SOTAVerified

Depth Estimation

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

Papers

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Showing 1351–1400 of 2454 papers

TitleStatusHype
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features—0
Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images—0
Distortion-aware Monocular Depth Estimation for Omnidirectional Images—0
Distortion-Aware Self-Supervised 360° Depth Estimation from A Single Equirectangular Projection Image—0
Distortion-Tolerant Monocular Depth Estimation On Omnidirectional Images Using Dual-cubemap—0
Diversity Matters: Fully Exploiting Depth Clues for Reliable Monocular 3D Object Detection—0
DnD: Dense Depth Estimation in Crowded Dynamic Indoor Scenes—0
D-NPC: Dynamic Neural Point Clouds for Non-Rigid View Synthesis from Monocular Video—0
DO3D: Self-supervised Learning of Decomposed Object-aware 3D Motion and Depth from Monocular Videos—0
DOC-Depth: A novel approach for dense depth ground truth generation—0
Does depth estimation help object detection?—0
Domain Adaptive Monocular Depth Estimation With Semantic Information—0
Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation—0
Domain-Transferred Synthetic Data Generation for Improving Monocular Depth Estimation—0
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video—0
Double Refinement Network for Efficient Indoor Monocular Depth Estimation—0
DoubleStar: Long-Range Attack Towards Depth Estimation based Obstacle Avoidance in Autonomous Systems—0
DoubleTake: Geometry Guided Depth Estimation—0
DPBridge: Latent Diffusion Bridge for Dense Prediction—0
DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion—0
DPS-Net: Deep Polarimetric Stereo Depth Estimation—0
DreamCube: 3D Panorama Generation via Multi-plane Synchronization—0
Driving Scene Perception Network: Real-time Joint Detection, Depth Estimation and Semantic Segmentation—0
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning—0
Drone Stereo Vision for Radiata Pine Branch Detection and Distance Measurement: Utilizing Deep Learning and YOLO Integration—0
DSCnet: Replicating Lidar Point Clouds with Deep Sensor Cloning—0
Du^2Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels—0
Dual Exposure Stereo for Extended Dynamic Range 3D Imaging—0
DualPM: Dual Posed-Canonical Point Maps for 3D Shape and Pose Reconstruction—0
DUNE: Distilling a Universal Encoder from Heterogeneous 2D and 3D Teachers—0
DwinFormer: Dual Window Transformers for End-to-End Monocular Depth Estimation—0
Dyna-DepthFormer: Multi-frame Transformer for Self-Supervised Depth Estimation in Dynamic Scenes—0
Dynamic Fusion Network For Light Field Depth Estimation—0
Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction—0
Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes—0
DynOcc: Learning Single-View Depth from Dynamic Occlusion Cues—0
Echo-Reconstruction: Audio-Augmented 3D Scene Reconstruction—0
Edge-aware Consistent Stereo Video Depth Estimation—0
EdgeConv with Attention Module for Monocular Depth Estimation—0
Estimating Indoor Scene Depth Maps from Ultrasonic Echoes—0
Efficient Multi-task Uncertainties for Joint Semantic Segmentation and Monocular Depth Estimation—0
Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report—0
Efficient Stereo Depth Estimation for Pseudo LiDAR: A Self-Supervised Approach Based on Multi-Input ResNet Encoder—0
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation—0
EGA-Depth: Efficient Guided Attention for Self-Supervised Multi-Camera Depth Estimation—0
EGformer: Equirectangular Geometry-biased Transformer for 360 Depth Estimation—0
EgoM2P: Egocentric Multimodal Multitask Pretraining—0
ElectricSight: 3D Hazard Monitoring for Power Lines Using Low-Cost Sensors—0
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery—0
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All datasetsStanford2D3D PanoramicNYU-Depth V2DCMeBDthequeScanNetV2Cityscapes testDIODEKITTI 2015Mars DTM EstimationScanNet4D Light Field DatasetKITTI Eigen split

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1OmniDepthRMSE0.62—Unverified
2SphereDepthRMSE0.45—Unverified
3Jin et al.RMSE0.42—Unverified
4BiFuse with fusionRMSE0.41—Unverified
5HoHoNet (ResNet-101)RMSE0.38—Unverified
6PanoDepthRMSE0.37—Unverified
7BiFuse++RMSE0.37—Unverified
8UniFuse with fusionRMSE0.37—Unverified
9DisConvRMSE0.37—Unverified
10SliceNetRMSE0.37—Unverified
#ModelMetricClaimedVerifiedStatus
1A2JmAP8.61—Unverified
2PAD-NetRMS0.79—Unverified
3MS-CRFRMS0.59—Unverified
4DORNRMS0.51—Unverified
5FreeformRMS0.43—Unverified
6Optimized, freeformRMS0.43—Unverified
7VNLRMS0.42—Unverified
8BTSRMS0.41—Unverified
9TransDepth (AGD+ ViT)RMS0.37—Unverified
10AdaBinsRMS0.36—Unverified
#ModelMetricClaimedVerifiedStatus
1T2NetAbs Rel0.35—Unverified
2MIDASAbs Rel0.31—Unverified
3Bhattacharjee et al.Abs Rel0.25—Unverified
#ModelMetricClaimedVerifiedStatus
1T2NetAbs Rel0.49—Unverified
2MIDASAbs Rel0.42—Unverified
3Bhattacharjee et al.Abs Rel0.38—Unverified
#ModelMetricClaimedVerifiedStatus
1LeReSabsolute relative error0.1—Unverified
2DELTASabsolute relative error0.09—Unverified
3Distill Any Depthabsolute relative error0.04—Unverified
#ModelMetricClaimedVerifiedStatus
1SDC-DepthRMSE6.92—Unverified
2SwinMTLRMSE6.35—Unverified
#ModelMetricClaimedVerifiedStatus
1AIP-BrownDelta < 1.250.36—Unverified
2LeResDelta < 1.250.23—Unverified
#ModelMetricClaimedVerifiedStatus
1H-Net (Ours)Absolute relative error (AbsRel)0.09—Unverified
2H-Net (Ours) Full EigenAbsolute relative error (AbsRel)0.08—Unverified
#ModelMetricClaimedVerifiedStatus
1GLPDepthDelta < 1.250.43—Unverified
2SRDINET (Model A)Delta < 1.250.4—Unverified
#ModelMetricClaimedVerifiedStatus
1Atlas (finetuned)RMSE0.17—Unverified
2Atlas (plain)RMSE0.17—Unverified
#ModelMetricClaimedVerifiedStatus
1LFattNetBadPix(0.01)17.23—Unverified
#ModelMetricClaimedVerifiedStatus
1LightDepthNumber of parameters (M)42.6—Unverified
#ModelMetricClaimedVerifiedStatus
1UniFuseAbs Rel0.11—Unverified
#ModelMetricClaimedVerifiedStatus
1X-TC (Cross-Task Consistency)L1 error1.63—Unverified