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Tang , Wei Zhi

University of British Columbia

Neural radiance fields (NeRF) have brought progress in rendering photorealistic 3D reconstruction. However, it requires clear images with correct camera poses. To address this problem, we propose to model camera imperfections that arise from the simple pinhole camera model and combine RGB images with event camera data in a stereo setup. Specifically, compared to conventional approaches that enforce physical priors on a camera model, we model measurement variation across the exposure time using embeddings using a data-driven approach. To incorporate event data into the NeRF pipeline, we propose a learnable mapper that bridges the event camera measurement space with that of the RGB camera. To validate our method, we collected our own high-resolution RGB and event stereo dataset. For further validation, we utilize the EVIMOv2 dataset consisting of limited indoor scenes.

Text

2024-09-09

Attribution 4.0 International

http://hdl.handle.net/2429/89208

Computer Science

University of British Columbia

UBCV

http://creativecommons.org/licenses/by/4.0/