#depth-estimation News & Analysis

5 articles tagged with #depth-estimation. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

5 articles

AIBullisharXiv – CS AI · Jun 17/10

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VLM3: Vision Language Models Are Native 3D Learners

Researchers introduce VLM3, a method that enables standard Vision Language Models to effectively learn 3D tasks through simple techniques like focal length unification and text-based pixel references, eliminating the need for complex task-specific architectures. The approach advances depth estimation accuracy and enables diverse 3D capabilities while maintaining standard VLM architecture, suggesting a paradigm shift toward simpler, more scalable 3D learning.

AIBearisharXiv – CS AI · Mar 177/10

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Cheating Stereo Matching in Full-scale: Physical Adversarial Attack against Binocular Depth Estimation in Autonomous Driving

Researchers have developed the first physical adversarial attack targeting stereo-based depth estimation in autonomous vehicles, using 3D camouflaged objects that can fool binocular vision systems. The attack employs global texture patterns and a novel merging technique to create nearly invisible threats that cause stereo matching models to produce incorrect depth information.

AINeutralarXiv – CS AI · Jun 26/10

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Modeling Depth Ambiguity: A Mixture-Density Representation for Flying-Point-Free Depth Estimation

Researchers introduce MDA (Mixture-Density Ambiguity), a depth estimation technique that predicts multiple depth hypotheses per pixel rather than a single value, effectively eliminating 'flying points'—spurious 3D artifacts that appear in empty space between foreground and background surfaces near object boundaries.

AINeutralarXiv – CS AI · Jun 26/10

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Self-supervised Monocular Depth and Pose Estimation for Endoscopy with Latent Priors

Researchers propose a self-supervised framework for monocular depth and pose estimation in endoscopy using a Generative Latent Bank and VAE to improve 3D mapping of the gastrointestinal tract. The method achieves superior performance over existing self-supervised approaches on standard endoscopic datasets without requiring synthetic training data.

AINeutralarXiv – CS AI · May 276/10

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Unified Panoramic Geometry Estimation via Multi-View Foundation Models

Researchers introduce PaGeR, a framework that adapts 3D foundation models trained on perspective images to work with panoramic imagery, enabling geometry estimation from 360-degree scenes. The unified model predicts depth, surface normals, and sky masks from both standard and panoramic images in a single pass, achieving state-of-the-art performance on indoor and outdoor scenes.