Tuning-free kernel density estimation for heaped and rounded data using a characteristic-function theory of heaping. Rounding to a grid is convolution with a box followed by lattice sampling, so the density is recovered by deconvolving the known box and tapering against a data-driven noise floor. Provides a box-deconvolution de-heaping estimator, a superposition variant, and a single combined estimator selected by a band-capacity gate; grid, heaped-fraction, and mixed-grain readers; and a spectral higher-order comb detector. Base-R replicas of the Heitjan-Rubin multiple-imputation and measurement-error deconvolution methods are included for comparison, and the 'Kernelheaping' stochastic expectation-maximization estimator is used when installed.