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arXiv Machine Learning By Juntang Wang, Yihan Wang, Sheng'en Li, Hao Wu, Dongmian Zou, Shixin Xu

DriftNeRF: Cross-Ray Density Drifting for Sparse-View Neural Radiance Fields

Read the original on arXiv Machine Learning →

DriftNeRF introduces a regularizer that addresses cross‑ray density disagreement in Neural Radiance Fields, a defect that causes poor performance when only a few views are available. By aggregating densities from nearby points on other rays into a stop‑gradient consensus target, DriftNeRF penalizes unsupported disagreement while preserving agreed boundaries. The method improves 3‑view PSNR by 0.42–0.65 dB on NeRF‑Synthetic, DTU, and LLFF datasets and remains competitive with sparse‑view Gaussian pipelines without requiring additional geometry or preprocessing.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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