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Neural Radiance Fields (NeRF) has demonstrated remarkable 3D reconstructioncapabilities with dense view images. However, its performance significantlydeteriorates under sparse view settings. We observe that learning the 3Dconsistency of pixels among different views is crucial for improvingreconstruction quality in such cases. In this paper, we propose ConsistentNeRF,a method that leverages depth information to regularize both multi-view andsingle-view 3D consistency among pixels. Specifically, ConsistentNeRF employsdepth-derived geometry information and a depth-invariant loss to concentrate onpixels that exhibit 3D correspondence and maintain consistent depthrelationships. Extensive experiments on recent representative works reveal thatour approach can considerably enhance model performance in sparse viewconditions, achieving improvements of up to 94% in PSNR, 76% in SSIM, and 31%in LPIPS compared to the vanilla baselines across various benchmarks, includingDTU, NeRF Synthetic, and LLFF.

ConsistentNeRF: Enhancing Neural Radiance Fields with 3D Consistency for Sparse View Synthesis