Date of Award

Spring 5-15-2024

Document Type

Thesis (Master's)

Department or Program

Computer Science

First Advisor

Adithya Pediredia

Second Advisor

Suren Jayasuriya

Third Advisor

Alberto Quattrini Li


The world’s information can be conceptualized as a holistic scene, often represented as a four-dimensional vector encapsulating spatial coordinates (x, y, z) and temporal data (t). Advancements in human technology have facilitated the measurement of this feature vector through various sensors. For instance, a video captures spatial dimensions (x, y) with temporal resolution (t), while sonar pulses gauge depth (z). Leveraging these captures, we can easily perform post-modification of a scene and provide computationally enhanced perception and visual results. However, capturing with only one type of sensor poses challenges, especially when capturing surround view (360◦ viewpoint) images is impossible or impractical in many real-world imaging scenarios, including underwater imaging, interior rooms, and autonomous navigation, a problem also known as the missing cone problem. This thesis delves into two main areas. Firstly, it explores a post-processing method for dynamic holistic scenes. The proposed method creates controllable long exposure images from video or light-field camera, effectively integrating temporal information into the mathematical representation. Secondly, it investigates a method to reconstruct holistic scenes within a small baseline by fusing camera and acoustic sensor data to combine 2D spatial (x, y) and depth (z) information. This fusion is achieved through Gaussian Splatting, a widely used 3D representation method. Referred to as Z-Splat, this novel method enables the reconstruction of holistic scenes with a small baseline.