Papers Stereo Matching
“Stereo Matching” 태그가 달린 논문 517편 · 필터 해제
$S^2M^2$: Scalable Stereo Matching Model for Reliable Depth Estimation
The pursuit of a generalizable stereo matching model, capable of performing across varying resolutions and disparity ranges without dataset-specific fine-tuning, has revealed a fundamental trade-off. Iterative local sear…
Depth EstimationStereo MatchingLearning Robust Stereo Matching in the Wild with Selective Mixture-of-Experts
Recently, learning-based stereo matching networks have advanced significantly. However, they often lack robustness and struggle to achieve impressive cross-domain performance due to domain shifts and imbalanced disparity…
Inductive BiasMixture-of-ExpertsStereo MatchingRobuSTereo: Robust Zero-Shot Stereo Matching under Adverse Weather
Learning-based stereo matching models struggle in adverse weather conditions due to the scarcity of corresponding training data and the challenges in extracting discriminative features from degraded images. These limitat…
DenoisingDepth EstimationDisparity EstimationStereo Matching+1ESMStereo: Enhanced ShuffleMixer Disparity Upsampling for Real-Time and Accurate Stereo Matching
Stereo matching has become an increasingly important component of modern autonomous systems. Developing deep learning-based stereo matching models that deliver high accuracy while operating in real-time continues to be a…
Disparity EstimationStereo MatchingStereoDiff: Stereo-Diffusion Synergy for Video Depth Estimation
Recent video depth estimation methods achieve great performance by following the paradigm of image depth estimation, i.e., typically fine-tuning pre-trained video diffusion models with massive data. However, we argue tha…
Depth EstimationStereo MatchingDiving into the Fusion of Monocular Priors for Generalized Stereo Matching
The matching formulation makes it naturally hard for the stereo matching to handle ill-posed regions like occlusions and non-Lambertian surfaces. Fusing monocular priors has been proven helpful for ill-posed matching, bu…
Stereo MatchingM3Depth: Wavelet-Enhanced Depth Estimation on Mars via Mutual Boosting of Dual-Modal Data
Depth estimation plays a great potential role in obstacle avoidance and navigation for further Mars exploration missions. Compared to traditional stereo matching, learning-based stereo depth estimation provides a data-dr…
Depth EstimationStereo Depth EstimationStereo MatchingMulti-Label Stereo Matching for Transparent Scene Depth Estimation
In this paper, we present a multi-label stereo matching method to simultaneously estimate the depth of the transparent objects and the occluded background in transparent scenes.Unlike previous methods that assume a unimo…
Depth EstimationregressionStereo MatchingTransparent objectsBoosting Zero-shot Stereo Matching using Large-scale Mixed Images Sources in the Real World
Stereo matching methods rely on dense pixel-wise ground truth labels, which are laborious to obtain, especially for real-world datasets. The scarcity of labeled data and domain gaps between synthetic and real-world image…
Depth EstimationMonocular Depth EstimationStereo MatchingCMD: Constraining Multimodal Distribution for Domain Adaptation in Stereo Matching
Recently, learning-based stereo matching methods have achieved great improvement in public benchmarks, where soft argmin and smooth L1 loss play a core contribution to their success. However, in unsupervised domain adapt…
Domain AdaptationStereo MatchingUnsupervised Domain AdaptationThermoStereoRT: Thermal Stereo Matching in Real Time via Knowledge Distillation and Attention-based Refinement
We introduce ThermoStereoRT, a real-time thermal stereo matching method designed for all-weather conditions that recovers disparity from two rectified thermal stereo images, envisioning applications such as night-time dr…
Knowledge DistillationStereo MatchingConsistency-aware Self-Training for Iterative-based Stereo Matching
Iterative-based methods have become mainstream in stereo matching due to their high performance. However, these methods heavily rely on labeled data and face challenges with unlabeled real-world data. To this end, we pro…
Stereo MatchingBoosting Omnidirectional Stereo Matching with a Pre-trained Depth Foundation Model
Omnidirectional depth perception is essential for mobile robotics applications that require scene understanding across a full 360{\deg} field of view. Camera-based setups offer a cost-effective option by using stereo dep…
Depth EstimationMonocular Depth EstimationOmnnidirectional Stereo Depth EstimationScene Understanding+2LeanStereo: A Leaner Backbone based Stereo Network
Recently, end-to-end deep networks based stereo matching methods, mainly because of their performance, have gained popularity. However, this improvement in performance comes at the cost of increased computational and mem…
Stereo MatchingDistilling Stereo Networks for Performant and Efficient Leaner Networks
Knowledge distillation has been quite popular in vision for tasks like classification and segmentation however not much work has been done for distilling state-of-the-art stereo matching methods despite their range of ap…
General KnowledgeKnowledge DistillationStereo MatchingGenStereo: Towards Open-World Generation of Stereo Images and Unsupervised Matching
Stereo images are fundamental to numerous applications, including extended reality (XR) devices, autonomous driving, and robotics. Unfortunately, acquiring high-quality stereo images remains challenging due to the precis…
Autonomous DrivingImage GenerationStereo MatchingRGB-Phase Speckle: Cross-Scene Stereo 3D Reconstruction via Wrapped Pre-Normalization
3D reconstruction garners increasing attention alongside the advancement of high-level image applications, where dense stereo matching (DSM) serves as a pivotal technique. Previous studies often rely on publicly availabl…
3D ReconstructionStereo MatchingStereo Any Video: Temporally Consistent Stereo Matching
This paper introduces Stereo Any Video, a powerful framework for video stereo matching. It can estimate spatially accurate and temporally consistent disparities without relying on auxiliary information such as camera pos…
Optical Flow EstimationStereo MatchingBANet: Bilateral Aggregation Network for Mobile Stereo Matching
State-of-the-art stereo matching methods typically use costly 3D convolutions to aggregate a full cost volume, but their computational demands make mobile deployment challenging. Directly applying 2D convolutions for cos…
Stereo MatchingLightEndoStereo: A Real-time Lightweight Stereo Matching Method for Endoscopy Images
Real-time acquisition of accurate depth of scene is essential for automated robotic minimally invasive surgery, and stereo matching with binocular endoscopy can generate such depth. However, existing algorithms struggle …
MambaStereo Matching