Multi-View Point Cloud Fusion

Improve a Python post-processor that combines noisy three-dimensional observations from several views into one clean scene cloud. The objective is low geometric error on unseen cases while retaining broad surface coverage and fine detail.

Hard Constraints

• Edit only /app/methods/main/solver.py, keeping predict(export_dir) as the entry point.
• Use the Python standard library and NumPy already installed in the image. Do not access the network, launch external programs, read files outside the supplied observation directory, or read verifier-owned files.
• Read every case from the supplied manifest. Case identifiers, case counts, view identifiers, view counts, and point counts all differ between the visible and sealed packages; nothing about them may be assumed or hard-coded.
• Return exactly one cloud per manifest case: 4 to 2,048 finite three-dimensional points, at least four of them distinct. Missing keys, extra keys, empty clouds, malformed points, and non-finite values are rejected outright and score nothing.
• predict must be deterministic for identical input and must not modify any input file.
• Private reference geometry is not present when predict runs.

What You Have

• /app/public/observations/ — the visible package: a manifest and the case files it references, in the same schema as the sealed one. Each view carries points in a shared scene coordinate system and a same-length confidence in [0, 1].
• /app/methods/main/solver.py — the editable starter.
• /app/selfcheck.py — checks the submission contract only: structure, coverage, finiteness, determinism, input immutability, standard-library-only imports. It contains no reference geometry, does not measure quality, and cannot predict sealed performance.

What You Submit

The edited /app/methods/main/solver.py, exposing:

def predict(export_dir: str) -> dict:
    ...

mapping each manifest case_id to its point cloud.

How It Is Judged

The sealed evaluator invokes predict on private cases with the same schema, first validating case coverage, cloud shape, point-count bounds, finiteness, distinct-point coverage, and input immutability.

For each valid case it computes symmetric precision/recall F-scores from nearest-neighbour distances at three fixed geometric tolerances (0.0125, 0.025, 0.05) and averages the three. Being symmetric, the score charges both for points that miss the surface and for surface that no point covers. Case results are calibrated separately before the final arithmetic mean; higher is better.