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++		79.88 8	80.14 8	79.10 20	88.17 1	64.80 1	86.59 12	83.70 66	65.37 15	78.78 25	90.64 19	58.63 24	87.24 55	79.00 10	90.37 14	85.26 129

zzz-MVS		77.61 32	77.36 32	78.35 37	86.08 41	63.57 2	83.37 60	80.97 132	65.13 18	75.77 38	90.88 15	48.63 121	86.66 77	77.23 19	88.17 38	84.81 141

MTAPA		76.90 39	76.42 41	78.35 37	86.08 41	63.57 2	74.92 211	80.97 132	65.13 18	75.77 38	90.88 15	48.63 121	86.66 77	77.23 19	88.17 38	84.81 141

mPP-MVS		76.54 42	75.93 46	78.34 39	86.47 28	63.50 4	85.74 25	82.28 96	62.90 55	71.77 97	90.26 34	46.61 152	86.55 83	71.71 58	85.66 66	84.97 137

MP-MVS		78.35 22	78.26 22	78.64 31	86.54 27	63.47 5	86.02 20	83.55 70	63.89 40	73.60 71	90.60 20	54.85 54	86.72 75	77.20 21	88.06 41	85.74 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mvs-test1		70.44 122	68.19 149	77.18 55	76.10 225	63.22 6	80.59 106	76.06 218	59.83 117	66.32 187	79.87 239	41.56 205	85.53 109	60.60 152	72.77 209	82.80 204

CP-MVS		77.12 37	76.68 38	78.43 35	86.05 43	63.18 7	87.55 10	83.45 73	62.44 67	72.68 87	90.50 24	48.18 127	87.34 54	73.59 46	85.71 65	84.76 145

region2R		77.67 31	77.18 34	79.15 17	86.76 18	62.95 8	86.29 14	84.16 53	62.81 60	73.30 74	90.58 21	49.90 106	88.21 35	73.78 43	87.03 52	86.29 87

ACMMPR		77.71 29	77.23 33	79.16 16	86.75 19	62.93 9	86.29 14	84.24 51	62.82 58	73.55 72	90.56 22	49.80 108	88.24 34	74.02 41	87.03 52	86.32 84

HFP-MVS		78.01 27	77.65 28	79.10 20	86.71 20	62.81 10	86.29 14	84.32 49	62.82 58	73.96 63	90.50 24	53.20 74	88.35 31	74.02 41	87.05 50	86.13 90

#test#		77.83 28	77.41 31	79.10 20	86.71 20	62.81 10	85.69 27	84.32 49	61.61 82	73.96 63	90.50 24	53.20 74	88.35 31	73.68 44	87.05 50	86.13 90

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 34	62.73 12	86.09 17	86.83 12	65.51 14	83.81 10	90.51 23	63.71 12	89.23 20	81.51 1	88.44 32	88.09 19
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		76.77 41	76.06 43	78.88 27	86.14 39	62.73 12	82.55 75	83.74 65	61.71 80	72.45 93	90.34 31	48.48 125	88.13 36	72.32 52	86.85 57	85.78 102

XVS		77.17 36	76.56 40	79.00 23	86.32 32	62.62 14	85.83 22	83.92 57	64.55 26	72.17 94	90.01 45	47.95 129	88.01 40	71.55 60	86.74 59	86.37 78

X-MVStestdata		70.21 127	67.28 173	79.00 23	86.32 32	62.62 14	85.83 22	83.92 57	64.55 26	72.17 94	6.49 376	47.95 129	88.01 40	71.55 60	86.74 59	86.37 78

NCCC		78.58 16	78.31 20	79.39 11	87.51 12	62.61 16	85.20 31	84.42 47	66.73 9	74.67 55	89.38 56	55.30 48	89.18 21	74.19 39	87.34 48	86.38 75

test_prior4						62.51 17	82.08 85

Effi-MVS+-dtu		69.64 142	67.53 162	75.95 76	76.10 225	62.29 18	80.20 110	76.06 218	59.83 117	65.26 209	77.09 279	41.56 205	84.02 144	60.60 152	71.09 231	81.53 222

SteuartSystems-ACMMP		79.48 10	79.31 11	79.98 2	83.01 82	62.18 19	87.60 9	85.83 23	66.69 10	78.03 31	90.98 14	54.26 58	90.06 12	78.42 17	89.02 27	87.69 34
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		79.84 9	79.97 9	79.45 10	87.90 2	62.17 20	84.37 37	85.03 38	66.96 5	77.58 32	90.06 40	59.47 21	89.13 22	78.67 12	89.73 20	87.03 58

ZNCC-MVS		78.82 13	78.67 17	79.30 13	86.43 30	62.05 21	86.62 11	86.01 22	63.32 46	75.08 44	90.47 27	53.96 62	88.68 27	76.48 26	89.63 24	87.16 55

SMA-MVS		80.28 6	80.39 7	79.95 3	86.60 25	61.95 22	86.33 13	85.75 25	62.49 65	82.20 15	92.28 1	56.53 36	89.70 15	79.85 3	91.48 1	88.19 15
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
ACMMP		76.02 48	75.33 52	78.07 41	85.20 56	61.91 23	85.49 30	84.44 46	63.04 52	69.80 122	89.74 52	45.43 165	87.16 59	72.01 55	82.87 89	85.14 130
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
SD-MVS		77.70 30	77.62 29	77.93 45	84.47 67	61.88 24	84.55 35	83.87 62	60.37 101	79.89 18	89.38 56	54.97 51	85.58 108	76.12 29	84.94 69	86.33 82
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
GST-MVS		78.14 24	77.85 27	78.99 25	86.05 43	61.82 25	85.84 21	85.21 34	63.56 44	74.29 60	90.03 43	52.56 78	88.53 29	74.79 34	88.34 34	86.63 72

DeepC-MVS	69.38 2	78.56 18	78.14 24	79.83 6	83.60 72	61.62 26	84.17 43	86.85 10	63.23 49	73.84 68	90.25 35	57.68 30	89.96 13	74.62 35	89.03 26	87.89 23
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TEST9					85.58 49	61.59 27	81.62 92	81.26 123	55.65 198	74.93 47	88.81 65	53.70 69	84.68 129

train_agg		76.27 45	76.15 42	76.64 66	85.58 49	61.59 27	81.62 92	81.26 123	55.86 189	74.93 47	88.81 65	53.70 69	84.68 129	75.24 32	88.33 35	83.65 183

TSAR-MVS + MP.		78.44 20	78.28 21	78.90 26	84.96 60	61.41 29	84.03 47	83.82 64	59.34 126	79.37 19	89.76 51	59.84 17	87.62 50	76.69 25	86.74 59	87.68 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CPTT-MVS		72.78 84	72.08 88	74.87 101	84.88 65	61.41 29	84.15 44	77.86 191	55.27 203	67.51 165	88.08 72	41.93 198	81.85 189	69.04 77	80.01 122	81.35 227

xxxxxxxxxxxxxcwj		78.37 21	78.25 23	78.76 29	86.17 36	61.30 31	83.98 49	79.95 147	59.00 129	79.16 20	90.75 17	57.96 26	87.09 64	77.08 23	90.18 15	87.87 25

save fliter					86.17 36	61.30 31	83.98 49	79.66 152	59.00 129

SR-MVS		76.13 47	75.70 49	77.40 54	85.87 45	61.20 33	85.52 28	82.19 97	59.99 112	75.10 43	90.35 29	47.66 133	86.52 84	71.64 59	82.99 84	84.47 151

1121		68.53 168	67.16 179	72.63 161	85.64 48	61.14 34	73.95 228	66.46 303	44.61 316	70.28 110	86.68 94	41.42 209	80.78 215	53.62 198	81.79 100	75.97 291

新几何1				70.76 199	85.66 46	61.13 35		66.43 304	44.68 315	70.29 109	86.64 95	41.29 211	75.23 282	49.72 229	81.75 104	75.93 293

MSC_two_6792asdad				79.95 3	87.24 14	61.04 36		85.62 27					90.96 1	79.31 7	90.65 8	87.85 27

No_MVS				79.95 3	87.24 14	61.04 36		85.62 27					90.96 1	79.31 7	90.65 8	87.85 27

ACMMP_NAP		78.77 15	78.78 15	78.74 30	85.44 52	61.04 36	83.84 54	85.16 35	62.88 56	78.10 28	91.26 13	52.51 79	88.39 30	79.34 6	90.52 13	86.78 69

test_8					85.40 53	60.96 39	81.54 95	81.18 126	55.86 189	74.81 50	88.80 67	53.70 69	84.45 134

OPU-MVS				79.83 6	87.54 11	60.93 40	87.82 7				89.89 47	67.01 1	90.33 11	73.16 49	91.15 4	88.23 13

DeepC-MVS_fast	68.24 3	77.25 35	76.63 39	79.12 19	86.15 38	60.86 41	84.71 33	84.85 43	61.98 78	73.06 82	88.88 64	53.72 68	89.06 23	68.27 78	88.04 42	87.42 46
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FOURS1					86.12 40	60.82 42	88.18 1	83.61 68	60.87 89	81.50 16

HPM-MVS		77.28 34	76.85 36	78.54 34	85.00 59	60.81 43	82.91 67	85.08 36	62.57 63	73.09 81	89.97 46	50.90 102	87.48 53	75.30 30	86.85 57	87.33 52
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS		78.02 25	78.04 26	77.98 44	86.44 29	60.81 43	85.52 28	84.36 48	60.61 94	79.05 23	90.30 32	55.54 47	88.32 33	73.48 48	87.03 52	84.83 140
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test1172		75.36 57	74.81 60	77.02 59	85.47 51	60.79 45	83.94 52	81.63 108	59.52 123	74.66 56	90.18 36	44.74 172	85.84 102	70.63 67	82.52 93	84.42 152

testtj		78.47 19	78.43 19	78.61 32	86.82 17	60.67 46	86.07 18	85.38 31	62.12 71	78.65 26	90.29 33	55.76 44	89.31 19	73.55 47	87.22 49	85.84 100

MVS_111021_HR		74.02 73	73.46 77	75.69 86	83.01 82	60.63 47	77.29 164	78.40 184	61.18 87	70.58 106	85.97 115	54.18 60	84.00 145	67.52 90	82.98 86	82.45 209

DPE-MVS		80.56 5	80.98 5	79.29 14	87.27 13	60.56 48	85.71 26	86.42 18	63.28 47	83.27 13	91.83 10	64.96 7	90.47 10	76.41 27	89.67 22	86.84 65
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					87.58 9	60.47 49				83.42 12

ZD-MVS					86.64 23	60.38 50		82.70 92	57.95 153	78.10 28	90.06 40	56.12 42	88.84 26	74.05 40	87.00 55

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 18	84.92 64	60.32 51	83.03 64	85.33 32	62.86 57	80.17 17	90.03 43	61.76 15	88.95 24	74.21 38	88.67 31	88.12 18

abl_6		74.34 69	73.50 74	76.86 61	82.43 86	60.16 52	83.48 59	81.86 103	58.81 133	73.95 65	89.86 48	41.87 199	86.62 79	67.98 83	81.23 107	83.80 175

APDe-MVS		80.16 7	80.59 6	78.86 28	86.64 23	60.02 53	88.12 3	86.42 18	62.94 54	82.40 14	92.12 2	59.64 19	89.76 14	78.70 11	88.32 36	86.79 68

agg_prior1		75.94 49	76.01 44	75.72 83	85.04 57	59.96 54	81.44 96	81.04 129	56.14 186	74.68 53	88.90 63	53.91 64	84.04 141	75.01 33	87.92 45	83.16 197

agg_prior					85.04 57	59.96 54		81.04 129		74.68 53			84.04 141

3Dnovator+	66.72 4	75.84 51	74.57 62	79.66 8	82.40 87	59.92 56	85.83 22	86.32 20	66.92 8	67.80 160	89.24 58	42.03 196	89.38 18	64.07 120	86.50 62	89.69 1

SR-MVS-dyc-post		74.57 66	73.90 69	76.58 67	83.49 74	59.87 57	84.29 38	81.36 116	58.07 150	73.14 78	90.07 38	44.74 172	85.84 102	68.20 79	81.76 102	84.03 162

RE-MVS-def			73.71 73		83.49 74	59.87 57	84.29 38	81.36 116	58.07 150	73.14 78	90.07 38	43.06 188		68.20 79	81.76 102	84.03 162

CSCG		76.92 38	76.75 37	77.41 52	83.96 71	59.60 59	82.95 65	86.50 17	60.78 92	75.27 42	84.83 133	60.76 16	86.56 82	67.86 85	87.87 46	86.06 93

h-mvs33		72.71 86	71.49 93	76.40 70	81.99 93	59.58 60	76.92 173	76.74 211	60.40 98	74.81 50	85.95 117	45.54 161	85.76 105	70.41 68	70.61 235	83.86 170

MP-MVS-pluss		78.35 22	78.46 18	78.03 43	84.96 60	59.52 61	82.93 66	85.39 30	62.15 70	76.41 36	91.51 11	52.47 81	86.78 74	80.66 2	89.64 23	87.80 30
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
原ACMM1				74.69 104	85.39 54	59.40 62		83.42 74	51.47 251	70.27 111	86.61 97	48.61 123	86.51 85	53.85 197	87.96 43	78.16 268

MAR-MVS		71.51 104	70.15 116	75.60 89	81.84 95	59.39 63	81.38 97	82.90 89	54.90 216	68.08 151	78.70 258	47.73 131	85.51 111	51.68 218	84.17 77	81.88 219
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
MVS_111021_LR		69.50 145	68.78 139	71.65 178	78.38 169	59.33 64	74.82 213	70.11 278	58.08 149	67.83 159	84.68 136	41.96 197	76.34 278	65.62 108	77.54 154	79.30 260

test_one_0601					87.58 9	59.30 65		86.84 11	65.01 23	83.80 11	91.86 6	64.03 11

MSLP-MVS++		73.77 77	73.47 76	74.66 106	83.02 81	59.29 66	82.30 82	81.88 102	59.34 126	71.59 100	86.83 87	45.94 156	83.65 151	65.09 113	85.22 68	81.06 234

DPM-MVS		75.47 55	75.00 55	76.88 60	81.38 104	59.16 67	79.94 115	85.71 26	56.59 176	72.46 91	86.76 89	56.89 34	87.86 45	66.36 99	88.91 30	83.64 184

DVP-MVS++		81.67 1	82.40 1	79.47 9	87.24 14	59.15 68	88.18 1	87.15 6	65.04 20	84.26 5	91.86 6	67.01 1	90.84 3	79.48 4	91.38 2	88.42 8

IU-MVS					87.77 4	59.15 68		85.53 29	53.93 227	84.64 3				79.07 9	90.87 5	88.37 10

CDPH-MVS		76.31 44	75.67 50	78.22 40	85.35 55	59.14 70	81.31 98	84.02 54	56.32 180	74.05 61	88.98 62	53.34 73	87.92 43	69.23 75	88.42 33	87.59 39

test_0728_SECOND				79.19 15	87.82 3	59.11 71	87.85 5	87.15 6					90.84 3	78.66 13	90.61 11	87.62 38

DVP-MVS		80.84 4	81.64 3	78.42 36	87.75 7	59.07 72	87.85 5	85.03 38	64.26 32	83.82 8	92.00 3	64.82 8	90.75 8	78.66 13	90.61 11	85.45 119
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					87.75 7	59.07 72	87.86 4	86.83 12	64.26 32	84.19 7	91.92 5	64.82 8

HPM-MVS_fast		74.30 71	73.46 77	76.80 62	84.45 68	59.04 74	83.65 57	81.05 128	60.15 109	70.43 107	89.84 49	41.09 214	85.59 107	67.61 89	82.90 88	85.77 105

OPM-MVS		74.73 63	74.25 66	76.19 73	80.81 113	59.01 75	82.60 74	83.64 67	63.74 42	72.52 90	87.49 79	47.18 143	85.88 101	69.47 73	80.78 108	83.66 182
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
3Dnovator	64.47 5	72.49 89	71.39 96	75.79 79	77.70 190	58.99 76	80.66 105	83.15 84	62.24 69	65.46 203	86.59 98	42.38 194	85.52 110	59.59 161	84.72 70	82.85 203

SED-MVS		81.56 2	82.30 2	79.32 12	87.77 4	58.90 77	87.82 7	86.78 14	64.18 35	85.97 1	91.84 8	66.87 3	90.83 5	78.63 15	90.87 5	88.23 13

test_241102_ONE					87.77 4	58.90 77		86.78 14	64.20 34	85.97 1	91.34 12	66.87 3	90.78 7

PVSNet_Blended_VisFu		71.45 106	70.39 112	74.65 107	82.01 91	58.82 79	79.93 116	80.35 143	55.09 208	65.82 198	82.16 192	49.17 115	82.64 177	60.34 154	78.62 146	82.50 208

TSAR-MVS + GP.		74.90 59	74.15 67	77.17 56	82.00 92	58.77 80	81.80 87	78.57 172	58.58 139	74.32 59	84.51 143	55.94 43	87.22 56	67.11 94	84.48 74	85.52 115

test222					83.14 77	58.68 81	72.57 250	63.45 321	41.78 336	67.56 164	86.12 108	37.13 250			78.73 144	74.98 304

ETH3D cwj APD-0.16		78.02 25	78.13 25	77.71 47	82.10 90	58.65 82	82.72 71	87.55 5	58.33 146	78.05 30	90.06 40	58.35 25	87.65 49	76.15 28	89.86 19	86.82 66

ACMM	61.98 7	70.80 115	69.73 121	74.02 121	80.59 119	58.59 83	82.68 72	82.02 101	55.46 201	67.18 170	84.39 145	38.51 232	83.17 160	60.65 151	76.10 171	80.30 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
test12				77.76 46	84.52 66	58.41 84		83.36 77		72.93 84		54.61 56	88.05 39		88.12 40	86.81 67

MCST-MVS		77.48 33	77.45 30	77.54 50	86.67 22	58.36 85	83.22 62	86.93 9	56.91 168	74.91 49	88.19 69	59.15 22	87.68 48	73.67 45	87.45 47	86.57 73

Regformer-2		75.63 53	74.99 56	77.54 50	80.43 120	58.32 86	79.50 125	82.92 87	67.84 1	75.94 37	80.75 223	55.73 45	86.80 72	71.44 62	80.38 116	87.50 42

APD-MVS_3200maxsize		74.96 58	74.39 65	76.67 65	82.20 89	58.24 87	83.67 56	83.29 80	58.41 143	73.71 69	90.14 37	45.62 158	85.99 97	69.64 71	82.85 90	85.78 102

ETH3 D test6400		79.14 11	79.32 10	78.61 32	86.34 31	58.11 88	84.65 34	87.66 4	58.56 141	78.87 24	89.54 53	63.67 13	89.57 16	74.60 36	89.98 17	88.14 17

CNLPA		65.43 220	64.02 220	69.68 219	78.73 162	58.07 89	77.82 151	70.71 275	51.49 250	61.57 257	83.58 164	38.23 237	70.82 300	43.90 274	70.10 245	80.16 247

DP-MVS Recon		72.15 97	70.73 107	76.40 70	86.57 26	57.99 90	81.15 100	82.96 86	57.03 165	66.78 176	85.56 124	44.50 176	88.11 37	51.77 216	80.23 121	83.10 198

ETH3D-3000-0.1		78.58 16	78.91 14	77.61 48	83.06 79	57.86 91	84.14 46	88.31 1	60.37 101	79.14 22	90.35 29	57.76 29	87.00 67	77.16 22	89.90 18	87.97 22

SF-MVS		78.82 13	79.22 12	77.60 49	82.88 84	57.83 92	84.99 32	88.13 3	61.86 79	79.16 20	90.75 17	57.96 26	87.09 64	77.08 23	90.18 15	87.87 25

AdaColmap		69.99 131	68.66 141	73.97 123	84.94 62	57.83 92	82.63 73	78.71 168	56.28 182	64.34 222	84.14 148	41.57 204	87.06 66	46.45 251	78.88 139	77.02 283

Fast-Effi-MVS+-dtu		67.37 188	65.33 211	73.48 142	72.94 271	57.78 94	77.47 159	76.88 207	57.60 159	61.97 251	76.85 283	39.31 224	80.49 222	54.72 189	70.28 242	82.17 214

Regformer-1		75.47 55	74.93 58	77.09 57	80.43 120	57.70 95	79.50 125	82.13 98	67.84 1	75.73 40	80.75 223	56.50 37	86.07 93	71.07 64	80.38 116	87.50 42

ACMP	63.53 6	72.30 92	71.20 101	75.59 90	80.28 122	57.54 96	82.74 70	82.84 91	60.58 95	65.24 210	86.18 107	39.25 225	86.03 96	66.95 97	76.79 167	83.22 192
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CANet		76.46 43	75.93 46	78.06 42	81.29 105	57.53 97	82.35 77	83.31 79	67.78 3	70.09 112	86.34 104	54.92 52	88.90 25	72.68 51	84.55 72	87.76 33

LPG-MVS_test		72.74 85	71.74 90	75.76 80	80.22 124	57.51 98	82.55 75	83.40 75	61.32 84	66.67 180	87.33 82	39.15 227	86.59 80	67.70 87	77.30 159	83.19 194

LGP-MVS_train				75.76 80	80.22 124	57.51 98		83.40 75	61.32 84	66.67 180	87.33 82	39.15 227	86.59 80	67.70 87	77.30 159	83.19 194

test_prior3		76.89 40	76.96 35	76.69 63	84.20 69	57.27 100	81.75 89	84.88 41	60.37 101	75.01 45	89.06 59	56.22 40	86.43 87	72.19 53	88.96 28	86.38 75

test_prior				76.69 63	84.20 69	57.27 100		84.88 41					86.43 87			86.38 75

XVG-OURS		68.76 160	67.37 169	72.90 155	74.32 257	57.22 102	70.09 284	78.81 165	55.24 204	67.79 161	85.81 122	36.54 256	78.28 254	62.04 140	75.74 174	83.19 194

API-MVS		72.17 95	71.41 95	74.45 115	81.95 94	57.22 102	84.03 47	80.38 142	59.89 116	68.40 142	82.33 186	49.64 109	87.83 46	51.87 214	84.16 78	78.30 266

xiu_mvs_v1_base_debu		68.58 163	67.28 173	72.48 164	78.19 176	57.19 104	75.28 200	75.09 234	51.61 246	70.04 113	81.41 207	32.79 290	79.02 245	63.81 124	77.31 156	81.22 229

xiu_mvs_v1_base		68.58 163	67.28 173	72.48 164	78.19 176	57.19 104	75.28 200	75.09 234	51.61 246	70.04 113	81.41 207	32.79 290	79.02 245	63.81 124	77.31 156	81.22 229

xiu_mvs_v1_base_debi		68.58 163	67.28 173	72.48 164	78.19 176	57.19 104	75.28 200	75.09 234	51.61 246	70.04 113	81.41 207	32.79 290	79.02 245	63.81 124	77.31 156	81.22 229

MVSFormer		71.50 105	70.38 113	74.88 100	78.76 160	57.15 107	82.79 68	78.48 177	51.26 255	69.49 125	83.22 168	43.99 181	83.24 158	66.06 101	79.37 130	84.23 157

lupinMVS		69.57 143	68.28 148	73.44 144	78.76 160	57.15 107	76.57 178	73.29 258	46.19 304	69.49 125	82.18 189	43.99 181	79.23 237	64.66 117	79.37 130	83.93 165

hse-mvs2		71.04 110	69.86 119	74.60 110	79.58 139	57.12 109	73.96 227	75.25 228	60.40 98	74.81 50	81.95 196	45.54 161	82.90 165	70.41 68	66.83 283	83.77 176

AUN-MVS		68.45 170	66.41 191	74.57 112	79.53 141	57.08 110	73.93 231	75.23 229	54.44 224	66.69 179	81.85 198	37.10 251	82.89 166	62.07 139	66.84 282	83.75 177

jason		69.65 141	68.39 147	73.43 145	78.27 174	56.88 111	77.12 167	73.71 254	46.53 301	69.34 129	83.22 168	43.37 185	79.18 238	64.77 116	79.20 135	84.23 157
jason: jason.
XVG-OURS-SEG-HR		68.81 157	67.47 166	72.82 158	74.40 255	56.87 112	70.59 277	79.04 161	54.77 217	66.99 172	86.01 114	39.57 222	78.21 255	62.54 135	73.33 198	83.37 188

DP-MVS		65.68 216	63.66 226	71.75 174	84.93 63	56.87 112	80.74 104	73.16 259	53.06 233	59.09 278	82.35 185	36.79 255	85.94 100	32.82 336	69.96 248	72.45 328

Regformer-4		74.25 72	73.48 75	76.57 68	79.75 134	56.54 114	78.54 139	81.49 112	66.93 7	73.90 66	80.30 231	53.84 66	85.98 98	69.76 70	76.84 165	87.17 54

HQP_MVS		74.31 70	73.73 72	76.06 74	81.41 102	56.31 115	84.22 41	84.01 55	64.52 28	69.27 130	86.10 109	45.26 169	87.21 57	68.16 81	80.58 112	84.65 146

plane_prior						56.31 115	83.58 58		63.19 51						80.48 115

EPNet		73.09 82	72.16 86	75.90 77	75.95 229	56.28 117	83.05 63	72.39 264	66.53 12	65.27 206	87.00 86	50.40 104	85.47 114	62.48 136	86.32 63	85.94 95
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		65.08 227	62.84 236	71.82 173	81.49 101	56.26 118	66.32 303	74.20 248	40.53 345	63.16 234	78.65 260	41.30 210	77.80 261	45.80 257	74.09 186	81.40 224

plane_prior6					81.20 109	56.24 119						45.26 169

anonymousdsp		67.00 199	64.82 216	73.57 140	70.09 309	56.13 120	76.35 182	77.35 202	48.43 281	64.99 216	80.84 221	33.01 287	80.34 223	64.66 117	67.64 278	84.23 157

plane_prior3						56.09 121			63.92 39	69.27 130

PatchMatch-RL		56.25 293	54.55 298	61.32 302	77.06 209	56.07 122	65.57 308	54.10 357	44.13 323	53.49 327	71.27 328	25.20 344	66.78 320	36.52 322	63.66 304	61.12 356

NP-MVS					80.98 112	56.05 123					85.54 126

plane_prior7					81.41 102	55.96 124

PS-MVSNAJss		72.24 93	71.21 100	75.31 93	78.50 166	55.93 125	81.63 91	82.12 99	56.24 183	70.02 116	85.68 123	47.05 145	84.34 137	65.27 112	74.41 184	85.67 109

PHI-MVS		75.87 50	75.36 51	77.41 52	80.62 118	55.91 126	84.28 40	85.78 24	56.08 187	73.41 73	86.58 99	50.94 101	88.54 28	70.79 65	89.71 21	87.79 31

PS-MVSNAJ		70.51 120	69.70 122	72.93 154	81.52 99	55.79 127	74.92 211	79.00 162	55.04 213	69.88 120	78.66 259	47.05 145	82.19 184	61.61 144	79.58 127	80.83 237

PCF-MVS	61.88 8	70.95 112	69.49 126	75.35 92	77.63 193	55.71 128	76.04 191	81.81 105	50.30 264	69.66 123	85.40 129	52.51 79	84.89 125	51.82 215	80.24 120	85.45 119
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LS3D		64.71 230	62.50 240	71.34 188	79.72 138	55.71 128	79.82 118	74.72 239	48.50 280	56.62 295	84.62 138	33.59 281	82.34 183	29.65 354	75.23 180	75.97 291

HyFIR lowres test		65.67 217	63.01 234	73.67 134	79.97 132	55.65 130	69.07 292	75.52 224	42.68 334	63.53 230	77.95 267	40.43 216	81.64 192	46.01 255	71.91 222	83.73 178

CS-MVS		76.25 46	75.98 45	77.06 58	80.15 129	55.63 131	84.51 36	83.90 59	63.24 48	73.30 74	87.27 84	55.06 50	86.30 92	71.78 57	84.58 71	89.25 3

xiu_mvs_v2_base		70.52 119	69.75 120	72.84 156	81.21 108	55.63 131	75.11 205	78.92 163	54.92 215	69.96 119	79.68 245	47.00 149	82.09 186	61.60 145	79.37 130	80.81 238

ET-MVSNet_ETH3D		67.96 179	65.72 205	74.68 105	76.67 216	55.62 133	75.11 205	74.74 238	52.91 235	60.03 266	80.12 235	33.68 279	82.64 177	61.86 142	76.34 170	85.78 102

MVP-Stereo		65.41 221	63.80 224	70.22 207	77.62 197	55.53 134	76.30 183	78.53 175	50.59 263	56.47 297	78.65 260	39.84 219	82.68 175	44.10 273	72.12 221	72.44 329
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
jajsoiax		68.25 173	66.45 187	73.66 135	75.62 233	55.49 135	80.82 102	78.51 176	52.33 241	64.33 223	84.11 149	28.28 324	81.81 191	63.48 128	70.62 234	83.67 180

Regformer-3		73.89 75	73.28 79	75.71 84	79.75 134	55.48 136	78.54 139	79.93 148	66.58 11	73.62 70	80.30 231	54.87 53	84.54 132	69.09 76	76.84 165	87.10 57

Vis-MVSNet		72.18 94	71.37 97	74.61 109	81.29 105	55.41 137	80.90 101	78.28 186	60.73 93	69.23 133	88.09 71	44.36 178	82.65 176	57.68 168	81.75 104	85.77 105
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
mvs_tets		68.18 175	66.36 193	73.63 138	75.61 234	55.35 138	80.77 103	78.56 173	52.48 240	64.27 225	84.10 150	27.45 330	81.84 190	63.45 129	70.56 236	83.69 179

ETV-MVS		74.46 68	73.84 71	76.33 72	79.27 147	55.24 139	79.22 129	85.00 40	64.97 24	72.65 88	79.46 250	53.65 72	87.87 44	67.45 92	82.91 87	85.89 99

CS-MVS-test		75.62 54	75.31 53	76.56 69	80.63 117	55.13 140	83.88 53	85.22 33	62.05 75	71.49 101	86.03 113	53.83 67	86.36 90	67.74 86	86.91 56	88.19 15

HQP5-MVS						54.94 141

HQP-MVS		73.45 78	72.80 82	75.40 91	80.66 114	54.94 141	82.31 79	83.90 59	62.10 72	67.85 155	85.54 126	45.46 163	86.93 68	67.04 95	80.35 118	84.32 154

test_djsdf		69.45 147	67.74 154	74.58 111	74.57 251	54.92 143	82.79 68	78.48 177	51.26 255	65.41 204	83.49 166	38.37 234	83.24 158	66.06 101	69.25 262	85.56 114

PLC	56.13 14	65.09 226	63.21 232	70.72 201	81.04 111	54.87 144	78.57 137	77.47 198	48.51 279	55.71 300	81.89 197	33.71 278	79.71 229	41.66 294	70.37 239	77.58 275
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
patch_mono-2		69.85 134	71.09 102	66.16 261	79.11 153	54.80 145	71.97 259	74.31 245	53.50 231	70.90 104	84.17 147	57.63 32	63.31 332	66.17 100	82.02 98	80.38 244

114514_t		70.83 113	69.56 123	74.64 108	86.21 34	54.63 146	82.34 78	81.81 105	48.22 283	63.01 235	85.83 120	40.92 215	87.10 63	57.91 167	79.79 123	82.18 212

UGNet		68.81 157	67.39 168	73.06 152	78.33 172	54.47 147	79.77 119	75.40 226	60.45 97	63.22 232	84.40 144	32.71 294	80.91 212	51.71 217	80.56 114	83.81 171
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
test_0402		63.25 244	61.01 257	69.96 212	80.00 131	54.37 148	76.86 175	72.02 266	54.58 221	58.71 281	80.79 222	35.00 266	84.36 136	26.41 362	64.71 297	71.15 340

EI-MVSNet-Vis-set		72.42 91	71.59 91	74.91 99	78.47 168	54.02 149	77.05 169	79.33 159	65.03 22	71.68 99	79.35 253	52.75 77	84.89 125	66.46 98	74.23 185	85.83 101

OpenMVS	61.03 9	68.85 156	67.56 159	72.70 160	74.26 258	53.99 150	81.21 99	81.34 120	52.70 237	62.75 239	85.55 125	38.86 230	84.14 139	48.41 239	83.01 83	79.97 250

pmmvs4		61.48 263	59.39 264	67.76 242	71.57 291	53.86 151	71.42 264	65.34 310	44.20 321	59.46 273	77.92 269	35.90 259	74.71 284	43.87 275	64.87 296	74.71 309

TAMVS		66.78 204	65.27 212	71.33 189	79.16 152	53.67 152	73.84 235	69.59 282	52.32 242	65.28 205	81.72 201	44.49 177	77.40 267	42.32 288	78.66 145	82.92 200

Effi-MVS+		73.31 80	72.54 84	75.62 88	77.87 186	53.64 153	79.62 123	79.61 153	61.63 81	72.02 96	82.61 179	56.44 38	85.97 99	63.99 123	79.07 138	87.25 53

F-COLMAP		63.05 247	60.87 260	69.58 223	76.99 212	53.63 154	78.12 146	76.16 215	47.97 287	52.41 330	81.61 203	27.87 326	78.11 256	40.07 300	66.66 284	77.00 284

EI-MVSNet-UG-set		71.92 98	71.06 103	74.52 114	77.98 184	53.56 155	76.62 177	79.16 160	64.40 30	71.18 102	78.95 257	52.19 85	84.66 131	65.47 109	73.57 193	85.32 126

EIA-MVS		71.78 100	70.60 108	75.30 94	79.85 133	53.54 156	77.27 165	83.26 82	57.92 154	66.49 183	79.39 251	52.07 87	86.69 76	60.05 156	79.14 137	85.66 110

EG-PatchMatch MVS		64.71 230	62.87 235	70.22 207	77.68 191	53.48 157	77.99 147	78.82 164	53.37 232	56.03 299	77.41 278	24.75 347	84.04 141	46.37 252	73.42 197	73.14 320

mvsmamba		71.15 108	69.54 124	75.99 75	77.61 198	53.46 158	81.95 86	75.11 233	57.73 158	66.95 174	85.96 116	37.14 249	87.56 51	67.94 84	75.49 177	86.97 59

QAPM		70.05 129	68.81 138	73.78 126	76.54 220	53.43 159	83.23 61	83.48 71	52.89 236	65.90 195	86.29 105	41.55 207	86.49 86	51.01 220	78.40 148	81.42 223

PAPM_NR		72.63 87	71.80 89	75.13 98	81.72 96	53.42 160	79.91 117	83.28 81	59.14 128	66.31 188	85.90 118	51.86 89	86.06 94	57.45 169	80.62 110	85.91 97

dcpmvs_2		74.55 67	75.23 54	72.48 164	82.34 88	53.34 161	77.87 148	81.46 113	57.80 157	75.49 41	86.81 88	62.22 14	77.75 262	71.09 63	82.02 98	86.34 80

CLD-MVS		73.33 79	72.68 83	75.29 95	78.82 159	53.33 162	78.23 143	84.79 44	61.30 86	70.41 108	81.04 213	52.41 82	87.12 62	64.61 119	82.49 95	85.41 123
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
bld_raw_dy_0_64		64.87 228	63.22 231	69.83 218	74.79 246	53.32 163	78.15 145	62.02 331	51.20 257	60.17 264	83.12 172	24.15 349	74.20 289	63.08 130	72.33 217	81.96 216

BH-untuned		68.27 172	67.29 172	71.21 190	79.74 136	53.22 164	76.06 189	77.46 200	57.19 163	66.10 190	81.61 203	45.37 167	83.50 154	45.42 265	76.68 169	76.91 287

旧先验1					83.04 80	53.15 165		67.52 294			87.85 77	44.08 179			80.76 109	78.03 273

OMC-MVS		71.40 107	70.60 108	73.78 126	76.60 218	53.15 165	79.74 121	79.78 149	58.37 144	68.75 137	86.45 102	45.43 165	80.60 218	62.58 134	77.73 152	87.58 40

CDS-MVSNet		66.80 203	65.37 209	71.10 194	78.98 155	53.13 167	73.27 241	71.07 272	52.15 243	64.72 218	80.23 234	43.56 184	77.10 269	45.48 263	78.88 139	83.05 199
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
bld_raw_conf005		70.66 117	68.94 136	75.84 78	77.34 204	53.03 168	81.79 88	74.40 242	58.13 148	66.63 182	86.04 112	33.24 285	87.56 51	67.51 91	75.34 179	86.92 61

testdata				64.66 279	81.52 99	52.93 169		65.29 311	46.09 305	73.88 67	87.46 80	38.08 239	66.26 324	53.31 203	78.48 147	74.78 308

iter_conf_final		69.82 135	68.02 152	75.23 96	79.38 144	52.91 170	80.11 111	73.96 251	54.99 214	68.04 152	83.59 161	29.05 318	87.16 59	65.41 110	77.62 153	85.63 112

ACMH+	57.40 11	66.12 213	64.06 219	72.30 170	77.79 189	52.83 171	80.39 107	78.03 189	57.30 161	57.47 291	82.55 181	27.68 328	84.17 138	45.54 261	69.78 252	79.90 251

IB-MVS	56.42 12	65.40 222	62.73 238	73.40 146	74.89 242	52.78 172	73.09 243	75.13 232	55.69 196	58.48 285	73.73 315	32.86 289	86.32 91	50.63 222	70.11 244	81.10 233
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
v7n		69.01 155	67.36 170	73.98 122	72.51 279	52.65 173	78.54 139	81.30 121	60.26 108	62.67 240	81.62 202	43.61 183	84.49 133	57.01 171	68.70 270	84.79 143

DROMVSNet		75.84 51	75.87 48	75.74 82	78.86 157	52.65 173	83.73 55	86.08 21	63.47 45	72.77 86	87.25 85	53.13 76	87.93 42	71.97 56	85.57 67	86.66 71

MSDG		61.81 259	59.23 265	69.55 224	72.64 275	52.63 175	70.45 280	75.81 220	51.38 252	53.70 322	76.11 293	29.52 314	81.08 207	37.70 311	65.79 291	74.93 305

cascas		65.98 214	63.42 229	73.64 137	77.26 206	52.58 176	72.26 255	77.21 204	48.56 278	61.21 259	74.60 309	32.57 298	85.82 104	50.38 224	76.75 168	82.52 207

RRT_MVS		69.42 148	67.49 165	75.21 97	78.01 183	52.56 177	82.23 83	78.15 187	55.84 191	65.65 199	85.07 130	30.86 305	86.83 71	61.56 147	70.00 246	86.24 89

BH-RMVSNet		68.81 157	67.42 167	72.97 153	80.11 130	52.53 178	74.26 222	76.29 214	58.48 142	68.38 143	84.20 146	42.59 190	83.83 147	46.53 250	75.91 172	82.56 205

COLMAP_ROB	52.97 17	61.27 265	58.81 267	68.64 234	74.63 249	52.51 179	78.42 142	73.30 257	49.92 269	50.96 335	81.51 206	23.06 350	79.40 234	31.63 344	65.85 289	74.01 316
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
BH-w/o		66.85 201	65.83 203	69.90 216	79.29 145	52.46 180	74.66 217	76.65 212	54.51 223	64.85 217	78.12 265	45.59 160	82.95 164	43.26 280	75.54 176	74.27 313

XVG-ACMP-BASELINE		64.36 234	62.23 243	70.74 200	72.35 281	52.45 181	70.80 276	78.45 180	53.84 228	59.87 269	81.10 212	16.24 362	79.32 236	55.64 183	71.76 223	80.47 241

pmmvs-eth3d		58.81 275	56.31 288	66.30 258	67.61 330	52.42 182	72.30 254	64.76 314	43.55 327	54.94 310	74.19 312	28.95 319	72.60 292	43.31 278	57.21 335	73.88 317

DELS-MVS		74.76 61	74.46 63	75.65 87	77.84 187	52.25 183	75.59 196	84.17 52	63.76 41	73.15 77	82.79 174	59.58 20	86.80 72	67.24 93	86.04 64	87.89 23
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
GeoE		71.01 111	70.15 116	73.60 139	79.57 140	52.17 184	78.93 131	78.12 188	58.02 152	67.76 163	83.87 155	52.36 83	82.72 174	56.90 172	75.79 173	85.92 96

MS-PatchMatch		62.42 251	61.46 251	65.31 276	75.21 241	52.10 185	72.05 257	74.05 249	46.41 302	57.42 292	74.36 310	34.35 273	77.57 265	45.62 260	73.67 190	66.26 353

CR-MVSNet		59.91 270	57.90 277	65.96 266	69.96 312	52.07 186	65.31 312	63.15 324	42.48 335	59.36 274	74.84 306	35.83 260	70.75 301	45.50 262	64.65 298	75.06 301

RPMNet		61.53 261	58.42 271	70.86 197	69.96 312	52.07 186	65.31 312	81.36 116	43.20 330	59.36 274	70.15 336	35.37 262	85.47 114	36.42 323	64.65 298	75.06 301

IterMVS		62.79 248	61.27 253	67.35 248	69.37 319	52.04 188	71.17 269	68.24 293	52.63 239	59.82 270	76.91 282	37.32 245	72.36 293	52.80 206	63.19 309	77.66 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	55.70 15	65.20 225	63.57 227	70.07 211	78.07 180	52.01 189	79.48 127	79.69 150	55.75 195	56.59 296	80.98 215	27.12 332	80.94 209	42.90 285	71.58 226	77.25 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UA-Net		73.13 81	72.93 81	73.76 128	83.58 73	51.66 190	78.75 132	77.66 195	67.75 4	72.61 89	89.42 54	49.82 107	83.29 157	53.61 200	83.14 81	86.32 84

Fast-Effi-MVS+		70.28 126	69.12 133	73.73 131	78.50 166	51.50 191	75.01 208	79.46 157	56.16 185	68.59 138	79.55 248	53.97 61	84.05 140	53.34 202	77.53 155	85.65 111

test_low_dy_conf_001		68.55 167	66.83 183	73.73 131	75.99 228	51.45 192	79.99 113	78.55 174	50.21 265	62.97 236	84.81 134	30.97 304	84.40 135	64.80 115	73.16 204	85.34 124

test_part1		74.74 62	74.42 64	75.70 85	81.69 97	51.26 193	83.98 49	87.05 8	65.31 16	73.10 80	86.20 106	53.94 63	88.06 38	65.32 111	73.17 203	87.77 32

PAPR		71.72 102	70.82 106	74.41 116	81.20 109	51.17 194	79.55 124	83.33 78	55.81 193	66.93 175	84.61 139	50.95 100	86.06 94	55.79 180	79.20 135	86.00 94

iter_conf05		69.40 149	67.62 158	74.73 102	77.84 187	51.13 195	79.28 128	73.71 254	54.62 218	68.17 147	83.59 161	28.68 322	87.16 59	65.74 107	76.95 162	85.91 97

thisisatest0530		67.92 180	65.78 204	74.33 118	76.29 222	51.03 196	76.89 174	74.25 247	53.67 229	65.59 201	81.76 200	35.15 264	85.50 112	55.94 176	72.47 214	86.47 74

v1192		69.97 132	68.68 140	73.85 124	73.19 265	50.94 197	77.68 153	81.36 116	57.51 160	68.95 136	80.85 220	45.28 168	85.33 118	62.97 132	70.37 239	85.27 128

MVS		67.37 188	66.33 194	70.51 205	75.46 237	50.94 197	73.95 228	81.85 104	41.57 340	62.54 244	78.57 263	47.98 128	85.47 114	52.97 205	82.05 97	75.14 300

v10		70.21 127	69.02 134	73.81 125	73.51 263	50.92 199	78.74 133	81.39 115	60.05 111	66.39 186	81.83 199	47.58 135	85.41 117	62.80 133	68.86 268	85.09 133

PMMVS		53.96 304	53.26 310	56.04 323	62.60 355	50.92 199	61.17 331	56.09 351	32.81 358	53.51 326	66.84 349	34.04 275	59.93 344	44.14 272	68.18 273	57.27 361

tttt0517		67.83 182	65.66 206	74.33 118	76.69 215	50.82 201	77.86 149	73.99 250	54.54 222	64.64 220	82.53 182	35.06 265	85.50 112	55.71 181	69.91 249	86.67 70

IterMVS-SCA-FT		62.49 249	61.52 250	65.40 274	71.99 286	50.80 202	71.15 271	69.63 281	45.71 310	60.61 261	77.93 268	37.45 243	65.99 325	55.67 182	63.50 306	79.42 258

JIA-IIPM		51.56 317	47.68 327	63.21 288	64.61 347	50.73 203	47.71 360	58.77 340	42.90 332	48.46 345	51.72 364	24.97 345	70.24 306	36.06 325	53.89 346	68.64 351

v1144		70.42 123	69.31 129	73.76 128	73.22 264	50.64 204	77.83 150	81.43 114	58.58 139	69.40 128	81.16 210	47.53 136	85.29 119	64.01 122	70.64 233	85.34 124

PVSNet_BlendedMVS		68.56 166	67.72 155	71.07 195	77.03 210	50.57 205	74.50 219	81.52 109	53.66 230	64.22 227	79.72 244	49.13 116	82.87 168	55.82 178	73.92 188	79.77 255

PVSNet_Blended		68.59 162	67.72 155	71.19 191	77.03 210	50.57 205	72.51 251	81.52 109	51.91 244	64.22 227	77.77 275	49.13 116	82.87 168	55.82 178	79.58 127	80.14 248

canonicalmvs		74.67 64	74.98 57	73.71 133	78.94 156	50.56 207	80.23 108	83.87 62	60.30 107	77.15 33	86.56 100	59.65 18	82.00 187	66.01 103	82.12 96	88.58 7

alignmvs		73.86 76	73.99 68	73.45 143	78.20 175	50.50 208	78.57 137	82.43 94	59.40 124	76.57 34	86.71 93	56.42 39	81.23 203	65.84 105	81.79 100	88.62 5

casdiffmvs		74.80 60	74.89 59	74.53 113	75.59 235	50.37 209	78.17 144	85.06 37	62.80 61	74.40 58	87.86 76	57.88 28	83.61 152	69.46 74	82.79 91	89.59 2

nrg030		72.96 83	73.01 80	72.84 156	75.41 238	50.24 210	80.02 112	82.89 90	58.36 145	74.44 57	86.73 91	58.90 23	80.83 213	65.84 105	74.46 182	87.44 45

v8		70.33 125	69.28 130	73.49 141	73.15 266	50.22 211	78.62 136	80.78 136	60.79 91	66.45 185	82.11 194	49.35 111	84.98 122	63.58 127	68.71 269	85.28 127

V42		68.65 161	67.35 171	72.56 162	68.93 323	50.18 212	72.90 245	79.47 156	56.92 167	69.45 127	80.26 233	46.29 154	82.99 162	64.07 120	67.82 276	84.53 148

v144192		69.71 137	68.51 142	73.33 148	73.10 267	50.13 213	77.54 157	80.64 137	56.65 170	68.57 140	80.55 225	46.87 150	84.96 124	62.98 131	69.66 256	84.89 139

v1921920		69.47 146	68.17 150	73.36 147	73.06 268	50.10 214	77.39 160	80.56 138	56.58 177	68.59 138	80.37 227	44.72 174	84.98 122	62.47 137	69.82 251	85.00 135

v2v482		70.50 121	69.45 128	73.66 135	72.62 276	50.03 215	77.58 154	80.51 140	59.90 113	69.52 124	82.14 193	47.53 136	84.88 127	65.07 114	70.17 243	86.09 92

baseline		74.61 65	74.70 61	74.34 117	75.70 231	49.99 216	77.54 157	84.63 45	62.73 62	73.98 62	87.79 78	57.67 31	83.82 148	69.49 72	82.74 92	89.20 4

v1240		69.24 152	67.91 153	73.25 151	73.02 270	49.82 217	77.21 166	80.54 139	56.43 179	68.34 144	80.51 226	43.33 186	84.99 120	62.03 141	69.77 254	84.95 138

CHOSEN 280x420		47.83 324	46.36 328	52.24 341	67.37 332	49.78 218	38.91 368	43.11 372	35.00 356	43.27 359	63.30 357	28.95 319	49.19 367	36.53 321	60.80 325	57.76 360

MVSTER		67.16 195	65.58 208	71.88 172	70.37 305	49.70 219	70.25 283	78.45 180	51.52 249	69.16 134	80.37 227	38.45 233	82.50 179	60.19 155	71.46 227	83.44 187

EPP-MVSNet		72.16 96	71.31 99	74.71 103	78.68 163	49.70 219	82.10 84	81.65 107	60.40 98	65.94 193	85.84 119	51.74 91	86.37 89	55.93 177	79.55 129	88.07 21

VDD-MVS		72.50 88	72.09 87	73.75 130	81.58 98	49.69 221	77.76 152	77.63 196	63.21 50	73.21 76	89.02 61	42.14 195	83.32 156	61.72 143	82.50 94	88.25 12

MG-MVS		73.96 74	73.89 70	74.16 120	85.65 47	49.69 221	81.59 94	81.29 122	61.45 83	71.05 103	88.11 70	51.77 90	87.73 47	61.05 149	83.09 82	85.05 134

TR-MVS		66.59 209	65.07 214	71.17 192	79.18 150	49.63 223	73.48 238	75.20 231	52.95 234	67.90 153	80.33 230	39.81 220	83.68 150	43.20 281	73.56 194	80.20 246

thisisatest0515		65.83 215	63.50 228	72.82 158	73.75 261	49.50 224	71.32 266	73.12 260	49.39 271	63.82 229	76.50 291	34.95 267	84.84 128	53.20 204	75.49 177	84.13 161

IterMVS-LS		69.22 153	68.48 143	71.43 184	74.44 254	49.40 225	76.23 185	77.55 197	59.60 119	65.85 197	81.59 205	51.28 95	81.58 195	59.87 159	69.90 250	83.30 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.27 151	68.44 146	71.73 175	74.47 252	49.39 226	75.20 203	78.45 180	59.60 119	69.16 134	76.51 289	51.29 94	82.50 179	59.86 160	71.45 228	83.30 189

AllTest		57.08 288	54.65 297	64.39 281	71.44 292	49.03 227	69.92 286	67.30 295	45.97 307	47.16 348	79.77 242	17.47 359	67.56 316	33.65 333	59.16 330	76.57 288

TestCases				64.39 281	71.44 292	49.03 227		67.30 295	45.97 307	47.16 348	79.77 242	17.47 359	67.56 316	33.65 333	59.16 330	76.57 288

PAPM		67.92 180	66.69 184	71.63 179	78.09 179	49.02 229	77.09 168	81.24 125	51.04 258	60.91 260	83.98 153	47.71 132	84.99 120	40.81 297	79.32 133	80.90 236

ppachtmachnet_test		58.06 282	55.38 293	66.10 264	69.51 316	48.99 230	68.01 296	66.13 305	44.50 318	54.05 320	70.74 330	32.09 301	72.34 294	36.68 319	56.71 338	76.99 286

diffmvs		70.69 116	70.43 111	71.46 181	69.45 318	48.95 231	72.93 244	78.46 179	57.27 162	71.69 98	83.97 154	51.48 93	77.92 259	70.70 66	77.95 151	87.53 41

Patchmatch-RL test		58.16 280	55.49 292	66.15 262	67.92 329	48.89 232	60.66 333	51.07 361	47.86 288	59.36 274	62.71 358	34.02 276	72.27 295	56.41 174	59.40 329	77.30 278

TAPA-MVS	59.36 10	66.60 207	65.20 213	70.81 198	76.63 217	48.75 233	76.52 180	80.04 146	50.64 262	65.24 210	84.93 132	39.15 227	78.54 251	36.77 316	76.88 164	85.14 130
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EGC-MVSNET		42.47 330	38.48 336	54.46 331	74.33 256	48.73 234	70.33 282	51.10 360	0.03 379	0.18 380	67.78 346	13.28 365	66.49 322	18.91 367	50.36 354	48.15 364

MDA-MVSNet-bldmvs		53.87 306	50.81 317	63.05 290	66.25 339	48.58 235	56.93 344	63.82 319	48.09 285	41.22 361	70.48 334	30.34 309	68.00 315	34.24 331	45.92 360	72.57 326

MVS_Test		72.45 90	72.46 85	72.42 168	74.88 243	48.50 236	76.28 184	83.14 85	59.40 124	72.46 91	84.68 136	55.66 46	81.12 204	65.98 104	79.66 126	87.63 37

D2MVS		62.30 253	60.29 262	68.34 239	66.46 338	48.42 237	65.70 306	73.42 256	47.71 289	58.16 287	75.02 305	30.51 307	77.71 263	53.96 196	71.68 225	78.90 264

eth_miper_zixun_eth		67.63 184	66.28 197	71.67 177	71.60 290	48.33 238	73.68 237	77.88 190	55.80 194	65.91 194	78.62 262	47.35 142	82.88 167	59.45 162	66.25 287	83.81 171

K. test v3		60.47 268	57.11 280	70.56 203	73.74 262	48.22 239	75.10 207	62.55 327	58.27 147	53.62 324	76.31 292	27.81 327	81.59 194	47.42 242	39.18 366	81.88 219

GA-MVS		65.53 219	63.70 225	71.02 196	70.87 298	48.10 240	70.48 279	74.40 242	56.69 169	64.70 219	76.77 284	33.66 280	81.10 205	55.42 185	70.32 241	83.87 169

SCA		60.49 267	58.38 272	66.80 251	74.14 260	48.06 241	63.35 320	63.23 323	49.13 274	59.33 277	72.10 322	37.45 243	74.27 287	44.17 270	62.57 313	78.05 270

OurMVSNet-221017-0		61.37 264	58.63 270	69.61 220	72.05 285	48.06 241	73.93 231	72.51 263	47.23 297	54.74 312	80.92 217	21.49 357	81.24 202	48.57 238	56.22 339	79.53 257

lessismore_v0				69.91 215	71.42 294	47.80 243		50.90 362		50.39 341	75.56 300	27.43 331	81.33 199	45.91 256	34.10 368	80.59 240

LTVRE_ROB	55.42 16	63.15 246	61.23 255	68.92 231	76.57 219	47.80 243	59.92 334	76.39 213	54.35 225	58.67 282	82.46 184	29.44 316	81.49 196	42.12 290	71.14 229	77.46 276
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
v148		68.24 174	67.19 178	71.40 185	70.43 303	47.77 245	75.76 195	77.03 206	58.91 131	67.36 166	80.10 236	48.60 124	81.89 188	60.01 157	66.52 286	84.53 148

Anonymous20240529		69.91 133	69.02 134	72.56 162	80.19 127	47.65 246	77.56 156	80.99 131	55.45 202	69.88 120	86.76 89	39.24 226	82.18 185	54.04 194	77.10 161	87.85 27

baseline2		63.42 240	61.26 254	69.89 217	72.55 278	47.62 247	71.54 263	68.38 292	50.11 266	54.82 311	75.55 301	43.06 188	80.96 208	48.13 240	67.16 281	81.11 232

VDDNet		71.81 99	71.33 98	73.26 150	82.80 85	47.60 248	78.74 133	75.27 227	59.59 122	72.94 83	89.40 55	41.51 208	83.91 146	58.75 165	82.99 84	88.26 11

1314		64.61 232	63.21 232	68.80 232	71.87 288	47.46 249	73.95 228	78.39 185	42.88 333	59.97 267	76.60 288	38.11 238	79.39 235	54.84 188	72.32 218	79.55 256

CMPMVS	42.80 21	57.81 284	55.97 289	63.32 286	60.98 362	47.38 250	64.66 316	69.50 283	32.06 359	46.83 350	77.80 273	29.50 315	71.36 298	48.68 236	73.75 189	71.21 339
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SixPastTwentyTwo		61.65 260	58.80 268	70.20 209	75.80 230	47.22 251	75.59 196	69.68 280	54.61 219	54.11 319	79.26 254	27.07 333	82.96 163	43.27 279	49.79 355	80.41 243

Anonymous20231211		69.28 150	68.47 144	71.73 175	80.28 122	47.18 252	79.98 114	82.37 95	54.61 219	67.24 168	84.01 152	39.43 223	82.41 182	55.45 184	72.83 208	85.62 113

tpm cat1		59.25 274	56.95 283	66.15 262	72.19 283	46.96 253	68.09 295	65.76 306	40.03 348	57.81 289	70.56 331	38.32 235	74.51 285	38.26 309	61.50 321	77.00 284

TDRefinement		53.44 310	50.72 318	61.60 299	64.31 349	46.96 253	70.89 275	65.27 312	41.78 336	44.61 356	77.98 266	11.52 369	66.36 323	28.57 357	51.59 350	71.49 337

PatchmatchNet		59.84 271	58.24 273	64.65 280	73.05 269	46.70 255	69.42 289	62.18 329	47.55 291	58.88 280	71.96 324	34.49 271	69.16 309	42.99 283	63.60 305	78.07 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cl22		67.47 187	66.45 187	70.54 204	69.85 314	46.49 256	73.85 234	77.35 202	55.07 211	65.51 202	77.92 269	47.64 134	81.10 205	61.58 146	69.32 259	84.01 164

LFMVS		71.78 100	71.59 91	72.32 169	83.40 76	46.38 257	79.75 120	71.08 271	64.18 35	72.80 85	88.64 68	42.58 191	83.72 149	57.41 170	84.49 73	86.86 64

miper_lstm_enhance		62.03 256	60.88 259	65.49 273	66.71 336	46.25 258	56.29 346	75.70 221	50.68 260	61.27 258	75.48 302	40.21 217	68.03 314	56.31 175	65.25 294	82.18 212

CANet_DTU		68.18 175	67.71 157	69.59 221	74.83 244	46.24 259	78.66 135	76.85 208	59.60 119	63.45 231	82.09 195	35.25 263	77.41 266	59.88 158	78.76 143	85.14 130

miper_ehance_all_eth		68.03 177	67.24 177	70.40 206	70.54 301	46.21 260	73.98 226	78.68 170	55.07 211	66.05 191	77.80 273	52.16 86	81.31 200	61.53 148	69.32 259	83.67 180

c3_l		68.33 171	67.56 159	70.62 202	70.87 298	46.21 260	74.47 220	78.80 166	56.22 184	66.19 189	78.53 264	51.88 88	81.40 197	62.08 138	69.04 265	84.25 156

miper_enhance_ethall		67.11 196	66.09 200	70.17 210	69.21 320	45.98 262	72.85 246	78.41 183	51.38 252	65.65 199	75.98 297	51.17 97	81.25 201	60.82 150	69.32 259	83.29 191

CostFormer		64.04 235	62.51 239	68.61 235	71.88 287	45.77 263	71.30 267	70.60 276	47.55 291	64.31 224	76.61 287	41.63 203	79.62 232	49.74 228	69.00 266	80.42 242

cl____		67.18 193	66.26 198	69.94 213	70.20 306	45.74 264	73.30 239	76.83 209	55.10 206	65.27 206	79.57 247	47.39 140	80.53 219	59.41 164	69.22 263	83.53 186

DIV-MVS_self_test		67.18 193	66.26 198	69.94 213	70.20 306	45.74 264	73.29 240	76.83 209	55.10 206	65.27 206	79.58 246	47.38 141	80.53 219	59.43 163	69.22 263	83.54 185

test_yl		69.69 138	69.13 131	71.36 186	78.37 170	45.74 264	74.71 215	80.20 144	57.91 155	70.01 117	83.83 156	42.44 192	82.87 168	54.97 186	79.72 124	85.48 117

DCV-MVSNet		69.69 138	69.13 131	71.36 186	78.37 170	45.74 264	74.71 215	80.20 144	57.91 155	70.01 117	83.83 156	42.44 192	82.87 168	54.97 186	79.72 124	85.48 117

IS-MVSNet		71.57 103	71.00 104	73.27 149	78.86 157	45.63 268	80.22 109	78.69 169	64.14 38	66.46 184	87.36 81	49.30 112	85.60 106	50.26 225	83.71 80	88.59 6

our_test_3		56.49 289	54.42 299	62.68 293	69.51 316	45.48 269	66.08 304	61.49 333	44.11 324	50.73 339	69.60 340	33.05 286	68.15 313	38.38 308	56.86 336	74.40 311

UniMVSNet (Re)		70.63 118	70.20 114	71.89 171	78.55 165	45.29 270	75.94 193	82.92 87	63.68 43	68.16 148	83.59 161	53.89 65	83.49 155	53.97 195	71.12 230	86.89 63

PM-MVS		52.33 314	50.19 319	58.75 312	62.10 356	45.14 271	65.75 305	40.38 374	43.60 326	53.52 325	72.65 319	9.16 374	65.87 326	50.41 223	54.18 345	65.24 355

OpenMVS_ROB	52.78 18	60.03 269	58.14 275	65.69 271	70.47 302	44.82 272	75.33 199	70.86 274	45.04 312	56.06 298	76.00 294	26.89 335	79.65 230	35.36 328	67.29 279	72.60 325

test-LLR		58.15 281	58.13 276	58.22 314	68.57 324	44.80 273	65.46 309	57.92 343	50.08 267	55.44 303	69.82 338	32.62 295	57.44 351	49.66 230	73.62 191	72.41 330

test-mter		56.42 291	55.82 290	58.22 314	68.57 324	44.80 273	65.46 309	57.92 343	39.94 349	55.44 303	69.82 338	21.92 354	57.44 351	49.66 230	73.62 191	72.41 330

PVSNet_0	43.31 20	47.46 326	45.64 329	52.92 337	67.60 331	44.65 275	54.06 351	54.64 353	41.59 339	46.15 352	58.75 360	30.99 303	58.66 347	32.18 337	24.81 369	55.46 362

ADS-MVSNet2		51.33 318	48.76 323	59.07 310	66.02 342	44.60 276	50.90 355	59.76 337	36.90 352	50.74 337	66.18 351	26.38 336	63.11 333	27.17 358	54.76 343	69.50 347

mvs_anonymous		68.03 177	67.51 163	69.59 221	72.08 284	44.57 277	71.99 258	75.23 229	51.67 245	67.06 171	82.57 180	54.68 55	77.94 258	56.56 173	75.71 175	86.26 88

ITE_SJBPF				62.09 296	66.16 340	44.55 278		64.32 317	47.36 294	55.31 305	80.34 229	19.27 358	62.68 335	36.29 324	62.39 315	79.04 261

UniMVSNet_NR-MVSNet		71.11 109	71.00 104	71.44 182	79.20 149	44.13 279	76.02 192	82.60 93	66.48 13	68.20 145	84.60 140	56.82 35	82.82 172	54.62 190	70.43 237	87.36 51

DU-MVS		70.01 130	69.53 125	71.44 182	78.05 181	44.13 279	75.01 208	81.51 111	64.37 31	68.20 145	84.52 141	49.12 118	82.82 172	54.62 190	70.43 237	87.37 49

PVSNet	50.76 19	58.40 278	57.39 278	61.42 300	75.53 236	44.04 281	61.43 327	63.45 321	47.04 299	56.91 293	73.61 316	27.00 334	64.76 328	39.12 305	72.40 215	75.47 298

tpm2		62.07 255	60.10 263	67.99 241	72.79 273	43.86 282	71.05 274	66.85 301	43.14 331	62.77 237	75.39 303	38.32 235	80.80 214	41.69 293	68.88 267	79.32 259

NR-MVSNet		69.54 144	68.85 137	71.59 180	78.05 181	43.81 283	74.20 223	80.86 135	65.18 17	62.76 238	84.52 141	52.35 84	83.59 153	50.96 221	70.78 232	87.37 49

TESTMET0.1,1		55.28 299	54.90 296	56.42 322	66.56 337	43.67 284	65.46 309	56.27 350	39.18 351	53.83 321	67.44 347	24.21 348	55.46 361	48.04 241	73.11 205	70.13 345

pmmvs3		44.92 328	41.95 332	53.86 332	52.58 371	43.55 285	62.11 326	46.90 370	26.05 366	40.63 362	60.19 359	11.08 371	57.91 350	31.83 343	46.15 359	60.11 357

GBi-Net		67.21 190	66.55 185	69.19 226	77.63 193	43.33 286	77.31 161	77.83 192	56.62 173	65.04 213	82.70 175	41.85 200	80.33 224	47.18 245	72.76 210	83.92 166

test1		67.21 190	66.55 185	69.19 226	77.63 193	43.33 286	77.31 161	77.83 192	56.62 173	65.04 213	82.70 175	41.85 200	80.33 224	47.18 245	72.76 210	83.92 166

FMVSNet1		66.70 205	65.87 202	69.19 226	77.49 201	43.33 286	77.31 161	77.83 192	56.45 178	64.60 221	82.70 175	38.08 239	80.33 224	46.08 254	72.31 219	83.92 166

FMVSNet2		66.93 200	66.31 196	68.79 233	77.63 193	42.98 289	76.11 187	77.47 198	56.62 173	65.22 212	82.17 191	41.85 200	80.18 227	47.05 248	72.72 213	83.20 193

TranMVSNet+NR-MVSNet		70.36 124	70.10 118	71.17 192	78.64 164	42.97 290	76.53 179	81.16 127	66.95 6	68.53 141	85.42 128	51.61 92	83.07 161	52.32 208	69.70 255	87.46 44

RPSCF		55.80 296	54.22 304	60.53 304	65.13 345	42.91 291	64.30 317	57.62 345	36.84 354	58.05 288	82.28 188	28.01 325	56.24 358	37.14 314	58.61 332	82.44 210

1112_ss		64.00 236	63.36 230	65.93 267	79.28 146	42.58 292	71.35 265	72.36 265	46.41 302	60.55 262	77.89 271	46.27 155	73.28 290	46.18 253	69.97 247	81.92 218

FMVSNet3		66.32 212	65.61 207	68.46 236	76.48 221	42.34 293	74.98 210	77.15 205	55.83 192	65.04 213	81.16 210	39.91 218	80.14 228	47.18 245	72.76 210	82.90 202

UniMVSNet_ETH3D		67.60 185	67.07 181	69.18 229	77.39 203	42.29 294	74.18 224	75.59 223	60.37 101	66.77 177	86.06 111	37.64 241	78.93 250	52.16 210	73.49 195	86.32 84

Anonymous202405211		66.84 202	65.99 201	69.40 225	80.19 127	42.21 295	71.11 272	71.31 270	58.80 134	67.90 153	86.39 103	29.83 313	79.65 230	49.60 232	78.78 142	86.33 82

TinyColmap		54.14 303	51.72 313	61.40 301	66.84 335	41.97 296	66.52 301	68.51 291	44.81 313	42.69 360	75.77 298	11.66 368	72.94 291	31.96 338	56.77 337	69.27 349

MDA-MVSNet_test_wron		50.71 320	48.95 321	56.00 325	61.17 360	41.84 297	51.90 354	56.45 347	40.96 343	44.79 355	67.84 344	30.04 312	55.07 363	36.71 318	50.69 353	71.11 341

MVS_0304		58.51 276	57.36 279	61.96 297	70.04 310	41.83 298	69.40 290	65.46 309	50.73 259	53.30 328	74.06 313	22.65 351	70.18 307	42.16 289	68.44 271	73.86 318

YYNet1		50.73 319	48.96 320	56.03 324	61.10 361	41.78 299	51.94 353	56.44 348	40.94 344	44.84 354	67.80 345	30.08 311	55.08 362	36.77 316	50.71 352	71.22 338

Anonymous20240521		55.30 298	54.41 300	57.96 317	60.92 364	41.73 300	71.09 273	71.06 273	41.18 341	48.65 344	73.31 317	16.93 361	59.25 346	42.54 286	64.01 301	72.90 322

ab-mvs		66.65 206	66.42 190	67.37 247	76.17 224	41.73 300	70.41 281	76.14 216	53.99 226	65.98 192	83.51 165	49.48 110	76.24 279	48.60 237	73.46 196	84.14 160

gm-plane-assit					71.40 295	41.72 302			48.85 277		73.31 317		82.48 181	48.90 235

VNet		69.68 140	70.19 115	68.16 240	79.73 137	41.63 303	70.53 278	77.38 201	60.37 101	70.69 105	86.63 96	51.08 98	77.09 270	53.61 200	81.69 106	85.75 107

tpmvs		58.47 277	56.95 283	63.03 291	70.20 306	41.21 304	67.90 297	67.23 298	49.62 270	54.73 313	70.84 329	34.14 274	76.24 279	36.64 320	61.29 322	71.64 336

HY-MVS	56.14 13	64.55 233	63.89 221	66.55 255	74.73 248	41.02 305	69.96 285	74.43 241	49.29 272	61.66 255	80.92 217	47.43 139	76.68 274	44.91 268	71.69 224	81.94 217

FPMVS		42.18 331	41.11 333	45.39 347	58.03 368	41.01 306	49.50 357	53.81 358	30.07 361	33.71 366	64.03 354	11.69 367	52.08 366	14.01 371	55.11 341	43.09 367

VPA-MVSNet		69.02 154	69.47 127	67.69 243	77.42 202	41.00 307	74.04 225	79.68 151	60.06 110	69.26 132	84.81 134	51.06 99	77.58 264	54.44 193	74.43 183	84.48 150

USDC		56.35 292	54.24 303	62.69 292	64.74 346	40.31 308	65.05 314	73.83 252	43.93 325	47.58 346	77.71 276	15.36 364	75.05 283	38.19 310	61.81 319	72.70 324

thres200		62.20 254	61.16 256	65.34 275	75.38 239	39.99 309	69.60 287	69.29 286	55.64 199	61.87 253	76.99 280	37.07 252	78.96 249	31.28 348	73.28 199	77.06 282

WR-MVS		68.47 169	68.47 144	68.44 237	80.20 126	39.84 310	73.75 236	76.07 217	64.68 25	68.11 150	83.63 160	50.39 105	79.14 243	49.78 226	69.66 256	86.34 80

EPNet_dtu		61.90 257	61.97 246	61.68 298	72.89 272	39.78 311	75.85 194	65.62 308	55.09 208	54.56 315	79.36 252	37.59 242	67.02 319	39.80 303	76.95 162	78.25 267
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		63.18 245	62.18 244	66.21 260	76.85 213	39.62 312	71.96 260	69.44 284	56.63 171	62.61 242	79.83 240	37.18 246	79.17 239	31.84 340	73.25 200	79.83 253

thres400		63.31 241	62.18 244	66.72 252	76.85 213	39.62 312	71.96 260	69.44 284	56.63 171	62.61 242	79.83 240	37.18 246	79.17 239	31.84 340	73.25 200	81.36 225

Test_1112_low_res		62.32 252	61.77 247	64.00 283	79.08 154	39.53 314	68.17 294	70.17 277	43.25 329	59.03 279	79.90 238	44.08 179	71.24 299	43.79 276	68.42 272	81.25 228

pm-mvs1		65.24 224	64.97 215	66.04 265	72.38 280	39.40 315	72.62 249	75.63 222	55.53 200	62.35 250	83.18 170	47.45 138	76.47 276	49.06 234	66.54 285	82.24 211

pmmvs6		63.69 238	62.82 237	66.27 259	70.63 300	39.27 316	73.13 242	75.47 225	52.69 238	59.75 272	82.30 187	39.71 221	77.03 271	47.40 243	64.35 300	82.53 206

tfpnnormal		62.47 250	61.63 249	64.99 278	74.81 245	39.01 317	71.22 268	73.72 253	55.22 205	60.21 263	80.09 237	41.26 213	76.98 272	30.02 352	68.09 274	78.97 263

thres600view7		63.30 242	62.27 242	66.41 256	77.18 207	38.87 318	72.35 253	69.11 288	56.98 166	62.37 249	80.96 216	37.01 253	79.00 248	31.43 347	73.05 206	81.36 225

CVMVSNet		59.63 273	59.14 266	61.08 303	74.47 252	38.84 319	75.20 203	68.74 290	31.15 360	58.24 286	76.51 289	32.39 299	68.58 312	49.77 227	65.84 290	75.81 294

thres100view900		63.28 243	62.41 241	65.89 268	77.31 205	38.66 320	72.65 247	69.11 288	57.07 164	62.45 247	81.03 214	37.01 253	79.17 239	31.84 340	73.25 200	79.83 253

TransMVSNet (Re)		64.72 229	64.33 218	65.87 269	75.22 240	38.56 321	74.66 217	75.08 237	58.90 132	61.79 254	82.63 178	51.18 96	78.07 257	43.63 277	55.87 340	80.99 235

XXY-MVS		60.68 266	61.67 248	57.70 320	70.43 303	38.45 322	64.19 318	66.47 302	48.05 286	63.22 232	80.86 219	49.28 113	60.47 341	45.25 267	67.28 280	74.19 314

MDTV_nov1_ep13			57.00 282		72.73 274	38.26 323	65.02 315	64.73 315	44.74 314	55.46 302	72.48 320	32.61 297	70.47 302	37.47 312	67.75 277

FIs		70.82 114	71.43 94	68.98 230	78.33 172	38.14 324	76.96 171	83.59 69	61.02 88	67.33 167	86.73 91	55.07 49	81.64 192	54.61 192	79.22 134	87.14 56

Gipuma		34.77 337	31.91 341	43.33 350	62.05 357	37.87 325	20.39 371	67.03 299	23.23 368	18.41 372	25.84 372	4.24 378	62.73 334	14.71 370	51.32 351	29.38 371
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
WTY-MVS		59.75 272	60.39 261	57.85 318	72.32 282	37.83 326	61.05 332	64.18 318	45.95 309	61.91 252	79.11 256	47.01 148	60.88 340	42.50 287	69.49 258	74.83 306

WR-MVS_H		67.02 198	66.92 182	67.33 249	77.95 185	37.75 327	77.57 155	82.11 100	62.03 77	62.65 241	82.48 183	50.57 103	79.46 233	42.91 284	64.01 301	84.79 143

Baseline_NR-MVSNet		67.05 197	67.56 159	65.50 272	75.65 232	37.70 328	75.42 198	74.65 240	59.90 113	68.14 149	83.15 171	49.12 118	77.20 268	52.23 209	69.78 252	81.60 221

MIMVSNet		57.35 285	57.07 281	58.22 314	74.21 259	37.18 329	62.46 323	60.88 335	48.88 276	55.29 306	75.99 296	31.68 302	62.04 337	31.87 339	72.35 216	75.43 299

KD-MVS_2432*1600		53.45 308	51.50 315	59.30 305	62.82 352	37.14 330	55.33 347	71.79 268	47.34 295	55.09 308	70.52 332	21.91 355	70.45 303	35.72 326	42.97 363	70.31 343

miper_refine_blended		53.45 308	51.50 315	59.30 305	62.82 352	37.14 330	55.33 347	71.79 268	47.34 295	55.09 308	70.52 332	21.91 355	70.45 303	35.72 326	42.97 363	70.31 343

ambc				65.13 277	63.72 351	37.07 332	47.66 361	78.78 167		54.37 318	71.42 326	11.24 370	80.94 209	45.64 259	53.85 347	77.38 277

GG-mvs-BLEND				62.34 294	71.36 296	37.04 333	69.20 291	57.33 346		54.73 313	65.48 353	30.37 308	77.82 260	34.82 329	74.93 181	72.17 334

CL-MVSNet_self_test		61.53 261	60.94 258	63.30 287	68.95 322	36.93 334	67.60 298	72.80 262	55.67 197	59.95 268	76.63 285	45.01 171	72.22 296	39.74 304	62.09 317	80.74 239

VPNet		67.52 186	68.11 151	65.74 270	79.18 150	36.80 335	72.17 256	72.83 261	62.04 76	67.79 161	85.83 120	48.88 120	76.60 275	51.30 219	72.97 207	83.81 171

pmmvs5		56.47 290	55.68 291	58.86 311	61.41 359	36.71 336	66.37 302	62.75 326	40.38 346	53.70 322	76.62 286	34.56 269	67.05 318	40.02 302	65.27 293	72.83 323

PEN-MVS		66.60 207	66.45 187	67.04 250	77.11 208	36.56 337	77.03 170	80.42 141	62.95 53	62.51 246	84.03 151	46.69 151	79.07 244	44.22 269	63.08 310	85.51 116

baseline1		63.81 237	63.87 223	63.62 284	76.29 222	36.36 338	71.78 262	67.29 297	56.05 188	64.23 226	82.95 173	47.11 144	74.41 286	47.30 244	61.85 318	80.10 249

FMVSNet5		55.86 295	54.93 295	58.66 313	71.05 297	36.35 339	64.18 319	62.48 328	46.76 300	50.66 340	74.73 308	25.80 341	64.04 330	33.11 335	65.57 292	75.59 297

CP-MVSNet		66.49 210	66.41 191	66.72 252	77.67 192	36.33 340	76.83 176	79.52 155	62.45 66	62.54 244	83.47 167	46.32 153	78.37 252	45.47 264	63.43 307	85.45 119

sss		56.17 294	56.57 285	54.96 327	66.93 334	36.32 341	57.94 340	61.69 332	41.67 338	58.64 283	75.32 304	38.72 231	56.25 357	42.04 291	66.19 288	72.31 333

PS-CasMVS		66.42 211	66.32 195	66.70 254	77.60 200	36.30 342	76.94 172	79.61 153	62.36 68	62.43 248	83.66 159	45.69 157	78.37 252	45.35 266	63.26 308	85.42 122

ECVR-MVS		67.72 183	67.51 163	68.35 238	79.46 142	36.29 343	74.79 214	66.93 300	58.72 135	67.19 169	88.05 73	36.10 257	81.38 198	52.07 211	84.25 75	87.39 47

PMVS	28.69 22	36.22 336	33.29 340	45.02 349	36.82 380	35.98 344	54.68 350	48.74 365	26.31 365	21.02 370	51.61 365	2.88 382	60.10 343	9.99 375	47.58 358	38.99 370
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
gg-mvs-nofinetune		57.86 283	56.43 287	62.18 295	72.62 276	35.35 345	66.57 300	56.33 349	50.65 261	57.64 290	57.10 361	30.65 306	76.36 277	37.38 313	78.88 139	74.82 307

DTE-MVSNet		65.58 218	65.34 210	66.31 257	76.06 227	34.79 346	76.43 181	79.38 158	62.55 64	61.66 255	83.83 156	45.60 159	79.15 242	41.64 296	60.88 324	85.00 135

tpm		57.34 286	58.16 274	54.86 328	71.80 289	34.77 347	67.47 299	56.04 352	48.20 284	60.10 265	76.92 281	37.17 248	53.41 364	40.76 298	65.01 295	76.40 290

test1111		67.21 190	67.14 180	67.42 246	79.24 148	34.76 348	73.89 233	65.65 307	58.71 137	66.96 173	87.95 75	36.09 258	80.53 219	52.03 212	83.79 79	86.97 59

FC-MVSNet-test		69.80 136	70.58 110	67.46 245	77.61 198	34.73 349	76.05 190	83.19 83	60.84 90	65.88 196	86.46 101	54.52 57	80.76 217	52.52 207	78.12 149	86.91 62

Patchmtry		57.16 287	56.47 286	59.23 307	69.17 321	34.58 350	62.98 321	63.15 324	44.53 317	56.83 294	74.84 306	35.83 260	68.71 311	40.03 301	60.91 323	74.39 312

tpmrst		58.24 279	58.70 269	56.84 321	66.97 333	34.32 351	69.57 288	61.14 334	47.17 298	58.58 284	71.60 325	41.28 212	60.41 342	49.20 233	62.84 311	75.78 295

test2506		65.33 223	64.61 217	67.50 244	79.46 142	34.19 352	74.43 221	51.92 359	58.72 135	66.75 178	88.05 73	25.99 340	80.92 211	51.94 213	84.25 75	87.39 47

MVS-HIRNet		45.52 327	44.48 330	48.65 345	68.49 326	34.05 353	59.41 337	44.50 371	27.03 364	37.96 365	50.47 367	26.16 339	64.10 329	26.74 361	59.52 328	47.82 365

Anonymous20231206		55.10 302	55.30 294	54.48 330	69.81 315	33.94 354	62.91 322	62.13 330	41.08 342	55.18 307	75.65 299	32.75 293	56.59 356	30.32 351	67.86 275	72.91 321

UnsupCasMVSNet_bld		50.07 321	48.87 322	53.66 333	60.97 363	33.67 355	57.62 342	64.56 316	39.47 350	47.38 347	64.02 356	27.47 329	59.32 345	34.69 330	43.68 362	67.98 352

EU-MVSNet		55.61 297	54.41 300	59.19 309	65.41 344	33.42 356	72.44 252	71.91 267	28.81 362	51.27 333	73.87 314	24.76 346	69.08 310	43.04 282	58.20 333	75.06 301

UnsupCasMVSNet_eth		53.16 313	52.47 311	55.23 326	59.45 366	33.39 357	59.43 336	69.13 287	45.98 306	50.35 342	72.32 321	29.30 317	58.26 349	42.02 292	44.30 361	74.05 315

LF4IMVS		42.95 329	42.26 331	45.04 348	48.30 373	32.50 358	54.80 349	48.49 366	28.03 363	40.51 363	70.16 335	9.24 373	43.89 370	31.63 344	49.18 357	58.72 358

dp		51.89 316	51.60 314	52.77 338	68.44 327	32.45 359	62.36 324	54.57 354	44.16 322	49.31 343	67.91 343	28.87 321	56.61 355	33.89 332	54.89 342	69.24 350

MIMVSNet1		55.17 301	54.31 302	57.77 319	70.03 311	32.01 360	65.68 307	64.81 313	49.19 273	46.75 351	76.00 294	25.53 343	64.04 330	28.65 356	62.13 316	77.26 280

EPMVS		53.96 304	53.69 307	54.79 329	66.12 341	31.96 361	62.34 325	49.05 364	44.42 320	55.54 301	71.33 327	30.22 310	56.70 354	41.65 295	62.54 314	75.71 296

LCM-MVSNet-Re		61.88 258	61.35 252	63.46 285	74.58 250	31.48 362	61.42 328	58.14 342	58.71 137	53.02 329	79.55 248	43.07 187	76.80 273	45.69 258	77.96 150	82.11 215

Vis-MVSNet (Re-imp)		63.69 238	63.88 222	63.14 289	74.75 247	31.04 363	71.16 270	63.64 320	56.32 180	59.80 271	84.99 131	44.51 175	75.46 281	39.12 305	80.62 110	82.92 200

Patchmatch-test		49.08 322	48.28 324	51.50 342	64.40 348	30.85 364	45.68 362	48.46 367	35.60 355	46.10 353	72.10 322	34.47 272	46.37 368	27.08 360	60.65 326	77.27 279

ADS-MVSNet		48.48 323	47.77 325	50.63 343	66.02 342	29.92 365	50.90 355	50.87 363	36.90 352	50.74 337	66.18 351	26.38 336	52.47 365	27.17 358	54.76 343	69.50 347

test0.0.03 1		53.32 311	53.59 308	52.50 339	62.81 354	29.45 366	59.51 335	54.11 356	50.08 267	54.40 317	74.31 311	32.62 295	55.92 359	30.50 350	63.95 303	72.15 335

LCM-MVSNet		40.30 333	35.88 338	53.57 334	42.24 375	29.15 367	45.21 364	60.53 336	22.23 370	28.02 368	50.98 366	3.72 380	61.78 338	31.22 349	38.76 367	69.78 346

test20.03		53.87 306	54.02 305	53.41 335	61.47 358	28.11 368	61.30 329	59.21 338	51.34 254	52.09 331	77.43 277	33.29 284	58.55 348	29.76 353	60.27 327	73.58 319

KD-MVS_self_test		55.22 300	53.89 306	59.21 308	57.80 369	27.47 369	57.75 341	74.32 244	47.38 293	50.90 336	70.00 337	28.45 323	70.30 305	40.44 299	57.92 334	79.87 252

wuyk23d		13.32 345	12.52 348	15.71 359	47.54 374	26.27 370	31.06 370	1.98 384	4.93 376	5.18 379	1.94 379	0.45 384	18.54 378	6.81 378	12.83 375	2.33 376

MDTV_nov1_ep13_2view						25.89 371	61.22 330		40.10 347	51.10 334		32.97 288		38.49 307		78.61 265

MVE	17.77 23	21.41 342	17.77 347	32.34 354	34.34 381	25.44 372	16.11 372	24.11 380	11.19 374	13.22 374	31.92 370	1.58 383	30.95 376	10.47 373	17.03 372	40.62 369
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PatchT		53.17 312	53.44 309	52.33 340	68.29 328	25.34 373	58.21 339	54.41 355	44.46 319	54.56 315	69.05 341	33.32 283	60.94 339	36.93 315	61.76 320	70.73 342

ANet_high		41.38 332	37.47 337	53.11 336	39.73 378	24.45 374	56.94 343	69.69 279	47.65 290	26.04 369	52.32 363	12.44 366	62.38 336	21.80 365	10.61 376	72.49 327

testgi		51.90 315	52.37 312	50.51 344	60.39 365	23.55 375	58.42 338	58.15 341	49.03 275	51.83 332	79.21 255	22.39 352	55.59 360	29.24 355	62.64 312	72.40 332

E-PMN		23.77 340	22.73 344	26.90 356	42.02 376	20.67 376	42.66 366	35.70 375	17.43 371	10.28 377	25.05 373	6.42 376	42.39 372	10.28 374	14.71 373	17.63 372

DSMNet-mixed		39.30 335	38.72 335	41.03 351	51.22 372	19.66 377	45.53 363	31.35 378	15.83 373	39.80 364	67.42 348	22.19 353	45.13 369	22.43 364	52.69 348	58.31 359

EMVS		22.97 341	21.84 345	26.36 357	40.20 377	19.53 378	41.95 367	34.64 376	17.09 372	9.73 378	22.83 374	7.29 375	42.22 373	9.18 376	13.66 374	17.32 373

new_pmnet		34.13 338	34.29 339	33.64 353	52.63 370	18.23 379	44.43 365	33.90 377	22.81 369	30.89 367	53.18 362	10.48 372	35.72 375	20.77 366	39.51 365	46.98 366

DeepMVS_CX				12.03 360	17.97 382	10.91 380		10.60 383	7.46 375	11.07 376	28.36 371	3.28 381	11.29 379	8.01 377	9.74 378	13.89 374

new-patchmatchnet		47.56 325	47.73 326	47.06 346	58.81 367	9.37 381	48.78 359	59.21 338	43.28 328	44.22 357	68.66 342	25.67 342	57.20 353	31.57 346	49.35 356	74.62 310

PMMVS2		27.40 339	25.91 342	31.87 355	39.46 379	6.57 382	31.17 369	28.52 379	23.96 367	20.45 371	48.94 368	4.20 379	37.94 374	16.51 368	19.97 371	51.09 363

tmp_tt		9.43 346	11.14 349	4.30 361	2.38 384	4.40 383	13.62 373	16.08 382	0.39 378	15.89 373	13.06 375	15.80 363	5.54 380	12.63 372	10.46 377	2.95 375

test_method		19.68 343	18.10 346	24.41 358	13.68 383	3.11 384	12.06 374	42.37 373	2.00 377	11.97 375	36.38 369	5.77 377	29.35 377	15.06 369	23.65 370	40.76 368

N_pmnet		39.35 334	40.28 334	36.54 352	63.76 350	1.62 385	49.37 358	0.76 385	34.62 357	43.61 358	66.38 350	26.25 338	42.57 371	26.02 363	51.77 349	65.44 354

test123		4.73 348	6.30 351	0.02 362	0.01 385	0.01 386	56.36 345	0.00 386	0.01 380	0.04 381	0.21 381	0.01 385	0.00 381	0.03 380	0.00 379	0.04 377

test_blank		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

uanet_test		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

DCPMVS		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

cdsmvs_eth3d_5k		17.50 344	23.34 343	0.00 364	0.00 387	0.00 387	0.00 375	78.63 171	0.00 382	0.00 383	82.18 189	49.25 114	0.00 381	0.00 381	0.00 379	0.00 379

pcd_1.5k_mvsjas		3.92 350	5.23 353	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	47.05 145	0.00 381	0.00 381	0.00 379	0.00 379

sosnet-low-res		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

sosnet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

uncertanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

Regformer		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

testmvs		4.52 349	6.03 352	0.01 363	0.01 385	0.00 387	53.86 352	0.00 386	0.01 380	0.04 381	0.27 380	0.00 386	0.00 381	0.04 379	0.00 379	0.03 378

ab-mvs-re		6.49 347	8.65 350	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	77.89 271	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

uanet		0.00 351	0.00 354	0.00 364	0.00 387	0.00 387	0.00 375	0.00 386	0.00 382	0.00 383	0.00 382	0.00 386	0.00 381	0.00 381	0.00 379	0.00 379

PC_three_1452									55.09 208	84.46 4	89.84 49	66.68 5	89.41 17	74.24 37	91.38 2	88.42 8

eth-test2					0.00 387

eth-test					0.00 387

test_241102_TWO								86.73 16	64.18 35	84.26 5	91.84 8	65.19 6	90.83 5	78.63 15	90.70 7	87.65 36

9.14			78.75 16		83.10 78		84.15 44	88.26 2	59.90 113	78.57 27	90.36 28	57.51 33	86.86 70	77.39 18	89.52 25

test_0728_THIRD									65.04 20	83.82 8	92.00 3	64.69 10	90.75 8	79.48 4	90.63 10	88.09 19

GSMVS																78.05 270

sam_mvs1												34.74 268				78.05 270

sam_mvs												33.43 282

MTGPA								80.97 132

test_post1							68.67 293				3.64 377	32.39 299	69.49 308	44.17 270

test_post											3.55 378	33.90 277	66.52 321

patchmatchnet-post											64.03 354	34.50 270	74.27 287

MTMP							86.03 19	17.08 381

test9_res														75.28 31	88.31 37	83.81 171

agg_prior2														73.09 50	87.93 44	84.33 153

test_prior2							81.75 89		60.37 101	75.01 45	89.06 59	56.22 40		72.19 53	88.96 28

旧先验2							76.08 188		45.32 311	76.55 35			65.56 327	58.75 165

新几何2							76.12 186

无先验							79.66 122	74.30 246	48.40 282				80.78 215	53.62 198		79.03 262

原ACMM2							79.02 130

testdata2													72.18 297	46.95 249

segment_acmp												54.23 59

testdata1							72.65 247		60.50 96

plane_prior5								84.01 55					87.21 57	68.16 81	80.58 112	84.65 146

plane_prior4											86.10 109

plane_prior2							84.22 41		64.52 28

plane_prior1					81.27 107

n2								0.00 386

nn								0.00 386

door-mid								47.19 369

test11								83.47 72

door								47.60 368

HQP-NCC					80.66 114		82.31 79		62.10 72	67.85 155

ACMP_Plane					80.66 114		82.31 79		62.10 72	67.85 155

BP-MVS														67.04 95

HQP4-MVS										67.85 155			86.93 68			84.32 154

HQP3-MVS								83.90 59							80.35 118

HQP2-MVS												45.46 163

ACMMP++_ref															74.07 187

ACMMP++															72.16 220

Test By Simon												48.33 126

High-resolution multi-view benchmark