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Neural Radiance Fields (NeRF) exhibit remarkable performance for Novel ViewSynthesis (NVS) given a set of 2D images. However, NeRF training requiresaccurate camera pose for each input view, typically obtained byStructure-from-Motion (SfM) pipelines. Recent works have attempted to relaxthis constraint, but they still often rely on decent initial poses which theycan refine. Here we aim at removing the requirement for pose initialization. Wepresent Incremental CONfidence (ICON), an optimization procedure for trainingNeRFs from 2D video frames. ICON only assumes smooth camera motion to estimateinitial guess for poses. Further, ICON introduces ``confidence": an adaptivemeasure of model quality used to dynamically reweight gradients. ICON relies onhigh-confidence poses to learn NeRF, and high-confidence 3D structure (asencoded by NeRF) to learn poses. We show that ICON, without prior poseinitialization, achieves superior performance in both CO3D and HO3D versusmethods which use SfM pose.

ICON: Incremental CONfidence for Joint Pose and Radiance Field Optimization