Enhancing Monocular Depth Estimation with Geometric and Language Priors

Current Trends in 3D Vision and Depth Estimation

Recent advancements in the field of 3D vision and depth estimation are marked by a shift towards leveraging self-supervised learning and diffusion models. The focus is increasingly on enhancing monocular depth estimation by integrating geometric priors and language models, which allows for more accurate and detailed depth predictions without the need for extensive labeled data. This approach not only improves the precision of depth maps but also sharpens the boundaries of objects within these maps, making the results more applicable to real-world scenarios. Additionally, there is a growing interest in evaluating and improving the mid-level vision capabilities of large pre-trained models, particularly in understanding depth cues and geometric relationships. The field is also witnessing the development of new datasets tailored for omnidirectional depth estimation, which address the limitations of traditional stereo methods in handling complex, real-world environments.

Noteworthy Developments

  • PriorDiffusion: Demonstrates the potential of integrating language priors with diffusion models to enhance monocular depth estimation, achieving state-of-the-art performance.
  • SharpDepth: Combines the strengths of discriminative and generative models to produce depth maps that are both metrically accurate and visually detailed.
  • Helvipad: Introduces a comprehensive dataset for omnidirectional depth estimation, facilitating advancements in handling complex, real-world scenes.

Sources

Self-Supervised Learning for Ordered Three-Dimensional Structures

PriorDiffusion: Leverage Language Prior in Diffusion Models for Monocular Depth Estimation

Boost 3D Reconstruction using Diffusion-based Monocular Camera Calibration

DepthCues: Evaluating Monocular Depth Perception in Large Vision Models

Probing the Mid-level Vision Capabilities of Self-Supervised Learning

SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation

Helvipad: A Real-World Dataset for Omnidirectional Stereo Depth Estimation

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