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.

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

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

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

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

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

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

DPE-MVS		80.56 5	80.98 5	79.29 14	87.27 13	60.56 48	85.71 26	86.42 18	63.28 47	83.27 13	91.83 10	64.96 7	90.47 10	76.41 27	89.67 22	86.84 64
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
DVP-MVS		80.84 4	81.64 3	78.42 36	87.75 7	59.07 72	87.85 5	85.03 38	64.26 32	83.82 8	92.00 3	64.82 8	90.75 8	78.66 13	90.61 11	85.45 119
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					87.75 7	59.07 72	87.86 4	86.83 12	64.26 32	84.19 7	91.92 5	64.82 8

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

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

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 34	62.73 12	86.09 17	86.83 12	65.51 14	83.81 10	90.51 23	63.71 12	89.23 20	81.51 1	88.44 32	88.09 19
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
ETH3 D test6400		79.14 11	79.32 10	78.61 32	86.34 31	58.11 88	84.65 34	87.66 4	58.56 141	78.87 24	89.54 53	63.67 13	89.57 16	74.60 36	89.98 17	88.14 17

dcpmvs_2		74.55 67	75.23 54	72.48 164	82.34 88	53.34 161	77.87 146	81.46 113	57.80 157	75.49 41	86.81 88	62.22 14	77.75 262	71.09 63	82.02 98	86.34 79

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

CSCG		76.92 38	76.75 37	77.41 52	83.96 71	59.60 59	82.95 65	86.50 17	60.78 92	75.27 42	84.83 132	60.76 16	86.56 81	67.86 85	87.87 46	86.06 92

TSAR-MVS + MP.		78.44 20	78.28 21	78.90 26	84.96 60	61.41 29	84.03 47	83.82 64	59.34 126	79.37 19	89.76 51	59.84 17	87.62 50	76.69 25	86.74 59	87.68 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
canonicalmvs		74.67 64	74.98 57	73.71 132	78.94 156	50.56 206	80.23 107	83.87 62	60.30 107	77.15 33	86.56 100	59.65 18	82.00 187	66.01 102	82.12 96	88.58 7

APDe-MVS		80.16 7	80.59 6	78.86 28	86.64 23	60.02 53	88.12 3	86.42 18	62.94 54	82.40 14	92.12 2	59.64 19	89.76 14	78.70 11	88.32 36	86.79 67

DELS-MVS		74.76 61	74.46 63	75.65 86	77.84 188	52.25 182	75.59 194	84.17 52	63.76 41	73.15 77	82.79 172	59.58 20	86.80 71	67.24 92	86.04 64	87.89 23
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
CNVR-MVS		79.84 9	79.97 9	79.45 10	87.90 2	62.17 20	84.37 37	85.03 38	66.96 5	77.58 32	90.06 40	59.47 21	89.13 22	78.67 12	89.73 20	87.03 58

MCST-MVS		77.48 33	77.45 30	77.54 50	86.67 22	58.36 85	83.22 62	86.93 9	56.91 168	74.91 49	88.19 69	59.15 22	87.68 48	73.67 45	87.45 47	86.57 72

nrg030		72.96 83	73.01 80	72.84 156	75.41 238	50.24 209	80.02 111	82.89 90	58.36 145	74.44 57	86.73 91	58.90 23	80.83 213	65.84 104	74.46 182	87.44 45

HPM-MVS++		79.88 8	80.14 8	79.10 20	88.17 1	64.80 1	86.59 12	83.70 66	65.37 15	78.78 25	90.64 19	58.63 24	87.24 54	79.00 10	90.37 14	85.26 128

ETH3D cwj APD-0.16		78.02 25	78.13 25	77.71 47	82.10 90	58.65 82	82.72 71	87.55 5	58.33 146	78.05 30	90.06 40	58.35 25	87.65 49	76.15 28	89.86 19	86.82 65

xxxxxxxxxxxxxcwj		78.37 21	78.25 23	78.76 29	86.17 36	61.30 31	83.98 49	79.95 147	59.00 129	79.16 20	90.75 17	57.96 26	87.09 63	77.08 23	90.18 15	87.87 25

SF-MVS		78.82 13	79.22 12	77.60 49	82.88 84	57.83 92	84.99 32	88.13 3	61.86 79	79.16 20	90.75 17	57.96 26	87.09 63	77.08 23	90.18 15	87.87 25

casdiffmvs		74.80 60	74.89 59	74.53 112	75.59 235	50.37 208	78.17 142	85.06 37	62.80 61	74.40 58	87.86 76	57.88 28	83.61 152	69.46 74	82.79 91	89.59 2

ETH3D-3000-0.1		78.58 16	78.91 14	77.61 48	83.06 79	57.86 91	84.14 46	88.31 1	60.37 101	79.14 22	90.35 29	57.76 29	87.00 66	77.16 22	89.90 18	87.97 22

DeepC-MVS	69.38 2	78.56 18	78.14 24	79.83 6	83.60 72	61.62 26	84.17 43	86.85 10	63.23 49	73.84 68	90.25 35	57.68 30	89.96 13	74.62 35	89.03 26	87.89 23
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
baseline		74.61 65	74.70 61	74.34 116	75.70 231	49.99 216	77.54 155	84.63 45	62.73 62	73.98 62	87.79 78	57.67 31	83.82 148	69.49 72	82.74 92	89.20 4

patch_mono-2		69.85 133	71.09 102	66.16 261	79.11 153	54.80 145	71.97 259	74.31 245	53.50 232	70.90 104	84.17 145	57.63 32	63.31 332	66.17 99	82.02 98	80.38 244

9.14			78.75 16		83.10 78		84.15 44	88.26 2	59.90 113	78.57 27	90.36 28	57.51 33	86.86 69	77.39 18	89.52 25

DPM-MVS		75.47 55	75.00 55	76.88 60	81.38 104	59.16 67	79.94 113	85.71 26	56.59 176	72.46 91	86.76 89	56.89 34	87.86 45	66.36 98	88.91 30	83.64 183

UniMVSNet_NR-MVSNet		71.11 109	71.00 104	71.44 182	79.20 149	44.13 279	76.02 190	82.60 93	66.48 13	68.20 145	84.60 138	56.82 35	82.82 172	54.62 189	70.43 237	87.36 51

SMA-MVS		80.28 6	80.39 7	79.95 3	86.60 25	61.95 22	86.33 13	85.75 25	62.49 65	82.20 15	92.28 1	56.53 36	89.70 15	79.85 3	91.48 1	88.19 15
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
Regformer-1		75.47 55	74.93 58	77.09 57	80.43 120	57.70 95	79.50 123	82.13 98	67.84 1	75.73 40	80.75 222	56.50 37	86.07 93	71.07 64	80.38 116	87.50 42

Effi-MVS+		73.31 80	72.54 84	75.62 87	77.87 187	53.64 153	79.62 121	79.61 153	61.63 81	72.02 96	82.61 177	56.44 38	85.97 99	63.99 121	79.07 138	87.25 53

alignmvs		73.86 76	73.99 68	73.45 143	78.20 176	50.50 207	78.57 135	82.43 94	59.40 124	76.57 34	86.71 93	56.42 39	81.23 203	65.84 104	81.79 100	88.62 5

test_prior3		76.89 40	76.96 35	76.69 63	84.20 69	57.27 100	81.75 88	84.88 41	60.37 101	75.01 45	89.06 59	56.22 40	86.43 86	72.19 53	88.96 28	86.38 74

test_prior2							81.75 88		60.37 101	75.01 45	89.06 59	56.22 40		72.19 53	88.96 28

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

TSAR-MVS + GP.		74.90 59	74.15 67	77.17 56	82.00 92	58.77 80	81.80 87	78.57 173	58.58 139	74.32 59	84.51 141	55.94 43	87.22 55	67.11 93	84.48 74	85.52 115

testtj		78.47 19	78.43 19	78.61 32	86.82 17	60.67 46	86.07 18	85.38 31	62.12 71	78.65 26	90.29 33	55.76 44	89.31 19	73.55 47	87.22 49	85.84 99

Regformer-2		75.63 53	74.99 56	77.54 50	80.43 120	58.32 86	79.50 123	82.92 87	67.84 1	75.94 37	80.75 222	55.73 45	86.80 71	71.44 62	80.38 116	87.50 42

MVS_Test		72.45 90	72.46 85	72.42 168	74.88 243	48.50 236	76.28 182	83.14 85	59.40 124	72.46 91	84.68 134	55.66 46	81.12 204	65.98 103	79.66 126	87.63 37

APD-MVS		78.02 25	78.04 26	77.98 44	86.44 29	60.81 43	85.52 28	84.36 48	60.61 94	79.05 23	90.30 32	55.54 47	88.32 33	73.48 48	87.03 52	84.83 139
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		78.58 16	78.31 20	79.39 11	87.51 12	62.61 16	85.20 31	84.42 47	66.73 9	74.67 55	89.38 56	55.30 48	89.18 21	74.19 39	87.34 48	86.38 74

FIs		70.82 114	71.43 94	68.98 230	78.33 173	38.14 324	76.96 169	83.59 69	61.02 88	67.33 168	86.73 91	55.07 49	81.64 192	54.61 191	79.22 134	87.14 56

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

SD-MVS		77.70 30	77.62 29	77.93 45	84.47 67	61.88 24	84.55 35	83.87 62	60.37 101	79.89 18	89.38 56	54.97 51	85.58 109	76.12 29	84.94 69	86.33 81
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
CANet		76.46 43	75.93 46	78.06 42	81.29 105	57.53 97	82.35 77	83.31 79	67.78 3	70.09 112	86.34 104	54.92 52	88.90 25	72.68 51	84.55 72	87.76 33

Regformer-3		73.89 75	73.28 79	75.71 83	79.75 134	55.48 136	78.54 137	79.93 148	66.58 11	73.62 70	80.30 230	54.87 53	84.54 133	69.09 76	76.84 165	87.10 57

MP-MVS		78.35 22	78.26 22	78.64 31	86.54 27	63.47 5	86.02 20	83.55 70	63.89 40	73.60 71	90.60 20	54.85 54	86.72 74	77.20 21	88.06 41	85.74 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mvs_anonymous		68.03 176	67.51 163	69.59 221	72.08 284	44.57 277	71.99 258	75.23 230	51.67 246	67.06 172	82.57 178	54.68 55	77.94 258	56.56 172	75.71 176	86.26 87

test12				77.76 46	84.52 66	58.41 84		83.36 77		72.93 84		54.61 56	88.05 39		88.12 40	86.81 66

FC-MVSNet-test		69.80 136	70.58 110	67.46 245	77.61 199	34.73 349	76.05 188	83.19 83	60.84 90	65.88 196	86.46 101	54.52 57	80.76 217	52.52 206	78.12 149	86.91 61

SteuartSystems-ACMMP		79.48 10	79.31 11	79.98 2	83.01 82	62.18 19	87.60 9	85.83 23	66.69 10	78.03 31	90.98 14	54.26 58	90.06 12	78.42 17	89.02 27	87.69 34
Skip Steuart: Steuart Systems R&D Blog.
segment_acmp												54.23 59

MVS_111021_HR		74.02 73	73.46 77	75.69 85	83.01 82	60.63 47	77.29 162	78.40 184	61.18 87	70.58 106	85.97 114	54.18 60	84.00 145	67.52 90	82.98 86	82.45 209

Fast-Effi-MVS+		70.28 125	69.12 133	73.73 131	78.50 166	51.50 191	75.01 207	79.46 157	56.16 185	68.59 138	79.55 247	53.97 61	84.05 140	53.34 201	77.53 155	85.65 111

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

test_part1		74.74 62	74.42 64	75.70 84	81.69 97	51.26 192	83.98 49	87.05 8	65.31 16	73.10 80	86.20 106	53.94 63	88.06 38	65.32 110	73.17 204	87.77 32

agg_prior1		75.94 49	76.01 44	75.72 82	85.04 57	59.96 54	81.44 95	81.04 129	56.14 186	74.68 53	88.90 63	53.91 64	84.04 141	75.01 33	87.92 45	83.16 196

UniMVSNet (Re)		70.63 117	70.20 114	71.89 171	78.55 165	45.29 270	75.94 191	82.92 87	63.68 43	68.16 148	83.59 159	53.89 65	83.49 155	53.97 194	71.12 230	86.89 62

Regformer-4		74.25 72	73.48 75	76.57 68	79.75 134	56.54 114	78.54 137	81.49 112	66.93 7	73.90 66	80.30 230	53.84 66	85.98 98	69.76 70	76.84 165	87.17 54

CS-MVS-test		75.62 54	75.31 53	76.56 69	80.63 117	55.13 140	83.88 53	85.22 33	62.05 75	71.49 101	86.03 112	53.83 67	86.36 90	67.74 86	86.91 56	88.19 15

DeepC-MVS_fast	68.24 3	77.25 35	76.63 39	79.12 19	86.15 38	60.86 41	84.71 33	84.85 43	61.98 78	73.06 82	88.88 64	53.72 68	89.06 23	68.27 78	88.04 42	87.42 46
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TEST9					85.58 49	61.59 27	81.62 91	81.26 123	55.65 198	74.93 47	88.81 65	53.70 69	84.68 130

train_agg		76.27 45	76.15 42	76.64 66	85.58 49	61.59 27	81.62 91	81.26 123	55.86 189	74.93 47	88.81 65	53.70 69	84.68 130	75.24 32	88.33 35	83.65 182

test_8					85.40 53	60.96 39	81.54 94	81.18 126	55.86 189	74.81 50	88.80 67	53.70 69	84.45 135

ETV-MVS		74.46 68	73.84 71	76.33 72	79.27 147	55.24 139	79.22 127	85.00 40	64.97 24	72.65 88	79.46 249	53.65 72	87.87 44	67.45 91	82.91 87	85.89 98

CDPH-MVS		76.31 44	75.67 50	78.22 40	85.35 55	59.14 70	81.31 97	84.02 54	56.32 180	74.05 61	88.98 62	53.34 73	87.92 43	69.23 75	88.42 33	87.59 39

HFP-MVS		78.01 27	77.65 28	79.10 20	86.71 20	62.81 10	86.29 14	84.32 49	62.82 58	73.96 63	90.50 24	53.20 74	88.35 31	74.02 41	87.05 50	86.13 89

#test#		77.83 28	77.41 31	79.10 20	86.71 20	62.81 10	85.69 27	84.32 49	61.61 82	73.96 63	90.50 24	53.20 74	88.35 31	73.68 44	87.05 50	86.13 89

DROMVSNet		75.84 51	75.87 48	75.74 81	78.86 157	52.65 172	83.73 55	86.08 21	63.47 45	72.77 86	87.25 85	53.13 76	87.93 42	71.97 56	85.57 67	86.66 70

EI-MVSNet-Vis-set		72.42 91	71.59 91	74.91 98	78.47 168	54.02 149	77.05 167	79.33 159	65.03 22	71.68 99	79.35 252	52.75 77	84.89 126	66.46 97	74.23 185	85.83 101

GST-MVS		78.14 24	77.85 27	78.99 25	86.05 43	61.82 25	85.84 21	85.21 34	63.56 44	74.29 60	90.03 43	52.56 78	88.53 29	74.79 34	88.34 34	86.63 71

ACMMP_NAP		78.77 15	78.78 15	78.74 30	85.44 52	61.04 36	83.84 54	85.16 35	62.88 56	78.10 28	91.26 13	52.51 79	88.39 30	79.34 6	90.52 13	86.78 68

PCF-MVS	61.88 8	70.95 112	69.49 126	75.35 91	77.63 194	55.71 128	76.04 189	81.81 105	50.30 265	69.66 123	85.40 128	52.51 79	84.89 126	51.82 214	80.24 120	85.45 119
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MP-MVS-pluss		78.35 22	78.46 18	78.03 43	84.96 60	59.52 61	82.93 66	85.39 30	62.15 70	76.41 36	91.51 11	52.47 81	86.78 73	80.66 2	89.64 23	87.80 30
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CLD-MVS		73.33 79	72.68 83	75.29 94	78.82 159	53.33 162	78.23 141	84.79 44	61.30 86	70.41 108	81.04 212	52.41 82	87.12 61	64.61 117	82.49 95	85.41 123
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
GeoE		71.01 111	70.15 116	73.60 139	79.57 140	52.17 183	78.93 129	78.12 188	58.02 152	67.76 163	83.87 153	52.36 83	82.72 174	56.90 170	75.79 174	85.92 95

NR-MVSNet		69.54 144	68.85 136	71.59 180	78.05 182	43.81 283	74.20 222	80.86 135	65.18 17	62.76 238	84.52 139	52.35 84	83.59 153	50.96 220	70.78 232	87.37 49

EI-MVSNet-UG-set		71.92 98	71.06 103	74.52 113	77.98 185	53.56 155	76.62 175	79.16 160	64.40 30	71.18 102	78.95 257	52.19 85	84.66 132	65.47 108	73.57 193	85.32 125

miper_ehance_all_eth		68.03 176	67.24 177	70.40 206	70.54 301	46.21 260	73.98 225	78.68 171	55.07 211	66.05 191	77.80 273	52.16 86	81.31 200	61.53 146	69.32 259	83.67 179

EIA-MVS		71.78 100	70.60 108	75.30 93	79.85 133	53.54 156	77.27 163	83.26 82	57.92 154	66.49 183	79.39 250	52.07 87	86.69 75	60.05 154	79.14 137	85.66 110

c3_l		68.33 170	67.56 159	70.62 202	70.87 298	46.21 260	74.47 219	78.80 167	56.22 184	66.19 189	78.53 264	51.88 88	81.40 197	62.08 136	69.04 265	84.25 155

PAPM_NR		72.63 87	71.80 89	75.13 97	81.72 96	53.42 160	79.91 115	83.28 81	59.14 128	66.31 188	85.90 117	51.86 89	86.06 94	57.45 167	80.62 110	85.91 96

MG-MVS		73.96 74	73.89 70	74.16 119	85.65 47	49.69 221	81.59 93	81.29 122	61.45 83	71.05 103	88.11 70	51.77 90	87.73 47	61.05 147	83.09 82	85.05 133

EPP-MVSNet		72.16 96	71.31 99	74.71 102	78.68 163	49.70 219	82.10 84	81.65 107	60.40 98	65.94 193	85.84 118	51.74 91	86.37 89	55.93 176	79.55 129	88.07 21

TranMVSNet+NR-MVSNet		70.36 123	70.10 118	71.17 192	78.64 164	42.97 290	76.53 177	81.16 127	66.95 6	68.53 141	85.42 127	51.61 92	83.07 161	52.32 207	69.70 255	87.46 44

diffmvs		70.69 116	70.43 111	71.46 181	69.45 318	48.95 231	72.93 243	78.46 179	57.27 162	71.69 98	83.97 152	51.48 93	77.92 259	70.70 66	77.95 151	87.53 41

EI-MVSNet		69.27 151	68.44 146	71.73 175	74.47 252	49.39 226	75.20 202	78.45 180	59.60 119	69.16 134	76.51 289	51.29 94	82.50 179	59.86 158	71.45 228	83.30 188

IterMVS-LS		69.22 153	68.48 142	71.43 184	74.44 254	49.40 225	76.23 183	77.55 197	59.60 119	65.85 197	81.59 204	51.28 95	81.58 195	59.87 157	69.90 250	83.30 188
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		64.72 229	64.33 217	65.87 269	75.22 240	38.56 321	74.66 216	75.08 238	58.90 132	61.79 254	82.63 176	51.18 96	78.07 257	43.63 277	55.87 340	80.99 235

miper_enhance_ethall		67.11 195	66.09 199	70.17 210	69.21 320	45.98 262	72.85 245	78.41 183	51.38 253	65.65 199	75.98 297	51.17 97	81.25 201	60.82 148	69.32 259	83.29 190

VNet		69.68 140	70.19 115	68.16 240	79.73 137	41.63 303	70.53 278	77.38 201	60.37 101	70.69 105	86.63 96	51.08 98	77.09 270	53.61 199	81.69 106	85.75 107

VPA-MVSNet		69.02 154	69.47 127	67.69 243	77.42 203	41.00 307	74.04 224	79.68 151	60.06 110	69.26 132	84.81 133	51.06 99	77.58 264	54.44 192	74.43 183	84.48 149

PAPR		71.72 102	70.82 106	74.41 115	81.20 109	51.17 193	79.55 122	83.33 78	55.81 193	66.93 176	84.61 137	50.95 100	86.06 94	55.79 179	79.20 135	86.00 93

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

HPM-MVS		77.28 34	76.85 36	78.54 34	85.00 59	60.81 43	82.91 67	85.08 36	62.57 63	73.09 81	89.97 46	50.90 102	87.48 52	75.30 30	86.85 57	87.33 52
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
WR-MVS_H		67.02 197	66.92 182	67.33 249	77.95 186	37.75 327	77.57 153	82.11 100	62.03 77	62.65 241	82.48 181	50.57 103	79.46 233	42.91 284	64.01 301	84.79 142

EPNet		73.09 82	72.16 86	75.90 77	75.95 229	56.28 117	83.05 63	72.39 264	66.53 12	65.27 207	87.00 86	50.40 104	85.47 115	62.48 134	86.32 63	85.94 94
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
WR-MVS		68.47 168	68.47 144	68.44 237	80.20 126	39.84 310	73.75 235	76.07 217	64.68 25	68.11 150	83.63 158	50.39 105	79.14 243	49.78 225	69.66 256	86.34 79

region2R		77.67 31	77.18 34	79.15 17	86.76 18	62.95 8	86.29 14	84.16 53	62.81 60	73.30 74	90.58 21	49.90 106	88.21 35	73.78 43	87.03 52	86.29 86

UA-Net		73.13 81	72.93 81	73.76 128	83.58 73	51.66 190	78.75 130	77.66 195	67.75 4	72.61 89	89.42 54	49.82 107	83.29 157	53.61 199	83.14 81	86.32 83

ACMMPR		77.71 29	77.23 33	79.16 16	86.75 19	62.93 9	86.29 14	84.24 51	62.82 58	73.55 72	90.56 22	49.80 108	88.24 34	74.02 41	87.03 52	86.32 83

API-MVS		72.17 95	71.41 95	74.45 114	81.95 94	57.22 102	84.03 47	80.38 142	59.89 116	68.40 142	82.33 184	49.64 109	87.83 46	51.87 213	84.16 78	78.30 266

ab-mvs		66.65 205	66.42 189	67.37 247	76.17 225	41.73 300	70.41 281	76.14 216	53.99 226	65.98 192	83.51 163	49.48 110	76.24 279	48.60 236	73.46 197	84.14 159

v8		70.33 124	69.28 130	73.49 141	73.15 266	50.22 210	78.62 134	80.78 136	60.79 91	66.45 185	82.11 193	49.35 111	84.98 123	63.58 125	68.71 269	85.28 126

IS-MVSNet		71.57 103	71.00 104	73.27 149	78.86 157	45.63 268	80.22 108	78.69 170	64.14 38	66.46 184	87.36 81	49.30 112	85.60 107	50.26 224	83.71 80	88.59 6

XXY-MVS		60.68 266	61.67 248	57.70 320	70.43 303	38.45 322	64.19 318	66.47 302	48.05 286	63.22 233	80.86 218	49.28 113	60.47 341	45.25 266	67.28 280	74.19 314

cdsmvs_eth3d_5k		17.50 344	23.34 343	0.00 364	0.00 387	0.00 387	0.00 375	78.63 172	0.00 382	0.00 383	82.18 187	49.25 114	0.00 381	0.00 381	0.00 379	0.00 379

PVSNet_Blended_VisFu		71.45 106	70.39 112	74.65 106	82.01 91	58.82 79	79.93 114	80.35 143	55.09 208	65.82 198	82.16 190	49.17 115	82.64 177	60.34 152	78.62 146	82.50 208

PVSNet_BlendedMVS		68.56 166	67.72 155	71.07 195	77.03 211	50.57 204	74.50 218	81.52 109	53.66 231	64.22 228	79.72 243	49.13 116	82.87 168	55.82 177	73.92 188	79.77 255

PVSNet_Blended		68.59 162	67.72 155	71.19 191	77.03 211	50.57 204	72.51 251	81.52 109	51.91 245	64.22 228	77.77 275	49.13 116	82.87 168	55.82 177	79.58 127	80.14 248

DU-MVS		70.01 129	69.53 125	71.44 182	78.05 182	44.13 279	75.01 207	81.51 111	64.37 31	68.20 145	84.52 139	49.12 118	82.82 172	54.62 189	70.43 237	87.37 49

Baseline_NR-MVSNet		67.05 196	67.56 159	65.50 272	75.65 232	37.70 328	75.42 197	74.65 241	59.90 113	68.14 149	83.15 169	49.12 118	77.20 268	52.23 208	69.78 252	81.60 221

VPNet		67.52 185	68.11 151	65.74 270	79.18 150	36.80 335	72.17 256	72.83 261	62.04 76	67.79 161	85.83 119	48.88 120	76.60 275	51.30 218	72.97 207	83.81 170

zzz-MVS		77.61 32	77.36 32	78.35 37	86.08 41	63.57 2	83.37 60	80.97 132	65.13 18	75.77 38	90.88 15	48.63 121	86.66 76	77.23 19	88.17 38	84.81 140

MTAPA		76.90 39	76.42 41	78.35 37	86.08 41	63.57 2	74.92 210	80.97 132	65.13 18	75.77 38	90.88 15	48.63 121	86.66 76	77.23 19	88.17 38	84.81 140

原ACMM1				74.69 103	85.39 54	59.40 62		83.42 74	51.47 252	70.27 111	86.61 97	48.61 123	86.51 84	53.85 196	87.96 43	78.16 268

v148		68.24 173	67.19 178	71.40 185	70.43 303	47.77 245	75.76 193	77.03 206	58.91 131	67.36 167	80.10 235	48.60 124	81.89 188	60.01 155	66.52 286	84.53 147

PGM-MVS		76.77 41	76.06 43	78.88 27	86.14 39	62.73 12	82.55 75	83.74 65	61.71 80	72.45 93	90.34 31	48.48 125	88.13 36	72.32 52	86.85 57	85.78 102

Test By Simon												48.33 126

CP-MVS		77.12 37	76.68 38	78.43 35	86.05 43	63.18 7	87.55 10	83.45 73	62.44 67	72.68 87	90.50 24	48.18 127	87.34 53	73.59 46	85.71 65	84.76 144

MVS		67.37 187	66.33 193	70.51 205	75.46 237	50.94 196	73.95 227	81.85 104	41.57 340	62.54 244	78.57 263	47.98 128	85.47 115	52.97 204	82.05 97	75.14 300

XVS		77.17 36	76.56 40	79.00 23	86.32 32	62.62 14	85.83 22	83.92 57	64.55 26	72.17 94	90.01 45	47.95 129	88.01 40	71.55 60	86.74 59	86.37 77

X-MVStestdata		70.21 126	67.28 173	79.00 23	86.32 32	62.62 14	85.83 22	83.92 57	64.55 26	72.17 94	6.49 376	47.95 129	88.01 40	71.55 60	86.74 59	86.37 77

MAR-MVS		71.51 104	70.15 116	75.60 88	81.84 95	59.39 63	81.38 96	82.90 89	54.90 216	68.08 151	78.70 258	47.73 131	85.51 112	51.68 217	84.17 77	81.88 219
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
PAPM		67.92 179	66.69 183	71.63 179	78.09 180	49.02 229	77.09 166	81.24 125	51.04 259	60.91 260	83.98 151	47.71 132	84.99 121	40.81 297	79.32 133	80.90 236

SR-MVS		76.13 47	75.70 49	77.40 54	85.87 45	61.20 33	85.52 28	82.19 97	59.99 112	75.10 43	90.35 29	47.66 133	86.52 83	71.64 59	82.99 84	84.47 150

cl22		67.47 186	66.45 186	70.54 204	69.85 314	46.49 256	73.85 233	77.35 202	55.07 211	65.51 202	77.92 269	47.64 134	81.10 205	61.58 144	69.32 259	84.01 163

v10		70.21 126	69.02 134	73.81 125	73.51 263	50.92 198	78.74 131	81.39 115	60.05 111	66.39 186	81.83 198	47.58 135	85.41 118	62.80 131	68.86 268	85.09 132

v1144		70.42 122	69.31 129	73.76 128	73.22 264	50.64 203	77.83 148	81.43 114	58.58 139	69.40 128	81.16 209	47.53 136	85.29 120	64.01 120	70.64 233	85.34 124

v2v482		70.50 120	69.45 128	73.66 134	72.62 276	50.03 215	77.58 152	80.51 140	59.90 113	69.52 124	82.14 191	47.53 136	84.88 128	65.07 113	70.17 243	86.09 91

pm-mvs1		65.24 224	64.97 214	66.04 265	72.38 280	39.40 315	72.62 248	75.63 223	55.53 200	62.35 250	83.18 168	47.45 138	76.47 276	49.06 233	66.54 285	82.24 211

HY-MVS	56.14 13	64.55 233	63.89 220	66.55 255	74.73 248	41.02 305	69.96 285	74.43 242	49.29 272	61.66 255	80.92 216	47.43 139	76.68 274	44.91 267	71.69 224	81.94 217

cl____		67.18 192	66.26 197	69.94 213	70.20 306	45.74 264	73.30 238	76.83 209	55.10 206	65.27 207	79.57 246	47.39 140	80.53 219	59.41 162	69.22 263	83.53 185

DIV-MVS_self_test		67.18 192	66.26 197	69.94 213	70.20 306	45.74 264	73.29 239	76.83 209	55.10 206	65.27 207	79.58 245	47.38 141	80.53 219	59.43 161	69.22 263	83.54 184

eth_miper_zixun_eth		67.63 183	66.28 196	71.67 177	71.60 290	48.33 238	73.68 236	77.88 190	55.80 194	65.91 194	78.62 262	47.35 142	82.88 167	59.45 160	66.25 287	83.81 170

OPM-MVS		74.73 63	74.25 66	76.19 73	80.81 113	59.01 75	82.60 74	83.64 67	63.74 42	72.52 90	87.49 79	47.18 143	85.88 101	69.47 73	80.78 108	83.66 181
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
baseline1		63.81 237	63.87 222	63.62 284	76.29 223	36.36 338	71.78 262	67.29 297	56.05 188	64.23 227	82.95 171	47.11 144	74.41 286	47.30 243	61.85 318	80.10 249

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

PS-MVSNAJss		72.24 93	71.21 100	75.31 92	78.50 166	55.93 125	81.63 90	82.12 99	56.24 183	70.02 116	85.68 122	47.05 145	84.34 137	65.27 111	74.41 184	85.67 109

PS-MVSNAJ		70.51 119	69.70 122	72.93 154	81.52 99	55.79 127	74.92 210	79.00 162	55.04 213	69.88 120	78.66 259	47.05 145	82.19 184	61.61 142	79.58 127	80.83 237

WTY-MVS		59.75 272	60.39 261	57.85 318	72.32 282	37.83 326	61.05 332	64.18 318	45.95 309	61.91 252	79.11 255	47.01 148	60.88 340	42.50 287	69.49 258	74.83 306

xiu_mvs_v2_base		70.52 118	69.75 120	72.84 156	81.21 108	55.63 131	75.11 204	78.92 164	54.92 215	69.96 119	79.68 244	47.00 149	82.09 186	61.60 143	79.37 130	80.81 238

v144192		69.71 137	68.51 141	73.33 148	73.10 267	50.13 212	77.54 155	80.64 137	56.65 170	68.57 140	80.55 224	46.87 150	84.96 125	62.98 129	69.66 256	84.89 138

PEN-MVS		66.60 206	66.45 186	67.04 250	77.11 209	36.56 337	77.03 168	80.42 141	62.95 53	62.51 246	84.03 149	46.69 151	79.07 244	44.22 268	63.08 310	85.51 116

mPP-MVS		76.54 42	75.93 46	78.34 39	86.47 28	63.50 4	85.74 25	82.28 96	62.90 55	71.77 97	90.26 34	46.61 152	86.55 82	71.71 58	85.66 66	84.97 136

CP-MVSNet		66.49 209	66.41 190	66.72 252	77.67 193	36.33 340	76.83 174	79.52 155	62.45 66	62.54 244	83.47 165	46.32 153	78.37 252	45.47 263	63.43 307	85.45 119

V42		68.65 161	67.35 171	72.56 162	68.93 323	50.18 211	72.90 244	79.47 156	56.92 167	69.45 127	80.26 232	46.29 154	82.99 162	64.07 118	67.82 276	84.53 147

1112_ss		64.00 236	63.36 229	65.93 267	79.28 146	42.58 292	71.35 265	72.36 265	46.41 302	60.55 262	77.89 271	46.27 155	73.28 290	46.18 252	69.97 247	81.92 218

MSLP-MVS++		73.77 77	73.47 76	74.66 105	83.02 81	59.29 66	82.30 82	81.88 102	59.34 126	71.59 100	86.83 87	45.94 156	83.65 151	65.09 112	85.22 68	81.06 234

PS-CasMVS		66.42 210	66.32 194	66.70 254	77.60 201	36.30 342	76.94 170	79.61 153	62.36 68	62.43 248	83.66 157	45.69 157	78.37 252	45.35 265	63.26 308	85.42 122

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

DTE-MVSNet		65.58 218	65.34 209	66.31 257	76.06 228	34.79 346	76.43 179	79.38 158	62.55 64	61.66 255	83.83 154	45.60 159	79.15 242	41.64 296	60.88 324	85.00 134

BH-w/o		66.85 200	65.83 202	69.90 216	79.29 145	52.46 179	74.66 216	76.65 212	54.51 223	64.85 218	78.12 265	45.59 160	82.95 164	43.26 280	75.54 177	74.27 313

h-mvs33		72.71 86	71.49 93	76.40 70	81.99 93	59.58 60	76.92 171	76.74 211	60.40 98	74.81 50	85.95 116	45.54 161	85.76 105	70.41 68	70.61 235	83.86 169

hse-mvs2		71.04 110	69.86 119	74.60 109	79.58 139	57.12 109	73.96 226	75.25 229	60.40 98	74.81 50	81.95 195	45.54 161	82.90 165	70.41 68	66.83 283	83.77 175

HQP2-MVS												45.46 163

HQP-MVS		73.45 78	72.80 82	75.40 90	80.66 114	54.94 141	82.31 79	83.90 59	62.10 72	67.85 155	85.54 125	45.46 163	86.93 67	67.04 94	80.35 118	84.32 153

ACMMP		76.02 48	75.33 52	78.07 41	85.20 56	61.91 23	85.49 30	84.44 46	63.04 52	69.80 122	89.74 52	45.43 165	87.16 58	72.01 55	82.87 89	85.14 129
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
OMC-MVS		71.40 107	70.60 108	73.78 126	76.60 219	53.15 165	79.74 119	79.78 149	58.37 144	68.75 137	86.45 102	45.43 165	80.60 218	62.58 132	77.73 152	87.58 40

BH-untuned		68.27 171	67.29 172	71.21 190	79.74 136	53.22 164	76.06 187	77.46 200	57.19 163	66.10 190	81.61 202	45.37 167	83.50 154	45.42 264	76.68 169	76.91 287

v1192		69.97 131	68.68 139	73.85 123	73.19 265	50.94 196	77.68 151	81.36 116	57.51 160	68.95 136	80.85 219	45.28 168	85.33 119	62.97 130	70.37 239	85.27 127

HQP_MVS		74.31 70	73.73 72	76.06 74	81.41 102	56.31 115	84.22 41	84.01 55	64.52 28	69.27 130	86.10 109	45.26 169	87.21 56	68.16 81	80.58 112	84.65 145

plane_prior6					81.20 109	56.24 119						45.26 169

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

test1172		75.36 57	74.81 60	77.02 59	85.47 51	60.79 45	83.94 52	81.63 108	59.52 123	74.66 56	90.18 36	44.74 172	85.84 102	70.63 67	82.52 93	84.42 151

SR-MVS-dyc-post		74.57 66	73.90 69	76.58 67	83.49 74	59.87 57	84.29 38	81.36 116	58.07 150	73.14 78	90.07 38	44.74 172	85.84 102	68.20 79	81.76 102	84.03 161

v1921920		69.47 146	68.17 150	73.36 147	73.06 268	50.10 213	77.39 158	80.56 138	56.58 177	68.59 138	80.37 226	44.72 174	84.98 123	62.47 135	69.82 251	85.00 134

Vis-MVSNet (Re-imp)		63.69 238	63.88 221	63.14 289	74.75 247	31.04 363	71.16 270	63.64 320	56.32 180	59.80 271	84.99 130	44.51 175	75.46 281	39.12 305	80.62 110	82.92 199

DP-MVS Recon		72.15 97	70.73 107	76.40 70	86.57 26	57.99 90	81.15 99	82.96 86	57.03 165	66.78 177	85.56 123	44.50 176	88.11 37	51.77 215	80.23 121	83.10 197

TAMVS		66.78 203	65.27 211	71.33 189	79.16 152	53.67 152	73.84 234	69.59 282	52.32 243	65.28 206	81.72 200	44.49 177	77.40 267	42.32 288	78.66 145	82.92 199

Vis-MVSNet		72.18 94	71.37 97	74.61 108	81.29 105	55.41 137	80.90 100	78.28 186	60.73 93	69.23 133	88.09 71	44.36 178	82.65 176	57.68 166	81.75 104	85.77 105
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
旧先验1					83.04 80	53.15 165		67.52 294			87.85 77	44.08 179			80.76 109	78.03 273

Test_1112_low_res		62.32 252	61.77 247	64.00 283	79.08 154	39.53 314	68.17 294	70.17 277	43.25 329	59.03 279	79.90 237	44.08 179	71.24 299	43.79 276	68.42 272	81.25 228

MVSFormer		71.50 105	70.38 113	74.88 99	78.76 160	57.15 107	82.79 68	78.48 177	51.26 256	69.49 125	83.22 166	43.99 181	83.24 158	66.06 100	79.37 130	84.23 156

lupinMVS		69.57 143	68.28 148	73.44 144	78.76 160	57.15 107	76.57 176	73.29 258	46.19 304	69.49 125	82.18 187	43.99 181	79.23 237	64.66 115	79.37 130	83.93 164

v7n		69.01 155	67.36 170	73.98 121	72.51 279	52.65 172	78.54 137	81.30 121	60.26 108	62.67 240	81.62 201	43.61 183	84.49 134	57.01 169	68.70 270	84.79 142

CDS-MVSNet		66.80 202	65.37 208	71.10 194	78.98 155	53.13 167	73.27 240	71.07 272	52.15 244	64.72 219	80.23 233	43.56 184	77.10 269	45.48 262	78.88 139	83.05 198
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
jason		69.65 141	68.39 147	73.43 145	78.27 175	56.88 111	77.12 165	73.71 254	46.53 301	69.34 129	83.22 166	43.37 185	79.18 238	64.77 114	79.20 135	84.23 156
jason: jason.
v1240		69.24 152	67.91 153	73.25 151	73.02 270	49.82 217	77.21 164	80.54 139	56.43 179	68.34 144	80.51 225	43.33 186	84.99 121	62.03 139	69.77 254	84.95 137

LCM-MVSNet-Re		61.88 258	61.35 252	63.46 285	74.58 250	31.48 362	61.42 328	58.14 342	58.71 137	53.02 329	79.55 247	43.07 187	76.80 273	45.69 257	77.96 150	82.11 215

RE-MVS-def			73.71 73		83.49 74	59.87 57	84.29 38	81.36 116	58.07 150	73.14 78	90.07 38	43.06 188		68.20 79	81.76 102	84.03 161

baseline2		63.42 240	61.26 254	69.89 217	72.55 278	47.62 247	71.54 263	68.38 292	50.11 266	54.82 311	75.55 301	43.06 188	80.96 208	48.13 239	67.16 281	81.11 232

FA-MVS(test-final)		69.82 134	68.48 142	73.84 124	78.44 169	50.04 214	75.58 196	78.99 163	58.16 148	67.59 164	82.14 191	42.66 190	85.63 106	56.60 171	76.19 171	85.84 99

BH-RMVSNet		68.81 157	67.42 167	72.97 153	80.11 130	52.53 177	74.26 221	76.29 214	58.48 142	68.38 143	84.20 144	42.59 191	83.83 147	46.53 249	75.91 173	82.56 204

LFMVS		71.78 100	71.59 91	72.32 169	83.40 76	46.38 257	79.75 118	71.08 271	64.18 35	72.80 85	88.64 68	42.58 192	83.72 149	57.41 168	84.49 73	86.86 63

test_yl		69.69 138	69.13 131	71.36 186	78.37 171	45.74 264	74.71 214	80.20 144	57.91 155	70.01 117	83.83 154	42.44 193	82.87 168	54.97 185	79.72 124	85.48 117

DCV-MVSNet		69.69 138	69.13 131	71.36 186	78.37 171	45.74 264	74.71 214	80.20 144	57.91 155	70.01 117	83.83 154	42.44 193	82.87 168	54.97 185	79.72 124	85.48 117

3Dnovator	64.47 5	72.49 89	71.39 96	75.79 78	77.70 191	58.99 76	80.66 104	83.15 84	62.24 69	65.46 203	86.59 98	42.38 195	85.52 111	59.59 159	84.72 70	82.85 202

VDD-MVS		72.50 88	72.09 87	73.75 130	81.58 98	49.69 221	77.76 150	77.63 196	63.21 50	73.21 76	89.02 61	42.14 196	83.32 156	61.72 141	82.50 94	88.25 12

3Dnovator+	66.72 4	75.84 51	74.57 62	79.66 8	82.40 87	59.92 56	85.83 22	86.32 20	66.92 8	67.80 160	89.24 58	42.03 197	89.38 18	64.07 118	86.50 62	89.69 1

MVS_111021_LR		69.50 145	68.78 138	71.65 178	78.38 170	59.33 64	74.82 212	70.11 278	58.08 149	67.83 159	84.68 134	41.96 198	76.34 278	65.62 107	77.54 154	79.30 260

CPTT-MVS		72.78 84	72.08 88	74.87 100	84.88 65	61.41 29	84.15 44	77.86 191	55.27 203	67.51 166	88.08 72	41.93 199	81.85 189	69.04 77	80.01 122	81.35 227

abl_6		74.34 69	73.50 74	76.86 61	82.43 86	60.16 52	83.48 59	81.86 103	58.81 133	73.95 65	89.86 48	41.87 200	86.62 78	67.98 83	81.23 107	83.80 174

GBi-Net		67.21 189	66.55 184	69.19 226	77.63 194	43.33 286	77.31 159	77.83 192	56.62 173	65.04 214	82.70 173	41.85 201	80.33 224	47.18 244	72.76 210	83.92 165

test1		67.21 189	66.55 184	69.19 226	77.63 194	43.33 286	77.31 159	77.83 192	56.62 173	65.04 214	82.70 173	41.85 201	80.33 224	47.18 244	72.76 210	83.92 165

FMVSNet2		66.93 199	66.31 195	68.79 233	77.63 194	42.98 289	76.11 185	77.47 198	56.62 173	65.22 213	82.17 189	41.85 201	80.18 227	47.05 247	72.72 213	83.20 192

CostFormer		64.04 235	62.51 239	68.61 235	71.88 287	45.77 263	71.30 267	70.60 276	47.55 291	64.31 225	76.61 287	41.63 204	79.62 232	49.74 227	69.00 266	80.42 242

AdaColmap		69.99 130	68.66 140	73.97 122	84.94 62	57.83 92	82.63 73	78.71 169	56.28 182	64.34 223	84.14 146	41.57 205	87.06 65	46.45 250	78.88 139	77.02 283

Effi-MVS+-dtu		69.64 142	67.53 162	75.95 76	76.10 226	62.29 18	80.20 109	76.06 218	59.83 117	65.26 210	77.09 279	41.56 206	84.02 144	60.60 150	71.09 231	81.53 222

mvs-test1		70.44 121	68.19 149	77.18 55	76.10 226	63.22 6	80.59 105	76.06 218	59.83 117	66.32 187	79.87 238	41.56 206	85.53 110	60.60 150	72.77 209	82.80 203

QAPM		70.05 128	68.81 137	73.78 126	76.54 221	53.43 159	83.23 61	83.48 71	52.89 237	65.90 195	86.29 105	41.55 208	86.49 85	51.01 219	78.40 148	81.42 223

VDDNet		71.81 99	71.33 98	73.26 150	82.80 85	47.60 248	78.74 131	75.27 228	59.59 122	72.94 83	89.40 55	41.51 209	83.91 146	58.75 163	82.99 84	88.26 11

1121		68.53 167	67.16 179	72.63 161	85.64 48	61.14 34	73.95 227	66.46 303	44.61 316	70.28 110	86.68 94	41.42 210	80.78 215	53.62 197	81.79 100	75.97 291

CHOSEN 1792x2688		65.08 227	62.84 236	71.82 173	81.49 101	56.26 118	66.32 303	74.20 248	40.53 345	63.16 235	78.65 260	41.30 211	77.80 261	45.80 256	74.09 186	81.40 224

新几何1				70.76 199	85.66 46	61.13 35		66.43 304	44.68 315	70.29 109	86.64 95	41.29 212	75.23 282	49.72 228	81.75 104	75.93 293

tpmrst		58.24 279	58.70 269	56.84 321	66.97 333	34.32 351	69.57 288	61.14 334	47.17 298	58.58 284	71.60 325	41.28 213	60.41 342	49.20 232	62.84 311	75.78 295

tfpnnormal		62.47 250	61.63 249	64.99 278	74.81 245	39.01 317	71.22 268	73.72 253	55.22 205	60.21 263	80.09 236	41.26 214	76.98 272	30.02 352	68.09 274	78.97 263

HPM-MVS_fast		74.30 71	73.46 77	76.80 62	84.45 68	59.04 74	83.65 57	81.05 128	60.15 109	70.43 107	89.84 49	41.09 215	85.59 108	67.61 89	82.90 88	85.77 105

114514_t		70.83 113	69.56 123	74.64 107	86.21 34	54.63 146	82.34 78	81.81 105	48.22 283	63.01 236	85.83 119	40.92 216	87.10 62	57.91 165	79.79 123	82.18 212

HyFIR lowres test		65.67 217	63.01 234	73.67 133	79.97 132	55.65 130	69.07 292	75.52 225	42.68 334	63.53 231	77.95 267	40.43 217	81.64 192	46.01 254	71.91 222	83.73 177

miper_lstm_enhance		62.03 256	60.88 259	65.49 273	66.71 336	46.25 258	56.29 346	75.70 222	50.68 261	61.27 258	75.48 302	40.21 218	68.03 314	56.31 174	65.25 294	82.18 212

FMVSNet3		66.32 211	65.61 206	68.46 236	76.48 222	42.34 293	74.98 209	77.15 205	55.83 192	65.04 214	81.16 209	39.91 219	80.14 228	47.18 244	72.76 210	82.90 201

MVP-Stereo		65.41 221	63.80 223	70.22 207	77.62 198	55.53 134	76.30 181	78.53 175	50.59 264	56.47 297	78.65 260	39.84 220	82.68 175	44.10 272	72.12 221	72.44 329
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
TR-MVS		66.59 208	65.07 213	71.17 192	79.18 150	49.63 223	73.48 237	75.20 232	52.95 235	67.90 153	80.33 229	39.81 221	83.68 150	43.20 281	73.56 194	80.20 246

pmmvs6		63.69 238	62.82 237	66.27 259	70.63 300	39.27 316	73.13 241	75.47 226	52.69 239	59.75 272	82.30 185	39.71 222	77.03 271	47.40 242	64.35 300	82.53 206

XVG-OURS-SEG-HR		68.81 157	67.47 166	72.82 158	74.40 255	56.87 112	70.59 277	79.04 161	54.77 217	66.99 173	86.01 113	39.57 223	78.21 255	62.54 133	73.33 199	83.37 187

Anonymous20231211		69.28 150	68.47 144	71.73 175	80.28 122	47.18 252	79.98 112	82.37 95	54.61 219	67.24 169	84.01 150	39.43 224	82.41 182	55.45 183	72.83 208	85.62 113

Fast-Effi-MVS+-dtu		67.37 187	65.33 210	73.48 142	72.94 271	57.78 94	77.47 157	76.88 207	57.60 159	61.97 251	76.85 283	39.31 225	80.49 222	54.72 188	70.28 242	82.17 214

ACMP	63.53 6	72.30 92	71.20 101	75.59 89	80.28 122	57.54 96	82.74 70	82.84 91	60.58 95	65.24 211	86.18 107	39.25 226	86.03 96	66.95 96	76.79 167	83.22 191
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
Anonymous20240529		69.91 132	69.02 134	72.56 162	80.19 127	47.65 246	77.56 154	80.99 131	55.45 202	69.88 120	86.76 89	39.24 227	82.18 185	54.04 193	77.10 161	87.85 27

LPG-MVS_test		72.74 85	71.74 90	75.76 79	80.22 124	57.51 98	82.55 75	83.40 75	61.32 84	66.67 181	87.33 82	39.15 228	86.59 79	67.70 87	77.30 159	83.19 193

LGP-MVS_train				75.76 79	80.22 124	57.51 98		83.40 75	61.32 84	66.67 181	87.33 82	39.15 228	86.59 79	67.70 87	77.30 159	83.19 193

TAPA-MVS	59.36 10	66.60 206	65.20 212	70.81 198	76.63 218	48.75 233	76.52 178	80.04 146	50.64 263	65.24 211	84.93 131	39.15 228	78.54 251	36.77 316	76.88 164	85.14 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OpenMVS	61.03 9	68.85 156	67.56 159	72.70 160	74.26 258	53.99 150	81.21 98	81.34 120	52.70 238	62.75 239	85.55 124	38.86 231	84.14 139	48.41 238	83.01 83	79.97 250

sss		56.17 294	56.57 285	54.96 327	66.93 334	36.32 341	57.94 340	61.69 332	41.67 338	58.64 283	75.32 304	38.72 232	56.25 357	42.04 291	66.19 288	72.31 333

ACMM	61.98 7	70.80 115	69.73 121	74.02 120	80.59 119	58.59 83	82.68 72	82.02 101	55.46 201	67.18 171	84.39 143	38.51 233	83.17 160	60.65 149	76.10 172	80.30 245
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MVSTER		67.16 194	65.58 207	71.88 172	70.37 305	49.70 219	70.25 283	78.45 180	51.52 250	69.16 134	80.37 226	38.45 234	82.50 179	60.19 153	71.46 227	83.44 186

test_djsdf		69.45 147	67.74 154	74.58 110	74.57 251	54.92 143	82.79 68	78.48 177	51.26 256	65.41 204	83.49 164	38.37 235	83.24 158	66.06 100	69.25 262	85.56 114

tpm2		62.07 255	60.10 263	67.99 241	72.79 273	43.86 282	71.05 274	66.85 301	43.14 331	62.77 237	75.39 303	38.32 236	80.80 214	41.69 293	68.88 267	79.32 259

tpm cat1		59.25 274	56.95 283	66.15 262	72.19 283	46.96 253	68.09 295	65.76 306	40.03 348	57.81 289	70.56 331	38.32 236	74.51 285	38.26 309	61.50 321	77.00 284

CNLPA		65.43 220	64.02 219	69.68 219	78.73 162	58.07 89	77.82 149	70.71 275	51.49 251	61.57 257	83.58 162	38.23 238	70.82 300	43.90 274	70.10 245	80.16 247

1314		64.61 232	63.21 232	68.80 232	71.87 288	47.46 249	73.95 227	78.39 185	42.88 333	59.97 267	76.60 288	38.11 239	79.39 235	54.84 187	72.32 218	79.55 256

testdata				64.66 279	81.52 99	52.93 168		65.29 311	46.09 305	73.88 67	87.46 80	38.08 240	66.26 324	53.31 202	78.48 147	74.78 308

FMVSNet1		66.70 204	65.87 201	69.19 226	77.49 202	43.33 286	77.31 159	77.83 192	56.45 178	64.60 222	82.70 173	38.08 240	80.33 224	46.08 253	72.31 219	83.92 165

UniMVSNet_ETH3D		67.60 184	67.07 181	69.18 229	77.39 204	42.29 294	74.18 223	75.59 224	60.37 101	66.77 178	86.06 111	37.64 242	78.93 250	52.16 209	73.49 195	86.32 83

EPNet_dtu		61.90 257	61.97 246	61.68 298	72.89 272	39.78 311	75.85 192	65.62 308	55.09 208	54.56 315	79.36 251	37.59 243	67.02 319	39.80 303	76.95 162	78.25 267
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-SCA-FT		62.49 249	61.52 250	65.40 274	71.99 286	50.80 201	71.15 271	69.63 281	45.71 310	60.61 261	77.93 268	37.45 244	65.99 325	55.67 181	63.50 306	79.42 258

SCA		60.49 267	58.38 272	66.80 251	74.14 260	48.06 241	63.35 320	63.23 323	49.13 274	59.33 277	72.10 322	37.45 244	74.27 287	44.17 269	62.57 313	78.05 270

IterMVS		62.79 248	61.27 253	67.35 248	69.37 319	52.04 187	71.17 269	68.24 293	52.63 240	59.82 270	76.91 282	37.32 246	72.36 293	52.80 205	63.19 309	77.66 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		63.18 245	62.18 244	66.21 260	76.85 214	39.62 312	71.96 260	69.44 284	56.63 171	62.61 242	79.83 239	37.18 247	79.17 239	31.84 340	73.25 201	79.83 253

thres400		63.31 241	62.18 244	66.72 252	76.85 214	39.62 312	71.96 260	69.44 284	56.63 171	62.61 242	79.83 239	37.18 247	79.17 239	31.84 340	73.25 201	81.36 225

tpm		57.34 286	58.16 274	54.86 328	71.80 289	34.77 347	67.47 299	56.04 352	48.20 284	60.10 265	76.92 281	37.17 249	53.41 364	40.76 298	65.01 295	76.40 290

mvsmamba		71.15 108	69.54 124	75.99 75	77.61 199	53.46 158	81.95 86	75.11 234	57.73 158	66.95 175	85.96 115	37.14 250	87.56 51	67.94 84	75.49 178	86.97 59

test222					83.14 77	58.68 81	72.57 250	63.45 321	41.78 336	67.56 165	86.12 108	37.13 251			78.73 144	74.98 304

AUN-MVS		68.45 169	66.41 190	74.57 111	79.53 141	57.08 110	73.93 230	75.23 230	54.44 224	66.69 180	81.85 197	37.10 252	82.89 166	62.07 137	66.84 282	83.75 176

thres200		62.20 254	61.16 256	65.34 275	75.38 239	39.99 309	69.60 287	69.29 286	55.64 199	61.87 253	76.99 280	37.07 253	78.96 249	31.28 348	73.28 200	77.06 282

thres100view900		63.28 243	62.41 241	65.89 268	77.31 206	38.66 320	72.65 246	69.11 288	57.07 164	62.45 247	81.03 213	37.01 254	79.17 239	31.84 340	73.25 201	79.83 253

thres600view7		63.30 242	62.27 242	66.41 256	77.18 208	38.87 318	72.35 253	69.11 288	56.98 166	62.37 249	80.96 215	37.01 254	79.00 248	31.43 347	73.05 206	81.36 225

DP-MVS		65.68 216	63.66 225	71.75 174	84.93 63	56.87 112	80.74 103	73.16 259	53.06 234	59.09 278	82.35 183	36.79 256	85.94 100	32.82 336	69.96 248	72.45 328

XVG-OURS		68.76 160	67.37 169	72.90 155	74.32 257	57.22 102	70.09 284	78.81 166	55.24 204	67.79 161	85.81 121	36.54 257	78.28 254	62.04 138	75.74 175	83.19 193

ECVR-MVS		67.72 182	67.51 163	68.35 238	79.46 142	36.29 343	74.79 213	66.93 300	58.72 135	67.19 170	88.05 73	36.10 258	81.38 198	52.07 210	84.25 75	87.39 47

test1111		67.21 189	67.14 180	67.42 246	79.24 148	34.76 348	73.89 232	65.65 307	58.71 137	66.96 174	87.95 75	36.09 259	80.53 219	52.03 211	83.79 79	86.97 59

pmmvs4		61.48 263	59.39 264	67.76 242	71.57 291	53.86 151	71.42 264	65.34 310	44.20 321	59.46 273	77.92 269	35.90 260	74.71 284	43.87 275	64.87 296	74.71 309

CR-MVSNet		59.91 270	57.90 277	65.96 266	69.96 312	52.07 185	65.31 312	63.15 324	42.48 335	59.36 274	74.84 306	35.83 261	70.75 301	45.50 261	64.65 298	75.06 301

Patchmtry		57.16 287	56.47 286	59.23 307	69.17 321	34.58 350	62.98 321	63.15 324	44.53 317	56.83 294	74.84 306	35.83 261	68.71 311	40.03 301	60.91 323	74.39 312

RPMNet		61.53 261	58.42 271	70.86 197	69.96 312	52.07 185	65.31 312	81.36 116	43.20 330	59.36 274	70.15 336	35.37 263	85.47 115	36.42 323	64.65 298	75.06 301

CANet_DTU		68.18 174	67.71 157	69.59 221	74.83 244	46.24 259	78.66 133	76.85 208	59.60 119	63.45 232	82.09 194	35.25 264	77.41 266	59.88 156	78.76 143	85.14 129

thisisatest0530		67.92 179	65.78 203	74.33 117	76.29 223	51.03 195	76.89 172	74.25 247	53.67 230	65.59 201	81.76 199	35.15 265	85.50 113	55.94 175	72.47 214	86.47 73

tttt0517		67.83 181	65.66 205	74.33 117	76.69 216	50.82 200	77.86 147	73.99 250	54.54 222	64.64 221	82.53 180	35.06 266	85.50 113	55.71 180	69.91 249	86.67 69

test_0402		63.25 244	61.01 257	69.96 212	80.00 131	54.37 148	76.86 173	72.02 266	54.58 221	58.71 281	80.79 221	35.00 267	84.36 136	26.41 362	64.71 297	71.15 340

thisisatest0515		65.83 215	63.50 227	72.82 158	73.75 261	49.50 224	71.32 266	73.12 260	49.39 271	63.82 230	76.50 291	34.95 268	84.84 129	53.20 203	75.49 178	84.13 160

sam_mvs1												34.74 269				78.05 270

pmmvs5		56.47 290	55.68 291	58.86 311	61.41 359	36.71 336	66.37 302	62.75 326	40.38 346	53.70 322	76.62 286	34.56 270	67.05 318	40.02 302	65.27 293	72.83 323

patchmatchnet-post											64.03 354	34.50 271	74.27 287

PatchmatchNet		59.84 271	58.24 273	64.65 280	73.05 269	46.70 255	69.42 289	62.18 329	47.55 291	58.88 280	71.96 324	34.49 272	69.16 309	42.99 283	63.60 305	78.07 269
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
Patchmatch-test		49.08 322	48.28 324	51.50 342	64.40 348	30.85 364	45.68 362	48.46 367	35.60 355	46.10 353	72.10 322	34.47 273	46.37 368	27.08 360	60.65 326	77.27 279

MS-PatchMatch		62.42 251	61.46 251	65.31 276	75.21 241	52.10 184	72.05 257	74.05 249	46.41 302	57.42 292	74.36 310	34.35 274	77.57 265	45.62 259	73.67 190	66.26 353

tpmvs		58.47 277	56.95 283	63.03 291	70.20 306	41.21 304	67.90 297	67.23 298	49.62 270	54.73 313	70.84 329	34.14 275	76.24 279	36.64 320	61.29 322	71.64 336

PMMVS		53.96 304	53.26 310	56.04 323	62.60 355	50.92 198	61.17 331	56.09 351	32.81 358	53.51 326	66.84 349	34.04 276	59.93 344	44.14 271	68.18 273	57.27 361

Patchmatch-RL test		58.16 280	55.49 292	66.15 262	67.92 329	48.89 232	60.66 333	51.07 361	47.86 288	59.36 274	62.71 358	34.02 277	72.27 295	56.41 173	59.40 329	77.30 278

test_post											3.55 378	33.90 278	66.52 321

PLC	56.13 14	65.09 226	63.21 232	70.72 201	81.04 111	54.87 144	78.57 135	77.47 198	48.51 279	55.71 300	81.89 196	33.71 279	79.71 229	41.66 294	70.37 239	77.58 275
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ET-MVSNet_ETH3D		67.96 178	65.72 204	74.68 104	76.67 217	55.62 133	75.11 204	74.74 239	52.91 236	60.03 266	80.12 234	33.68 280	82.64 177	61.86 140	76.34 170	85.78 102

GA-MVS		65.53 219	63.70 224	71.02 196	70.87 298	48.10 240	70.48 279	74.40 243	56.69 169	64.70 220	76.77 284	33.66 281	81.10 205	55.42 184	70.32 241	83.87 168

LS3D		64.71 230	62.50 240	71.34 188	79.72 138	55.71 128	79.82 116	74.72 240	48.50 280	56.62 295	84.62 136	33.59 282	82.34 183	29.65 354	75.23 180	75.97 291

sam_mvs												33.43 283

PatchT		53.17 312	53.44 309	52.33 340	68.29 328	25.34 373	58.21 339	54.41 355	44.46 319	54.56 315	69.05 341	33.32 284	60.94 339	36.93 315	61.76 320	70.73 342

test20.03		53.87 306	54.02 305	53.41 335	61.47 358	28.11 368	61.30 329	59.21 338	51.34 255	52.09 331	77.43 277	33.29 285	58.55 348	29.76 353	60.27 327	73.58 319

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

anonymousdsp		67.00 198	64.82 215	73.57 140	70.09 309	56.13 120	76.35 180	77.35 202	48.43 281	64.99 217	80.84 220	33.01 287	80.34 223	64.66 115	67.64 278	84.23 156

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

IB-MVS	56.42 12	65.40 222	62.73 238	73.40 146	74.89 242	52.78 171	73.09 242	75.13 233	55.69 196	58.48 285	73.73 315	32.86 289	86.32 91	50.63 221	70.11 244	81.10 233
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
xiu_mvs_v1_base_debu		68.58 163	67.28 173	72.48 164	78.19 177	57.19 104	75.28 199	75.09 235	51.61 247	70.04 113	81.41 206	32.79 290	79.02 245	63.81 122	77.31 156	81.22 229

xiu_mvs_v1_base		68.58 163	67.28 173	72.48 164	78.19 177	57.19 104	75.28 199	75.09 235	51.61 247	70.04 113	81.41 206	32.79 290	79.02 245	63.81 122	77.31 156	81.22 229

xiu_mvs_v1_base_debi		68.58 163	67.28 173	72.48 164	78.19 177	57.19 104	75.28 199	75.09 235	51.61 247	70.04 113	81.41 206	32.79 290	79.02 245	63.81 122	77.31 156	81.22 229

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

UGNet		68.81 157	67.39 168	73.06 152	78.33 173	54.47 147	79.77 117	75.40 227	60.45 97	63.22 233	84.40 142	32.71 294	80.91 212	51.71 216	80.56 114	83.81 170
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
test-LLR		58.15 281	58.13 276	58.22 314	68.57 324	44.80 273	65.46 309	57.92 343	50.08 267	55.44 303	69.82 338	32.62 295	57.44 351	49.66 229	73.62 191	72.41 330

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

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

cascas		65.98 213	63.42 228	73.64 136	77.26 207	52.58 175	72.26 255	77.21 204	48.56 278	61.21 259	74.60 309	32.57 298	85.82 104	50.38 223	76.75 168	82.52 207

test_post1							68.67 293				3.64 377	32.39 299	69.49 308	44.17 269

CVMVSNet		59.63 273	59.14 266	61.08 303	74.47 252	38.84 319	75.20 202	68.74 290	31.15 360	58.24 286	76.51 289	32.39 299	68.58 312	49.77 226	65.84 290	75.81 294

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

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

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

RRT_MVS		69.42 148	67.49 165	75.21 96	78.01 184	52.56 176	82.23 83	78.15 187	55.84 191	65.65 199	85.07 129	30.86 304	86.83 70	61.56 145	70.00 246	86.24 88

gg-mvs-nofinetune		57.86 283	56.43 287	62.18 295	72.62 276	35.35 345	66.57 300	56.33 349	50.65 262	57.64 290	57.10 361	30.65 305	76.36 277	37.38 313	78.88 139	74.82 307

D2MVS		62.30 253	60.29 262	68.34 239	66.46 338	48.42 237	65.70 306	73.42 256	47.71 289	58.16 287	75.02 305	30.51 306	77.71 263	53.96 195	71.68 225	78.90 264

GG-mvs-BLEND				62.34 294	71.36 296	37.04 333	69.20 291	57.33 346		54.73 313	65.48 353	30.37 307	77.82 260	34.82 329	74.93 181	72.17 334

MDA-MVSNet-bldmvs		53.87 306	50.81 317	63.05 290	66.25 339	48.58 235	56.93 344	63.82 319	48.09 285	41.22 361	70.48 334	30.34 308	68.00 315	34.24 331	45.92 360	72.57 326

EPMVS		53.96 304	53.69 307	54.79 329	66.12 341	31.96 361	62.34 325	49.05 364	44.42 320	55.54 301	71.33 327	30.22 309	56.70 354	41.65 295	62.54 314	75.71 296

YYNet1		50.73 319	48.96 320	56.03 324	61.10 361	41.78 299	51.94 353	56.44 348	40.94 344	44.84 354	67.80 345	30.08 310	55.08 362	36.77 316	50.71 352	71.22 338

MDA-MVSNet_test_wron		50.71 320	48.95 321	56.00 325	61.17 360	41.84 297	51.90 354	56.45 347	40.96 343	44.79 355	67.84 344	30.04 311	55.07 363	36.71 318	50.69 353	71.11 341

Anonymous202405211		66.84 201	65.99 200	69.40 225	80.19 127	42.21 295	71.11 272	71.31 270	58.80 134	67.90 153	86.39 103	29.83 312	79.65 230	49.60 231	78.78 142	86.33 81

MSDG		61.81 259	59.23 265	69.55 224	72.64 275	52.63 174	70.45 280	75.81 220	51.38 253	53.70 322	76.11 293	29.52 313	81.08 207	37.70 311	65.79 291	74.93 305

CMPMVS	42.80 21	57.81 284	55.97 289	63.32 286	60.98 362	47.38 250	64.66 316	69.50 283	32.06 359	46.83 350	77.80 273	29.50 314	71.36 298	48.68 235	73.75 189	71.21 339
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
LTVRE_ROB	55.42 16	63.15 246	61.23 255	68.92 231	76.57 220	47.80 243	59.92 334	76.39 213	54.35 225	58.67 282	82.46 182	29.44 315	81.49 196	42.12 290	71.14 229	77.46 276
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
UnsupCasMVSNet_eth		53.16 313	52.47 311	55.23 326	59.45 366	33.39 357	59.43 336	69.13 287	45.98 306	50.35 342	72.32 321	29.30 316	58.26 349	42.02 292	44.30 361	74.05 315

iter_conf_final		69.82 134	68.02 152	75.23 95	79.38 144	52.91 169	80.11 110	73.96 251	54.99 214	68.04 152	83.59 159	29.05 317	87.16 58	65.41 109	77.62 153	85.63 112

CHOSEN 280x420		47.83 324	46.36 328	52.24 341	67.37 332	49.78 218	38.91 368	43.11 372	35.00 356	43.27 359	63.30 357	28.95 318	49.19 367	36.53 321	60.80 325	57.76 360

pmmvs-eth3d		58.81 275	56.31 288	66.30 258	67.61 330	52.42 181	72.30 254	64.76 314	43.55 327	54.94 310	74.19 312	28.95 318	72.60 292	43.31 278	57.21 335	73.88 317

dp		51.89 316	51.60 314	52.77 338	68.44 327	32.45 359	62.36 324	54.57 354	44.16 322	49.31 343	67.91 343	28.87 320	56.61 355	33.89 332	54.89 342	69.24 350

FE-MVS		65.91 214	63.33 230	73.63 137	77.36 205	51.95 189	72.62 248	75.81 220	53.70 229	65.31 205	78.96 256	28.81 321	86.39 88	43.93 273	73.48 196	82.55 205

iter_conf05		69.40 149	67.62 158	74.73 101	77.84 188	51.13 194	79.28 126	73.71 254	54.62 218	68.17 147	83.59 159	28.68 322	87.16 58	65.74 106	76.95 162	85.91 96

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

jajsoiax		68.25 172	66.45 186	73.66 134	75.62 233	55.49 135	80.82 101	78.51 176	52.33 242	64.33 224	84.11 147	28.28 324	81.81 191	63.48 126	70.62 234	83.67 179

RPSCF		55.80 296	54.22 304	60.53 304	65.13 345	42.91 291	64.30 317	57.62 345	36.84 354	58.05 288	82.28 186	28.01 325	56.24 358	37.14 314	58.61 332	82.44 210

F-COLMAP		63.05 247	60.87 260	69.58 223	76.99 213	53.63 154	78.12 144	76.16 215	47.97 287	52.41 330	81.61 202	27.87 326	78.11 256	40.07 300	66.66 284	77.00 284

K. test v3		60.47 268	57.11 280	70.56 203	73.74 262	48.22 239	75.10 206	62.55 327	58.27 147	53.62 324	76.31 292	27.81 327	81.59 194	47.42 241	39.18 366	81.88 219

ACMH+	57.40 11	66.12 212	64.06 218	72.30 170	77.79 190	52.83 170	80.39 106	78.03 189	57.30 161	57.47 291	82.55 179	27.68 328	84.17 138	45.54 260	69.78 252	79.90 251

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

mvs_tets		68.18 174	66.36 192	73.63 137	75.61 234	55.35 138	80.77 102	78.56 174	52.48 241	64.27 226	84.10 148	27.45 330	81.84 190	63.45 127	70.56 236	83.69 178

lessismore_v0				69.91 215	71.42 294	47.80 243		50.90 362		50.39 341	75.56 300	27.43 331	81.33 199	45.91 255	34.10 368	80.59 240

ACMH	55.70 15	65.20 225	63.57 226	70.07 211	78.07 181	52.01 188	79.48 125	79.69 150	55.75 195	56.59 296	80.98 214	27.12 332	80.94 209	42.90 285	71.58 226	77.25 281
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
SixPastTwentyTwo		61.65 260	58.80 268	70.20 209	75.80 230	47.22 251	75.59 194	69.68 280	54.61 219	54.11 319	79.26 253	27.07 333	82.96 163	43.27 279	49.79 355	80.41 243

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

OpenMVS_ROB	52.78 18	60.03 269	58.14 275	65.69 271	70.47 302	44.82 272	75.33 198	70.86 274	45.04 312	56.06 298	76.00 294	26.89 335	79.65 230	35.36 328	67.29 279	72.60 325

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

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

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

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

test2506		65.33 223	64.61 216	67.50 244	79.46 142	34.19 352	74.43 220	51.92 359	58.72 135	66.75 179	88.05 73	25.99 340	80.92 211	51.94 212	84.25 75	87.39 47

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

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

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

PatchMatch-RL		56.25 293	54.55 298	61.32 302	77.06 210	56.07 122	65.57 308	54.10 357	44.13 323	53.49 327	71.27 328	25.20 344	66.78 320	36.52 322	63.66 304	61.12 356

JIA-IIPM		51.56 317	47.68 327	63.21 288	64.61 347	50.73 202	47.71 360	58.77 340	42.90 332	48.46 345	51.72 364	24.97 345	70.24 306	36.06 325	53.89 346	68.64 351

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

EG-PatchMatch MVS		64.71 230	62.87 235	70.22 207	77.68 192	53.48 157	77.99 145	78.82 165	53.37 233	56.03 299	77.41 278	24.75 347	84.04 141	46.37 251	73.42 198	73.14 320

TESTMET0.1,1		55.28 299	54.90 296	56.42 322	66.56 337	43.67 284	65.46 309	56.27 350	39.18 351	53.83 321	67.44 347	24.21 348	55.46 361	48.04 240	73.11 205	70.13 345

bld_raw_dy_0_64		64.87 228	63.22 231	69.83 218	74.79 246	53.32 163	78.15 143	62.02 331	51.20 258	60.17 264	83.12 170	24.15 349	74.20 289	63.08 128	72.33 217	81.96 216

COLMAP_ROB	52.97 17	61.27 265	58.81 267	68.64 234	74.63 249	52.51 178	78.42 140	73.30 257	49.92 269	50.96 335	81.51 205	23.06 350	79.40 234	31.63 344	65.85 289	74.01 316
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MVS_0304		58.51 276	57.36 279	61.96 297	70.04 310	41.83 298	69.40 290	65.46 309	50.73 260	53.30 328	74.06 313	22.65 351	70.18 307	42.16 289	68.44 271	73.86 318

testgi		51.90 315	52.37 312	50.51 344	60.39 365	23.55 375	58.42 338	58.15 341	49.03 275	51.83 332	79.21 254	22.39 352	55.59 360	29.24 355	62.64 312	72.40 332

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

test-mter		56.42 291	55.82 290	58.22 314	68.57 324	44.80 273	65.46 309	57.92 343	39.94 349	55.44 303	69.82 338	21.92 354	57.44 351	49.66 229	73.62 191	72.41 330

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

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

OurMVSNet-221017-0		61.37 264	58.63 270	69.61 220	72.05 285	48.06 241	73.93 230	72.51 263	47.23 297	54.74 312	80.92 216	21.49 357	81.24 202	48.57 237	56.22 339	79.53 257

ITE_SJBPF				62.09 296	66.16 340	44.55 278		64.32 317	47.36 294	55.31 305	80.34 228	19.27 358	62.68 335	36.29 324	62.39 315	79.04 261

AllTest		57.08 288	54.65 297	64.39 281	71.44 292	49.03 227	69.92 286	67.30 295	45.97 307	47.16 348	79.77 241	17.47 359	67.56 316	33.65 333	59.16 330	76.57 288

TestCases				64.39 281	71.44 292	49.03 227		67.30 295	45.97 307	47.16 348	79.77 241	17.47 359	67.56 316	33.65 333	59.16 330	76.57 288

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

XVG-ACMP-BASELINE		64.36 234	62.23 243	70.74 200	72.35 281	52.45 180	70.80 276	78.45 180	53.84 228	59.87 269	81.10 211	16.24 362	79.32 236	55.64 182	71.76 223	80.47 241

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

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

EGC-MVSNET		42.47 330	38.48 336	54.46 331	74.33 256	48.73 234	70.33 282	51.10 360	0.03 379	0.18 380	67.78 346	13.28 365	66.49 322	18.91 367	50.36 354	48.15 364

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

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

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

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

ambc				65.13 277	63.72 351	37.07 332	47.66 361	78.78 168		54.37 318	71.42 326	11.24 370	80.94 209	45.64 258	53.85 347	77.38 277

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

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

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

PM-MVS		52.33 314	50.19 319	58.75 312	62.10 356	45.14 271	65.75 305	40.38 374	43.60 326	53.52 325	72.65 319	9.16 374	65.87 326	50.41 222	54.18 345	65.24 355

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

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

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

Gipuma		34.77 337	31.91 341	43.33 350	62.05 357	37.87 325	20.39 371	67.03 299	23.23 368	18.41 372	25.84 372	4.24 378	62.73 334	14.71 370	51.32 351	29.38 371
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		27.40 339	25.91 342	31.87 355	39.46 379	6.57 382	31.17 369	28.52 379	23.96 367	20.45 371	48.94 368	4.20 379	37.94 374	16.51 368	19.97 371	51.09 363

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

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

PMVS	28.69 22	36.22 336	33.29 340	45.02 349	36.82 380	35.98 344	54.68 350	48.74 365	26.31 365	21.02 370	51.61 365	2.88 382	60.10 343	9.99 375	47.58 358	38.99 370
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	17.77 23	21.41 342	17.77 347	32.34 354	34.34 381	25.44 372	16.11 372	24.11 380	11.19 374	13.22 374	31.92 370	1.58 383	30.95 376	10.47 373	17.03 372	40.62 369
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		13.32 345	12.52 348	15.71 359	47.54 374	26.27 370	31.06 370	1.98 384	4.93 376	5.18 379	1.94 379	0.45 384	18.54 378	6.81 378	12.83 375	2.33 376

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

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

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

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

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

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

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

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

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

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

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

FOURS1					86.12 40	60.82 42	88.18 1	83.61 68	60.87 89	81.50 16

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

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

eth-test2					0.00 387

eth-test					0.00 387

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

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

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

GSMVS																78.05 270

test_part2					87.58 9	60.47 49				83.42 12

MTGPA								80.97 132

MTMP							86.03 19	17.08 381

gm-plane-assit					71.40 295	41.72 302			48.85 277		73.31 317		82.48 181	48.90 234

test9_res														75.28 31	88.31 37	83.81 170

agg_prior2														73.09 50	87.93 44	84.33 152

agg_prior					85.04 57	59.96 54		81.04 129		74.68 53			84.04 141

test_prior4						62.51 17	82.08 85

test_prior				76.69 63	84.20 69	57.27 100		84.88 41					86.43 86			86.38 74

旧先验2							76.08 186		45.32 311	76.55 35			65.56 327	58.75 163

新几何2							76.12 184

无先验							79.66 120	74.30 246	48.40 282				80.78 215	53.62 197		79.03 262

原ACMM2							79.02 128

testdata2													72.18 297	46.95 248

testdata1							72.65 246		60.50 96

plane_prior7					81.41 102	55.96 124

plane_prior5								84.01 55					87.21 56	68.16 81	80.58 112	84.65 145

plane_prior4											86.10 109

plane_prior3						56.09 121			63.92 39	69.27 130

plane_prior2							84.22 41		64.52 28

plane_prior1					81.27 107

plane_prior						56.31 115	83.58 58		63.19 51						80.48 115

n2								0.00 386

nn								0.00 386

door-mid								47.19 369

test11								83.47 72

door								47.60 368

HQP5-MVS						54.94 141

HQP-NCC					80.66 114		82.31 79		62.10 72	67.85 155

ACMP_Plane					80.66 114		82.31 79		62.10 72	67.85 155

BP-MVS														67.04 94

HQP4-MVS										67.85 155			86.93 67			84.32 153

HQP3-MVS								83.90 59							80.35 118

NP-MVS					80.98 112	56.05 123					85.54 125

ACMMP++_ref															74.07 187

ACMMP++															72.16 220

High-resolution multi-view benchmark