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Depth Estimation
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Depth Estimation is the task of measuring the distance of each pixel relative to the camera. Depth is extracted from either monocular (single) or stereo (multiple views of a scene) images. Traditional methods use multi-view geometry to find the relationship between the images. Newer methods can directly estimate depth by minimizing the regression loss, or by learning to generate a novel view from a sequence. The most popular benchmarks are KITTI and NYUv2. Models are typically evaluated according to a RMS metric.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
MonoCoP: Chain-of-Prediction for Monocular 3D Object Detection
LiftFeat: 3D Geometry-Aware Local Feature Matching
Learning Unknown Spoof Prompts for Generalized Face Anti-Spoofing Using Only Real Face Images
Self-Supervised Learning for Robotic Leaf Manipulation: A Hybrid Geometric-Neural Approach
DELTA: Dense Depth from Events and LiDAR using Transformer's Attention
VGLD: Visually-Guided Linguistic Disambiguation for Monocular Depth Scale Recovery
PosePilot: Steering Camera Pose for Generative World Models with Self-supervised Depth
In-situ and Non-contact Etch Depth Prediction in Plasma Etching via Machine Learning (ANN & BNN) and Digital Image Colorimetry
LMDepth: Lightweight Mamba-based Monocular Depth Estimation for Real-World Deployment
JointDiT: Enhancing RGB-Depth Joint Modeling with Diffusion Transformers
HoloTime: Taming Video Diffusion Models for Panoramic 4D Scene Generation
Dense Geometry Supervision for Underwater Depth Estimation
Occlusion-Aware Self-Supervised Monocular Depth Estimation for Weak-Texture Endoscopic Images
UniVG: A Generalist Diffusion Model for Unified Image Generation and Editing
DERD-Net: Learning Depth from Event-based Ray Densities
VistaDepth: Frequency Modulation With Bias Reweighting For Enhanced Long-Range Depth Estimation
Seurat: From Moving Points to Depth
TAPIP3D: Tracking Any Point in Persistent 3D Geometry
Enhancing Pothole Detection and Characterization: Integrated Segmentation and Depth Estimation in Road Anomaly Systems
Occlusion-Ordered Semantic Instance Segmentation
Privacy-Preserving Operating Room Workflow Analysis using Digital Twins
TSGS: Improving Gaussian Splatting for Transparent Surface Reconstruction via Normal and De-lighting Priors
Perception Encoder: The best visual embeddings are not at the output of the network
An Online Adaptation Method for Robust Depth Estimation and Visual Odometry in the Open World
TacoDepth: Towards Efficient Radar-Camera Depth Estimation with One-stage Fusion
Metric-Solver: Sliding Anchored Metric Depth Estimation from a Single Image
DeepWheel: Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation
Deep Learning-based Bathymetry Retrieval without In-situ Depths using Remote Sensing Imagery and SfM-MVS DSMs with Data Gaps
Aligning Generative Denoising with Discriminative Objectives Unleashes Diffusion for Visual Perception
Cut-and-Splat: Leveraging Gaussian Splatting for Synthetic Data Generation
Geo4D: Leveraging Video Generators for Geometric 4D Scene Reconstruction
FlashDepth: Real-time Streaming Video Depth Estimation at 2K Resolution
POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction
Stereo-LiDAR Fusion by Semi-Global Matching With Discrete Disparity-Matching Cost and Semidensification
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.