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Performance evaluation of a class of M-estimators for surface parameter estimation in noisy range data

Depth maps are frequently analyzed as if, to an adequate approximation, the errors are normally, identically, and independently distributed. This noise model does not consider at least two types of anomalies encountered in sampling: A few large deviations in the data, often thought of as outliers; and a uniformly distributed error component arising from rounding and quantization. The theory of robust statistics formally addresses these problems and is efficiently used in a robust sequential estimator (RSE) of our own design. The specific implementation was based on a t-distribution error model, and this work extends this concept to several well known M-estimators. We evaluate the performance of these estimators under different noise conditions and highlight the effects of tuning constants and the necessity of simultaneous scale and parameter estimation.