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Research map / Detection and segmentation / 3D detection and driving

PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points

NeurIPS 2019 · Detection and segmentation › 3D detection and driving · approach: Unified LiDAR 3D detection · 18 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use PerspectiveNet with ground-truth camera extrinsic parameters instead of CooPPerspectiveNet · SUN RGB-DmAP: 23.65 -> 39.09Helped
Use ground-truth camera extrinsic parameters instead of learning them jointlyPerspectiveNet · SUN RGB-DmAP: 34.96 -> 39.09Helped
Add the perspective point branch instead of predicting the 3D center from the 2D offset (ablation)PerspectiveNet · SUN RGB-DmAP: 35.23 -> 39.09Helped
Use gated feature aggregation instead of direct aggregation (ablation)PerspectiveNet · SUN RGB-DmAP: 38.63 -> 39.09Helped
Use a soft gate instead of a hard 0-or-1 gate (ablation)PerspectiveNet · SUN RGB-DmAP: 38.87 -> 39.09Helped
Add the perspective loss (ablation)PerspectiveNet · SUN RGB-DmAP: 39.01 -> 39.09Helped
Add the 2D-3D consistency loss (ablation)PerspectiveNet · SUN RGB-DmAP: 37.43 -> 39.09Helped
Add the perspective branch, perspective loss, and consistency loss (ablation)PerspectiveNet · SUN RGB-DmAP: 32.97 -> 39.09Helped
Use template-based rather than heatmap-based perspective-point estimation (ablation)PerspectiveNet perspective-point estimator · SUN RGB-Daverage perspective-point absolute distance (pix): 10.25 pix -> 6.37 pixHelped

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