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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 2251–2275 of 2454 papers

Title	Date	Tasks	Status	Hype
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360° Panoramic Imagery	Aug 19, 2018	3D Object DetectionAutonomous Vehicles	CodeCode Available	0
Simultaneous Localization And Mapping with depth Prediction using Capsule Networks for UAVs	Aug 16, 2018	Depth EstimationDepth Prediction	—Unverified	0
DeepTAM: Deep Tracking and Mapping	Aug 6, 2018	Depth EstimationDepth Prediction	CodeCode Available	0
Learning monocular depth estimation with unsupervised trinocular assumptions	Aug 5, 2018	Depth EstimationImage Reconstruction	CodeCode Available	0
T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks	Aug 4, 2018	Depth EstimationDepth Prediction	CodeCode Available	0
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network	Aug 1, 2018	Depth EstimationDepth Prediction	CodeCode Available	0
Geo-Supervised Visual Depth Prediction	Jul 30, 2018	3D ReconstructionDepth Estimation	CodeCode Available	0
Unsupervised Adversarial Depth Estimation using Cycled Generative Networks	Jul 28, 2018	Depth EstimationMonocular Depth Estimation	CodeCode Available	0
OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas	Jul 25, 2018	Depth EstimationMonocular Depth Estimation	CodeCode Available	0
CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks	Jul 24, 2018	Depth EstimationDepth Prediction	—Unverified	0
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction	Jul 24, 2018	Depth EstimationDepth Prediction	CodeCode Available	0
MVDepthNet: Real-time Multiview Depth Estimation Neural Network	Jul 23, 2018	Data AugmentationDecoder	CodeCode Available	0
Peeking Behind Objects: Layered Depth Prediction from a Single Image	Jul 23, 2018	Depth EstimationDepth Prediction	—Unverified	0
ENG: End-to-end Neural Geometry for Robust Depth and Pose Estimation using CNNs	Jul 16, 2018	Depth EstimationDepth Prediction	—Unverified	0
Deep attention-based classification network for robust depth prediction	Jul 11, 2018	ClassificationDeep Attention	CodeCode Available	0
Deep Virtual Stereo Odometry: Leveraging Deep Depth Prediction for Monocular Direct Sparse Odometry	Jul 6, 2018	3D ReconstructionDepth Estimation	—Unverified	0
Towards real-time unsupervised monocular depth estimation on CPU	Jun 29, 2018	Autonomous NavigationCPU	CodeCode Available	1
Every Pixel Counts: Unsupervised Geometry Learning with Holistic 3D Motion Understanding	Jun 27, 2018	3D geometryDepth And Camera Motion	—Unverified	0
Learning Single-Image Depth from Videos using Quality Assessment Networks	Jun 25, 2018	Depth Estimation	—Unverified	0
Self-supervised Learning for Dense Depth Estimation in Monocular Endoscopy	Jun 25, 2018	AnatomyDepth Estimation	—Unverified	0
Deep multi-scale architectures for monocular depth estimation	Jun 8, 2018	Depth EstimationMonocular Depth Estimation	—Unverified	0
Deep Ordinal Regression Network for Monocular Depth Estimation	Jun 6, 2018	Depth EstimationMonocular Depth Estimation	CodeCode Available	1
Digging Into Self-Supervised Monocular Depth Estimation	Jun 4, 2018	Camera Pose EstimationDepth Estimation	CodeCode Available	1
Soccer on Your Tabletop	Jun 3, 2018	3D ReconstructionDepth Estimation	—Unverified	0
Monocular Depth Estimation with Augmented Ordinal Depth Relationships	Jun 2, 2018	Depth EstimationDepth Prediction	—Unverified	0

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All datasets Stanford2D3D Panoramic NYU-Depth V2 DCM eBDtheque ScanNetV2 Cityscapes test DIODE KITTI 2015 Mars DTM Estimation ScanNet 4D Light Field Dataset KITTI Eigen split

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	OmniDepth	RMSE	0.62	—	Unverified
2	SphereDepth	RMSE	0.45	—	Unverified
3	Jin et al.	RMSE	0.42	—	Unverified
4	BiFuse with fusion	RMSE	0.41	—	Unverified
5	HoHoNet (ResNet-101)	RMSE	0.38	—	Unverified
6	PanoDepth	RMSE	0.37	—	Unverified
7	BiFuse++	RMSE	0.37	—	Unverified
8	UniFuse with fusion	RMSE	0.37	—	Unverified
9	DisConv	RMSE	0.37	—	Unverified
10	SliceNet	RMSE	0.37	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	A2J	mAP	8.61	—	Unverified
2	PAD-Net	RMS	0.79	—	Unverified
3	MS-CRF	RMS	0.59	—	Unverified
4	DORN	RMS	0.51	—	Unverified
5	Freeform	RMS	0.43	—	Unverified
6	Optimized, freeform	RMS	0.43	—	Unverified
7	VNL	RMS	0.42	—	Unverified
8	BTS	RMS	0.41	—	Unverified
9	TransDepth (AGD+ ViT)	RMS	0.37	—	Unverified
10	AdaBins	RMS	0.36	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	T2Net	Abs Rel	0.35	—	Unverified
2	MIDAS	Abs Rel	0.31	—	Unverified
3	Bhattacharjee et al.	Abs Rel	0.25	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	T2Net	Abs Rel	0.49	—	Unverified
2	MIDAS	Abs Rel	0.42	—	Unverified
3	Bhattacharjee et al.	Abs Rel	0.38	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LeReS	absolute relative error	0.1	—	Unverified
2	DELTAS	absolute relative error	0.09	—	Unverified
3	Distill Any Depth	absolute relative error	0.04	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SDC-Depth	RMSE	6.92	—	Unverified
2	SwinMTL	RMSE	6.35	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	AIP-Brown	Delta < 1.25	0.36	—	Unverified
2	LeRes	Delta < 1.25	0.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	H-Net (Ours)	Absolute relative error (AbsRel)	0.09	—	Unverified
2	H-Net (Ours) Full Eigen	Absolute relative error (AbsRel)	0.08	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GLPDepth	Delta < 1.25	0.43	—	Unverified
2	SRDINET (Model A)	Delta < 1.25	0.4	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Atlas (finetuned)	RMSE	0.17	—	Unverified
2	Atlas (plain)	RMSE	0.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LFattNet	BadPix(0.01)	17.23	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LightDepth	Number of parameters (M)	42.6	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniFuse	Abs Rel	0.11	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	X-TC (Cross-Task Consistency)	L1 error	1.63	—	Unverified