Papers Unsupervised Monocular Depth Estimation
“Unsupervised Monocular Depth Estimation” 태그가 달린 논문 59편 · 필터 해제
Toward Better SSIM Loss for Unsupervised Monocular Depth Estimation
Unsupervised monocular depth learning generally relies on the photometric relation among temporally adjacent images. Most of previous works use both mean absolute error (MAE) and structure similarity index measure (SSIM)…
Depth EstimationFormMonocular Depth EstimationSSIM+1Advancing Depth Anything Model for Unsupervised Monocular Depth Estimation in Endoscopy
Depth estimation is a cornerstone of 3D reconstruction and plays a vital role in minimally invasive endoscopic surgeries. However, most current depth estimation networks rely on traditional convolutional neural networks,…
3D ReconstructionDepth EstimationMonocular Depth EstimationUnsupervised Monocular Depth EstimationProDepth: Boosting Self-Supervised Multi-Frame Monocular Depth with Probabilistic Fusion
Self-supervised multi-frame monocular depth estimation relies on the geometric consistency between successive frames under the assumption of a static scene. However, the presence of moving objects in dynamic scenes intro…
DecoderDepth EstimationDepth PredictionMonocular Depth Estimation+1SCIPaD: Incorporating Spatial Clues into Unsupervised Pose-Depth Joint Learning
Unsupervised monocular depth estimation frameworks have shown promising performance in autonomous driving. However, existing solutions primarily rely on a simple convolutional neural network for ego-motion recovery, whic…
Autonomous DrivingCamera Pose EstimationDepth EstimationMonocular Depth Estimation+1Unsupervised Monocular Depth Estimation Based on Hierarchical Feature-Guided Diffusion
Unsupervised monocular depth estimation has received widespread attention because of its capability to train without ground truth. In real-world scenarios, the images may be blurry or noisy due to the influence of weathe…
DenoisingDepth EstimationMonocular Depth EstimationUnsupervised Monocular Depth EstimationBack to the Color: Learning Depth to Specific Color Transformation for Unsupervised Depth Estimation
Virtual engines can generate dense depth maps for various synthetic scenes, making them invaluable for training depth estimation models. However, discrepancies between synthetic and real-world colors pose significant cha…
Depth EstimationMonocular Depth EstimationUnsupervised Monocular Depth EstimationDCPI-Depth: Explicitly Infusing Dense Correspondence Prior to Unsupervised Monocular Depth Estimation
There has been a recent surge of interest in learning to perceive depth from monocular videos in an unsupervised fashion. A key challenge in this field is achieving robust and accurate depth estimation in challenging sce…
Depth EstimationMonocular Depth EstimationOptical Flow EstimationUnsupervised Monocular Depth EstimationSPIdepth: Strengthened Pose Information for Self-supervised Monocular Depth Estimation
Self-supervised monocular depth estimation has garnered considerable attention for its applications in autonomous driving and robotics. While recent methods have made strides in leveraging techniques like the Self Query …
Autonomous DrivingDepth EstimationMonocular Depth EstimationScene Understanding+1Digging into contrastive learning for robust depth estimation with diffusion models
Recently, diffusion-based depth estimation methods have drawn widespread attention due to their elegant denoising patterns and promising performance. However, they are typically unreliable under adverse conditions preval…
Contrastive LearningDenoisingDepth EstimationKnowledge Distillation+1Deeper into Self-Supervised Monocular Indoor Depth Estimation
Monocular depth estimation using Convolutional Neural Networks (CNNs) has shown impressive performance in outdoor driving scenes. However, self-supervised learning of indoor depth from monocular sequences is quite challe…
Depth EstimationMonocular Depth Estimationmotion predictionSelf-Supervised Learning+2MonoProb: Self-Supervised Monocular Depth Estimation with Interpretable Uncertainty
Self-supervised monocular depth estimation methods aim to be used in critical applications such as autonomous vehicles for environment analysis. To circumvent the potential imperfections of these approaches, a quantifica…
Autonomous VehiclesDecision MakingDepth EstimationDepth Prediction+2Continual Learning of Unsupervised Monocular Depth from Videos
Spatial scene understanding, including monocular depth estimation, is an important problem in various applications, such as robotics and autonomous driving. While improvements in unsupervised monocular depth estimation h…
Autonomous DrivingContinual LearningDepth Estimationimage-classification+4Dynamo-Depth: Fixing Unsupervised Depth Estimation for Dynamical Scenes
Unsupervised monocular depth estimation techniques have demonstrated encouraging results but typically assume that the scene is static. These techniques suffer when trained on dynamical scenes, where apparent object moti…
Depth EstimationMonocular Depth EstimationMotion SegmentationSegmentation+1EC-Depth: Exploring the consistency of self-supervised monocular depth estimation in challenging scenes
Self-supervised monocular depth estimation holds significant importance in the fields of autonomous driving and robotics. However, existing methods are typically trained and tested on standard datasets, overlooking the i…
Autonomous DrivingDepth EstimationDepth PredictionMonocular Depth Estimation+3WeatherDepth: Curriculum Contrastive Learning for Self-Supervised Depth Estimation under Adverse Weather Conditions
Depth estimation models have shown promising performance on clear scenes but fail to generalize to adverse weather conditions due to illumination variations, weather particles, etc. In this paper, we propose WeatherDepth…
Contrastive LearningDepth EstimationDomain AdaptationUnsupervised Monocular Depth EstimationInfraParis: A multi-modal and multi-task autonomous driving dataset
Current deep neural networks (DNNs) for autonomous driving computer vision are typically trained on specific datasets that only involve a single type of data and urban scenes. Consequently, these models struggle to handl…
Autonomous DrivingMonocular Depth EstimationObject DetectionSemantic Segmentation+2DS-Depth: Dynamic and Static Depth Estimation via a Fusion Cost Volume
Self-supervised monocular depth estimation methods typically rely on the reprojection error to capture geometric relationships between successive frames in static environments. However, this assumption does not hold in d…
Depth EstimationMonocular Depth EstimationOptical Flow EstimationUnsupervised Monocular Depth EstimationSelf-Supervised Monocular Depth Estimation by Direction-aware Cumulative Convolution Network
Monocular depth estimation is known as an ill-posed task in which objects in a 2D image usually do not contain sufficient information to predict their depth. Thus, it acts differently from other tasks (e.g., classificati…
Depth EstimationMonocular Depth EstimationUnsupervised Monocular Depth EstimationSelf-supervised Monocular Depth Estimation: Let's Talk About The Weather
Current, self-supervised depth estimation architectures rely on clear and sunny weather scenes to train deep neural networks. However, in many locations, this assumption is too strong. For example in the UK (2021), 149 d…
Depth EstimationMonocular Depth EstimationPose EstimationUnsupervised Monocular Depth EstimationPose Constraints for Consistent Self-supervised Monocular Depth and Ego-motion
Self-supervised monocular depth estimation approaches suffer not only from scale ambiguity but also infer temporally inconsistent depth maps w.r.t. scale. While disambiguating scale during training is not possible withou…
Camera Pose EstimationDepth EstimationEgocentric Pose EstimationMonocular Depth Estimation+2