Every example for both comparisons below — pick one from each dropdown to switch scenes. This page is separate from the main project page so the front page loads instantly.
Drag on the GT point cloud below to rotate the view — every other point cloud follows it in sync.
From the visualizations above, we observe that point clouds obtained by projecting the depth-diffusion baselines' predicted depth maps (WM-DD and VGGT-DD) are considerably noisier, with scattered, uneven surfaces. Their Z3D counterparts, by contrast, produce smooth, coherent point clouds.
Drag to rotate — all point clouds stay in sync. Pick an example above to switch scenes.
Source Views
Target Views (4 target viewpoints)
Combined Point Clouds (all target views fused)
Drag on the GT point cloud below to rotate the view — the other two follow it in sync.
From the visualizations above, we observe that point clouds obtained by projecting Z3D's predicted depth maps are smooth and coherent.
Drag to rotate — all three point clouds stay in sync. Pick an example above to switch scenes.
Pick an example above to switch scenes.
From the visualizations above, we observe that depth maps predicted by the depth-diffusion baseline (WM-DD) contain patchy artifacts. The corresponding depth gradients further reveal that WM-DD produces noisy, inconsistent gradients, whereas WM-Z3D yields smoother, more coherent depth structure. The last column shows the zoomed in view of the highlighted section in the depth gradient images.