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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 1–10 of 2454 papers

TitleStatusHype
π^3: Scalable Permutation-Equivariant Visual Geometry Learning—0
S^2M^2: Scalable Stereo Matching Model for Reliable Depth Estimation—0
Vision-based Perception for Autonomous Vehicles in Obstacle Avoidance Scenarios—0
Efficient Calisthenics Skills Classification through Foreground Instance Selection and Depth EstimationCode0
MonoMVSNet: Monocular Priors Guided Multi-View Stereo NetworkCode1
Towards Depth Foundation Model: Recent Trends in Vision-Based Depth Estimation—0
Cameras as Relative Positional Encoding—0
ByDeWay: Boost Your multimodal LLM with DEpth prompting in a Training-Free Way—0
LighthouseGS: Indoor Structure-aware 3D Gaussian Splatting for Panorama-Style Mobile Captures—0
Beyond Appearance: Geometric Cues for Robust Video Instance Segmentation—0
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All datasetsStanford2D3D PanoramicNYU-Depth V2DCMeBDthequeScanNetV2Cityscapes testDIODEKITTI 2015Mars DTM EstimationScanNet4D Light Field DatasetKITTI Eigen split

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1A2JmAP8.61—Unverified
2PAD-NetRMS0.79—Unverified
3MS-CRFRMS0.59—Unverified
4DORNRMS0.51—Unverified
5FreeformRMS0.43—Unverified
6Optimized, freeformRMS0.43—Unverified
7VNLRMS0.42—Unverified
8BTSRMS0.41—Unverified
9TransDepth (AGD+ ViT)RMS0.37—Unverified
10AdaBinsRMS0.36—Unverified