Large Depth Completion Model from Sparse Observations

2026-05-28 · Source: Computer Vision and Pattern Recognition · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Expert, quick

Summary

The Large Depth Completion Model (LDCM) is a robust, transformer-based framework designed for single-view metric depth estimation using sparse observations. This model generates metric-accurate dense depth maps and consistently outperforms existing approaches across diverse datasets and varying sparsity levels. LDCM achieves its superior performance by leveraging existing monocular foundation models to enhance sparse depth input quality and by reformulating training objectives to better capture geometric structure and metric consistency. A key innovation is the Poisson-based depth initialization strategy, which creates a uniform coarse dense depth map, providing a strong structural prior. Furthermore, LDCM replaces the traditional depth head with a point map head that regresses per-pixel 3D coordinates in camera space. This design allows the model to directly learn the underlying 3D scene structure, eliminating the need for camera intrinsic parameters and naturally producing metric-scaled 3D point maps.

Key takeaway

For Computer Vision Engineers developing robust depth estimation systems, LDCM presents a compelling alternative to complex architectures. If you are working with sparse observations and require metric-accurate dense depth maps, consider adopting LDCM's point map head for direct 3D coordinate regression. This approach simplifies deployment by eliminating the need for camera intrinsic parameters, offering strong generalization and superior performance across diverse datasets. Your systems could benefit from its enhanced geometric structure learning.

Key insights

LDCM achieves robust, metric-accurate dense depth completion from sparse observations via a transformer, novel initialization, and 3D coordinate regression.

Principles

• Leveraging monocular foundation models enhances sparse depth inputs.
• Direct 3D coordinate regression improves geometric structure learning.
• Poisson-based initialization provides strong structural priors.

Method

Introduces a Poisson-based depth initialization for coarse dense maps, then uses a point map head to regress per-pixel 3D coordinates in camera space, bypassing camera intrinsics.

In practice

• Generate dense depth maps from sparse inputs.
• Estimate metric-scaled 3D point maps.
• Improve depth estimation using structural priors.

Topics

• Depth Completion
• Sparse Depth Estimation
• Transformers
• 3D Point Maps
• Computer Vision
• Foundation Models

Best for: Research Scientist, AI Scientist, Computer Vision Engineer

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Editorial summary, takeaway, and curation by AIssential. Original article published by Computer Vision and Pattern Recognition.