Many interest point detectors have been designed so far to work in two dimensional (2-D) images. However, expansion of these detectors into the third dimension for three dimensional (3-D) images can refine their representational power. This paper presents how the Harris corner, LoG filtering-based blob, and salient regions detectors can be expanded to find interest points in volumetric images handling multiple slices collectively. Performances of 2-D and 3-D detector implementations were assessed both qualitatively and quantitatively with value combinations of different parameters using metrics such as F1-score, localization error, and repeatability in binary images of twenty 3-D object models from the Princeton Shape Benchmark (PSB). Computation of F1-score and localization error depended on some manually marked ground truth points, while repeatability measurement was according to the proximity of the detected point sets. The 3-D detectors were evaluated as more successful in capturing distinctive and sparse interest points on 3-D object surfaces in qualitative analyses. Despite having greater computational complexities, most of the 3-D detectors yielded better average F1-score, localization accuracy, and repeatability given uniqueness constraint on the matched points in quantitative analyses. Therefore, the 3-D detectors appear preferable when longer working durations or sparser representations would not constitute any disadvantage.