SlowGuess/NeurIPS_2023_OCR huggingface dataset:
The purpose of this work is to facilitate the development of general object inverse rendering methods by contributing a real-world evaluation dataset and benchmark. We believe there is no direct negative societal impact resulting from this benchmark. The technology of inverse rendering has numerous applications that will have positive societal impact, such as improving the realism of computer graphics for virtual and augmented reality, or aiding in scene understanding for robotics applications. However, if leveraged for malicious intentions, it could create a significant negative impact on society. For instance, extremists might exploit an inverse rendering methods to generate fake imagery, commonly known as 'deepfakes', which may lead to the spread of misinformation or even intimidate the public. Nevertheless, we believe these possibilities can be eliminated with sufficient supervision from the governments and the research communities. It is important to note that our work focuses solely on common everyday objects, steering clear of any personal information or hazardous items.
Table 3: Full Benchmark Comparison of Existing Methods. †denotes models trained with the ground-truth 3D scans and pseudo materials optimized from light-box captures. Depth SI-MSE ×10-3. Shape Chamfer distance ×10-3.
Geometry	Novel Scene Relighting	Novel View Synthesis	Albedo
Depth↓	Normal↓	Shape↓	PSNR-H↑	PSNR-L↑	SSIM↑	LPIPS↓	PSNR-H↑	PSNR-L↑	SSIM↑	LPIPS↓	PSNR-L↑	SSIM↑	LPIPS↓
NVDiffRecMC [24]†	N/A	25.08±2.72	32.28±2.48	0.974±0.013	0.027±0.012	N/A	N/A
NVDiffRec [44]†	N/A	24.93±2.48	32.42±2.35	0.975±0.012	0.027±0.012	N/A	N/A
IDR [79]	0.35±0.55	0.05±0.05	0.30±0.35	N/A	30.11±4.34	39.66±2.82	0.990±0.008	0.017±0.009	N/A
NeRF [43]	2.19±4.27	0.62±0.34	62.05±179.10	N/A	26.31±5.63	33.59±7.28	0.968±0.032	0.044±0.036	N/A
Neural-PIL [9]	0.86±1.96	0.29±0.26	4.14±16.09	N/A	25.79±3.75	33.35±3.63	0.963±0.017	0.051±0.022	36.89±3.76	0.970±0.014	0.058±0.023
PhySG [85]	1.90±5.47	0.17±0.22	9.28±20.50	21.81±2.68	28.11±3.15	0.960±0.024	0.055±0.028	24.24±3.84	32.15±4.44	0.974±0.014	0.047±0.023	36.82±4.32	0.960±0.039	0.065±0.041
NVDiffRec [44]	0.31±0.50	0.06±0.07	0.62±0.74	22.91±2.35	29.72±2.28	0.963±0.011	0.039±0.013	21.94±3.14	28.44±3.54	0.969±0.014	0.030±0.011	40.70±4.14	0.983±0.011	0.043±0.021
NeRD [7]	1.39±2.42	0.28±0.32	13.70±30.28	23.29±2.44	29.65±2.16	0.957±0.019	0.059±0.023	25.83±3.99	32.61±3.92	0.963±0.016	0.054±0.023	40.59±3.51	0.981±0.012	0.058±0.026
NeRFactor [88]	0.87±1.23	0.29±0.41	9.53±21.43	23.54±3.15	30.38±3.08	0.969±0.013	0.048±0.019	26.06±4.22	33.47±4.48	0.973±0.014	0.046±0.020	40.11±3.82	0.985±0.007	0.051±0.020
InvRender [75]	0.59±0.69	0.06±0.06	0.44±0.55	23.76±2.72	30.83±2.57	0.970±0.013	0.046±0.019	25.91±3.58	34.01±3.04	0.977±0.011	0.042±0.017	40.32±3.85	0.982±0.010	0.049±0.020
NVDiffRecMC [24]	0.32±0.54	0.04±0.04	0.51±0.57	24.43±2.95	31.60±3.15	0.972±0.014	0.036±0.015	28.03±4.18	36.40±3.47	0.982±0.008	0.028±0.012	41.60±3.87	0.986±0.009	0.038±0.018
SI-SVBRDF [30]	81.48±17.85	0.29±0.07	N/A	N/A	N/A	34.94±3.51	0.957±0.021	0.064±0.023
SIRFS [2]	N/A	0.59±0.07	N/A	N/A	N/A	37.90±3.01	0.978±0.012	0.039±0.018