GUMBEL-NERF: Representing Unseen Objects as Part-Compositional Neural Radiance Fields
We propose Gumbel-NeRF, a mixture-of-expert (MoE) neural radiance fields (NeRF) model with a hindsight expert selection mechanism for synthesizing novel views of unseen objects. Previous studies have shown that the MoE structure provides high-quality representations of a given large-scale scene cons...
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Zusammenfassung: | We propose Gumbel-NeRF, a mixture-of-expert (MoE) neural radiance fields
(NeRF) model with a hindsight expert selection mechanism for synthesizing novel
views of unseen objects. Previous studies have shown that the MoE structure
provides high-quality representations of a given large-scale scene consisting
of many objects. However, we observe that such a MoE NeRF model often produces
low-quality representations in the vicinity of experts' boundaries when applied
to the task of novel view synthesis of an unseen object from one/few-shot
input. We find that this deterioration is primarily caused by the foresight
expert selection mechanism, which may leave an unnatural discontinuity in the
object shape near the experts' boundaries. Gumbel-NeRF adopts a hindsight
expert selection mechanism, which guarantees continuity in the density field
even near the experts' boundaries. Experiments using the SRN cars dataset
demonstrate the superiority of Gumbel-NeRF over the baselines in terms of
various image quality metrics. |
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DOI: | 10.48550/arxiv.2410.20306 |