SlowGuess/NeurIPS_2023_OCR huggingface dataset:
We compare surface reconstruction results on DTU [26] with SDF-based NeRF models. We follow the convention to use the same 15 scenes from DTU as previous works [21, 49]. The selected training views and test views are the same across all methods. Three input views are used for training.
Table 1: Quantitative results on LLFF. We compare novel view synthesis on the LLFF dataset for 3 input views. Our method outperforms the baseline NeRF model and the sparse-view optimized models. Our model is built with the vanilla NeRF as the direct baseline.
PSNR ↑	SSIM ↑	LPIPS ↓	Depth MAE ↓
NeRF [4]	16.79	0.56	0.37	1.66
DS-NeRF [13]	17.09	0.57	0.38	1.64
RegNeRF [29]	19.08	0.59	0.34	1.02
Ours	19.83	0.70	0.29	0.91
Table 2: Quantitative results on DTU. We compare novel view synthesis and surface reconstruction with SDF-based methods on the DTU dataset for 3 input views. All models are trained without foreground masks. For evaluation, we report photometric metrics for the masked foreground object and the full image. The Chamfer-  $L_{1}$  geometric metric is computed with the official DTU evaluation code with foreground masks applied. Our model is built with NeuS as the direct baseline, achieving the best results across photometric and geometric metrics.
PSNR ↑	SSIM ↑	LPIPS ↓	Chamfer-L1 ↓
Object	Image	Object	Image	Object	Image	Object
UNISURF [20]	12.90	13.85	0.47	0.58	0.28	0.57	7.23
VolSDF [52]	15.68	15.44	0.58	0.65	0.21	0.47	4.44
NeuS [21]	16.06	16.37	0.59	0.66	0.21	0.46	6.16
Ours	20.58	18.23	0.77	0.76	0.13	0.33	2.63
For the novel view synthesis task, we report photometric metrics, including PSNR, structural similarity index (SSIM) [50], and the learned perceptual metric LPIPS [51]. In addition to the image metrics, we also report the mean absolute error (MAE) for depth prediction. We compute the pseudo ground truth depth using the baseline NeRF model given all input views. The depth error is calculated as the mean absolute difference between the predicted depths and the pseudo ground truth depths. Both the predicted depths and the pseudo ground truth depths are in the normalized coordinates.
For the surface reconstruction task, we report the Chamfer distance with DTU's official evaluation code [26], where the Chamfer distance is computed in the unnormalized world coordinates for the foreground objects. We also report the PSNR, SSIM, and LPIPS metrics for the rendered images with (object) and without (image) the foreground masks.
4.3 Novel View Synthesis
Quantitative comparison. For the novel view synthesis task, we compare our method with the baseline NeRF [4] as well as few-view optimized DS-NeRF [13] and RegNeRF [29]. DS-NeRF uses external SfM module COLMAP [19] to generate sparse point clouds for depth supervision, while RegNeRF applies additional regularizations on depths and colors rendered from unobserved views. The experiments are performed on the LLFF dataset with 3 input views. Our method outperforms all other methods regarding photometric metrics and depth prediction. The results are shown in Table 1. For DS-NeRF, we observe that if COLMAP is only given sparse-view inputs with known camera poses for SfM (as opposed to providing all views for SfM and selecting visible points from sparse views), COLMAP does not generate a sufficient number of points for depth supervision. In contrast, our method relies on image correspondences as inputs that are computed according to various characteristics. Therefore, it can leverage much denser prior information for supervision, as shown in Fig. 3.
Qualitative comparison. Besides the quantitative comparison, we provide the qualitative comparison to demonstrate the effects of our approach in improving novel view synthesis performance. The visual samples are shown in Fig. 5. Compared with baselines, our results have sharper and more accurate details and fewer artifacts. Such an improvement in performance is most visually evident in challenging cases such as small or thin structures.
4.4 Surface Reconstruction
Quantitative comparison. We apply the same correspondence loss terms to the SDF-based neural implicit field method NeuS [21] and compare the results with the baseline NeuS model as well as other two SOTA SDF-based surface reconstruction method, UNISURF [20] and VolSDF [52]. The experiments are performed on the DTU dataset with 3 input views. The results are shown in Table 2. Our method outperforms all baseline models in terms of Chamfer-  $L_{1}$  distances, i.e., ours generates more accurate surfaces. Moreover, ours also is also significantly better than the baselines in terms of rendering metrics, including PSNR, SSIM, and LPIPS. This further demonstrates that our approach can be plugged into different types of network backbones (density-based and SDF-based) and can be applied to different tasks (novel view synthesis and surface reconstruction).
Qualitative comparison. The visual comparisons between our method and the baseline for surface reconstruction are shown in Fig. 6. It's apparent that our 3D reconstruction results have more precise geometrical shapes. Combined with the correspondence priors, our CorresNeRF achieves clean and high-quality reconstruction even when given sparse input views.
We perform ablation studies of the correspondence generation process, correspondence loss terms, robustness to correspondence noise, and the effect of foreground masks.
Correspondence generation process. We compare the result with augmentation removed, with automatic filtering removed, and with the full pipeline. The results are shown in the first two rows in Table 3. The result shows that both the augmentation and the automatic outlier filtering process are able to improve the reconstruction quality measured in photometric and geometric metrics. The augmentation step can provide denser supervision signals, while the filtering step is able to remove the noisy correspondences to further improve the reconstruction quality.
Correspondence loss terms. We evaluate the effectiveness of the correspondence pixel reprojection loss and correspondence depth loss terms. We compare the results with only the reprojection loss removed, with only the depth loss removed, and the full pipeline. The results are displayed in Table 3. Our results show that both correspondence reprojection loss and the correspondence depth loss contribute significantly to the reconstruction quality, each adding  $\sim 1$  dB PSNR compared to the vanilla NeRF. The combined loss term yields the best performance, adding  $\sim 3$  dB PSNR.
Robustness to correspondence noise. To evaluate the robustness of CorresNeRF to noisy correspondences, we introduce Gaussian noise with standard deviations of 1, 2, and 4 pixels to both the  $x$  and  $y$  pixel coordinates. We measure the performance of CorresNeRF on LLFF dataset with 3 input views. The results are shown in Table 4. The automatic filtering process is able to remove the noisy correspondences, demonstrating the robustness of CorresNeRF to noisy correspondences.
Correspondence with mask supervision. For the surface reconstruction task, we evaluate the performance of CorresNeRF when the foreground mask is provided. When the foreground mask is available, the correspondences are filtered by the mask, allowing the model to focus on the foreground region. The results are shown in Table 5. Our model outperforms the NeuS model in both the with-mask and without-mask settings. Qualitative visualization results are shown in Fig. 8 in the supplementary material.
We presented CorresNeRF, a method that can utilize the image correspondence priors for training neural radiance fields with sparse-view inputs. We propose automatic augmentation and filtering methods to generate dense and high-quality image correspondences from sparse-view inputs. We design reprojection and depth loss terms based on the correspondence priors to regularize the neural radiance field training. Experiments show that our method can significantly improve the reconstruction quality measured in photometric and geometric metrics with only a few input images.
Limitations and future work. In CorresNeRF, convincing correspondences are obtained by using SOTA matching networks and further processed via adaptive augmentation and outlier removal. An
NeRF [4]
DS-NeRF [13]
RegNeRF [29]
CorresNeRF
Figure 5: Qualitative results from the LLFF dataset.
UNISURF [18]
VolSDF [52]
Figure 6: Qualitative results from the DTU dataset.
NeuS [21]
CorresNeRF
Ground Truth
Table 3: Ablation study on correspondence generation and loss terms. We verify the effectiveness of correspondence pre-processing and correspondence loss terms. We find that both correspondence augmentation and outlier filtering are helpful for the final performance. We also find that both the pixel reprojection loss and correspondence depth loss contribute to the final performance on both photometric and geometric metrics.
PSNR ↑	SSIM ↑	LPIPS ↓	Depth MAE ↓
NeRF [4]	16.79	0.56	0.37	1.66
Ours w/o Corres Augment	18.50	0.66	0.32	1.15
Ours w/o Corres Filter	19.32	0.69	0.29	0.93
Ours w/o Pixel Loss	18.16	0.62	0.42	1.33
Ours w/o Depth Loss	17.75	0.59	0.51	1.47
Ours	19.83	0.70	0.29	0.91
Table 4: Ablation study on robustness to correspondence noise. We add Gaussian noise to the correspondences generated by our method and evaluate the performance of CorresNeRF on the LLFF dataset with 3 input views.
Corres % After Filter	PSNR ↑	SSIM ↑	LPIPS ↓	Depth MAE ↓
NeRF [4]	N/A	16.79	0.56	0.37	1.66
Ours (Noise STD = 4 px)	13.96%	18.31	0.61	0.48	1.04
Ours (Noise STD = 2 px)	27.04%	19.16	0.66	0.33	1.06
Ours (Noise STD = 1 px)	48.91%	19.31	0.67	0.28	1.06
Ours (Noise STD = 0 px)	100.00%	19.83	0.70	0.29	0.91
Table 5: Ablation study on the effects of foreground masks. We report the results of NeuS and our model trained with and without foreground masks on the DTU dataset with 3 input views. The photometric metrics are calculated with the foreground masks applied regardless of the training settings.
PSNR ↑	SSIM ↑	LPIPS ↓	Depth MAE ↓
NeuS w/o Mask	16.06	0.59	0.21	6.16
Ours w/o Mask	20.58	0.77	0.13	2.63
NeuS w/ Mask	20.85	0.78	0.12	2.36
Ours w/ Mask	21.85	0.81	0.11	1.57
admired number of correspondences can be acquired for most practical scenes (proved by experiments on various benchmarks). However, there are still some extreme cases where few correspondences are computed via matching networks (due to unreasonable camera positions or some specific textures of target scenes). Correspondence generation methods are needed for these cases to synthesize convincing correspondences along with the optimization of NeRF. Some recent works [53, 54] have proved that neural implicit representations can be utilized to learn correspondences in turn. Dealing with such extreme scenes will be our future work.
This work is supported in part by the National Natural Science Foundation of China (No. 62201484), HKU Startup Fund, and HKU Seed Fund for Basic Research.
[51] Richard Zhang, Phillip Isola, Alexei A. Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In CVPR, 2018. 7
[52] Lior Yariv, Jiatao Gu, Yoni Kasten, and Yaron Lipman. Volume rendering of neural implicit surfaces. In NeurIPS, 2021. 7, 8, 9, 17
[53] Lin Yen-Chen, Pete Florence, Jonathan T Barron, Tsung-Yi Lin, Alberto Rodriguez, and Phillip Isola. Nerf-supervision: Learning dense object descriptors from neural radiance fields. In ICRA, 2022. 10
[54] Jianfei Guo, Zhiyuan Yang, Xi Lin, and Qingfu Zhang. Template nerf: Towards modeling dense shape correspondences from category-specific object images. arXiv:2111.04237, 2021. 10
A Discussions on Correspondence
In this section, we provide additional discussions and visualizations on the correspondence generation pipeline, including direct correspondence reconstruction, comparing correspondence with SfM, and the correspondence propagation process.
A.1 Comparing with SfM
We compare the DS-NeRF [13]'s SfM pipeline with our CorresNeRF's correspondence generation procedure in terms of the number of points generated and the number of correspondences generated. The results are reported in Table 6. The table shows that CorresNeRF is able to leverage much denser supervision compared to SfM-based depth supervision methods.
Table 6: Comparison of DS-NeRF's SfM point cloud number, CorresNeRF's correspondence number, and their pixel coverage (\%). We compare the number of points in the supervision point cloud used in DS-NeRF [13] generated by COLMAP SfM [19] with the number of correspondences used in CorresNeRF. The numbers are reported in counts (total number of points and total number of correspondences) and pixel coverage percentage (the percentage of pixels that are supervised by sparse point cloud in DS-NeRF and the percentage of pixels having correspondences in CorresNeRF). For DS-NeRF, we run COLMAP only on the selected input views with camera poses provided (as opposed to providing all views for SfM and selecting visible points from input views). The results are reported on the LLFF dataset with 3 input views.
DS-NeRF [13] with SfM	Ours
Base	Base+Filter	Base+Filter+Aug
fern	362 (0.19%)	325,633 (57%)	281,382 (49%)	368,798 (65%)
flower	685 (0.35%)	415,459 (73%)	278,180 (49%)	356,044 (62%)
fortress	609 (0.31%)	435,676 (76%)	377,956 (66%)	430,044 (75%)
horns	512 (0.27%)	270,778 (47%)	224,812 (39%)	271,705 (48%)
leaves	201 (0.11%)	272,158 (48%)	153,024 (27%)	198,412 (35%)
orchids	229 (0.12%)	213,660 (37%)	153,250 (27%)	242,620 (42%)
room	345 (0.18%)	226,156 (40%)	190,302 (33%)	260,308 (46%)
trex	644 (0.34%)	202,650 (35%)	164,047 (29%)	233,950 (41%)
A.2 Correspondence Propagation
We provide additional visualizations to demonstrate the correspondence propagation process. With correspondence propagation, image pairs initially with few or no correspondences may still obtain correspondence relationships propagated by other image pairs. For the propagated correspondences, we assign a new confidence score as the cumulative product of the confidence scores in the propagation path. These confidence levels are used to weigh the loss terms, as described in the main paper. See Fig. 7 for the visualizations.
LLFF dataset. For the novel view synthesis task on LLFF, we based our code on NeRF [4]. For most parameters, we use the same settings as the original NeRF paper.
Number of input views: 3
- Image scale factor: 8
Correspondence pixel reprojection weight  $(\mathcal{L}_{\mathrm{pixel}})$  : 0.1
Correspondence depth weight  $(\mathcal{L}_{\mathrm{depth}})$  .. 0.1
- Training iterations:  $50\mathrm{K}^2$
- Learning rate:  $5e^{-4}$
Similar to RegNeRF [29]'s 44K iterations in a 3-view setting. More iterations could enhance results.
Corres(A, B) with Propagation
Input B
Input C
Figure 7: Correspondence propagation visualizations. Column 1 shows the sparse set of input images. Column 2 shows the correspondences without correspondence propagation but with all other augmentations and filtering enabled. Column 3 shows the propagated correspondences. The correspondence colors represent the confidence scores, where the higher confidence scores are greener. The highlighted area shows that the image pair (A, B) can receive additional correspondence relationships by propagating their correspondences from C.
Corres(C, B) with Propagation
Corres(C, A) with Propagation
DTU dataset. For the novel view synthesis and surface reconstruction task on DTU, we based our code on NeuS [21]. Important parameters are listed below. For most parameters, we utilize the same settings as in the original NeuS paper.
Number of input views: 3
- Image scale factor: 4
Correspondence pixel reprojection weight  $(\mathcal{L}_{\mathrm{pixel}})$  : 0.1
Correspondence depth weight  $(\mathcal{L}_{\mathrm{depth}})$  .. 0.1
- Training iterations: 200K
- Learning rate:  $5e^{-4}$
C.1 Effects of Foreground Masks
We provide additional qualitative results to demonstrate the effects of foreground masks with correspondence priors in Fig. 8.
C.2 Per-Scene Results
We provide per-scene results for the novel view synthesis task on the LLFF dataset in Table 7, as well as novel view synthesis and surface reconstruction tasks on the DTU dataset in Table 8 and Table 9.
Figure 8: Effects of foreground masks. Columns 1 and 2 compare NeuS [21] and our model trained without foreground masks. Our model outperforms NeuS in terms of novel-view rendering quality and mesh reconstruction quality. Columns 3 and 4 compare NeuS and our model trained with foreground masks. Our model trained with foreground masks further improves the rendering quality and geometric reconstruction quality. We observe more geometric details and fewer artifacts in our reconstruction compared with NeuS.
Table 7: Per-scene results on the LLFF dataset. We compare novel view synthesis on the LLFF dataset for 3 input views. Our method outperforms the baseline NeRF model and the sparse-view optimized DS-NeRF and RegNeRF models. Our model is built with the vanilla NeRF as the direct baseline. Note that for RegNeRF [29], the numbers reported here are results from our own evaluation, which are slightly better than the numbers (PSNR: 19.08, SSIM: 0.59, and LPIPS: 0.34) reported in the original author's paper and Table 1.
NeRF [4] / DS-NeRF [13] / RegNeRF [29] / Ours
PSNR ↑	SSIM ↑	LPIPS ↓	Depth MAE ↓
fern	20.47 / 21.28 / 19.87 / 21.15	0.71 / 0.75 / 0.70 / 0.74	0.33 / 0.27 / 0.30 / 0.33	0.28 / 0.29 / 0.34 / 0.31
flower	17.35 / 19.35 / 19.93 / 20.44	0.56 / 0.67 / 0.68 / 0.69	0.34 / 0.26 / 0.23 / 0.25	1.78 / 0.85 / 0.91 / 0.85
fortress	19.48 / 20.87 / 23.33 / 22.53	0.68 / 0.67 / 0.74 / 0.76	0.38 / 0.37 / 0.26 / 0.35	0.95 / 0.67 / 0.46 / 0.51
horns	14.46 / 14.50 / 15.64 / 19.18	0.49 / 0.48 / 0.61 / 0.71	0.51 / 0.49 / 0.36 / 0.32	3.06 / 2.92 / 1.68 / 1.09
leaves	12.73 / 14.83 / 16.60 / 16.89	0.28 / 0.43 / 0.61 / 0.59	0.45 / 0.41 / 0.22 / 0.32	4.16 / 2.91 / 3.08 / 3.10
orchids	13.92 / 14.44 / 15.56 / 15.85	0.38 / 0.41 / 0.50 / 0.48	0.33 / 0.32 / 0.25 / 0.38	1.13 / 0.96 / 0.71 / 0.56
room	19.97 / 17.69 / 21.53 / 22.41	0.82 / 0.71 / 0.86 / 0.87	0.25 / 0.40 / 0.18 / 0.19	0.77 / 2.14 / 0.45 / 0.38
trex	15.89 / 13.79 / 20.16 / 20.18	0.59 / 0.43 / 0.77 / 0.76	0.36 / 0.50 / 0.20 / 0.22	1.19 / 2.39 / 0.51 / 0.51
mean	16.79 / 17.09 / 19.08 / 19.83	0.56 / 0.57 / 0.68 / 0.70	0.37 / 0.38 / 0.25 / 0.29	1.66 / 1.64 / 1.02 / 0.91
Table 8: Per-scene results on the DTU dataset (image-level eval). We compare novel view synthesis and surface reconstruction on the DTU dataset for 3 input views. The models are trained without mask supervision. The models are evaluated without foreground masks applied (image-level).
UNISURF [20] / VolSDF [52] / NeuS [21] / Ours
PSNR ↑	SSIM ↑	LPIPS ↓
scan24	8.98 / 10.53 / 10.36 / 13.99	0.43 / 0.46 / 0.46 / 0.64	0.65 / 0.58 / 0.61 / 0.36
scan37	7.26 / 10.40 / 10.31 / 13.38	0.38 / 0.57 / 0.47 / 0.59	0.67 / 0.43 / 0.58 / 0.40
scan40	12.14 / 7.48 / 10.61 / 13.51	0.58 / 0.39 / 0.48 / 0.70	0.59 / 0.67 / 0.63 / 0.38
scan55	14.68 / 17.51 / 17.75 / 18.99	0.52 / 0.68 / 0.62 / 0.73	0.71 / 0.45 / 0.52 / 0.41
scan63	4.79 / 5.80 / 10.92 / 12.66	0.42 / 0.53 / 0.65 / 0.78	0.75 / 0.61 / 0.50 / 0.31
scan65	12.78 / 19.93 / 19.15 / 20.13	0.60 / 0.82 / 0.76 / 0.79	0.59 / 0.32 / 0.37 / 0.30
scan69	17.05 / 16.62 / 17.87 / 19.71	0.57 / 0.62 / 0.62 / 0.75	0.51 / 0.46 / 0.48 / 0.31
scan83	8.37 / 13.19 / 13.73 / 13.98	0.48 / 0.68 / 0.69 / 0.74	0.68 / 0.49 / 0.47 / 0.39
scan97	13.28 / 10.93 / 14.57 / 15.80	0.59 / 0.54 / 0.67 / 0.74	0.46 / 0.51 / 0.39 / 0.31
scan105	11.78 / 13.69 / 12.91 / 14.68	0.57 / 0.63 / 0.60 / 0.72	0.56 / 0.49 / 0.51 / 0.37
scan106	18.10 / 21.36 / 20.09 / 22.03	0.71 / 0.78 / 0.76 / 0.83	0.44 / 0.35 / 0.40 / 0.30
scan110	18.07 / 16.21 / 20.51 / 22.05	0.67 / 0.60 / 0.77 / 0.81	0.48 / 0.62 / 0.39 / 0.34
scan114	20.90 / 23.00 / 23.12 / 24.25	0.79 / 0.85 / 0.83 / 0.86	0.37 / 0.26 / 0.28 / 0.24
scan118	18.84 / 20.93 / 20.55 / 25.01	0.66 / 0.78 / 0.73 / 0.85	0.57 / 0.46 / 0.44 / 0.27
scan122	20.69 / 24.02 / 23.08 / 23.26	0.70 / 0.85 / 0.80 / 0.81	0.53 / 0.32 / 0.38 / 0.33
mean	13.85 / 15.44 / 16.37 / 18.23	0.58 / 0.65 / 0.66 / 0.76	0.57 / 0.47 / 0.46 / 0.33
Table 9: Per-scene results on the DTU dataset (object-level eval). We compare novel view synthesis and surface reconstruction on the DTU dataset for 3 input views. Note that VolSDF fails to reconstruct a mesh for one of the scenes (scan110), thus its Chamfer-  $L_{1}$  is averaged over the remaining scenes. The models are trained without mask supervision. The models are evaluated with foreground masks applied (object-level).
UNISURF [20] / VolSDF [52] / NeuS [21] / Ours
PSNR ↑	SSIM ↑	LPIPS ↓	Chamfer-L1 ↓
scan24	9.45 / 10.95 / 10.65 / 16.82	0.37 / 0.36 / 0.40 / 0.61	0.54 / 0.49 / 0.49 / 0.29	7.81 / 7.00 / 6.06 / 2.73
scan37	7.19 / 11.17 / 10.07 / 13.44	0.22 / 0.35 / 0.30 / 0.45	0.33 / 0.21 / 0.27 / 0.22	7.54 / 6.95 / 7.24 / 4.92
scan40	15.37 / 10.56 / 12.36 / 19.08	0.52 / 0.34 / 0.41 / 0.71	0.49 / 0.48 / 0.47 / 0.28	6.37 / 7.47 / 7.68 / 3.00
scan55	12.71 / 16.35 / 15.93 / 19.55	0.30 / 0.58 / 0.43 / 0.73	0.39 / 0.21 / 0.28 / 0.17	8.38 / 2.90 / 5.85 / 2.37
scan63	5.62 / 12.83 / 12.05 / 20.37	0.38 / 0.63 / 0.53 / 0.82	0.27 / 0.17 / 0.18 / 0.08	8.40 / 4.58 / 8.84 / 2.52
scan65	11.23 / 17.76 / 18.10 / 19.00	0.57 / 0.81 / 0.79 / 0.86	0.18 / 0.08 / 0.09 / 0.07	5.08 / 2.30 / 4.65 / 2.71
scan69	16.44 / 17.01 / 18.21 / 21.94	0.49 / 0.57 / 0.60 / 0.81	0.27 / 0.23 / 0.23 / 0.13	7.42 / 3.85 / 6.30 / 2.05
scan83	7.62 / 12.72 / 13.52 / 22.63	0.37 / 0.49 / 0.52 / 0.85	0.19 / 0.15 / 0.13 / 0.05	7.92 / 9.14 / 9.62 / 3.14
scan97	12.13 / 13.32 / 15.03 / 18.36	0.44 / 0.49 / 0.59 / 0.70	0.27 / 0.21 / 0.18 / 0.13	8.73 / 3.50 / 4.82 / 2.27
scan105	12.12 / 14.60 / 15.94 / 21.48	0.46 / 0.54 / 0.62 / 0.80	0.29 / 0.25 / 0.21 / 0.15	8.89 / 6.52 / 8.19 / 3.61
scan106	15.83 / 20.44 / 18.65 / 21.37	0.61 / 0.77 / 0.73 / 0.86	0.22 / 0.13 / 0.17 / 0.10	5.89 / 1.76 / 4.99 / 2.08
scan110	14.89 / 13.69 / 17.68 / 19.79	0.51 / 0.39 / 0.70 / 0.79	0.19 / 0.26 / 0.14 / 0.11	7.68 / N/A / 5.75 / 2.03
scan114	18.90 / 21.14 / 21.69 / 23.44	0.74 / 0.81 / 0.82 / 0.87	0.21 / 0.11 / 0.13 / 0.10	3.43 / 0.81 / 2.01 / 1.37
scan118	16.09 / 21.03 / 18.29 / 27.44	0.48 / 0.74 / 0.61 / 0.91	0.24 / 0.14 / 0.16 / 0.05	6.47 / 3.93 / 6.16 / 1.83
scan122	17.91 / 21.66 / 22.72 / 24.00	0.52 / 0.79 / 0.79 / 0.85	0.15 / 0.06 / 0.07 / 0.05	8.51 / 1.45 / 4.25 / 2.85
mean	12.90 / 15.68 / 16.06 / 20.58	0.47 / 0.58 / 0.59 / 0.77	0.28 / 0.21 / 0.21 / 0.13	7.23 / 4.44 / 6.16 / 2.63