Neural energy regularization smooths LiDAR maps without extra inference cost
Curvature energy on neural signed-distance level sets reduces jitter on million-point maps while keeping geometric fidelity.
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Curvature energy on neural signed-distance level sets reduces jitter on million-point maps while keeping geometric fidelity.
Claude Opus 5.5 scores 41.71% mean overlap; only 3.25% of targets exceed IoU 0.9.
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