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Description: IL-NeRF Incremental Learning for Neural Radiance Fields with Camera Pose Alignment
Existing incremental learning methods assume that the camera pose parameters are estimated in advance based on the complete dataset, which poses a paradox as the setting of incremental learning is that the data arrives sequentially. Our IL-NeRF addresses a more practical scenario where pre-estimated camera poses are unavailable for each training data chunk.
Since the previous training data have been discarded, the incoming training data cannot simply be used directly for camera pose estimation because the isolated estimated camera pose will not be in the same coordinate system as the previous camera pose, which will lead to NeRF training misalignment and failure to render the 3D scene. Therefore, accurately estimating the camera poses of the sequential coming data within the same coordinate system in incremental NeRF training becomes a crucial issue that needs
The original NeRF demonstrates severe catastrophic forgetting, leading to the loss of early-task scene information. In contrast, IL-NeRF is able to preserve the scene of interest throughout the training process.
Ming Li - Homepage