Background Three dimensional (3D) genome spatial organization is critical for numerous cellular functions, including transcription. Genome architecture had been difficult to elucidate but the suite of chromatin conformation capture assays, notably Hi-C transformed understanding of chromatin organization, yielding numerous insights, many deriving from 3D reconstructions. In part, these benefits derive from the ability to superpose genomic features on the reconstruction. However, the advantages of 3D structure-based analyses are clearly conditional on the accuracy of the corresponding reconstruction, which is difficult to assess due to an absence of gold standards. Proponents of competing reconstruction algorithms have evaluated their accuracy by recourse to simulation of toy structures and limited FISH imaging that features a handful of low resolution probes. While newly advanced multiplexed FISH imaging offers possibilities for refined 3D reconstruction accuracy evaluation, availability of such data remains limited due to assay complexity and the resolution thereof is appreciably lower than the reconstructions being assessed. Accordingly, there is demand for new methods of reconstruction accuracy appraisal. Results Here we explore the potential of recently proposed stationary distributions, StatDns, derived from Hi-C contact matrices, to serve as a basis for reconstruction accuracy assessment. Current usage of such StatDns has focussed on the identification of highly interactive regions (HIRs): computationally defined regions of the genome purportedly involved in numerous long-range intra-chromosomal contacts. Consistent identification of HIRs would be informative with respect to inferred 3D architecture since the corresponding regions of the reconstruction would have an elevated number of k nearest neighbors ( k NNs). More generally, we anticipate a monotone decreasing relationship between StatDn values and k NN distances. After initially evaluating the reproducibility of StatDns across replicate Hi-C data sets, we use this implied StatDn - k NN relationship to gauge the utility of StatDns for reconstruction validation, making recourse to both real and simulated examples. Conclusions Our analyses demonstrate that, as constructed, StatDns do not provide a suitable measure for assessing the accuracy of 3D genome reconstructions. Whether this is attributable to specific choices surrounding normalization in defining StatDns or to the logic underlying their very formulation remains to be determined.