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Segment Anything in 3D with NeRFs

NeurIPS 2023 · 3D vision › NeRF and radiance fields · approach: Few-shot neural rendering architectures · 34 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
add confidence decay term to self-prompting (vs plain 2D NMS) (ablation)SA3D · NVOS meanmIoU (%): 87.9 -> 90.3Helped
add confidence decay term to self-prompting (ablation)SA3D · NVOS TrexIoU (%): 64.0 -> 82.0Helped
alternate mask inverse rendering and self-prompting across views (vs single view) (ablation)SA3D · SPIn-NeRF meanIoU (%): 74.6 -> 92.4Helped
alternate mask inverse rendering and self-prompting across views (vs single view) (ablation)SA3D · Replica meanmIoU (%): 59.8 -> 83.0Helped
use more views for 3D mask generation on a 360 scene (ablation)SA3D · Lego (360)IoU (%): 84.5 -> 92.2Helped
use more views on a forward-facing scene (ablation)SA3D · FortressIoU (%): 97.8 -> 98.3No clear effect
IoU rejection threshold tau varied from 0.3 to 0.7 (ablation)SA3D · Replica office0mIoU: 74.9 (0.3), 84.4 (0.5), 82.0 (0.7)Mixed
negative term coefficient lambda varied from 0.05 to 0.5 (ablation)SA3D · Replica office0mIoU: 79.1 (0.05), 84.9 (0.3), 83.3 (0.5)Mixed
replace SAM with other prompt-based 2D segmenters (SEEM, SimpleClick, RITM, FocalClick) (ablation)SA3D · NVOSmIoU (%): 86.0 / 87.7 / 81.2 / 88.9 vs 90.3 with SAMMixed
SA3D vs ISRFNVOS · NVOSmIoU (%): 83.8 -> 90.3Helped

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