In this paper, the performance of various panoramic image stitching techniques applicable across diverse domains, including virtual reality, robotics, remote sensing, and multimedia content creation, is investigated. The assessment involves methodologies such as, Oriented FAST and Rotated BRIEF (ORB), Speeded Up Robust Features (SURF), Binary Robust Independent Elementary Features (BRISK) and Scale-Invariant Feature Transform (SIFT). The study utilizes images of Koneru Lakshmaiah Education Foundation, Hyderabad’s main building and library building as input, along with generated noisy images derived from these captures. These images undergo processing using different panoramic image stitching techniques in quiet and noisy environments. Evaluation of these techniques considers simulation time, memory consumption, and a step-by-step analysis of the output panoramic images in noisy environments. From the results, it is observed that the stitched image using BRISK does not produce a correct panoramic image, indicating that under noisy conditions, the BRISK algorithm may not function effectively.