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Research map / 3D vision / Depth and stereo

RGBD Based Dimensional Decomposition Residual Network for 3D Semantic Scene Completion

CVPR 2019 · 3D vision › Depth and stereo · approach: Dense depth prediction and completion · 99 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Fuse RGB and depth inputs instead of using depth alone (ablation)DDRNet · NYUSSC-IoU: 28.9 -> 30.4Helped
Fuse RGB and depth inputs instead of using RGB alone (ablation)DDRNet · NYUSSC-IoU: 27.2 -> 30.4Helped
Add LW-ASPP (ablation)DDRNet without ASPP · NYUSC-IoU: 56.8 -> 61.0Helped
Add LW-ASPP (ablation)DDRNet without ASPP · NYUSSC-IoU: 26.8 -> 30.4Helped
Replace LW-ASPP with full 3D-ASPP (ablation)DDRNet · NYUSSC-IoU: 30.4 -> 30.8; 195.0 -> 431.0 Params/k; 27.22 -> 63.28 FLOPs/GMixed
Replace DDR blocks with 3D-ResNet blocks (ablation)RGBD SSC network · NYUSSC-IoU: 30.4 -> 30.8Helped
Use DDR blocks instead of 3D-ResNet blocks (ablation)RGBD SSC network · NYUParams (k): 1540.5 -> 195.0Helped

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