Sample Output

AquaMVS supports three reconstruction pathways, each offering a different tradeoff between speed and point density. Below are representative results from the example dataset showing what each pathway produces on the same frame.

3D Reconstruction Comparison

Side-by-side point cloud renders from all three pathways on the same input frame.

Depth Map Comparison

Colormapped depth maps for the two dense pathways (LG+SP full and RoMa full). Sparse mode does not produce depth maps – it triangulates directly from feature correspondences.

Performance

LG+SP sparse is the fastest pathway but produces the fewest points. RoMa full is the slowest but yields the densest reconstruction. LG+SP full offers a balanced middle ground suitable for most use cases.

Choosing a Pathway

Use this table to guide your choice of reconstruction pathway:

Pathway	Speed	Point Density	Use Case
LG+SP sparse	Fastest	Low	Quick preview, debugging, sparse structure
LG+SP full	Moderate	High	General-purpose dense reconstruction
RoMa full	Slowest	Highest	Maximum quality, challenging lighting/texture

Recommendations:

• Start with LG+SP sparse to verify your dataset and configuration are correct.

• Use LG+SP full for most production reconstructions.

• Switch to RoMa full if you see sparse reconstruction failures or low point density (often in textureless or highly reflective underwater scenes).

To lock in a pathway, set these fields in your config.yaml:

matcher_type: lightglue  # or roma
pipeline_mode: full       # or sparse

Or use the --preset flag when initializing:

aquamvs init --preset fast

This applies speed-optimized parameter defaults across all pathways.

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See also the CLI Guide for command-line options and the API Reference for programmatic access to the benchmark runner.