Rethinking Deblurring Strategies for 3D Reconstruction: Joint Optimization vs. Modular Approaches

Akash Malhotra, Nacera seghouani

Abstract:

In this paper, we present a comparison between joint optimization and modular frameworks for addressing
deblurring in multiview 3D reconstruction. Casual captures, especially with handheld devices, often contain
blurry images that degrade the quality of 3D reconstruction. Joint optimization frameworks tackle this issue by
integrating deblurring and 3D reconstruction into a unified learning process, leveraging information from over-
lapping blurry images. While effective, these methods increase the complexity and training time. Conversely,
modular approaches decouple deblurring from 3D reconstruction, enabling the use of stand-alone deblurring
algorithms such as Richardson-Lucy, DeepRFT, and Restormer. In this study, we evaluate the trade-offs be-
tween these strategies in terms of reconstruction quality, computational complexity, and suitability for varying
levels of blur. Our findings reveal that modular approaches are more effective for low to medium blur scenar-
ios, while Deblur-NeRF, a joint optimization framework, excels at handling extreme blur when computational
costs are not a constraint.