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 376–400 of 2454 papers

Title	Date	Tasks	Status	Hype
Event-based Stereo Depth Estimation from Ego-motion using Ray Density Fusion	Oct 17, 2022	Depth EstimationStereo Depth Estimation	CodeCode Available	1
Attention Attention Everywhere: Monocular Depth Prediction with Skip Attention	Oct 17, 2022	DecoderDepth Estimation	CodeCode Available	1
Frequency-Aware Self-Supervised Monocular Depth Estimation	Oct 11, 2022	Depth EstimationDepth Prediction	CodeCode Available	1
Detaching and Boosting: Dual Engine for Scale-Invariant Self-Supervised Monocular Depth Estimation	Oct 8, 2022	Data AugmentationDepth Estimation	CodeCode Available	1
Image Masking for Robust Self-Supervised Monocular Depth Estimation	Oct 5, 2022	Autonomous DrivingDepth Estimation	CodeCode Available	1
PlaneDepth: Self-supervised Depth Estimation via Orthogonal Planes	Oct 4, 2022	Autonomous DrivingData Augmentation	CodeCode Available	1
FreDSNet: Joint Monocular Depth and Semantic Segmentation with Fast Fourier Convolutions	Oct 4, 2022	Depth EstimationMonocular Depth Estimation	CodeCode Available	1
Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning	Oct 3, 2022	ClusteringDepth Estimation	CodeCode Available	1
Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem	Oct 2, 2022	Depth EstimationDepth Prediction	CodeCode Available	1
CrossDTR: Cross-view and Depth-guided Transformers for 3D Object Detection	Sep 27, 2022	3D Object DetectionAutonomous Driving	CodeCode Available	1
SAPA: Similarity-Aware Point Affiliation for Feature Upsampling	Sep 26, 2022	DecoderDepth Estimation	CodeCode Available	1
UDepth: Fast Monocular Depth Estimation for Visually-guided Underwater Robots	Sep 26, 2022	CPUDepth Estimation	CodeCode Available	1
3D-PL: Domain Adaptive Depth Estimation with 3D-aware Pseudo-Labeling	Sep 19, 2022	Depth EstimationDomain Adaptation	CodeCode Available	1
TODE-Trans: Transparent Object Depth Estimation with Transformer	Sep 18, 2022	Depth EstimationObject	CodeCode Available	1
SF2SE3: Clustering Scene Flow into SE(3)-Motions via Proposal and Selection	Sep 18, 2022	ClusteringDepth Estimation	CodeCode Available	1
Self-distilled Feature Aggregation for Self-supervised Monocular Depth Estimation	Sep 15, 2022	Depth EstimationDepth Prediction	CodeCode Available	1
DevNet: Self-supervised Monocular Depth Learning via Density Volume Construction	Sep 14, 2022	Depth Estimation	CodeCode Available	1
A Benchmark and a Baseline for Robust Multi-view Depth Estimation	Sep 13, 2022	Depth Estimation	CodeCode Available	1
BiFuse++: Self-supervised and Efficient Bi-projection Fusion for 360 Depth Estimation	Sep 7, 2022	Depth EstimationMonocular Depth Estimation	CodeCode Available	1
LiteDepth: Digging into Fast and Accurate Depth Estimation on Mobile Devices	Sep 2, 2022	Data AugmentationDepth Estimation	CodeCode Available	1
Synthehicle: Multi-Vehicle Multi-Camera Tracking in Virtual Cities	Aug 30, 2022	Depth EstimationMulti-Object Tracking	CodeCode Available	1
Depth Map Decomposition for Monocular Depth Estimation	Aug 23, 2022	Depth EstimationMonocular Depth Estimation	CodeCode Available	1
Learning an Efficient Multimodal Depth Completion Model	Aug 23, 2022	Depth CompletionDepth Estimation	CodeCode Available	1
Learning Sub-Pixel Disparity Distribution for Light Field Depth Estimation	Aug 20, 2022	Depth Estimation	CodeCode Available	1
Net2Brain: A Toolbox to compare artificial vision models with human brain responses	Aug 20, 2022	Action RecognitionDepth Estimation	CodeCode Available	1

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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