A robust scale invariant feature transform (SIFT) algorithm based on bidirectional pre-screening is proposed to resolve the problem of the traditional SIFT algorithm, which includes redundant feature points and large computation. First, feature points are pre-screened using the pixel 8-neighborhood standard deviation similarity method before constructing difference of gauss space. Then, feature points are precisely located via the extremum detection method. Considering low matching efficiency of the traditional random sample consensus (RANSAC) algorithm, an adaptive 3D multi-peak histogram voting method is proposed to filter initial matching, whose results are considered as the initial interior point set of RANSAC to purge initial matching. Finally, the model parameters are calculated in the optimal point set. The experimental results in different types of images indicated that in contrast to SIFT+RANSAC algorithm, the feature point detection time and total running time adopting the proposed algorithm are reduced by the average of 11.7% and 10.7%, respectively. Further, the correct matching numbers are increased by 2.8% on average, while the recall rate and F value are raised averagely by 4.9 percentage and 2.7 percentage, respectively. The effectiveness of the comprehensive performance with the proposed algorithm is validated.