Scene understanding is a sub-field of computer vision, concentrating on interpreting the context of a scene through perceptive and analytical processes that mimic human understandings. On a fundamental level, it is a technology that digitally recreates human visual perception via algorithms and machine learning to interpret, analyze, and understand digital imagery or video frames with high precision and speed.

Scene Understanding in Computer Vision encapsulates certain notable characteristics:

• Real-time processing: As computer vision technology, it is capable of processing vast quantities of visual data in real time, thus serving diverse environments and applications.
• Precision: Scene understanding aims to elucidate visual scenes with impeccable accuracy to ensure a comprehensive and error-free interpretation of the visual information presented.
• Underlying algorithms: The technology applies a sophisticated blend of algorithms, including machine learning, artificial intelligence, and neural networks, to precisely decipher and interpret visual data.
• Applicability: Its applicability ranges from robotics, surveillance, healthcare, to autonomous driving and more, promising far-reaching implications for various sectors.

Several inherent advantages have accelerated the adoption of scene understanding in computer vision:

• Efficient Processing: Compared to human analysts, scene understanding in computer vision can process visual data more swiftly and efficiently, proving its worth in time-critical applications like surveillance or autonomous driving.
• Accuracy and Precision: Leveraging advanced algorithms and machine learning capabilities, scene understanding delivers greater accuracy and precision in visual data interpretation.
• Scalability: The technology has inherent scalability and can be employed in various capacities across different industries, improving efficiency, safety, and operations.
• Constant Development: Frequent software updates ensure regular improvements in accuracy, speed, and reproducibility, thereby extending its potential application scope.

Despite its considerable advantages, scene understanding in computer vision has several disadvantages:

• Lack of Contextual Understanding: While AI has made significant strides, it can still struggle with contextual understanding, a significant shortcoming in scene understanding.
• Limited Adaptability: Unless equipped with advanced machine learning capabilities, more simplistic applications might struggle to adapt to new or unfamiliar scenarios.
• Integration Challenges: The integration of scene understanding into pre-existing systems or infrastructures may present obstacles due to compatibility issues or operational complexities.

The implementation of scene understanding in computer vision requires strategic planning and assessment. The first step involves a comprehensive analysis of organizational needs, following which most suitable software must be chosen. Calculating cost-benefit is also critical, along with vendor assessment to ensure concordance between product offerings and company requirements.

In conclusion, while scene understanding in computer vision has substantial potential, successful implementation depends greatly on comprehensive understanding, careful planning, and attentive execution. The ongoing advancements in artificial intelligence and machine learning capabilities promise increased accuracy and adaptability in scene understanding, bringing us closer to to the goal of replicating human-like perception and understanding.