SlowGuess/NeurIPS_2023_OCR huggingface dataset:
We additionally train and evaluate our method and baselines on the cars category of the ShapeNet [5] synthetic dataset of object renderings. We train all methods on 2458 training car objects each containing 50 views. We randomly sample 1 to 3 context views during training. For evaluation, we randomly pick 10 objects from the test set with 251 views per object. We randomly sample one context view and evaluate on all other novel views. The final metrics are averaged across all views and objects. Quantitative results in Table 8 show improvements in FID and LPIPS over other baselines, indicating sharper image quality and diversity in synthesized results in comparison to the image distribution of ShapeNet cars and more faithful structure and texture of novel view reconstructions.
Table 8: Quantitative evaluation metrics for ShapeNet [5] cars category. For all baselines, only a single context view was provided as input to compute the metrics over novel views.
FID↓	LPIPS↓	PSNR↑
PixelNeRF [69]	154.96	0.14	23.32
SparseFusion [76]	127.61	0.19	19.82
Ours	101.86	0.13	20.29
F.1 Object-Centric Scenes
F.2 Open-Set Categories