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DepthAgent uses multiple models for better depth estimation

Researchers have developed DepthAgent, a novel vision-language agent designed to improve monocular depth estimation across various camera types. Unlike previous methods that use a single estimator, DepthAgent leverages multiple pre-existing depth models as tools. It intelligently analyzes scene and camera geometry to select or fuse predictions from these experts, particularly excelling on challenging samples where individual models falter. This adaptive approach significantly enhances accuracy and robustness for depth estimation tasks. AI

IMPACT Enhances depth estimation accuracy and robustness across diverse camera geometries by adaptively selecting and fusing multiple expert models.

RANK_REASON The cluster contains a research paper detailing a new method for depth estimation.

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 English(EN) · Jie Zhu, Girish Chandar Ganesan, Xiaoming Liu ·

    DepthAgent: Towards Better Universal Depth Estimation via Sample-wise Expert Selection

    arXiv:2605.23281v1 Announce Type: new Abstract: Monocular metric depth estimation has achieved strong progress with large-scale training and universal-camera modeling, yet robust deployment across diverse camera settings, such as perspective, fisheye, and panoramic images, remain…

  2. arXiv cs.CV TIER_1 English(EN) · Xiaoming Liu ·

    DepthAgent: Towards Better Universal Depth Estimation via Sample-wise Expert Selection

    Monocular metric depth estimation has achieved strong progress with large-scale training and universal-camera modeling, yet robust deployment across diverse camera settings, such as perspective, fisheye, and panoramic images, remains challenging. Existing methods typically rely o…