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 2251–2275 of 2454 papers

Title	Date	Tasks	Status
Visual Relationship Prediction via Label Clustering and Incorporation of Depth Information	Sep 9, 2018	ClusteringDepth Estimation	—Unverified
Deep Depth from Defocus: how can defocus blur improve 3D estimation using dense neural networks?	Sep 5, 2018	3D ReconstructionDepth Estimation	CodeCode Available
DF-Net: Unsupervised Joint Learning of Depth and Flow using Cross-Task Consistency	Sep 5, 2018	Depth And Camera MotionDepth Estimation	CodeCode Available
Detail Preserving Depth Estimation from a Single Image Using Attention Guided Networks	Sep 3, 2018	Depth Estimation	—Unverified
Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement	Sep 1, 2018	Depth EstimationMonocular Depth Estimation	—Unverified
Look Deeper into Depth: Monocular Depth Estimation with Semantic Booster and Attention-Driven Loss	Sep 1, 2018	Depth EstimationMonocular Depth Estimation	—Unverified
Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360Â° Panoramic Imagery	Sep 1, 2018	3D Object DetectionAutonomous Vehicles	—Unverified
End-to-end View Synthesis for Light Field Imaging with Pseudo 4DCNN	Sep 1, 2018	Computational EfficiencyDepth Estimation	—Unverified
Distortion-Aware Convolutional Filters for Dense Prediction in Panoramic Images	Sep 1, 2018	Depth EstimationSemantic Segmentation	—Unverified
Into the Twilight Zone: Depth Estimation using Joint Structure-Stereo Optimization	Sep 1, 2018	DenoisingDepth Estimation	—Unverified
Joint Task-Recursive Learning for Semantic Segmentation and Depth Estimation	Sep 1, 2018	Depth EstimationMonocular Depth Estimation	—Unverified
Monocular Depth Estimation Using Whole Strip Masking and Reliability-Based Refinement	Sep 1, 2018	Depth EstimationMonocular Depth Estimation	—Unverified
A Deeper Insight into the UnDEMoN: Unsupervised Deep Network for Depth and Ego-Motion Estimation	Aug 27, 2018	Depth EstimationMotion Estimation	—Unverified
Rethinking Monocular Depth Estimation with Adversarial Training	Aug 22, 2018	Depth EstimationMonocular Depth Estimation	—Unverified
Learning Monocular Depth by Distilling Cross-domain Stereo Networks	Aug 20, 2018	Autonomous DrivingDepth Estimation	CodeCode Available
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
Simultaneous Localization And Mapping with depth Prediction using Capsule Networks for UAVs	Aug 16, 2018	Depth EstimationDepth Prediction	—Unverified
DeepTAM: Deep Tracking and Mapping	Aug 6, 2018	Depth EstimationDepth Prediction	CodeCode Available
Learning monocular depth estimation with unsupervised trinocular assumptions	Aug 5, 2018	Depth EstimationImage Reconstruction	CodeCode Available
T2Net: Synthetic-to-Realistic Translation for Solving Single-Image Depth Estimation Tasks	Aug 4, 2018	Depth EstimationDepth Prediction	CodeCode Available
Depth Estimation via Affinity Learned with Convolutional Spatial Propagation Network	Aug 1, 2018	Depth EstimationDepth Prediction	CodeCode Available
Geo-Supervised Visual Depth Prediction	Jul 30, 2018	3D ReconstructionDepth Estimation	CodeCode Available
Unsupervised Adversarial Depth Estimation using Cycled Generative Networks	Jul 28, 2018	Depth EstimationMonocular Depth Estimation	CodeCode Available
OmniDepth: Dense Depth Estimation for Indoors Spherical Panoramas	Jul 25, 2018	Depth EstimationMonocular Depth Estimation	CodeCode Available
CReaM: Condensed Real-time Models for Depth Prediction using Convolutional Neural Networks	Jul 24, 2018	Depth EstimationDepth Prediction	—Unverified

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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