One-sided visibility-motivated metric residual

Compare the frozen symmetric metric fit against a single asymmetric modification. A camera records the first visible surface: a LiDAR hit behind that surface can be occluded, whereas a hit in front contradicts observed free space. This motivates a one-sided bounded influence approximation, not a complete probabilistic visibility model or proof of occlusion.

Let r = transformed LiDAR z minus bilinear camera z. For supported r>0 replace r with r/sqrt(1+(r/.2)^2); otherwise retain r. The residual saturates toward .2 m behind the surface but preserves local unit slope. Keep every unsupported point's 1 m penalty unchanged. Apply the same outer soft-L1(.1 m), TRF, bounds, scales, tolerances, 100 nfev/800 actual-call cap and support-retention checks as baseline. Do not remove points or use true pose to identify occlusions within the estimator. No depth-bias parameter is added. Record large positive/negative residual counts, but do not label every positive residual as a true occlusion.

Potential failure: reducing behind-surface influence can favor moving geometry behind the measured surface or produce one-sided pose bias. Require exact-visible recovery and true-start drift checks rather than only reporting all-return gains. Thresholds, saturation and scene layouts are fixed before optimization results.

Use three scene layouts: original five spheres, a nearer foreground arrangement, and a staggered arrangement (all constants in tools/occlusion_geometry.py). Same slanted background, cameras/extrinsics and three views. New layouts are additional synthetic controls sharing generator/surface family, not independent real sensors. For each layout use exact-z visible-only (oracle population as favorable control) and exact-z all-return observations (no camera visibility filtering). Both methods receive the identical population in each condition. Two original signed seeds plus truth start: 12 perturbation cases + 6 drift controls, two methods, 36 fits. No outcomes omitted, retries or altered pose gates. Fitting receives no reference extrinsics. Truth labels outcomes only after solving.

Compare converged recovery (<.5 deg/<.20 m), status, support, runtime/call budgets, and all truth-start drift. Reproduce the six original-scene symmetric outputs. No held-out thresholds select candidates or parameters. Failure in either control blocks production promotion; success is still only a synthetic diagnostic. Existing bias failure remains unresolved, not assumed cured by this experiment.

PYTHONPATH=src:. OPENBLAS_NUM_THREADS=1 /workspace/work/p0-venv/bin/python \
  tools/evaluate_occlusion_pose.py docs/assets/occlusion_pose_20261029/plan.json \
  docs/assets/occlusion_pose_20261029/evaluation.json