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 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 65	65.37 13	78.78 22	90.64 21	58.63 25	87.24 54	79.00 13	90.37 14	85.26 138

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 39	85.03 36	66.96 5	77.58 30	90.06 39	59.47 21	89.13 22	78.67 16	89.73 16	87.03 60

test_0728_SECOND				79.19 16	87.82 3	59.11 66	87.85 5	87.15 3					90.84 3	78.66 17	90.61 11	87.62 43

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 72	87.82 7	86.78 10	64.18 32	85.97 1	91.84 8	66.87 3	90.83 5	78.63 19	90.87 5	88.23 22

IU-MVS					87.77 4	59.15 63		85.53 26	53.93 245	84.64 3				79.07 12	90.87 5	88.37 18

test_241102_ONE					87.77 4	58.90 72		86.78 10	64.20 31	85.97 1	91.34 15	66.87 3	90.78 7

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 67	87.85 5	85.03 36	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 17	90.61 11	85.45 126
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					87.75 7	59.07 67	87.86 4	86.83 8	64.26 29	84.19 7	91.92 5	64.82 8

test_one_0601					87.58 9	59.30 60		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

test_part2					87.58 9	60.47 42				83.42 12

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 45	67.01 1	90.33 12	73.16 61	91.15 4	88.23 22

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 30	84.42 45	66.73 8	74.67 64	89.38 52	55.30 47	89.18 21	74.19 53	87.34 44	86.38 82

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 26	86.42 14	63.28 45	83.27 13	91.83 10	64.96 7	90.47 11	76.41 33	89.67 18	86.84 66
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 14	59.15 63	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 6	91.38 2	88.42 16

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

No_MVS				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 50	62.81 58	73.30 81	90.58 23	49.90 118	88.21 34	73.78 57	87.03 46	86.29 94

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 48	62.82 56	73.55 79	90.56 24	49.80 121	88.24 33	74.02 55	87.03 46	86.32 90

HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 47	62.82 56	73.96 73	90.50 26	53.20 75	88.35 31	74.02 55	87.05 45	86.13 97

MCST-MVS		77.48 28	77.45 27	77.54 47	86.67 20	58.36 79	83.22 58	86.93 5	56.91 174	74.91 56	88.19 67	59.15 23	87.68 50	73.67 58	87.45 43	86.57 77

ZD-MVS					86.64 21	60.38 45		82.70 93	57.95 157	78.10 25	90.06 39	56.12 42	88.84 26	74.05 54	87.00 49

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 52	82.40 14	92.12 2	59.64 19	89.76 16	78.70 14	88.32 31	86.79 68
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 21	62.49 63	82.20 15	92.28 1	56.53 37	89.70 17	79.85 5	91.48 1	88.19 24
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
DP-MVS Recon		72.15 100	70.73 113	76.40 65	86.57 24	57.99 82	81.15 90	82.96 87	57.03 171	66.78 190	85.56 139	44.50 191	88.11 38	51.77 233	80.23 119	83.10 211

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 69	63.89 37	73.60 78	90.60 22	54.85 53	86.72 71	77.20 27	88.06 36	85.74 114
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mPP-MVS		76.54 39	75.93 44	78.34 36	86.47 26	63.50 3	85.74 25	82.28 97	62.90 53	71.77 109	90.26 34	46.61 167	86.55 77	71.71 76	85.66 61	84.97 149

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 27	84.36 46	60.61 94	79.05 21	90.30 33	55.54 46	88.32 32	73.48 60	87.03 46	84.83 152
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 44	75.08 51	90.47 28	53.96 63	88.68 27	76.48 32	89.63 20	87.16 58

XVS		77.17 31	76.56 36	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 105	90.01 43	47.95 142	88.01 40	71.55 78	86.74 53	86.37 84

X-MVStestdata		70.21 133	67.28 185	79.00 23	86.32 29	62.62 11	85.83 22	83.92 55	64.55 23	72.17 105	6.49 432	47.95 142	88.01 40	71.55 78	86.74 53	86.37 84

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 25	63.71 12	89.23 20	81.51 2	88.44 27	88.09 27
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		70.83 120	69.56 133	74.64 96	86.21 31	54.63 140	82.34 73	81.81 104	48.22 314	63.01 259	85.83 134	40.92 233	87.10 62	57.91 181	79.79 122	82.18 228

save fliter					86.17 33	61.30 28	83.98 50	79.66 150	59.00 133

DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 34	84.85 40	61.98 75	73.06 91	88.88 59	53.72 68	89.06 23	68.27 92	88.04 37	87.42 49
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 37	76.06 42	78.88 28	86.14 35	62.73 9	82.55 70	83.74 64	61.71 77	72.45 104	90.34 32	48.48 138	88.13 37	72.32 68	86.85 51	85.78 108

FOURS1					86.12 36	60.82 37	88.18 1	83.61 67	60.87 88	81.50 16

MTAPA		76.90 34	76.42 38	78.35 35	86.08 37	63.57 2	74.92 219	80.97 132	65.13 15	75.77 41	90.88 19	48.63 135	86.66 73	77.23 26	88.17 33	84.81 153

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 21	85.21 30	63.56 41	74.29 70	90.03 41	52.56 81	88.53 29	74.79 49	88.34 29	86.63 76

CP-MVS		77.12 32	76.68 32	78.43 33	86.05 38	63.18 5	87.55 10	83.45 72	62.44 65	72.68 98	90.50 26	48.18 140	87.34 53	73.59 59	85.71 60	84.76 156

SR-MVS		76.13 46	75.70 47	77.40 51	85.87 40	61.20 29	85.52 27	82.19 98	59.99 113	75.10 50	90.35 31	47.66 147	86.52 78	71.64 77	82.99 83	84.47 162

新几何1				70.76 208	85.66 41	61.13 30		66.43 321	44.68 351	70.29 123	86.64 104	41.29 227	75.23 299	49.72 248	81.75 103	75.93 321

MG-MVS		73.96 68	73.89 67	74.16 111	85.65 42	49.69 230	81.59 85	81.29 121	61.45 79	71.05 117	88.11 68	51.77 97	87.73 47	61.05 159	83.09 81	85.05 145

TEST9					85.58 43	61.59 24	81.62 83	81.26 122	55.65 205	74.93 54	88.81 60	53.70 69	84.68 123

train_agg		76.27 43	76.15 40	76.64 62	85.58 43	61.59 24	81.62 83	81.26 122	55.86 197	74.93 54	88.81 60	53.70 69	84.68 123	75.24 45	88.33 30	83.65 194

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 52	85.16 31	62.88 54	78.10 25	91.26 16	52.51 82	88.39 30	79.34 8	90.52 13	86.78 69

test_8					85.40 46	60.96 34	81.54 86	81.18 125	55.86 197	74.81 59	88.80 62	53.70 69	84.45 127

原ACMM1				74.69 92	85.39 47	59.40 57		83.42 73	51.47 271	70.27 124	86.61 107	48.61 136	86.51 79	53.85 215	87.96 39	78.16 291

CDPH-MVS		76.31 42	75.67 48	78.22 37	85.35 48	59.14 65	81.31 88	84.02 51	56.32 189	74.05 71	88.98 57	53.34 74	87.92 43	69.23 90	88.42 28	87.59 44

ACMMP		76.02 47	75.33 51	78.07 38	85.20 49	61.91 20	85.49 29	84.44 44	63.04 50	69.80 135	89.74 49	45.43 180	87.16 60	72.01 71	82.87 88	85.14 140
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 50	59.96 50		81.04 130		74.68 63			84.04 133

HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 63	85.08 33	62.57 61	73.09 90	89.97 44	50.90 111	87.48 52	75.30 43	86.85 51	87.33 55
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 56	82.93 62	85.39 27	62.15 68	76.41 39	91.51 11	52.47 84	86.78 70	80.66 4	89.64 19	87.80 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 48	83.82 63	59.34 129	79.37 19	89.76 48	59.84 16	87.62 51	76.69 30	86.74 53	87.68 40
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
AdaColmap		69.99 137	68.66 153	73.97 115	84.94 54	57.83 84	82.63 68	78.71 168	56.28 191	64.34 238	84.14 164	41.57 222	87.06 64	46.45 276	78.88 139	77.02 310

DP-MVS		65.68 229	63.66 240	71.75 177	84.93 55	56.87 102	80.74 95	73.16 264	53.06 252	59.09 312	82.35 201	36.79 278	85.94 92	32.82 374	69.96 270	72.45 358

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 60	85.33 28	62.86 55	80.17 17	90.03 41	61.76 14	88.95 24	74.21 52	88.67 26	88.12 26

CPTT-MVS		72.78 82	72.08 89	74.87 90	84.88 57	61.41 26	84.15 46	77.86 189	55.27 213	67.51 179	88.08 70	41.93 217	81.85 182	69.04 91	80.01 121	81.35 244

test12				77.76 45	84.52 58	58.41 78		83.36 76		72.93 94		54.61 56	88.05 39		88.12 34	86.81 67

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 37	83.87 60	60.37 101	79.89 18	89.38 52	54.97 51	85.58 100	76.12 36	84.94 64	86.33 88
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 66	73.46 72	76.80 56	84.45 60	59.04 69	83.65 55	81.05 129	60.15 110	70.43 121	89.84 46	41.09 231	85.59 99	67.61 101	82.90 87	85.77 111

test_prior				76.69 58	84.20 61	57.27 91		84.88 39					86.43 81			86.38 82

reproduce-ours		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 108	77.85 28	91.42 13	50.67 112	87.69 48	72.46 66	84.53 68	85.46 124

our_new_method		76.90 34	76.58 34	77.87 43	83.99 62	60.46 43	84.75 32	83.34 77	60.22 108	77.85 28	91.42 13	50.67 112	87.69 48	72.46 66	84.53 68	85.46 124

CSCG		76.92 33	76.75 31	77.41 49	83.96 64	59.60 54	82.95 61	86.50 13	60.78 91	75.27 46	84.83 148	60.76 15	86.56 76	67.86 97	87.87 41	86.06 99

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 65	61.62 23	84.17 45	86.85 6	63.23 47	73.84 76	90.25 35	57.68 29	89.96 15	74.62 50	89.03 22	87.89 30
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 77	72.93 77	73.76 121	83.58 66	51.66 197	78.75 122	77.66 193	67.75 4	72.61 100	89.42 50	49.82 120	83.29 148	53.61 217	83.14 80	86.32 90

SR-MVS-dyc-post		74.57 62	73.90 66	76.58 63	83.49 67	59.87 52	84.29 40	81.36 115	58.07 152	73.14 87	90.07 37	44.74 187	85.84 94	68.20 93	81.76 101	84.03 173

RE-MVS-def			73.71 70		83.49 67	59.87 52	84.29 40	81.36 115	58.07 152	73.14 87	90.07 37	43.06 204		68.20 93	81.76 101	84.03 173

reproduce_model		76.43 41	76.08 41	77.49 48	83.47 69	60.09 47	84.60 36	82.90 89	59.65 120	77.31 31	91.43 12	49.62 123	87.24 54	71.99 72	83.75 78	85.14 140

LFMVS		71.78 104	71.59 93	72.32 167	83.40 70	46.38 269	79.75 109	71.08 280	64.18 32	72.80 96	88.64 64	42.58 209	83.72 140	57.41 185	84.49 70	86.86 65

test222					83.14 71	58.68 76	72.57 261	63.45 346	41.78 372	67.56 178	86.12 123	37.13 273			78.73 144	74.98 334

9.14			78.75 15		83.10 72		84.15 46	88.26 1	59.90 114	78.57 24	90.36 30	57.51 32	86.86 68	77.39 25	89.52 21

旧先验1					83.04 73	53.15 165		67.52 310			87.85 77	44.08 194			80.76 109	78.03 296

MSLP-MVS++		73.77 70	73.47 71	74.66 94	83.02 74	59.29 61	82.30 77	81.88 102	59.34 129	71.59 112	86.83 97	45.94 171	83.65 142	65.09 123	85.22 63	81.06 251

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 75	62.18 16	87.60 9	85.83 19	66.69 9	78.03 27	90.98 18	54.26 58	90.06 14	78.42 21	89.02 23	87.69 39
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		74.02 67	73.46 72	75.69 76	83.01 75	60.63 40	77.29 162	78.40 183	61.18 84	70.58 120	85.97 129	54.18 60	84.00 136	67.52 102	82.98 85	82.45 223

SF-MVS		78.82 13	79.22 12	77.60 46	82.88 77	57.83 84	84.99 31	88.13 2	61.86 76	79.16 20	90.75 20	57.96 26	87.09 63	77.08 29	90.18 15	87.87 32

VDDNet		71.81 103	71.33 101	73.26 147	82.80 78	47.60 260	78.74 123	75.27 231	59.59 125	72.94 93	89.40 51	41.51 225	83.91 137	58.75 177	82.99 83	88.26 20

3Dnovator+	66.72 4	75.84 49	74.57 59	79.66 9	82.40 79	59.92 51	85.83 22	86.32 16	66.92 7	67.80 173	89.24 54	42.03 214	89.38 19	64.07 130	86.50 57	89.69 3

dcpmvs_2		74.55 63	75.23 53	72.48 161	82.34 80	53.34 161	77.87 143	81.46 111	57.80 162	75.49 43	86.81 98	62.22 13	77.75 261	71.09 81	82.02 97	86.34 86

APD-MVS_3200maxsize		74.96 54	74.39 61	76.67 60	82.20 81	58.24 80	83.67 54	83.29 81	58.41 146	73.71 77	90.14 36	45.62 173	85.99 90	69.64 86	82.85 89	85.78 108

MM		80.20 7	80.28 8	79.99 2	82.19 82	60.01 49	86.19 17	83.93 54	73.19 1	77.08 35	91.21 17	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

PVSNet_Blended_VisFu		71.45 112	70.39 119	74.65 95	82.01 83	58.82 74	79.93 105	80.35 143	55.09 218	65.82 212	82.16 209	49.17 129	82.64 168	60.34 164	78.62 147	82.50 222

TSAR-MVS + GP.		74.90 55	74.15 64	77.17 52	82.00 84	58.77 75	81.80 80	78.57 172	58.58 143	74.32 69	84.51 159	55.94 43	87.22 57	67.11 105	84.48 71	85.52 120

h-mvs33		72.71 84	71.49 96	76.40 65	81.99 85	59.58 55	76.92 172	76.74 210	60.40 98	74.81 59	85.95 130	45.54 176	85.76 96	70.41 84	70.61 255	83.86 182

API-MVS		72.17 97	71.41 98	74.45 103	81.95 86	57.22 92	84.03 48	80.38 142	59.89 118	68.40 156	82.33 202	49.64 122	87.83 46	51.87 231	84.16 75	78.30 289

MAR-MVS		71.51 109	70.15 125	75.60 80	81.84 87	59.39 58	81.38 87	82.90 89	54.90 228	68.08 165	78.70 274	47.73 145	85.51 102	51.68 235	84.17 74	81.88 234
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
balanced_conf03		76.58 38	76.55 37	76.68 59	81.73 88	52.90 171	80.94 91	85.70 23	61.12 86	74.90 57	87.17 91	56.46 38	88.14 36	72.87 63	88.03 38	89.00 8

PAPM_NR		72.63 87	71.80 91	75.13 87	81.72 89	53.42 160	79.91 106	83.28 82	59.14 131	66.31 201	85.90 131	51.86 95	86.06 87	57.45 184	80.62 110	85.91 104

VDD-MVS		72.50 89	72.09 88	73.75 123	81.58 90	49.69 230	77.76 149	77.63 194	63.21 48	73.21 84	89.02 56	42.14 213	83.32 147	61.72 154	82.50 92	88.25 21

PS-MVSNAJ		70.51 126	69.70 131	72.93 151	81.52 91	55.79 119	74.92 219	79.00 161	55.04 224	69.88 133	78.66 276	47.05 160	82.19 176	61.61 155	79.58 126	80.83 255

testdata				64.66 297	81.52 91	52.93 170		65.29 330	46.09 340	73.88 74	87.46 84	38.08 262	66.26 352	53.31 220	78.48 148	74.78 338

CHOSEN 1792x2688		65.08 240	62.84 252	71.82 175	81.49 93	56.26 108	66.32 324	74.20 253	40.53 382	63.16 255	78.65 277	41.30 226	77.80 260	45.80 282	74.09 200	81.40 241

HQP_MVS		74.31 65	73.73 69	76.06 69	81.41 94	56.31 105	84.22 43	84.01 52	64.52 25	69.27 143	86.10 124	45.26 184	87.21 58	68.16 95	80.58 112	84.65 157

plane_prior7					81.41 94	55.96 114

DPM-MVS		75.47 53	75.00 54	76.88 54	81.38 96	59.16 62	79.94 104	85.71 22	56.59 183	72.46 102	86.76 99	56.89 35	87.86 45	66.36 111	88.91 25	83.64 195

CANet		76.46 40	75.93 44	78.06 39	81.29 97	57.53 88	82.35 72	83.31 80	67.78 3	70.09 125	86.34 117	54.92 52	88.90 25	72.68 65	84.55 67	87.76 38

Vis-MVSNet		72.18 96	71.37 100	74.61 97	81.29 97	55.41 129	80.90 92	78.28 185	60.73 92	69.23 146	88.09 69	44.36 193	82.65 167	57.68 182	81.75 103	85.77 111
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
plane_prior1					81.27 99

xiu_mvs_v2_base		70.52 125	69.75 129	72.84 153	81.21 100	55.63 123	75.11 212	78.92 163	54.92 227	69.96 132	79.68 259	47.00 164	82.09 178	61.60 156	79.37 129	80.81 256

plane_prior6					81.20 101	56.24 109						45.26 184

PAPR		71.72 107	70.82 111	74.41 104	81.20 101	51.17 200	79.55 115	83.33 79	55.81 200	66.93 189	84.61 155	50.95 109	86.06 87	55.79 196	79.20 134	86.00 100

PLC	56.13 14	65.09 239	63.21 248	70.72 210	81.04 103	54.87 138	78.57 128	77.47 196	48.51 310	55.71 340	81.89 215	33.71 306	79.71 224	41.66 322	70.37 259	77.58 301
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
NP-MVS					80.98 104	56.05 113					85.54 142

MVSMamba_PlusPlus		75.75 51	75.44 49	76.67 60	80.84 105	53.06 168	78.62 126	85.13 32	59.65 120	71.53 113	87.47 83	56.92 34	88.17 35	72.18 70	86.63 56	88.80 10

OPM-MVS		74.73 58	74.25 63	76.19 68	80.81 106	59.01 70	82.60 69	83.64 66	63.74 39	72.52 101	87.49 82	47.18 158	85.88 93	69.47 88	80.78 107	83.66 193
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
MVS_0304		78.45 18	78.28 19	78.98 26	80.73 107	57.91 83	84.68 35	81.64 107	68.35 2	75.77 41	90.38 29	53.98 61	90.26 13	81.30 3	87.68 42	88.77 11

HQP-NCC					80.66 108		82.31 74		62.10 69	67.85 168

ACMP_Plane					80.66 108		82.31 74		62.10 69	67.85 168

HQP-MVS		73.45 72	72.80 79	75.40 82	80.66 108	54.94 135	82.31 74	83.90 57	62.10 69	67.85 168	85.54 142	45.46 178	86.93 66	67.04 106	80.35 116	84.32 164

SPE-MVS-test		75.62 52	75.31 52	76.56 64	80.63 111	55.13 133	83.88 51	85.22 29	62.05 72	71.49 114	86.03 127	53.83 65	86.36 84	67.74 98	86.91 50	88.19 24

PHI-MVS		75.87 48	75.36 50	77.41 49	80.62 112	55.91 116	84.28 42	85.78 20	56.08 195	73.41 80	86.58 109	50.94 110	88.54 28	70.79 82	89.71 17	87.79 37

ACMM	61.98 7	70.80 122	69.73 130	74.02 113	80.59 113	58.59 77	82.68 67	82.02 101	55.46 209	67.18 184	84.39 161	38.51 254	83.17 151	60.65 162	76.10 183	80.30 264
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20231211		69.28 161	68.47 158	71.73 178	80.28 114	47.18 264	79.98 103	82.37 96	54.61 232	67.24 182	84.01 168	39.43 244	82.41 174	55.45 201	72.83 227	85.62 118

ACMP	63.53 6	72.30 94	71.20 105	75.59 81	80.28 114	57.54 87	82.74 66	82.84 92	60.58 95	65.24 224	86.18 121	39.25 247	86.03 89	66.95 109	76.79 176	83.22 204
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		72.74 83	71.74 92	75.76 73	80.22 116	57.51 89	82.55 70	83.40 74	61.32 80	66.67 194	87.33 87	39.15 249	86.59 74	67.70 99	77.30 168	83.19 206

LGP-MVS_train				75.76 73	80.22 116	57.51 89		83.40 74	61.32 80	66.67 194	87.33 87	39.15 249	86.59 74	67.70 99	77.30 168	83.19 206

WR-MVS		68.47 178	68.47 158	68.44 248	80.20 118	39.84 332	73.75 244	76.07 217	64.68 22	68.11 164	83.63 176	50.39 116	79.14 238	49.78 245	69.66 278	86.34 86

Anonymous20240529		69.91 139	69.02 144	72.56 159	80.19 119	47.65 258	77.56 153	80.99 131	55.45 210	69.88 133	86.76 99	39.24 248	82.18 177	54.04 212	77.10 172	87.85 33

Anonymous202405211		66.84 214	65.99 213	69.40 234	80.19 119	42.21 313	71.11 283	71.31 279	58.80 137	67.90 166	86.39 116	29.83 346	79.65 225	49.60 251	78.78 142	86.33 88

CS-MVS		76.25 44	75.98 43	77.06 53	80.15 121	55.63 123	84.51 38	83.90 57	63.24 46	73.30 81	87.27 89	55.06 49	86.30 86	71.78 75	84.58 66	89.25 5

BH-RMVSNet		68.81 168	67.42 179	72.97 150	80.11 122	52.53 180	74.26 231	76.29 213	58.48 145	68.38 157	84.20 162	42.59 208	83.83 138	46.53 275	75.91 184	82.56 218

test_0402		63.25 260	61.01 279	69.96 221	80.00 123	54.37 144	76.86 175	72.02 275	54.58 234	58.71 315	80.79 240	35.00 291	84.36 128	26.41 407	64.71 324	71.15 377

HyFIR lowres test		65.67 230	63.01 250	73.67 128	79.97 124	55.65 122	69.07 307	75.52 226	42.68 370	63.53 249	77.95 287	40.43 235	81.64 185	46.01 280	71.91 241	83.73 189

EIA-MVS		71.78 104	70.60 115	75.30 85	79.85 125	53.54 157	77.27 163	83.26 83	57.92 158	66.49 196	79.39 266	52.07 92	86.69 72	60.05 166	79.14 137	85.66 116

BH-untuned		68.27 182	67.29 184	71.21 197	79.74 126	53.22 163	76.06 192	77.46 198	57.19 168	66.10 203	81.61 221	45.37 182	83.50 145	45.42 291	76.68 178	76.91 314

VNet		69.68 147	70.19 124	68.16 251	79.73 127	41.63 320	70.53 290	77.38 199	60.37 101	70.69 119	86.63 106	51.08 107	77.09 274	53.61 217	81.69 105	85.75 113

LS3D		64.71 242	62.50 256	71.34 195	79.72 128	55.71 120	79.82 107	74.72 243	48.50 311	56.62 333	84.62 154	33.59 309	82.34 175	29.65 395	75.23 192	75.97 320

mvsmamba		68.47 178	66.56 196	74.21 110	79.60 129	52.95 169	74.94 218	75.48 227	52.09 263	60.10 296	83.27 182	36.54 279	84.70 122	59.32 176	77.69 160	84.99 148

hse-mvs2		71.04 115	69.86 128	74.60 98	79.58 130	57.12 99	73.96 236	75.25 232	60.40 98	74.81 59	81.95 214	45.54 176	82.90 156	70.41 84	66.83 309	83.77 187

GeoE		71.01 116	70.15 125	73.60 134	79.57 131	52.17 187	78.93 121	78.12 186	58.02 154	67.76 176	83.87 171	52.36 86	82.72 165	56.90 187	75.79 186	85.92 103

AUN-MVS		68.45 180	66.41 203	74.57 100	79.53 132	57.08 100	73.93 239	75.23 233	54.44 237	66.69 193	81.85 216	37.10 274	82.89 157	62.07 150	66.84 308	83.75 188

test2506		65.33 236	64.61 229	67.50 256	79.46 133	34.19 386	74.43 230	51.92 395	58.72 138	66.75 192	88.05 71	25.99 377	80.92 204	51.94 230	84.25 72	87.39 50

ECVR-MVS		67.72 195	67.51 176	68.35 249	79.46 133	36.29 371	74.79 222	66.93 317	58.72 138	67.19 183	88.05 71	36.10 281	81.38 191	52.07 228	84.25 72	87.39 50

testing3-2		62.06 275	62.36 258	61.17 324	79.29 135	30.31 404	64.09 348	63.49 345	63.50 42	62.84 260	82.22 205	32.35 332	69.02 332	40.01 331	73.43 216	84.17 170

BH-w/o		66.85 213	65.83 215	69.90 225	79.29 135	52.46 183	74.66 225	76.65 211	54.51 236	64.85 233	78.12 283	45.59 175	82.95 155	43.26 308	75.54 190	74.27 344

1112_ss		64.00 252	63.36 244	65.93 283	79.28 137	42.58 309	71.35 276	72.36 272	46.41 337	60.55 293	77.89 291	46.27 170	73.28 307	46.18 278	69.97 269	81.92 233

ETV-MVS		74.46 64	73.84 68	76.33 67	79.27 138	55.24 132	79.22 117	85.00 38	64.97 21	72.65 99	79.46 264	53.65 72	87.87 44	67.45 103	82.91 86	85.89 105

test1111		67.21 202	67.14 192	67.42 258	79.24 139	34.76 380	73.89 241	65.65 326	58.71 140	66.96 188	87.95 75	36.09 282	80.53 211	52.03 229	83.79 77	86.97 62

UniMVSNet_NR-MVSNet		71.11 114	71.00 109	71.44 189	79.20 140	44.13 293	76.02 195	82.60 94	66.48 11	68.20 159	84.60 156	56.82 36	82.82 163	54.62 207	70.43 257	87.36 54

VPNet		67.52 198	68.11 165	65.74 286	79.18 141	36.80 363	72.17 267	72.83 267	62.04 73	67.79 174	85.83 134	48.88 134	76.60 288	51.30 236	72.97 225	83.81 183

TR-MVS		66.59 221	65.07 226	71.17 200	79.18 141	49.63 232	73.48 246	75.20 235	52.95 253	67.90 166	80.33 246	39.81 241	83.68 141	43.20 309	73.56 212	80.20 265

TAMVS		66.78 216	65.27 224	71.33 196	79.16 143	53.67 153	73.84 243	69.59 294	52.32 261	65.28 219	81.72 219	44.49 192	77.40 268	42.32 316	78.66 146	82.92 213

patch_mono-2		69.85 140	71.09 107	66.16 277	79.11 144	54.80 139	71.97 270	74.31 249	53.50 250	70.90 118	84.17 163	57.63 31	63.31 363	66.17 112	82.02 97	80.38 263

Test_1112_low_res		62.32 270	61.77 265	64.00 303	79.08 145	39.53 337	68.17 311	70.17 287	43.25 365	59.03 313	79.90 252	44.08 194	71.24 319	43.79 303	68.42 296	81.25 245

CDS-MVSNet		66.80 215	65.37 221	71.10 202	78.98 146	53.13 167	73.27 251	71.07 281	52.15 262	64.72 234	80.23 248	43.56 200	77.10 273	45.48 289	78.88 139	83.05 212
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
sasdasda		74.67 59	74.98 55	73.71 126	78.94 147	50.56 214	80.23 98	83.87 60	60.30 105	77.15 33	86.56 110	59.65 17	82.00 179	66.01 115	82.12 94	88.58 14

canonicalmvs		74.67 59	74.98 55	73.71 126	78.94 147	50.56 214	80.23 98	83.87 60	60.30 105	77.15 33	86.56 110	59.65 17	82.00 179	66.01 115	82.12 94	88.58 14

EC-MVSNet		75.84 49	75.87 46	75.74 75	78.86 149	52.65 176	83.73 53	86.08 17	63.47 43	72.77 97	87.25 90	53.13 76	87.93 42	71.97 73	85.57 62	86.66 74

IS-MVSNet		71.57 108	71.00 109	73.27 146	78.86 149	45.63 280	80.22 100	78.69 169	64.14 35	66.46 197	87.36 86	49.30 126	85.60 98	50.26 244	83.71 79	88.59 13

CLD-MVS		73.33 74	72.68 81	75.29 86	78.82 151	53.33 162	78.23 133	84.79 41	61.30 82	70.41 122	81.04 231	52.41 85	87.12 61	64.61 129	82.49 93	85.41 130
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVSFormer		71.50 110	70.38 120	74.88 89	78.76 152	57.15 97	82.79 64	78.48 176	51.26 275	69.49 138	83.22 183	43.99 197	83.24 149	66.06 113	79.37 129	84.23 167

lupinMVS		69.57 152	68.28 163	73.44 141	78.76 152	57.15 97	76.57 180	73.29 263	46.19 339	69.49 138	82.18 206	43.99 197	79.23 232	64.66 127	79.37 129	83.93 177

CNLPA		65.43 233	64.02 233	69.68 228	78.73 154	58.07 81	77.82 147	70.71 284	51.49 270	61.57 284	83.58 178	38.23 260	70.82 320	43.90 301	70.10 267	80.16 266

EPP-MVSNet		72.16 99	71.31 102	74.71 91	78.68 155	49.70 228	82.10 78	81.65 106	60.40 98	65.94 206	85.84 133	51.74 98	86.37 83	55.93 193	79.55 128	88.07 29

TranMVSNet+NR-MVSNet		70.36 130	70.10 127	71.17 200	78.64 156	42.97 307	76.53 181	81.16 127	66.95 6	68.53 154	85.42 144	51.61 100	83.07 152	52.32 225	69.70 277	87.46 47

UniMVSNet (Re)		70.63 124	70.20 123	71.89 172	78.55 157	45.29 283	75.94 196	82.92 88	63.68 40	68.16 162	83.59 177	53.89 64	83.49 146	53.97 213	71.12 250	86.89 64

Fast-Effi-MVS+		70.28 132	69.12 143	73.73 125	78.50 158	51.50 198	75.01 215	79.46 155	56.16 194	68.59 151	79.55 262	53.97 62	84.05 132	53.34 219	77.53 162	85.65 117

PS-MVSNAJss		72.24 95	71.21 104	75.31 84	78.50 158	55.93 115	81.63 82	82.12 99	56.24 192	70.02 129	85.68 138	47.05 160	84.34 129	65.27 122	74.41 198	85.67 115

EI-MVSNet-Vis-set		72.42 93	71.59 93	74.91 88	78.47 160	54.02 147	77.05 168	79.33 157	65.03 18	71.68 111	79.35 268	52.75 79	84.89 118	66.46 110	74.23 199	85.83 107

FA-MVS(test-final)		69.82 141	68.48 156	73.84 117	78.44 161	50.04 223	75.58 204	78.99 162	58.16 150	67.59 177	82.14 210	42.66 207	85.63 97	56.60 188	76.19 182	85.84 106

testing91		64.46 246	63.80 237	66.47 270	78.43 162	40.06 330	67.63 315	69.59 294	59.06 132	63.18 254	78.05 285	34.05 300	76.99 278	48.30 261	75.87 185	82.37 225

testing11		62.81 264	61.90 264	65.54 288	78.38 163	40.76 327	67.59 317	66.78 319	55.48 208	60.13 295	77.11 303	31.67 335	76.79 283	45.53 287	74.45 196	79.06 282

MVS_111021_LR		69.50 156	68.78 150	71.65 182	78.38 163	59.33 59	74.82 221	70.11 288	58.08 151	67.83 172	84.68 151	41.96 215	76.34 293	65.62 120	77.54 161	79.30 281

test_yl		69.69 145	69.13 141	71.36 193	78.37 165	45.74 276	74.71 223	80.20 144	57.91 159	70.01 130	83.83 172	42.44 210	82.87 159	54.97 203	79.72 123	85.48 122

DCV-MVSNet		69.69 145	69.13 141	71.36 193	78.37 165	45.74 276	74.71 223	80.20 144	57.91 159	70.01 130	83.83 172	42.44 210	82.87 159	54.97 203	79.72 123	85.48 122

FIs		70.82 121	71.43 97	68.98 241	78.33 167	38.14 348	76.96 170	83.59 68	61.02 87	67.33 181	86.73 101	55.07 48	81.64 185	54.61 209	79.22 133	87.14 59

UGNet		68.81 168	67.39 180	73.06 149	78.33 167	54.47 141	79.77 108	75.40 229	60.45 97	63.22 252	84.40 160	32.71 322	80.91 205	51.71 234	80.56 114	83.81 183
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		69.65 148	68.39 162	73.43 142	78.27 169	56.88 101	77.12 166	73.71 258	46.53 336	69.34 142	83.22 183	43.37 201	79.18 233	64.77 126	79.20 134	84.23 167
jason: jason.
alignmvs		73.86 69	73.99 65	73.45 140	78.20 170	50.50 216	78.57 128	82.43 95	59.40 127	76.57 37	86.71 103	56.42 40	81.23 196	65.84 118	81.79 100	88.62 12

xiu_mvs_v1_base_debu		68.58 174	67.28 185	72.48 161	78.19 171	57.19 94	75.28 207	75.09 237	51.61 266	70.04 126	81.41 225	32.79 318	79.02 240	63.81 136	77.31 165	81.22 246

xiu_mvs_v1_base		68.58 174	67.28 185	72.48 161	78.19 171	57.19 94	75.28 207	75.09 237	51.61 266	70.04 126	81.41 225	32.79 318	79.02 240	63.81 136	77.31 165	81.22 246

xiu_mvs_v1_base_debi		68.58 174	67.28 185	72.48 161	78.19 171	57.19 94	75.28 207	75.09 237	51.61 266	70.04 126	81.41 225	32.79 318	79.02 240	63.81 136	77.31 165	81.22 246

testing99		64.05 250	63.29 247	66.34 272	78.17 174	39.76 334	67.33 320	68.00 308	58.60 142	63.03 257	78.10 284	32.57 328	76.94 280	48.22 262	75.58 189	82.34 226

UBG		59.62 300	59.53 289	59.89 329	78.12 175	35.92 374	64.11 347	60.81 365	49.45 297	61.34 285	75.55 331	33.05 313	67.39 345	38.68 338	74.62 194	76.35 318

PAPM		67.92 191	66.69 195	71.63 183	78.09 176	49.02 239	77.09 167	81.24 124	51.04 278	60.91 290	83.98 169	47.71 146	84.99 112	40.81 325	79.32 132	80.90 254

ACMH	55.70 15	65.20 238	63.57 241	70.07 220	78.07 177	52.01 192	79.48 116	79.69 148	55.75 202	56.59 334	80.98 233	27.12 368	80.94 202	42.90 313	71.58 245	77.25 308
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		70.01 136	69.53 134	71.44 189	78.05 178	44.13 293	75.01 215	81.51 110	64.37 28	68.20 159	84.52 157	49.12 132	82.82 163	54.62 207	70.43 257	87.37 52

NR-MVSNet		69.54 153	68.85 147	71.59 184	78.05 178	43.81 298	74.20 232	80.86 134	65.18 14	62.76 263	84.52 157	52.35 87	83.59 144	50.96 240	70.78 252	87.37 52

WBMVS		60.54 289	60.61 283	60.34 328	78.00 180	35.95 373	64.55 343	64.89 332	49.63 294	63.39 251	78.70 274	33.85 305	67.65 341	42.10 318	70.35 261	77.43 303

EI-MVSNet-UG-set		71.92 102	71.06 108	74.52 102	77.98 181	53.56 156	76.62 178	79.16 158	64.40 27	71.18 116	78.95 273	52.19 89	84.66 125	65.47 121	73.57 211	85.32 134

WR-MVS_H		67.02 210	66.92 194	67.33 261	77.95 182	37.75 352	77.57 152	82.11 100	62.03 74	62.65 266	82.48 199	50.57 114	79.46 228	42.91 312	64.01 330	84.79 154

testing222		62.29 272	61.31 272	65.25 294	77.87 183	38.53 345	68.34 310	66.31 323	56.37 188	63.15 256	77.58 299	28.47 356	76.18 296	37.04 348	76.65 179	81.05 252

Effi-MVS+		73.31 75	72.54 83	75.62 79	77.87 183	53.64 154	79.62 113	79.61 151	61.63 78	72.02 107	82.61 193	56.44 39	85.97 91	63.99 133	79.07 138	87.25 57

DELS-MVS		74.76 57	74.46 60	75.65 78	77.84 185	52.25 186	75.59 202	84.17 49	63.76 38	73.15 86	82.79 188	59.58 20	86.80 69	67.24 104	86.04 59	87.89 30
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	66.12 225	64.06 232	72.30 168	77.79 186	52.83 174	80.39 97	78.03 187	57.30 166	57.47 327	82.55 195	27.68 363	84.17 130	45.54 286	69.78 274	79.90 271

MGCFI-Net		72.45 91	73.34 74	69.81 227	77.77 187	43.21 304	75.84 199	81.18 125	59.59 125	75.45 44	86.64 104	57.74 28	77.94 255	63.92 134	81.90 99	88.30 19

RRT-MVS		71.46 111	70.70 114	73.74 124	77.76 188	49.30 236	76.60 179	80.45 140	61.25 83	68.17 161	84.78 150	44.64 189	84.90 117	64.79 125	77.88 158	87.03 60

GDP-MVS		72.64 86	71.28 103	76.70 57	77.72 189	54.22 145	79.57 114	84.45 43	55.30 212	71.38 115	86.97 94	39.94 237	87.00 65	67.02 108	79.20 134	88.89 9

3Dnovator	64.47 5	72.49 90	71.39 99	75.79 72	77.70 190	58.99 71	80.66 96	83.15 85	62.24 67	65.46 216	86.59 108	42.38 212	85.52 101	59.59 172	84.72 65	82.85 216

EG-PatchMatch MVS		64.71 242	62.87 251	70.22 216	77.68 191	53.48 158	77.99 141	78.82 164	53.37 251	56.03 339	77.41 301	24.75 385	84.04 133	46.37 277	73.42 217	73.14 350

UWE-MVS		60.18 293	59.78 287	61.39 322	77.67 192	33.92 389	69.04 308	63.82 342	48.56 308	64.27 241	77.64 298	27.20 367	70.40 325	33.56 371	76.24 181	79.83 273

CP-MVSNet		66.49 222	66.41 203	66.72 264	77.67 192	36.33 368	76.83 176	79.52 153	62.45 64	62.54 269	83.47 181	46.32 168	78.37 248	45.47 290	63.43 337	85.45 126

GBi-Net		67.21 202	66.55 197	69.19 236	77.63 194	43.33 301	77.31 159	77.83 190	56.62 180	65.04 229	82.70 189	41.85 218	80.33 216	47.18 270	72.76 228	83.92 178

test1		67.21 202	66.55 197	69.19 236	77.63 194	43.33 301	77.31 159	77.83 190	56.62 180	65.04 229	82.70 189	41.85 218	80.33 216	47.18 270	72.76 228	83.92 178

FMVSNet2		66.93 212	66.31 208	68.79 244	77.63 194	42.98 306	76.11 190	77.47 196	56.62 180	65.22 226	82.17 208	41.85 218	80.18 222	47.05 273	72.72 231	83.20 205

PCF-MVS	61.88 8	70.95 118	69.49 135	75.35 83	77.63 194	55.71 120	76.04 194	81.81 104	50.30 286	69.66 136	85.40 145	52.51 82	84.89 118	51.82 232	80.24 118	85.45 126
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		65.41 234	63.80 237	70.22 216	77.62 198	55.53 127	76.30 185	78.53 174	50.59 284	56.47 337	78.65 277	39.84 240	82.68 166	44.10 299	72.12 240	72.44 359
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FC-MVSNet-test		69.80 143	70.58 117	67.46 257	77.61 199	34.73 381	76.05 193	83.19 84	60.84 89	65.88 210	86.46 114	54.52 57	80.76 209	52.52 224	78.12 154	86.91 63

PS-CasMVS		66.42 223	66.32 207	66.70 266	77.60 200	36.30 370	76.94 171	79.61 151	62.36 66	62.43 274	83.66 175	45.69 172	78.37 248	45.35 292	63.26 338	85.42 129

testing3		56.54 320	55.92 322	58.41 341	77.52 201	27.93 412	69.72 301	56.36 382	54.75 231	58.63 318	77.80 293	20.88 396	71.75 317	25.31 409	62.25 346	75.53 326

FMVSNet1		66.70 217	65.87 214	69.19 236	77.49 202	43.33 301	77.31 159	77.83 190	56.45 185	64.60 237	82.70 189	38.08 262	80.33 216	46.08 279	72.31 237	83.92 178

ETVMVS		59.51 301	58.81 295	61.58 319	77.46 203	34.87 377	64.94 341	59.35 368	54.06 242	61.08 289	76.67 310	29.54 347	71.87 316	32.16 376	74.07 201	78.01 297

VPA-MVSNet		69.02 165	69.47 136	67.69 255	77.42 204	41.00 325	74.04 234	79.68 149	60.06 111	69.26 145	84.81 149	51.06 108	77.58 264	54.44 210	74.43 197	84.48 161

UniMVSNet_ETH3D		67.60 197	67.07 193	69.18 239	77.39 205	42.29 311	74.18 233	75.59 224	60.37 101	66.77 191	86.06 126	37.64 264	78.93 245	52.16 227	73.49 213	86.32 90

FE-MVS		65.91 227	63.33 245	73.63 132	77.36 206	51.95 194	72.62 259	75.81 219	53.70 247	65.31 218	78.96 272	28.81 355	86.39 82	43.93 300	73.48 214	82.55 219

myMVS_eth3d28		60.66 287	61.04 278	59.51 331	77.32 207	31.58 400	63.11 352	63.87 341	59.00 133	60.90 291	78.26 282	32.69 323	66.15 353	36.10 359	78.13 153	80.81 256

thres100view900		63.28 259	62.41 257	65.89 284	77.31 208	38.66 343	72.65 257	69.11 301	57.07 169	62.45 272	81.03 232	37.01 276	79.17 234	31.84 380	73.25 220	79.83 273

cascas		65.98 226	63.42 243	73.64 131	77.26 209	52.58 179	72.26 266	77.21 202	48.56 308	61.21 287	74.60 341	32.57 328	85.82 95	50.38 243	76.75 177	82.52 221

thres600view7		63.30 258	62.27 259	66.41 271	77.18 210	38.87 341	72.35 264	69.11 301	56.98 172	62.37 275	80.96 234	37.01 276	79.00 243	31.43 387	73.05 224	81.36 242

SDMVSNet		68.03 187	68.10 166	67.84 253	77.13 211	48.72 246	65.32 336	79.10 159	58.02 154	65.08 227	82.55 195	47.83 144	73.40 306	63.92 134	73.92 203	81.41 239

sd_testset		64.46 246	64.45 230	64.51 299	77.13 211	42.25 312	62.67 355	72.11 274	58.02 154	65.08 227	82.55 195	41.22 230	69.88 328	47.32 268	73.92 203	81.41 239

PEN-MVS		66.60 219	66.45 199	67.04 262	77.11 213	36.56 365	77.03 169	80.42 141	62.95 51	62.51 271	84.03 167	46.69 166	79.07 239	44.22 295	63.08 340	85.51 121

PatchMatch-RL		56.25 325	54.55 332	61.32 323	77.06 214	56.07 112	65.57 330	54.10 392	44.13 358	53.49 368	71.27 366	25.20 382	66.78 348	36.52 356	63.66 333	61.12 400

PVSNet_BlendedMVS		68.56 177	67.72 169	71.07 203	77.03 215	50.57 212	74.50 227	81.52 108	53.66 249	64.22 244	79.72 258	49.13 130	82.87 159	55.82 194	73.92 203	79.77 276

PVSNet_Blended		68.59 173	67.72 169	71.19 198	77.03 215	50.57 212	72.51 262	81.52 108	51.91 264	64.22 244	77.77 296	49.13 130	82.87 159	55.82 194	79.58 126	80.14 267

F-COLMAP		63.05 263	60.87 282	69.58 232	76.99 217	53.63 155	78.12 137	76.16 214	47.97 319	52.41 371	81.61 221	27.87 360	78.11 252	40.07 328	66.66 310	77.00 311

tfpn200view9		63.18 261	62.18 261	66.21 276	76.85 218	39.62 335	71.96 271	69.44 297	56.63 178	62.61 267	79.83 253	37.18 270	79.17 234	31.84 380	73.25 220	79.83 273

thres400		63.31 257	62.18 261	66.72 264	76.85 218	39.62 335	71.96 271	69.44 297	56.63 178	62.61 267	79.83 253	37.18 270	79.17 234	31.84 380	73.25 220	81.36 242

tttt0517		67.83 193	65.66 218	74.33 106	76.69 220	50.82 208	77.86 144	73.99 255	54.54 235	64.64 236	82.53 198	35.06 290	85.50 103	55.71 197	69.91 271	86.67 73

BP-MVS1		73.41 73	72.25 86	76.88 54	76.68 221	53.70 152	79.15 118	81.07 128	60.66 93	71.81 108	87.39 85	40.93 232	87.24 54	71.23 80	81.29 106	89.71 2

ET-MVSNet_ETH3D		67.96 190	65.72 217	74.68 93	76.67 222	55.62 125	75.11 212	74.74 242	52.91 254	60.03 298	80.12 249	33.68 307	82.64 168	61.86 153	76.34 180	85.78 108

TAPA-MVS	59.36 10	66.60 219	65.20 225	70.81 207	76.63 223	48.75 244	76.52 182	80.04 146	50.64 283	65.24 224	84.93 147	39.15 249	78.54 247	36.77 350	76.88 174	85.14 140
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OMC-MVS		71.40 113	70.60 115	73.78 119	76.60 224	53.15 165	79.74 110	79.78 147	58.37 147	68.75 150	86.45 115	45.43 180	80.60 210	62.58 145	77.73 159	87.58 45

LTVRE_ROB	55.42 16	63.15 262	61.23 275	68.92 242	76.57 225	47.80 255	59.92 371	76.39 212	54.35 238	58.67 316	82.46 200	29.44 350	81.49 189	42.12 317	71.14 249	77.46 302
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		70.05 135	68.81 149	73.78 119	76.54 226	53.43 159	83.23 57	83.48 70	52.89 255	65.90 208	86.29 118	41.55 224	86.49 80	51.01 238	78.40 150	81.42 238

FMVSNet3		66.32 224	65.61 219	68.46 247	76.48 227	42.34 310	74.98 217	77.15 203	55.83 199	65.04 229	81.16 228	39.91 238	80.14 223	47.18 270	72.76 228	82.90 215

casdiffmvs_mvg		76.14 45	76.30 39	75.66 77	76.46 228	51.83 196	79.67 111	85.08 33	65.02 19	75.84 40	88.58 65	59.42 22	85.08 111	72.75 64	83.93 76	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
thisisatest0530		67.92 191	65.78 216	74.33 106	76.29 229	51.03 203	76.89 173	74.25 251	53.67 248	65.59 214	81.76 218	35.15 289	85.50 103	55.94 192	72.47 233	86.47 81

baseline1		63.81 253	63.87 236	63.62 304	76.29 229	36.36 366	71.78 273	67.29 313	56.05 196	64.23 243	82.95 187	47.11 159	74.41 303	47.30 269	61.85 349	80.10 268

ab-mvs		66.65 218	66.42 202	67.37 259	76.17 231	41.73 317	70.41 293	76.14 216	53.99 243	65.98 205	83.51 179	49.48 124	76.24 294	48.60 258	73.46 215	84.14 171

Effi-MVS+-dtu		69.64 149	67.53 175	75.95 70	76.10 232	62.29 15	80.20 101	76.06 218	59.83 119	65.26 223	77.09 304	41.56 223	84.02 135	60.60 163	71.09 251	81.53 237

DTE-MVSNet		65.58 231	65.34 222	66.31 273	76.06 233	34.79 378	76.43 183	79.38 156	62.55 62	61.66 282	83.83 172	45.60 174	79.15 237	41.64 324	60.88 355	85.00 146

EPNet		73.09 78	72.16 87	75.90 71	75.95 234	56.28 107	83.05 59	72.39 271	66.53 10	65.27 220	87.00 93	50.40 115	85.47 105	62.48 147	86.32 58	85.94 102
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SixPastTwentyTwo		61.65 280	58.80 297	70.20 218	75.80 235	47.22 263	75.59 202	69.68 292	54.61 232	54.11 359	79.26 269	27.07 369	82.96 154	43.27 307	49.79 396	80.41 262

baseline		74.61 61	74.70 58	74.34 105	75.70 236	49.99 225	77.54 154	84.63 42	62.73 60	73.98 72	87.79 79	57.67 30	83.82 139	69.49 87	82.74 91	89.20 7

Baseline_NR-MVSNet		67.05 209	67.56 172	65.50 289	75.65 237	37.70 354	75.42 205	74.65 245	59.90 114	68.14 163	83.15 186	49.12 132	77.20 272	52.23 226	69.78 274	81.60 236

jajsoiax		68.25 183	66.45 199	73.66 129	75.62 238	55.49 128	80.82 93	78.51 175	52.33 260	64.33 239	84.11 165	28.28 358	81.81 184	63.48 140	70.62 254	83.67 191

mvs_tets		68.18 185	66.36 205	73.63 132	75.61 239	55.35 131	80.77 94	78.56 173	52.48 259	64.27 241	84.10 166	27.45 365	81.84 183	63.45 141	70.56 256	83.69 190

casdiffmvs		74.80 56	74.89 57	74.53 101	75.59 240	50.37 217	78.17 136	85.06 35	62.80 59	74.40 67	87.86 76	57.88 27	83.61 143	69.46 89	82.79 90	89.59 4
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	58.40 306	57.39 308	61.42 320	75.53 241	44.04 296	61.43 361	63.45 346	47.04 332	56.91 331	73.61 348	27.00 370	64.76 358	39.12 336	72.40 234	75.47 327

MVS		67.37 200	66.33 206	70.51 214	75.46 242	50.94 204	73.95 237	81.85 103	41.57 376	62.54 269	78.57 280	47.98 141	85.47 105	52.97 222	82.05 96	75.14 330

nrg030		72.96 80	73.01 76	72.84 153	75.41 243	50.24 218	80.02 102	82.89 91	58.36 148	74.44 66	86.73 101	58.90 24	80.83 206	65.84 118	74.46 195	87.44 48

thres200		62.20 273	61.16 277	65.34 292	75.38 244	39.99 331	69.60 302	69.29 299	55.64 206	61.87 279	76.99 305	37.07 275	78.96 244	31.28 388	73.28 219	77.06 309

TransMVSNet (Re)		64.72 241	64.33 231	65.87 285	75.22 245	38.56 344	74.66 225	75.08 240	58.90 136	61.79 280	82.63 192	51.18 105	78.07 253	43.63 305	55.87 378	80.99 253

MS-PatchMatch		62.42 269	61.46 269	65.31 293	75.21 246	52.10 188	72.05 268	74.05 254	46.41 337	57.42 329	74.36 342	34.35 298	77.57 265	45.62 285	73.67 207	66.26 396

WB-MVSnew		59.66 298	59.69 288	59.56 330	75.19 247	35.78 375	69.34 305	64.28 338	46.88 333	61.76 281	75.79 327	40.61 234	65.20 357	32.16 376	71.21 248	77.70 299

fmvsm_s_conf0.5_n_6		72.59 88	72.87 78	71.73 178	75.14 248	51.96 193	76.28 186	77.12 204	57.63 163	73.85 75	86.91 95	51.54 101	77.87 258	77.18 28	80.18 120	85.37 132

IB-MVS	56.42 12	65.40 235	62.73 254	73.40 143	74.89 249	52.78 175	73.09 253	75.13 236	55.69 203	58.48 320	73.73 347	32.86 317	86.32 85	50.63 241	70.11 266	81.10 250
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 91	72.46 84	72.42 165	74.88 250	48.50 248	76.28 186	83.14 86	59.40 127	72.46 102	84.68 151	55.66 45	81.12 197	65.98 117	79.66 125	87.63 42

tt0805		67.77 194	67.24 189	69.34 235	74.87 251	40.08 329	77.36 158	81.37 114	55.31 211	66.33 200	84.65 153	37.35 268	82.55 170	55.65 199	72.28 238	85.39 131

CANet_DTU		68.18 185	67.71 171	69.59 230	74.83 252	46.24 271	78.66 125	76.85 207	59.60 122	63.45 250	82.09 213	35.25 288	77.41 267	59.88 169	78.76 143	85.14 140

tfpnnormal		62.47 268	61.63 267	64.99 296	74.81 253	39.01 340	71.22 279	73.72 257	55.22 215	60.21 294	80.09 251	41.26 229	76.98 279	30.02 393	68.09 299	78.97 285

Vis-MVSNet (Re-imp)		63.69 254	63.88 235	63.14 309	74.75 254	31.04 402	71.16 281	63.64 344	56.32 189	59.80 303	84.99 146	44.51 190	75.46 298	39.12 336	80.62 110	82.92 213

HY-MVS	56.14 13	64.55 245	63.89 234	66.55 269	74.73 255	41.02 322	69.96 299	74.43 246	49.29 300	61.66 282	80.92 235	47.43 154	76.68 287	44.91 294	71.69 243	81.94 232

Syy-MVS		56.00 327	56.23 320	55.32 359	74.69 256	26.44 418	65.52 331	57.49 377	50.97 279	56.52 335	72.18 355	39.89 239	68.09 336	24.20 410	64.59 327	71.44 373

myMVS_eth3d		54.86 337	54.61 331	55.61 358	74.69 256	27.31 415	65.52 331	57.49 377	50.97 279	56.52 335	72.18 355	21.87 394	68.09 336	27.70 401	64.59 327	71.44 373

COLMAP_ROB	52.97 17	61.27 285	58.81 295	68.64 245	74.63 258	52.51 181	78.42 131	73.30 262	49.92 292	50.96 376	81.51 224	23.06 388	79.40 229	31.63 384	65.85 315	74.01 347
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet-Re		61.88 278	61.35 271	63.46 305	74.58 259	31.48 401	61.42 362	58.14 373	58.71 140	53.02 369	79.55 262	43.07 203	76.80 282	45.69 283	77.96 156	82.11 231

test_djsdf		69.45 158	67.74 168	74.58 99	74.57 260	54.92 137	82.79 64	78.48 176	51.26 275	65.41 217	83.49 180	38.37 256	83.24 149	66.06 113	69.25 285	85.56 119

EI-MVSNet		69.27 162	68.44 160	71.73 178	74.47 261	49.39 235	75.20 210	78.45 179	59.60 122	69.16 147	76.51 316	51.29 103	82.50 171	59.86 171	71.45 247	83.30 201

CVMVSNet		59.63 299	59.14 292	61.08 326	74.47 261	38.84 342	75.20 210	68.74 303	31.15 402	58.24 321	76.51 316	32.39 330	68.58 334	49.77 246	65.84 316	75.81 322

IterMVS-LS		69.22 164	68.48 156	71.43 191	74.44 263	49.40 234	76.23 188	77.55 195	59.60 122	65.85 211	81.59 223	51.28 104	81.58 188	59.87 170	69.90 272	83.30 201
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 76	73.13 75	73.55 136	74.40 264	55.13 133	78.97 120	74.96 241	56.64 177	74.76 62	88.75 63	55.02 50	78.77 246	76.33 34	78.31 152	86.74 70

XVG-OURS-SEG-HR		68.81 168	67.47 178	72.82 155	74.40 264	56.87 102	70.59 289	79.04 160	54.77 230	66.99 187	86.01 128	39.57 243	78.21 251	62.54 146	73.33 218	83.37 200

EGC-MVSNET		42.47 375	38.48 383	54.46 365	74.33 266	48.73 245	70.33 295	51.10 398	0.03 435	0.18 436	67.78 387	13.28 410	66.49 350	18.91 418	50.36 394	48.15 415

XVG-OURS		68.76 171	67.37 181	72.90 152	74.32 267	57.22 92	70.09 298	78.81 165	55.24 214	67.79 174	85.81 137	36.54 279	78.28 250	62.04 151	75.74 187	83.19 206

SSC-MVS3.2		60.57 288	61.39 270	58.12 346	74.29 268	32.63 395	59.52 372	65.53 328	59.90 114	62.45 272	79.75 257	41.96 215	63.90 362	39.47 334	69.65 280	77.84 298

OpenMVS	61.03 9	68.85 167	67.56 172	72.70 157	74.26 269	53.99 148	81.21 89	81.34 119	52.70 256	62.75 264	85.55 141	38.86 252	84.14 131	48.41 260	83.01 82	79.97 269

MIMVSNet		57.35 313	57.07 310	58.22 343	74.21 270	37.18 357	62.46 356	60.88 364	48.88 305	55.29 346	75.99 325	31.68 334	62.04 368	31.87 379	72.35 235	75.43 328

SCA		60.49 290	58.38 301	66.80 263	74.14 271	48.06 253	63.35 351	63.23 348	49.13 302	59.33 311	72.10 357	37.45 266	74.27 304	44.17 296	62.57 343	78.05 293

fmvsm_s_conf0.5_n_5		72.69 85	72.80 79	72.37 166	74.11 272	53.21 164	78.12 137	73.31 261	53.98 244	76.81 36	88.05 71	53.38 73	77.37 269	76.64 31	80.78 107	86.53 79

fmvsm_s_conf0.5_n_3		73.55 71	74.39 61	71.03 204	74.09 273	51.86 195	77.77 148	75.60 223	61.18 84	78.67 23	88.98 57	55.88 44	77.73 262	78.69 15	78.68 145	83.50 198

thisisatest0515		65.83 228	63.50 242	72.82 155	73.75 274	49.50 233	71.32 277	73.12 266	49.39 298	63.82 246	76.50 318	34.95 292	84.84 121	53.20 221	75.49 191	84.13 172

fmvsm_s_conf0.5_n_4		72.04 101	71.85 90	72.58 158	73.74 275	52.49 182	76.69 177	72.42 270	56.42 187	75.32 45	87.04 92	52.13 91	78.01 254	79.29 11	73.65 208	87.26 56

K. test v3		60.47 291	57.11 309	70.56 212	73.74 275	48.22 251	75.10 214	62.55 352	58.27 149	53.62 365	76.31 320	27.81 361	81.59 187	47.42 266	39.18 411	81.88 234

v10		70.21 133	69.02 144	73.81 118	73.51 277	50.92 206	78.74 123	81.39 113	60.05 112	66.39 199	81.83 217	47.58 149	85.41 108	62.80 144	68.86 292	85.09 144

fmvsm_s_conf0.5_n_7		69.54 153	69.67 132	69.15 240	73.47 278	51.41 199	70.35 294	73.34 260	57.05 170	68.41 155	85.83 134	49.86 119	72.84 309	71.86 74	76.83 175	83.19 206

v1144		70.42 129	69.31 138	73.76 121	73.22 279	50.64 211	77.83 146	81.43 112	58.58 143	69.40 141	81.16 228	47.53 151	85.29 110	64.01 132	70.64 253	85.34 133

v1192		69.97 138	68.68 152	73.85 116	73.19 280	50.94 204	77.68 150	81.36 115	57.51 165	68.95 149	80.85 238	45.28 183	85.33 109	62.97 143	70.37 259	85.27 137

v8		70.33 131	69.28 139	73.49 138	73.15 281	50.22 219	78.62 126	80.78 135	60.79 90	66.45 198	82.11 212	49.35 125	84.98 114	63.58 139	68.71 293	85.28 136

v144192		69.71 144	68.51 155	73.33 145	73.10 282	50.13 221	77.54 154	80.64 136	56.65 176	68.57 153	80.55 241	46.87 165	84.96 116	62.98 142	69.66 278	84.89 151

v1921920		69.47 157	68.17 164	73.36 144	73.06 283	50.10 222	77.39 157	80.56 137	56.58 184	68.59 151	80.37 243	44.72 188	84.98 114	62.47 148	69.82 273	85.00 146

PatchmatchNet		59.84 296	58.24 302	64.65 298	73.05 284	46.70 267	69.42 304	62.18 358	47.55 324	58.88 314	71.96 359	34.49 296	69.16 330	42.99 311	63.60 334	78.07 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		69.24 163	67.91 167	73.25 148	73.02 285	49.82 226	77.21 164	80.54 138	56.43 186	68.34 158	80.51 242	43.33 202	84.99 112	62.03 152	69.77 276	84.95 150

Fast-Effi-MVS+-dtu		67.37 200	65.33 223	73.48 139	72.94 286	57.78 86	77.47 156	76.88 206	57.60 164	61.97 277	76.85 308	39.31 245	80.49 214	54.72 206	70.28 263	82.17 230

EPNet_dtu		61.90 277	61.97 263	61.68 317	72.89 287	39.78 333	75.85 198	65.62 327	55.09 218	54.56 355	79.36 267	37.59 265	67.02 347	39.80 332	76.95 173	78.25 290
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		62.07 274	60.10 286	67.99 252	72.79 288	43.86 297	71.05 285	66.85 318	43.14 367	62.77 262	75.39 335	38.32 258	80.80 207	41.69 321	68.88 290	79.32 280

MDTV_nov1_ep13			57.00 311		72.73 289	38.26 347	65.02 340	64.73 335	44.74 350	55.46 342	72.48 353	32.61 327	70.47 322	37.47 344	67.75 302

MSDG		61.81 279	59.23 291	69.55 233	72.64 290	52.63 178	70.45 292	75.81 219	51.38 272	53.70 362	76.11 321	29.52 348	81.08 200	37.70 343	65.79 317	74.93 335

gg-mvs-nofinetune		57.86 311	56.43 318	62.18 315	72.62 291	35.35 376	66.57 321	56.33 383	50.65 282	57.64 326	57.10 410	30.65 338	76.36 292	37.38 345	78.88 139	74.82 337

v2v482		70.50 127	69.45 137	73.66 129	72.62 291	50.03 224	77.58 151	80.51 139	59.90 114	69.52 137	82.14 210	47.53 151	84.88 120	65.07 124	70.17 265	86.09 98

baseline2		63.42 256	61.26 274	69.89 226	72.55 293	47.62 259	71.54 274	68.38 305	50.11 288	54.82 351	75.55 331	43.06 204	80.96 201	48.13 263	67.16 307	81.11 249

test_fmvsm_n_1920		71.73 106	71.14 106	73.50 137	72.52 294	56.53 104	75.60 201	76.16 214	48.11 316	77.22 32	85.56 139	53.10 77	77.43 266	74.86 47	77.14 170	86.55 78

v7n		69.01 166	67.36 182	73.98 114	72.51 295	52.65 176	78.54 130	81.30 120	60.26 107	62.67 265	81.62 220	43.61 199	84.49 126	57.01 186	68.70 294	84.79 154

fmvsm_s_conf0.5_n_a		69.54 153	68.74 151	71.93 171	72.47 296	53.82 150	78.25 132	62.26 357	49.78 293	73.12 89	86.21 120	52.66 80	76.79 283	75.02 46	68.88 290	85.18 139

mamv4		56.85 318	58.00 306	53.43 372	72.46 297	54.47 141	57.56 385	54.74 387	38.81 390	57.42 329	79.45 265	47.57 150	38.70 425	60.88 160	53.07 386	67.11 395

pm-mvs1		65.24 237	64.97 227	66.04 281	72.38 298	39.40 338	72.62 259	75.63 222	55.53 207	62.35 276	83.18 185	47.45 153	76.47 291	49.06 255	66.54 311	82.24 227

XVG-ACMP-BASELINE		64.36 248	62.23 260	70.74 209	72.35 299	52.45 184	70.80 287	78.45 179	53.84 246	59.87 301	81.10 230	16.24 404	79.32 231	55.64 200	71.76 242	80.47 260

WTY-MVS		59.75 297	60.39 284	57.85 348	72.32 300	37.83 351	61.05 367	64.18 339	45.95 344	61.91 278	79.11 271	47.01 163	60.88 371	42.50 315	69.49 281	74.83 336

fmvsm_s_conf0.5_n		69.58 151	68.84 148	71.79 176	72.31 301	52.90 171	77.90 142	62.43 355	49.97 291	72.85 95	85.90 131	52.21 88	76.49 289	75.75 38	70.26 264	85.97 101

tpm cat1		59.25 302	56.95 312	66.15 278	72.19 302	46.96 265	68.09 312	65.76 325	40.03 386	57.81 325	70.56 369	38.32 258	74.51 302	38.26 341	61.50 352	77.00 311

mvs_anonymous		68.03 187	67.51 176	69.59 230	72.08 303	44.57 290	71.99 269	75.23 233	51.67 265	67.06 186	82.57 194	54.68 55	77.94 255	56.56 189	75.71 188	86.26 95

OurMVSNet-221017-0		61.37 284	58.63 299	69.61 229	72.05 304	48.06 253	73.93 239	72.51 269	47.23 330	54.74 352	80.92 235	21.49 395	81.24 195	48.57 259	56.22 377	79.53 278

fmvsm_s_conf0.5_n_2		69.82 141	69.27 140	71.46 187	72.00 305	51.08 201	73.30 248	67.79 309	55.06 223	75.24 47	87.51 81	44.02 196	77.00 277	75.67 39	72.86 226	86.31 93

IterMVS-SCA-FT		62.49 267	61.52 268	65.40 291	71.99 306	50.80 209	71.15 282	69.63 293	45.71 345	60.61 292	77.93 288	37.45 266	65.99 354	55.67 198	63.50 336	79.42 279

CostFormer		64.04 251	62.51 255	68.61 246	71.88 307	45.77 275	71.30 278	70.60 285	47.55 324	64.31 240	76.61 314	41.63 221	79.62 227	49.74 247	69.00 289	80.42 261

1314		64.61 244	63.21 248	68.80 243	71.87 308	47.46 261	73.95 237	78.39 184	42.88 369	59.97 299	76.60 315	38.11 261	79.39 230	54.84 205	72.32 236	79.55 277

tpm		57.34 314	58.16 303	54.86 362	71.80 309	34.77 379	67.47 319	56.04 386	48.20 315	60.10 296	76.92 306	37.17 272	53.41 406	40.76 326	65.01 321	76.40 317

fmvsm_s_conf0.1_n_2		69.64 149	69.01 146	71.52 185	71.66 310	51.04 202	73.39 247	67.14 315	55.02 225	75.11 49	87.64 80	42.94 206	77.01 276	75.55 40	72.63 232	86.52 80

eth_miper_zixun_eth		67.63 196	66.28 209	71.67 181	71.60 311	48.33 250	73.68 245	77.88 188	55.80 201	65.91 207	78.62 279	47.35 157	82.88 158	59.45 173	66.25 313	83.81 183

pmmvs4		61.48 283	59.39 290	67.76 254	71.57 312	53.86 149	71.42 275	65.34 329	44.20 356	59.46 307	77.92 289	35.90 283	74.71 301	43.87 302	64.87 323	74.71 340

fmvsm_l_conf0.5_n		70.99 117	70.82 111	71.48 186	71.45 313	54.40 143	77.18 165	70.46 286	48.67 307	75.17 48	86.86 96	53.77 67	76.86 281	76.33 34	77.51 163	83.17 210

AllTest		57.08 316	54.65 330	64.39 300	71.44 314	49.03 237	69.92 300	67.30 311	45.97 342	47.16 391	79.77 255	17.47 398	67.56 343	33.65 368	59.16 365	76.57 315

TestCases				64.39 300	71.44 314	49.03 237		67.30 311	45.97 342	47.16 391	79.77 255	17.47 398	67.56 343	33.65 368	59.16 365	76.57 315

lessismore_v0				69.91 224	71.42 316	47.80 255		50.90 400		50.39 382	75.56 330	27.43 366	81.33 192	45.91 281	34.10 417	80.59 259

gm-plane-assit					71.40 317	41.72 319			48.85 306		73.31 349		82.48 173	48.90 256

GG-mvs-BLEND				62.34 314	71.36 318	37.04 361	69.20 306	57.33 379		54.73 353	65.48 398	30.37 340	77.82 259	34.82 364	74.93 193	72.17 364

fmvsm_l_conf0.5_n_a		70.50 127	70.27 122	71.18 199	71.30 319	54.09 146	76.89 173	69.87 290	47.90 320	74.37 68	86.49 113	53.07 78	76.69 286	75.41 42	77.11 171	82.76 217

test_fmvsmconf_n		73.01 79	72.59 82	74.27 108	71.28 320	55.88 117	78.21 135	75.56 225	54.31 239	74.86 58	87.80 78	54.72 54	80.23 220	78.07 23	78.48 148	86.70 71

test_fmvsmvis_n_1920		70.84 119	70.38 120	72.22 169	71.16 321	55.39 130	75.86 197	72.21 273	49.03 303	73.28 83	86.17 122	51.83 96	77.29 271	75.80 37	78.05 155	83.98 176

fmvsm_s_conf0.1_n		69.41 159	68.60 154	71.83 174	71.07 322	52.88 173	77.85 145	62.44 354	49.58 296	72.97 92	86.22 119	51.68 99	76.48 290	75.53 41	70.10 267	86.14 96

FMVSNet5		55.86 328	54.93 328	58.66 340	71.05 323	36.35 367	64.18 346	62.48 353	46.76 335	50.66 381	74.73 340	25.80 378	64.04 360	33.11 372	65.57 318	75.59 325

fmvsm_s_conf0.1_n_a		69.32 160	68.44 160	71.96 170	70.91 324	53.78 151	78.12 137	62.30 356	49.35 299	73.20 85	86.55 112	51.99 93	76.79 283	74.83 48	68.68 295	85.32 134

c3_l		68.33 181	67.56 172	70.62 211	70.87 325	46.21 272	74.47 228	78.80 166	56.22 193	66.19 202	78.53 281	51.88 94	81.40 190	62.08 149	69.04 288	84.25 166

GA-MVS		65.53 232	63.70 239	71.02 205	70.87 325	48.10 252	70.48 291	74.40 247	56.69 175	64.70 235	76.77 309	33.66 308	81.10 198	55.42 202	70.32 262	83.87 181

pmmvs6		63.69 254	62.82 253	66.27 275	70.63 327	39.27 339	73.13 252	75.47 228	52.69 257	59.75 305	82.30 203	39.71 242	77.03 275	47.40 267	64.35 329	82.53 220

reproduce_monomvs		62.56 266	61.20 276	66.62 268	70.62 328	44.30 292	70.13 297	73.13 265	54.78 229	61.13 288	76.37 319	25.63 380	75.63 297	58.75 177	60.29 361	79.93 270

miper_ehance_all_eth		68.03 187	67.24 189	70.40 215	70.54 329	46.21 272	73.98 235	78.68 170	55.07 221	66.05 204	77.80 293	52.16 90	81.31 193	61.53 158	69.32 282	83.67 191

MonoMVSNet		64.15 249	63.31 246	66.69 267	70.51 330	44.12 295	74.47 228	74.21 252	57.81 161	63.03 257	76.62 312	38.33 257	77.31 270	54.22 211	60.59 360	78.64 287

OpenMVS_ROB	52.78 18	60.03 294	58.14 304	65.69 287	70.47 331	44.82 285	75.33 206	70.86 283	45.04 348	56.06 338	76.00 323	26.89 372	79.65 225	35.36 363	67.29 305	72.60 355

v148		68.24 184	67.19 191	71.40 192	70.43 332	47.77 257	75.76 200	77.03 205	58.91 135	67.36 180	80.10 250	48.60 137	81.89 181	60.01 167	66.52 312	84.53 159

XXY-MVS		60.68 286	61.67 266	57.70 350	70.43 332	38.45 346	64.19 345	66.47 320	48.05 318	63.22 252	80.86 237	49.28 127	60.47 372	45.25 293	67.28 306	74.19 345

MVSTER		67.16 207	65.58 220	71.88 173	70.37 334	49.70 228	70.25 296	78.45 179	51.52 269	69.16 147	80.37 243	38.45 255	82.50 171	60.19 165	71.46 246	83.44 199

cl____		67.18 205	66.26 210	69.94 222	70.20 335	45.74 276	73.30 248	76.83 208	55.10 216	65.27 220	79.57 261	47.39 155	80.53 211	59.41 175	69.22 286	83.53 197

DIV-MVS_self_test		67.18 205	66.26 210	69.94 222	70.20 335	45.74 276	73.29 250	76.83 208	55.10 216	65.27 220	79.58 260	47.38 156	80.53 211	59.43 174	69.22 286	83.54 196

tpmvs		58.47 305	56.95 312	63.03 311	70.20 335	41.21 321	67.90 314	67.23 314	49.62 295	54.73 353	70.84 367	34.14 299	76.24 294	36.64 354	61.29 353	71.64 369

anonymousdsp		67.00 211	64.82 228	73.57 135	70.09 338	56.13 110	76.35 184	77.35 200	48.43 312	64.99 232	80.84 239	33.01 315	80.34 215	64.66 127	67.64 303	84.23 167

MIMVSNet1		55.17 335	54.31 336	57.77 349	70.03 339	32.01 398	65.68 329	64.81 333	49.19 301	46.75 394	76.00 323	25.53 381	64.04 360	28.65 398	62.13 347	77.26 307

CR-MVSNet		59.91 295	57.90 307	65.96 282	69.96 340	52.07 189	65.31 337	63.15 349	42.48 371	59.36 308	74.84 338	35.83 284	70.75 321	45.50 288	64.65 325	75.06 331

RPMNet		61.53 281	58.42 300	70.86 206	69.96 340	52.07 189	65.31 337	81.36 115	43.20 366	59.36 308	70.15 374	35.37 287	85.47 105	36.42 357	64.65 325	75.06 331

test_fmvsmconf0.1_n		72.81 81	72.33 85	74.24 109	69.89 342	55.81 118	78.22 134	75.40 229	54.17 241	75.00 53	88.03 74	53.82 66	80.23 220	78.08 22	78.34 151	86.69 72

cl22		67.47 199	66.45 199	70.54 213	69.85 343	46.49 268	73.85 242	77.35 200	55.07 221	65.51 215	77.92 289	47.64 148	81.10 198	61.58 157	69.32 282	84.01 175

Anonymous20231206		55.10 336	55.30 327	54.48 364	69.81 344	33.94 388	62.91 354	62.13 359	41.08 378	55.18 347	75.65 329	32.75 321	56.59 395	30.32 392	67.86 300	72.91 351

mmtdpeth		60.40 292	59.12 293	64.27 302	69.59 345	48.99 240	70.67 288	70.06 289	54.96 226	62.78 261	73.26 351	27.00 370	67.66 340	58.44 180	45.29 403	76.16 319

our_test_3		56.49 321	54.42 333	62.68 313	69.51 346	45.48 281	66.08 325	61.49 361	44.11 359	50.73 380	69.60 379	33.05 313	68.15 335	38.38 340	56.86 373	74.40 342

ppachtmachnet_test		58.06 310	55.38 326	66.10 280	69.51 346	48.99 240	68.01 313	66.13 324	44.50 353	54.05 360	70.74 368	32.09 333	72.34 312	36.68 353	56.71 376	76.99 313

diffmvs		70.69 123	70.43 118	71.46 187	69.45 348	48.95 242	72.93 254	78.46 178	57.27 167	71.69 110	83.97 170	51.48 102	77.92 257	70.70 83	77.95 157	87.53 46
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		62.79 265	61.27 273	67.35 260	69.37 349	52.04 191	71.17 280	68.24 307	52.63 258	59.82 302	76.91 307	37.32 269	72.36 311	52.80 223	63.19 339	77.66 300
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_re		56.77 319	56.83 314	56.61 353	69.23 350	41.02 322	58.37 377	64.18 339	50.59 284	57.45 328	71.42 363	35.54 286	58.94 382	37.23 346	67.45 304	69.87 386

miper_enhance_ethall		67.11 208	66.09 212	70.17 219	69.21 351	45.98 274	72.85 256	78.41 182	51.38 272	65.65 213	75.98 326	51.17 106	81.25 194	60.82 161	69.32 282	83.29 203

Patchmtry		57.16 315	56.47 317	59.23 334	69.17 352	34.58 382	62.98 353	63.15 349	44.53 352	56.83 332	74.84 338	35.83 284	68.71 333	40.03 329	60.91 354	74.39 343

CL-MVSNet_self_test		61.53 281	60.94 280	63.30 307	68.95 353	36.93 362	67.60 316	72.80 268	55.67 204	59.95 300	76.63 311	45.01 186	72.22 314	39.74 333	62.09 348	80.74 258

V42		68.65 172	67.35 183	72.56 159	68.93 354	50.18 220	72.90 255	79.47 154	56.92 173	69.45 140	80.26 247	46.29 169	82.99 153	64.07 130	67.82 301	84.53 159

test-LLR		58.15 309	58.13 305	58.22 343	68.57 355	44.80 286	65.46 333	57.92 374	50.08 289	55.44 343	69.82 376	32.62 325	57.44 389	49.66 249	73.62 209	72.41 360

test-mter		56.42 323	55.82 323	58.22 343	68.57 355	44.80 286	65.46 333	57.92 374	39.94 387	55.44 343	69.82 376	21.92 391	57.44 389	49.66 249	73.62 209	72.41 360

MVS-HIRNet		45.52 369	44.48 371	48.65 388	68.49 357	34.05 387	59.41 375	44.50 416	27.03 409	37.96 416	50.47 418	26.16 376	64.10 359	26.74 406	59.52 363	47.82 417

dp		51.89 352	51.60 351	52.77 377	68.44 358	32.45 397	62.36 357	54.57 389	44.16 357	49.31 386	67.91 384	28.87 354	56.61 394	33.89 367	54.89 380	69.24 391

PatchT		53.17 347	53.44 344	52.33 380	68.29 359	25.34 422	58.21 378	54.41 390	44.46 354	54.56 355	69.05 382	33.32 311	60.94 370	36.93 349	61.76 351	70.73 380

test_fmvsmconf0.01_n		72.17 97	71.50 95	74.16 111	67.96 360	55.58 126	78.06 140	74.67 244	54.19 240	74.54 65	88.23 66	50.35 117	80.24 219	78.07 23	77.46 164	86.65 75

Patchmatch-RL test		58.16 308	55.49 325	66.15 278	67.92 361	48.89 243	60.66 369	51.07 399	47.86 321	59.36 308	62.71 404	34.02 302	72.27 313	56.41 190	59.40 364	77.30 305

pmmvs-eth3d		58.81 304	56.31 319	66.30 274	67.61 362	52.42 185	72.30 265	64.76 334	43.55 362	54.94 350	74.19 344	28.95 352	72.60 310	43.31 306	57.21 372	73.88 348

PVSNet_0	43.31 20	47.46 367	45.64 370	52.92 376	67.60 363	44.65 288	54.06 396	54.64 388	41.59 375	46.15 396	58.75 407	30.99 337	58.66 383	32.18 375	24.81 422	55.46 410

CHOSEN 280x420		47.83 365	46.36 369	52.24 382	67.37 364	49.78 227	38.91 422	43.11 419	35.00 396	43.27 404	63.30 403	28.95 352	49.19 413	36.53 355	60.80 356	57.76 407

UWE-MVS-28		52.25 350	52.35 348	51.93 383	66.99 365	22.79 426	63.48 350	48.31 407	46.78 334	52.73 370	76.11 321	27.78 362	57.82 388	20.58 416	68.41 297	75.17 329

tpmrst		58.24 307	58.70 298	56.84 352	66.97 366	34.32 384	69.57 303	61.14 363	47.17 331	58.58 319	71.60 362	41.28 228	60.41 373	49.20 253	62.84 341	75.78 323

sss		56.17 326	56.57 316	54.96 361	66.93 367	36.32 369	57.94 380	61.69 360	41.67 374	58.64 317	75.32 336	38.72 253	56.25 396	42.04 319	66.19 314	72.31 363

TinyColmap		54.14 338	51.72 350	61.40 321	66.84 368	41.97 314	66.52 322	68.51 304	44.81 349	42.69 405	75.77 328	11.66 414	72.94 308	31.96 378	56.77 375	69.27 390

miper_lstm_enhance		62.03 276	60.88 281	65.49 290	66.71 369	46.25 270	56.29 390	75.70 221	50.68 281	61.27 286	75.48 333	40.21 236	68.03 338	56.31 191	65.25 320	82.18 228

TESTMET0.1,1		55.28 333	54.90 329	56.42 354	66.56 370	43.67 299	65.46 333	56.27 384	39.18 389	53.83 361	67.44 388	24.21 386	55.46 400	48.04 264	73.11 223	70.13 384

dmvs_testset		50.16 359	51.90 349	44.94 394	66.49 371	11.78 434	61.01 368	51.50 396	51.17 277	50.30 384	67.44 388	39.28 246	60.29 374	22.38 413	57.49 371	62.76 399

D2MVS		62.30 271	60.29 285	68.34 250	66.46 372	48.42 249	65.70 328	73.42 259	47.71 322	58.16 322	75.02 337	30.51 339	77.71 263	53.96 214	71.68 244	78.90 286

MDA-MVSNet-bldmvs		53.87 341	50.81 354	63.05 310	66.25 373	48.58 247	56.93 388	63.82 342	48.09 317	41.22 406	70.48 372	30.34 341	68.00 339	34.24 366	45.92 402	72.57 356

ITE_SJBPF				62.09 316	66.16 374	44.55 291		64.32 337	47.36 327	55.31 345	80.34 245	19.27 397	62.68 366	36.29 358	62.39 345	79.04 283

EPMVS		53.96 339	53.69 342	54.79 363	66.12 375	31.96 399	62.34 358	49.05 403	44.42 355	55.54 341	71.33 365	30.22 342	56.70 392	41.65 323	62.54 344	75.71 324

ADS-MVSNet2		51.33 355	48.76 362	59.07 337	66.02 376	44.60 289	50.90 404	59.76 367	36.90 391	50.74 378	66.18 396	26.38 373	63.11 364	27.17 403	54.76 381	69.50 388

ADS-MVSNet		48.48 364	47.77 365	50.63 385	66.02 376	29.92 405	50.90 404	50.87 401	36.90 391	50.74 378	66.18 396	26.38 373	52.47 408	27.17 403	54.76 381	69.50 388

EU-MVSNet		55.61 331	54.41 334	59.19 336	65.41 378	33.42 391	72.44 263	71.91 276	28.81 404	51.27 374	73.87 346	24.76 384	69.08 331	43.04 310	58.20 368	75.06 331

RPSCF		55.80 329	54.22 338	60.53 327	65.13 379	42.91 308	64.30 344	57.62 376	36.84 393	58.05 324	82.28 204	28.01 359	56.24 397	37.14 347	58.61 367	82.44 224

USDC		56.35 324	54.24 337	62.69 312	64.74 380	40.31 328	65.05 339	73.83 256	43.93 360	47.58 389	77.71 297	15.36 407	75.05 300	38.19 342	61.81 350	72.70 354

JIA-IIPM		51.56 353	47.68 367	63.21 308	64.61 381	50.73 210	47.71 410	58.77 371	42.90 368	48.46 388	51.72 414	24.97 383	70.24 327	36.06 360	53.89 384	68.64 392

Patchmatch-test		49.08 362	48.28 364	51.50 384	64.40 382	30.85 403	45.68 414	48.46 406	35.60 395	46.10 397	72.10 357	34.47 297	46.37 417	27.08 405	60.65 358	77.27 306

TDRefinement		53.44 345	50.72 355	61.60 318	64.31 383	46.96 265	70.89 286	65.27 331	41.78 372	44.61 400	77.98 286	11.52 416	66.36 351	28.57 399	51.59 390	71.49 372

test_vis1_n_1920		58.86 303	59.06 294	58.25 342	63.76 384	43.14 305	67.49 318	66.36 322	40.22 384	65.89 209	71.95 360	31.04 336	59.75 377	59.94 168	64.90 322	71.85 367

N_pmnet		39.35 382	40.28 379	36.54 405	63.76 384	1.62 442	49.37 407	0.76 441	34.62 397	43.61 403	66.38 395	26.25 375	42.57 421	26.02 408	51.77 389	65.44 397

ambc				65.13 295	63.72 386	37.07 360	47.66 411	78.78 167		54.37 358	71.42 363	11.24 417	80.94 202	45.64 284	53.85 385	77.38 304

WB-MVS		43.26 372	43.41 372	42.83 398	63.32 387	10.32 436	58.17 379	45.20 414	45.42 346	40.44 409	67.26 391	34.01 303	58.98 381	11.96 427	24.88 421	59.20 402

KD-MVS_2432*1600		53.45 343	51.50 352	59.30 332	62.82 388	37.14 358	55.33 391	71.79 277	47.34 328	55.09 348	70.52 370	21.91 392	70.45 323	35.72 361	42.97 406	70.31 382

miper_refine_blended		53.45 343	51.50 352	59.30 332	62.82 388	37.14 358	55.33 391	71.79 277	47.34 328	55.09 348	70.52 370	21.91 392	70.45 323	35.72 361	42.97 406	70.31 382

test0.0.03 1		53.32 346	53.59 343	52.50 379	62.81 390	29.45 406	59.51 373	54.11 391	50.08 289	54.40 357	74.31 343	32.62 325	55.92 398	30.50 391	63.95 332	72.15 365

PMMVS		53.96 339	53.26 345	56.04 355	62.60 391	50.92 206	61.17 365	56.09 385	32.81 399	53.51 367	66.84 393	34.04 301	59.93 376	44.14 298	68.18 298	57.27 408

SSC-MVS		41.96 377	41.99 376	41.90 399	62.46 392	9.28 438	57.41 386	44.32 417	43.38 363	38.30 415	66.45 394	32.67 324	58.42 385	10.98 428	21.91 424	57.99 406

PM-MVS		52.33 349	50.19 358	58.75 339	62.10 393	45.14 284	65.75 327	40.38 421	43.60 361	53.52 366	72.65 352	9.16 422	65.87 355	50.41 242	54.18 383	65.24 398

Gipuma		34.77 386	31.91 391	43.33 396	62.05 394	37.87 349	20.39 427	67.03 316	23.23 415	18.41 428	25.84 428	4.24 429	62.73 365	14.71 421	51.32 391	29.38 426
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test20.03		53.87 341	54.02 339	53.41 373	61.47 395	28.11 411	61.30 363	59.21 369	51.34 274	52.09 372	77.43 300	33.29 312	58.55 384	29.76 394	60.27 362	73.58 349

pmmvs5		56.47 322	55.68 324	58.86 338	61.41 396	36.71 364	66.37 323	62.75 351	40.38 383	53.70 362	76.62 312	34.56 294	67.05 346	40.02 330	65.27 319	72.83 353

MDA-MVSNet_test_wron		50.71 358	48.95 360	56.00 357	61.17 397	41.84 315	51.90 402	56.45 380	40.96 379	44.79 399	67.84 385	30.04 344	55.07 403	36.71 352	50.69 393	71.11 378

YYNet1		50.73 357	48.96 359	56.03 356	61.10 398	41.78 316	51.94 401	56.44 381	40.94 380	44.84 398	67.80 386	30.08 343	55.08 402	36.77 350	50.71 392	71.22 375

dongtai		34.52 387	34.94 387	33.26 408	61.06 399	16.00 433	52.79 400	23.78 434	40.71 381	39.33 413	48.65 422	16.91 402	48.34 414	12.18 426	19.05 426	35.44 425

CMPMVS	42.80 21	57.81 312	55.97 321	63.32 306	60.98 400	47.38 262	64.66 342	69.50 296	32.06 400	46.83 393	77.80 293	29.50 349	71.36 318	48.68 257	73.75 206	71.21 376
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		50.07 360	48.87 361	53.66 369	60.97 401	33.67 390	57.62 384	64.56 336	39.47 388	47.38 390	64.02 402	27.47 364	59.32 378	34.69 365	43.68 405	67.98 394

Anonymous20240521		55.30 332	54.41 334	57.96 347	60.92 402	41.73 317	71.09 284	71.06 282	41.18 377	48.65 387	73.31 349	16.93 401	59.25 379	42.54 314	64.01 330	72.90 352

testgi		51.90 351	52.37 347	50.51 386	60.39 403	23.55 425	58.42 376	58.15 372	49.03 303	51.83 373	79.21 270	22.39 389	55.59 399	29.24 397	62.64 342	72.40 362

UnsupCasMVSNet_eth		53.16 348	52.47 346	55.23 360	59.45 404	33.39 392	59.43 374	69.13 300	45.98 341	50.35 383	72.32 354	29.30 351	58.26 386	42.02 320	44.30 404	74.05 346

mvs5depth		55.64 330	53.81 341	61.11 325	59.39 405	40.98 326	65.89 326	68.28 306	50.21 287	58.11 323	75.42 334	17.03 400	67.63 342	43.79 303	46.21 400	74.73 339

test_cas_vis1_n_1920		56.91 317	56.71 315	57.51 351	59.13 406	45.40 282	63.58 349	61.29 362	36.24 394	67.14 185	71.85 361	29.89 345	56.69 393	57.65 183	63.58 335	70.46 381

new-patchmatchnet		47.56 366	47.73 366	47.06 389	58.81 407	9.37 437	48.78 408	59.21 369	43.28 364	44.22 401	68.66 383	25.67 379	57.20 391	31.57 386	49.35 397	74.62 341

FPMVS		42.18 376	41.11 378	45.39 391	58.03 408	41.01 324	49.50 406	53.81 393	30.07 403	33.71 418	64.03 400	11.69 413	52.08 411	14.01 422	55.11 379	43.09 419

KD-MVS_self_test		55.22 334	53.89 340	59.21 335	57.80 409	27.47 414	57.75 383	74.32 248	47.38 326	50.90 377	70.00 375	28.45 357	70.30 326	40.44 327	57.92 369	79.87 272

test_vis1_n		49.89 361	48.69 363	53.50 371	53.97 410	37.38 356	61.53 360	47.33 411	28.54 405	59.62 306	67.10 392	13.52 409	52.27 409	49.07 254	57.52 370	70.84 379

test_fmvs1		51.32 356	50.48 356	53.81 368	53.57 411	37.51 355	60.63 370	51.16 397	28.02 408	63.62 248	69.23 381	16.41 403	53.93 405	51.01 238	60.70 357	69.99 385

kuosan		29.62 394	30.82 393	26.02 413	52.99 412	16.22 432	51.09 403	22.71 435	33.91 398	33.99 417	40.85 423	15.89 405	33.11 430	7.59 434	18.37 427	28.72 427

test_fmvs1_n		51.37 354	50.35 357	54.42 366	52.85 413	37.71 353	61.16 366	51.93 394	28.15 406	63.81 247	69.73 378	13.72 408	53.95 404	51.16 237	60.65 358	71.59 370

new_pmnet		34.13 388	34.29 389	33.64 407	52.63 414	18.23 431	44.43 417	33.90 427	22.81 417	30.89 420	53.18 412	10.48 420	35.72 429	20.77 415	39.51 410	46.98 418

pmmvs3		44.92 370	41.95 377	53.86 367	52.58 415	43.55 300	62.11 359	46.90 413	26.05 411	40.63 407	60.19 406	11.08 419	57.91 387	31.83 383	46.15 401	60.11 401

ttmdpeth		45.56 368	42.95 373	53.39 374	52.33 416	29.15 407	57.77 381	48.20 408	31.81 401	49.86 385	77.21 302	8.69 423	59.16 380	27.31 402	33.40 418	71.84 368

DSMNet-mixed		39.30 383	38.72 382	41.03 400	51.22 417	19.66 429	45.53 415	31.35 428	15.83 427	39.80 411	67.42 390	22.19 390	45.13 418	22.43 412	52.69 388	58.31 405

mvsany_test1		39.38 381	38.16 384	43.02 397	49.05 418	34.28 385	44.16 418	25.94 432	22.74 418	46.57 395	62.21 405	23.85 387	41.16 424	33.01 373	35.91 414	53.63 411

APD_test1		37.39 384	34.94 387	44.72 395	48.88 419	33.19 393	52.95 399	44.00 418	19.49 421	27.28 422	58.59 408	3.18 434	52.84 407	18.92 417	41.17 409	48.14 416

test_fmvs2		48.69 363	47.49 368	52.29 381	48.63 420	33.06 394	57.76 382	48.05 409	25.71 412	59.76 304	69.60 379	11.57 415	52.23 410	49.45 252	56.86 373	71.58 371

LF4IMVS		42.95 373	42.26 375	45.04 392	48.30 421	32.50 396	54.80 393	48.49 405	28.03 407	40.51 408	70.16 373	9.24 421	43.89 420	31.63 384	49.18 398	58.72 404

wuyk23d		13.32 401	12.52 404	15.71 415	47.54 422	26.27 419	31.06 426	1.98 440	4.93 432	5.18 435	1.94 435	0.45 440	18.54 434	6.81 435	12.83 431	2.33 432

MVStest1		42.65 374	39.29 381	52.71 378	47.26 423	34.58 382	54.41 395	50.84 402	23.35 414	39.31 414	74.08 345	12.57 411	55.09 401	23.32 411	28.47 420	68.47 393

test_vis1_rt		41.35 379	39.45 380	47.03 390	46.65 424	37.86 350	47.76 409	38.65 422	23.10 416	44.21 402	51.22 416	11.20 418	44.08 419	39.27 335	53.02 387	59.14 403

test_fmvs3		44.30 371	42.55 374	49.55 387	42.83 425	27.15 417	53.03 398	44.93 415	22.03 420	53.69 364	64.94 399	4.21 430	49.63 412	47.47 265	49.82 395	71.88 366

LCM-MVSNet		40.30 380	35.88 386	53.57 370	42.24 426	29.15 407	45.21 416	60.53 366	22.23 419	28.02 421	50.98 417	3.72 432	61.78 369	31.22 389	38.76 412	69.78 387

E-PMN		23.77 396	22.73 400	26.90 411	42.02 427	20.67 428	42.66 419	35.70 425	17.43 423	10.28 433	25.05 429	6.42 425	42.39 422	10.28 430	14.71 429	17.63 428

testf1		31.46 392	28.89 396	39.16 401	41.99 428	28.78 409	46.45 412	37.56 423	14.28 428	21.10 424	48.96 419	1.48 438	47.11 415	13.63 423	34.56 415	41.60 420

APD_test2		31.46 392	28.89 396	39.16 401	41.99 428	28.78 409	46.45 412	37.56 423	14.28 428	21.10 424	48.96 419	1.48 438	47.11 415	13.63 423	34.56 415	41.60 420

EMVS		22.97 397	21.84 401	26.36 412	40.20 430	19.53 430	41.95 420	34.64 426	17.09 424	9.73 434	22.83 430	7.29 424	42.22 423	9.18 432	13.66 430	17.32 429

ANet_high		41.38 378	37.47 385	53.11 375	39.73 431	24.45 423	56.94 387	69.69 291	47.65 323	26.04 423	52.32 413	12.44 412	62.38 367	21.80 414	10.61 432	72.49 357

PMMVS2		27.40 395	25.91 398	31.87 410	39.46 432	6.57 439	31.17 425	28.52 430	23.96 413	20.45 427	48.94 421	4.20 431	37.94 426	16.51 419	19.97 425	51.09 412

PMVS	28.69 22	36.22 385	33.29 390	45.02 393	36.82 433	35.98 372	54.68 394	48.74 404	26.31 410	21.02 426	51.61 415	2.88 435	60.10 375	9.99 431	47.58 399	38.99 424
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
mvsany_test3		32.62 389	30.57 394	38.77 403	36.16 434	24.20 424	38.10 423	20.63 436	19.14 422	40.36 410	57.43 409	5.06 427	36.63 428	29.59 396	28.66 419	55.49 409

test_vis3_rt		32.09 390	30.20 395	37.76 404	35.36 435	27.48 413	40.60 421	28.29 431	16.69 425	32.52 419	40.53 424	1.96 436	37.40 427	33.64 370	42.21 408	48.39 414

MVE	17.77 23	21.41 398	17.77 403	32.34 409	34.34 436	25.44 421	16.11 428	24.11 433	11.19 430	13.22 430	31.92 426	1.58 437	30.95 432	10.47 429	17.03 428	40.62 423
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_f		31.86 391	31.05 392	34.28 406	32.33 437	21.86 427	32.34 424	30.46 429	16.02 426	39.78 412	55.45 411	4.80 428	32.36 431	30.61 390	37.66 413	48.64 413

DeepMVS_CX				12.03 416	17.97 438	10.91 435		10.60 439	7.46 431	11.07 432	28.36 427	3.28 433	11.29 435	8.01 433	9.74 434	13.89 430

test_method		19.68 399	18.10 402	24.41 414	13.68 439	3.11 441	12.06 430	42.37 420	2.00 433	11.97 431	36.38 425	5.77 426	29.35 433	15.06 420	23.65 423	40.76 422

tmp_tt		9.43 402	11.14 405	4.30 417	2.38 440	4.40 440	13.62 429	16.08 438	0.39 434	15.89 429	13.06 431	15.80 406	5.54 436	12.63 425	10.46 433	2.95 431

testmvs		4.52 405	6.03 408	0.01 419	0.01 441	0.00 444	53.86 397	0.00 442	0.01 436	0.04 437	0.27 436	0.00 442	0.00 437	0.04 436	0.00 435	0.03 434

test123		4.73 404	6.30 407	0.02 418	0.01 441	0.01 443	56.36 389	0.00 442	0.01 436	0.04 437	0.21 437	0.01 441	0.00 437	0.03 437	0.00 435	0.04 433

mmdepth		0.00 407	0.00 410	0.00 420	0.00 443	0.00 444	0.00 431	0.00 442	0.00 438	0.00 439	0.00 438	0.00 442	0.00 437	0.00 438	0.00 435	0.00 435

monomultidepth		0.00 407	0.00 410	0.00 420	0.00 443	0.00 444	0.00 431	0.00 442	0.00 438	0.00 439	0.00 438	0.00 442	0.00 437	0.00 438	0.00 435	0.00 435

test_blank		0.00 407	0.00 410	0.00 420	0.00 443	0.00 444	0.00 431	0.00 442	0.00 438	0.00 439	0.00 438	0.00 442	0.00 437	0.00 438	0.00 435	0.00 435

eth-test2					0.00 443

eth-test					0.00 443

uanet_test		0.00 407	0.00 410	0.00 420	0.00 443	0.00 444	0.00 431	0.00 442	0.00 438	0.00 439	0.00 438	0.00 442	0.00 437	0.00 438	0.00 435	0.00 435

DCPMVS		0.00 407	0.00 410	0.00 420	0.00 443	0.00 444	0.00 431	0.00 442	0.00 438	0.00 439	0.00 438	0.00 442	0.00 437	0.00 438	0.00 435	0.00 435

cdsmvs_eth3d_5k		17.50 400	23.34 399	0.00 420	0.00 443	0.00 444	0.00 431	78.63 171	0.00 438	0.00 439	82.18 206	49.25 128	0.00 437	0.00 438	0.00 435	0.00 435

pcd_1.5k_mvsjas		3.92 406	5.23 409	0.00 420	0.00 443	0.00 444	0.00 431	0.00 442	0.00 438	0.00 439	0.00 438	47.05 160	0.00 437	0.00 438	0.00 435	0.00 435

sosnet-low-res		0.00 407	0.00 410	0.00 420	0.00 443	0.00 444	0.00 431	0.00 442	0.00 438	0.00 439	0.00 438	0.00 442	0.00 437	0.00 438	0.00 435	0.00 435

sosnet		0.00 407	0.00 410	0.00 420	0.00 443	0.00 444	0.00 431	0.00 442	0.00 438	0.00 439	0.00 438	0.00 442	0.00 437	0.00 438	0.00 435	0.00 435

uncertanet		0.00 407	0.00 410	0.00 420	0.00 443	0.00 444	0.00 431	0.00 442	0.00 438	0.00 439	0.00 438	0.00 442	0.00 437	0.00 438	0.00 435	0.00 435

Regformer		0.00 407	0.00 410	0.00 420	0.00 443	0.00 444	0.00 431	0.00 442	0.00 438	0.00 439	0.00 438	0.00 442	0.00 437	0.00 438	0.00 435	0.00 435

ab-mvs-re		6.49 403	8.65 406	0.00 420	0.00 443	0.00 444	0.00 431	0.00 442	0.00 438	0.00 439	77.89 291	0.00 442	0.00 437	0.00 438	0.00 435	0.00 435

uanet		0.00 407	0.00 410	0.00 420	0.00 443	0.00 444	0.00 431	0.00 442	0.00 438	0.00 439	0.00 438	0.00 442	0.00 437	0.00 438	0.00 435	0.00 435

WAC-MVS						27.31 415								27.77 400

PC_three_1452									55.09 218	84.46 4	89.84 46	66.68 5	89.41 18	74.24 51	91.38 2	88.42 16

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 19	90.70 7	87.65 41

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 6	90.63 10	88.09 27

GSMVS																78.05 293

sam_mvs1												34.74 293				78.05 293

sam_mvs												33.43 310

MTGPA								80.97 132

test_post1							68.67 309				3.64 433	32.39 330	69.49 329	44.17 296

test_post											3.55 434	33.90 304	66.52 349

patchmatchnet-post											64.03 400	34.50 295	74.27 304

MTMP							86.03 19	17.08 437

test9_res														75.28 44	88.31 32	83.81 183

agg_prior2														73.09 62	87.93 40	84.33 163

test_prior4						62.51 14	82.08 79

test_prior2							81.75 81		60.37 101	75.01 52	89.06 55	56.22 41		72.19 69	88.96 24

旧先验2							76.08 191		45.32 347	76.55 38			65.56 356	58.75 177

新几何2							76.12 189

无先验							79.66 112	74.30 250	48.40 313				80.78 208	53.62 216		79.03 284

原ACMM2							79.02 119

testdata2													72.18 315	46.95 274

segment_acmp												54.23 59

testdata1							72.65 257		60.50 96

plane_prior5								84.01 52					87.21 58	68.16 95	80.58 112	84.65 157

plane_prior4											86.10 124

plane_prior3						56.09 111			63.92 36	69.27 143

plane_prior2							84.22 43		64.52 25

plane_prior						56.31 105	83.58 56		63.19 49						80.48 115

n2								0.00 442

nn								0.00 442

door-mid								47.19 412

test11								83.47 71

door								47.60 410

HQP5-MVS						54.94 135

BP-MVS														67.04 106

HQP4-MVS										67.85 168			86.93 66			84.32 164

HQP3-MVS								83.90 57							80.35 116

HQP2-MVS												45.46 178

MDTV_nov1_ep13_2view						25.89 420	61.22 364		40.10 385	51.10 375		32.97 316		38.49 339		78.61 288

ACMMP++_ref															74.07 201

ACMMP++															72.16 239

Test By Simon												48.33 139

High-resolution multi-view benchmark