Segmentation and Depth Estimation—0
BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation—0
MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environments—0
MSFNet:Multi-scale features network for monocular depth estimation—0
A Weakly-Supervised Depth Estimation Network Using Attention Mechanism—0
Neighbor-Vote: Improving Monocular 3D Object Detection through Neighbor Distance VotingCode0
Extraction of Key-frames of Endoscopic Videos by using Depth Information—0
Fractal Pyramid Networks—0
Exploring Depth Contribution for Camouflaged Object Detection—0
SGTBN: Generating Dense Depth Maps from Single-Line LiDAR—0
EdgeConv with Attention Module for Monocular Depth Estimation—0
A Hybrid mmWave and Camera System for Long-Range Depth Imaging—0
Real-time Monocular Depth Estimation with Sparse Supervision on Mobile—0
Improving Online Performance Prediction for Semantic Segmentation—0
Domain Adaptive Monocular Depth Estimation With Semantic Information—0
Geometric Unsupervised Domain Adaptation for Semantic Segmentation—0
SaccadeCam: Adaptive Visual Attention for Monocular Depth SensingCode0
Revisiting Self-Supervised Monocular Depth EstimationCode0
Learning a Domain-Agnostic Visual Representation for Autonomous Driving via Contrastive Loss—0
Multimodal Scale Consistency and Awareness for Monocular Self-Supervised Depth EstimationCode0
ADAADepth: Adapting Data Augmentation and Attention for Self-Supervised Monocular Depth Estimation—0
Improved Point Transformation Methods For Self-Supervised Depth PredictionCode0
Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidance—0
Deep Learning--Based Scene Simplification for Bionic VisionCode0
SOSD-Net: Joint Semantic Object Segmentation and Depth Estimation from Monocular images—0
Probabilistic Graph Attention Network with Conditional Kernels for Pixel-Wise Prediction—0
Monocular Depth Estimation for Soft Visuotactile Sensors—0
Revealing the Reciprocal Relations Between Self-Supervised Stereo and Monocular Depth Estimation—0
Pseudo Label-Guided Multi Task Learning for Scene Understanding—0
Can Scale-Consistent Monocular Depth Be Learned in a Self-Supervised Scale-Invariant Manner?—0
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization—0
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