Papers Stereo Depth Estimation
“Stereo Depth Estimation” 태그가 달린 논문 111편 · 필터 해제
WHTMix: Efficient Stereo Depth Estimation via Walsh-Hadamard Token Mixing
Stereo depth estimation for driving, robotics and augmented reality must run at high resolution under tight latency budgets, yet in transformer-based matchers the global self-attention that aggregates scene context grows…
Stereo Depth EstimationSeeing Through Smoke: Surgical Desmoking for Improved Visual Perception
Minimally invasive and robot-assisted surgery relies heavily on endoscopic imaging, yet surgical smoke produced by electrocautery and vessel-sealing instruments can severely degrade visual perception and hinder vision-ba…
Synthetic Data GenerationStereo Depth EstimationImage ReconstructionStereoAdapter-2: Globally Structure-Consistent Underwater Stereo Depth Estimation
Stereo depth estimation is fundamental to underwater robotic perception, yet suffers from severe domain shifts caused by wavelength-dependent light attenuation, scattering, and refraction. Recent approaches leverage mono…
Stereo Depth EstimationNovel View SynthesisStyle TransferRayRoPE: Projective Ray Positional Encoding for Multi-view Attention
We study positional encodings for multi-view transformers that process tokens from a set of posed input images, and seek a mechanism that encodes patches uniquely, allows SE(3)-invariant attention with multi-frequency si…
Stereo Depth EstimationSurfSLAM: 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 ReconstructionA Modular Architecture Design for Autonomous Driving Racing in Controlled Environments
This paper presents a modular autonomous driving architecture for Formula Student Driverless competition vehicles operating in closed-circuit environments. The perception module employs YOLOv11 for real-time traffic cone…
Stereo Depth EstimationAutonomous DrivingMODEST: Multi-Optics Depth-of-Field Stereo Dataset
Reliable depth estimation under real optical conditions remains a core challenge for camera vision in systems such as autonomous robotics and augmented reality. Despite recent progress in depth estimation and depth-of-fi…
Stereo Depth EstimationNovel View SynthesisDepthFocus: Controllable Depth Estimation for See-Through Scenes
Depth in the real world is rarely singular. Transmissive materials create layered ambiguities that confound conventional perception systems. Existing models remain passive; conventional approaches typically estimate stat…
Stereo Depth EstimationLite Any Stereo: Efficient Zero-Shot Stereo Matching
Recent advances in stereo matching have focused on accuracy, often at the cost of significantly increased model size. Traditionally, the community has regarded efficient models as incapable of zero-shot ability due to th…
Zero-shot GeneralizationStereo Depth EstimationRobIA: Robust Instance-aware Continual Test-time Adaptation for Deep Stereo
Stereo Depth Estimation in real-world environments poses significant challenges due to dynamic domain shifts, sparse or unreliable supervision, and the high cost of acquiring dense ground-truth labels. While recent Test-…
Computational EfficiencyStereo Depth EstimationTest-time AdaptationCCNeXt: An Effective Self-Supervised Stereo Depth Estimation Approach
Depth Estimation plays a crucial role in recent applications in robotics, autonomous vehicles, and augmented reality. These scenarios commonly operate under constraints imposed by computational power. Stereo image pairs …
Stereo Depth EstimationAutonomous VehiclesStereoAdapter: Adapting Stereo Depth Estimation to Underwater Scenes
Underwater stereo depth estimation provides accurate 3D geometry for robotics tasks such as navigation, inspection, and mapping, offering metric depth from low-cost passive cameras while avoiding the scale ambiguity of m…
Stereo Depth EstimationURNet: Uncertainty-aware Refinement Network for Event-based Stereo Depth Estimation
Event cameras provide high temporal resolution, high dynamic range, and low latency, offering significant advantages over conventional frame-based cameras. In this work, we introduce an uncertainty-aware refinement netwo…
Stereo Depth EstimationBridgeDepth: Bridging Monocular and Stereo Reasoning with Latent Alignment
Monocular and stereo depth estimation offer complementary strengths: monocular methods capture rich contextual priors but lack geometric precision, while stereo approaches leverage epipolar geometry yet struggle with amb…
Zero-shot GeneralizationStereo Depth EstimationCameras as Relative Positional Encoding
Transformers are increasingly prevalent for multi-view computer vision tasks, where geometric relationships between viewpoints are critical for 3D perception. To leverage these relationships, multi-view transformers must…
Depth EstimationNovel View SynthesisStereo Depth EstimationSpikeStereoNet: A Brain-Inspired Framework for Stereo Depth Estimation from Spike Streams
Conventional frame-based cameras often struggle with stereo depth estimation in rapidly changing scenes. In contrast, bio-inspired spike cameras emit asynchronous events at microsecond-level resolution, providing an alte…
Depth EstimationStereo Depth EstimationM3Depth: 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 MatchingDERD-Net: Learning Depth from Event-based Ray Densities
Event cameras offer a promising avenue for multi-view stereo depth estimation and Simultaneous Localization And Mapping (SLAM) due to their ability to detect blur-free 3D edges at high-speed and over broad illumination c…
Depth EstimationDepth PredictionSimultaneous Localization and MappingStereo Depth EstimationBoosting 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+2Deep Depth Estimation from Thermal Image: Dataset, Benchmark, and Challenges
Achieving robust and accurate spatial perception under adverse weather and lighting conditions is crucial for the high-level autonomy of self-driving vehicles and robots. However, existing perception algorithms relying o…
Depth EstimationStereo Depth Estimation