Papers Stereo Disparity Estimation
“Stereo Disparity Estimation” 태그가 달린 논문 30편 · 필터 해제
SurfSLAM: Sim-to-Real Underwater Stereo Reconstruction For Real-Time SLAM
Localization and mapping are core perceptual capabilities for underwater robots. Stereo cameras provide a low-cost means of directly estimating metric depth to support these tasks. However, despite recent advances in ste…
Stereo Disparity EstimationStereo Depth Estimation3D ReconstructionDiFuse-Net: RGB and Dual-Pixel Depth Estimation using Window Bi-directional Parallax Attention and Cross-modal Transfer Learning
Depth estimation is crucial for intelligent systems, enabling applications from autonomous navigation to augmented reality. While traditional stereo and active depth sensors have limitations in cost, power, and robustnes…
Autonomous NavigationDepth EstimationDepth PredictionDisparity Estimation+2EV-MGDispNet: Motion-Guided Event-Based Stereo Disparity Estimation Network with Left-Right Consistency
Event cameras have the potential to revolutionize the field of robot vision, particularly in areas like stereo disparity estimation, owing to their high temporal resolution and high dynamic range. Many studies use deep l…
Disparity EstimationStereo Disparity EstimationMoCha-Stereo: Motif Channel Attention Network for Stereo Matching
Learning-based stereo matching techniques have made significant progress. However, existing methods inevitably lose geometrical structure information during the feature channel generation process, resulting in edge detai…
Disparity EstimationStereo Depth EstimationStereo Disparity EstimationStereo MatchingNeural Markov Random Field for Stereo Matching
Stereo matching is a core task for many computer vision and robotics applications. Despite their dominance in traditional stereo methods, the hand-crafted Markov Random Field (MRF) models lack sufficient modeling accurac…
Domain GeneralizationInductive BiasStereo Disparity EstimationStereo Matching+1An evaluation of Deep Learning based stereo dense matching dataset shift from aerial images and a large scale stereo dataset
Dense matching is crucial for 3D scene reconstruction since it enables the recovery of scene 3D geometry from image acquisition. Deep Learning (DL)-based methods have shown effectiveness in the special case of epipolar s…
3D geometry3D Scene ReconstructionDisparity EstimationStereo Disparity EstimationHinge-Wasserstein: Estimating Multimodal Aleatoric Uncertainty in Regression Tasks
Computer vision systems that are deployed in safety-critical applications need to quantify their output uncertainty. We study regression from images to parameter values and here it is common to detect uncertainty by pred…
Density EstimationDisparity EstimationHorizon Line EstimationLine Detection+2VisiTherS: Visible-thermal infrared stereo disparity estimation of human silhouette
This paper presents a novel approach for visible-thermal infrared stereoscopy, focusing on the estimation of disparities of human silhouettes. Visible-thermal infrared stereo poses several challenges, including occlusion…
Disparity EstimationStereo Disparity EstimationSpring: A High-Resolution High-Detail Dataset and Benchmark for Scene Flow, Optical Flow and Stereo
While recent methods for motion and stereo estimation recover an unprecedented amount of details, such highly detailed structures are neither adequately reflected in the data of existing benchmarks nor their evaluation m…
Optical Flow EstimationScene Flow EstimationStereo Depth EstimationStereo Disparity EstimationORA3D: Overlap Region Aware Multi-view 3D Object Detection
Current multi-view 3D object detection methods often fail to detect objects in the overlap region properly, and the networks' understanding of the scene is often limited to that of a monocular detection network. Moreover…
3D Object DetectionDisparity EstimationObjectobject-detection+3Sparse LiDAR Assisted Self-supervised Stereo Disparity Estimation
Deep stereo matching has made significant progress in recent years. However, state-of-the-art methods are based on expensive 4D cost volume, which limits their use in real-world applications. To address this issue, 3D co…
Disparity EstimationSelf-Driving CarsStereo Disparity EstimationStereo MatchingStereo Hybrid Event-Frame (SHEF) Cameras for 3D Perception
Stereo camera systems play an important role in robotics applications to perceive the 3D world. However, conventional cameras have drawbacks such as low dynamic range, motion blur and latency due to the underlying frame-…
Depth EstimationDisparity EstimationStereo Depth EstimationStereo Disparity Estimation+1RAFT-Stereo: Multilevel Recurrent Field Transforms for Stereo Matching
We introduce RAFT-Stereo, a new deep architecture for rectified stereo based on the optical flow network RAFT. We introduce multi-level convolutional GRUs, which more efficiently propagate information across the image. A…
Optical Flow EstimationStereo Depth EstimationStereo Disparity EstimationStereo MatchingCodedStereo: Learned Phase Masks for Large Depth-of-field Stereo
Conventional stereo suffers from a fundamental trade-off between imaging volume and signal-to-noise ratio (SNR) -- due to the conflicting impact of aperture size on both these variables. Inspired by the extended depth of…
Disparity EstimationImage ReconstructionStereo Disparity EstimationHierarchical Neural Architecture Search for Deep Stereo Matching
To reduce the human efforts in neural network design, Neural Architecture Search (NAS) has been applied with remarkable success to various high-level vision tasks such as classification and semantic segmentation. The und…
Neural Architecture SearchSemantic SegmentationStereo Depth EstimationStereo Disparity Estimation+1Movement-induced Priors for Deep Stereo
We propose a method for fusing stereo disparity estimation with movement-induced prior information. Instead of independent inference frame-by-frame, we formulate the problem as a non-parametric learning task in terms of …
DecoderDisparity EstimationStereo Disparity EstimationStereopagnosia: Fooling Stereo Networks with Adversarial Perturbations
We study the effect of adversarial perturbations of images on the estimates of disparity by deep learning models trained for stereo. We show that imperceptible additive perturbations can significantly alter the disparity…
Adversarial AttackAdversarial DefenseData Augmentationimage-classification+2Learning Stereo Matchability in Disparity Regression Networks
Learning-based stereo matching has recently achieved promising results, yet still suffers difficulties in establishing reliable matches in weakly matchable regions that are textureless, non-Lambertian, or occluded. In th…
regressionStereo Disparity EstimationStereo MatchingHITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching
This paper presents HITNet, a novel neural network architecture for real-time stereo matching. Contrary to many recent neural network approaches that operate on a full cost volume and rely on 3D convolutions, our approac…
Stereo Depth EstimationStereo Disparity EstimationStereo MatchingWasserstein Distances for Stereo Disparity Estimation
Existing approaches to depth or disparity estimation output a distribution over a set of pre-defined discrete values. This leads to inaccurate results when the true depth or disparity does not match any of these values. …
3D Object Detection3D Object Detection From Stereo ImagesAutonomous DrivingDepth Estimation+6