Converting MASt3R Scenes to COLMAP: A Comparison of Three Pipelines

Introduction

MASt3R is a next-generation multi-view network capable of high-precision 3D reconstruction. It works across a wide range of scales—from challenging image pairs to large-scale collections of thousands of images—without requiring prior information such as camera calibration or capture positions. By leveraging powerful pre-trained 3D spatial priors, it can directly estimate dense 3D structures even in feature-poor environments or extreme viewpoint changes where traditional SfM often fails.

This report aims to compare and evaluate three technical approaches (process1, process2, and process3) for converting 3D scene data generated by MASt3R into the COLMAP format, which is widely used for downstream tasks like 3D Gaussian Splatting (3DGS). We will examine how these approaches differ in camera parameter determination, point cloud generation, and final data structure.

1. Overview of Key Methodologies

Before diving into the technical details, this table summarizes the core characteristics of each script across three major categories.

Feature	process1	process2	process3
Primary Focus	Speed and Efficiency	Balance of Fidelity and Quality	Data Richness and Self-containment
Filtering	None (High Speed)	Confidence Score-based	Confidence + Local Sampling
Output Files	Standard 3 Files	Standard 3 Files	Rich (incl. Depth/Normal Maps)

2. Technical Comparison

2.1. Handling Camera Parameters: Accuracy vs. Fidelity

process1 and process2: These scripts adopt a "data-driven transformation" philosophy. They trust the focal lengths and principal points provided by MASt3R, scaling them to match the original image resolution. This preserves the intrinsic camera characteristics estimated by the network.

process3: In contrast, this script uses a "data-derived ab initio estimation" approach. It ignores MASt3R's intrinsics and instead estimates them using heuristics based on image dimensions (e.g., max(w, h) * 1.2). While versatile, this may sacrifice precision regarding lens-specific optical traits.

2.2. 3D Point Cloud and Color Generation

process1: Extracts the entire scene's point cloud at once and applies global random sampling (e.g., 1 million points) to minimize memory usage and maximize processing speed.
process2: Combines points per image and filters out low-quality points based on MASt3R’s confidence threshold. It ensures strict consistency between 3D points and color information by applying the same confidence mask to both.
process3: Performs filtering and local random sampling per image. It retrieves color directly from the `scene.imgs` tensor, enhancing efficiency by avoiding additional image loading.

3. Optimal Use Case Analysis

3.1. process1: Large-scale Processing prioritizing Speed

Best for batch-processing hundreds or thousands of MASt3R scenes where memory resources are limited or fast previews are required during prototyping.

3.2. process2: Standard 3DGS requiring High-Quality Sparse Points

The "gold standard" for typical 3D visualization and rendering. It respects the geometric structure of the scene while actively improving quality through confidence filtering.

3.3. process3: Comprehensive Dataset for Dense Reconstruction (MVS)

Targeted at advanced computer vision pipelines that utilize depth and normal maps. However, its self-estimation logic makes it a "high-risk, high-reward" option that should be used only when MASt3R's own parameters are unavailable.

4. Conclusion

The choice of pipeline significantly impacts the final 3D reconstruction. process1 is for speed, process2 is the de facto standard for balanced quality in 3DGS, and process3 provides data richness for MVS at the cost of potential estimation risks.