SlowGuess/NeurIPS_2023_OCR huggingface dataset:
In Figure 7, we provide a toy experiment to support what we motivated in §1 of the main paper, which demonstrates how standard DM easily fails even with a single data sample. Note that we have clarified the definition of standard DM in §5.3 of the main paper. In this experiment, the data contains a single toy sample with 6 rectangular shapes, so there are permutation-equivalent representations. After sufficient training, we draw four samples using the image-conditioned denoising process. The DDIM sampler is used with 10 sampling steps, so the randomness only comes from the initial noise. As the figure shows, only the third sample gets the correct reconstruction result. With the challenges of set ambiguity, a standard conditional DM has trouble overfitting a single data sample and easily gets wrong outputs when the initial noise is inappropriate.
Toy input image
Sampling results of Standard DM (DDIM sampler, 10 steps)
Figure 7: A simple toy experiment of using a standard DM to fit a single data sample with 6 elements. Four sampling results with different initial noises are shown. The DDIM sampler is used with 10 denoising steps. Only one of the four samples (i.e., Sample 3) gets the correct final result due to the challenges induced by the set ambiguity, as explained in the main paper.
D.3 More qualitative examples
We provide additional qualitative results to compare PolyDiffuse against the state-of-the-art floorplan and HD map reconstruction methods, respectively. Note that the state-of-the-art method (i.e., Room-Former or MapTR) produces the initial reconstruction for PolyDiffuse. Figure 8 to Figure 10 are for the floorplan reconstruction task, while Figure 11 to Figure 13 are for the HD map reconstruction task.
Since the likelihood-based refinement is not employed in the qualitative examples of this subsection, PolyDiffuse cannot discover missing instances that are not covered by the proposal generator (i.e., RoomFormer and MapTR in the qualitative results). However, the visual comparisons clearly demonstrate that PolyDiffuse significantly improves the structural regularity of the reconstructed polygonal shapes.
RoomFormer
+PolyDiffuse
Figure 8: Additional qualitative results for the floorplan reconstruction task.
Figure 9: Additional qualitative results for the floorplan reconstruction task.
Figure 10: Additional qualitative results for the floorplan reconstruction task.
Figure 11: Additional qualitative results of the HD map reconstruction task.
MapTR +PolyDiffuse
Figure 12: Additional qualitative results of the HD map reconstruction task.
G.T.
MapTR
+PolyDiffuse
Figure 13: Additional qualitative results of the HD map reconstruction task.