SlowGuess/NeurIPS_2023_OCR huggingface dataset:
As depicted in Fig. 6 (a), the utilization of handheld cameras in the capture process frequently gives rise to inconsistent illumination between different viewpoints because of the changing occlusion of light caused by the moving photographer, thereby breaching the static illumination assumption for most inverse rendering methods. In Fig. 6 (b), we use a handheld smartphone to capture data under a similar setup in the dome. Experiments on handheld cameras tends to inadequately ensure an extensive range of viewpoints, thereby frequently resulting in the incompleteness of the reconstructed objects. Conversely, our dataset delivers a superior range of viewpoints and maintains consistency across different objects, thereby producing a more complete reconstruction. This demonstrates the high quality of our dataset and establishes its suitability as an evaluation benchmark for real-world objects.
There are several limitations and future directions to our work. (1) Since we use the light stage to capture the images in a dark room, the illumination is controlled strictly. Thus there exists a gap between the images in this dataset and in-the-wild captured images. (2) Although we use state-of-the-art methods for segmentation, the mask consistency across different views for smaller objects with fine details, such as hair, is not considered yet. (3) Due to the limited space, the sizes of the objects in the dataset are restricted to  $10\sim 20\mathrm{cm}$ , and the cameras are not highly densely distributed.
In this paper, we introduce a multi-illumination dataset OpenIllumination for inverse rendering evaluation on real objects. This dataset offers crucial components such as precise camera parameters, ground-truth illumination information, and segmentation masks for all the images. OpenIllumination provides a valuable resource for quantitatively evaluating inverse rendering and material decomposition techniques applied to real objects for researchers. By analyzing various state-of-the-art inverse
rendering pipelines using our dataset, we have been able to assess and compare their performance effectively. The release of both the dataset and accompanying code will be made available, encouraging further exploration and advancement in this field.
This work was supported in part by ONR grant N00014-23-1-2526 and NSF grant 2110409. We acknowledge gift support from Adobe, Google, Meta, Qualcomm, Oppo, the Ronald L. Graham Chair and the UC San Diego Center for Visual Computing.