The available set of spin and shape modelled asteroids is strongly biased against slowly rotating targets and those with low lightcurve amplitudes. This is due to the observing selection effects. As a consequence, the current picture of asteroid spin axis distribution, rotation rates, radiometric properties, or aspects related to the object's internal structure might be affected too. To counteract these selection effects, we are running a photometric campaign of a large sample of main belt asteroids omitted in most previous studies. Using least chi-squared fitting we determined synodic rotation periods and verified previous determinations. When a dataset for a given target was sufficiently large and varied, we performed spin and shape modelling with two different methods to compare their performance. We used the convex inversion method and the non-convex SAGE algorithm, applied on the same datasets of dense lightcurves. Both methods search for the lowest deviations between observed and modelled lightcurves, though using different approaches. Unlike convex inversion, the SAGE method allows for the existence of valleys and indentations on the shapes based only on lightcurves. We obtain detailed spin and shape models for the first five targets of our sample: (159) Aemilia, (227) Philosophia, (329) Svea, (478) Tergeste, and (487) Venetia. When compared to stellar occultation chords, our models obtained an absolute size scale and major topographic features of the shape models were also confirmed. When applied to thermophysical modelling (TPM), they provided a very good fit to the infrared data and allowed their size, albedo, and thermal inertia to be determined. Convex and non-convex shape models provide comparable fits to lightcurves. However, some non-convex models fit notably better to stellar occultation chords and to infrared data in sophisticated thermophysical modelling (TPM). In some cases TPM showed strong preference for one of the spin and shape solutions. Also, we confirmed that slowly rotating asteroids tend to have higher-than-average values of thermal inertia, which might be caused by properties of the surface layers underlying the skin depth.