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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
Index NetworkCode0
Deep Neighbor Layer Aggregation for Lightweight Self-Supervised Monocular Depth EstimationCode0
Deep Learning--Based Scene Simplification for Bionic VisionCode0
Improving Self-Supervised Single View Depth Estimation by Masking OcclusionCode0
D4D: An RGBD diffusion model to boost monocular depth estimationCode0
D^3epth: Self-Supervised Depth Estimation with Dynamic Mask in Dynamic ScenesCode0
Structure-Aware Residual Pyramid Network for Monocular Depth EstimationCode0
UniNet: A Unified Scene Understanding Network and Exploring Multi-Task Relationships through the Lens of Adversarial AttacksCode0
Structured Attention Guided Convolutional Neural Fields for Monocular Depth EstimationCode0
Cut-and-Splat: Leveraging Gaussian Splatting for Synthetic Data GenerationCode0
An Online Adaptation Method for Robust Depth Estimation and Visual Odometry in the Open WorldCode0
Improved Point Transformation Methods For Self-Supervised Depth PredictionCode0
HQDec: Self-Supervised Monocular Depth Estimation Based on a High-Quality DecoderCode0
Style Augmentation: Data Augmentation via Style RandomizationCode0
Hierarchical Neural Memory Network for Low Latency Event ProcessingCode0
Unsupervised Adversarial Depth Estimation using Cycled Generative NetworksCode0
Continual Learning of Unsupervised Monocular Depth from VideosCode0
Geometry meets semantics for semi-supervised monocular depth estimationCode0
Adversarial Manhole: Challenging Monocular Depth Estimation and Semantic Segmentation Models with Patch AttackCode0
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric ConstraintsCode0
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth EstimationCode0
Generating and Exploiting Probabilistic Monocular Depth EstimatesCode0
Consistency Regularisation for Unsupervised Domain Adaptation in Monocular Depth EstimationCode0
Adversarial Structure Matching for Structured Prediction TasksCode0
FUSE: Label-Free Image-Event Joint Monocular Depth Estimation via Frequency-Decoupled Alignment and Degradation-Robust FusionCode0
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