Data to 14 Oct 2026

Learning Depth from Monocular Videos using Direct Methods

arXiv 1712.00175 ↗First posted 1 Dec 2017cs.CVcited by 154

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Abstract (arXiv)

The ability to predict depth from a single image - using recent advances in CNNs - is of increasing interest to the vision community. Unsupervised strategies to learning are particularly appealing as they can utilize much larger and varied monocular video datasets during learning without the need for ground truth depth or stereo. In previous works, separate pose and depth CNN predictors had to be determined such that their joint outputs minimized the photometric error. Inspired by recent advances in direct visual odometry (DVO), we argue that the depth CNN predictor can be learned without a pose CNN predictor. Further, we demonstrate empirically that incorporation of a differentiable implementation of DVO, along with a novel depth normalization strategy - substantially improves performance over state of the art that use monocular videos for training.

Key facts. Cited by 154 papers in the map (data through 14 Oct 2026). First posted on arXiv 1 Dec 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 49; 2022: 41; 2023: 29; 2024: 17; 2025: 5; 2026: 13.

154 papers.

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2021BARF: Bundle-Adjusting Neural Radiance Fieldscited by 642
2022SinNeRF: Training Neural Radiance Fields on Complex Scenes from a Single Imagecited by 165
2022NeuralLift-360: Lifting An In-the-wild 2D Photo to A 3D Object with 360deg Viewscited by 148
2022MonoViT: Self-Supervised Monocular Depth Estimation with a Vision Transformercited by 136
2021MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesiscited by 133
2022Lite-Mono: A Lightweight CNN and Transformer Architecture for Self-Supervised Monocular Depth Estimationcited by 100
2021Combining Events and Frames using Recurrent Asynchronous Multimodal Networks for Monocular Depth Predictioncited by 90
2022SurroundDepth: Entangling Surrounding Views for Self-Supervised Multi-Camera Depth Estimationcited by 76
2021Unsupervised Scale-consistent Depth Learning from Videocited by 69
2021Channel-Wise Attention-Based Network for Self-Supervised Monocular Depth Estimationcited by 63
2021360MonoDepth: High-Resolution 360deg Monocular Depth Estimationcited by 60
2021Learning Monocular Depth in Dynamic Scenes via Instance-Aware Projection Consistencycited by 58

38 more

2022SC-DepthV3: Robust Self-supervised Monocular Depth Estimation for Dynamic Scenescited by 52
2022Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depthcited by 48
2021Learning to Segment Rigid Motions from Two Framescited by 46
2021Multi-View Depth Estimation by Fusing Single-View Depth Probability with Multi-View Geometrycited by 46
2021Self-supervised Monocular Depth Estimation for All Day Images using Domain Separationcited by 44
2023OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environmentscited by 41
2023R3D3: Dense 3D Reconstruction of Dynamic Scenes from Multiple Camerascited by 39
2021MonoIndoor: Towards Good Practice of Self-Supervised Monocular Depth Estimation for Indoor Environmentscited by 38
2021Single Image Depth Estimation: An Overviewcited by 35
2024Diffusion Models for Monocular Depth Estimation: Overcoming Challenging Conditionscited by 29
2021Self-supervised Learning of Depth Inference for Multi-view Stereocited by 28
2021Unsupervised Monocular Depth Estimation in Highly Complex Environmentscited by 28
2022MGNet: Monocular Geometric Scene Understanding for Autonomous Drivingcited by 27
2021Self-supervised Geometric Perceptioncited by 26
2021Attentive and Contrastive Learning for Joint Depth and Motion Field Estimationcited by 24
2022DevNet: Self-supervised Monocular Depth Learning via Density Volume Constructioncited by 22
2023RM-Depth: Unsupervised Learning of Recurrent Monocular Depth in Dynamic Scenescited by 22
2023Kick Back & Relax: Learning to Reconstruct the World by Watching SlowTVcited by 21
2023SQLdepth: Generalizable Self-Supervised Fine-Structured Monocular Depth Estimationcited by 21
2023Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenescited by 20
2021Learning Depth via Leveraging Semantics: Self-supervised Monocular Depth Estimation with Both Implicit and Explicit Semantic Guidancecited by 19
2021Outdoor inverse rendering from a single image using multiview self-supervisioncited by 18
2022Deconstructing Self-Supervised Monocular Reconstruction: The Design Decisions that Mattercited by 18
2021VOLDOR: Visual Odometry from Log-logistic Dense Optical flow Residualscited by 17
2023Adversarial Training of Self-supervised Monocular Depth Estimation against Physical-World Attackscited by 17
2026FD-VLA: Force-Distilled Vision-Language-Action Model for Contact-Rich Manipulationcited by 17
2022Multi-Camera Collaborative Depth Prediction via Consistent Structure Estimationcited by 16
2023DualRefine: Self-Supervised Depth and Pose Estimation Through Iterative Epipolar Sampling and Refinement Toward Equilibriumcited by 16
2023Multi-Object Discovery by Low-Dimensional Object Motioncited by 16
2023AugUndo: Scaling Up Augmentations for Monocular Depth Completion and Estimationcited by 15
2022PanopticDepth: A Unified Framework for Depth-aware Panoptic Segmentationcited by 14
2021Advancing Self-supervised Monocular Depth Learning with Sparse LiDARcited by 13
2022The Monocular Depth Estimation Challengecited by 13
2023The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimationcited by 13
2023MonoDiffusion: Self-Supervised Monocular Depth Estimation Using Diffusion Modelcited by 13
2021H-Net: Unsupervised Attention-based Stereo Depth Estimation Leveraging Epipolar Geometrycited by 12
2021RGB-D Salient Object Detection with Ubiquitous Target Awarenesscited by 12
2021PLNet: Plane and Line Priors for Unsupervised Indoor Depth Estimationcited by 12

50 most cited of 154 All 154 (JSON)