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 11	80.14 11	79.10 21	88.17 1	64.80 1	86.59 17	83.70 82	65.37 14	78.78 32	90.64 26	58.63 32	87.24 62	79.00 14	90.37 14	85.26 182

CNVR-MVS		79.84 12	79.97 12	79.45 11	87.90 2	62.17 18	84.37 46	85.03 43	66.96 5	77.58 42	90.06 47	59.47 26	89.13 29	78.67 17	89.73 16	87.03 92

test_0728_SECOND				79.19 16	87.82 3	59.11 73	87.85 6	87.15 3					90.84 3	78.66 18	90.61 11	87.62 66

RA-MVS			78.88 17		87.79 4	62.77 9	88.20 1	84.29 56	57.50 201	81.17 23	91.78 11	55.70 65	89.55 24	77.26 30	86.23 66

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 5	58.90 80	87.82 8	86.78 10	64.18 35	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 40

IU-MVS					87.77 5	59.15 70		85.53 33	53.93 294	84.64 3				79.07 13	90.87 5	88.37 34

test_241102_ONE					87.77 5	58.90 80		86.78 10	64.20 34	85.97 1	91.34 20	66.87 3	90.78 7

DVP-MVS		80.84 4	81.64 3	78.42 38	87.75 8	59.07 75	87.85 6	85.03 43	64.26 32	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 170
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 8	59.07 75	87.86 5	86.83 8	64.26 32	84.19 7	91.92 5	64.82 8

test_one_0601					87.58 10	59.30 63		86.84 7	65.01 21	83.80 11	91.86 6	64.03 12

test_part2					87.58 10	60.47 43				83.42 14

OPU-MVS				79.83 7	87.54 12	60.93 36	87.82 8				89.89 53	67.01 1	90.33 12	73.16 73	91.15 4	88.23 40

NCCC		78.58 19	78.31 22	79.39 12	87.51 13	62.61 14	85.20 37	84.42 53	66.73 8	74.67 79	89.38 60	55.30 68	89.18 28	74.19 65	87.34 50	86.38 120

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 14	60.56 42	85.71 32	86.42 16	63.28 52	83.27 15	91.83 10	64.96 7	90.47 11	76.41 42	89.67 20	86.84 100
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 15	59.15 70	88.18 2	87.15 3	65.04 17	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 32

MSC_two_6792asdad				79.95 4	87.24 15	61.04 32		85.62 31					90.96 1	79.31 10	90.65 8	87.85 55

No_MVS				79.95 4	87.24 15	61.04 32		85.62 31					90.96 1	79.31 10	90.65 8	87.85 55

region2R		77.67 31	77.18 34	79.15 18	86.76 18	62.95 6	86.29 19	84.16 59	62.81 67	73.30 107	90.58 28	49.90 158	88.21 41	73.78 69	87.03 52	86.29 133

ACMMPR		77.71 29	77.23 33	79.16 17	86.75 19	62.93 7	86.29 19	84.24 57	62.82 65	73.55 102	90.56 31	49.80 161	88.24 40	74.02 67	87.03 52	86.32 129

HFP-MVS		78.01 27	77.65 30	79.10 21	86.71 20	62.81 8	86.29 19	84.32 55	62.82 65	73.96 90	90.50 33	53.20 101	88.35 38	74.02 67	87.05 51	86.13 136

MCST-MVS		77.48 32	77.45 32	77.54 53	86.67 21	58.36 87	83.22 67	86.93 5	56.91 213	74.91 70	88.19 79	59.15 29	87.68 58	73.67 70	87.45 49	86.57 113

ZD-MVS					86.64 22	60.38 46		82.70 120	57.95 189	78.10 37	90.06 47	56.12 55	88.84 33	74.05 66	87.00 55

APDe-MVS		80.16 9	80.59 7	78.86 33	86.64 22	60.02 49	88.12 4	86.42 16	62.94 61	82.40 16	92.12 2	59.64 24	89.76 20	78.70 15	88.32 35	86.79 102
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
aaEdge-Enhanced		80.04 10	80.36 9	79.08 24	86.63 24	59.25 65	85.62 33	86.73 12	63.10 58	82.27 19	90.57 29	61.90 17	89.88 19	77.02 36	89.43 24	88.10 45

SMA-MVS		80.28 7	80.39 8	79.95 4	86.60 25	61.95 20	86.33 18	85.75 28	62.49 72	82.20 20	92.28 1	56.53 45	89.70 21	79.85 6	91.48 1	88.19 42
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
test-260524					86.59 26	59.16 68		86.47 15		82.32 18		62.54 14	89.91 16	77.25 31	89.69 18

DP-MVS Recon		72.15 129	70.73 146	76.40 74	86.57 27	57.99 91	81.15 99	82.96 114	57.03 209	66.78 244	85.56 172	44.50 238	88.11 45	51.77 290	80.23 140	83.10 263

MP-MVS		78.35 23	78.26 25	78.64 35	86.54 28	63.47 4	86.02 25	83.55 88	63.89 40	73.60 100	90.60 27	54.85 74	86.72 79	77.20 33	88.06 40	85.74 156
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		76.54 44	75.93 50	78.34 40	86.47 29	63.50 3	85.74 31	82.28 125	62.90 62	71.77 141	90.26 41	46.61 210	86.55 88	71.71 90	85.66 70	84.97 193

APD-MVS		78.02 26	78.04 27	77.98 46	86.44 30	60.81 39	85.52 34	84.36 54	60.61 116	79.05 30	90.30 40	55.54 67	88.32 39	73.48 72	87.03 52	84.83 197
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZNCC-MVS		78.82 16	78.67 20	79.30 14	86.43 31	62.05 19	86.62 16	86.01 21	63.32 48	75.08 65	90.47 35	53.96 86	88.68 34	76.48 41	89.63 22	87.16 89

XVS		77.17 35	76.56 41	79.00 26	86.32 32	62.62 12	85.83 28	83.92 64	64.55 26	72.17 136	90.01 51	47.95 186	88.01 47	71.55 92	86.74 59	86.37 122

X-MVStestdata		70.21 170	67.28 234	79.00 26	86.32 32	62.62 12	85.83 28	83.92 64	64.55 26	72.17 136	6.49 532	47.95 186	88.01 47	71.55 92	86.74 59	86.37 122

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 34	62.73 10	86.09 23	86.83 8	65.51 13	83.81 10	90.51 32	63.71 13	89.23 27	81.51 2	88.44 31	88.09 47
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
114514_t		70.83 156	69.56 169	74.64 113	86.21 34	54.63 151	82.34 82	81.81 132	48.22 388	63.01 319	85.83 165	40.92 288	87.10 70	57.91 236	79.79 147	82.18 285

save fliter					86.17 36	61.30 29	83.98 59	79.66 183	59.00 160

DeepC-MVS_fast	68.24 3	77.25 34	76.63 38	79.12 20	86.15 37	60.86 37	84.71 41	84.85 47	61.98 89	73.06 119	88.88 69	53.72 92	89.06 30	68.27 110	88.04 41	87.42 74
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		76.77 41	76.06 48	78.88 32	86.14 38	62.73 10	82.55 79	83.74 79	61.71 91	72.45 134	90.34 39	48.48 182	88.13 44	72.32 80	86.85 57	85.78 150

FOURS1					86.12 39	60.82 38	88.18 2	83.61 86	60.87 109	81.50 21

MTAPA		76.90 38	76.42 44	78.35 39	86.08 40	63.57 2	74.92 260	80.97 162	65.13 16	75.77 54	90.88 24	48.63 179	86.66 81	77.23 32	88.17 37	84.81 198

GST-MVS		78.14 25	77.85 28	78.99 28	86.05 41	61.82 23	85.84 27	85.21 37	63.56 44	74.29 85	90.03 49	52.56 110	88.53 36	74.79 61	88.34 33	86.63 112

CP-MVS		77.12 36	76.68 37	78.43 37	86.05 41	63.18 5	87.55 11	83.45 91	62.44 74	72.68 128	90.50 33	48.18 184	87.34 61	73.59 71	85.71 69	84.76 201

SR-MVS		76.13 52	75.70 54	77.40 58	85.87 43	61.20 30	85.52 34	82.19 126	59.99 138	75.10 64	90.35 38	47.66 191	86.52 89	71.64 91	82.99 96	84.47 210

新几何1				70.76 259	85.66 44	61.13 31		66.43 395	44.68 430	70.29 165	86.64 130	41.29 281	75.23 370	49.72 305	81.75 117	75.93 398

MG-MVS		73.96 83	73.89 83	74.16 133	85.65 45	49.69 275	81.59 94	81.29 149	61.45 96	71.05 153	88.11 82	51.77 128	87.73 55	61.05 206	83.09 93	85.05 189

TEST9					85.58 46	61.59 25	81.62 92	81.26 150	55.65 248	74.93 68	88.81 70	53.70 93	84.68 141

train_agg		76.27 48	76.15 46	76.64 71	85.58 46	61.59 25	81.62 92	81.26 150	55.86 240	74.93 68	88.81 70	53.70 93	84.68 141	75.24 57	88.33 34	83.65 245

ACMMP_NAP		78.77 18	78.78 18	78.74 34	85.44 48	61.04 32	83.84 61	85.16 38	62.88 63	78.10 37	91.26 21	52.51 111	88.39 37	79.34 9	90.52 13	86.78 103

test_8					85.40 49	60.96 35	81.54 95	81.18 154	55.86 240	74.81 73	88.80 72	53.70 93	84.45 145

原ACMM1				74.69 109	85.39 50	59.40 60		83.42 92	51.47 340	70.27 166	86.61 134	48.61 180	86.51 90	53.85 272	87.96 43	78.16 367

CDPH-MVS		76.31 47	75.67 55	78.22 41	85.35 51	59.14 72	81.31 97	84.02 60	56.32 231	74.05 88	88.98 65	53.34 98	87.92 50	69.23 105	88.42 32	87.59 68

aaatest				79.09 23	85.30 52	59.25 65	86.84 12	85.86 24	60.95 107	83.65 12	90.57 29		89.91 16	77.02 36	89.43 24	88.10 45

MED-MVS		80.42 6	80.87 6	79.07 25	85.30 52	59.25 65	86.84 12	85.86 24	63.31 49	83.65 12	91.48 13	64.70 10	89.91 16	77.02 36	89.69 18	88.06 50

TestfortrainingZip a		79.61 13	79.84 13	78.92 30	85.30 52	59.08 74	86.84 12	86.01 21	63.31 49	82.37 17	91.48 13	60.88 19	89.61 22	76.25 45	86.13 67	88.06 50

ACMMP		76.02 53	75.33 58	78.07 42	85.20 55	61.91 21	85.49 36	84.44 52	63.04 59	69.80 177	89.74 57	45.43 224	87.16 68	72.01 85	82.87 102	85.14 184
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
agg_prior					85.04 56	59.96 51		81.04 159		74.68 78			84.04 152

HPM-MVS		77.28 33	76.85 35	78.54 36	85.00 57	60.81 39	82.91 72	85.08 40	62.57 70	73.09 118	89.97 52	50.90 145	87.48 60	75.30 55	86.85 57	87.33 83
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS-pluss		78.35 23	78.46 21	78.03 45	84.96 58	59.52 59	82.93 71	85.39 34	62.15 82	76.41 52	91.51 12	52.47 113	86.78 78	80.66 4	89.64 21	87.80 58
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		78.44 22	78.28 23	78.90 31	84.96 58	61.41 27	84.03 57	83.82 77	59.34 156	79.37 28	89.76 56	59.84 21	87.62 59	76.69 39	86.74 59	87.68 63
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
AdaColmap		69.99 176	68.66 193	73.97 146	84.94 60	57.83 93	82.63 77	78.71 204	56.28 234	64.34 298	84.14 209	41.57 276	87.06 72	46.45 341	78.88 173	77.02 386

DP-MVS		65.68 284	63.66 298	71.75 219	84.93 61	56.87 112	80.74 105	73.16 334	53.06 309	59.09 375	82.35 256	36.79 341	85.94 109	32.82 453	69.96 334	72.45 437

DeepPCF-MVS	69.58 1	79.03 15	79.00 16	79.13 19	84.92 62	60.32 47	83.03 69	85.33 35	62.86 64	80.17 24	90.03 49	61.76 18	88.95 31	74.21 64	88.67 30	88.12 44

CPTT-MVS		72.78 109	72.08 118	74.87 105	84.88 63	61.41 27	84.15 55	77.86 231	55.27 258	67.51 231	88.08 84	41.93 266	81.85 217	69.04 106	80.01 142	81.35 303

TestfortrainingZip				78.05 44	84.66 64	58.22 89	86.84 12	85.98 23	63.31 49	79.39 27	88.94 67	62.01 16	89.61 22		86.45 64	86.34 124

test12				77.76 51	84.52 65	58.41 86		83.36 95		72.93 122		54.61 77	88.05 46		88.12 38	86.81 101

SD-MVS		77.70 30	77.62 31	77.93 47	84.47 66	61.88 22	84.55 44	83.87 70	60.37 125	79.89 25	89.38 60	54.97 72	85.58 117	76.12 47	84.94 73	86.33 127
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
HPM-MVS_fast		74.30 75	73.46 92	76.80 65	84.45 67	59.04 77	83.65 64	81.05 158	60.15 134	70.43 162	89.84 54	41.09 286	85.59 116	67.61 128	82.90 101	85.77 153

test_prior				76.69 67	84.20 68	57.27 101		84.88 46					86.43 92			86.38 120

reproduce-ours		76.90 38	76.58 39	77.87 48	83.99 69	60.46 44	84.75 39	83.34 96	60.22 132	77.85 40	91.42 17	50.67 146	87.69 56	72.46 78	84.53 77	85.46 168

our_new_method		76.90 38	76.58 39	77.87 48	83.99 69	60.46 44	84.75 39	83.34 96	60.22 132	77.85 40	91.42 17	50.67 146	87.69 56	72.46 78	84.53 77	85.46 168

CSCG		76.92 37	76.75 36	77.41 56	83.96 71	59.60 57	82.95 70	86.50 14	60.78 112	75.27 60	84.83 186	60.76 20	86.56 85	67.86 122	87.87 45	86.06 138

SymmetryMVS		75.28 60	74.60 68	77.30 59	83.85 72	59.89 53	84.36 47	75.51 289	64.69 23	74.21 86	87.40 99	49.48 165	86.17 100	68.04 119	83.88 86	85.85 147

DeepC-MVS	69.38 2	78.56 20	78.14 26	79.83 7	83.60 73	61.62 24	84.17 54	86.85 6	63.23 54	73.84 97	90.25 42	57.68 36	89.96 15	74.62 62	89.03 26	87.89 52
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UA-Net		73.13 102	72.93 102	73.76 153	83.58 74	51.66 224	78.75 136	77.66 236	67.75 4	72.61 130	89.42 58	49.82 160	83.29 169	53.61 274	83.14 92	86.32 129

SR-MVS-dyc-post		74.57 71	73.90 82	76.58 72	83.49 75	59.87 55	84.29 49	81.36 143	58.07 183	73.14 114	90.07 45	44.74 234	85.84 111	68.20 111	81.76 115	84.03 222

RE-MVS-def			73.71 87		83.49 75	59.87 55	84.29 49	81.36 143	58.07 183	73.14 114	90.07 45	43.06 253		68.20 111	81.76 115	84.03 222

reproduce_model		76.43 46	76.08 47	77.49 55	83.47 77	60.09 48	84.60 43	82.90 116	59.65 146	77.31 43	91.43 16	49.62 164	87.24 62	71.99 86	83.75 89	85.14 184

LFMVS		71.78 134	71.59 124	72.32 206	83.40 78	46.38 329	79.75 121	71.08 351	64.18 35	72.80 126	88.64 75	42.58 258	83.72 159	57.41 240	84.49 79	86.86 99

test222					83.14 79	58.68 84	72.57 316	63.45 426	41.78 452	67.56 230	86.12 152	37.13 335			78.73 179	74.98 411

9.14			78.75 19		83.10 80		84.15 55	88.26 1	59.90 140	78.57 34	90.36 37	57.51 39	86.86 76	77.39 29	89.52 23

旧先验1					83.04 81	53.15 184		67.52 384			87.85 91	44.08 241			80.76 127	78.03 372

MSLP-MVS++		73.77 86	73.47 91	74.66 111	83.02 82	59.29 64	82.30 86	81.88 130	59.34 156	71.59 145	86.83 121	45.94 215	83.65 161	65.09 158	85.22 72	81.06 314

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 83	62.18 17	87.60 10	85.83 26	66.69 10	78.03 39	90.98 23	54.26 79	90.06 14	78.42 23	89.02 27	87.69 62
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MVS_111021_HR		74.02 82	73.46 92	75.69 88	83.01 83	60.63 41	77.29 191	78.40 223	61.18 103	70.58 160	85.97 159	54.18 81	84.00 155	67.52 129	82.98 98	82.45 280

SF-MVS		78.82 16	79.22 15	77.60 52	82.88 85	57.83 93	84.99 38	88.13 2	61.86 90	79.16 29	90.75 25	57.96 33	87.09 71	77.08 35	90.18 15	87.87 54

VDDNet		71.81 133	71.33 132	73.26 179	82.80 86	47.60 320	78.74 137	75.27 294	59.59 151	72.94 121	89.40 59	41.51 279	83.91 156	58.75 231	82.99 96	88.26 37

NormalMVS		76.26 49	75.74 53	77.83 50	82.75 87	59.89 53	84.36 47	83.21 104	64.69 23	74.21 86	87.40 99	49.48 165	86.17 100	68.04 119	87.55 47	87.42 74

lecture		77.75 28	77.84 29	77.50 54	82.75 87	57.62 96	85.92 26	86.20 19	60.53 118	78.99 31	91.45 15	51.51 133	87.78 54	75.65 51	87.55 47	87.10 91

3Dnovator+	66.72 4	75.84 55	74.57 69	79.66 9	82.40 89	59.92 52	85.83 28	86.32 18	66.92 7	67.80 225	89.24 62	42.03 263	89.38 26	64.07 166	86.50 63	89.69 4

dcpmvs_2		74.55 72	75.23 60	72.48 200	82.34 90	53.34 179	77.87 168	81.46 139	57.80 194	75.49 57	86.81 122	62.22 15	77.75 327	71.09 95	82.02 111	86.34 124

APD-MVS_3200maxsize		74.96 62	74.39 71	76.67 69	82.20 91	58.24 88	83.67 63	83.29 100	58.41 176	73.71 98	90.14 43	45.62 217	85.99 107	69.64 101	82.85 103	85.78 150

MM		80.20 8	80.28 10	79.99 2	82.19 92	60.01 50	86.19 22	83.93 63	73.19 1	77.08 48	91.21 22	57.23 40	90.73 10	83.35 1	88.12 38	89.22 9

PVSNet_Blended_VisFu		71.45 142	70.39 153	74.65 112	82.01 93	58.82 82	79.93 117	80.35 174	55.09 264	65.82 271	82.16 265	49.17 173	82.64 200	60.34 211	78.62 183	82.50 279

TSAR-MVS + GP.		74.90 63	74.15 75	77.17 60	82.00 94	58.77 83	81.80 89	78.57 212	58.58 172	74.32 84	84.51 202	55.94 63	87.22 65	67.11 135	84.48 80	85.52 164

h-mvs33		72.71 111	71.49 127	76.40 74	81.99 95	59.58 58	76.92 206	76.74 263	60.40 122	74.81 73	85.95 160	45.54 220	85.76 113	70.41 99	70.61 318	83.86 233

API-MVS		72.17 126	71.41 129	74.45 122	81.95 96	57.22 102	84.03 57	80.38 173	59.89 144	68.40 200	82.33 257	49.64 163	87.83 53	51.87 288	84.16 84	78.30 365

MAR-MVS		71.51 139	70.15 161	75.60 92	81.84 97	59.39 61	81.38 96	82.90 116	54.90 276	68.08 214	78.70 332	47.73 189	85.51 119	51.68 292	84.17 83	81.88 291
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
BridgeMVS		76.58 43	76.55 42	76.68 68	81.73 98	52.90 190	80.94 100	85.70 30	61.12 105	74.90 71	87.17 114	56.46 46	88.14 43	72.87 75	88.03 42	89.00 12

PAPM_NR		72.63 114	71.80 121	75.13 100	81.72 99	53.42 178	79.91 118	83.28 102	59.14 158	66.31 257	85.90 162	51.86 125	86.06 104	57.45 239	80.62 129	85.91 143

VDD-MVS		72.50 116	72.09 117	73.75 155	81.58 100	49.69 275	77.76 175	77.63 237	63.21 55	73.21 110	89.02 64	42.14 262	83.32 168	61.72 199	82.50 106	88.25 38

PS-MVSNAJ		70.51 162	69.70 167	72.93 186	81.52 101	55.79 129	74.92 260	79.00 196	55.04 270	69.88 175	78.66 334	47.05 203	82.19 210	61.61 201	79.58 151	80.83 318

testdata				64.66 369	81.52 101	52.93 189		65.29 405	46.09 419	73.88 95	87.46 98	38.08 324	66.26 429	53.31 277	78.48 186	74.78 415

CHOSEN 1792x2688		65.08 295	62.84 313	71.82 216	81.49 103	56.26 118	66.32 398	74.20 318	40.53 462	63.16 315	78.65 335	41.30 280	77.80 326	45.80 349	74.09 257	81.40 300

HQP_MVS		74.31 74	73.73 86	76.06 79	81.41 104	56.31 115	84.22 52	84.01 61	64.52 28	69.27 186	86.10 153	45.26 228	87.21 66	68.16 115	80.58 131	84.65 202

plane_prior7					81.41 104	55.96 124

DPM-MVS		75.47 59	75.00 63	76.88 63	81.38 106	59.16 68	79.94 116	85.71 29	56.59 224	72.46 132	86.76 123	56.89 43	87.86 52	66.36 145	88.91 29	83.64 246

CANet		76.46 45	75.93 50	78.06 43	81.29 107	57.53 98	82.35 81	83.31 99	67.78 3	70.09 167	86.34 145	54.92 73	88.90 32	72.68 77	84.55 76	87.76 60

Vis-MVSNet		72.18 125	71.37 131	74.61 114	81.29 107	55.41 139	80.90 101	78.28 226	60.73 113	69.23 189	88.09 83	44.36 240	82.65 199	57.68 237	81.75 117	85.77 153
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
plane_prior1					81.27 109

xiu_mvs_v2_base		70.52 161	69.75 165	72.84 188	81.21 110	55.63 133	75.11 252	78.92 198	54.92 275	69.96 174	79.68 318	47.00 207	82.09 212	61.60 202	79.37 154	80.81 319

plane_prior6					81.20 111	56.24 119						45.26 228

PAPR		71.72 137	70.82 144	74.41 123	81.20 111	51.17 227	79.55 127	83.33 98	55.81 243	66.93 243	84.61 196	50.95 143	86.06 104	55.79 253	79.20 164	86.00 139

PLC	56.13 14	65.09 294	63.21 309	70.72 262	81.04 113	54.87 149	78.57 143	77.47 239	48.51 383	55.71 413	81.89 272	33.71 374	79.71 270	41.66 397	70.37 322	77.58 377
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
NP-MVS					80.98 114	56.05 123					85.54 176

MVSMamba_PlusPlus		75.75 57	75.44 56	76.67 69	80.84 115	53.06 187	78.62 141	85.13 39	59.65 146	71.53 147	87.47 97	56.92 42	88.17 42	72.18 84	86.63 62	88.80 16

OPM-MVS		74.73 67	74.25 74	76.19 78	80.81 116	59.01 78	82.60 78	83.64 85	63.74 42	72.52 131	87.49 96	47.18 201	85.88 110	69.47 103	80.78 125	83.66 244
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MGCNet		78.45 21	78.28 23	78.98 29	80.73 117	57.91 92	84.68 42	81.64 135	68.35 2	75.77 54	90.38 36	53.98 84	90.26 13	81.30 3	87.68 46	88.77 19

HQP-NCC					80.66 118		82.31 83		62.10 83	67.85 219

ACMP_Plane					80.66 118		82.31 83		62.10 83	67.85 219

HQP-MVS		73.45 92	72.80 105	75.40 94	80.66 118	54.94 146	82.31 83	83.90 66	62.10 83	67.85 219	85.54 176	45.46 222	86.93 74	67.04 137	80.35 137	84.32 212

SPE-MVS-test		75.62 58	75.31 59	76.56 73	80.63 121	55.13 144	83.88 60	85.22 36	62.05 86	71.49 148	86.03 156	53.83 88	86.36 95	67.74 124	86.91 56	88.19 42

PHI-MVS		75.87 54	75.36 57	77.41 56	80.62 122	55.91 126	84.28 51	85.78 27	56.08 238	73.41 103	86.58 136	50.94 144	88.54 35	70.79 97	89.71 17	87.79 59

ACMM	61.98 7	70.80 158	69.73 166	74.02 142	80.59 123	58.59 85	82.68 76	82.02 129	55.46 253	67.18 238	84.39 205	38.51 316	83.17 172	60.65 209	76.10 232	80.30 334
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		69.28 202	68.47 198	71.73 220	80.28 124	47.18 324	79.98 115	82.37 124	54.61 281	67.24 236	84.01 213	39.43 300	82.41 207	55.45 258	72.83 285	85.62 162

ACMP	63.53 6	72.30 122	71.20 136	75.59 93	80.28 124	57.54 97	82.74 75	82.84 119	60.58 117	65.24 283	86.18 150	39.25 305	86.03 106	66.95 141	76.79 219	83.22 256
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		72.74 110	71.74 123	75.76 85	80.22 126	57.51 99	82.55 79	83.40 93	61.32 98	66.67 249	87.33 104	39.15 307	86.59 83	67.70 126	77.30 210	83.19 258

LGP-MVS_train				75.76 85	80.22 126	57.51 99		83.40 93	61.32 98	66.67 249	87.33 104	39.15 307	86.59 83	67.70 126	77.30 210	83.19 258

WR-MVS		68.47 225	68.47 198	68.44 304	80.20 128	39.84 409	73.75 289	76.07 275	64.68 25	68.11 212	83.63 224	50.39 151	79.14 288	49.78 302	69.66 343	86.34 124

Anonymous20240529		69.91 178	69.02 182	72.56 196	80.19 129	47.65 317	77.56 179	80.99 161	55.45 254	69.88 175	86.76 123	39.24 306	82.18 211	54.04 269	77.10 214	87.85 55

Anonymous202405211		66.84 266	65.99 266	69.40 288	80.19 129	42.21 384	71.11 343	71.31 350	58.80 164	67.90 216	86.39 143	29.83 419	79.65 271	49.60 308	78.78 176	86.33 127

CS-MVS		76.25 50	75.98 49	77.06 61	80.15 131	55.63 133	84.51 45	83.90 66	63.24 53	73.30 107	87.27 106	55.06 70	86.30 97	71.78 89	84.58 75	89.25 8

BH-RMVSNet		68.81 214	67.42 228	72.97 185	80.11 132	52.53 204	74.26 274	76.29 271	58.48 174	68.38 201	84.20 207	42.59 257	83.83 157	46.53 340	75.91 234	82.56 274

test_0402		63.25 319	61.01 340	69.96 275	80.00 133	54.37 154	76.86 210	72.02 345	54.58 283	58.71 378	80.79 298	35.00 357	84.36 146	26.41 488	64.71 390	71.15 456

HyFIR lowres test		65.67 285	63.01 311	73.67 160	79.97 134	55.65 132	69.07 376	75.52 288	42.68 450	63.53 309	77.95 346	40.43 291	81.64 221	46.01 347	71.91 301	83.73 240

EIA-MVS		71.78 134	70.60 149	75.30 97	79.85 135	53.54 172	77.27 193	83.26 103	57.92 190	66.49 251	79.39 324	52.07 122	86.69 80	60.05 213	79.14 169	85.66 160

BH-untuned		68.27 229	67.29 233	71.21 244	79.74 136	53.22 182	76.06 230	77.46 241	57.19 204	66.10 261	81.61 279	45.37 226	83.50 165	45.42 360	76.68 221	76.91 390

VNet		69.68 187	70.19 159	68.16 309	79.73 137	41.63 391	70.53 353	77.38 244	60.37 125	70.69 157	86.63 132	51.08 140	77.09 343	53.61 274	81.69 119	85.75 155

LS3D		64.71 298	62.50 317	71.34 242	79.72 138	55.71 130	79.82 119	74.72 306	48.50 384	56.62 404	84.62 195	33.59 377	82.34 208	29.65 475	75.23 246	75.97 397

mvsmamba		68.47 225	66.56 249	74.21 132	79.60 139	52.95 188	74.94 259	75.48 290	52.09 327	60.10 358	83.27 233	36.54 342	84.70 140	59.32 223	77.69 199	84.99 192

hse-mvs2		71.04 148	69.86 164	74.60 115	79.58 140	57.12 109	73.96 281	75.25 295	60.40 122	74.81 73	81.95 271	45.54 220	82.90 188	70.41 99	66.83 375	83.77 238

GeoE		71.01 150	70.15 161	73.60 166	79.57 141	52.17 212	78.93 134	78.12 228	58.02 185	67.76 228	83.87 216	52.36 115	82.72 197	56.90 242	75.79 236	85.92 142

AUN-MVS		68.45 227	66.41 256	74.57 117	79.53 142	57.08 110	73.93 284	75.23 296	54.44 286	66.69 247	81.85 273	37.10 336	82.89 189	62.07 195	66.84 374	83.75 239

balanced_ft_v1		72.98 105	72.55 110	74.27 127	79.52 143	50.64 243	77.78 173	83.29 100	56.76 214	67.88 218	85.95 160	49.42 168	85.29 127	68.64 107	83.76 88	86.87 98

test2506		65.33 291	64.61 286	67.50 316	79.46 144	34.19 466	74.43 272	51.92 477	58.72 166	66.75 246	88.05 85	25.99 457	80.92 245	51.94 287	84.25 81	87.39 77

ECVR-MVS		67.72 247	67.51 225	68.35 305	79.46 144	36.29 450	74.79 263	66.93 391	58.72 166	67.19 237	88.05 85	36.10 346	81.38 229	52.07 285	84.25 81	87.39 77

testing3-2		62.06 339	62.36 319	61.17 401	79.29 146	30.31 486	64.09 425	63.49 424	63.50 45	62.84 320	82.22 261	32.35 402	69.02 408	40.01 408	73.43 274	84.17 219

BH-w/o		66.85 265	65.83 268	69.90 279	79.29 146	52.46 207	74.66 266	76.65 264	54.51 285	64.85 293	78.12 342	45.59 219	82.95 182	43.26 383	75.54 240	74.27 422

1112_ss		64.00 311	63.36 305	65.93 351	79.28 148	42.58 380	71.35 336	72.36 342	46.41 416	60.55 355	77.89 352	46.27 214	73.28 379	46.18 345	69.97 333	81.92 290

ETV-MVS		74.46 73	73.84 84	76.33 76	79.27 149	55.24 143	79.22 130	85.00 45	64.97 22	72.65 129	79.46 323	53.65 96	87.87 51	67.45 132	82.91 100	85.89 144

test1111		67.21 254	67.14 242	67.42 320	79.24 150	34.76 460	73.89 286	65.65 401	58.71 168	66.96 242	87.95 89	36.09 347	80.53 254	52.03 286	83.79 87	86.97 94

FBQ-MVS		66.84 266	65.39 277	71.18 246	79.22 151	47.61 319	76.89 207	74.70 307	56.31 233	65.84 270	77.22 364	36.21 345	82.07 213	45.20 363	76.94 216	83.87 231

SSM_0404		70.84 154	69.41 175	75.12 101	79.20 152	53.86 161	77.89 167	80.00 178	53.88 295	69.40 183	84.61 196	43.21 250	86.56 85	58.80 229	77.68 200	84.95 194

UniMVSNet_NR-MVSNet		71.11 147	71.00 141	71.44 233	79.20 152	44.13 355	76.02 233	82.60 121	66.48 12	68.20 204	84.60 199	56.82 44	82.82 195	54.62 264	70.43 320	87.36 81

VPNet		67.52 250	68.11 212	65.74 355	79.18 154	36.80 442	72.17 325	72.83 337	62.04 87	67.79 226	85.83 165	48.88 178	76.60 359	51.30 293	72.97 283	83.81 234

TR-MVS		66.59 274	65.07 283	71.17 248	79.18 154	49.63 277	73.48 292	75.20 298	52.95 310	67.90 216	80.33 304	39.81 297	83.68 160	43.20 384	73.56 270	80.20 336

TAMVS		66.78 269	65.27 281	71.33 243	79.16 156	53.67 166	73.84 288	69.59 368	52.32 324	65.28 278	81.72 277	44.49 239	77.40 337	42.32 391	78.66 182	82.92 265

patch_mono-2		69.85 180	71.09 138	66.16 345	79.11 157	54.80 150	71.97 328	74.31 313	53.50 304	70.90 156	84.17 208	57.63 38	63.31 444	66.17 146	82.02 111	80.38 329

Test_1112_low_res		62.32 334	61.77 326	64.00 376	79.08 158	39.53 415	68.17 383	70.17 361	43.25 444	59.03 376	79.90 311	44.08 241	71.24 394	43.79 377	68.42 361	81.25 305

CDS-MVSNet		66.80 268	65.37 278	71.10 252	78.98 159	53.13 186	73.27 300	71.07 352	52.15 325	64.72 294	80.23 306	43.56 247	77.10 342	45.48 358	78.88 173	83.05 264
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
sasdasda		74.67 68	74.98 64	73.71 158	78.94 160	50.56 248	80.23 109	83.87 70	60.30 129	77.15 45	86.56 137	59.65 22	82.00 214	66.01 149	82.12 108	88.58 29

canonicalmvs		74.67 68	74.98 64	73.71 158	78.94 160	50.56 248	80.23 109	83.87 70	60.30 129	77.15 45	86.56 137	59.65 22	82.00 214	66.01 149	82.12 108	88.58 29

EC-MVSNet		75.84 55	75.87 52	75.74 87	78.86 162	52.65 199	83.73 62	86.08 20	63.47 46	72.77 127	87.25 111	53.13 102	87.93 49	71.97 87	85.57 71	86.66 110

IS-MVSNet		71.57 138	71.00 141	73.27 178	78.86 162	45.63 340	80.22 111	78.69 205	64.14 38	66.46 252	87.36 102	49.30 170	85.60 115	50.26 301	83.71 90	88.59 28

CLD-MVS		73.33 96	72.68 107	75.29 98	78.82 164	53.33 180	78.23 155	84.79 48	61.30 100	70.41 164	81.04 289	52.41 114	87.12 69	64.61 164	82.49 107	85.41 174
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVSFormer		71.50 140	70.38 154	74.88 104	78.76 165	57.15 107	82.79 73	78.48 216	51.26 344	69.49 180	83.22 234	43.99 244	83.24 170	66.06 147	79.37 154	84.23 216

lupinMVS		69.57 192	68.28 208	73.44 173	78.76 165	57.15 107	76.57 217	73.29 331	46.19 418	69.49 180	82.18 262	43.99 244	79.23 282	64.66 162	79.37 154	83.93 227

CNLPA		65.43 288	64.02 291	69.68 282	78.73 167	58.07 90	77.82 172	70.71 358	51.49 338	61.57 346	83.58 228	38.23 322	70.82 396	43.90 375	70.10 331	80.16 337

PRO-TEST		71.42 143	71.02 140	72.62 193	78.68 168	52.64 201	78.04 164	81.04 159	56.33 230	68.21 203	82.15 266	50.03 155	81.69 220	64.20 165	80.51 134	83.52 249

EPP-MVSNet		72.16 128	71.31 133	74.71 108	78.68 168	49.70 273	82.10 87	81.65 134	60.40 122	65.94 264	85.84 164	51.74 129	86.37 94	55.93 250	79.55 153	88.07 49

mamba_0408		67.78 245	65.42 275	74.85 106	78.65 170	53.46 174	50.83 485	79.09 193	53.75 298	68.14 208	83.83 218	41.79 272	86.56 85	56.58 244	76.11 229	84.54 204

SSM_04072		64.98 296	65.42 275	63.68 378	78.65 170	53.46 174	50.83 485	79.09 193	53.75 298	68.14 208	83.83 218	41.79 272	53.03 488	56.58 244	76.11 229	84.54 204

SSM_0407		70.41 166	68.96 185	74.75 107	78.65 170	53.46 174	77.28 192	80.00 178	53.88 295	68.14 208	84.61 196	43.21 250	86.26 99	58.80 229	76.11 229	84.54 204

TranMVSNet+NR-MVSNet		70.36 167	70.10 163	71.17 248	78.64 173	42.97 376	76.53 218	81.16 156	66.95 6	68.53 198	85.42 178	51.61 131	83.07 173	52.32 282	69.70 342	87.46 72

UniMVSNet (Re)		70.63 160	70.20 158	71.89 213	78.55 174	45.29 343	75.94 234	82.92 115	63.68 43	68.16 207	83.59 225	53.89 87	83.49 166	53.97 270	71.12 311	86.89 97

Fast-Effi-MVS+		70.28 169	69.12 181	73.73 157	78.50 175	51.50 225	75.01 256	79.46 188	56.16 237	68.59 195	79.55 321	53.97 85	84.05 151	53.34 276	77.53 202	85.65 161

PS-MVSNAJss		72.24 124	71.21 135	75.31 96	78.50 175	55.93 125	81.63 91	82.12 127	56.24 235	70.02 171	85.68 171	47.05 203	84.34 147	65.27 157	74.41 255	85.67 159

EI-MVSNet-Vis-set		72.42 120	71.59 124	74.91 103	78.47 177	54.02 159	77.05 200	79.33 190	65.03 19	71.68 143	79.35 326	52.75 108	84.89 136	66.46 144	74.23 256	85.83 149

FA-MVS(test-final)		69.82 181	68.48 196	73.84 149	78.44 178	50.04 263	75.58 243	78.99 197	58.16 181	67.59 229	82.14 267	42.66 256	85.63 114	56.60 243	76.19 228	85.84 148

testing91		64.46 303	63.80 295	66.47 337	78.43 179	40.06 406	67.63 387	69.59 368	59.06 159	63.18 314	78.05 344	34.05 368	76.99 348	48.30 319	75.87 235	82.37 282

testing11		62.81 324	61.90 325	65.54 357	78.38 180	40.76 400	67.59 389	66.78 393	55.48 252	60.13 357	77.11 367	31.67 405	76.79 353	45.53 355	74.45 253	79.06 356

MVS_111021_LR		69.50 196	68.78 190	71.65 225	78.38 180	59.33 62	74.82 262	70.11 362	58.08 182	67.83 224	84.68 191	41.96 264	76.34 364	65.62 154	77.54 201	79.30 353

test_yl		69.69 185	69.13 179	71.36 240	78.37 182	45.74 336	74.71 264	80.20 175	57.91 191	70.01 172	83.83 218	42.44 259	82.87 191	54.97 260	79.72 148	85.48 166

DCV-MVSNet		69.69 185	69.13 179	71.36 240	78.37 182	45.74 336	74.71 264	80.20 175	57.91 191	70.01 172	83.83 218	42.44 259	82.87 191	54.97 260	79.72 148	85.48 166

testing915		67.86 242	68.34 205	66.42 338	78.35 184	40.04 407	75.04 255	71.84 347	58.41 176	66.43 254	83.87 216	50.32 153	78.04 317	49.26 310	81.49 120	81.19 309

fmvsm_s_conf0.5_n_9		75.16 61	75.22 61	75.01 102	78.34 185	55.37 141	77.30 190	73.95 322	61.40 97	79.46 26	90.14 43	57.07 41	81.15 235	80.00 5	79.31 159	88.51 31

FIs		70.82 157	71.43 128	68.98 296	78.33 186	38.14 427	76.96 204	83.59 87	61.02 106	67.33 233	86.73 127	55.07 69	81.64 221	54.61 266	79.22 163	87.14 90

UGNet		68.81 214	67.39 229	73.06 182	78.33 186	54.47 152	79.77 120	75.40 292	60.45 120	63.22 312	84.40 204	32.71 390	80.91 246	51.71 291	80.56 133	83.81 234
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
jason		69.65 188	68.39 202	73.43 174	78.27 188	56.88 111	77.12 198	73.71 325	46.53 415	69.34 185	83.22 234	43.37 248	79.18 283	64.77 161	79.20 164	84.23 216
jason: jason.
alignmvs		73.86 85	73.99 80	73.45 172	78.20 189	50.50 250	78.57 143	82.43 123	59.40 154	76.57 50	86.71 129	56.42 48	81.23 234	65.84 152	81.79 114	88.62 26

xiu_mvs_v1_base_debu		68.58 220	67.28 234	72.48 200	78.19 190	57.19 104	75.28 247	75.09 300	51.61 333	70.04 168	81.41 283	32.79 386	79.02 296	63.81 173	77.31 207	81.22 306

xiu_mvs_v1_base		68.58 220	67.28 234	72.48 200	78.19 190	57.19 104	75.28 247	75.09 300	51.61 333	70.04 168	81.41 283	32.79 386	79.02 296	63.81 173	77.31 207	81.22 306

xiu_mvs_v1_base_debi		68.58 220	67.28 234	72.48 200	78.19 190	57.19 104	75.28 247	75.09 300	51.61 333	70.04 168	81.41 283	32.79 386	79.02 296	63.81 173	77.31 207	81.22 306

testing99		64.05 309	63.29 308	66.34 340	78.17 193	39.76 411	67.33 392	68.00 382	58.60 171	63.03 317	78.10 343	32.57 397	76.94 350	48.22 320	75.58 239	82.34 283

UBG		59.62 369	59.53 357	59.89 407	78.12 194	35.92 454	64.11 424	60.81 447	49.45 368	61.34 347	75.55 396	33.05 381	67.39 421	38.68 416	74.62 251	76.35 395

PAPM		67.92 240	66.69 248	71.63 226	78.09 195	49.02 288	77.09 199	81.24 152	51.04 349	60.91 352	83.98 214	47.71 190	84.99 130	40.81 401	79.32 158	80.90 317

ACMH	55.70 15	65.20 293	63.57 299	70.07 274	78.07 196	52.01 218	79.48 128	79.69 181	55.75 245	56.59 405	80.98 291	27.12 447	80.94 243	42.90 388	71.58 306	77.25 384
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		70.01 175	69.53 170	71.44 233	78.05 197	44.13 355	75.01 256	81.51 138	64.37 31	68.20 204	84.52 200	49.12 176	82.82 195	54.62 264	70.43 320	87.37 79

NR-MVSNet		69.54 193	68.85 187	71.59 227	78.05 197	43.81 360	74.20 276	80.86 164	65.18 15	62.76 323	84.52 200	52.35 116	83.59 163	50.96 297	70.78 315	87.37 79

WBMVS		60.54 357	60.61 348	60.34 406	78.00 199	35.95 453	64.55 418	64.89 407	49.63 365	63.39 311	78.70 332	33.85 373	67.65 417	42.10 393	70.35 324	77.43 379

EI-MVSNet-UG-set		71.92 131	71.06 139	74.52 120	77.98 200	53.56 171	76.62 215	79.16 191	64.40 30	71.18 151	78.95 331	52.19 119	84.66 143	65.47 155	73.57 269	85.32 178

WR-MVS_H		67.02 262	66.92 244	67.33 323	77.95 201	37.75 431	77.57 178	82.11 128	62.03 88	62.65 326	82.48 254	50.57 148	79.46 277	42.91 387	64.01 396	84.79 199

Casviewmamba		76.62 42	76.52 43	76.90 62	77.91 202	53.66 167	80.76 104	84.47 50	66.73 8	75.75 56	88.63 76	59.17 28	86.66 81	72.28 81	83.01 94	90.39 1

testing222		62.29 336	61.31 333	65.25 366	77.87 203	38.53 423	68.34 381	66.31 397	56.37 229	63.15 316	77.58 361	28.47 431	76.18 367	37.04 427	76.65 222	81.05 315

Effi-MVS+		73.31 97	72.54 111	75.62 91	77.87 203	53.64 168	79.62 125	79.61 184	61.63 95	72.02 139	82.61 244	56.44 47	85.97 108	63.99 169	79.07 170	87.25 85

DELS-MVS		74.76 66	74.46 70	75.65 90	77.84 205	52.25 211	75.59 241	84.17 58	63.76 41	73.15 113	82.79 239	59.58 25	86.80 77	67.24 133	86.04 68	87.89 52
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
ACMH+	57.40 11	66.12 280	64.06 290	72.30 207	77.79 206	52.83 195	80.39 107	78.03 229	57.30 202	57.47 396	82.55 250	27.68 442	84.17 148	45.54 354	69.78 338	79.90 342

MGCFI-Net		72.45 118	73.34 96	69.81 281	77.77 207	43.21 369	75.84 238	81.18 154	59.59 151	75.45 58	86.64 130	57.74 35	77.94 319	63.92 170	81.90 113	88.30 36

RRT-MVS		71.46 141	70.70 147	73.74 156	77.76 208	49.30 283	76.60 216	80.45 171	61.25 101	68.17 206	84.78 188	44.64 236	84.90 135	64.79 160	77.88 197	87.03 92

GDP-MVS		72.64 113	71.28 134	76.70 66	77.72 209	54.22 157	79.57 126	84.45 51	55.30 257	71.38 149	86.97 118	39.94 293	87.00 73	67.02 139	79.20 164	88.89 15

3Dnovator	64.47 5	72.49 117	71.39 130	75.79 84	77.70 210	58.99 79	80.66 106	83.15 109	62.24 80	65.46 275	86.59 135	42.38 261	85.52 118	59.59 219	84.72 74	82.85 268

EG-PatchMatch MVS		64.71 298	62.87 312	70.22 270	77.68 211	53.48 173	77.99 165	78.82 200	53.37 305	56.03 412	77.41 363	24.75 465	84.04 152	46.37 342	73.42 275	73.14 428

UWE-MVS		60.18 361	59.78 355	61.39 399	77.67 212	33.92 469	69.04 377	63.82 421	48.56 381	64.27 301	77.64 360	27.20 446	70.40 401	33.56 450	76.24 226	79.83 345

CP-MVSNet		66.49 275	66.41 256	66.72 328	77.67 212	36.33 447	76.83 212	79.52 186	62.45 73	62.54 329	83.47 231	46.32 212	78.37 310	45.47 359	63.43 405	85.45 170

GBi-Net		67.21 254	66.55 250	69.19 290	77.63 214	43.33 366	77.31 187	77.83 233	56.62 220	65.04 288	82.70 240	41.85 269	80.33 259	47.18 332	72.76 286	83.92 228

test1		67.21 254	66.55 250	69.19 290	77.63 214	43.33 366	77.31 187	77.83 233	56.62 220	65.04 288	82.70 240	41.85 269	80.33 259	47.18 332	72.76 286	83.92 228

FMVSNet2		66.93 264	66.31 261	68.79 299	77.63 214	42.98 375	76.11 228	77.47 239	56.62 220	65.22 285	82.17 264	41.85 269	80.18 266	47.05 338	72.72 289	83.20 257

PCF-MVS	61.88 8	70.95 153	69.49 171	75.35 95	77.63 214	55.71 130	76.04 232	81.81 132	50.30 357	69.66 178	85.40 179	52.51 111	84.89 136	51.82 289	80.24 139	85.45 170
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		65.41 289	63.80 295	70.22 270	77.62 218	55.53 137	76.30 222	78.53 214	50.59 355	56.47 408	78.65 335	39.84 296	82.68 198	44.10 373	72.12 300	72.44 438
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FC-MVSNet-test		69.80 183	70.58 151	67.46 319	77.61 219	34.73 461	76.05 231	83.19 108	60.84 110	65.88 268	86.46 141	54.52 78	80.76 251	52.52 281	78.12 193	86.91 96

PS-CasMVS		66.42 276	66.32 260	66.70 330	77.60 220	36.30 449	76.94 205	79.61 184	62.36 75	62.43 334	83.66 223	45.69 216	78.37 310	45.35 361	63.26 407	85.42 173

testing3		56.54 394	55.92 395	58.41 421	77.52 221	27.93 494	69.72 364	56.36 464	54.75 279	58.63 382	77.80 355	20.88 476	71.75 391	25.31 491	62.25 422	75.53 403

FMVSNet1		66.70 270	65.87 267	69.19 290	77.49 222	43.33 366	77.31 187	77.83 233	56.45 226	64.60 297	82.70 240	38.08 324	80.33 259	46.08 346	72.31 295	83.92 228

ETVMVS		59.51 370	58.81 364	61.58 396	77.46 223	34.87 457	64.94 416	59.35 450	54.06 291	61.08 351	76.67 375	29.54 420	71.87 390	32.16 455	74.07 258	78.01 373

VPA-MVSNet		69.02 209	69.47 172	67.69 315	77.42 224	41.00 398	74.04 279	79.68 182	60.06 135	69.26 188	84.81 187	51.06 141	77.58 333	54.44 267	74.43 254	84.48 209

UniMVSNet_ETH3D		67.60 249	67.07 243	69.18 293	77.39 225	42.29 382	74.18 277	75.59 286	60.37 125	66.77 245	86.06 155	37.64 326	78.93 302	52.16 284	73.49 271	86.32 129

FE-MVS		65.91 282	63.33 306	73.63 164	77.36 226	51.95 220	72.62 313	75.81 281	53.70 301	65.31 277	78.96 330	28.81 429	86.39 93	43.93 374	73.48 272	82.55 275

myMVS_eth3d28		60.66 355	61.04 339	59.51 409	77.32 227	31.58 481	63.11 430	63.87 420	59.00 160	60.90 353	78.26 341	32.69 392	66.15 431	36.10 438	78.13 192	80.81 319

thres100view900		63.28 318	62.41 318	65.89 352	77.31 228	38.66 421	72.65 311	69.11 375	57.07 207	62.45 332	81.03 290	37.01 338	79.17 284	31.84 459	73.25 278	79.83 345

cascas		65.98 281	63.42 304	73.64 163	77.26 229	52.58 203	72.26 324	77.21 248	48.56 381	61.21 349	74.60 406	32.57 397	85.82 112	50.38 300	76.75 220	82.52 278

viewdifsd2359ckpt09		73.42 93	72.45 113	76.30 77	77.25 230	53.27 181	80.36 108	82.48 122	57.96 188	72.24 135	85.73 169	53.22 99	86.27 98	63.79 176	79.06 171	89.36 7

thres600view7		63.30 317	62.27 320	66.41 339	77.18 231	38.87 419	72.35 321	69.11 375	56.98 210	62.37 335	80.96 292	37.01 338	79.00 299	31.43 466	73.05 282	81.36 301

E2		73.72 88	73.60 89	74.06 140	77.16 232	50.40 252	76.97 202	83.74 79	61.64 93	73.36 104	86.75 126	56.14 53	82.99 176	67.50 130	79.18 167	88.80 16

E3		73.72 88	73.60 89	74.06 140	77.16 232	50.40 252	76.97 202	83.74 79	61.64 93	73.36 104	86.76 123	56.13 54	82.99 176	67.50 130	79.18 167	88.80 16

E5new		74.10 78	74.09 76	74.15 135	77.14 234	50.74 238	78.24 150	83.86 73	62.34 76	73.95 91	87.27 106	55.97 61	82.95 182	68.16 115	79.86 143	88.77 19

E6new		74.10 78	74.09 76	74.15 135	77.14 234	50.74 238	78.24 150	83.85 75	62.34 76	73.95 91	87.27 106	55.98 59	82.95 182	68.17 113	79.85 145	88.77 19

E6		74.10 78	74.09 76	74.15 135	77.14 234	50.74 238	78.24 150	83.85 75	62.34 76	73.95 91	87.27 106	55.98 59	82.95 182	68.17 113	79.85 145	88.77 19

E5		74.10 78	74.09 76	74.15 135	77.14 234	50.74 238	78.24 150	83.86 73	62.34 76	73.95 91	87.27 106	55.97 61	82.95 182	68.16 115	79.86 143	88.77 19

E4		73.91 84	73.83 85	74.15 135	77.13 238	50.47 251	77.15 197	83.79 78	62.21 81	73.61 99	87.19 113	56.08 57	83.03 174	67.91 121	79.35 157	88.94 14

viewcassd2359sk11		73.56 90	73.41 94	74.00 144	77.13 238	50.35 255	76.86 210	83.69 83	61.23 102	73.14 114	86.38 144	56.09 56	82.96 180	67.15 134	79.01 172	88.70 25

SDMVSNet		68.03 236	68.10 213	67.84 311	77.13 238	48.72 296	65.32 410	79.10 192	58.02 185	65.08 286	82.55 250	47.83 188	73.40 378	63.92 170	73.92 260	81.41 298

sd_testset		64.46 303	64.45 287	64.51 371	77.13 238	42.25 383	62.67 433	72.11 344	58.02 185	65.08 286	82.55 250	41.22 285	69.88 404	47.32 330	73.92 260	81.41 298

PEN-MVS		66.60 272	66.45 252	67.04 325	77.11 242	36.56 444	77.03 201	80.42 172	62.95 60	62.51 331	84.03 212	46.69 209	79.07 291	44.22 369	63.08 409	85.51 165

E3new		73.41 94	73.22 97	73.95 147	77.06 243	50.31 256	76.78 213	83.66 84	60.90 108	72.93 122	86.02 157	55.99 58	82.95 182	66.89 142	78.77 177	88.61 27

icg_test_0407_2		66.41 277	66.75 247	65.37 363	77.06 243	49.73 269	63.79 426	78.60 208	52.70 314	66.19 258	82.58 245	45.17 230	63.65 443	59.20 224	75.46 242	82.74 270

IMVS_0407		68.90 212	67.93 214	71.82 216	77.06 243	49.73 269	74.40 273	78.60 208	52.70 314	66.19 258	82.58 245	45.17 230	83.00 175	59.20 224	75.46 242	82.74 270

IMVS_0404		64.63 300	64.22 289	65.88 353	77.06 243	49.73 269	64.40 419	78.60 208	52.70 314	53.16 447	82.58 245	34.82 359	65.16 437	59.20 224	75.46 242	82.74 270

IMVS_0403		69.09 208	68.14 211	71.95 211	77.06 243	49.73 269	74.51 268	78.60 208	52.70 314	66.69 247	82.58 245	46.43 211	83.38 167	59.20 224	75.46 242	82.74 270

PatchMatch-RL		56.25 399	54.55 409	61.32 400	77.06 243	56.07 122	65.57 404	54.10 474	44.13 437	53.49 445	71.27 437	25.20 462	66.78 424	36.52 435	63.66 400	61.12 481

PVSNet_BlendedMVS		68.56 223	67.72 218	71.07 253	77.03 249	50.57 246	74.50 269	81.52 136	53.66 303	64.22 304	79.72 317	49.13 174	82.87 191	55.82 251	73.92 260	79.77 348

PVSNet_Blended		68.59 219	67.72 218	71.19 245	77.03 249	50.57 246	72.51 318	81.52 136	51.91 329	64.22 304	77.77 358	49.13 174	82.87 191	55.82 251	79.58 151	80.14 338

F-COLMAP		63.05 323	60.87 344	69.58 286	76.99 251	53.63 169	78.12 159	76.16 272	47.97 394	52.41 451	81.61 279	27.87 439	78.11 315	40.07 405	66.66 376	77.00 387

tfpn200view9		63.18 320	62.18 322	66.21 344	76.85 252	39.62 413	71.96 329	69.44 371	56.63 218	62.61 327	79.83 312	37.18 332	79.17 284	31.84 459	73.25 278	79.83 345

thres400		63.31 316	62.18 322	66.72 328	76.85 252	39.62 413	71.96 329	69.44 371	56.63 218	62.61 327	79.83 312	37.18 332	79.17 284	31.84 459	73.25 278	81.36 301

casdiffseed414692147		73.73 87	73.22 97	75.28 99	76.76 254	52.16 213	80.05 113	83.01 113	63.38 47	73.35 106	87.11 115	53.22 99	84.14 149	61.71 200	80.38 136	89.55 6

tttt0517		67.83 244	65.66 271	74.33 125	76.69 255	50.82 236	77.86 169	73.99 321	54.54 284	64.64 296	82.53 253	35.06 356	85.50 120	55.71 254	69.91 335	86.67 109

BP-MVS1		73.41 94	72.25 115	76.88 63	76.68 256	53.70 165	79.15 131	81.07 157	60.66 115	71.81 140	87.39 101	40.93 287	87.24 62	71.23 94	81.29 122	89.71 3

ET-MVSNet_ETH3D		67.96 239	65.72 270	74.68 110	76.67 257	55.62 135	75.11 252	74.74 305	52.91 311	60.03 360	80.12 308	33.68 375	82.64 200	61.86 198	76.34 225	85.78 150

TAPA-MVS	59.36 10	66.60 272	65.20 282	70.81 258	76.63 258	48.75 294	76.52 219	80.04 177	50.64 354	65.24 283	84.93 183	39.15 307	78.54 309	36.77 429	76.88 217	85.14 184
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OMC-MVS		71.40 144	70.60 149	73.78 151	76.60 259	53.15 184	79.74 122	79.78 180	58.37 178	68.75 194	86.45 142	45.43 224	80.60 252	62.58 190	77.73 198	87.58 69

LTVRE_ROB	55.42 16	63.15 321	61.23 336	68.92 297	76.57 260	47.80 314	59.92 450	76.39 268	54.35 287	58.67 380	82.46 255	29.44 423	81.49 226	42.12 392	71.14 310	77.46 378
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
QAPM		70.05 174	68.81 189	73.78 151	76.54 261	53.43 177	83.23 66	83.48 89	52.89 312	65.90 266	86.29 147	41.55 278	86.49 91	51.01 295	78.40 189	81.42 297

FMVSNet3		66.32 279	65.61 272	68.46 303	76.48 262	42.34 381	74.98 258	77.15 249	55.83 242	65.04 288	81.16 286	39.91 294	80.14 267	47.18 332	72.76 286	82.90 267

casdiffmvs_mvg		76.14 51	76.30 45	75.66 89	76.46 263	51.83 222	79.67 123	85.08 40	65.02 20	75.84 53	88.58 77	59.42 27	85.08 129	72.75 76	83.93 85	90.08 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
hybridcas		74.86 64	75.07 62	74.24 129	76.30 264	50.58 245	79.30 129	83.88 69	63.15 57	74.69 77	88.13 81	58.91 30	82.98 179	68.30 109	82.93 99	89.15 11

thisisatest0530		67.92 240	65.78 269	74.33 125	76.29 265	51.03 230	76.89 207	74.25 316	53.67 302	65.59 273	81.76 276	35.15 355	85.50 120	55.94 249	72.47 291	86.47 118

baseline1		63.81 312	63.87 294	63.62 379	76.29 265	36.36 445	71.78 332	67.29 387	56.05 239	64.23 303	82.95 238	47.11 202	74.41 374	47.30 331	61.85 425	80.10 339

ab-mvs		66.65 271	66.42 255	67.37 321	76.17 267	41.73 388	70.41 356	76.14 274	53.99 292	65.98 263	83.51 229	49.48 165	76.24 365	48.60 316	73.46 273	84.14 220

Effi-MVS+-dtu		69.64 189	67.53 224	75.95 80	76.10 268	62.29 16	80.20 112	76.06 276	59.83 145	65.26 282	77.09 368	41.56 277	84.02 154	60.60 210	71.09 314	81.53 296

DTE-MVSNet		65.58 286	65.34 279	66.31 341	76.06 269	34.79 458	76.43 220	79.38 189	62.55 71	61.66 344	83.83 218	45.60 218	79.15 287	41.64 399	60.88 431	85.00 190

EPNet		73.09 103	72.16 116	75.90 81	75.95 270	56.28 117	83.05 68	72.39 341	66.53 11	65.27 279	87.00 117	50.40 150	85.47 122	62.48 192	86.32 65	85.94 141
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SixPastTwentyTwo		61.65 347	58.80 366	70.20 272	75.80 271	47.22 323	75.59 241	69.68 366	54.61 281	54.11 435	79.26 327	27.07 448	82.96 180	43.27 382	49.79 479	80.41 328

tt0320-xc		58.33 380	56.41 391	64.08 375	75.79 272	41.34 392	68.30 382	62.72 433	47.90 395	56.29 409	74.16 411	28.53 430	71.04 395	41.50 400	52.50 470	79.88 343

baseline		74.61 70	74.70 67	74.34 124	75.70 273	49.99 265	77.54 180	84.63 49	62.73 69	73.98 89	87.79 93	57.67 37	83.82 158	69.49 102	82.74 105	89.20 10

Baseline_NR-MVSNet		67.05 261	67.56 221	65.50 359	75.65 274	37.70 433	75.42 244	74.65 309	59.90 140	68.14 208	83.15 237	49.12 176	77.20 341	52.23 283	69.78 338	81.60 293

viewdifsd2359ckpt13		72.40 121	71.79 122	74.22 131	75.63 275	51.77 223	78.67 139	83.13 111	57.08 206	71.59 145	85.36 180	53.10 103	82.64 200	63.07 186	78.51 185	88.24 39

jajsoiax		68.25 230	66.45 252	73.66 161	75.62 276	55.49 138	80.82 102	78.51 215	52.33 322	64.33 299	84.11 210	28.28 435	81.81 219	63.48 180	70.62 317	83.67 242

mvs_tets		68.18 233	66.36 258	73.63 164	75.61 277	55.35 142	80.77 103	78.56 213	52.48 321	64.27 301	84.10 211	27.45 444	81.84 218	63.45 181	70.56 319	83.69 241

casdiffmvs		74.80 65	74.89 66	74.53 119	75.59 278	50.37 254	78.17 158	85.06 42	62.80 68	74.40 82	87.86 90	57.88 34	83.61 162	69.46 104	82.79 104	89.59 5
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PVSNet	50.76 19	58.40 378	57.39 377	61.42 397	75.53 279	44.04 358	61.43 440	63.45 426	47.04 411	56.91 402	73.61 415	27.00 449	64.76 438	39.12 414	72.40 292	75.47 404

fmvsm_s_conf0.5_n_11		73.16 100	73.35 95	72.58 194	75.48 280	52.41 210	78.84 135	76.85 257	58.64 170	73.58 101	87.25 111	54.09 83	79.47 276	76.19 46	79.27 160	85.86 146

tt0320		58.59 375	56.81 385	63.92 377	75.46 281	41.32 393	68.63 379	64.06 419	47.05 410	56.19 410	74.19 409	30.34 411	71.36 392	39.92 409	55.45 457	79.09 355

MVS		67.37 252	66.33 259	70.51 268	75.46 281	50.94 231	73.95 282	81.85 131	41.57 456	62.54 329	78.57 338	47.98 185	85.47 122	52.97 279	82.05 110	75.14 407

nrg030		72.96 106	73.01 101	72.84 188	75.41 283	50.24 257	80.02 114	82.89 118	58.36 179	74.44 81	86.73 127	58.90 31	80.83 248	65.84 152	74.46 252	87.44 73

thres200		62.20 337	61.16 338	65.34 364	75.38 284	39.99 408	69.60 368	69.29 373	55.64 249	61.87 339	76.99 369	37.07 337	78.96 301	31.28 467	73.28 277	77.06 385

fmvsm_s_conf0.5_n_8		74.30 75	74.39 71	74.01 143	75.33 285	52.89 192	78.24 150	77.32 247	61.65 92	78.13 36	88.90 68	52.82 107	81.54 225	78.46 22	78.67 181	87.60 67

TransMVSNet (Re)		64.72 297	64.33 288	65.87 354	75.22 286	38.56 422	74.66 266	75.08 303	58.90 163	61.79 340	82.63 243	51.18 138	78.07 316	43.63 380	55.87 456	80.99 316

MS-PatchMatch		62.42 333	61.46 330	65.31 365	75.21 287	52.10 214	72.05 326	74.05 319	46.41 416	57.42 398	74.36 407	34.35 365	77.57 334	45.62 352	73.67 265	66.26 476

WB-MVSnew		59.66 367	59.69 356	59.56 408	75.19 288	35.78 455	69.34 373	64.28 415	46.88 412	61.76 341	75.79 392	40.61 290	65.20 436	32.16 455	71.21 309	77.70 375

viewmanbaseed2359cas		72.92 107	72.89 103	73.00 183	75.16 289	49.25 285	77.25 194	83.11 112	59.52 153	72.93 122	86.63 132	54.11 82	80.98 241	66.63 143	80.67 128	88.76 24

SD_0403		63.07 322	63.49 303	61.82 393	75.16 289	31.14 483	71.89 331	73.47 326	53.34 306	58.22 387	81.81 275	45.17 230	73.86 377	37.43 423	74.87 249	80.45 326

viewmacassd2359aftdt		73.15 101	73.16 99	73.11 181	75.15 291	49.31 282	77.53 182	83.21 104	60.42 121	73.20 111	87.34 103	53.82 89	81.05 240	67.02 139	80.79 124	88.96 13

fmvsm_s_conf0.5_n_6		72.59 115	72.87 104	71.73 220	75.14 292	51.96 219	76.28 223	77.12 250	57.63 198	73.85 96	86.91 119	51.54 132	77.87 324	77.18 34	80.18 141	85.37 176

IB-MVS	56.42 12	65.40 290	62.73 315	73.40 175	74.89 293	52.78 196	73.09 305	75.13 299	55.69 246	58.48 384	73.73 414	32.86 385	86.32 96	50.63 298	70.11 330	81.10 312
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
MVS_Test		72.45 118	72.46 112	72.42 204	74.88 294	48.50 300	76.28 223	83.14 110	59.40 154	72.46 132	84.68 191	55.66 66	81.12 236	65.98 151	79.66 150	87.63 65

sc_t1		59.76 365	57.84 376	65.54 357	74.87 295	42.95 377	69.61 367	64.16 418	48.90 376	58.68 379	77.12 366	28.19 437	72.35 385	43.75 379	55.28 458	81.31 304

tt0805		67.77 246	67.24 238	69.34 289	74.87 295	40.08 405	77.36 186	81.37 142	55.31 256	66.33 256	84.65 194	37.35 330	82.55 203	55.65 256	72.28 296	85.39 175

CANet_DTU		68.18 233	67.71 220	69.59 284	74.83 297	46.24 331	78.66 140	76.85 257	59.60 148	63.45 310	82.09 270	35.25 354	77.41 336	59.88 216	78.76 178	85.14 184

viewdifsd2359ckpt07		71.90 132	71.97 119	71.69 223	74.81 298	48.08 310	75.30 246	80.49 170	60.00 137	71.63 144	86.33 146	56.34 49	79.25 281	65.40 156	77.41 205	87.76 60

tfpnnormal		62.47 329	61.63 328	64.99 368	74.81 298	39.01 418	71.22 339	73.72 324	55.22 261	60.21 356	80.09 310	41.26 283	76.98 349	30.02 473	68.09 364	78.97 359

Vis-MVSNet (Re-imp)		63.69 313	63.88 293	63.14 384	74.75 300	31.04 484	71.16 341	63.64 423	56.32 231	59.80 365	84.99 182	44.51 237	75.46 369	39.12 414	80.62 129	82.92 265

HY-MVS	56.14 13	64.55 302	63.89 292	66.55 336	74.73 301	41.02 395	69.96 362	74.43 310	49.29 371	61.66 344	80.92 293	47.43 197	76.68 358	44.91 366	71.69 304	81.94 289

Syy-MVS		56.00 401	56.23 393	55.32 439	74.69 302	26.44 500	65.52 405	57.49 459	50.97 350	56.52 406	72.18 424	39.89 295	68.09 412	24.20 492	64.59 393	71.44 452

myMVS_eth3d		54.86 414	54.61 408	55.61 438	74.69 302	27.31 497	65.52 405	57.49 459	50.97 350	56.52 406	72.18 424	21.87 474	68.09 412	27.70 482	64.59 393	71.44 452

COLMAP_ROB	52.97 17	61.27 353	58.81 364	68.64 300	74.63 304	52.51 205	78.42 146	73.30 330	49.92 363	50.96 456	81.51 282	23.06 468	79.40 278	31.63 463	65.85 381	74.01 425
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_10		74.11 77	73.98 81	74.48 121	74.61 305	52.86 194	78.10 162	77.06 252	57.14 205	78.24 35	88.79 73	52.83 106	82.26 209	77.79 28	81.30 121	88.32 35

KinetiMVS		71.26 145	70.16 160	74.57 117	74.59 306	52.77 197	75.91 235	81.20 153	60.72 114	69.10 192	85.71 170	41.67 274	83.53 164	63.91 172	78.62 183	87.42 74

LCM-MVSNet-Re		61.88 345	61.35 332	63.46 380	74.58 307	31.48 482	61.42 441	58.14 455	58.71 168	53.02 449	79.55 321	43.07 252	76.80 352	45.69 350	77.96 195	82.11 288

test_djsdf		69.45 198	67.74 217	74.58 116	74.57 308	54.92 148	82.79 73	78.48 216	51.26 344	65.41 276	83.49 230	38.37 318	83.24 170	66.06 147	69.25 350	85.56 163

EI-MVSNet		69.27 203	68.44 200	71.73 220	74.47 309	49.39 280	75.20 250	78.45 219	59.60 148	69.16 190	76.51 381	51.29 136	82.50 204	59.86 218	71.45 308	83.30 253

CVMVSNet		59.63 368	59.14 360	61.08 403	74.47 309	38.84 420	75.20 250	68.74 377	31.15 484	58.24 386	76.51 381	32.39 400	68.58 410	49.77 303	65.84 382	75.81 399

IterMVS-LS		69.22 205	68.48 196	71.43 235	74.44 311	49.40 279	76.23 225	77.55 238	59.60 148	65.85 269	81.59 281	51.28 137	81.58 224	59.87 217	69.90 336	83.30 253
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
fmvsm_l_conf0.5_n_3		73.23 99	73.13 100	73.55 168	74.40 312	55.13 144	78.97 133	74.96 304	56.64 217	74.76 76	88.75 74	55.02 71	78.77 306	76.33 43	78.31 191	86.74 105

XVG-OURS-SEG-HR		68.81 214	67.47 227	72.82 190	74.40 312	56.87 112	70.59 352	79.04 195	54.77 278	66.99 241	86.01 158	39.57 299	78.21 314	62.54 191	73.33 276	83.37 252

EGC-MVSNET		42.47 453	38.48 462	54.46 445	74.33 314	48.73 295	70.33 358	51.10 480	0.03 559	0.18 558	67.78 464	13.28 491	66.49 427	18.91 502	50.36 477	48.15 496

XVG-OURS		68.76 217	67.37 230	72.90 187	74.32 315	57.22 102	70.09 361	78.81 201	55.24 259	67.79 226	85.81 168	36.54 342	78.28 313	62.04 196	75.74 237	83.19 258

SSC-MVS3.2		60.57 356	61.39 331	58.12 426	74.29 316	32.63 476	59.52 451	65.53 403	59.90 140	62.45 332	79.75 316	41.96 264	63.90 442	39.47 412	69.65 345	77.84 374

OpenMVS	61.03 9	68.85 213	67.56 221	72.70 192	74.26 317	53.99 160	81.21 98	81.34 147	52.70 314	62.75 324	85.55 174	38.86 311	84.14 149	48.41 318	83.01 94	79.97 340

MIMVSNet		57.35 388	57.07 380	58.22 423	74.21 318	37.18 436	62.46 434	60.88 446	48.88 377	55.29 420	75.99 390	31.68 404	62.04 449	31.87 458	72.35 293	75.43 405

Elysia		70.19 172	68.29 206	75.88 82	74.15 319	54.33 155	78.26 147	83.21 104	55.04 270	67.28 234	83.59 225	30.16 414	86.11 102	63.67 177	79.26 161	87.20 87

StellarMVS		70.19 172	68.29 206	75.88 82	74.15 319	54.33 155	78.26 147	83.21 104	55.04 270	67.28 234	83.59 225	30.16 414	86.11 102	63.67 177	79.26 161	87.20 87

SCA		60.49 358	58.38 370	66.80 327	74.14 321	48.06 311	63.35 429	63.23 428	49.13 373	59.33 374	72.10 426	37.45 328	74.27 375	44.17 370	62.57 418	78.05 369

fmvsm_s_conf0.5_n_5		72.69 112	72.80 105	72.37 205	74.11 322	53.21 183	78.12 159	73.31 329	53.98 293	76.81 49	88.05 85	53.38 97	77.37 338	76.64 40	80.78 125	86.53 115

fmvsm_s_conf0.5_n_3		73.55 91	74.39 71	71.03 254	74.09 323	51.86 221	77.77 174	75.60 285	61.18 103	78.67 33	88.98 65	55.88 64	77.73 328	78.69 16	78.68 180	83.50 250

fmvsm_l_conf0.5_n_9		73.27 98	73.66 88	72.09 209	73.82 324	52.72 198	77.45 184	74.28 315	56.61 223	77.10 47	88.16 80	56.17 52	77.09 343	78.27 24	81.13 123	86.48 117

VortexMVS		66.41 277	65.50 274	69.16 294	73.75 325	48.14 307	73.41 294	78.28 226	53.73 300	64.98 292	78.33 340	40.62 289	79.07 291	58.88 228	67.50 369	80.26 335

thisisatest0515		65.83 283	63.50 302	72.82 190	73.75 325	49.50 278	71.32 337	73.12 336	49.39 369	63.82 306	76.50 383	34.95 358	84.84 139	53.20 278	75.49 241	84.13 221

fmvsm_s_conf0.5_n_4		72.04 130	71.85 120	72.58 194	73.74 327	52.49 206	76.69 214	72.42 340	56.42 228	75.32 59	87.04 116	52.13 121	78.01 318	79.29 12	73.65 266	87.26 84

K. test v3		60.47 359	57.11 379	70.56 266	73.74 327	48.22 304	75.10 254	62.55 434	58.27 180	53.62 441	76.31 385	27.81 440	81.59 223	47.42 326	39.18 494	81.88 291

guyue		68.10 235	67.23 240	70.71 263	73.67 329	49.27 284	73.65 291	76.04 277	55.62 250	67.84 223	82.26 260	41.24 284	78.91 304	61.01 207	73.72 264	83.94 226

v10		70.21 170	69.02 182	73.81 150	73.51 330	50.92 233	78.74 137	81.39 141	60.05 136	66.39 255	81.83 274	47.58 193	85.41 125	62.80 189	68.86 357	85.09 188

AstraMVS		67.86 242	66.83 245	70.93 256	73.50 331	49.34 281	73.28 299	74.01 320	55.45 254	68.10 213	83.28 232	38.93 310	79.14 288	63.22 184	71.74 303	84.30 214

fmvsm_s_conf0.5_n_7		69.54 193	69.67 168	69.15 295	73.47 332	51.41 226	70.35 357	73.34 328	57.05 208	68.41 199	85.83 165	49.86 159	72.84 381	71.86 88	76.83 218	83.19 258

LuminaMVS		68.24 231	66.82 246	72.51 199	73.46 333	53.60 170	76.23 225	78.88 199	52.78 313	68.08 214	80.13 307	32.70 391	81.41 227	63.16 185	75.97 233	82.53 276

v1144		70.42 165	69.31 176	73.76 153	73.22 334	50.64 243	77.83 171	81.43 140	58.58 172	69.40 183	81.16 286	47.53 194	85.29 127	64.01 168	70.64 316	85.34 177

v1192		69.97 177	68.68 192	73.85 148	73.19 335	50.94 231	77.68 176	81.36 143	57.51 200	68.95 193	80.85 296	45.28 227	85.33 126	62.97 188	70.37 322	85.27 181

v8		70.33 168	69.28 177	73.49 170	73.15 336	50.22 258	78.62 141	80.78 165	60.79 111	66.45 253	82.11 269	49.35 169	84.98 132	63.58 179	68.71 358	85.28 180

v144192		69.71 184	68.51 195	73.33 177	73.10 337	50.13 260	77.54 180	80.64 166	56.65 216	68.57 197	80.55 299	46.87 208	84.96 134	62.98 187	69.66 343	84.89 196

v1921920		69.47 197	68.17 210	73.36 176	73.06 338	50.10 261	77.39 185	80.56 167	56.58 225	68.59 195	80.37 301	44.72 235	84.98 132	62.47 193	69.82 337	85.00 190

PatchmatchNet		59.84 364	58.24 371	64.65 370	73.05 339	46.70 327	69.42 372	62.18 440	47.55 402	58.88 377	71.96 428	34.49 363	69.16 406	42.99 386	63.60 402	78.07 368
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		69.24 204	67.91 215	73.25 180	73.02 340	49.82 267	77.21 195	80.54 168	56.43 227	68.34 202	80.51 300	43.33 249	84.99 130	62.03 197	69.77 340	84.95 194

fmvsm_l_mol_unc0.5_1		72.30 122	72.61 108	71.37 239	72.96 341	48.16 306	72.91 308	64.68 411	58.47 175	81.24 22	91.38 19	56.26 50	79.00 299	72.19 82	83.35 91	86.95 95

Fast-Effi-MVS+-dtu		67.37 252	65.33 280	73.48 171	72.94 342	57.78 95	77.47 183	76.88 256	57.60 199	61.97 337	76.85 372	39.31 303	80.49 257	54.72 263	70.28 326	82.17 287

EPNet_dtu		61.90 344	61.97 324	61.68 394	72.89 343	39.78 410	75.85 237	65.62 402	55.09 264	54.56 431	79.36 325	37.59 327	67.02 423	39.80 410	76.95 215	78.25 366
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		62.07 338	60.10 354	67.99 310	72.79 344	43.86 359	71.05 345	66.85 392	43.14 446	62.77 322	75.39 400	38.32 320	80.80 249	41.69 396	68.88 355	79.32 352

MDTV_nov1_ep13			57.00 381		72.73 345	38.26 426	65.02 415	64.73 410	44.74 429	55.46 415	72.48 421	32.61 396	70.47 398	37.47 422	67.75 367

MSDG		61.81 346	59.23 359	69.55 287	72.64 346	52.63 202	70.45 355	75.81 281	51.38 341	53.70 438	76.11 386	29.52 421	81.08 239	37.70 421	65.79 383	74.93 412

gg-mvs-nofinetune		57.86 386	56.43 390	62.18 390	72.62 347	35.35 456	66.57 395	56.33 465	50.65 353	57.64 393	57.10 491	30.65 408	76.36 363	37.38 424	78.88 173	74.82 414

v2v482		70.50 163	69.45 173	73.66 161	72.62 347	50.03 264	77.58 177	80.51 169	59.90 140	69.52 179	82.14 267	47.53 194	84.88 138	65.07 159	70.17 329	86.09 137

baseline2		63.42 315	61.26 335	69.89 280	72.55 349	47.62 318	71.54 334	68.38 379	50.11 359	54.82 426	75.55 396	43.06 253	80.96 242	48.13 321	67.16 373	81.11 311

test_fmvsm_n_1920		71.73 136	71.14 137	73.50 169	72.52 350	56.53 114	75.60 240	76.16 272	48.11 391	77.22 44	85.56 172	53.10 103	77.43 335	74.86 59	77.14 212	86.55 114

v7n		69.01 210	67.36 231	73.98 145	72.51 351	52.65 199	78.54 145	81.30 148	60.26 131	62.67 325	81.62 278	43.61 246	84.49 144	57.01 241	68.70 359	84.79 199

fmvsm_s_conf0.5_n_a		69.54 193	68.74 191	71.93 212	72.47 352	53.82 163	78.25 149	62.26 439	49.78 364	73.12 117	86.21 149	52.66 109	76.79 353	75.02 58	68.88 355	85.18 183

usedtu_dtu_shiyan1		64.34 306	63.57 299	66.66 332	72.44 353	40.74 401	69.60 368	76.80 261	53.21 307	61.73 342	77.92 348	41.92 267	77.68 330	46.23 343	72.25 297	81.57 294

FE-MVSNET3		64.34 306	63.57 299	66.66 332	72.44 353	40.74 401	69.60 368	76.80 261	53.21 307	61.73 342	77.92 348	41.92 267	77.68 330	46.23 343	72.25 297	81.57 294

pm-mvs1		65.24 292	64.97 284	66.04 349	72.38 355	39.40 416	72.62 313	75.63 284	55.53 251	62.35 336	83.18 236	47.45 196	76.47 362	49.06 313	66.54 377	82.24 284

XVG-ACMP-BASELINE		64.36 305	62.23 321	70.74 261	72.35 356	52.45 208	70.80 350	78.45 219	53.84 297	59.87 363	81.10 288	16.24 485	79.32 280	55.64 257	71.76 302	80.47 325

WTY-MVS		59.75 366	60.39 350	57.85 428	72.32 357	37.83 430	61.05 446	64.18 416	45.95 423	61.91 338	79.11 329	47.01 206	60.88 452	42.50 390	69.49 346	74.83 413

fmvsm_s_conf0.5_n		69.58 191	68.84 188	71.79 218	72.31 358	52.90 190	77.90 166	62.43 437	49.97 362	72.85 125	85.90 162	52.21 118	76.49 360	75.75 49	70.26 327	85.97 140

tpm cat1		59.25 372	56.95 382	66.15 346	72.19 359	46.96 325	68.09 384	65.76 400	40.03 466	57.81 391	70.56 440	38.32 320	74.51 373	38.26 419	61.50 428	77.00 387

mvs_anonymous		68.03 236	67.51 225	69.59 284	72.08 360	44.57 352	71.99 327	75.23 296	51.67 331	67.06 240	82.57 249	54.68 76	77.94 319	56.56 246	75.71 238	86.26 134

OurMVSNet-221017-0		61.37 352	58.63 368	69.61 283	72.05 361	48.06 311	73.93 284	72.51 339	47.23 408	54.74 427	80.92 293	21.49 475	81.24 233	48.57 317	56.22 455	79.53 350

fmvsm_s_conf0.5_n_2		69.82 181	69.27 178	71.46 230	72.00 362	51.08 228	73.30 296	67.79 383	55.06 269	75.24 61	87.51 95	44.02 243	77.00 347	75.67 50	72.86 284	86.31 132

IterMVS-SCA-FT		62.49 328	61.52 329	65.40 362	71.99 363	50.80 237	71.15 342	69.63 367	45.71 424	60.61 354	77.93 347	37.45 328	65.99 432	55.67 255	63.50 404	79.42 351

CostFormer		64.04 310	62.51 316	68.61 301	71.88 364	45.77 335	71.30 338	70.60 359	47.55 402	64.31 300	76.61 379	41.63 275	79.62 273	49.74 304	69.00 354	80.42 327

1314		64.61 301	63.21 309	68.80 298	71.87 365	47.46 321	73.95 282	78.39 224	42.88 449	59.97 361	76.60 380	38.11 323	79.39 279	54.84 262	72.32 294	79.55 349

tpm		57.34 389	58.16 372	54.86 442	71.80 366	34.77 459	67.47 391	56.04 469	48.20 390	60.10 358	76.92 370	37.17 334	53.41 487	40.76 402	65.01 387	76.40 394

fmvsm_s_conf0.1_n_2		69.64 189	69.01 184	71.52 228	71.66 367	51.04 229	73.39 295	67.14 389	55.02 273	75.11 63	87.64 94	42.94 255	77.01 346	75.55 52	72.63 290	86.52 116

eth_miper_zixun_eth		67.63 248	66.28 262	71.67 224	71.60 368	48.33 302	73.68 290	77.88 230	55.80 244	65.91 265	78.62 337	47.35 200	82.88 190	59.45 220	66.25 379	83.81 234

viewdifsd2359ckpt11		69.13 206	68.38 203	71.38 237	71.57 369	48.61 297	73.22 301	73.18 332	57.65 196	70.67 158	84.73 189	50.03 155	79.80 268	63.25 182	71.10 312	85.74 156

viewmsd2359difaftdt		69.13 206	68.38 203	71.38 237	71.57 369	48.61 297	73.22 301	73.18 332	57.65 196	70.67 158	84.73 189	50.03 155	79.80 268	63.25 182	71.10 312	85.74 156

pmmvs4		61.48 350	59.39 358	67.76 312	71.57 369	53.86 161	71.42 335	65.34 404	44.20 435	59.46 370	77.92 348	35.90 348	74.71 372	43.87 376	64.87 389	74.71 417

fmvsm_l_conf0.5_n		70.99 152	70.82 144	71.48 229	71.45 372	54.40 153	77.18 196	70.46 360	48.67 379	75.17 62	86.86 120	53.77 91	76.86 351	76.33 43	77.51 203	83.17 262

AllTest		57.08 391	54.65 407	64.39 372	71.44 373	49.03 286	69.92 363	67.30 385	45.97 421	47.16 472	79.77 314	17.47 479	67.56 419	33.65 447	59.16 442	76.57 392

TestCases				64.39 372	71.44 373	49.03 286		67.30 385	45.97 421	47.16 472	79.77 314	17.47 479	67.56 419	33.65 447	59.16 442	76.57 392

lessismore_v0				69.91 278	71.42 375	47.80 314		50.90 482		50.39 462	75.56 395	27.43 445	81.33 230	45.91 348	34.10 500	80.59 324

gm-plane-assit					71.40 376	41.72 390			48.85 378		73.31 417		82.48 206	48.90 314

GG-mvs-BLEND				62.34 389	71.36 377	37.04 440	69.20 374	57.33 461		54.73 428	65.48 476	30.37 410	77.82 325	34.82 443	74.93 248	72.17 443

fmvsm_l_conf0.5_n_a		70.50 163	70.27 157	71.18 246	71.30 378	54.09 158	76.89 207	69.87 364	47.90 395	74.37 83	86.49 140	53.07 105	76.69 357	75.41 54	77.11 213	82.76 269

test_fmvsmconf_n		73.01 104	72.59 109	74.27 127	71.28 379	55.88 127	78.21 157	75.56 287	54.31 288	74.86 72	87.80 92	54.72 75	80.23 264	78.07 26	78.48 186	86.70 106

test_fmvsmvis_n_1920		70.84 154	70.38 154	72.22 208	71.16 380	55.39 140	75.86 236	72.21 343	49.03 374	73.28 109	86.17 151	51.83 127	77.29 340	75.80 48	78.05 194	83.98 225

fmvsm_s_conf0.1_n		69.41 199	68.60 194	71.83 215	71.07 381	52.88 193	77.85 170	62.44 436	49.58 367	72.97 120	86.22 148	51.68 130	76.48 361	75.53 53	70.10 331	86.14 135

FMVSNet5		55.86 403	54.93 404	58.66 420	71.05 382	36.35 446	64.18 423	62.48 435	46.76 414	50.66 461	74.73 405	25.80 458	64.04 440	33.11 451	65.57 384	75.59 402

fmvsm_s_conf0.1_n_a		69.32 201	68.44 200	71.96 210	70.91 383	53.78 164	78.12 159	62.30 438	49.35 370	73.20 111	86.55 139	51.99 123	76.79 353	74.83 60	68.68 360	85.32 178

c3_l		68.33 228	67.56 221	70.62 265	70.87 384	46.21 332	74.47 270	78.80 202	56.22 236	66.19 258	78.53 339	51.88 124	81.40 228	62.08 194	69.04 353	84.25 215

GA-MVS		65.53 287	63.70 297	71.02 255	70.87 384	48.10 308	70.48 354	74.40 311	56.69 215	64.70 295	76.77 373	33.66 376	81.10 237	55.42 259	70.32 325	83.87 231

pmmvs6		63.69 313	62.82 314	66.27 343	70.63 386	39.27 417	73.13 304	75.47 291	52.69 319	59.75 367	82.30 258	39.71 298	77.03 345	47.40 327	64.35 395	82.53 276

reproduce_monomvs		62.56 327	61.20 337	66.62 335	70.62 387	44.30 354	70.13 360	73.13 335	54.78 277	61.13 350	76.37 384	25.63 460	75.63 368	58.75 231	60.29 438	79.93 341

miper_ehance_all_eth		68.03 236	67.24 238	70.40 269	70.54 388	46.21 332	73.98 280	78.68 206	55.07 267	66.05 262	77.80 355	52.16 120	81.31 231	61.53 205	69.32 347	83.67 242

MonoMVSNet		64.15 308	63.31 307	66.69 331	70.51 389	44.12 357	74.47 270	74.21 317	57.81 193	63.03 317	76.62 377	38.33 319	77.31 339	54.22 268	60.59 437	78.64 362

OpenMVS_ROB	52.78 18	60.03 362	58.14 373	65.69 356	70.47 390	44.82 345	75.33 245	70.86 357	45.04 427	56.06 411	76.00 388	26.89 451	79.65 271	35.36 442	67.29 371	72.60 433

v148		68.24 231	67.19 241	71.40 236	70.43 391	47.77 316	75.76 239	77.03 253	58.91 162	67.36 232	80.10 309	48.60 181	81.89 216	60.01 214	66.52 378	84.53 207

XXY-MVS		60.68 354	61.67 327	57.70 430	70.43 391	38.45 424	64.19 422	66.47 394	48.05 393	63.22 312	80.86 295	49.28 171	60.47 453	45.25 362	67.28 372	74.19 423

onestephybrid01		71.00 151	70.34 156	72.99 184	70.38 393	50.88 235	74.14 278	77.41 242	58.80 164	71.36 150	84.93 183	50.96 142	80.87 247	67.73 125	77.35 206	87.23 86

MVSTER		67.16 259	65.58 273	71.88 214	70.37 394	49.70 273	70.25 359	78.45 219	51.52 336	69.16 190	80.37 301	38.45 317	82.50 204	60.19 212	71.46 307	83.44 251

dtuplus		68.48 224	67.76 216	70.63 264	70.33 395	48.09 309	72.62 313	75.88 280	52.33 322	71.09 152	84.66 193	50.09 154	77.93 321	58.02 235	74.82 250	85.87 145

viewmamba		71.13 146	70.66 148	72.56 196	70.23 396	50.07 262	74.25 275	77.85 232	59.92 139	70.94 154	85.55 174	52.30 117	80.25 262	68.42 108	76.47 224	87.35 82

viewmambaseed2359dif		68.91 211	68.18 209	71.11 251	70.21 397	48.05 313	72.28 323	75.90 278	51.96 328	70.93 155	84.47 203	51.37 135	78.59 308	61.55 204	74.97 247	86.68 108

cl____		67.18 257	66.26 263	69.94 276	70.20 398	45.74 336	73.30 296	76.83 259	55.10 262	65.27 279	79.57 320	47.39 198	80.53 254	59.41 222	69.22 351	83.53 248

DIV-MVS_self_test		67.18 257	66.26 263	69.94 276	70.20 398	45.74 336	73.29 298	76.83 259	55.10 262	65.27 279	79.58 319	47.38 199	80.53 254	59.43 221	69.22 351	83.54 247

tpmvs		58.47 376	56.95 382	63.03 386	70.20 398	41.21 394	67.90 386	67.23 388	49.62 366	54.73 428	70.84 438	34.14 367	76.24 365	36.64 433	61.29 429	71.64 448

anonymousdsp		67.00 263	64.82 285	73.57 167	70.09 401	56.13 120	76.35 221	77.35 245	48.43 385	64.99 291	80.84 297	33.01 383	80.34 258	64.66 162	67.64 368	84.23 216

MIMVSNet1		55.17 410	54.31 413	57.77 429	70.03 402	32.01 479	65.68 403	64.81 408	49.19 372	46.75 475	76.00 388	25.53 461	64.04 440	28.65 478	62.13 423	77.26 383

CR-MVSNet		59.91 363	57.90 375	65.96 350	69.96 403	52.07 215	65.31 411	63.15 429	42.48 451	59.36 371	74.84 403	35.83 349	70.75 397	45.50 356	64.65 391	75.06 408

RPMNet		61.53 348	58.42 369	70.86 257	69.96 403	52.07 215	65.31 411	81.36 143	43.20 445	59.36 371	70.15 445	35.37 353	85.47 122	36.42 436	64.65 391	75.06 408

diffmvs_AUTHOR		71.02 149	70.87 143	71.45 232	69.89 405	48.97 291	73.16 303	78.33 225	57.79 195	72.11 138	85.26 181	51.84 126	77.89 323	71.00 96	78.47 188	87.49 71

test_fmvsmconf0.1_n		72.81 108	72.33 114	74.24 129	69.89 405	55.81 128	78.22 156	75.40 292	54.17 290	75.00 67	88.03 88	53.82 89	80.23 264	78.08 25	78.34 190	86.69 107

cl22		67.47 251	66.45 252	70.54 267	69.85 407	46.49 328	73.85 287	77.35 245	55.07 267	65.51 274	77.92 348	47.64 192	81.10 237	61.58 203	69.32 347	84.01 224

Anonymous20231206		55.10 412	55.30 402	54.48 444	69.81 408	33.94 468	62.91 432	62.13 441	41.08 458	55.18 421	75.65 394	32.75 389	56.59 476	30.32 472	67.86 365	72.91 429

mmtdpeth		60.40 360	59.12 361	64.27 374	69.59 409	48.99 289	70.67 351	70.06 363	54.96 274	62.78 321	73.26 419	27.00 449	67.66 416	58.44 234	45.29 486	76.16 396

our_test_3		56.49 395	54.42 410	62.68 388	69.51 410	45.48 341	66.08 399	61.49 443	44.11 438	50.73 460	69.60 455	33.05 381	68.15 411	38.38 418	56.86 451	74.40 420

ppachtmachnet_test		58.06 385	55.38 401	66.10 348	69.51 410	48.99 289	68.01 385	66.13 399	44.50 432	54.05 436	70.74 439	32.09 403	72.34 386	36.68 432	56.71 454	76.99 389

diffmvs		70.69 159	70.43 152	71.46 230	69.45 412	48.95 292	72.93 306	78.46 218	57.27 203	71.69 142	83.97 215	51.48 134	77.92 322	70.70 98	77.95 196	87.53 70
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
IterMVS		62.79 325	61.27 334	67.35 322	69.37 413	52.04 217	71.17 340	68.24 381	52.63 320	59.82 364	76.91 371	37.32 331	72.36 384	52.80 280	63.19 408	77.66 376
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
0.4-1-1-0.1		59.29 371	56.70 387	67.07 324	69.35 414	43.16 370	66.59 394	70.87 356	48.59 380	55.11 422	62.25 483	28.22 436	78.92 303	45.49 357	63.79 399	79.14 354

dmvs_re		56.77 393	56.83 384	56.61 433	69.23 415	41.02 395	58.37 456	64.18 416	50.59 355	57.45 397	71.42 432	35.54 351	58.94 463	37.23 425	67.45 370	69.87 467

miper_enhance_ethall		67.11 260	66.09 265	70.17 273	69.21 416	45.98 334	72.85 310	78.41 222	51.38 341	65.65 272	75.98 391	51.17 139	81.25 232	60.82 208	69.32 347	83.29 255

Patchmtry		57.16 390	56.47 389	59.23 413	69.17 417	34.58 462	62.98 431	63.15 429	44.53 431	56.83 403	74.84 403	35.83 349	68.71 409	40.03 406	60.91 430	74.39 421

blended_shiyan8		62.46 330	60.71 346	67.71 313	69.15 418	43.43 364	70.83 347	76.52 265	51.49 338	57.67 392	71.36 435	39.38 301	79.07 291	47.37 328	62.67 412	80.62 323

blended_shiyan6		62.46 330	60.71 346	67.71 313	69.14 419	43.42 365	70.82 348	76.52 265	51.50 337	57.64 393	71.37 434	39.38 301	79.08 290	47.36 329	62.67 412	80.65 322

CL-MVSNet_self_test		61.53 348	60.94 341	63.30 382	68.95 420	36.93 441	67.60 388	72.80 338	55.67 247	59.95 362	76.63 376	45.01 233	72.22 388	39.74 411	62.09 424	80.74 321

blend_shiyan4		61.38 351	59.10 362	68.20 307	68.94 421	44.64 349	70.81 349	76.52 265	51.63 332	57.56 395	69.94 450	28.30 434	79.61 274	47.44 324	60.78 433	80.36 333

V42		68.65 218	67.35 232	72.56 196	68.93 422	50.18 259	72.90 309	79.47 187	56.92 212	69.45 182	80.26 305	46.29 213	82.99 176	64.07 166	67.82 366	84.53 207

gbinet_0.2-2-1-0.02		62.43 332	60.41 349	68.49 302	68.91 423	43.71 361	71.73 333	75.89 279	52.10 326	58.33 385	69.67 454	36.86 340	80.59 253	47.18 332	63.05 410	81.16 310

FE-MVSNET2		62.01 341	60.88 342	65.42 361	68.74 424	38.43 425	72.92 307	77.39 243	54.74 280	55.40 418	76.71 374	35.46 352	76.72 356	44.25 368	62.31 421	81.10 312

nomal-1		58.46 377	57.31 378	61.90 392	68.64 425	49.90 266	55.10 471	63.49 424	48.22 388	59.51 369	72.40 422	32.56 399	65.29 435	45.60 353	70.25 328	70.51 461

test-LLR		58.15 384	58.13 374	58.22 423	68.57 426	44.80 346	65.46 407	57.92 456	50.08 360	55.44 416	69.82 451	32.62 394	57.44 470	49.66 306	73.62 267	72.41 439

test-mter		56.42 397	55.82 396	58.22 423	68.57 426	44.80 346	65.46 407	57.92 456	39.94 468	55.44 416	69.82 451	21.92 471	57.44 470	49.66 306	73.62 267	72.41 439

wanda-best-256-512		62.00 342	60.17 352	67.49 317	68.53 428	43.07 373	69.65 365	76.38 269	51.26 344	57.10 399	69.95 447	38.83 312	79.04 294	47.14 336	62.67 412	80.37 330

FE-blended-shiyan7		62.00 342	60.17 352	67.49 317	68.53 428	43.07 373	69.65 365	76.38 269	51.26 344	57.10 399	69.95 447	38.83 312	79.04 294	47.14 336	62.67 412	80.37 330

usedtu_blend_shiyan5		62.63 326	60.77 345	68.20 307	68.53 428	44.64 349	73.47 293	77.00 254	51.91 329	57.10 399	69.95 447	38.83 312	79.61 274	47.44 324	62.67 412	80.37 330

MVS-HIRNet		45.52 447	44.48 450	48.65 468	68.49 431	34.05 467	59.41 454	44.50 499	27.03 491	37.96 498	50.47 501	26.16 456	64.10 439	26.74 487	59.52 440	47.82 498

dp		51.89 430	51.60 429	52.77 457	68.44 432	32.45 478	62.36 435	54.57 471	44.16 436	49.31 467	67.91 461	28.87 428	56.61 475	33.89 446	54.89 460	69.24 472

dtuonlycased		55.96 402	54.88 406	59.22 414	68.38 433	40.38 403	69.17 375	63.12 431	40.00 467	53.62 441	68.84 459	36.27 344	66.23 430	40.57 403	53.92 465	71.06 458

PatchT		53.17 425	53.44 422	52.33 460	68.29 434	25.34 504	58.21 457	54.41 472	44.46 433	54.56 431	69.05 458	33.32 379	60.94 451	36.93 428	61.76 427	70.73 460

hybridnocas07		69.86 179	69.44 174	71.14 250	68.10 435	48.28 303	72.52 317	77.08 251	56.94 211	70.50 161	84.91 185	50.48 149	78.37 310	67.84 123	76.55 223	86.76 104

0.3-1-1-0.015		58.40 378	55.56 398	66.91 326	68.08 436	43.09 372	65.25 413	70.96 355	47.89 397	53.10 448	59.82 486	26.48 452	78.79 305	45.07 365	63.43 405	78.84 361

test_fmvsmconf0.01_n		72.17 126	71.50 126	74.16 133	67.96 437	55.58 136	78.06 163	74.67 308	54.19 289	74.54 80	88.23 78	50.35 152	80.24 263	78.07 26	77.46 204	86.65 111

Patchmatch-RL test		58.16 383	55.49 400	66.15 346	67.92 438	48.89 293	60.66 448	51.07 481	47.86 398	59.36 371	62.71 482	34.02 370	72.27 387	56.41 247	59.40 441	77.30 381

hybrid		69.38 200	68.93 186	70.75 260	67.86 439	48.20 305	72.49 319	76.90 255	55.23 260	70.42 163	84.34 206	49.76 162	77.62 332	67.11 135	76.20 227	86.42 119

pmmvs-eth3d		58.81 374	56.31 392	66.30 342	67.61 440	52.42 209	72.30 322	64.76 409	43.55 441	54.94 425	74.19 409	28.95 426	72.60 382	43.31 381	57.21 450	73.88 426

PVSNet_0	43.31 20	47.46 445	45.64 449	52.92 456	67.60 441	44.65 348	54.06 475	54.64 470	41.59 455	46.15 477	58.75 488	30.99 407	58.66 464	32.18 454	24.81 505	55.46 491

0.4-1-1-0.2		58.31 381	55.53 399	66.64 334	67.46 442	42.78 379	64.38 420	70.97 354	47.65 400	53.38 446	59.02 487	28.39 433	78.72 307	44.86 367	63.63 401	78.42 364

CHOSEN 280x420		47.83 443	46.36 448	52.24 462	67.37 443	49.78 268	38.91 504	43.11 502	35.00 477	43.27 486	63.30 481	28.95 426	49.19 496	36.53 434	60.80 432	57.76 488

UWE-MVS-28		52.25 428	52.35 426	51.93 463	66.99 444	22.79 508	63.48 428	48.31 489	46.78 413	52.73 450	76.11 386	27.78 441	57.82 469	20.58 500	68.41 362	75.17 406

tpmrst		58.24 382	58.70 367	56.84 432	66.97 445	34.32 464	69.57 371	61.14 445	47.17 409	58.58 383	71.60 431	41.28 282	60.41 454	49.20 311	62.84 411	75.78 400

sss		56.17 400	56.57 388	54.96 441	66.93 446	36.32 448	57.94 459	61.69 442	41.67 454	58.64 381	75.32 401	38.72 315	56.25 477	42.04 394	66.19 380	72.31 442

TinyColmap		54.14 415	51.72 428	61.40 398	66.84 447	41.97 385	66.52 396	68.51 378	44.81 428	42.69 487	75.77 393	11.66 495	72.94 380	31.96 457	56.77 453	69.27 471

miper_lstm_enhance		62.03 340	60.88 342	65.49 360	66.71 448	46.25 330	56.29 468	75.70 283	50.68 352	61.27 348	75.48 398	40.21 292	68.03 414	56.31 248	65.25 386	82.18 285

TESTMET0.1,1		55.28 408	54.90 405	56.42 434	66.56 449	43.67 362	65.46 407	56.27 467	39.18 470	53.83 437	67.44 466	24.21 466	55.46 481	48.04 322	73.11 281	70.13 465

dmvs_testset		50.16 437	51.90 427	44.94 474	66.49 450	11.78 519	61.01 447	51.50 478	51.17 348	50.30 464	67.44 466	39.28 304	60.29 455	22.38 495	57.49 449	62.76 480

D2MVS		62.30 335	60.29 351	68.34 306	66.46 451	48.42 301	65.70 402	73.42 327	47.71 399	58.16 388	75.02 402	30.51 409	77.71 329	53.96 271	71.68 305	78.90 360

MDA-MVSNet-bldmvs		53.87 418	50.81 432	63.05 385	66.25 452	48.58 299	56.93 466	63.82 421	48.09 392	41.22 488	70.48 443	30.34 411	68.00 415	34.24 445	45.92 485	72.57 434

ITE_SJBPF				62.09 391	66.16 453	44.55 353		64.32 414	47.36 405	55.31 419	80.34 303	19.27 477	62.68 447	36.29 437	62.39 420	79.04 357

EPMVS		53.96 416	53.69 420	54.79 443	66.12 454	31.96 480	62.34 436	49.05 485	44.42 434	55.54 414	71.33 436	30.22 413	56.70 473	41.65 398	62.54 419	75.71 401

ADS-MVSNet2		51.33 433	48.76 441	59.07 417	66.02 455	44.60 351	50.90 483	59.76 449	36.90 472	50.74 458	66.18 474	26.38 453	63.11 445	27.17 484	54.76 461	69.50 469

ADS-MVSNet		48.48 442	47.77 444	50.63 465	66.02 455	29.92 487	50.90 483	50.87 483	36.90 472	50.74 458	66.18 474	26.38 453	52.47 490	27.17 484	54.76 461	69.50 469

EU-MVSNet		55.61 406	54.41 411	59.19 416	65.41 457	33.42 471	72.44 320	71.91 346	28.81 486	51.27 454	73.87 413	24.76 464	69.08 407	43.04 385	58.20 446	75.06 408

FE-MVSNET		55.16 411	53.75 419	59.41 410	65.29 458	33.20 473	67.21 393	66.21 398	48.39 387	49.56 466	73.53 416	29.03 425	72.51 383	30.38 471	54.10 464	72.52 435

RPSCF		55.80 404	54.22 415	60.53 405	65.13 459	42.91 378	64.30 421	57.62 458	36.84 474	58.05 390	82.28 259	28.01 438	56.24 478	37.14 426	58.61 445	82.44 281

USDC		56.35 398	54.24 414	62.69 387	64.74 460	40.31 404	65.05 414	73.83 323	43.93 439	47.58 470	77.71 359	15.36 488	75.05 371	38.19 420	61.81 426	72.70 432

JIA-IIPM		51.56 431	47.68 446	63.21 383	64.61 461	50.73 242	47.71 492	58.77 453	42.90 448	48.46 469	51.72 495	24.97 463	70.24 403	36.06 439	53.89 466	68.64 473

Patchmatch-test		49.08 440	48.28 443	51.50 464	64.40 462	30.85 485	45.68 496	48.46 488	35.60 476	46.10 478	72.10 426	34.47 364	46.37 500	27.08 486	60.65 435	77.27 382

TDRefinement		53.44 422	50.72 434	61.60 395	64.31 463	46.96 325	70.89 346	65.27 406	41.78 452	44.61 482	77.98 345	11.52 497	66.36 428	28.57 479	51.59 473	71.49 451

test_vis1_n_1920		58.86 373	59.06 363	58.25 422	63.76 464	43.14 371	67.49 390	66.36 396	40.22 464	65.89 267	71.95 429	31.04 406	59.75 458	59.94 215	64.90 388	71.85 446

N_pmnet		39.35 460	40.28 458	36.54 485	63.76 464	1.62 541	49.37 488	0.76 539	34.62 478	43.61 485	66.38 473	26.25 455	42.57 504	26.02 489	51.77 472	65.44 477

ambc				65.13 367	63.72 466	37.07 439	47.66 493	78.78 203		54.37 434	71.42 432	11.24 498	80.94 243	45.64 351	53.85 467	77.38 380

WB-MVS		43.26 450	43.41 451	42.83 478	63.32 467	10.32 521	58.17 458	45.20 496	45.42 425	40.44 491	67.26 469	34.01 371	58.98 462	11.96 511	24.88 504	59.20 483

KD-MVS_2432*1600		53.45 420	51.50 430	59.30 411	62.82 468	37.14 437	55.33 469	71.79 348	47.34 406	55.09 423	70.52 441	21.91 472	70.45 399	35.72 440	42.97 489	70.31 463

miper_refine_blended		53.45 420	51.50 430	59.30 411	62.82 468	37.14 437	55.33 469	71.79 348	47.34 406	55.09 423	70.52 441	21.91 472	70.45 399	35.72 440	42.97 489	70.31 463

test0.0.03 1		53.32 424	53.59 421	52.50 459	62.81 470	29.45 488	59.51 452	54.11 473	50.08 360	54.40 433	74.31 408	32.62 394	55.92 479	30.50 470	63.95 398	72.15 444

PMMVS		53.96 416	53.26 423	56.04 435	62.60 471	50.92 233	61.17 444	56.09 468	32.81 481	53.51 444	66.84 471	34.04 369	59.93 457	44.14 372	68.18 363	57.27 489

SSC-MVS		41.96 455	41.99 455	41.90 479	62.46 472	9.28 523	57.41 464	44.32 500	43.38 442	38.30 497	66.45 472	32.67 393	58.42 466	10.98 513	21.91 507	57.99 487

PM-MVS		52.33 427	50.19 437	58.75 419	62.10 473	45.14 344	65.75 401	40.38 504	43.60 440	53.52 443	72.65 420	9.16 503	65.87 433	50.41 299	54.18 463	65.24 479

Gipuma		34.77 464	31.91 470	43.33 476	62.05 474	37.87 428	20.39 511	67.03 390	23.23 497	18.41 512	25.84 518	4.24 510	62.73 446	14.71 505	51.32 474	29.38 509
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
usedtu_dtu_shiyan2		53.34 423	50.78 433	61.00 404	61.86 475	39.63 412	68.47 380	64.58 412	42.94 447	45.22 479	67.61 465	19.25 478	66.71 425	28.08 480	59.05 444	76.66 391

test20.03		53.87 418	54.02 416	53.41 453	61.47 476	28.11 493	61.30 442	59.21 451	51.34 343	52.09 452	77.43 362	33.29 380	58.55 465	29.76 474	60.27 439	73.58 427

pmmvs5		56.47 396	55.68 397	58.86 418	61.41 477	36.71 443	66.37 397	62.75 432	40.38 463	53.70 438	76.62 377	34.56 361	67.05 422	40.02 407	65.27 385	72.83 431

MDA-MVSNet_test_wron		50.71 436	48.95 439	56.00 437	61.17 478	41.84 386	51.90 481	56.45 462	40.96 459	44.79 481	67.84 462	30.04 417	55.07 484	36.71 431	50.69 476	71.11 457

YYNet1		50.73 435	48.96 438	56.03 436	61.10 479	41.78 387	51.94 480	56.44 463	40.94 460	44.84 480	67.80 463	30.08 416	55.08 483	36.77 429	50.71 475	71.22 454

dongtai		34.52 465	34.94 466	33.26 488	61.06 480	16.00 515	52.79 479	23.78 517	40.71 461	39.33 495	48.65 505	16.91 483	48.34 497	12.18 510	19.05 509	35.44 508

CMPMVS	42.80 21	57.81 387	55.97 394	63.32 381	60.98 481	47.38 322	64.66 417	69.50 370	32.06 482	46.83 474	77.80 355	29.50 422	71.36 392	48.68 315	73.75 263	71.21 455
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		50.07 438	48.87 440	53.66 450	60.97 482	33.67 470	57.62 463	64.56 413	39.47 469	47.38 471	64.02 480	27.47 443	59.32 459	34.69 444	43.68 488	67.98 475

Anonymous20240521		55.30 407	54.41 411	57.96 427	60.92 483	41.73 388	71.09 344	71.06 353	41.18 457	48.65 468	73.31 417	16.93 482	59.25 460	42.54 389	64.01 396	72.90 430

testgi		51.90 429	52.37 425	50.51 466	60.39 484	23.55 507	58.42 455	58.15 454	49.03 374	51.83 453	79.21 328	22.39 469	55.59 480	29.24 477	62.64 417	72.40 441

UnsupCasMVSNet_eth		53.16 426	52.47 424	55.23 440	59.45 485	33.39 472	59.43 453	69.13 374	45.98 420	50.35 463	72.32 423	29.30 424	58.26 467	42.02 395	44.30 487	74.05 424

mvs5depth		55.64 405	53.81 418	61.11 402	59.39 486	40.98 399	65.89 400	68.28 380	50.21 358	58.11 389	75.42 399	17.03 481	67.63 418	43.79 377	46.21 483	74.73 416

test_cas_vis1_n_1920		56.91 392	56.71 386	57.51 431	59.13 487	45.40 342	63.58 427	61.29 444	36.24 475	67.14 239	71.85 430	29.89 418	56.69 474	57.65 238	63.58 403	70.46 462

dtuonly		54.95 413	55.26 403	54.01 447	59.03 488	35.99 451	61.92 438	56.33 465	38.48 471	54.61 430	77.85 354	34.27 366	51.60 494	45.10 364	69.74 341	74.43 419

new-patchmatchnet		47.56 444	47.73 445	47.06 469	58.81 489	9.37 522	48.78 489	59.21 451	43.28 443	44.22 483	68.66 460	25.67 459	57.20 472	31.57 465	49.35 480	74.62 418

FPMVS		42.18 454	41.11 457	45.39 471	58.03 490	41.01 397	49.50 487	53.81 475	30.07 485	33.71 500	64.03 478	11.69 494	52.08 493	14.01 506	55.11 459	43.09 500

KD-MVS_self_test		55.22 409	53.89 417	59.21 415	57.80 491	27.47 496	57.75 462	74.32 312	47.38 404	50.90 457	70.00 446	28.45 432	70.30 402	40.44 404	57.92 447	79.87 344

test_vis1_n		49.89 439	48.69 442	53.50 452	53.97 492	37.38 435	61.53 439	47.33 493	28.54 487	59.62 368	67.10 470	13.52 490	52.27 491	49.07 312	57.52 448	70.84 459

test_fmvs1		51.32 434	50.48 435	53.81 449	53.57 493	37.51 434	60.63 449	51.16 479	28.02 490	63.62 308	69.23 457	16.41 484	53.93 486	51.01 295	60.70 434	69.99 466

kuosan		29.62 472	30.82 472	26.02 493	52.99 494	16.22 514	51.09 482	22.71 518	33.91 480	33.99 499	40.85 507	15.89 486	33.11 513	7.59 524	18.37 510	28.72 510

test_fmvs1_n		51.37 432	50.35 436	54.42 446	52.85 495	37.71 432	61.16 445	51.93 476	28.15 488	63.81 307	69.73 453	13.72 489	53.95 485	51.16 294	60.65 435	71.59 449

new_pmnet		34.13 466	34.29 468	33.64 487	52.63 496	18.23 513	44.43 499	33.90 510	22.81 499	30.89 502	53.18 493	10.48 501	35.72 512	20.77 499	39.51 493	46.98 499

pmmvs3		44.92 448	41.95 456	53.86 448	52.58 497	43.55 363	62.11 437	46.90 495	26.05 493	40.63 489	60.19 485	11.08 500	57.91 468	31.83 462	46.15 484	60.11 482

ttmdpeth		45.56 446	42.95 452	53.39 454	52.33 498	29.15 489	57.77 460	48.20 490	31.81 483	49.86 465	77.21 365	8.69 504	59.16 461	27.31 483	33.40 501	71.84 447

DSMNet-mixed		39.30 461	38.72 461	41.03 480	51.22 499	19.66 511	45.53 497	31.35 511	15.83 509	39.80 493	67.42 468	22.19 470	45.13 501	22.43 494	52.69 469	58.31 486

mvsany_test1		39.38 459	38.16 463	43.02 477	49.05 500	34.28 465	44.16 500	25.94 515	22.74 500	46.57 476	62.21 484	23.85 467	41.16 508	33.01 452	35.91 497	53.63 492

APD_test1		37.39 462	34.94 466	44.72 475	48.88 501	33.19 474	52.95 478	44.00 501	19.49 503	27.28 504	58.59 489	3.18 515	52.84 489	18.92 501	41.17 492	48.14 497

test_fmvs2		48.69 441	47.49 447	52.29 461	48.63 502	33.06 475	57.76 461	48.05 491	25.71 494	59.76 366	69.60 455	11.57 496	52.23 492	49.45 309	56.86 451	71.58 450

LF4IMVS		42.95 451	42.26 454	45.04 472	48.30 503	32.50 477	54.80 472	48.49 487	28.03 489	40.51 490	70.16 444	9.24 502	43.89 503	31.63 463	49.18 481	58.72 485

wuyk23d		13.32 482	12.52 486	15.71 498	47.54 504	26.27 501	31.06 509	1.98 529	4.93 519	5.18 530	1.94 545	0.45 526	18.54 519	6.81 525	12.83 515	2.33 531

MVStest1		42.65 452	39.29 460	52.71 458	47.26 505	34.58 462	54.41 474	50.84 484	23.35 496	39.31 496	74.08 412	12.57 492	55.09 482	23.32 493	28.47 503	68.47 474

test_vis1_rt		41.35 457	39.45 459	47.03 470	46.65 506	37.86 429	47.76 491	38.65 505	23.10 498	44.21 484	51.22 499	11.20 499	44.08 502	39.27 413	53.02 468	59.14 484

test_fmvs3		44.30 449	42.55 453	49.55 467	42.83 507	27.15 499	53.03 477	44.93 497	22.03 502	53.69 440	64.94 477	4.21 511	49.63 495	47.47 323	49.82 478	71.88 445

LCM-MVSNet		40.30 458	35.88 465	53.57 451	42.24 508	29.15 489	45.21 498	60.53 448	22.23 501	28.02 503	50.98 500	3.72 513	61.78 450	31.22 468	38.76 495	69.78 468

E-PMN		23.77 474	22.73 479	26.90 491	42.02 509	20.67 510	42.66 501	35.70 508	17.43 505	10.28 522	25.05 519	6.42 506	42.39 506	10.28 516	14.71 512	17.63 515

testf1		31.46 470	28.89 475	39.16 481	41.99 510	28.78 491	46.45 494	37.56 506	14.28 510	21.10 508	48.96 502	1.48 521	47.11 498	13.63 507	34.56 498	41.60 502

APD_test2		31.46 470	28.89 475	39.16 481	41.99 510	28.78 491	46.45 494	37.56 506	14.28 510	21.10 508	48.96 502	1.48 521	47.11 498	13.63 507	34.56 498	41.60 502

EMVS		22.97 475	21.84 480	26.36 492	40.20 512	19.53 512	41.95 502	34.64 509	17.09 506	9.73 523	22.83 521	7.29 505	42.22 507	9.18 519	13.66 514	17.32 516

ANet_high		41.38 456	37.47 464	53.11 455	39.73 513	24.45 505	56.94 465	69.69 365	47.65 400	26.04 505	52.32 494	12.44 493	62.38 448	21.80 496	10.61 516	72.49 436

PMMVS2		27.40 473	25.91 477	31.87 490	39.46 514	6.57 526	31.17 508	28.52 513	23.96 495	20.45 511	48.94 504	4.20 512	37.94 509	16.51 503	19.97 508	51.09 493

PMVS	28.69 22	36.22 463	33.29 469	45.02 473	36.82 515	35.98 452	54.68 473	48.74 486	26.31 492	21.02 510	51.61 497	2.88 516	60.10 456	9.99 517	47.58 482	38.99 506
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
mvsany_test3		32.62 467	30.57 473	38.77 483	36.16 516	24.20 506	38.10 505	20.63 519	19.14 504	40.36 492	57.43 490	5.06 508	36.63 511	29.59 476	28.66 502	55.49 490

test_vis3_rt		32.09 468	30.20 474	37.76 484	35.36 517	27.48 495	40.60 503	28.29 514	16.69 507	32.52 501	40.53 509	1.96 519	37.40 510	33.64 449	42.21 491	48.39 495

MVE	17.77 23	21.41 476	17.77 484	32.34 489	34.34 518	25.44 503	16.11 513	24.11 516	11.19 513	13.22 516	31.92 513	1.58 520	30.95 515	10.47 515	17.03 511	40.62 505
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_f		31.86 469	31.05 471	34.28 486	32.33 519	21.86 509	32.34 507	30.46 512	16.02 508	39.78 494	55.45 492	4.80 509	32.36 514	30.61 469	37.66 496	48.64 494

ArgMatch-Sym		21.00 477	19.89 481	24.35 496	23.32 520	15.10 516	32.50 506	4.90 525	11.83 512	24.09 506	51.35 498	0.56 524	19.55 518	21.24 497	9.18 519	38.40 507

ArgMatch-SfM		20.82 478	19.10 482	25.97 494	21.54 521	13.77 517	29.84 510	6.08 524	9.69 514	22.36 507	51.71 496	0.53 525	21.69 517	20.98 498	9.18 519	42.43 501

DeepMVS_CX				12.03 500	17.97 522	10.91 520		10.60 522	7.46 516	11.07 520	28.36 517	3.28 514	11.29 522	8.01 521	9.74 518	13.89 520

DenseAffine		14.16 481	13.16 485	17.15 497	17.01 523	8.89 524	19.68 512	2.17 528	7.89 515	15.00 514	40.64 508	0.19 528	15.28 520	11.16 512	4.69 524	27.27 511

PDCNetPlus		9.23 487	8.89 492	10.23 503	13.70 524	3.70 531	12.27 517	1.51 531	3.98 520	6.73 528	29.50 516	0.24 527	8.07 527	7.83 522	4.30 525	18.93 513

test_method		19.68 479	18.10 483	24.41 495	13.68 525	3.11 535	12.06 518	42.37 503	2.00 524	11.97 518	36.38 510	5.77 507	29.35 516	15.06 504	23.65 506	40.76 504

DKM		10.33 484	10.10 489	11.02 501	10.54 526	5.43 527	14.18 515	1.03 535	4.97 518	11.74 519	36.09 511	0.11 533	9.09 525	9.38 518	2.85 528	18.53 514

RoMa-SfM		11.96 483	11.39 487	13.68 499	10.24 527	6.80 525	15.83 514	1.33 532	6.34 517	13.06 517	41.41 506	0.16 529	12.72 521	10.58 514	3.56 527	21.52 512

LoFTR		9.45 485	9.00 491	10.79 502	10.22 528	4.31 529	11.11 519	4.11 526	2.40 523	10.53 521	30.89 514	0.13 530	10.75 523	3.12 529	8.52 521	17.31 517

MatchFormer		7.03 491	6.96 496	7.26 506	7.64 529	3.36 534	10.21 520	3.04 527	1.31 526	9.02 526	22.94 520	0.08 540	8.15 526	1.46 533	6.91 522	10.26 522

DKM-HiRes		7.91 490	7.93 495	7.83 505	7.35 530	3.58 533	10.03 522	0.66 542	3.58 522	9.05 525	30.62 515	0.08 540	5.66 529	8.09 520	1.91 535	14.26 518

VLMVS_CLIP		8.61 488	9.36 490	6.34 508	7.07 531	4.23 530	8.66 523	10.16 523	1.75 525	13.91 515	20.41 523	2.33 517	10.32 524	6.21 526	13.74 513	4.49 526

RoMa-HiRes		8.28 489	8.27 494	8.28 504	6.12 532	3.67 532	10.07 521	0.74 540	3.93 521	9.17 524	34.46 512	0.12 532	7.12 528	7.80 523	2.05 534	14.04 519

ALIKED-LG		2.35 502	2.54 506	1.78 515	5.54 533	1.79 538	3.81 526	0.96 536	0.33 534	1.86 537	7.18 531	0.13 530	1.60 535	0.20 544	2.81 529	1.94 532

MVS_clip		4.22 497	4.98 500	1.95 514	5.46 534	1.99 536	3.96 525	0.34 546	0.36 533	7.04 527	17.25 524	0.66 523	0.80 540	4.04 527	5.70 523	3.07 528

ALIKED-MNN		2.09 504	2.23 508	1.67 516	5.15 535	1.82 537	3.53 528	0.77 537	0.25 535	1.45 539	6.03 534	0.09 538	1.52 536	0.17 545	2.64 531	1.66 533

ALIKED-NN		1.96 505	2.12 509	1.48 518	4.72 536	1.65 539	3.19 532	0.77 537	0.23 536	1.43 540	5.87 535	0.10 535	1.37 538	0.16 546	2.61 532	1.42 539

GLUNet-SfM		4.33 496	3.64 503	6.41 507	3.38 537	1.65 539	3.23 531	1.54 530	0.66 531	6.36 529	15.13 528	0.08 540	5.54 530	0.94 535	1.44 538	12.05 521

PMatch-SfM		4.42 495	4.43 501	4.39 510	2.90 538	1.50 542	4.85 524	0.36 545	1.17 527	4.73 532	20.99 522	0.01 560	3.26 533	3.74 528	1.10 542	8.40 524

SP-LightGlue		0.94 509	0.99 513	0.78 519	2.60 539	0.38 554	1.71 533	0.34 546	0.17 538	0.50 544	2.14 541	0.09 538	0.38 544	0.26 540	1.13 541	1.59 534

SP-SuperGlue		0.93 510	0.98 514	0.77 520	2.54 540	0.38 554	1.70 534	0.34 546	0.17 538	0.52 543	2.13 542	0.10 535	0.36 546	0.26 540	1.10 542	1.57 536

VLMVS		2.25 503	2.47 507	1.62 517	2.41 541	1.01 545	1.61 537	0.72 541	0.07 558	4.27 533	6.17 533	2.11 518	1.03 539	1.17 534	3.66 526	2.83 530

tmp_tt		9.43 486	11.14 488	4.30 511	2.38 542	4.40 528	13.62 516	16.08 521	0.39 532	15.89 513	13.06 529	15.80 487	5.54 530	12.63 509	10.46 517	2.95 529

ELoFTR		4.04 498	3.55 504	5.50 509	2.33 543	1.25 543	3.58 527	1.18 533	0.90 528	4.23 534	16.28 526	0.03 548	5.46 532	1.95 532	1.42 539	9.81 523

SP-MNN		0.89 511	0.93 516	0.77 520	2.32 544	0.34 558	1.68 535	0.33 549	0.13 542	0.49 545	2.07 543	0.08 540	0.39 543	0.25 542	1.07 544	1.58 535

SP-NN		0.85 513	0.90 517	0.73 524	2.22 545	0.33 560	1.63 536	0.31 550	0.14 541	0.47 546	1.97 544	0.08 540	0.38 544	0.25 542	1.01 545	1.47 538

MASt3R-SfM		3.33 500	3.70 502	2.21 513	2.02 546	1.04 544	3.52 529	1.05 534	0.67 530	4.93 531	16.68 525	0.10 535	1.50 537	2.06 531	2.29 533	4.09 527

PMatch-Up-SfM		3.14 501	3.26 505	2.81 512	1.97 547	1.00 546	3.35 530	0.23 552	0.79 529	3.44 535	16.19 527	0.01 560	2.11 534	2.62 530	0.70 555	5.32 525

SIFT-NN		0.60 514	0.65 518	0.45 526	1.90 548	0.55 547	0.90 541	0.16 553	0.10 544	0.34 547	1.43 546	0.02 549	0.28 548	0.04 547	0.95 546	0.50 543

SIFT-MNN		0.56 515	0.61 519	0.43 527	1.75 549	0.50 548	0.82 542	0.16 553	0.10 544	0.30 548	1.38 547	0.02 549	0.28 548	0.04 547	0.92 548	0.50 543

SIFT-NCM-Cal		0.51 517	0.55 521	0.38 530	1.66 550	0.45 550	0.75 544	0.12 556	0.09 547	0.21 555	1.18 553	0.02 549	0.27 550	0.03 555	0.89 549	0.43 549

SIFT-NN-NCMNet		0.53 516	0.58 520	0.40 528	1.60 551	0.49 549	0.80 543	0.15 555	0.09 547	0.28 550	1.29 548	0.02 549	0.27 550	0.04 547	0.94 547	0.44 547

SIFT-ConvMatch		0.48 519	0.52 523	0.35 533	1.51 552	0.42 551	0.64 548	0.11 559	0.09 547	0.26 551	1.24 551	0.02 549	0.25 554	0.04 547	0.76 553	0.38 550

SIFT-UMatch		0.45 521	0.50 525	0.32 535	1.46 553	0.34 558	0.66 547	0.10 561	0.09 547	0.22 554	1.19 552	0.02 549	0.25 554	0.04 547	0.73 554	0.36 552

SIFT-CM-Cal		0.42 523	0.46 527	0.31 536	1.40 554	0.35 557	0.56 551	0.09 562	0.09 547	0.20 556	1.09 556	0.02 549	0.23 557	0.03 555	0.66 557	0.34 553

SIFT-UM-Cal		0.41 524	0.46 527	0.28 537	1.35 555	0.29 562	0.57 550	0.08 563	0.09 547	0.20 556	1.10 555	0.02 549	0.23 557	0.03 555	0.68 556	0.30 555

SIFT-NN-CMatch		0.49 518	0.53 522	0.38 530	1.35 555	0.41 552	0.70 546	0.12 556	0.09 547	0.30 548	1.28 550	0.02 549	0.26 552	0.04 547	0.83 551	0.47 545

SIFT-NN-UMatch		0.48 519	0.52 523	0.36 532	1.27 557	0.36 556	0.75 544	0.12 556	0.10 544	0.25 552	1.29 548	0.02 549	0.26 552	0.04 547	0.85 550	0.44 547

SIFT-NN-PointCN		0.44 522	0.47 526	0.33 534	1.17 558	0.29 562	0.64 548	0.11 559	0.09 547	0.25 552	1.14 554	0.02 549	0.25 554	0.03 555	0.78 552	0.46 546

SIFT-PCN-Cal		0.36 525	0.39 529	0.26 538	1.16 559	0.21 565	0.46 553	0.07 565	0.08 555	0.17 559	0.92 557	0.01 560	0.20 560	0.03 555	0.59 559	0.37 551

SIFT-PointCN		0.36 525	0.39 529	0.25 539	1.14 560	0.21 565	0.50 552	0.08 563	0.08 555	0.17 559	0.89 558	0.01 560	0.21 559	0.03 555	0.60 558	0.34 553

MVS_baseline		1.38 506	1.71 510	0.39 529	1.08 561	0.02 568	0.39 554	0.06 566	0.01 560	2.77 536	7.83 530	0.07 545	0.00 562	0.47 537	2.72 530	1.14 541

SIFT-NCMNet		0.30 527	0.33 531	0.19 540	1.04 562	0.18 567	0.39 554	0.05 567	0.08 555	0.14 561	0.77 559	0.01 560	0.16 561	0.02 562	0.49 560	0.22 556

SP-DiffGlue		0.98 508	1.05 512	0.75 523	0.81 563	0.40 553	1.24 538	0.37 544	0.19 537	1.26 542	3.80 537	0.11 533	0.34 547	0.51 536	1.18 540	1.52 537

XFeat-MNN		1.07 507	1.17 511	0.77 520	0.52 564	0.31 561	1.15 539	0.41 543	0.15 540	1.62 538	4.35 536	0.07 545	0.77 541	0.38 538	1.88 536	1.22 540

XFeat-NN		0.87 512	0.97 515	0.59 525	0.48 565	0.24 564	0.94 540	0.29 551	0.12 543	1.41 541	3.45 540	0.06 547	0.56 542	0.29 539	1.65 537	0.95 542

testmvs		4.52 494	6.03 498	0.01 542	0.01 566	0.00 570	53.86 476	0.00 568	0.01 560	0.04 562	0.27 560	0.00 566	0.00 562	0.04 547	0.00 561	0.03 558

test123		4.73 493	6.30 497	0.02 541	0.01 566	0.01 569	56.36 467	0.00 568	0.01 560	0.04 562	0.21 561	0.01 560	0.00 562	0.03 555	0.00 561	0.04 557

PatchmatchNet2					0.00 568	13.27 518	48.02 490	44.92 498	34.52 479
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
mmdepth		0.00 528	0.00 532	0.00 543	0.00 568	0.00 570	0.00 556	0.00 568	0.00 563	0.00 564	0.00 562	0.00 566	0.00 562	0.00 563	0.00 561	0.00 559

monomultidepth		0.00 528	0.00 532	0.00 543	0.00 568	0.00 570	0.00 556	0.00 568	0.00 563	0.00 564	0.00 562	0.00 566	0.00 562	0.00 563	0.00 561	0.00 559

test_blank		0.00 528	0.00 532	0.00 543	0.00 568	0.00 570	0.00 556	0.00 568	0.00 563	0.00 564	0.00 562	0.00 566	0.00 562	0.00 563	0.00 561	0.00 559

eth-test2					0.00 568

eth-test					0.00 568

uanet_test		0.00 528	0.00 532	0.00 543	0.00 568	0.00 570	0.00 556	0.00 568	0.00 563	0.00 564	0.00 562	0.00 566	0.00 562	0.00 563	0.00 561	0.00 559

DCPMVS		0.00 528	0.00 532	0.00 543	0.00 568	0.00 570	0.00 556	0.00 568	0.00 563	0.00 564	0.00 562	0.00 566	0.00 562	0.00 563	0.00 561	0.00 559

cdsmvs_eth3d_5k		17.50 480	23.34 478	0.00 543	0.00 568	0.00 570	0.00 556	78.63 207	0.00 563	0.00 564	82.18 262	49.25 172	0.00 562	0.00 563	0.00 561	0.00 559

pcd_1.5k_mvsjas		3.92 499	5.23 499	0.00 543	0.00 568	0.00 570	0.00 556	0.00 568	0.00 563	0.00 564	0.00 562	47.05 203	0.00 562	0.00 563	0.00 561	0.00 559

sosnet-low-res		0.00 528	0.00 532	0.00 543	0.00 568	0.00 570	0.00 556	0.00 568	0.00 563	0.00 564	0.00 562	0.00 566	0.00 562	0.00 563	0.00 561	0.00 559

sosnet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 570	0.00 556	0.00 568	0.00 563	0.00 564	0.00 562	0.00 566	0.00 562	0.00 563	0.00 561	0.00 559

uncertanet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 570	0.00 556	0.00 568	0.00 563	0.00 564	0.00 562	0.00 566	0.00 562	0.00 563	0.00 561	0.00 559

Regformer		0.00 528	0.00 532	0.00 543	0.00 568	0.00 570	0.00 556	0.00 568	0.00 563	0.00 564	0.00 562	0.00 566	0.00 562	0.00 563	0.00 561	0.00 559

ab-mvs-re		6.49 492	8.65 493	0.00 543	0.00 568	0.00 570	0.00 556	0.00 568	0.00 563	0.00 564	77.89 352	0.00 566	0.00 562	0.00 563	0.00 561	0.00 559

uanet		0.00 528	0.00 532	0.00 543	0.00 568	0.00 570	0.00 556	0.00 568	0.00 563	0.00 564	0.00 562	0.00 566	0.00 562	0.00 563	0.00 561	0.00 559

Meshroom													0.00 562
: In preparation.
AliceVision / Meshro													0.00 562
: In preparation.
AliceVision_Meshroom													0.00 562
: In preparation.
PatchmatchNet1														25.92 490	51.90 471	65.44 477
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
PatchmatchNet3													42.51 505
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo. CVPR 2021
WAC-MVS						27.31 497								27.77 481

PC_three_1452									55.09 264	84.46 4	89.84 54	66.68 5	89.41 25	74.24 63	91.38 2	88.42 32

test_241102_TWO								86.73 12	64.18 35	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 64

test_0728_THIRD									65.04 17	83.82 8	92.00 3	64.69 11	90.75 8	79.48 7	90.63 10	88.09 47

GSMVS																78.05 369

sam_mvs1												34.74 360				78.05 369

sam_mvs												33.43 378

MTGPA								80.97 162

test_post1							68.67 378				3.64 538	32.39 400	69.49 405	44.17 370

test_post											3.55 539	33.90 372	66.52 426

patchmatchnet-post											64.03 478	34.50 362	74.27 375

MTMP							86.03 24	17.08 520

test9_res														75.28 56	88.31 36	83.81 234

agg_prior2														73.09 74	87.93 44	84.33 211

test_prior4						62.51 15	82.08 88

test_prior2							81.75 90		60.37 125	75.01 66	89.06 63	56.22 51		72.19 82	88.96 28

旧先验2							76.08 229		45.32 426	76.55 51			65.56 434	58.75 231

新几何2							76.12 227

无先验							79.66 124	74.30 314	48.40 386				80.78 250	53.62 273		79.03 358

原ACMM2							79.02 132

testdata2													72.18 389	46.95 339

segment_acmp												54.23 80

testdata1							72.65 311		60.50 119

plane_prior5								84.01 61					87.21 66	68.16 115	80.58 131	84.65 202

plane_prior4											86.10 153

plane_prior3						56.09 121			63.92 39	69.27 186

plane_prior2							84.22 52		64.52 28

plane_prior						56.31 115	83.58 65		63.19 56						80.48 135

n2								0.00 568

nn								0.00 568

door-mid								47.19 494

test11								83.47 90

door								47.60 492

HQP5-MVS						54.94 146

BP-MVS														67.04 137

HQP4-MVS										67.85 219			86.93 74			84.32 212

HQP3-MVS								83.90 66							80.35 137

HQP2-MVS												45.46 222

MDTV_nov1_ep13_2view						25.89 502	61.22 443		40.10 465	51.10 455		32.97 384		38.49 417		78.61 363

ACMMP++_ref															74.07 258

ACMMP++															72.16 299

Test By Simon												48.33 183

High-resolution multi-view benchmark