High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

HPM-MVS++		89.02 8	89.15 8	88.63 4	95.01 9	76.03 1	92.38 26	92.85 60	80.26 13	87.78 30	94.27 36	75.89 20	96.81 22	87.45 19	96.44 9	93.05 97

MSC_two_6792asdad				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 32

No_MVS				89.16 1	94.34 29	75.53 2		92.99 49					97.53 1	89.67 1	96.44 9	94.41 32

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 47	82.45 3	96.87 19	83.77 54	96.48 8	94.88 11

zzz-MVS		87.53 24	87.41 27	87.90 21	94.18 37	74.25 5	90.23 76	92.02 94	79.45 20	85.88 43	94.80 16	68.07 88	96.21 43	86.69 24	95.34 36	93.23 89

MTAPA		87.23 33	87.00 34	87.90 21	94.18 37	74.25 5	86.58 189	92.02 94	79.45 20	85.88 43	94.80 16	68.07 88	96.21 43	86.69 24	95.34 36	93.23 89

MP-MVS		87.71 21	87.64 23	87.93 20	94.36 28	73.88 7	92.71 22	92.65 70	77.57 43	83.84 83	94.40 34	72.24 52	96.28 41	85.65 28	95.30 40	93.62 76
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		87.11 35	86.92 37	87.68 37	94.20 36	73.86 8	93.98 3	92.82 64	76.62 73	83.68 85	94.46 27	67.93 90	95.95 56	84.20 49	94.39 60	93.23 89

CNVR-MVS		88.93 9	89.13 9	88.33 7	94.77 12	73.82 9	90.51 66	93.00 47	80.90 9	88.06 28	94.06 46	76.43 17	96.84 20	88.48 14	95.99 19	94.34 37

SMA-MVS		89.08 7	89.23 7	88.61 5	94.25 33	73.73 10	92.40 23	93.63 24	74.77 112	92.29 7	95.97 2	74.28 36	97.24 11	88.58 13	96.91 1	94.87 13
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
NCCC		88.06 14	88.01 18	88.24 10	94.41 24	73.62 11	91.22 54	92.83 61	81.50 6	85.79 46	93.47 59	73.02 47	97.00 17	84.90 33	94.94 44	94.10 45

ACMMPR		87.44 26	87.23 31	88.08 13	94.64 13	73.59 12	93.04 12	93.20 39	76.78 68	84.66 67	94.52 23	68.81 86	96.65 29	84.53 41	94.90 45	94.00 52

region2R		87.42 28	87.20 32	88.09 12	94.63 14	73.55 13	93.03 14	93.12 42	76.73 71	84.45 71	94.52 23	69.09 82	96.70 26	84.37 44	94.83 51	94.03 49

mPP-MVS		86.67 43	86.32 47	87.72 32	94.41 24	73.55 13	92.74 20	92.22 87	76.87 65	82.81 98	94.25 38	66.44 104	96.24 42	82.88 66	94.28 63	93.38 83

HFP-MVS		87.58 23	87.47 25	87.94 17	94.58 16	73.54 15	93.04 12	93.24 37	76.78 68	84.91 58	94.44 30	70.78 64	96.61 32	84.53 41	94.89 46	93.66 68

#test#		87.33 31	87.13 33	87.94 17	94.58 16	73.54 15	92.34 29	93.24 37	75.23 100	84.91 58	94.44 30	70.78 64	96.61 32	83.75 55	94.89 46	93.66 68

3Dnovator+	77.84 4	85.48 62	84.47 76	88.51 6	91.08 93	73.49 17	93.18 11	93.78 21	80.79 10	76.66 191	93.37 60	60.40 190	96.75 25	77.20 117	93.73 69	95.29 3

MSP-MVS		89.51 4	89.91 5	88.30 9	94.28 32	73.46 18	92.90 16	94.11 8	80.27 12	91.35 14	94.16 41	78.35 13	96.77 23	89.59 3	94.22 65	94.67 24
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
PGM-MVS		86.68 42	86.27 48	87.90 21	94.22 35	73.38 19	90.22 77	93.04 43	75.53 94	83.86 82	94.42 33	67.87 92	96.64 30	82.70 71	94.57 56	93.66 68

ZNCC-MVS		87.94 18	87.85 20	88.20 11	94.39 26	73.33 20	93.03 14	93.81 20	76.81 66	85.24 52	94.32 35	71.76 56	96.93 18	85.53 29	95.79 25	94.32 38

XVS		87.18 34	86.91 38	88.00 15	94.42 22	73.33 20	92.78 18	92.99 49	79.14 23	83.67 86	94.17 40	67.45 95	96.60 34	83.06 62	94.50 57	94.07 47

X-MVStestdata		80.37 147	77.83 184	88.00 15	94.42 22	73.33 20	92.78 18	92.99 49	79.14 23	83.67 86	12.47 374	67.45 95	96.60 34	83.06 62	94.50 57	94.07 47

ACMMP_NAP		88.05 16	88.08 17	87.94 17	93.70 47	73.05 23	90.86 59	93.59 26	76.27 82	88.14 26	95.09 15	71.06 62	96.67 28	87.67 16	96.37 14	94.09 46

DPM-MVS		84.93 72	84.29 77	86.84 53	90.20 112	73.04 24	87.12 171	93.04 43	69.80 204	82.85 96	91.22 108	73.06 46	96.02 51	76.72 125	94.63 54	91.46 150

GST-MVS		87.42 28	87.26 29	87.89 24	94.12 39	72.97 25	92.39 25	93.43 32	76.89 64	84.68 64	93.99 50	70.67 67	96.82 21	84.18 50	95.01 42	93.90 57

TEST9					93.26 56	72.96 26	88.75 117	91.89 103	68.44 236	85.00 56	93.10 65	74.36 35	95.41 76

train_agg		86.43 46	86.20 50	87.13 49	93.26 56	72.96 26	88.75 117	91.89 103	68.69 232	85.00 56	93.10 65	74.43 32	95.41 76	84.97 32	95.71 30	93.02 99

SteuartSystems-ACMMP		88.72 10	88.86 10	88.32 8	92.14 80	72.96 26	93.73 5	93.67 23	80.19 14	88.10 27	94.80 16	73.76 41	97.11 13	87.51 18	95.82 24	94.90 10
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator	76.31 5	83.38 87	82.31 96	86.59 60	87.94 192	72.94 29	90.64 63	92.14 91	77.21 54	75.47 215	92.83 74	58.56 197	94.72 111	73.24 155	92.71 77	92.13 130

SD-MVS		88.06 14	88.50 13	86.71 57	92.60 76	72.71 30	91.81 42	93.19 40	77.87 38	90.32 17	94.00 48	74.83 28	93.78 149	87.63 17	94.27 64	93.65 73
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
MVS_111021_HR		85.14 69	84.75 73	86.32 65	91.65 87	72.70 31	85.98 204	90.33 150	76.11 84	82.08 103	91.61 98	71.36 61	94.17 132	81.02 83	92.58 79	92.08 131

DPE-MVS		89.48 5	89.98 4	88.01 14	94.80 11	72.69 32	91.59 44	94.10 10	75.90 88	92.29 7	95.66 10	81.67 6	97.38 9	87.44 20	96.34 15	93.95 54
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					95.06 8	72.65 33				91.80 13

ACMMP		85.89 56	85.39 62	87.38 44	93.59 51	72.63 34	92.74 20	93.18 41	76.78 68	80.73 122	93.82 53	64.33 125	96.29 40	82.67 72	90.69 102	93.23 89
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
test_prior4						72.60 35	89.01 106

test_8					93.13 58	72.57 36	88.68 121	91.84 107	68.69 232	84.87 62	93.10 65	74.43 32	95.16 87

TSAR-MVS + GP.		85.71 59	85.33 63	86.84 53	91.34 90	72.50 37	89.07 105	87.28 233	76.41 75	85.80 45	90.22 132	74.15 39	95.37 82	81.82 78	91.88 87	92.65 112

CSCG		86.41 48	86.19 51	87.07 50	92.91 65	72.48 38	90.81 60	93.56 27	73.95 131	83.16 92	91.07 114	75.94 19	95.19 86	79.94 95	94.38 61	93.55 79

testtj		87.78 20	87.78 21	87.77 26	94.55 18	72.47 39	92.23 33	93.49 29	74.75 113	88.33 25	94.43 32	73.27 44	97.02 16	84.18 50	94.84 49	93.82 62

MCST-MVS		87.37 30	87.25 30	87.73 30	94.53 19	72.46 40	89.82 85	93.82 19	73.07 152	84.86 63	92.89 72	76.22 18	96.33 39	84.89 35	95.13 41	94.40 34

ETH3D cwj APD-0.16		87.31 32	87.27 28	87.44 43	91.60 88	72.45 41	90.02 80	94.37 4	71.76 166	87.28 34	94.27 36	75.18 26	96.08 49	85.16 30	95.77 26	93.80 65

FOURS1					95.00 10	72.39 42	95.06 1	93.84 18	74.49 119	91.30 15

APD-MVS		87.44 26	87.52 24	87.19 47	94.24 34	72.39 42	91.86 41	92.83 61	73.01 154	88.58 23	94.52 23	73.36 42	96.49 37	84.26 46	95.01 42	92.70 108
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	79.65 3	86.91 38	86.62 42	87.76 29	93.52 52	72.37 44	91.26 50	93.04 43	76.62 73	84.22 76	93.36 61	71.44 60	96.76 24	80.82 86	95.33 38	94.16 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
xxxxxxxxxxxxxcwj		87.88 19	87.92 19	87.77 26	93.80 44	72.35 45	90.47 69	89.69 168	74.31 123	89.16 19	95.10 13	75.65 22	96.19 45	87.07 21	96.01 17	94.79 18

save fliter					93.80 44	72.35 45	90.47 69	91.17 129	74.31 123

DeepC-MVS	79.81 2	87.08 37	86.88 39	87.69 36	91.16 92	72.32 47	90.31 74	93.94 17	77.12 58	82.82 97	94.23 39	72.13 54	97.09 14	84.83 36	95.37 35	93.65 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZD-MVS					94.38 27	72.22 48		92.67 67	70.98 183	87.75 31	94.07 45	74.01 40	96.70 26	84.66 39	94.84 49

HPM-MVS		87.11 35	86.98 35	87.50 41	93.88 43	72.16 49	92.19 34	93.33 35	76.07 85	83.81 84	93.95 51	69.77 76	96.01 52	85.15 31	94.66 53	94.32 38
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		88.02 17	88.11 16	87.72 32	93.68 49	72.13 50	91.41 49	92.35 80	74.62 117	88.90 21	93.85 52	75.75 21	96.00 53	87.80 15	94.63 54	95.04 5
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS		86.73 39	86.67 41	86.91 52	94.11 40	72.11 51	92.37 27	92.56 73	74.50 118	86.84 37	94.65 20	67.31 97	95.77 60	84.80 37	92.85 75	92.84 106

MP-MVS-pluss		87.67 22	87.72 22	87.54 39	93.64 50	72.04 52	89.80 87	93.50 28	75.17 103	86.34 41	95.29 12	70.86 63	96.00 53	88.78 12	96.04 16	94.58 27
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 11	88.74 11	87.64 38	92.78 69	71.95 53	92.40 23	94.74 2	75.71 90	89.16 19	95.10 13	75.65 22	96.19 45	87.07 21	96.01 17	94.79 18

agg_prior1		86.22 51	86.09 55	86.62 59	92.85 66	71.94 54	88.59 123	91.78 110	68.96 227	84.41 72	93.18 64	74.94 27	94.93 98	84.75 38	95.33 38	93.01 100

agg_prior					92.85 66	71.94 54		91.78 110		84.41 72			94.93 98

ETH3D-3000-0.1		88.09 13	88.29 14	87.50 41	92.76 70	71.89 56	91.43 48	94.70 3	74.47 120	88.86 22	94.61 21	75.23 25	95.84 58	86.62 26	95.92 21	94.78 20

ETH3 D test6400		87.50 25	87.44 26	87.70 35	93.71 46	71.75 57	90.62 64	94.05 15	70.80 185	87.59 33	93.51 56	77.57 14	96.63 31	83.31 57	95.77 26	94.72 23

APDe-MVS		89.15 6	89.63 6	87.73 30	94.49 20	71.69 58	93.83 4	93.96 16	75.70 92	91.06 16	96.03 1	76.84 15	97.03 15	89.09 6	95.65 32	94.47 31

MVS_111021_LR		82.61 99	82.11 98	84.11 123	88.82 162	71.58 59	85.15 224	86.16 250	74.69 114	80.47 125	91.04 115	62.29 154	90.55 257	80.33 92	90.08 112	90.20 190

MAR-MVS		81.84 109	80.70 120	85.27 84	91.32 91	71.53 60	89.82 85	90.92 134	69.77 205	78.50 149	86.21 245	62.36 153	94.52 117	65.36 224	92.05 86	89.77 215
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
test_one_0601					95.07 7	71.46 61		94.14 7	78.27 37	92.05 11	95.74 6	80.83 11

test_0728_SECOND				87.71 34	95.34 1	71.43 62	93.49 9	94.23 5					97.49 3	89.08 7	96.41 12	94.21 42

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 29	71.25 63	95.06 1	94.23 5	78.38 35	92.78 4	95.74 6	82.45 3	97.49 3	89.42 4	96.68 2	94.95 7

IU-MVS					95.30 2	71.25 63		92.95 56	66.81 246	92.39 6				88.94 11	96.63 4	94.85 16

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 63	93.49 9	92.73 65	77.33 50	92.12 9	95.78 4	80.98 9	97.40 7	89.08 7	96.41 12	93.33 86
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					95.27 5	71.25 63	93.60 6	94.11 8	77.33 50	92.81 3	95.79 3	80.98 9

Regformer-2		86.63 44	86.53 43	86.95 51	89.33 139	71.24 67	88.43 126	92.05 93	82.50 1	86.88 36	90.09 134	74.45 31	95.61 63	84.38 43	90.63 103	94.01 51

CDPH-MVS		85.76 58	85.29 66	87.17 48	93.49 53	71.08 68	88.58 124	92.42 78	68.32 237	84.61 68	93.48 57	72.32 51	96.15 48	79.00 99	95.43 34	94.28 40

CNLPA		78.08 200	76.79 209	81.97 197	90.40 109	71.07 69	87.59 159	84.55 268	66.03 260	72.38 260	89.64 146	57.56 205	86.04 308	59.61 268	83.35 192	88.79 246

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 70	93.57 7	94.06 12	77.24 52	93.10 1	95.72 8	82.99 1	97.44 5	89.07 9	96.63 4	94.88 11

test_241102_ONE					95.30 2	70.98 70		94.06 12	77.17 56	93.10 1	95.39 11	82.99 1	97.27 10

PHI-MVS		86.43 46	86.17 52	87.24 46	90.88 99	70.96 72	92.27 32	94.07 11	72.45 157	85.22 53	91.90 90	69.47 78	96.42 38	83.28 60	95.94 20	94.35 36

OPM-MVS		83.50 83	82.95 87	85.14 87	88.79 165	70.95 73	89.13 104	91.52 117	77.55 46	80.96 120	91.75 93	60.71 182	94.50 118	79.67 96	86.51 155	89.97 207
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CANet		86.45 45	86.10 54	87.51 40	90.09 114	70.94 74	89.70 91	92.59 72	81.78 4	81.32 113	91.43 104	70.34 69	97.23 12	84.26 46	93.36 71	94.37 35

Regformer-1		86.41 48	86.33 46	86.64 58	89.33 139	70.93 75	88.43 126	91.39 123	82.14 3	86.65 38	90.09 134	74.39 34	95.01 97	83.97 52	90.63 103	93.97 53

DP-MVS Recon		83.11 92	82.09 99	86.15 68	94.44 21	70.92 76	88.79 115	92.20 88	70.53 192	79.17 137	91.03 117	64.12 127	96.03 50	68.39 199	90.14 110	91.50 147

CPTT-MVS		83.73 78	83.33 82	84.92 96	93.28 55	70.86 77	92.09 37	90.38 146	68.75 231	79.57 133	92.83 74	60.60 186	93.04 187	80.92 85	91.56 93	90.86 168

h-mvs33		83.15 89	82.19 97	86.02 73	90.56 105	70.85 78	88.15 143	89.16 184	76.02 86	84.67 65	91.39 105	61.54 165	95.50 69	82.71 69	75.48 283	91.72 141

1121		80.84 129	79.77 136	84.05 129	93.11 60	70.78 79	84.66 234	85.42 258	57.37 338	81.76 111	92.02 87	63.41 133	94.12 133	67.28 207	92.93 73	87.26 277

新几何1				83.42 149	93.13 58	70.71 80		85.48 257	57.43 337	81.80 108	91.98 88	63.28 135	92.27 210	64.60 231	92.99 72	87.27 276

test12				86.80 55	92.63 73	70.70 81		91.79 109		82.71 99		71.67 57	96.16 47		94.50 57	93.54 80

SR-MVS-dyc-post		85.77 57	85.61 59	86.23 66	93.06 62	70.63 82	91.88 39	92.27 83	73.53 144	85.69 47	94.45 28	65.00 122	95.56 65	82.75 67	91.87 88	92.50 115

RE-MVS-def			85.48 60		93.06 62	70.63 82	91.88 39	92.27 83	73.53 144	85.69 47	94.45 28	63.87 129		82.75 67	91.87 88	92.50 115

HPM-MVS_fast		85.35 66	84.95 71	86.57 61	93.69 48	70.58 84	92.15 36	91.62 114	73.89 134	82.67 100	94.09 44	62.60 147	95.54 68	80.93 84	92.93 73	93.57 78

abl_6		85.23 67	84.95 71	86.07 71	92.23 79	70.48 85	90.80 61	92.08 92	73.51 146	85.26 51	94.16 41	62.75 146	95.92 57	82.46 74	91.30 97	91.81 139

MSLP-MVS++		85.43 64	85.76 58	84.45 112	91.93 83	70.24 86	90.71 62	92.86 59	77.46 49	84.22 76	92.81 76	67.16 99	92.94 189	80.36 91	94.35 62	90.16 191

MVSFormer		82.85 95	82.05 101	85.24 85	87.35 211	70.21 87	90.50 67	90.38 146	68.55 234	81.32 113	89.47 152	61.68 162	93.46 166	78.98 100	90.26 108	92.05 132

lupinMVS		81.39 121	80.27 130	84.76 103	87.35 211	70.21 87	85.55 217	86.41 245	62.85 295	81.32 113	88.61 176	61.68 162	92.24 212	78.41 106	90.26 108	91.83 137

xiu_mvs_v1_base_debu		80.80 134	79.72 138	84.03 132	87.35 211	70.19 89	85.56 214	88.77 200	69.06 223	81.83 105	88.16 190	50.91 263	92.85 191	78.29 108	87.56 138	89.06 230

xiu_mvs_v1_base		80.80 134	79.72 138	84.03 132	87.35 211	70.19 89	85.56 214	88.77 200	69.06 223	81.83 105	88.16 190	50.91 263	92.85 191	78.29 108	87.56 138	89.06 230

xiu_mvs_v1_base_debi		80.80 134	79.72 138	84.03 132	87.35 211	70.19 89	85.56 214	88.77 200	69.06 223	81.83 105	88.16 190	50.91 263	92.85 191	78.29 108	87.56 138	89.06 230

API-MVS		81.99 107	81.23 111	84.26 120	90.94 97	70.18 92	91.10 55	89.32 176	71.51 175	78.66 146	88.28 186	65.26 117	95.10 93	64.74 230	91.23 98	87.51 270

OpenMVS	72.83 10	79.77 158	78.33 173	84.09 125	85.17 244	69.91 93	90.57 65	90.97 133	66.70 249	72.17 262	91.91 89	54.70 225	93.96 137	61.81 252	90.95 100	88.41 255

jason		81.39 121	80.29 129	84.70 104	86.63 228	69.90 94	85.95 205	86.77 241	63.24 288	81.07 119	89.47 152	61.08 178	92.15 214	78.33 107	90.07 113	92.05 132
jason: jason.
test1172		86.20 52	86.22 49	86.12 70	93.95 42	69.89 95	91.79 43	92.28 82	75.07 104	86.40 40	94.58 22	65.00 122	95.56 65	84.34 45	92.60 78	92.90 104

MVP-Stereo		76.12 237	74.46 243	81.13 218	85.37 242	69.79 96	84.42 245	87.95 219	65.03 270	67.46 304	85.33 262	53.28 238	91.73 229	58.01 285	83.27 193	81.85 341
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PVSNet_Blended_VisFu		82.62 98	81.83 106	84.96 93	90.80 101	69.76 97	88.74 119	91.70 113	69.39 211	78.96 139	88.46 181	65.47 116	94.87 106	74.42 140	88.57 127	90.24 189

mvs-test1		80.88 127	79.40 145	85.29 83	85.13 247	69.75 98	89.28 96	88.10 214	74.99 106	76.44 197	86.72 225	57.27 209	94.26 127	73.53 148	83.18 195	91.87 136

Regformer-4		85.68 60	85.45 61	86.35 62	88.95 157	69.67 99	88.29 136	91.29 125	81.73 5	85.36 50	90.01 138	72.62 49	95.35 83	83.28 60	87.57 136	94.03 49

test_prior3		86.73 39	86.86 40	86.33 63	92.61 74	69.59 100	88.85 112	92.97 54	75.41 96	84.91 58	93.54 54	74.28 36	95.48 70	83.31 57	95.86 22	93.91 55

test_prior				86.33 63	92.61 74	69.59 100		92.97 54					95.48 70			93.91 55

APD-MVS_3200maxsize		85.97 54	85.88 56	86.22 67	92.69 72	69.53 102	91.93 38	92.99 49	73.54 143	85.94 42	94.51 26	65.80 114	95.61 63	83.04 64	92.51 80	93.53 81

EPNet		83.72 79	82.92 88	86.14 69	84.22 260	69.48 103	91.05 57	85.27 259	81.30 7	76.83 186	91.65 95	66.09 109	95.56 65	76.00 130	93.85 67	93.38 83
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ET-MVSNet_ETH3D		78.63 187	76.63 215	84.64 105	86.73 227	69.47 104	85.01 227	84.61 267	69.54 209	66.51 318	86.59 234	50.16 272	91.75 227	76.26 126	84.24 180	92.69 110

alignmvs		85.48 62	85.32 64	85.96 74	89.51 131	69.47 104	89.74 89	92.47 74	76.17 83	87.73 32	91.46 103	70.32 70	93.78 149	81.51 79	88.95 121	94.63 26

DP-MVS		76.78 227	74.57 239	83.42 149	93.29 54	69.46 106	88.55 125	83.70 281	63.98 285	70.20 278	88.89 169	54.01 233	94.80 108	46.66 341	81.88 211	86.01 303

canonicalmvs		85.91 55	85.87 57	86.04 72	89.84 124	69.44 107	90.45 72	93.00 47	76.70 72	88.01 29	91.23 107	73.28 43	93.91 144	81.50 80	88.80 124	94.77 21

nrg030		83.88 76	83.53 79	84.96 93	86.77 226	69.28 108	90.46 71	92.67 67	74.79 111	82.95 93	91.33 106	72.70 48	93.09 183	80.79 88	79.28 241	92.50 115

DeepPCF-MVS	80.84 1	88.10 12	88.56 12	86.73 56	92.24 78	69.03 109	89.57 93	93.39 34	77.53 47	89.79 18	94.12 43	78.98 12	96.58 36	85.66 27	95.72 29	94.58 27

XVG-OURS		80.41 145	79.23 152	83.97 136	85.64 238	69.02 110	83.03 271	90.39 145	71.09 181	77.63 170	91.49 102	54.62 227	91.35 238	75.71 131	83.47 191	91.54 144

Regformer-3		85.23 67	85.07 68	85.70 78	88.95 157	69.01 111	88.29 136	89.91 162	80.95 8	85.01 54	90.01 138	72.45 50	94.19 130	82.50 73	87.57 136	93.90 57

PCF-MVS	73.52 7	80.38 146	78.84 161	85.01 91	87.71 201	68.99 112	83.65 258	91.46 122	63.00 292	77.77 168	90.28 129	66.10 108	95.09 94	61.40 255	88.22 133	90.94 166
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
QAPM		80.88 127	79.50 143	85.03 90	88.01 191	68.97 113	91.59 44	92.00 97	66.63 253	75.15 229	92.16 85	57.70 203	95.45 72	63.52 234	88.76 125	90.66 174

AdaColmap		80.58 143	79.42 144	84.06 127	93.09 61	68.91 114	89.36 95	88.97 194	69.27 214	75.70 212	89.69 144	57.20 211	95.77 60	63.06 239	88.41 131	87.50 271

原ACMM1				84.35 116	93.01 64	68.79 115		92.44 75	63.96 286	81.09 118	91.57 99	66.06 110	95.45 72	67.19 210	94.82 52	88.81 245

XVG-OURS-SEG-HR		80.81 132	79.76 137	83.96 137	85.60 239	68.78 116	83.54 263	90.50 143	70.66 190	76.71 190	91.66 94	60.69 183	91.26 240	76.94 120	81.58 214	91.83 137

LPG-MVS_test		82.08 104	81.27 110	84.50 109	89.23 148	68.76 117	90.22 77	91.94 101	75.37 98	76.64 192	91.51 100	54.29 229	94.91 100	78.44 104	83.78 183	89.83 212

LGP-MVS_train				84.50 109	89.23 148	68.76 117		91.94 101	75.37 98	76.64 192	91.51 100	54.29 229	94.91 100	78.44 104	83.78 183	89.83 212

Effi-MVS+-dtu		80.03 154	78.57 166	84.42 113	85.13 247	68.74 119	88.77 116	88.10 214	74.99 106	74.97 234	83.49 289	57.27 209	93.36 169	73.53 148	80.88 220	91.18 156

Vis-MVSNet		83.46 84	82.80 90	85.43 81	90.25 111	68.74 119	90.30 75	90.13 156	76.33 81	80.87 121	92.89 72	61.00 179	94.20 129	72.45 162	90.97 99	93.35 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HQP_MVS		83.64 81	83.14 83	85.14 87	90.08 115	68.71 121	91.25 52	92.44 75	79.12 25	78.92 141	91.00 118	60.42 188	95.38 79	78.71 102	86.32 157	91.33 151

plane_prior						68.71 121	90.38 73		77.62 41						86.16 160

plane_prior6					89.84 124	68.70 123						60.42 188

ACMP	74.13 6	81.51 120	80.57 122	84.36 115	89.42 134	68.69 124	89.97 82	91.50 121	74.46 121	75.04 233	90.41 128	53.82 234	94.54 115	77.56 113	82.91 198	89.86 211
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ETV-MVS		84.90 74	84.67 74	85.59 79	89.39 136	68.66 125	88.74 119	92.64 71	79.97 17	84.10 79	85.71 253	69.32 80	95.38 79	80.82 86	91.37 95	92.72 107

plane_prior3						68.60 126			78.44 33	78.92 141

CHOSEN 1792x2688		77.63 213	75.69 224	83.44 148	89.98 121	68.58 127	78.70 311	87.50 229	56.38 343	75.80 211	86.84 221	58.67 196	91.40 237	61.58 254	85.75 166	90.34 186

plane_prior7					90.08 115	68.51 128

ACMM	73.20 8	80.78 137	79.84 135	83.58 145	89.31 144	68.37 129	89.99 81	91.60 115	70.28 196	77.25 177	89.66 145	53.37 237	93.53 162	74.24 143	82.85 199	88.85 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		74.03 258	71.91 264	80.39 232	81.96 306	68.32 130	81.45 284	82.14 301	59.32 323	69.87 287	85.13 267	52.40 243	88.13 293	60.21 264	74.74 296	84.73 318

NP-MVS					89.62 126	68.32 130					90.24 130

test222					91.50 89	68.26 132	84.16 250	83.20 293	54.63 349	79.74 131	91.63 97	58.97 195			91.42 94	86.77 289

CDS-MVSNet		79.07 178	77.70 190	83.17 161	87.60 205	68.23 133	84.40 246	86.20 249	67.49 243	76.36 199	86.54 238	61.54 165	90.79 253	61.86 251	87.33 142	90.49 181
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		81.69 113	81.02 115	83.70 143	89.51 131	68.21 134	84.28 248	90.09 157	70.79 186	81.26 117	85.62 257	63.15 140	94.29 121	75.62 133	88.87 123	88.59 251

UGNet		80.83 131	79.59 141	84.54 108	88.04 189	68.09 135	89.42 94	88.16 212	76.95 62	76.22 202	89.46 154	49.30 283	93.94 140	68.48 197	90.31 106	91.60 142
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
UA-Net		85.08 71	84.96 70	85.45 80	92.07 81	68.07 136	89.78 88	90.86 137	82.48 2	84.60 69	93.20 63	69.35 79	95.22 85	71.39 168	90.88 101	93.07 96

xiu_mvs_v2_base		81.69 113	81.05 114	83.60 144	89.15 151	68.03 137	84.46 242	90.02 158	70.67 189	81.30 116	86.53 239	63.17 139	94.19 130	75.60 134	88.54 128	88.57 252

DELS-MVS		85.41 65	85.30 65	85.77 76	88.49 175	67.93 138	85.52 221	93.44 31	78.70 31	83.63 88	89.03 165	74.57 29	95.71 62	80.26 93	94.04 66	93.66 68
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
EI-MVSNet-Vis-set		84.19 75	83.81 78	85.31 82	88.18 184	67.85 139	87.66 157	89.73 167	80.05 16	82.95 93	89.59 149	70.74 66	94.82 107	80.66 90	84.72 172	93.28 88

PLC	70.83 11	78.05 202	76.37 220	83.08 165	91.88 85	67.80 140	88.19 140	89.46 173	64.33 279	69.87 287	88.38 183	53.66 235	93.58 157	58.86 276	82.73 201	87.86 262
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAMVS		78.89 183	77.51 195	83.03 168	87.80 197	67.79 141	84.72 233	85.05 262	67.63 240	76.75 189	87.70 199	62.25 155	90.82 252	58.53 280	87.13 145	90.49 181

CLD-MVS		82.31 101	81.65 107	84.29 119	88.47 176	67.73 142	85.81 212	92.35 80	75.78 89	78.33 154	86.58 236	64.01 128	94.35 120	76.05 128	87.48 141	90.79 169
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
iter_conf_final		80.63 139	79.35 148	84.46 111	89.36 138	67.70 143	89.85 83	84.49 269	73.19 150	78.30 155	88.94 166	45.98 305	94.56 113	79.59 97	84.48 176	91.11 158

hse-mvs2		81.72 111	80.94 117	84.07 126	88.72 168	67.68 144	85.87 208	87.26 234	76.02 86	84.67 65	88.22 189	61.54 165	93.48 164	82.71 69	73.44 309	91.06 160

AUN-MVS		79.21 174	77.60 193	84.05 129	88.71 169	67.61 145	85.84 210	87.26 234	69.08 222	77.23 179	88.14 194	53.20 239	93.47 165	75.50 136	73.45 308	91.06 160

CS-MVS		86.69 41	86.95 36	85.90 75	90.76 102	67.57 146	92.83 17	93.30 36	79.67 19	84.57 70	92.27 83	71.47 59	95.02 96	84.24 48	93.46 70	95.13 4

EI-MVSNet-UG-set		83.81 77	83.38 81	85.09 89	87.87 193	67.53 147	87.44 163	89.66 169	79.74 18	82.23 102	89.41 158	70.24 71	94.74 110	79.95 94	83.92 182	92.99 102

Effi-MVS+		83.62 82	83.08 84	85.24 85	88.38 180	67.45 148	88.89 110	89.15 185	75.50 95	82.27 101	88.28 186	69.61 77	94.45 119	77.81 111	87.84 134	93.84 61

EG-PatchMatch MVS		74.04 257	71.82 265	80.71 227	84.92 251	67.42 149	85.86 209	88.08 216	66.04 259	64.22 332	83.85 282	35.10 353	92.56 198	57.44 289	80.83 221	82.16 340

OMC-MVS		82.69 97	81.97 104	84.85 99	88.75 167	67.42 149	87.98 146	90.87 136	74.92 108	79.72 132	91.65 95	62.19 157	93.96 137	75.26 137	86.42 156	93.16 94

PatchMatch-RL		72.38 274	70.90 274	76.80 290	88.60 172	67.38 151	79.53 301	76.17 344	62.75 297	69.36 292	82.00 308	45.51 311	84.89 317	53.62 307	80.58 225	78.12 354

LS3D		76.95 225	74.82 237	83.37 152	90.45 107	67.36 152	89.15 103	86.94 239	61.87 305	69.52 290	90.61 124	51.71 257	94.53 116	46.38 344	86.71 152	88.21 257

PS-MVSNAJss		82.07 105	81.31 109	84.34 117	86.51 229	67.27 153	89.27 97	91.51 118	71.75 167	79.37 135	90.22 132	63.15 140	94.27 123	77.69 112	82.36 206	91.49 148

114514_t		80.68 138	79.51 142	84.20 121	94.09 41	67.27 153	89.64 92	91.11 131	58.75 329	74.08 243	90.72 122	58.10 199	95.04 95	69.70 184	89.42 119	90.30 187

CS-MVS-test		86.29 50	86.48 44	85.71 77	91.02 95	67.21 155	92.36 28	93.78 21	78.97 30	83.51 89	91.20 109	70.65 68	95.15 88	81.96 77	94.89 46	94.77 21

anonymousdsp		78.60 188	77.15 200	82.98 171	80.51 326	67.08 156	87.24 169	89.53 171	65.66 264	75.16 228	87.19 215	52.52 240	92.25 211	77.17 118	79.34 240	89.61 219

MVS		78.19 198	76.99 204	81.78 199	85.66 237	66.99 157	84.66 234	90.47 144	55.08 348	72.02 264	85.27 263	63.83 130	94.11 135	66.10 218	89.80 115	84.24 322

HQP5-MVS						66.98 158

HQP-MVS		82.61 99	82.02 102	84.37 114	89.33 139	66.98 158	89.17 99	92.19 89	76.41 75	77.23 179	90.23 131	60.17 191	95.11 90	77.47 114	85.99 163	91.03 162

test_part1		82.78 96	82.08 100	84.89 98	90.66 103	66.97 160	90.96 58	92.93 57	77.19 55	80.53 124	90.04 136	63.44 132	95.39 78	76.04 129	76.90 260	92.31 122

Fast-Effi-MVS+-dtu		78.02 203	76.49 216	82.62 186	83.16 283	66.96 161	86.94 176	87.45 231	72.45 157	71.49 269	84.17 278	54.79 224	91.58 231	67.61 203	80.31 229	89.30 226

F-COLMAP		76.38 235	74.33 244	82.50 188	89.28 146	66.95 162	88.41 129	89.03 189	64.05 283	66.83 312	88.61 176	46.78 299	92.89 190	57.48 288	78.55 244	87.67 265

mvsmamba		81.69 113	80.74 119	84.56 107	87.45 210	66.72 163	91.26 50	85.89 254	74.66 115	78.23 157	90.56 125	54.33 228	94.91 100	80.73 89	83.54 190	92.04 134

HyFIR lowres test		77.53 214	75.40 230	83.94 138	89.59 127	66.62 164	80.36 293	88.64 207	56.29 344	76.45 194	85.17 266	57.64 204	93.28 171	61.34 257	83.10 197	91.91 135

ACMH	67.68 16	75.89 240	73.93 247	81.77 200	88.71 169	66.61 165	88.62 122	89.01 191	69.81 203	66.78 313	86.70 230	41.95 331	91.51 235	55.64 300	78.14 250	87.17 279
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		79.29 172	77.96 179	83.27 155	84.68 254	66.57 166	89.25 98	90.16 155	69.20 218	75.46 217	89.49 151	45.75 310	93.13 181	76.84 121	80.80 222	90.11 195

VDD-MVS		83.01 94	82.36 95	84.96 93	91.02 95	66.40 167	88.91 109	88.11 213	77.57 43	84.39 74	93.29 62	52.19 246	93.91 144	77.05 119	88.70 126	94.57 29

mvs_tets		79.13 176	77.77 187	83.22 159	84.70 253	66.37 168	89.17 99	90.19 154	69.38 212	75.40 220	89.46 154	44.17 315	93.15 179	76.78 123	80.70 224	90.14 192

PAPM_NR		83.02 93	82.41 93	84.82 100	92.47 77	66.37 168	87.93 150	91.80 108	73.82 135	77.32 176	90.66 123	67.90 91	94.90 103	70.37 176	89.48 118	93.19 93

DROMVSNet		86.01 53	86.38 45	84.91 97	89.31 144	66.27 170	92.32 30	93.63 24	79.37 22	84.17 78	91.88 91	69.04 85	95.43 74	83.93 53	93.77 68	93.01 100

pmmvs-eth3d		70.50 287	67.83 298	78.52 266	77.37 348	66.18 171	81.82 278	81.51 307	58.90 327	63.90 335	80.42 320	42.69 323	86.28 307	58.56 279	65.30 343	83.11 333

IB-MVS	68.01 15	75.85 241	73.36 253	83.31 153	84.76 252	66.03 172	83.38 264	85.06 261	70.21 198	69.40 291	81.05 312	45.76 309	94.66 112	65.10 227	75.49 282	89.25 227
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
MS-PatchMatch		73.83 259	72.67 258	77.30 284	83.87 267	66.02 173	81.82 278	84.66 266	61.37 309	68.61 297	82.82 296	47.29 295	88.21 291	59.27 270	84.32 178	77.68 355

test_0402		72.79 271	70.44 278	79.84 244	88.13 185	65.99 174	85.93 206	84.29 273	65.57 265	67.40 306	85.49 259	46.92 298	92.61 196	35.88 362	74.38 299	80.94 346

BH-RMVSNet		79.61 160	78.44 169	83.14 162	89.38 137	65.93 175	84.95 229	87.15 236	73.56 142	78.19 159	89.79 142	56.67 214	93.36 169	59.53 269	86.74 151	90.13 193

BH-untuned		79.47 165	78.60 165	82.05 194	89.19 150	65.91 176	86.07 203	88.52 209	72.18 162	75.42 219	87.69 200	61.15 176	93.54 161	60.38 262	86.83 150	86.70 291

cascas		76.72 228	74.64 238	82.99 170	85.78 236	65.88 177	82.33 275	89.21 182	60.85 311	72.74 254	81.02 313	47.28 296	93.75 153	67.48 205	85.02 168	89.34 224

patch_mono-2		83.65 80	84.54 75	80.99 221	90.06 119	65.83 178	84.21 249	88.74 204	71.60 173	85.01 54	92.44 81	74.51 30	83.50 326	82.15 76	92.15 84	93.64 75

iter_conf05		80.00 156	78.70 162	83.91 139	87.84 195	65.83 178	88.84 114	84.92 264	71.61 172	78.70 143	88.94 166	43.88 317	94.56 113	79.28 98	84.28 179	91.33 151

MSDG		73.36 264	70.99 273	80.49 231	84.51 256	65.80 180	80.71 289	86.13 251	65.70 263	65.46 323	83.74 286	44.60 313	90.91 251	51.13 317	76.89 261	84.74 317

旧先验1					91.96 82	65.79 181		86.37 247			93.08 69	69.31 81			92.74 76	88.74 248

casdiffmvs		85.11 70	85.14 67	85.01 91	87.20 218	65.77 182	87.75 155	92.83 61	77.84 39	84.36 75	92.38 82	72.15 53	93.93 143	81.27 82	90.48 105	95.33 2

COLMAP_ROB	66.92 17	73.01 268	70.41 279	80.81 225	87.13 220	65.63 183	88.30 135	84.19 276	62.96 293	63.80 336	87.69 200	38.04 345	92.56 198	46.66 341	74.91 294	84.24 322
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EIA-MVS		83.31 88	82.80 90	84.82 100	89.59 127	65.59 184	88.21 139	92.68 66	74.66 115	78.96 139	86.42 241	69.06 83	95.26 84	75.54 135	90.09 111	93.62 76

v7n		78.97 181	77.58 194	83.14 162	83.45 274	65.51 185	88.32 134	91.21 127	73.69 138	72.41 259	86.32 244	57.93 200	93.81 148	69.18 189	75.65 279	90.11 195

V42		79.38 171	78.24 175	82.83 176	81.10 320	65.50 186	85.55 217	89.82 163	71.57 174	78.21 158	86.12 247	60.66 184	93.18 178	75.64 132	75.46 285	89.81 214

bld_raw_dy_0_64		77.29 220	75.98 223	81.22 214	85.04 250	65.47 187	88.14 144	77.56 335	69.20 218	73.77 245	89.40 160	42.24 328	88.85 285	76.78 123	81.64 213	89.33 225

RRT_MVS		80.35 148	79.22 153	83.74 142	87.63 204	65.46 188	91.08 56	88.92 197	73.82 135	76.44 197	90.03 137	49.05 287	94.25 128	76.84 121	79.20 243	91.51 145

PVSNet_BlendedMVS		80.60 141	80.02 131	82.36 191	88.85 159	65.40 189	86.16 201	92.00 97	69.34 213	78.11 161	86.09 248	66.02 111	94.27 123	71.52 165	82.06 208	87.39 272

PVSNet_Blended		80.98 126	80.34 127	82.90 174	88.85 159	65.40 189	84.43 244	92.00 97	67.62 241	78.11 161	85.05 270	66.02 111	94.27 123	71.52 165	89.50 117	89.01 235

baseline		84.93 72	84.98 69	84.80 102	87.30 216	65.39 191	87.30 167	92.88 58	77.62 41	84.04 81	92.26 84	71.81 55	93.96 137	81.31 81	90.30 107	95.03 6

test_djsdf		80.30 149	79.32 149	83.27 155	83.98 265	65.37 192	90.50 67	90.38 146	68.55 234	76.19 203	88.70 172	56.44 215	93.46 166	78.98 100	80.14 232	90.97 165

ACMH+	68.96 14	76.01 239	74.01 246	82.03 195	88.60 172	65.31 193	88.86 111	87.55 227	70.25 197	67.75 301	87.47 207	41.27 332	93.19 177	58.37 281	75.94 276	87.60 267

CR-MVSNet		73.37 262	71.27 271	79.67 249	81.32 318	65.19 194	75.92 327	80.30 320	59.92 318	72.73 255	81.19 310	52.50 241	86.69 303	59.84 266	77.71 251	87.11 283

RPMNet		73.51 261	70.49 277	82.58 187	81.32 318	65.19 194	75.92 327	92.27 83	57.60 336	72.73 255	76.45 346	52.30 244	95.43 74	48.14 336	77.71 251	87.11 283

BH-w/o		78.21 196	77.33 198	80.84 224	88.81 163	65.13 196	84.87 230	87.85 223	69.75 206	74.52 239	84.74 273	61.34 171	93.11 182	58.24 283	85.84 165	84.27 321

thisisatest0530		79.40 169	77.76 188	84.31 118	87.69 203	65.10 197	87.36 164	84.26 275	70.04 199	77.42 173	88.26 188	49.94 275	94.79 109	70.20 177	84.70 173	93.03 98

v10		79.74 159	78.67 163	82.97 172	84.06 263	64.95 198	87.88 153	90.62 140	73.11 151	75.11 230	86.56 237	61.46 168	94.05 136	73.68 146	75.55 281	89.90 209

dcpmvs_2		85.63 61	86.15 53	84.06 127	91.71 86	64.94 199	86.47 192	91.87 105	73.63 139	86.60 39	93.02 70	76.57 16	91.87 225	83.36 56	92.15 84	95.35 1

IterMVS-SCA-FT		75.43 246	73.87 249	80.11 239	82.69 294	64.85 200	81.57 283	83.47 287	69.16 220	70.49 275	84.15 279	51.95 252	88.15 292	69.23 188	72.14 318	87.34 274

MVSTER		79.01 179	77.88 183	82.38 190	83.07 284	64.80 201	84.08 254	88.95 195	69.01 226	78.69 144	87.17 216	54.70 225	92.43 202	74.69 139	80.57 226	89.89 210

Anonymous20240529		80.19 152	78.89 160	84.10 124	90.60 104	64.75 202	88.95 108	90.90 135	65.97 261	80.59 123	91.17 111	49.97 274	93.73 155	69.16 190	82.70 203	93.81 63

XVG-ACMP-BASELINE		76.11 238	74.27 245	81.62 202	83.20 280	64.67 203	83.60 261	89.75 166	69.75 206	71.85 265	87.09 218	32.78 357	92.11 215	69.99 181	80.43 228	88.09 258

v1192		79.59 162	78.43 170	83.07 166	83.55 272	64.52 204	86.93 177	90.58 141	70.83 184	77.78 167	85.90 249	59.15 194	93.94 140	73.96 145	77.19 257	90.76 170

Fast-Effi-MVS+		80.81 132	79.92 133	83.47 147	88.85 159	64.51 205	85.53 219	89.39 174	70.79 186	78.49 150	85.06 269	67.54 94	93.58 157	67.03 213	86.58 153	92.32 121

v1144		80.03 154	79.03 157	83.01 169	83.78 268	64.51 205	87.11 172	90.57 142	71.96 165	78.08 163	86.20 246	61.41 169	93.94 140	74.93 138	77.23 255	90.60 177

v8		79.97 157	79.02 158	82.80 179	84.09 262	64.50 207	87.96 147	90.29 153	74.13 130	75.24 227	86.81 222	62.88 145	93.89 146	74.39 141	75.40 287	90.00 203

EPP-MVSNet		83.40 86	83.02 86	84.57 106	90.13 113	64.47 208	92.32 30	90.73 138	74.45 122	79.35 136	91.10 112	69.05 84	95.12 89	72.78 159	87.22 144	94.13 44

GeoE		81.71 112	81.01 116	83.80 141	89.51 131	64.45 209	88.97 107	88.73 205	71.27 178	78.63 147	89.76 143	66.32 106	93.20 175	69.89 182	86.02 162	93.74 66

UniMVSNet (Re)		81.60 117	81.11 113	83.09 164	88.38 180	64.41 210	87.60 158	93.02 46	78.42 34	78.56 148	88.16 190	69.78 75	93.26 172	69.58 186	76.49 267	91.60 142

LTVRE_ROB	69.57 13	76.25 236	74.54 241	81.41 207	88.60 172	64.38 211	79.24 304	89.12 188	70.76 188	69.79 289	87.86 197	49.09 285	93.20 175	56.21 299	80.16 230	86.65 292
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
Anonymous20231211		78.97 181	77.69 191	82.81 178	90.54 106	64.29 212	90.11 79	91.51 118	65.01 271	76.16 207	88.13 195	50.56 268	93.03 188	69.68 185	77.56 254	91.11 158

testdata				79.97 241	90.90 98	64.21 213		84.71 265	59.27 324	85.40 49	92.91 71	62.02 160	89.08 278	68.95 192	91.37 95	86.63 293

v2v482		80.23 150	79.29 150	83.05 167	83.62 270	64.14 214	87.04 173	89.97 159	73.61 140	78.18 160	87.22 213	61.10 177	93.82 147	76.11 127	76.78 265	91.18 156

VDDNet		81.52 118	80.67 121	84.05 129	90.44 108	64.13 215	89.73 90	85.91 253	71.11 180	83.18 91	93.48 57	50.54 269	93.49 163	73.40 152	88.25 132	94.54 30

PAPR		81.66 116	80.89 118	83.99 135	90.27 110	64.00 216	86.76 185	91.77 112	68.84 230	77.13 184	89.50 150	67.63 93	94.88 105	67.55 204	88.52 129	93.09 95

v144192		79.47 165	78.37 171	82.78 182	83.35 275	63.96 217	86.96 175	90.36 149	69.99 200	77.50 171	85.67 255	60.66 184	93.77 151	74.27 142	76.58 266	90.62 175

v1921920		79.22 173	78.03 178	82.80 179	83.30 277	63.94 218	86.80 181	90.33 150	69.91 202	77.48 172	85.53 258	58.44 198	93.75 153	73.60 147	76.85 263	90.71 173

tttt0517		79.40 169	77.91 181	83.90 140	88.10 187	63.84 219	88.37 133	84.05 277	71.45 176	76.78 188	89.12 162	49.93 277	94.89 104	70.18 178	83.18 195	92.96 103

thisisatest0515		77.33 218	75.38 231	83.18 160	85.27 243	63.80 220	82.11 277	83.27 290	65.06 269	75.91 208	83.84 283	49.54 279	94.27 123	67.24 209	86.19 159	91.48 149

diffmvs		82.10 103	81.88 105	82.76 184	83.00 287	63.78 221	83.68 257	89.76 165	72.94 155	82.02 104	89.85 141	65.96 113	90.79 253	82.38 75	87.30 143	93.71 67

test_yl		81.17 123	80.47 125	83.24 157	89.13 152	63.62 222	86.21 199	89.95 160	72.43 160	81.78 109	89.61 147	57.50 206	93.58 157	70.75 171	86.90 148	92.52 113

DCV-MVSNet		81.17 123	80.47 125	83.24 157	89.13 152	63.62 222	86.21 199	89.95 160	72.43 160	81.78 109	89.61 147	57.50 206	93.58 157	70.75 171	86.90 148	92.52 113

AllTest		70.96 282	68.09 294	79.58 251	85.15 245	63.62 222	84.58 239	79.83 324	62.31 301	60.32 346	86.73 223	32.02 358	88.96 282	50.28 322	71.57 322	86.15 299

TestCases				79.58 251	85.15 245	63.62 222		79.83 324	62.31 301	60.32 346	86.73 223	32.02 358	88.96 282	50.28 322	71.57 322	86.15 299

v1240		78.99 180	77.78 186	82.64 185	83.21 279	63.54 226	86.62 188	90.30 152	69.74 208	77.33 175	85.68 254	57.04 212	93.76 152	73.13 156	76.92 259	90.62 175

CHOSEN 280x420		66.51 312	64.71 313	71.90 322	81.45 313	63.52 227	57.98 364	68.95 363	53.57 350	62.59 341	76.70 344	46.22 303	75.29 360	55.25 301	79.68 233	76.88 357

IterMVS		74.29 253	72.94 257	78.35 268	81.53 312	63.49 228	81.58 282	82.49 299	68.06 239	69.99 284	83.69 287	51.66 258	85.54 311	65.85 221	71.64 321	86.01 303
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		81.88 108	81.54 108	82.92 173	88.46 177	63.46 229	87.13 170	92.37 79	80.19 14	78.38 152	89.14 161	71.66 58	93.05 185	70.05 179	76.46 268	92.25 125

DU-MVS		81.12 125	80.52 124	82.90 174	87.80 197	63.46 229	87.02 174	91.87 105	79.01 28	78.38 152	89.07 163	65.02 120	93.05 185	70.05 179	76.46 268	92.20 127

LFMVS		81.82 110	81.23 111	83.57 146	91.89 84	63.43 231	89.84 84	81.85 305	77.04 61	83.21 90	93.10 65	52.26 245	93.43 168	71.98 163	89.95 114	93.85 59

NR-MVSNet		80.23 150	79.38 146	82.78 182	87.80 197	63.34 232	86.31 196	91.09 132	79.01 28	72.17 262	89.07 163	67.20 98	92.81 194	66.08 219	75.65 279	92.20 127

IS-MVSNet		83.15 89	82.81 89	84.18 122	89.94 122	63.30 233	91.59 44	88.46 210	79.04 27	79.49 134	92.16 85	65.10 119	94.28 122	67.71 202	91.86 90	94.95 7

TR-MVS		77.44 215	76.18 221	81.20 215	88.24 183	63.24 234	84.61 238	86.40 246	67.55 242	77.81 166	86.48 240	54.10 231	93.15 179	57.75 287	82.72 202	87.20 278

MVS_Test		83.15 89	83.06 85	83.41 151	86.86 222	63.21 235	86.11 202	92.00 97	74.31 123	82.87 95	89.44 157	70.03 72	93.21 173	77.39 116	88.50 130	93.81 63

IterMVS-LS		80.06 153	79.38 146	82.11 193	85.89 234	63.20 236	86.79 182	89.34 175	74.19 127	75.45 218	86.72 225	66.62 101	92.39 204	72.58 160	76.86 262	90.75 171
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 144	79.98 132	82.12 192	84.28 258	63.19 237	86.41 193	88.95 195	74.18 128	78.69 144	87.54 205	66.62 101	92.43 202	72.57 161	80.57 226	90.74 172

CANet_DTU		80.61 140	79.87 134	82.83 176	85.60 239	63.17 238	87.36 164	88.65 206	76.37 79	75.88 209	88.44 182	53.51 236	93.07 184	73.30 153	89.74 116	92.25 125

GBi-Net		78.40 191	77.40 196	81.40 208	87.60 205	63.01 239	88.39 130	89.28 177	71.63 169	75.34 222	87.28 209	54.80 221	91.11 243	62.72 240	79.57 235	90.09 197

test1		78.40 191	77.40 196	81.40 208	87.60 205	63.01 239	88.39 130	89.28 177	71.63 169	75.34 222	87.28 209	54.80 221	91.11 243	62.72 240	79.57 235	90.09 197

FMVSNet1		77.44 215	76.12 222	81.40 208	86.81 225	63.01 239	88.39 130	89.28 177	70.49 193	74.39 240	87.28 209	49.06 286	91.11 243	60.91 259	78.52 245	90.09 197

TAPA-MVS	73.13 9	79.15 175	77.94 180	82.79 181	89.59 127	62.99 242	88.16 142	91.51 118	65.77 262	77.14 183	91.09 113	60.91 180	93.21 173	50.26 324	87.05 146	92.17 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet2		78.20 197	77.21 199	81.20 215	87.60 205	62.89 243	87.47 162	89.02 190	71.63 169	75.29 226	87.28 209	54.80 221	91.10 246	62.38 244	79.38 239	89.61 219

GA-MVS		76.87 226	75.17 235	81.97 197	82.75 292	62.58 244	81.44 285	86.35 248	72.16 164	74.74 237	82.89 294	46.20 304	92.02 218	68.85 194	81.09 218	91.30 154

D2MVS		74.82 250	73.21 254	79.64 250	79.81 333	62.56 245	80.34 294	87.35 232	64.37 278	68.86 294	82.66 298	46.37 301	90.10 262	67.91 201	81.24 217	86.25 296

FMVSNet3		77.88 207	76.85 207	80.97 222	86.84 224	62.36 246	86.52 191	88.77 200	71.13 179	75.34 222	86.66 232	54.07 232	91.10 246	62.72 240	79.57 235	89.45 222

TranMVSNet+NR-MVSNet		80.84 129	80.31 128	82.42 189	87.85 194	62.33 247	87.74 156	91.33 124	80.55 11	77.99 164	89.86 140	65.23 118	92.62 195	67.05 212	75.24 292	92.30 123

1314		76.53 229	75.30 234	80.21 237	83.93 266	62.32 248	84.66 234	88.81 198	60.23 315	70.16 281	84.07 280	55.30 219	90.73 255	67.37 206	83.21 194	87.59 269

MG-MVS		83.41 85	83.45 80	83.28 154	92.74 71	62.28 249	88.17 141	89.50 172	75.22 101	81.49 112	92.74 80	66.75 100	95.11 90	72.85 158	91.58 92	92.45 118

SCA		74.22 255	72.33 262	79.91 242	84.05 264	62.17 250	79.96 298	79.29 328	66.30 256	72.38 260	80.13 322	51.95 252	88.60 287	59.25 271	77.67 253	88.96 239

PMMVS		69.34 295	68.67 287	71.35 327	75.67 353	62.03 251	75.17 331	73.46 352	50.00 357	68.68 295	79.05 329	52.07 250	78.13 347	61.16 258	82.77 200	73.90 358

eth_miper_zixun_eth		77.92 206	76.69 213	81.61 204	83.00 287	61.98 252	83.15 267	89.20 183	69.52 210	74.86 236	84.35 276	61.76 161	92.56 198	71.50 167	72.89 313	90.28 188

v148		78.72 185	77.80 185	81.47 206	82.73 293	61.96 253	86.30 197	88.08 216	73.26 149	76.18 204	85.47 260	62.46 151	92.36 206	71.92 164	73.82 305	90.09 197

PAPM		77.68 212	76.40 219	81.51 205	87.29 217	61.85 254	83.78 256	89.59 170	64.74 273	71.23 270	88.70 172	62.59 148	93.66 156	52.66 311	87.03 147	89.01 235

cl22		78.07 201	77.01 202	81.23 213	82.37 302	61.83 255	83.55 262	87.98 218	68.96 227	75.06 232	83.87 281	61.40 170	91.88 224	73.53 148	76.39 270	89.98 206

baseline2		75.70 242	73.83 250	81.30 211	83.26 278	61.79 256	82.57 274	80.65 314	66.81 246	66.88 310	83.42 290	57.86 202	92.19 213	63.47 235	79.57 235	89.91 208

JIA-IIPM		66.32 314	62.82 324	76.82 289	77.09 349	61.72 257	65.34 359	75.38 345	58.04 333	64.51 330	62.32 361	42.05 330	86.51 305	51.45 316	69.22 331	82.21 339

miper_ehance_all_eth		78.59 189	77.76 188	81.08 219	82.66 295	61.56 258	83.65 258	89.15 185	68.87 229	75.55 214	83.79 285	66.49 103	92.03 217	73.25 154	76.39 270	89.64 218

c3_l		78.75 184	77.91 181	81.26 212	82.89 290	61.56 258	84.09 253	89.13 187	69.97 201	75.56 213	84.29 277	66.36 105	92.09 216	73.47 151	75.48 283	90.12 194

miper_enhance_ethall		77.87 208	76.86 206	80.92 223	81.65 309	61.38 260	82.68 272	88.98 192	65.52 266	75.47 215	82.30 302	65.76 115	92.00 219	72.95 157	76.39 270	89.39 223

ppachtmachnet_test		70.04 291	67.34 304	78.14 270	79.80 334	61.13 261	79.19 306	80.59 315	59.16 325	65.27 325	79.29 328	46.75 300	87.29 300	49.33 328	66.72 337	86.00 305

TDRefinement		67.49 305	64.34 314	76.92 288	73.47 363	61.07 262	84.86 231	82.98 295	59.77 319	58.30 352	85.13 267	26.06 363	87.89 295	47.92 338	60.59 353	81.81 342

VNet		82.21 102	82.41 93	81.62 202	90.82 100	60.93 263	84.47 240	89.78 164	76.36 80	84.07 80	91.88 91	64.71 124	90.26 259	70.68 173	88.89 122	93.66 68

ab-mvs		79.51 163	78.97 159	81.14 217	88.46 177	60.91 264	83.84 255	89.24 181	70.36 194	79.03 138	88.87 170	63.23 138	90.21 261	65.12 226	82.57 204	92.28 124

PatchmatchNet		73.12 267	71.33 270	78.49 267	83.18 281	60.85 265	79.63 300	78.57 330	64.13 280	71.73 266	79.81 327	51.20 261	85.97 309	57.40 290	76.36 273	88.66 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		80.60 141	80.55 123	80.76 226	88.07 188	60.80 266	86.86 179	91.58 116	75.67 93	80.24 127	89.45 156	63.34 134	90.25 260	70.51 175	79.22 242	91.23 155

EGC-MVSNET		52.07 332	47.05 336	67.14 341	83.51 273	60.71 267	80.50 292	67.75 364	0.07 377	0.43 378	75.85 349	24.26 365	81.54 335	28.82 365	62.25 348	59.16 365

Anonymous202405211		78.25 194	77.01 202	81.99 196	91.03 94	60.67 268	84.77 232	83.90 279	70.65 191	80.00 130	91.20 109	41.08 334	91.43 236	65.21 225	85.26 167	93.85 59

ITE_SJBPF				78.22 269	81.77 308	60.57 269		83.30 289	69.25 215	67.54 303	87.20 214	36.33 350	87.28 301	54.34 304	74.62 297	86.80 288

MDA-MVSNet-bldmvs		66.68 310	63.66 318	75.75 295	79.28 341	60.56 270	73.92 338	78.35 331	64.43 276	50.13 363	79.87 326	44.02 316	83.67 324	46.10 345	56.86 356	83.03 335

cl____		77.72 210	76.76 210	80.58 229	82.49 299	60.48 271	83.09 268	87.87 221	69.22 216	74.38 241	85.22 265	62.10 158	91.53 233	71.09 169	75.41 286	89.73 217

DIV-MVS_self_test		77.72 210	76.76 210	80.58 229	82.48 300	60.48 271	83.09 268	87.86 222	69.22 216	74.38 241	85.24 264	62.10 158	91.53 233	71.09 169	75.40 287	89.74 216

1112_ss		77.40 217	76.43 218	80.32 235	89.11 156	60.41 273	83.65 258	87.72 225	62.13 303	73.05 252	86.72 225	62.58 149	89.97 263	62.11 249	80.80 222	90.59 178

UniMVSNet_ETH3D		79.10 177	78.24 175	81.70 201	86.85 223	60.24 274	87.28 168	88.79 199	74.25 126	76.84 185	90.53 127	49.48 280	91.56 232	67.98 200	82.15 207	93.29 87

HY-MVS	69.67 12	77.95 205	77.15 200	80.36 233	87.57 209	60.21 275	83.37 265	87.78 224	66.11 257	75.37 221	87.06 220	63.27 136	90.48 258	61.38 256	82.43 205	90.40 185

RPSCF		73.23 266	71.46 267	78.54 265	82.50 298	59.85 276	82.18 276	82.84 297	58.96 326	71.15 272	89.41 158	45.48 312	84.77 318	58.82 277	71.83 320	91.02 164

MVS_0304		72.48 272	70.89 275	77.24 285	82.20 303	59.68 277	84.11 252	83.49 286	67.10 245	66.87 311	80.59 318	35.00 354	87.40 299	59.07 274	79.58 234	84.63 319

miper_lstm_enhance		74.11 256	73.11 256	77.13 287	80.11 329	59.62 278	72.23 341	86.92 240	66.76 248	70.40 276	82.92 293	56.93 213	82.92 330	69.06 191	72.63 314	88.87 242

OurMVSNet-221017-0		74.26 254	72.42 261	79.80 245	83.76 269	59.59 279	85.92 207	86.64 242	66.39 255	66.96 309	87.58 202	39.46 338	91.60 230	65.76 222	69.27 330	88.22 256

Patchmatch-RL test		70.24 289	67.78 300	77.61 279	77.43 347	59.57 280	71.16 343	70.33 356	62.94 294	68.65 296	72.77 354	50.62 267	85.49 312	69.58 186	66.58 339	87.77 264

OpenMVS_ROB	64.09 19	70.56 286	68.19 291	77.65 278	80.26 327	59.41 281	85.01 227	82.96 296	58.76 328	65.43 324	82.33 301	37.63 347	91.23 242	45.34 349	76.03 275	82.32 338

our_test_3		69.14 296	67.00 306	75.57 298	79.80 334	58.80 282	77.96 317	77.81 333	59.55 321	62.90 340	78.25 337	47.43 294	83.97 322	51.71 314	67.58 336	83.93 326

ADS-MVSNet2		66.20 317	63.33 319	74.82 306	79.92 331	58.75 283	67.55 356	75.19 346	53.37 351	65.25 326	75.86 347	42.32 325	80.53 340	41.57 356	68.91 332	85.18 311

pm-mvs1		77.25 221	76.68 214	78.93 259	84.22 260	58.62 284	86.41 193	88.36 211	71.37 177	73.31 248	88.01 196	61.22 175	89.15 277	64.24 232	73.01 312	89.03 234

WR-MVS		79.49 164	79.22 153	80.27 236	88.79 165	58.35 285	85.06 226	88.61 208	78.56 32	77.65 169	88.34 184	63.81 131	90.66 256	64.98 228	77.22 256	91.80 140

FIs		82.07 105	82.42 92	81.04 220	88.80 164	58.34 286	88.26 138	93.49 29	76.93 63	78.47 151	91.04 115	69.92 74	92.34 208	69.87 183	84.97 169	92.44 119

CostFormer		75.24 249	73.90 248	79.27 255	82.65 296	58.27 287	80.80 286	82.73 298	61.57 306	75.33 225	83.13 292	55.52 217	91.07 249	64.98 228	78.34 249	88.45 253

Test_1112_low_res		76.40 234	75.44 228	79.27 255	89.28 146	58.09 288	81.69 281	87.07 237	59.53 322	72.48 258	86.67 231	61.30 172	89.33 273	60.81 261	80.15 231	90.41 184

tfpnnormal		74.39 252	73.16 255	78.08 271	86.10 233	58.05 289	84.65 237	87.53 228	70.32 195	71.22 271	85.63 256	54.97 220	89.86 264	43.03 353	75.02 293	86.32 295

test-LLR		72.94 270	72.43 260	74.48 308	81.35 316	58.04 290	78.38 312	77.46 336	66.66 250	69.95 285	79.00 331	48.06 292	79.24 342	66.13 216	84.83 170	86.15 299

test-mter		71.41 279	70.39 280	74.48 308	81.35 316	58.04 290	78.38 312	77.46 336	60.32 314	69.95 285	79.00 331	36.08 351	79.24 342	66.13 216	84.83 170	86.15 299

mvs_anonymous		79.42 168	79.11 156	80.34 234	84.45 257	57.97 292	82.59 273	87.62 226	67.40 244	76.17 206	88.56 179	68.47 87	89.59 269	70.65 174	86.05 161	93.47 82

tpm cat1		70.57 285	68.31 290	77.35 283	82.41 301	57.95 293	78.08 316	80.22 322	52.04 354	68.54 298	77.66 341	52.00 251	87.84 296	51.77 313	72.07 319	86.25 296

SixPastTwentyTwo		73.37 262	71.26 272	79.70 247	85.08 249	57.89 294	85.57 213	83.56 284	71.03 182	65.66 322	85.88 250	42.10 329	92.57 197	59.11 273	63.34 347	88.65 250

thres200		75.55 244	74.47 242	78.82 260	87.78 200	57.85 295	83.07 270	83.51 285	72.44 159	75.84 210	84.42 275	52.08 249	91.75 227	47.41 339	83.64 189	86.86 287

XXY-MVS		75.41 247	75.56 226	74.96 304	83.59 271	57.82 296	80.59 291	83.87 280	66.54 254	74.93 235	88.31 185	63.24 137	80.09 341	62.16 247	76.85 263	86.97 285

K. test v3		71.19 280	68.51 288	79.21 257	83.04 286	57.78 297	84.35 247	76.91 342	72.90 156	62.99 339	82.86 295	39.27 339	91.09 248	61.65 253	52.66 361	88.75 247

tfpn200view9		76.42 233	75.37 232	79.55 253	89.13 152	57.65 298	85.17 222	83.60 282	73.41 147	76.45 194	86.39 242	52.12 247	91.95 220	48.33 332	83.75 185	89.07 228

thres400		76.50 230	75.37 232	79.86 243	89.13 152	57.65 298	85.17 222	83.60 282	73.41 147	76.45 194	86.39 242	52.12 247	91.95 220	48.33 332	83.75 185	90.00 203

CMPMVS	51.72 21	70.19 290	68.16 292	76.28 292	73.15 365	57.55 300	79.47 302	83.92 278	48.02 358	56.48 357	84.81 271	43.13 320	86.42 306	62.67 243	81.81 212	84.89 315
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		74.69 251	73.39 252	78.61 263	81.38 315	57.48 301	86.64 187	87.95 219	64.99 272	70.18 279	86.61 233	50.43 270	89.52 270	62.12 248	70.18 328	88.83 244

PVSNet_0	57.27 20	61.67 325	59.27 328	68.85 337	79.61 337	57.44 302	68.01 355	73.44 353	55.93 345	58.54 351	70.41 357	44.58 314	77.55 351	47.01 340	35.91 367	71.55 360

thres600view7		76.50 230	75.44 228	79.68 248	89.40 135	57.16 303	85.53 219	83.23 291	73.79 137	76.26 201	87.09 218	51.89 254	91.89 223	48.05 337	83.72 188	90.00 203

lessismore_v0				78.97 258	81.01 321	57.15 304		65.99 366		61.16 344	82.82 296	39.12 340	91.34 239	59.67 267	46.92 366	88.43 254

TransMVSNet (Re)		75.39 248	74.56 240	77.86 273	85.50 241	57.10 305	86.78 183	86.09 252	72.17 163	71.53 268	87.34 208	63.01 144	89.31 274	56.84 295	61.83 349	87.17 279

thres100view900		76.50 230	75.55 227	79.33 254	89.52 130	56.99 306	85.83 211	83.23 291	73.94 132	76.32 200	87.12 217	51.89 254	91.95 220	48.33 332	83.75 185	89.07 228

TESTMET0.1,1		69.89 293	69.00 286	72.55 320	79.27 342	56.85 307	78.38 312	74.71 350	57.64 335	68.09 299	77.19 343	37.75 346	76.70 353	63.92 233	84.09 181	84.10 325

WTY-MVS		75.65 243	75.68 225	75.57 298	86.40 230	56.82 308	77.92 319	82.40 300	65.10 268	76.18 204	87.72 198	63.13 143	80.90 338	60.31 263	81.96 209	89.00 237

MDA-MVSNet_test_wron		65.03 318	62.92 321	71.37 325	75.93 351	56.73 309	69.09 354	74.73 349	57.28 339	54.03 360	77.89 338	45.88 306	74.39 363	49.89 326	61.55 350	82.99 336

pmmvs3		57.79 327	54.26 331	68.37 339	64.02 371	56.72 310	75.12 334	65.17 367	40.20 362	52.93 361	69.86 358	20.36 368	75.48 359	45.45 348	55.25 359	72.90 359

tpm2		73.26 265	71.46 267	78.63 262	83.34 276	56.71 311	80.65 290	80.40 319	56.63 342	73.55 246	82.02 307	51.80 256	91.24 241	56.35 298	78.42 248	87.95 259

TinyColmap		67.30 308	64.81 312	74.76 307	81.92 307	56.68 312	80.29 295	81.49 308	60.33 313	56.27 358	83.22 291	24.77 364	87.66 298	45.52 347	69.47 329	79.95 350

YYNet1		65.03 318	62.91 322	71.38 324	75.85 352	56.60 313	69.12 353	74.66 351	57.28 339	54.12 359	77.87 339	45.85 307	74.48 362	49.95 325	61.52 351	83.05 334

PM-MVS		66.41 313	64.14 315	73.20 318	73.92 360	56.45 314	78.97 308	64.96 369	63.88 287	64.72 329	80.24 321	19.84 369	83.44 327	66.24 215	64.52 345	79.71 351

PVSNet	64.34 18	72.08 277	70.87 276	75.69 296	86.21 232	56.44 315	74.37 337	80.73 313	62.06 304	70.17 280	82.23 304	42.86 322	83.31 328	54.77 303	84.45 177	87.32 275

pmmvs5		71.55 278	70.20 281	75.61 297	77.83 345	56.39 316	81.74 280	80.89 310	57.76 334	67.46 304	84.49 274	49.26 284	85.32 314	57.08 293	75.29 290	85.11 314

WR-MVS_H		78.51 190	78.49 167	78.56 264	88.02 190	56.38 317	88.43 126	92.67 67	77.14 57	73.89 244	87.55 204	66.25 107	89.24 275	58.92 275	73.55 307	90.06 201

MIMVSNet		70.69 284	69.30 283	74.88 305	84.52 255	56.35 318	75.87 329	79.42 327	64.59 274	67.76 300	82.41 300	41.10 333	81.54 335	46.64 343	81.34 215	86.75 290

USDC		70.33 288	68.37 289	76.21 293	80.60 324	56.23 319	79.19 306	86.49 244	60.89 310	61.29 343	85.47 260	31.78 360	89.47 272	53.37 308	76.21 274	82.94 337

Baseline_NR-MVSNet		78.15 199	78.33 173	77.61 279	85.79 235	56.21 320	86.78 183	85.76 255	73.60 141	77.93 165	87.57 203	65.02 120	88.99 279	67.14 211	75.33 289	87.63 266

tpmvs		71.09 281	69.29 284	76.49 291	82.04 305	56.04 321	78.92 309	81.37 309	64.05 283	67.18 308	78.28 336	49.74 278	89.77 265	49.67 327	72.37 315	83.67 327

FC-MVSNet-test		81.52 118	82.02 102	80.03 240	88.42 179	55.97 322	87.95 148	93.42 33	77.10 59	77.38 174	90.98 120	69.96 73	91.79 226	68.46 198	84.50 174	92.33 120

GG-mvs-BLEND				75.38 301	81.59 311	55.80 323	79.32 303	69.63 359		67.19 307	73.67 353	43.24 319	88.90 284	50.41 319	84.50 174	81.45 343

VPNet		78.69 186	78.66 164	78.76 261	88.31 182	55.72 324	84.45 243	86.63 243	76.79 67	78.26 156	90.55 126	59.30 193	89.70 268	66.63 214	77.05 258	90.88 167

baseline1		76.98 224	76.75 212	77.66 277	88.13 185	55.66 325	85.12 225	81.89 303	73.04 153	76.79 187	88.90 168	62.43 152	87.78 297	63.30 238	71.18 324	89.55 221

FMVSNet5		69.50 294	67.96 295	74.15 312	82.97 289	55.35 326	80.01 297	82.12 302	62.56 299	63.02 337	81.53 309	36.92 348	81.92 333	48.42 331	74.06 301	85.17 313

sss		73.60 260	73.64 251	73.51 315	82.80 291	55.01 327	76.12 325	81.69 306	62.47 300	74.68 238	85.85 252	57.32 208	78.11 348	60.86 260	80.93 219	87.39 272

ECVR-MVS		79.61 160	79.26 151	80.67 228	90.08 115	54.69 328	87.89 152	77.44 338	74.88 109	80.27 126	92.79 77	48.96 289	92.45 201	68.55 196	92.50 81	94.86 14

EPNet_dtu		75.46 245	74.86 236	77.23 286	82.57 297	54.60 329	86.89 178	83.09 294	71.64 168	66.25 320	85.86 251	55.99 216	88.04 294	54.92 302	86.55 154	89.05 233
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVSNet		78.22 195	78.34 172	77.84 274	87.83 196	54.54 330	87.94 149	91.17 129	77.65 40	73.48 247	88.49 180	62.24 156	88.43 289	62.19 246	74.07 300	90.55 179

gg-mvs-nofinetune		69.95 292	67.96 295	75.94 294	83.07 284	54.51 331	77.23 322	70.29 357	63.11 290	70.32 277	62.33 360	43.62 318	88.69 286	53.88 306	87.76 135	84.62 320

PS-CasMVS		78.01 204	78.09 177	77.77 276	87.71 201	54.39 332	88.02 145	91.22 126	77.50 48	73.26 249	88.64 175	60.73 181	88.41 290	61.88 250	73.88 304	90.53 180

Anonymous20240521		68.80 299	67.22 305	73.55 314	74.33 358	54.11 333	83.18 266	85.61 256	58.15 331	61.68 342	80.94 315	30.71 361	81.27 337	57.00 294	73.34 311	85.28 310

Patchmtry		70.74 283	69.16 285	75.49 300	80.72 322	54.07 334	74.94 336	80.30 320	58.34 330	70.01 282	81.19 310	52.50 241	86.54 304	53.37 308	71.09 325	85.87 306

PEN-MVS		77.73 209	77.69 191	77.84 274	87.07 221	53.91 335	87.91 151	91.18 128	77.56 45	73.14 251	88.82 171	61.23 174	89.17 276	59.95 265	72.37 315	90.43 183

gm-plane-assit					81.40 314	53.83 336			62.72 298		80.94 315		92.39 204	63.40 237

CL-MVSNet_self_test		72.37 275	71.46 267	75.09 303	79.49 339	53.53 337	80.76 288	85.01 263	69.12 221	70.51 274	82.05 306	57.92 201	84.13 321	52.27 312	66.00 341	87.60 267

MDTV_nov1_ep13			69.97 282		83.18 281	53.48 338	77.10 323	80.18 323	60.45 312	69.33 293	80.44 319	48.89 290	86.90 302	51.60 315	78.51 246

KD-MVS_2432*1600		66.22 315	63.89 316	73.21 316	75.47 356	53.42 339	70.76 346	84.35 271	64.10 281	66.52 316	78.52 334	34.55 355	84.98 315	50.40 320	50.33 364	81.23 344

miper_refine_blended		66.22 315	63.89 316	73.21 316	75.47 356	53.42 339	70.76 346	84.35 271	64.10 281	66.52 316	78.52 334	34.55 355	84.98 315	50.40 320	50.33 364	81.23 344

test1111		79.43 167	79.18 155	80.15 238	89.99 120	53.31 341	87.33 166	77.05 341	75.04 105	80.23 128	92.77 79	48.97 288	92.33 209	68.87 193	92.40 83	94.81 17

LF4IMVS		64.02 322	62.19 325	69.50 334	70.90 367	53.29 342	76.13 324	77.18 340	52.65 353	58.59 350	80.98 314	23.55 366	76.52 354	53.06 310	66.66 338	78.68 353

DTE-MVSNet		76.99 223	76.80 208	77.54 281	86.24 231	53.06 343	87.52 160	90.66 139	77.08 60	72.50 257	88.67 174	60.48 187	89.52 270	57.33 291	70.74 326	90.05 202

test2506		77.30 219	76.49 216	79.74 246	90.08 115	52.02 344	87.86 154	63.10 371	74.88 109	80.16 129	92.79 77	38.29 344	92.35 207	68.74 195	92.50 81	94.86 14

tpm		72.37 275	71.71 266	74.35 310	82.19 304	52.00 345	79.22 305	77.29 339	64.56 275	72.95 253	83.68 288	51.35 259	83.26 329	58.33 282	75.80 277	87.81 263

MIMVSNet1		68.58 301	66.78 308	73.98 313	80.07 330	51.82 346	80.77 287	84.37 270	64.40 277	59.75 349	82.16 305	36.47 349	83.63 325	42.73 354	70.33 327	86.48 294

Vis-MVSNet (Re-imp)		78.36 193	78.45 168	78.07 272	88.64 171	51.78 347	86.70 186	79.63 326	74.14 129	75.11 230	90.83 121	61.29 173	89.75 266	58.10 284	91.60 91	92.69 110

LCM-MVSNet-Re		77.05 222	76.94 205	77.36 282	87.20 218	51.60 348	80.06 296	80.46 318	75.20 102	67.69 302	86.72 225	62.48 150	88.98 280	63.44 236	89.25 120	91.51 145

Gipuma		45.18 335	41.86 338	55.16 349	77.03 350	51.52 349	32.50 370	80.52 316	32.46 368	27.12 369	35.02 370	9.52 377	75.50 358	22.31 370	60.21 354	38.45 369
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		67.33 307	65.99 310	71.37 325	73.48 362	51.47 350	75.16 332	85.19 260	65.20 267	60.78 345	80.93 317	42.35 324	77.20 352	57.12 292	53.69 360	85.44 308

UnsupCasMVSNet_bld		63.70 323	61.53 327	70.21 332	73.69 361	51.39 351	72.82 339	81.89 303	55.63 346	57.81 353	71.80 356	38.67 341	78.61 345	49.26 329	52.21 362	80.63 347

FPMVS		53.68 330	51.64 333	59.81 347	65.08 370	51.03 352	69.48 350	69.58 360	41.46 361	40.67 365	72.32 355	16.46 372	70.00 367	24.24 369	65.42 342	58.40 366

CVMVSNet		72.99 269	72.58 259	74.25 311	84.28 258	50.85 353	86.41 193	83.45 288	44.56 359	73.23 250	87.54 205	49.38 281	85.70 310	65.90 220	78.44 247	86.19 298

Anonymous20231206		68.60 300	67.80 299	71.02 329	80.23 328	50.75 354	78.30 315	80.47 317	56.79 341	66.11 321	82.63 299	46.35 302	78.95 344	43.62 352	75.70 278	83.36 330

ambc				75.24 302	73.16 364	50.51 355	63.05 363	87.47 230		64.28 331	77.81 340	17.80 370	89.73 267	57.88 286	60.64 352	85.49 307

tpmrst		72.39 273	72.13 263	73.18 319	80.54 325	49.91 356	79.91 299	79.08 329	63.11 290	71.69 267	79.95 324	55.32 218	82.77 331	65.66 223	73.89 303	86.87 286

Patchmatch-test		64.82 320	63.24 320	69.57 333	79.42 340	49.82 357	63.49 362	69.05 362	51.98 355	59.95 348	80.13 322	50.91 263	70.98 366	40.66 358	73.57 306	87.90 261

EPMVS		69.02 297	68.16 292	71.59 323	79.61 337	49.80 358	77.40 321	66.93 365	62.82 296	70.01 282	79.05 329	45.79 308	77.86 350	56.58 296	75.26 291	87.13 282

dp		66.80 309	65.43 311	70.90 330	79.74 336	48.82 359	75.12 334	74.77 348	59.61 320	64.08 333	77.23 342	42.89 321	80.72 339	48.86 330	66.58 339	83.16 332

test0.0.03 1		68.00 304	67.69 301	68.90 336	77.55 346	47.43 360	75.70 330	72.95 354	66.66 250	66.56 314	82.29 303	48.06 292	75.87 357	44.97 350	74.51 298	83.41 329

ADS-MVSNet		64.36 321	62.88 323	68.78 338	79.92 331	47.17 361	67.55 356	71.18 355	53.37 351	65.25 326	75.86 347	42.32 325	73.99 364	41.57 356	68.91 332	85.18 311

EU-MVSNet		68.53 302	67.61 302	71.31 328	78.51 344	47.01 362	84.47 240	84.27 274	42.27 360	66.44 319	84.79 272	40.44 336	83.76 323	58.76 278	68.54 335	83.17 331

KD-MVS_self_test		68.81 298	67.59 303	72.46 321	74.29 359	45.45 363	77.93 318	87.00 238	63.12 289	63.99 334	78.99 333	42.32 325	84.77 318	56.55 297	64.09 346	87.16 281

LCM-MVSNet		54.25 329	49.68 335	67.97 340	53.73 374	45.28 364	66.85 358	80.78 312	35.96 366	39.45 366	62.23 362	8.70 378	78.06 349	48.24 335	51.20 363	80.57 348

test20.03		67.45 306	66.95 307	68.94 335	75.48 355	44.84 365	77.50 320	77.67 334	66.66 250	63.01 338	83.80 284	47.02 297	78.40 346	42.53 355	68.86 334	83.58 328

PatchT		68.46 303	67.85 297	70.29 331	80.70 323	43.93 366	72.47 340	74.88 347	60.15 316	70.55 273	76.57 345	49.94 275	81.59 334	50.58 318	74.83 295	85.34 309

MVS-HIRNet		59.14 326	57.67 329	63.57 344	81.65 309	43.50 367	71.73 342	65.06 368	39.59 364	51.43 362	57.73 364	38.34 343	82.58 332	39.53 359	73.95 302	64.62 363

PMVS	37.38 22	44.16 336	40.28 339	55.82 348	40.82 379	42.54 368	65.12 360	63.99 370	34.43 367	24.48 370	57.12 366	3.92 380	76.17 356	17.10 372	55.52 358	48.75 367
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
testgi		66.67 311	66.53 309	67.08 342	75.62 354	41.69 369	75.93 326	76.50 343	66.11 257	65.20 328	86.59 234	35.72 352	74.71 361	43.71 351	73.38 310	84.84 316

ANet_high		50.57 334	46.10 337	63.99 343	48.67 377	39.13 370	70.99 345	80.85 311	61.39 308	31.18 368	57.70 365	17.02 371	73.65 365	31.22 364	15.89 374	79.18 352

MDTV_nov1_ep13_2view						37.79 371	75.16 332		55.10 347	66.53 315		49.34 282		53.98 305		87.94 260

DSMNet-mixed		57.77 328	56.90 330	60.38 346	67.70 369	35.61 372	69.18 351	53.97 374	32.30 369	57.49 354	79.88 325	40.39 337	68.57 368	38.78 360	72.37 315	76.97 356

MVE	26.22 23	30.37 341	25.89 345	43.81 353	44.55 378	35.46 373	28.87 371	39.07 378	18.20 372	18.58 374	40.18 369	2.68 381	47.37 375	17.07 373	23.78 371	48.60 368
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		50.91 333	50.29 334	52.78 350	68.58 368	34.94 374	63.71 361	56.63 373	39.73 363	44.95 364	65.47 359	21.93 367	58.48 370	34.98 363	56.62 357	64.92 362

wuyk23d		16.82 344	15.94 347	19.46 358	58.74 372	31.45 375	39.22 368	3.74 383	6.84 374	6.04 377	2.70 377	1.27 382	24.29 377	10.54 376	14.40 376	2.63 374

E-PMN		31.77 338	30.64 341	35.15 355	52.87 375	27.67 376	57.09 365	47.86 376	24.64 370	16.40 375	33.05 371	11.23 375	54.90 372	14.46 374	18.15 372	22.87 371

DeepMVS_CX				27.40 357	40.17 380	26.90 377		24.59 381	17.44 373	23.95 371	48.61 368	9.77 376	26.48 376	18.06 371	24.47 370	28.83 370

EMVS		30.81 340	29.65 342	34.27 356	50.96 376	25.95 378	56.58 366	46.80 377	24.01 371	15.53 376	30.68 372	12.47 374	54.43 373	12.81 375	17.05 373	22.43 372

new-patchmatchnet		61.73 324	61.73 326	61.70 345	72.74 366	24.50 379	69.16 352	78.03 332	61.40 307	56.72 356	75.53 350	38.42 342	76.48 355	45.95 346	57.67 355	84.13 324

PMMVS2		40.82 337	38.86 340	46.69 352	53.84 373	16.45 380	48.61 367	49.92 375	37.49 365	31.67 367	60.97 363	8.14 379	56.42 371	28.42 366	30.72 369	67.19 361

tmp_tt		18.61 343	21.40 346	10.23 359	4.82 382	10.11 381	34.70 369	30.74 380	1.48 376	23.91 372	26.07 373	28.42 362	13.41 378	27.12 367	15.35 375	7.17 373

N_pmnet		52.79 331	53.26 332	51.40 351	78.99 343	7.68 382	69.52 349	3.89 382	51.63 356	57.01 355	74.98 351	40.83 335	65.96 369	37.78 361	64.67 344	80.56 349

test_method		31.52 339	29.28 343	38.23 354	27.03 381	6.50 383	20.94 372	62.21 372	4.05 375	22.35 373	52.50 367	13.33 373	47.58 374	27.04 368	34.04 368	60.62 364

test123		6.12 346	8.11 349	0.14 360	0.06 384	0.09 384	71.05 344	0.03 385	0.04 379	0.25 380	1.30 379	0.05 383	0.03 380	0.21 378	0.01 378	0.29 375

testmvs		6.04 347	8.02 350	0.10 361	0.08 383	0.03 385	69.74 348	0.04 384	0.05 378	0.31 379	1.68 378	0.02 384	0.04 379	0.24 377	0.02 377	0.25 376

test_blank		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uanet_test		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

DCPMVS		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

cdsmvs_eth3d_5k		19.96 342	26.61 344	0.00 362	0.00 385	0.00 386	0.00 373	89.26 180	0.00 380	0.00 381	88.61 176	61.62 164	0.00 381	0.00 379	0.00 379	0.00 377

pcd_1.5k_mvsjas		5.26 348	7.02 351	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	63.15 140	0.00 381	0.00 379	0.00 379	0.00 377

sosnet-low-res		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

sosnet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uncertanet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

Regformer		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

ab-mvs-re		7.23 345	9.64 348	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	86.72 225	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

uanet		0.00 349	0.00 352	0.00 362	0.00 385	0.00 386	0.00 373	0.00 386	0.00 380	0.00 381	0.00 380	0.00 385	0.00 381	0.00 379	0.00 379	0.00 377

PC_three_1452									68.21 238	92.02 12	94.00 48	82.09 5	95.98 55	84.58 40	96.68 2	94.95 7

eth-test2					0.00 385

eth-test					0.00 385

test_241102_TWO								94.06 12	77.24 52	92.78 4	95.72 8	81.26 8	97.44 5	89.07 9	96.58 6	94.26 41

9.14			88.26 15		92.84 68		91.52 47	94.75 1	73.93 133	88.57 24	94.67 19	75.57 24	95.79 59	86.77 23	95.76 28

test_0728_THIRD									78.38 35	92.12 9	95.78 4	81.46 7	97.40 7	89.42 4	96.57 7	94.67 24

GSMVS																88.96 239

sam_mvs1												51.32 260				88.96 239

sam_mvs												50.01 273

MTGPA								92.02 94

test_post1							78.90 310				5.43 376	48.81 291	85.44 313	59.25 271

test_post											5.46 375	50.36 271	84.24 320

patchmatchnet-post											74.00 352	51.12 262	88.60 287

MTMP							92.18 35	32.83 379

test9_res														84.90 33	95.70 31	92.87 105

agg_prior2														82.91 65	95.45 33	92.70 108

test_prior2							88.85 112		75.41 96	84.91 58	93.54 54	74.28 36		83.31 57	95.86 22

旧先验2							86.56 190		58.10 332	87.04 35			88.98 280	74.07 144

新几何2							86.29 198

无先验							87.48 161	88.98 192	60.00 317				94.12 133	67.28 207		88.97 238

原ACMM2							86.86 179

testdata2													91.01 250	62.37 245

segment_acmp												73.08 45

testdata1							84.14 251		75.71 90

plane_prior5								92.44 75					95.38 79	78.71 102	86.32 157	91.33 151

plane_prior4											91.00 118

plane_prior2							91.25 52		79.12 25

plane_prior1					89.90 123

n2								0.00 386

nn								0.00 386

door-mid								69.98 358

test11								92.23 86

door								69.44 361

HQP-NCC					89.33 139		89.17 99		76.41 75	77.23 179

ACMP_Plane					89.33 139		89.17 99		76.41 75	77.23 179

BP-MVS														77.47 114

HQP4-MVS										77.24 178			95.11 90			91.03 162

HQP3-MVS								92.19 89							85.99 163

HQP2-MVS												60.17 191

ACMMP++_ref															81.95 210

ACMMP++															81.25 216

Test By Simon												64.33 125

High-resolution multi-view benchmark