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Bi3D: Stereo Depth Estimation via Binary Classifications

CVPR 2020 · Architectures and efficiency › Quantization · approach: Post-training quantization of ViTs · 88 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Estimate depth using binary closer/farther classifications at selected planes instead of enumerating candidate disparitiesBi3D · Scene Flow, 2 levelsmean IOU: 0.9677 -> 0.9702Helped
Estimate depth using binary closer/farther classifications at selected planes instead of enumerating candidate disparitiesBi3D · Scene Flow, 4 levelsmean IOU: 0.9350 -> 0.9372Helped
Estimate depth using binary closer/farther classifications at selected planes instead of enumerating candidate disparitiesBi3D · Scene Flow, 8 levelsmean IOU: 0.8826 -> 0.8909Helped
Estimate depth using binary closer/farther classifications at selected planes instead of enumerating candidate disparitiesBi3D · Scene Flow, 16 levelsmean IOU: 0.8066 -> 0.8307Helped
Use Bi3D for continuous stereo depth estimationBi3D · Scene FlowEPE: 0.86 -> 0.73Helped
Use Bi3D for continuous stereo depth estimationBi3D · KITTI 2015, All, allerror (%): 1.74 -> 2.21Hurt
Run binary depth estimation with one tested planeBi3D · Scene Flowlatency: 5.3 msHelped
Allocate disparity planes to a selected depth interval rather than the full rangeBi3D selective depth · Selected disparity rangedepth quality within rangeHelped

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