Recently,　coordinate-based　MLPs　have　been　shown　to　be　powerful　representations　for　3D　surfaces,　where　learning　high-frequency　details　is　facilitated　by　modulating　surface　functions　with　periodic　functions　[1],　[2].　While　shortening　the　periodicity　helps　in　learning　high　frequencies,　it　leads　to　increasing　ambiguity,　i.e.,　more　points　along　the　axis　directions　become　similar　in　the　embedded　space,　so　that　many　points　on　the　surface　and　outside　the　surface　have　similar　predictions.　In　addition,　short　periodicity　increases　local　geometric　variations,　leading　to　unexpected　noisy　artifacts　in　untrained　regions.　Unlike　existing　methods　that　learn　surface　functions　in　a　regular　cube,　we　find　surfaces　within　shells,　a　coarse　form　of　the　target　surfaces　constructed　by　a　binary　classifier.　The　advantage　of　build　surfaces　in　shells　is　that　MLPs　focus　on　regions　of　interest,　which　inherently　reduces　ambiguity　and　also　promotes　training　efficiency　and　test　accuracy.　We　demonstrate　the　effectiveness　of　shells　and　show　significant　improvements　over　baseline　methods　in　3D　surface　reconstruction　from　raw　point　clouds.