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 1–10 of 2454 papers

Title	Date	Tasks	Status	Hype
π^3: Scalable Permutation-Equivariant Visual Geometry Learning	Jul 17, 2025	Camera Pose EstimationDepth Estimation	—Unverified	0
S^2M^2: Scalable Stereo Matching Model for Reliable Depth Estimation	Jul 17, 2025	Depth EstimationStereo Matching	—Unverified	0
Vision-based Perception for Autonomous Vehicles in Obstacle Avoidance Scenarios	Jul 16, 2025	Autonomous NavigationAutonomous Vehicles	—Unverified	0
Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth Estimation	Jul 16, 2025	ClassificationDepth Estimation	CodeCode Available	0
MonoMVSNet: Monocular Priors Guided Multi-View Stereo Network	Jul 15, 2025	Depth EstimationDepth Prediction	CodeCode Available	1
Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation	Jul 15, 2025	3D ReconstructionAutonomous Driving	—Unverified	0
Cameras as Relative Positional Encoding	Jul 14, 2025	Depth EstimationNovel View Synthesis	—Unverified	0
ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way	Jul 11, 2025	Depth EstimationHallucination	—Unverified	0
LighthouseGS: Indoor Structure-aware 3D Gaussian Splatting for Panorama-Style Mobile Captures	Jul 8, 2025	3DGSDepth Estimation	—Unverified	0
Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation	Jul 8, 2025	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