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 2276–2300 of 2454 papers

Title	Date	Tasks	Status	Hype
Trust Your Model: Light Field Depth Estimation With Inline Occlusion Handling	Jun 1, 2018	Depth EstimationOcclusion Handling	—Unverified	0
Real-Time Monocular Depth Estimation Using Synthetic Data With Domain Adaptation via Image Style Transfer	Jun 1, 2018	Depth EstimationDomain Adaptation	CodeCode Available	1
Free Supervision From Video Games	Jun 1, 2018	Depth EstimationInstance Segmentation	—Unverified	0
Matching Adversarial Networks	Jun 1, 2018	Depth EstimationLine Detection	—Unverified	0
Monocular Relative Depth Perception With Web Stereo Data Supervision	Jun 1, 2018	Depth EstimationSemantic Segmentation	—Unverified	0
Single-Image Depth Estimation Based on Fourier Domain Analysis	Jun 1, 2018	Depth Estimation	CodeCode Available	0
GeoNet: Geometric Neural Network for Joint Depth and Surface Normal Estimation	Jun 1, 2018	Depth EstimationSurface Normal Estimation	CodeCode Available	1
Competitive Collaboration: Joint Unsupervised Learning of Depth, Camera Motion, Optical Flow and Motion Segmentation	May 24, 2018	Depth EstimationDepth Prediction	CodeCode Available	0
Deep Learning with Cinematic Rendering: Fine-Tuning Deep Neural Networks Using Photorealistic Medical Images	May 22, 2018	Depth EstimationMonocular Depth Estimation	—Unverified	0
Adversarial Structure Matching for Structured Prediction Tasks	May 18, 2018	Depth Estimationimage-classification	CodeCode Available	0
Recurrent Neural Network for Learning DenseDepth and Ego-Motion from Video	May 17, 2018	3D ReconstructionDepth Estimation	—Unverified	0
Auxiliary Tasks in Multi-task Learning	May 16, 2018	Depth EstimationMulti-Task Learning	CodeCode Available	0
Just-in-Time Reconstruction: Inpainting Sparse Maps using Single View Depth Predictors as Priors	May 11, 2018	Depth EstimationDepth Prediction	—Unverified	0
PAD-Net: Multi-Tasks Guided Prediction-and-Distillation Network for Simultaneous Depth Estimation and Scene Parsing	May 11, 2018	Depth EstimationMulti-Task Learning	—Unverified	0
Position Estimation of Camera Based on Unsupervised Learning	May 5, 2018	Camera Pose EstimationDepth Estimation	—Unverified	0
Evaluation of CNN-based Single-Image Depth Estimation Methods	May 3, 2018	Depth Estimation	—Unverified	0
Deep cross-domain building extraction for selective depth estimation from oblique aerial imagery	Apr 23, 2018	3D ReconstructionDepth Estimation	—Unverified	0
PlaneNet: Piece-wise Planar Reconstruction from a Single RGB Image	Apr 17, 2018	Depth EstimationDepth Prediction	CodeCode Available	0
Dual CNN Models for Unsupervised Monocular Depth Estimation	Apr 16, 2018	Depth EstimationImage Reconstruction	CodeCode Available	0
Estimating Depth from RGB and Sparse Sensing	Apr 8, 2018	Depth EstimationMonocular Depth Estimation	CodeCode Available	0
EPINET: A Fully-Convolutional Neural Network Using Epipolar Geometry for Depth from Light Field Images	Apr 6, 2018	Data AugmentationDepth Estimation	CodeCode Available	0
MegaDepth: Learning Single-View Depth Prediction from Internet Photos	Apr 2, 2018	Depth EstimationDepth Prediction	CodeCode Available	1
Structured Attention Guided Convolutional Neural Fields for Monocular Depth Estimation	Mar 29, 2018	Depth EstimationMonocular Depth Estimation	CodeCode Available	0
Motion Guided LIDAR-camera Self-calibration and Accelerated Depth Upsampling for Autonomous Vehicles	Mar 28, 2018	Autonomous VehiclesDepth Estimation	—Unverified	0
Learning Depth from Single Images with Deep Neural Network Embedding Focal Length	Mar 27, 2018	Depth EstimationNetwork Embedding	—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