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Template-free Articulated Neural Point Clouds for Reposable View Synthesis

NeurIPS 2023 · 3D vision › NeRF and radiance fields · approach: Few-shot neural rendering architectures · 2 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
random initialization instead of TiNeuVox-pretrained features (ablation)Articulated point-cloud NeRF · RobotsPSNR: 30.19 -> 29.97No clear effect
skip fine-tuning of feature points (ablation)Articulated point-cloud NeRF · BlenderPSNR: 29.10 -> 29.16No clear effect
condition feature network on learned skeletal poses (Ours-pose)Articulated point-cloud NeRF · ZJU-MoCapPSNR: 29.60 -> 32.05Helped
replace learned skeleton with ground-truth SMPL skeletonOurs-pose · ZJU-MoCapPSNR: 32.05 -> 32.01No clear effect
use our method instead of WIM at 10h trainingArticulated point-cloud NeRF vs WIM · RobotsPSNR: 27.61 -> 30.19Helped
use our method instead of WIM at 2h trainingArticulated point-cloud NeRF vs WIM · RobotsPSNR: 25.05 -> 28.81Helped
use our method instead of WIMArticulated point-cloud NeRF vs WIM · BlenderPSNR: 23.81 -> 29.10Helped
use base Ours instead of WIMArticulated point-cloud NeRF vs WIM · ZJU-MoCapPSNR: 31.08 -> 29.60Hurt
only 100 iterations of TiNeuVox pre-training (ablation)Articulated point-cloud NeRF · Blender Jumping JacksPSNR: over 30 dBNo clear effect
add L_tranf regularization (ablation)Articulated point-cloud NeRF · Atlas (Robots)removed components after simplification: 49 -> 62Helped
add regularization terms (ARAP, smooth, skel, tranf) (ablation)Articulated point-cloud NeRF · Robotsimage qualityNo clear effect
add L_ARAP (ablation)Articulated point-cloud NeRF · Robotsrigidity of deformationHelped

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