Analysis of The Problem of Low Sales of Insta360 Panoramic Cameras
DOI:
https://doi.org/10.54691/bcpbm.v39i.4260Keywords:
Panoramic camera; Technical conditions; Market atmosphere; Application scenarios.Abstract
The Insta 360 panoramic camera simulates a two-dimensional floor plan into real three-dimensional space. Panoramic landscapes diversify the way an image is viewed, and at the same time, add interest. In addition to good interactive performance, the sense of realism is also very strong. Most importantly, objects can be seen in perspective, also known as naked-eye 3D. The purpose of this study was to explore why Insta 360's product is new and unique, and why sales are so low that the company has faced two innovation failures. After further analyzing its technical conditions, market atmosphere, and application scenarios, this paper improved product performance and development direction. This study concluded that only the novelty of the product is considered, ignoring immature technologies and markets and limited application scenarios. These led to low sales of the product in the first two times. Therefore, the panoramic camera is an emerging product, and it is necessary to determine the target user. Then, discuss the development model suitable for the Insta 360 panoramic camera under the above conditions.
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References
Christensen, Clayton M. The innovator's dilemma: when new technologies cause great firms to fail. Harvard Business Review Press, 2013.
Subramanian, Praseedha, and Masoud Gheisari. "Using 360-Degree Panoramic Photogrammetry and Laser Scanning Techniques to Create Point Cloud Data: A Comparative Pilot Study." 55th ASC Annual International Conference, Denver. 2019 p742 - 750.
CUI, Hainan; SHEN, Shuhan. MMA: Multi-camera Based Global Motion Averaging. 2022.
BARATH, Daniel, et al. Efficient initial pose-graph generation for global sfm. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. p. 14546 - 14555.
BOYD, Stephen, et al. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine learning, 2011, 3.1: 1 - 122.
CHATTERJEE, Avishek; GOVINDU, Venu Madhav. Robust relative rotation averaging. IEEE transactions on pattern analysis and machine intelligence, 2017, 40.4: 958 - 972.
CHEN, Changan, et al. Crowd-robot interaction: Crowd-aware robot navigation with attention-based deep reinforcement learning. In: 2019 International Conference on Robotics and Automation (ICRA). IEEE, 2019. p. 6015 - 6022.
CHEN, Xieyuanli, et al. Range image-based LiDAR localization for autonomous vehicles. In: 2021 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2021. p. 5802 - 5808.
CHEN, Yu; ZHAO, Ji; KNEIP, Laurent. Hybrid rotation averaging: A fast and robust rotation averaging approach. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021. p. 10358 - 10367.
COLLEDANCHISE, Michele; MALAFRONTE, Damiano; NATALE, Lorenzo. Act, perceive, and plan in belief space for robot localization. In: 2020 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2020. p. 3763 - 3769.
Cornelis, K.; Verbiest, F.; and Gool, L. V., Drift detection and removal for sequential structure from motion algorithms. IEEE-Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2004, 26 (10): 1249 – 1259.






