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Depth Estimation

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하위 태스크: 10
평가표: 14
보존된 평가 기록: 58

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.

Source: DIODE: A Dense Indoor and Outdoor DEpth Dataset

논문

이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.

Depth Estimation

논문

Dark Channel-Assisted Depth-from-Defocus from a Single Image

Aerial Multi-View Stereo via Adaptive Depth Range Inference and Normal Cues

Token Transforming: A Unified and Training-Free Token Compression Framework for Vision Transformer Acceleration

Toward Better SSIM Loss for Unsupervised Monocular Depth Estimation

Structure-Aware Radar-Camera Depth Estimation

Voyager: Long-Range and World-Consistent Video Diffusion for Explorable 3D Scene Generation

Attacking Attention of Foundation Models Disrupts Downstream Tasks

Harnessing Foundation Models for Robust and Generalizable 6-DOF Bronchoscopy Localization

Leveraging Intermediate Features of Vision Transformer for Face Anti-Spoofing

GeoMan: Temporally Consistent Human Geometry Estimation using Image-to-Video Diffusion

Bridging Geometric and Semantic Foundation Models for Generalized Monocular Depth Estimation

Ultrafast High-Flux Single-Photon LiDAR Simulator via Neural Mapping

Spatial RoboGrasp: Generalized Robotic Grasping Control Policy

SpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams

From Single Images to Motion Policies via Video-Generation Environment Representations

EvidenceMoE: A Physics-Guided Mixture-of-Experts with Evidential Critics for Advancing Fluorescence Light Detection and Ranging in Scattering Media

BadDepth: Backdoor Attacks Against Monocular Depth Estimation in the Physical World

RadarRGBD A Multi-Sensor Fusion Dataset for Perception with RGB-D and mmWave Radar

Multi-Label Stereo Matching for Transparent Scene Depth Estimation

M3Depth: Wavelet-Enhanced Depth Estimation on Mars via Mutual Boosting of Dual-Modal Data

IA-MVS: Instance-Focused Adaptive Depth Sampling for Multi-View Stereo

3D Visual Illusion Depth Estimation

DB3D-L: Depth-aware BEV Feature Transformation for Accurate 3D Lane Detection

Always Clear Depth: Robust Monocular Depth Estimation under Adverse Weather

MonoMobility: Zero-Shot 3D Mobility Analysis from Monocular Videos

Attention on the Sphere

SurgPose: Generalisable Surgical Instrument Pose Estimation using Zero-Shot Learning and Stereo Vision

JointDistill: Adaptive Multi-Task Distillation for Joint Depth Estimation and Scene Segmentation

Depth Anything with Any Prior

Marigold: Affordable Adaptation of Diffusion-Based Image Generators for Image Analysis

FaceShield: Explainable Face Anti-Spoofing with Multimodal Large Language Models

Denoising and Alignment: Rethinking Domain Generalization for Multimodal Face Anti-Spoofing

Boosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World

Reinforcement Learning-Based Monocular Vision Approach for Autonomous UAV Landing

Edge-Enabled VIO with Long-Tracked Features for High-Accuracy Low-Altitude IoT Navigation

ElectricSight: 3D Hazard Monitoring for Power Lines Using Low-Cost Sensors