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.

MCST-MVS		83.01 1	83.30 2	82.15 5	92.84 2	57.58 13	93.77 1	91.10 4	75.95 2	77.10 15	93.09 8	54.15 13	95.57 3	85.80 1	85.87 22	93.31 6

CNVR-MVS		81.76 4	81.90 4	81.33 9	90.04 5	57.70 11	91.71 3	88.87 16	70.31 14	77.64 14	93.87 2	52.58 19	93.91 17	84.17 2	87.92 10	92.39 19

CANet		80.90 5	81.17 6	80.09 22	87.62 22	54.21 73	91.60 6	86.47 59	73.13 5	79.89 8	93.10 7	49.88 34	92.98 23	84.09 3	84.75 34	93.08 11

MVS_0304		79.84 12	79.71 10	80.25 17	85.64 40	54.62 66	90.58 14	84.48 99	72.51 8	79.22 10	93.09 8	42.01 128	93.28 21	84.00 4	85.84 23	92.87 15

HSP-MVS		82.45 2	83.62 1	78.96 35	82.99 90	52.71 123	85.04 109	89.99 10	66.08 45	86.77 1	92.75 12	72.05 1	91.46 43	83.35 5	93.53 1	92.72 17

PS-MVSNAJ		80.06 10	79.52 11	81.68 7	85.58 43	60.97 3	91.69 4	87.02 50	70.62 12	80.75 6	93.22 6	37.77 167	92.50 30	82.75 6	86.25 19	91.57 33

xiu_mvs_v2_base		79.86 11	79.31 12	81.53 8	85.03 54	60.73 4	91.65 5	86.86 53	70.30 15	80.77 5	93.07 10	37.63 172	92.28 33	82.73 7	85.71 24	91.57 33

DeepPCF-MVS	69.37 1	80.65 6	81.56 5	77.94 63	85.46 48	49.56 181	90.99 11	86.66 57	70.58 13	80.07 7	95.30 1	56.18 9	90.97 50	82.57 8	86.22 20	93.28 7

lupinMVS		78.38 17	78.11 17	79.19 31	83.02 88	55.24 45	91.57 7	84.82 91	69.12 19	76.67 17	92.02 22	44.82 77	90.23 69	80.83 9	80.09 65	92.08 24

HPM-MVS++		80.50 7	80.71 7	79.88 24	87.34 24	55.20 48	89.93 20	87.55 45	66.04 48	79.46 9	93.00 11	53.10 17	91.76 40	80.40 10	89.56 4	92.68 18

test_part3							89.59 26		56.02 209		93.65 3		95.22 6	79.73 11

ESAPD		80.50 7	80.42 8	80.74 12	89.33 9	55.48 37	89.59 26	88.42 25	56.02 209	82.27 2	93.65 3	58.18 6	95.22 6	79.73 11	86.59 15	91.53 35

APDe-MVS		78.44 15	78.20 16	79.19 31	88.56 12	54.55 68	89.76 24	87.77 40	55.91 211	78.56 11	92.49 15	48.20 38	92.65 28	79.49 13	83.04 42	90.39 62

jason		77.01 31	76.45 31	78.69 41	79.69 157	54.74 61	90.56 15	83.99 120	68.26 24	74.10 27	90.91 44	42.14 124	89.99 73	79.30 14	79.12 73	91.36 41
jason: jason.
DELS-MVS		82.32 3	82.50 3	81.79 6	86.80 27	56.89 21	92.77 2	86.30 63	77.83 1	77.88 12	92.13 18	60.24 2	94.78 12	78.97 15	89.61 3	93.69 3
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
NCCC		79.57 13	79.23 13	80.59 13	89.50 8	56.99 19	91.38 8	88.17 32	67.71 29	73.81 28	92.75 12	46.88 51	93.28 21	78.79 16	84.07 39	91.50 38

test9_res														78.72 17	85.44 27	91.39 40

CSCG		80.41 9	79.72 9	82.49 4	89.12 11	57.67 12	89.29 30	91.54 2	59.19 143	71.82 49	90.05 63	59.72 3	96.04 1	78.37 18	88.40 8	93.75 2

MP-MVS-pluss		75.54 50	75.03 46	77.04 81	81.37 132	52.65 125	84.34 123	84.46 100	61.16 112	69.14 61	91.76 28	39.98 151	88.99 100	78.19 19	84.89 33	89.48 78
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
train_agg		76.91 32	76.40 32	78.45 49	85.68 37	55.42 39	87.59 47	84.00 118	57.84 178	72.99 35	90.98 40	44.99 73	88.58 116	78.19 19	85.32 28	91.34 43

agg_prior3		76.73 37	76.15 36	78.48 47	85.66 39	55.59 34	87.54 50	83.95 122	57.78 180	71.78 50	90.81 48	44.33 81	88.52 121	78.19 19	85.32 28	91.34 43

canonicalmvs		78.17 20	77.86 20	79.12 34	84.30 61	54.22 72	87.71 42	84.57 98	67.70 30	77.70 13	92.11 21	50.90 28	89.95 74	78.18 22	77.54 84	93.20 9

VDD-MVS		76.08 45	74.97 47	79.44 27	84.27 64	53.33 98	91.13 10	85.88 67	65.33 58	72.37 45	89.34 73	32.52 230	92.76 26	77.90 23	75.96 98	92.22 22

agg_prior1		76.68 38	76.24 35	78.00 60	85.64 40	54.92 56	87.55 49	83.61 129	57.99 175	72.53 42	91.05 38	45.36 70	88.10 138	77.76 24	84.68 35	90.99 48

alignmvs		78.08 21	77.98 18	78.39 51	83.53 73	53.22 106	89.77 23	85.45 72	66.11 43	76.59 19	91.99 24	54.07 14	89.05 91	77.34 25	77.00 90	92.89 14

SteuartSystems-ACMMP		77.08 30	76.33 33	79.34 29	80.98 136	55.31 43	89.76 24	86.91 52	62.94 93	71.65 51	91.56 33	42.33 120	92.56 29	77.14 26	83.69 41	90.15 69
Skip Steuart: Steuart Systems R&D Blog.
ACMMP_Plus		76.43 40	75.66 39	78.73 40	81.92 116	54.67 65	84.06 130	85.35 77	61.10 114	72.99 35	91.50 34	40.25 144	91.00 48	76.84 27	86.98 12	90.51 58

CLD-MVS		75.60 49	75.39 43	76.24 94	80.69 143	52.40 129	90.69 13	86.20 65	74.40 3	65.01 99	88.93 77	42.05 127	90.58 58	76.57 28	73.96 111	85.73 144
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MP-MVS		74.99 55	74.33 51	76.95 84	82.89 94	53.05 116	85.63 84	83.50 132	57.86 177	67.25 74	90.24 58	43.38 102	88.85 109	76.03 29	82.23 48	88.96 91

TSAR-MVS + GP.		77.82 24	77.59 22	78.49 46	85.25 52	50.27 172	90.02 17	90.57 5	56.58 199	74.26 26	91.60 32	54.26 11	92.16 35	75.87 30	79.91 69	93.05 12

agg_prior2														75.65 31	85.11 31	91.01 46

DeepC-MVS	67.15 4	76.90 34	76.27 34	78.80 39	80.70 142	55.02 53	86.39 68	86.71 55	66.96 36	67.91 70	89.97 65	48.03 40	91.41 44	75.60 32	84.14 38	89.96 72
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PVSNet_BlendedMVS		73.42 71	73.30 57	73.76 152	85.91 34	51.83 141	86.18 73	84.24 107	65.40 55	69.09 62	80.86 185	46.70 53	88.13 136	75.43 33	65.92 169	81.33 215

PVSNet_Blended		76.53 39	76.54 30	76.50 90	85.91 34	51.83 141	88.89 34	84.24 107	67.82 27	69.09 62	89.33 75	46.70 53	88.13 136	75.43 33	81.48 53	89.55 77

LFMVS		78.52 14	77.14 26	82.67 3	89.58 7	58.90 6	91.27 9	88.05 33	63.22 89	74.63 24	90.83 47	41.38 137	94.40 13	75.42 35	79.90 70	94.72 1

MVS_111021_HR		76.39 41	75.38 44	79.42 28	85.33 50	56.47 26	88.15 37	84.97 88	65.15 61	66.06 85	89.88 66	43.79 91	92.16 35	75.03 36	80.03 68	89.64 76

test_prior3		77.59 26	77.33 25	78.39 51	86.35 31	54.91 58	89.04 32	85.45 72	61.88 104	73.55 30	91.46 36	48.01 41	89.70 80	74.73 37	85.46 25	90.55 54

test_prior2							89.04 32		61.88 104	73.55 30	91.46 36	48.01 41		74.73 37	85.46 25

SD-MVS		76.18 43	74.85 49	80.18 19	85.39 49	56.90 20	85.75 81	82.45 151	56.79 194	74.48 25	91.81 25	43.72 96	90.75 54	74.61 39	78.65 76	92.91 13

APD-MVS		76.15 44	75.68 38	77.54 69	88.52 13	53.44 88	87.26 57	85.03 87	53.79 226	74.91 22	91.68 31	43.80 90	90.31 64	74.36 40	81.82 50	88.87 93
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
VDDNet		74.37 58	72.13 73	81.09 11	79.58 158	56.52 25	90.02 17	86.70 56	52.61 243	71.23 54	87.20 102	31.75 240	93.96 16	74.30 41	75.77 101	92.79 16

TSAR-MVS + MP.		78.31 19	78.26 15	78.48 47	81.33 133	56.31 29	81.59 184	86.41 60	69.61 18	81.72 4	88.16 89	55.09 10	88.04 140	74.12 42	86.31 18	91.09 45

PHI-MVS		77.49 27	77.00 27	78.95 36	85.33 50	50.69 159	88.57 35	88.59 22	58.14 172	73.60 29	93.31 5	43.14 105	93.79 18	73.81 43	88.53 7	92.37 20

MPTG		74.15 63	73.11 61	77.27 77	81.54 126	53.57 83	84.02 132	81.31 170	59.41 136	68.39 66	90.96 42	36.07 196	89.01 96	73.80 44	82.45 46	89.23 83

MTAPA		72.73 78	71.22 85	77.27 77	81.54 126	53.57 83	67.06 302	81.31 170	59.41 136	68.39 66	90.96 42	36.07 196	89.01 96	73.80 44	82.45 46	89.23 83

Regformer-1		77.80 25	77.44 24	78.88 37	87.78 20	52.44 128	87.60 44	90.08 8	68.86 20	72.49 44	91.79 26	47.69 43	94.90 10	73.57 46	77.05 87	89.31 81

VNet		77.99 23	77.92 19	78.19 56	87.43 23	50.12 173	90.93 12	91.41 3	67.48 32	75.12 21	90.15 62	46.77 52	91.00 48	73.52 47	78.46 77	93.44 4

EPNet		78.36 18	78.49 14	77.97 62	85.49 45	52.04 136	89.36 29	84.07 116	73.22 4	77.03 16	91.72 29	49.32 36	90.17 71	73.46 48	82.77 43	91.69 29
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DI_MVS_plusplus_test		71.30 98	68.98 113	78.26 55	72.76 247	54.08 76	81.72 176	83.22 137	65.75 51	51.94 236	78.47 205	36.01 201	90.31 64	73.33 49	77.60 82	90.40 61

xiu_mvs_v1_base_debu		71.60 94	70.29 94	75.55 107	77.26 195	53.15 109	85.34 89	79.37 200	55.83 212	72.54 39	90.19 59	22.38 293	86.66 175	73.28 50	76.39 94	86.85 125

xiu_mvs_v1_base		71.60 94	70.29 94	75.55 107	77.26 195	53.15 109	85.34 89	79.37 200	55.83 212	72.54 39	90.19 59	22.38 293	86.66 175	73.28 50	76.39 94	86.85 125

xiu_mvs_v1_base_debi		71.60 94	70.29 94	75.55 107	77.26 195	53.15 109	85.34 89	79.37 200	55.83 212	72.54 39	90.19 59	22.38 293	86.66 175	73.28 50	76.39 94	86.85 125

test_normal		71.31 97	68.95 114	78.39 51	72.30 265	54.25 71	81.67 177	84.05 117	65.94 50	51.31 240	78.09 211	36.06 198	90.43 61	73.00 53	78.09 80	90.50 59

Regformer-2		77.15 29	76.82 29	78.14 57	87.78 20	51.84 140	87.60 44	89.12 13	67.23 33	71.93 48	91.79 26	46.03 64	93.53 20	72.85 54	77.05 87	89.05 89

PMMVS		72.98 75	72.05 75	75.78 105	83.57 72	48.60 198	84.08 128	82.85 145	61.62 108	68.24 68	90.33 57	28.35 257	87.78 149	72.71 55	76.69 92	90.95 49

MVS		76.91 32	75.48 41	81.23 10	84.56 59	55.21 47	80.23 206	91.64 1	58.65 162	65.37 93	91.48 35	45.72 67	95.05 8	72.11 56	89.52 5	93.44 4

#test#		74.86 56	73.78 55	78.10 58	84.30 61	53.68 80	86.95 61	84.36 101	59.00 154	65.78 88	90.56 52	40.70 141	90.90 51	71.48 57	80.88 54	89.71 73

nrg030		72.27 88	71.56 79	74.42 135	75.93 208	50.60 161	86.97 60	83.21 138	62.75 95	67.15 75	84.38 129	50.07 32	86.66 175	71.19 58	62.37 204	85.99 139

DeepC-MVS_fast	67.50 3	78.00 22	77.63 21	79.13 33	88.52 13	55.12 50	89.95 19	85.98 66	68.31 23	71.33 53	92.75 12	45.52 69	90.37 63	71.15 59	85.14 30	91.91 26
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CHOSEN 1792x2688		76.24 42	74.03 53	82.88 1	83.09 85	62.84 2	85.73 82	85.39 75	69.79 16	64.87 101	83.49 147	41.52 136	93.69 19	70.55 60	81.82 50	92.12 23

CDPH-MVS		76.05 46	75.19 45	78.62 44	86.51 30	54.98 55	87.32 53	84.59 97	58.62 163	70.75 55	90.85 46	43.10 109	90.63 57	70.50 61	84.51 37	90.24 65

HPM-MVS		72.60 80	71.50 80	75.89 103	82.02 113	51.42 149	80.70 199	83.05 141	56.12 208	64.03 112	89.53 71	37.55 174	88.37 125	70.48 62	80.04 67	87.88 109

Regformer-3		76.02 47	75.47 42	77.70 65	85.49 45	51.47 147	85.12 99	90.19 7	68.52 22	69.36 60	90.66 50	46.45 61	94.81 11	70.25 63	73.16 116	86.81 128

MVS_111021_LR		69.07 136	67.91 124	72.54 172	77.27 194	49.56 181	79.77 214	73.96 277	59.33 141	60.73 146	87.82 93	30.19 249	81.53 251	69.94 64	72.19 130	86.53 131

Test4		68.64 149	65.68 169	77.53 70	67.78 296	53.34 96	79.42 221	82.84 146	65.96 49	46.54 281	76.15 237	25.16 277	88.83 110	69.74 65	77.53 85	90.43 60

Regformer-4		75.06 54	74.59 50	76.47 91	85.49 45	50.33 168	85.12 99	88.61 20	66.42 38	68.48 65	90.66 50	44.15 86	92.68 27	69.24 66	73.16 116	86.39 136

HFP-MVS		74.37 58	73.13 60	78.10 58	84.30 61	53.68 80	85.58 85	84.36 101	56.82 192	65.78 88	90.56 52	40.70 141	90.90 51	69.18 67	80.88 54	89.71 73

ACMMPR		73.76 67	72.61 62	77.24 80	83.92 70	52.96 120	85.58 85	84.29 103	56.82 192	65.12 95	90.45 54	37.24 182	90.18 70	69.18 67	80.84 56	88.58 100

region2R		73.75 68	72.55 64	77.33 74	83.90 71	52.98 119	85.54 88	84.09 109	56.83 191	65.10 96	90.45 54	37.34 180	90.24 68	68.89 69	80.83 57	88.77 96

CP-MVS		72.59 82	71.46 81	76.00 102	82.93 93	52.32 133	86.93 63	82.48 150	55.15 218	63.65 118	90.44 56	35.03 208	88.53 120	68.69 70	77.83 81	87.15 121

Effi-MVS+		75.24 52	73.61 56	80.16 20	81.92 116	57.42 14	85.21 93	76.71 248	60.68 121	73.32 34	89.34 73	47.30 46	91.63 41	68.28 71	79.72 71	91.42 39

CostFormer		73.89 66	72.30 69	78.66 42	82.36 107	56.58 22	75.56 253	85.30 78	66.06 46	70.50 58	76.88 225	57.02 8	89.06 89	68.27 72	68.74 147	90.33 64

CANet_DTU		73.71 69	73.14 58	75.40 111	82.61 103	50.05 174	84.67 119	79.36 203	69.72 17	75.39 20	90.03 64	29.41 252	85.93 196	67.99 73	79.11 74	90.22 66

PVSNet_Blended_VisFu		73.40 72	72.44 66	76.30 92	81.32 134	54.70 63	85.81 77	78.82 210	63.70 81	64.53 105	85.38 121	47.11 49	87.38 158	67.75 74	77.55 83	86.81 128

MSLP-MVS++		74.21 60	72.25 71	80.11 21	81.45 130	56.47 26	86.32 70	79.65 197	58.19 171	66.36 82	92.29 17	36.11 195	90.66 56	67.39 75	82.49 45	93.18 10

PGM-MVS		72.60 80	71.20 86	76.80 88	82.95 91	52.82 122	83.07 153	82.14 152	56.51 204	63.18 122	89.81 67	35.68 203	89.76 79	67.30 76	80.19 64	87.83 110

HY-MVS	67.03 5	73.90 65	73.14 58	76.18 96	84.70 58	47.36 218	75.56 253	86.36 62	66.27 41	70.66 56	83.91 134	51.05 27	89.31 86	67.10 77	72.61 125	91.88 27

testing_2		63.60 212	59.86 233	74.82 128	61.87 318	52.39 130	73.06 272	82.76 147	61.49 111	39.96 304	67.39 298	21.06 302	88.34 127	67.07 78	64.10 178	83.72 176

BP-MVS														66.70 79

HQP-MVS		72.34 84	71.44 82	75.03 117	79.02 166	51.56 144	88.00 38	83.68 126	65.45 52	64.48 106	85.13 122	37.35 178	88.62 113	66.70 79	73.12 118	84.91 156

gm-plane-assit					83.24 81	54.21 73			70.91 11		88.23 88		95.25 5	66.37 81

PAPR		75.20 53	74.13 52	78.41 50	88.31 17	55.10 52	84.31 124	85.66 69	63.76 80	67.55 71	90.73 49	43.48 101	89.40 85	66.36 82	77.03 89	90.73 52

WTY-MVS		77.47 28	77.52 23	77.30 75	88.33 16	46.25 233	88.46 36	90.32 6	71.40 10	72.32 46	91.72 29	53.44 15	92.37 31	66.28 83	75.42 103	93.28 7

tpmrst		71.04 102	69.77 102	74.86 127	83.19 82	55.86 33	75.64 252	78.73 213	67.88 26	64.99 100	73.73 252	49.96 33	79.56 272	65.92 84	67.85 155	89.14 88

MVS_Test		75.85 48	74.93 48	78.62 44	84.08 66	55.20 48	83.99 133	85.17 83	68.07 25	73.38 33	82.76 156	50.44 29	89.00 98	65.90 85	80.61 58	91.64 30

ACMMP		70.81 106	69.29 109	75.39 112	81.52 129	51.92 138	83.43 144	83.03 142	56.67 197	58.80 172	88.91 78	31.92 238	88.58 116	65.89 86	73.39 115	85.67 145
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
XVS		72.92 76	71.62 78	76.81 86	83.41 74	52.48 126	84.88 114	83.20 139	58.03 173	63.91 113	89.63 70	35.50 204	89.78 77	65.50 87	80.50 60	88.16 103

X-MVStestdata		65.85 193	62.20 202	76.81 86	83.41 74	52.48 126	84.88 114	83.20 139	58.03 173	63.91 113	4.82 353	35.50 204	89.78 77	65.50 87	80.50 60	88.16 103

PAPM		76.76 35	76.07 37	78.81 38	80.20 155	59.11 5	86.86 64	86.23 64	68.60 21	70.18 59	88.84 80	51.57 24	87.16 161	65.48 89	86.68 13	90.15 69

HQP_MVS		70.96 104	69.91 101	74.12 141	77.95 184	49.57 179	85.76 79	82.59 148	63.60 84	62.15 129	83.28 151	36.04 199	88.30 131	65.46 90	72.34 128	84.49 159

plane_prior5								82.59 148					88.30 131	65.46 90	72.34 128	84.49 159

mPP-MVS		71.79 93	70.38 92	76.04 100	82.65 102	52.06 135	84.45 121	81.78 163	55.59 215	62.05 132	89.68 69	33.48 221	88.28 133	65.45 92	78.24 79	87.77 112

OPM-MVS		70.75 107	69.58 103	74.26 139	75.55 212	51.34 151	86.05 75	83.29 136	61.94 103	62.95 125	85.77 117	34.15 214	88.44 123	65.44 93	71.07 134	82.99 188

Effi-MVS+-dtu		66.24 190	64.96 185	70.08 222	75.17 213	49.64 178	82.01 169	74.48 270	62.15 99	57.83 183	76.08 238	30.59 246	83.79 232	65.40 94	60.93 210	76.81 278

mvs-test1		69.04 137	67.57 137	73.44 159	75.17 213	51.68 143	86.57 67	74.48 270	62.15 99	62.07 131	85.79 116	30.59 246	87.48 155	65.40 94	65.94 168	81.18 219

EI-MVSNet-Vis-set		73.19 74	72.60 63	74.99 120	82.56 104	49.80 177	82.55 162	89.00 15	66.17 42	65.89 87	88.98 76	43.83 89	92.29 32	65.38 96	69.01 145	82.87 191

TESTMET0.1,1		72.86 77	72.33 67	74.46 134	81.98 114	50.77 157	85.13 96	85.47 71	66.09 44	67.30 72	83.69 139	37.27 181	83.57 235	65.06 97	78.97 75	89.05 89

MVSTER		73.25 73	72.33 67	76.01 101	85.54 44	53.76 79	83.52 139	87.16 48	67.06 35	63.88 115	81.66 175	52.77 18	90.44 59	64.66 98	64.69 175	83.84 174

CPTT-MVS		67.15 173	65.84 164	71.07 204	80.96 137	50.32 169	81.94 170	74.10 274	46.18 280	57.91 182	87.64 98	29.57 251	81.31 253	64.10 99	70.18 140	81.56 210

EI-MVSNet-UG-set		72.37 83	71.73 77	74.29 138	81.60 122	49.29 186	81.85 173	88.64 19	65.29 60	65.05 97	88.29 86	43.18 103	91.83 39	63.74 100	67.97 153	81.75 208

ab-mvs		70.65 108	69.11 111	75.29 113	80.87 139	46.23 234	73.48 266	85.24 82	59.99 128	66.65 78	80.94 184	43.13 106	88.69 111	63.58 101	68.07 151	90.95 49

VPA-MVSNet		71.12 100	70.66 89	72.49 174	78.75 171	44.43 249	87.64 43	90.02 9	63.97 75	65.02 98	81.58 176	42.14 124	87.42 157	63.42 102	63.38 187	85.63 148

APD-MVS_3200maxsize		69.62 126	68.23 121	73.80 151	81.58 124	48.22 210	81.91 171	79.50 199	48.21 267	64.24 111	89.75 68	31.91 239	87.55 154	63.08 103	73.85 113	85.64 147

v2v482		69.55 127	67.64 132	75.26 115	72.32 264	53.83 78	84.93 113	81.94 158	65.37 57	60.80 145	79.25 194	41.62 134	88.98 101	63.03 104	59.51 222	82.98 189

PS-MVSNAJss		68.78 145	67.17 144	73.62 156	73.01 237	48.33 209	84.95 112	84.81 92	59.30 142	58.91 169	79.84 191	37.77 167	88.86 108	62.83 105	63.12 194	83.67 177

v6		69.43 132	67.61 135	74.88 126	72.87 246	53.30 103	85.12 99	81.10 176	64.29 69	60.99 140	78.52 203	42.88 113	88.98 101	62.67 106	60.06 213	82.37 198

v1neww		69.43 132	67.62 133	74.89 124	72.90 242	53.31 99	85.12 99	81.11 174	64.29 69	61.00 138	78.53 201	42.88 113	88.98 101	62.66 107	60.06 213	82.37 198

v7new		69.43 132	67.62 133	74.89 124	72.90 242	53.31 99	85.12 99	81.11 174	64.29 69	61.00 138	78.53 201	42.88 113	88.98 101	62.66 107	60.06 213	82.37 198

v1141		69.50 129	67.67 129	74.98 121	72.73 249	53.41 91	85.08 105	82.14 152	64.79 64	60.88 141	78.19 208	43.09 110	89.04 92	62.51 109	59.61 219	82.47 196

divwei89l23v2f112		69.50 129	67.67 129	74.98 121	72.72 250	53.41 91	85.08 105	82.14 152	64.79 64	60.88 141	78.19 208	43.11 107	89.04 92	62.51 109	59.62 218	82.48 195

v1		69.49 131	67.67 129	74.98 121	72.69 251	53.41 91	85.08 105	82.13 155	64.80 63	60.87 143	78.19 208	43.11 107	89.04 92	62.51 109	59.61 219	82.49 194

V42		67.66 161	65.60 172	73.86 148	70.69 278	53.63 82	81.50 185	78.61 216	63.85 77	59.49 160	77.49 217	37.98 164	87.65 152	62.33 112	58.43 234	80.29 235

MG-MVS		78.42 16	76.99 28	82.73 2	93.17 1	64.46 1	89.93 20	88.51 23	64.83 62	73.52 32	88.09 90	48.07 39	92.19 34	62.24 113	84.53 36	91.53 35

Patchmatch-RL test		58.72 256	54.32 265	71.92 188	63.91 311	44.25 251	61.73 314	55.19 330	57.38 185	49.31 256	54.24 329	37.60 173	80.89 255	62.19 114	47.28 293	90.63 53

mvs_anonymous		72.29 86	70.74 88	76.94 85	82.85 95	54.72 62	78.43 232	81.54 166	63.77 79	61.69 134	79.32 193	51.11 26	85.31 204	62.15 115	75.79 100	90.79 51

HyFIR lowres test		69.94 121	67.58 136	77.04 81	77.11 200	57.29 15	81.49 187	79.11 208	58.27 169	58.86 170	80.41 187	42.33 120	86.96 167	61.91 116	68.68 148	86.87 123

sss		70.49 110	70.13 98	71.58 198	81.59 123	39.02 287	80.78 198	84.71 96	59.34 139	66.61 80	88.09 90	37.17 183	85.52 200	61.82 117	71.02 135	90.20 67

1314		71.11 101	69.41 105	76.22 95	79.32 161	50.49 164	80.23 206	85.14 86	59.44 135	58.93 167	88.89 79	33.83 220	89.60 84	61.49 118	77.42 86	88.57 101

GA-MVS		69.04 137	66.70 153	76.06 99	75.11 215	52.36 131	83.12 151	80.23 187	63.32 87	60.65 147	79.22 195	30.98 244	88.37 125	61.25 119	66.41 161	87.46 116

VPNet		72.07 89	71.42 83	74.04 143	78.64 176	47.17 222	89.91 22	87.97 34	72.56 7	64.66 103	85.04 124	41.83 131	88.33 129	61.17 120	60.97 209	86.62 130

ACMP	61.11 9	66.24 190	64.33 189	72.00 183	74.89 219	49.12 187	83.18 150	79.83 192	55.41 217	52.29 232	82.68 160	25.83 272	86.10 189	60.89 121	63.94 181	80.78 224
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MVSFormer		73.53 70	72.19 72	77.57 68	83.02 88	55.24 45	81.63 181	81.44 167	50.28 259	76.67 17	90.91 44	44.82 77	86.11 187	60.83 122	80.09 65	91.36 41

test_djsdf		63.84 208	61.56 217	70.70 210	68.78 287	44.69 247	81.63 181	81.44 167	50.28 259	52.27 233	76.26 232	26.72 267	86.11 187	60.83 122	55.84 258	81.29 218

v148		68.24 154	66.35 155	73.88 147	71.76 269	51.47 147	84.23 125	81.90 162	63.69 82	58.94 166	76.44 229	43.72 96	87.78 149	60.63 124	55.86 257	82.39 197

test-LLR		69.65 125	69.01 112	71.60 196	78.67 173	48.17 211	85.13 96	79.72 194	59.18 145	63.13 123	82.58 161	36.91 187	80.24 264	60.56 125	75.17 105	86.39 136

test-mter		68.36 151	67.29 142	71.60 196	78.67 173	48.17 211	85.13 96	79.72 194	53.38 230	63.13 123	82.58 161	27.23 265	80.24 264	60.56 125	75.17 105	86.39 136

IB-MVS	68.87 2	74.01 64	72.03 76	79.94 23	83.04 87	55.50 36	90.24 16	88.65 18	67.14 34	61.38 135	81.74 174	53.21 16	94.28 14	60.45 127	62.41 203	90.03 71
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
v1144		68.81 142	66.82 147	74.80 130	72.34 263	53.46 86	84.68 118	81.77 164	64.25 72	60.28 150	77.91 212	40.23 145	88.95 105	60.37 128	59.52 221	81.97 201

v7		68.76 146	66.79 149	74.68 131	72.60 254	53.37 94	84.72 117	80.88 179	63.80 78	60.43 149	78.21 207	40.05 150	88.89 107	60.34 129	60.07 212	81.77 207

LPG-MVS_test		66.44 186	64.58 187	72.02 181	74.42 223	48.60 198	83.07 153	80.64 183	54.69 222	53.75 221	83.83 135	25.73 274	86.98 165	60.33 130	64.71 173	80.48 232

LGP-MVS_train				72.02 181	74.42 223	48.60 198		80.64 183	54.69 222	53.75 221	83.83 135	25.73 274	86.98 165	60.33 130	64.71 173	80.48 232

abl_6		68.03 155	66.15 159	73.66 153	78.54 178	48.48 204	79.77 214	78.04 228	47.39 271	63.70 117	88.25 87	28.21 258	89.06 89	60.17 132	71.25 133	83.45 179

MVP-Stereo		70.97 103	70.44 91	72.59 171	76.03 207	51.36 150	85.02 111	86.99 51	60.31 125	56.53 201	78.92 198	40.11 148	90.00 72	60.00 133	90.01 2	76.41 283
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
jajsoiax		63.21 219	60.84 223	70.32 218	68.33 292	44.45 248	81.23 189	81.05 177	53.37 231	50.96 244	77.81 214	17.49 317	85.49 202	59.31 134	58.05 240	81.02 221

mvs_tets		62.96 223	60.55 228	70.19 219	68.22 294	44.24 252	80.90 196	80.74 182	52.99 235	50.82 252	77.56 215	16.74 320	85.44 203	59.04 135	57.94 241	80.89 222

HPM-MVS_fast		67.86 157	66.28 156	72.61 170	80.67 144	48.34 208	81.18 190	75.95 259	50.81 258	59.55 159	88.05 92	27.86 260	85.98 192	58.83 136	73.58 114	83.51 178

v144192		67.86 157	65.76 166	74.16 140	71.68 270	53.09 113	84.14 127	80.83 181	62.85 94	59.21 164	77.28 221	39.30 155	88.00 141	58.67 137	57.88 244	81.40 214

diffmvs		70.02 116	68.35 119	75.03 117	79.19 164	51.48 146	78.50 231	76.65 250	59.71 130	67.10 76	80.32 188	42.81 116	87.12 162	58.48 138	72.37 127	86.49 132

v8		67.25 170	64.99 183	74.04 143	72.89 244	53.31 99	82.37 165	80.11 189	61.54 109	54.29 217	76.02 239	42.89 112	88.41 124	58.43 139	56.36 250	80.39 234

XXY-MVS		70.18 112	69.28 110	72.89 166	77.64 188	42.88 263	85.06 108	87.50 46	62.58 96	62.66 127	82.34 166	43.64 98	89.83 76	58.42 140	63.70 183	85.96 141

3Dnovator	64.70 6	74.46 57	72.48 65	80.41 15	82.84 96	55.40 42	83.08 152	88.61 20	67.61 31	59.85 152	88.66 81	34.57 211	93.97 15	58.42 140	88.70 6	91.85 28

旧先验2							81.73 175		45.53 283	74.66 23			70.48 322	58.31 142

v1192		67.96 156	65.74 167	74.63 132	71.79 268	53.43 90	84.06 130	80.99 178	63.19 90	59.56 158	77.46 218	37.50 177	88.65 112	58.20 143	58.93 227	81.79 206

EPP-MVSNet		71.14 99	70.07 99	74.33 137	79.18 165	46.52 227	83.81 135	86.49 58	56.32 207	57.95 181	84.90 126	54.23 12	89.14 88	58.14 144	69.65 142	87.33 118

OMC-MVS		65.97 192	65.06 182	68.71 242	72.97 238	42.58 268	78.61 229	75.35 266	54.72 221	59.31 162	86.25 115	33.30 222	77.88 284	57.99 145	67.05 157	85.66 146

MS-PatchMatch		72.34 84	71.26 84	75.61 106	82.38 106	55.55 35	88.00 38	89.95 11	65.38 56	56.51 202	80.74 186	32.28 233	92.89 24	57.95 146	88.10 9	78.39 258

MAR-MVS		76.76 35	75.60 40	80.21 18	90.87 3	54.68 64	89.14 31	89.11 14	62.95 92	70.54 57	92.33 16	41.05 138	94.95 9	57.90 147	86.55 17	91.00 47
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
anonymousdsp		60.46 245	57.65 246	68.88 233	63.63 312	45.09 242	72.93 273	78.63 215	46.52 277	51.12 241	72.80 264	21.46 300	83.07 238	57.79 148	53.97 267	78.47 252

Fast-Effi-MVS+-dtu		66.53 184	64.10 192	73.84 149	72.41 261	52.30 134	84.73 116	75.66 262	59.51 133	56.34 203	79.11 197	28.11 259	85.85 197	57.74 149	63.29 189	83.35 180

v1921920		67.45 166	65.23 180	74.10 142	71.51 273	52.90 121	83.75 137	80.44 186	62.48 97	59.12 165	77.13 222	36.98 185	87.90 142	57.53 150	58.14 239	81.49 211

IterMVS-LS		66.63 182	65.36 179	70.42 216	75.10 216	48.90 192	81.45 188	76.69 249	61.05 115	55.71 209	77.10 224	45.86 66	83.65 234	57.44 151	57.88 244	78.70 248
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.70 124	68.70 115	72.68 169	75.00 217	48.90 192	79.54 218	87.16 48	61.05 115	63.88 115	83.74 137	45.87 65	90.44 59	57.42 152	64.68 176	78.70 248

CDS-MVSNet		70.48 111	69.43 104	73.64 154	77.56 190	48.83 194	83.51 143	77.45 240	63.27 88	62.33 128	85.54 120	43.85 88	83.29 237	57.38 153	74.00 110	88.79 95
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
3Dnovator+	62.71 7	72.29 86	70.50 90	77.65 67	83.40 77	51.29 153	87.32 53	86.40 61	59.01 153	58.49 177	88.32 85	32.40 231	91.27 45	57.04 154	82.15 49	90.38 63

v18		64.36 203	61.80 205	72.05 178	72.97 238	53.31 99	81.16 191	77.76 235	59.14 147	48.50 259	68.97 284	42.91 111	84.38 220	56.62 155	48.17 285	78.47 252

PAPM_NR		71.80 92	69.98 100	77.26 79	81.54 126	53.34 96	78.60 230	85.25 81	53.46 229	60.53 148	88.66 81	45.69 68	89.24 87	56.49 156	79.62 72	89.19 86

v10		66.61 183	64.20 191	73.83 150	72.59 256	53.37 94	81.88 172	79.91 191	61.11 113	54.09 219	75.60 241	40.06 149	88.26 134	56.47 157	56.10 254	79.86 239

v1240		66.99 177	64.68 186	73.93 145	71.38 274	52.66 124	83.39 148	79.98 190	61.97 102	58.44 179	77.11 223	35.25 206	87.81 144	56.46 158	58.15 237	81.33 215

Fast-Effi-MVS+		72.73 78	71.15 87	77.48 71	82.75 98	54.76 60	86.77 65	80.64 183	63.05 91	65.93 86	84.01 132	44.42 80	89.03 95	56.45 159	76.36 97	88.64 98

v16		64.25 205	61.66 214	72.03 179	72.91 241	53.28 104	80.93 194	77.81 231	58.86 158	48.30 260	68.80 287	42.70 117	84.37 221	56.44 160	48.14 286	78.44 255

v17		64.19 206	61.58 215	72.03 179	72.89 244	53.28 104	80.91 195	77.80 232	58.87 157	48.22 261	68.77 288	42.69 118	84.37 221	56.43 161	47.66 289	78.43 256

114514_t		69.87 122	67.88 126	75.85 104	88.38 15	52.35 132	86.94 62	83.68 126	53.70 227	55.68 210	85.60 118	30.07 250	91.20 46	55.84 162	71.02 135	83.99 168

v15		63.83 209	61.13 220	71.93 187	72.60 254	53.21 107	80.44 201	78.22 220	58.80 160	47.57 266	68.22 290	42.50 119	84.18 223	55.82 163	46.02 301	78.39 258

tpm2		70.82 105	68.44 117	77.98 61	80.78 140	56.11 31	74.21 263	81.28 173	60.24 126	68.04 69	75.27 243	52.26 22	88.50 122	55.82 163	68.03 152	89.33 80

V14		63.72 211	60.99 222	71.91 189	72.58 257	53.18 108	80.24 205	78.19 221	58.53 167	47.35 272	68.10 291	42.28 122	84.18 223	55.68 165	45.97 302	78.36 261

V9		63.60 212	60.84 223	71.87 191	72.51 259	53.12 112	80.04 210	78.15 223	58.25 170	47.14 274	67.98 292	42.08 126	84.18 223	55.47 166	45.92 304	78.32 262

PCF-MVS	61.03 10	70.10 113	68.40 118	75.22 116	77.15 199	51.99 137	79.30 226	82.12 156	56.47 205	61.88 133	86.48 114	43.98 87	87.24 160	55.37 167	72.79 123	86.43 135
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
v12		63.47 214	60.68 226	71.85 192	72.45 260	53.08 114	79.83 212	78.13 225	57.95 176	46.89 276	67.87 294	41.81 132	84.17 226	55.30 168	45.87 305	78.29 264

v13		63.36 216	60.54 229	71.82 193	72.41 261	53.03 117	79.64 217	78.10 226	57.66 182	46.67 279	67.75 295	41.68 133	84.17 226	55.11 169	45.82 306	78.25 267

PVSNet	62.49 8	69.27 135	67.81 128	73.64 154	84.41 60	51.85 139	84.63 120	77.80 232	66.42 38	59.80 153	84.95 125	22.14 297	80.44 261	55.03 170	75.11 107	88.62 99

v11		63.44 215	60.66 227	71.79 194	72.61 253	53.02 118	79.80 213	78.08 227	58.30 168	47.27 273	67.91 293	40.67 143	84.14 228	54.93 171	46.39 299	78.23 268

CHOSEN 280x420		57.53 264	56.38 257	60.97 297	74.01 227	48.10 213	46.30 333	54.31 332	48.18 268	50.88 245	77.43 219	38.37 163	59.16 336	54.83 172	63.14 193	75.66 288

GG-mvs-BLEND				77.77 64	86.68 28	50.61 160	68.67 299	88.45 24		68.73 64	87.45 101	59.15 4	90.67 55	54.83 172	87.67 11	92.03 25

TAMVS		69.51 128	68.16 122	73.56 157	76.30 203	48.71 195	82.57 161	77.17 244	62.10 101	61.32 136	84.23 130	41.90 129	83.46 236	54.80 174	73.09 120	88.50 102

DWT-MVSNet_test		75.47 51	73.87 54	80.29 16	87.33 25	57.05 18	82.86 158	87.96 35	72.59 6	67.29 73	87.79 94	51.61 23	91.52 42	54.75 175	72.63 124	92.29 21

IterMVS		63.77 210	61.67 213	70.08 222	72.68 252	51.24 154	80.44 201	75.51 263	60.51 123	51.41 238	73.70 255	32.08 236	78.91 273	54.30 176	54.35 266	80.08 237
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
PatchFormer-LS_test		74.17 61	72.30 69	79.77 25	86.61 29	57.26 16	82.02 168	84.80 93	71.85 9	64.73 102	87.52 99	50.33 31	90.40 62	54.23 177	68.63 149	91.64 30

DP-MVS Recon		71.99 90	70.31 93	77.01 83	90.65 4	53.44 88	89.37 28	82.97 143	56.33 206	63.56 120	89.47 72	34.02 215	92.15 37	54.05 178	72.41 126	85.43 150

tpm		68.36 151	67.48 140	70.97 206	79.93 156	51.34 151	76.58 242	78.75 212	67.73 28	63.54 121	74.86 245	48.33 37	72.36 315	53.93 179	63.71 182	89.21 85

XVG-OURS-SEG-HR		62.02 236	59.54 235	69.46 229	65.30 304	45.88 236	65.06 304	73.57 282	46.45 278	57.42 195	83.35 150	26.95 266	78.09 280	53.77 180	64.03 179	84.42 161

cascas		69.01 139	66.13 160	77.66 66	79.36 159	55.41 41	86.99 59	83.75 125	56.69 196	58.92 168	81.35 177	24.31 281	92.10 38	53.23 181	70.61 137	85.46 149

UniMVSNet_NR-MVSNet		68.82 141	68.29 120	70.40 217	75.71 211	42.59 266	84.23 125	86.78 54	66.31 40	58.51 174	82.45 163	51.57 24	84.64 218	53.11 182	55.96 255	83.96 171

DU-MVS		66.84 180	65.74 167	70.16 220	73.27 235	42.59 266	81.50 185	82.92 144	63.53 86	58.51 174	82.11 171	40.75 139	84.64 218	53.11 182	55.96 255	83.24 184

1112_ss		70.05 115	69.37 106	72.10 177	80.77 141	42.78 264	85.12 99	76.75 247	59.69 131	61.19 137	92.12 19	47.48 44	83.84 231	53.04 184	68.21 150	89.66 75

XVG-OURS		61.88 237	59.34 237	69.49 228	65.37 303	46.27 232	64.80 306	73.49 283	47.04 273	57.41 196	82.85 155	25.15 278	78.18 278	53.00 185	64.98 170	84.01 167

UGNet		68.71 147	67.11 146	73.50 158	80.55 153	47.61 216	84.08 128	78.51 218	59.45 134	65.68 91	82.73 159	23.78 283	85.08 209	52.80 186	76.40 93	87.80 111
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
无先验							85.19 94	78.00 230	49.08 266				85.13 207	52.78 187		87.45 117

1121		68.79 144	66.77 150	74.82 128	83.08 86	53.46 86	80.23 206	71.53 294	45.47 284	66.31 83	87.19 103	34.02 215	85.13 207	52.78 187	80.36 62	85.87 143

PVSNet_0	57.04 13	61.19 241	57.24 249	73.02 162	77.45 192	50.31 170	79.43 220	77.36 242	63.96 76	47.51 269	72.45 267	25.03 279	83.78 233	52.76 189	19.22 341	84.96 155

FIs		70.00 119	70.24 97	69.30 230	77.93 186	38.55 289	83.99 133	87.72 42	66.86 37	57.66 188	84.17 131	52.28 21	85.31 204	52.72 190	68.80 146	84.02 166

Vis-MVSNet		70.61 109	69.34 107	74.42 135	80.95 138	48.49 203	86.03 76	77.51 239	58.74 161	65.55 92	87.78 95	34.37 212	85.95 195	52.53 191	80.61 58	88.80 94
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
testdata				67.08 254	77.59 189	45.46 240		69.20 306	44.47 289	71.50 52	88.34 84	31.21 243	70.76 321	52.20 192	75.88 99	85.03 153

v52		59.82 246	56.41 255	70.06 224	61.49 321	48.67 196	69.46 297	75.80 260	52.55 244	47.49 270	68.82 286	28.60 255	85.70 198	52.13 193	51.34 279	75.80 286

V4		59.82 246	56.41 255	70.05 225	61.49 321	48.67 196	69.46 297	75.79 261	52.55 244	47.49 270	68.83 285	28.60 255	85.70 198	52.13 193	51.35 278	75.80 286

API-MVS		74.17 61	72.07 74	80.49 14	90.02 6	58.55 7	87.30 55	84.27 104	57.51 184	65.77 90	87.77 96	41.61 135	95.97 2	51.71 195	82.63 44	86.94 122

ACMM	58.35 12	64.35 204	62.01 204	71.38 200	74.21 226	48.51 202	82.25 166	79.66 196	47.61 269	54.54 214	80.11 189	25.26 276	86.00 191	51.26 196	63.16 192	79.64 240
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
原ACMM1				76.13 97	84.89 56	54.59 67		85.26 80	51.98 249	66.70 77	87.07 106	40.15 147	89.70 80	51.23 197	85.06 32	84.10 164

UniMVSNet (Re)		67.71 160	66.80 148	70.45 215	74.44 222	42.93 262	82.42 164	84.90 90	63.69 82	59.63 156	80.99 183	47.18 47	85.23 206	51.17 198	56.75 249	83.19 186

semantic-postprocess				60.08 299	70.68 279	45.07 243		74.25 273	43.54 296	50.02 253	73.73 252	32.22 234	56.74 337	51.06 199	53.60 271	78.42 257

Test_1112_low_res		67.18 172	66.23 157	70.02 227	78.75 171	41.02 278	83.43 144	73.69 280	57.29 186	58.45 178	82.39 165	45.30 71	80.88 256	50.50 200	66.26 167	88.16 103

pmmvs4		63.34 217	61.07 221	70.16 220	70.14 280	50.53 163	79.97 211	71.41 296	55.08 219	54.12 218	78.58 200	32.79 228	82.09 249	50.33 201	57.22 248	77.86 270

Baseline_NR-MVSNet		65.49 195	64.27 190	69.13 231	74.37 225	41.65 273	83.39 148	78.85 209	59.56 132	59.62 157	76.88 225	40.75 139	87.44 156	49.99 202	55.05 261	78.28 265

BH-w/o		70.02 116	68.51 116	74.56 133	82.77 97	50.39 166	86.60 66	78.14 224	59.77 129	59.65 155	85.57 119	39.27 156	87.30 159	49.86 203	74.94 108	85.99 139

LCM-MVSNet-Re		58.82 255	56.54 253	65.68 269	79.31 162	29.09 327	61.39 317	45.79 338	60.73 120	37.65 310	72.47 266	31.42 242	81.08 254	49.66 204	70.41 138	86.87 123

gg-mvs-nofinetune		67.43 167	64.53 188	76.13 97	85.95 33	47.79 215	64.38 307	88.28 31	39.34 307	66.62 79	41.27 335	58.69 5	89.00 98	49.64 205	86.62 14	91.59 32

TranMVSNet+NR-MVSNet		66.94 178	65.61 171	70.93 208	73.45 232	43.38 258	83.02 155	84.25 105	65.31 59	58.33 180	81.90 173	39.92 152	85.52 200	49.43 206	54.89 263	83.89 173

WR-MVS		67.58 162	66.76 151	70.04 226	75.92 209	45.06 246	86.23 72	85.28 79	64.31 68	58.50 176	81.00 182	44.80 79	82.00 250	49.21 207	55.57 260	83.06 187

test_post1							70.84 288				14.72 351	34.33 213	83.86 230	48.80 208

pmmvs5		62.80 229	61.18 219	67.66 249	69.53 285	42.37 271	82.65 160	75.19 267	54.30 225	52.03 235	78.51 204	31.64 241	80.67 257	48.60 209	58.15 237	79.95 238

新几何1				73.30 160	83.10 83	53.48 85		71.43 295	45.55 282	66.14 84	87.17 104	33.88 219	80.54 259	48.50 210	80.33 63	85.88 142

pm-mvs1		64.12 207	62.56 200	68.78 241	71.68 270	38.87 288	82.89 157	81.57 165	55.54 216	53.89 220	77.82 213	37.73 170	86.74 172	48.46 211	53.49 272	80.72 225

tpmp4_e23		70.01 118	67.13 145	78.65 43	81.93 115	57.90 10	73.99 264	81.35 169	60.61 122	65.28 94	73.78 251	52.48 20	88.60 115	48.40 212	66.35 166	89.44 79

PM-MVS		46.92 302	43.76 304	56.41 310	52.18 335	32.26 317	63.21 311	38.18 344	37.99 312	40.78 302	66.20 301	5.09 345	65.42 330	48.19 213	41.99 313	71.54 314

FC-MVSNet-test		67.49 165	67.91 124	66.21 262	76.06 205	33.06 313	80.82 197	87.18 47	64.44 67	54.81 212	82.87 154	50.40 30	82.60 246	48.05 214	66.55 160	82.98 189

CMPMVS	40.41 21	55.34 275	52.64 276	63.46 283	60.88 324	43.84 254	61.58 316	71.06 297	30.43 332	36.33 312	74.63 247	24.14 282	75.44 296	48.05 214	66.62 159	71.12 316
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
NR-MVSNet		67.25 170	65.99 163	71.04 205	73.27 235	43.91 253	85.32 92	84.75 95	66.05 47	53.65 223	82.11 171	45.05 72	85.97 194	47.55 216	56.18 253	83.24 184

QAPM		71.88 91	69.33 108	79.52 26	82.20 108	54.30 70	86.30 71	88.77 17	56.61 198	59.72 154	87.48 100	33.90 218	95.36 4	47.48 217	81.49 52	88.90 92

EPMVS		68.45 150	65.44 176	77.47 72	84.91 55	56.17 30	71.89 284	81.91 161	61.72 107	60.85 144	72.49 265	36.21 194	87.06 164	47.32 218	71.62 131	89.17 87

GBi-Net		67.09 174	65.47 174	71.96 184	82.71 99	46.36 229	83.52 139	83.31 133	58.55 164	57.58 189	76.23 233	36.72 190	86.20 183	47.25 219	63.40 184	83.32 181

test1		67.09 174	65.47 174	71.96 184	82.71 99	46.36 229	83.52 139	83.31 133	58.55 164	57.58 189	76.23 233	36.72 190	86.20 183	47.25 219	63.40 184	83.32 181

FMVSNet3		68.84 140	67.40 141	73.19 161	85.05 53	48.53 201	85.71 83	85.36 76	60.90 117	57.58 189	79.15 196	42.16 123	86.77 171	47.25 219	63.40 184	84.27 163

v7n		62.50 232	59.27 238	72.20 176	67.25 298	49.83 176	77.87 235	80.12 188	52.50 246	48.80 258	73.07 260	32.10 235	87.90 142	46.83 222	54.92 262	78.86 247

v748		61.35 240	58.24 244	70.69 211	66.28 299	47.35 219	76.58 242	79.17 207	53.09 233	46.37 283	71.50 273	33.18 224	86.33 182	46.78 223	51.19 280	78.39 258

CVMVSNet		60.85 243	60.44 230	62.07 288	75.00 217	32.73 315	79.54 218	73.49 283	36.98 315	56.28 204	83.74 137	29.28 254	69.53 324	46.48 224	63.23 190	83.94 172

TR-MVS		69.71 123	67.85 127	75.27 114	82.94 92	48.48 204	87.40 52	80.86 180	57.15 188	64.61 104	87.08 105	32.67 229	89.64 83	46.38 225	71.55 132	87.68 114

MDTV_nov1_ep13_2view						43.62 256	71.13 287		54.95 220	59.29 163		36.76 189		46.33 226		87.32 119

FMVSNet2		67.57 163	65.79 165	72.90 164	82.71 99	47.97 214	85.15 95	84.93 89	58.55 164	56.71 199	78.26 206	36.72 190	86.67 174	46.15 227	62.94 197	84.07 165

UnsupCasMVSNet_eth		57.56 263	55.15 263	64.79 278	64.57 309	33.12 312	73.17 270	83.87 124	58.98 155	41.75 298	70.03 280	22.54 292	79.92 268	46.12 228	35.31 325	81.32 217

testdata2													77.81 286	45.64 229

XVG-ACMP-BASELINE		56.03 272	52.85 274	65.58 270	61.91 317	40.95 279	63.36 308	72.43 286	45.20 285	46.02 284	74.09 248	9.20 336	78.12 279	45.13 230	58.27 235	77.66 273

AdaColmap		67.86 157	65.48 173	75.00 119	88.15 19	54.99 54	86.10 74	76.63 251	49.30 265	57.80 184	86.65 111	29.39 253	88.94 106	45.10 231	70.21 139	81.06 220

BH-untuned		68.28 153	66.40 154	73.91 146	81.62 121	50.01 175	85.56 87	77.39 241	57.63 183	57.47 194	83.69 139	36.36 193	87.08 163	44.81 232	73.08 121	84.65 158

BH-RMVSNet		70.08 114	68.01 123	76.27 93	84.21 65	51.22 155	87.29 56	79.33 205	58.96 156	63.63 119	86.77 108	33.29 223	90.30 67	44.63 233	73.96 111	87.30 120

IS-MVSNet		68.80 143	67.55 138	72.54 172	78.50 179	43.43 257	81.03 193	79.35 204	59.12 149	57.27 197	86.71 109	46.05 63	87.70 151	44.32 234	75.60 102	86.49 132

pmmvs-eth3d		55.97 273	52.78 275	65.54 271	61.02 323	46.44 228	75.36 257	67.72 308	49.61 264	43.65 290	67.58 297	21.63 299	77.04 289	44.11 235	44.33 310	73.15 309

pmmvs6		59.64 249	57.15 250	67.09 253	66.01 300	36.86 297	80.50 200	78.64 214	45.05 286	49.05 257	73.94 250	27.28 264	86.10 189	43.96 236	49.94 283	78.31 263

EPNet_dtu		66.25 189	66.71 152	64.87 277	78.66 175	34.12 308	82.80 159	75.51 263	61.75 106	64.47 109	86.90 107	37.06 184	72.46 314	43.65 237	69.63 143	88.02 108
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm cat1		66.28 188	62.78 199	76.77 89	81.40 131	57.14 17	70.03 293	77.19 243	53.00 234	58.76 173	70.73 278	46.17 62	86.73 173	43.27 238	64.46 177	86.44 134

OpenMVS	61.00 11	69.99 120	67.55 138	77.30 75	78.37 182	54.07 77	84.36 122	85.76 68	57.22 187	56.71 199	87.67 97	30.79 245	92.83 25	43.04 239	84.06 40	85.01 154

PatchmatchNet		67.07 176	63.63 193	77.40 73	83.10 83	58.03 8	72.11 281	77.77 234	58.85 159	59.37 161	70.83 276	37.84 166	84.93 214	42.96 240	69.83 141	89.26 82
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CR-MVSNet		62.47 233	59.04 241	72.77 167	73.97 229	56.57 23	60.52 318	71.72 290	60.04 127	57.49 192	65.86 303	38.94 157	80.31 262	42.86 241	59.93 216	81.42 212

FMVSNet1		64.57 200	62.11 203	71.96 184	77.32 193	46.36 229	83.52 139	83.31 133	52.43 247	54.42 215	76.23 233	27.80 261	86.20 183	42.59 242	61.34 207	83.32 181

UA-Net		67.32 169	66.23 157	70.59 213	78.85 169	41.23 277	73.60 265	75.45 265	61.54 109	66.61 80	84.53 127	38.73 160	86.57 180	42.48 243	74.24 109	83.98 169

testpf		45.92 304	45.81 298	46.27 323	69.56 284	27.86 330	23.18 346	73.91 278	44.10 293	36.99 311	57.16 327	20.56 303	71.77 316	42.17 244	44.64 309	39.18 342

MIMVSNet		63.12 220	60.29 231	71.61 195	75.92 209	46.65 225	65.15 303	81.94 158	59.14 147	54.65 213	69.47 282	25.74 273	80.63 258	41.03 245	69.56 144	87.55 115

Patchmatch-test1		63.23 218	59.16 239	75.43 110	78.58 177	57.92 9	61.61 315	77.53 238	56.71 195	57.75 187	70.98 275	31.97 237	78.19 277	40.97 246	56.36 250	90.18 68

EG-PatchMatch MVS		62.40 235	59.59 234	70.81 209	73.29 234	49.05 188	85.81 77	84.78 94	51.85 252	44.19 286	73.48 258	15.52 325	89.85 75	40.16 247	67.24 156	73.54 304

UnsupCasMVSNet_bld		53.86 283	50.53 283	63.84 280	63.52 313	34.75 306	71.38 285	81.92 160	46.53 276	38.95 308	57.93 324	20.55 304	80.20 266	39.91 248	34.09 332	76.57 281

dp		64.41 202	61.58 215	72.90 164	82.40 105	54.09 75	72.53 275	76.59 252	60.39 124	55.68 210	70.39 279	35.18 207	76.90 292	39.34 249	61.71 206	87.73 113

TransMVSNet (Re)		62.82 228	60.76 225	69.02 232	73.98 228	41.61 274	86.36 69	79.30 206	56.90 189	52.53 231	76.44 229	41.85 130	87.60 153	38.83 250	40.61 316	77.86 270

USDC		54.36 279	51.23 281	63.76 281	64.29 310	37.71 294	62.84 313	73.48 285	56.85 190	35.47 316	71.94 272	9.23 335	78.43 275	38.43 251	48.57 284	75.13 292

PLC	52.38 18	60.89 242	58.97 242	66.68 260	81.77 119	45.70 238	78.96 228	74.04 276	43.66 295	47.63 265	83.19 153	23.52 288	77.78 287	37.47 252	60.46 211	76.55 282
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
test0.0.03 1		62.54 231	62.44 201	62.86 287	72.28 266	29.51 324	82.93 156	78.78 211	59.18 145	53.07 229	82.41 164	36.91 187	77.39 288	37.45 253	58.96 226	81.66 209

OurMVSNet-221017-0		52.39 289	48.73 289	63.35 284	65.21 305	38.42 290	68.54 300	64.95 315	38.19 310	39.57 305	71.43 274	13.23 328	79.92 268	37.16 254	40.32 317	71.72 312

CNLPA		60.59 244	58.44 243	67.05 255	79.21 163	47.26 221	79.75 216	64.34 318	42.46 302	51.90 237	83.94 133	27.79 262	75.41 297	37.12 255	59.49 223	78.47 252

K. test v3		54.04 281	49.42 288	67.92 248	68.55 289	42.57 269	75.51 255	63.07 321	52.07 248	39.21 306	64.59 307	19.34 308	82.21 247	37.11 256	25.31 337	78.97 245

Vis-MVSNet (Re-imp)		65.52 194	65.63 170	65.17 275	77.49 191	30.54 321	75.49 256	77.73 236	59.34 139	52.26 234	86.69 110	49.38 35	80.53 260	37.07 257	75.28 104	84.42 161

PatchMatch-RL		56.66 266	53.75 268	65.37 274	77.91 187	45.28 241	69.78 295	60.38 325	41.35 303	47.57 266	73.73 252	16.83 319	76.91 291	36.99 258	59.21 225	73.92 301

Patchmtry		56.56 268	52.95 273	67.42 251	72.53 258	50.59 162	59.05 320	71.72 290	37.86 313	46.92 275	65.86 303	38.94 157	80.06 267	36.94 259	46.72 298	71.60 313

FMVSNet5		58.61 257	56.45 254	65.10 276	77.20 198	39.74 285	74.77 259	77.12 245	50.27 261	43.28 293	67.71 296	26.15 271	76.90 292	36.78 260	54.78 264	78.65 250

MDTV_nov1_ep13			61.56 217		81.68 120	55.12 50	72.41 277	78.18 222	59.19 143	58.85 171	69.29 283	34.69 210	86.16 186	36.76 261	62.96 196

JIA-IIPM		52.33 290	47.77 293	66.03 267	71.20 275	46.92 223	40.00 341	76.48 253	37.10 314	46.73 277	37.02 337	32.96 225	77.88 284	35.97 262	52.45 275	73.29 307

lessismore_v0				67.98 247	64.76 308	41.25 276		45.75 339		36.03 314	65.63 305	19.29 309	84.11 229	35.67 263	21.24 340	78.59 251

CP-MVSNet		58.54 259	57.57 248	61.46 295	68.50 290	33.96 309	76.90 240	78.60 217	51.67 253	47.83 263	76.60 228	34.99 209	72.79 312	35.45 264	47.58 290	77.64 274

ambc				62.06 289	53.98 333	29.38 325	35.08 343	79.65 197		41.37 299	59.96 317	6.27 343	82.15 248	35.34 265	38.22 322	74.65 294

PS-CasMVS		58.12 262	57.03 252	61.37 296	68.24 293	33.80 311	76.73 241	78.01 229	51.20 256	47.54 268	76.20 236	32.85 226	72.76 313	35.17 266	47.37 292	77.55 275

EU-MVSNet		52.63 288	50.72 282	58.37 305	62.69 316	28.13 329	72.60 274	75.97 258	30.94 331	40.76 303	72.11 271	20.16 305	70.80 320	35.11 267	46.11 300	76.19 285

ACMH+	54.58 15	58.55 258	55.24 262	68.50 245	74.68 221	45.80 237	80.27 203	70.21 303	47.15 272	42.77 295	75.48 242	16.73 321	85.98 192	35.10 268	54.78 264	73.72 302

pmmvs3		45.53 306	41.55 308	57.44 308	48.97 338	39.68 286	70.06 292	57.66 328	28.32 334	34.06 323	57.29 325	8.50 337	66.85 329	34.86 269	34.26 330	65.80 325

PEN-MVS		58.35 261	57.15 250	61.94 290	67.55 297	34.39 307	77.01 238	78.35 219	51.87 251	47.72 264	76.73 227	33.91 217	73.75 306	34.03 270	47.17 294	77.68 272

tpmvs		62.45 234	59.42 236	71.53 199	83.93 69	54.32 69	70.03 293	77.61 237	51.91 250	53.48 224	68.29 289	37.91 165	86.66 175	33.36 271	58.27 235	73.62 303

YYNet1		53.82 284	49.96 285	65.41 273	70.09 282	48.95 190	72.30 278	71.66 292	44.25 291	31.89 329	63.07 311	23.73 284	73.95 304	33.26 272	39.40 318	73.34 306

MDA-MVSNet_test_wron		53.82 284	49.95 286	65.43 272	70.13 281	49.05 188	72.30 278	71.65 293	44.23 292	31.85 330	63.13 310	23.68 287	74.01 303	33.25 273	39.35 319	73.23 308

Anonymous20231206		59.08 253	57.59 247	63.55 282	68.77 288	32.14 318	80.26 204	79.78 193	50.00 262	49.39 255	72.39 268	26.64 268	78.36 276	33.12 274	57.94 241	80.14 236

F-COLMAP		55.96 274	53.65 270	62.87 286	72.76 247	42.77 265	74.70 261	70.37 301	40.03 305	41.11 301	79.36 192	17.77 315	73.70 307	32.80 275	53.96 268	72.15 310

PatchT		56.60 267	52.97 272	67.48 250	72.94 240	46.16 235	57.30 324	73.78 279	38.77 309	54.37 216	57.26 326	37.52 175	78.06 281	32.02 276	52.79 273	78.23 268

SixPastTwentyTwo		54.37 278	50.10 284	67.21 252	70.70 277	41.46 275	74.73 260	64.69 316	47.56 270	39.12 307	69.49 281	18.49 313	84.69 217	31.87 277	34.20 331	75.48 289

WR-MVS_H		58.91 254	58.04 245	61.54 293	69.07 286	33.83 310	76.91 239	81.99 157	51.40 254	48.17 262	74.67 246	40.23 145	74.15 301	31.78 278	48.10 287	76.64 280

ACMH	53.70 16	59.78 248	55.94 260	71.28 201	76.59 202	48.35 207	80.15 209	76.11 255	49.74 263	41.91 297	73.45 259	16.50 322	90.31 64	31.42 279	57.63 246	75.17 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MSDG		59.44 250	55.14 264	72.32 175	74.69 220	50.71 158	74.39 262	73.58 281	44.44 290	43.40 292	77.52 216	19.45 307	90.87 53	31.31 280	57.49 247	75.38 290

thres200		68.71 147	67.27 143	73.02 162	84.73 57	46.76 224	85.03 110	87.73 41	62.34 98	59.87 151	83.45 148	43.15 104	88.32 130	31.25 281	67.91 154	83.98 169

DTE-MVSNet		57.03 265	55.73 261	60.95 298	65.94 301	32.57 316	75.71 251	77.09 246	51.16 257	46.65 280	76.34 231	32.84 227	73.22 310	30.94 282	44.87 308	77.06 277

MVS-HIRNet		49.01 294	44.71 299	61.92 291	76.06 205	46.61 226	63.23 310	54.90 331	24.77 336	33.56 325	36.60 338	21.28 301	75.88 295	29.49 283	62.54 202	63.26 333

test20.03		55.22 276	54.07 266	58.68 303	63.14 314	25.00 333	77.69 236	74.78 269	52.64 236	43.43 291	72.39 268	26.21 270	74.76 300	29.31 284	47.05 296	76.28 284

testgi		54.25 280	52.57 277	59.29 301	62.76 315	21.65 339	72.21 280	70.47 299	53.25 232	41.94 296	77.33 220	14.28 326	77.95 283	29.18 285	51.72 277	78.28 265

conf200view11		66.80 181	65.42 177	70.95 207	83.29 78	43.15 259	81.67 177	87.78 36	59.04 150	55.92 206	82.18 168	43.73 92	87.80 145	28.80 286	66.36 162	81.89 202

thres100view900		66.87 179	65.42 177	71.24 202	83.29 78	43.15 259	81.67 177	87.78 36	59.04 150	55.92 206	82.18 168	43.73 92	87.80 145	28.80 286	66.36 162	82.78 192

tfpn200view9		67.57 163	66.13 160	71.89 190	84.05 67	45.07 243	83.40 146	87.71 43	60.79 118	57.79 185	82.76 156	43.53 99	87.80 145	28.80 286	66.36 162	82.78 192

thres400		67.40 168	66.13 160	71.19 203	84.05 67	45.07 243	83.40 146	87.71 43	60.79 118	57.79 185	82.76 156	43.53 99	87.80 145	28.80 286	66.36 162	80.71 226

test2356		53.94 282	52.37 278	58.64 304	61.58 319	27.53 332	78.20 233	74.33 272	46.92 274	44.01 287	66.04 302	18.91 310	74.11 302	28.80 286	52.55 274	74.28 297

ADS-MVSNet2		55.21 277	51.44 280	66.51 261	80.60 145	49.56 181	55.03 326	65.44 314	44.72 287	51.00 242	61.19 314	22.83 289	75.41 297	28.54 291	53.63 269	74.57 295

ADS-MVSNet		56.17 271	51.95 279	68.84 234	80.60 145	53.07 115	55.03 326	70.02 304	44.72 287	51.00 242	61.19 314	22.83 289	78.88 274	28.54 291	53.63 269	74.57 295

LTVRE_ROB	45.45 19	52.73 287	49.74 287	61.69 292	69.78 283	34.99 305	44.52 335	67.60 309	43.11 299	43.79 289	74.03 249	18.54 312	81.45 252	28.39 293	57.94 241	68.62 320
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
new-patchmatchnet		48.21 296	46.55 295	53.18 315	57.73 328	18.19 348	70.24 291	71.02 298	45.70 281	33.70 324	60.23 316	18.00 314	69.86 323	27.97 294	34.35 329	71.49 315

OpenMVS_ROB	53.19 17	59.20 252	56.00 259	68.83 235	71.13 276	44.30 250	83.64 138	75.02 268	46.42 279	46.48 282	73.03 261	18.69 311	88.14 135	27.74 295	61.80 205	74.05 300

RPSCF		45.77 305	44.13 302	50.68 318	57.67 329	29.66 323	54.92 328	45.25 340	26.69 335	45.92 285	75.92 240	17.43 318	45.70 346	27.44 296	45.95 303	76.67 279

MDA-MVSNet-bldmvs		51.56 291	47.75 294	63.00 285	71.60 272	47.32 220	69.70 296	72.12 288	43.81 294	27.65 335	63.38 309	21.97 298	75.96 294	27.30 297	32.19 333	65.70 326

RPMNet		58.49 260	53.74 269	72.77 167	73.97 229	56.57 23	60.52 318	72.39 287	35.72 320	57.49 192	58.87 323	37.73 170	80.31 262	27.01 298	59.93 216	81.42 212

thres600view7		66.46 185	65.12 181	70.47 214	83.41 74	43.80 255	82.15 167	87.78 36	59.37 138	56.02 205	82.21 167	43.73 92	86.90 168	26.51 299	64.94 171	80.71 226

TAPA-MVS	56.12 14	61.82 238	60.18 232	66.71 258	78.48 180	37.97 293	75.19 258	76.41 254	46.82 275	57.04 198	86.52 113	27.67 263	77.03 290	26.50 300	67.02 158	85.14 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
tfpn111		66.40 187	64.99 183	70.63 212	83.29 78	43.15 259	81.67 177	87.78 36	59.04 150	55.92 206	82.18 168	43.73 92	86.83 170	26.34 301	64.92 172	81.89 202

ITE_SJBPF				51.84 317	58.03 326	31.94 319		53.57 335	36.67 318	41.32 300	75.23 244	11.17 331	51.57 341	25.81 302	48.04 288	72.02 311

Patchmatch-test		53.33 286	48.17 290	68.81 240	73.31 233	42.38 270	42.98 337	58.23 327	32.53 328	38.79 309	70.77 277	39.66 153	73.51 308	25.18 303	52.06 276	90.55 54

testus		48.97 295	46.53 296	56.31 311	57.39 330	24.08 335	73.40 268	70.45 300	43.37 297	35.52 315	63.95 308	4.77 347	71.36 318	24.88 304	45.02 307	73.50 305

TinyColmap		48.15 297	44.49 301	59.13 302	65.73 302	38.04 292	63.34 309	62.86 322	38.78 308	29.48 333	67.23 300	6.46 342	73.30 309	24.59 305	41.90 314	66.04 324

tfpn_ndepth		64.50 201	63.34 198	67.99 246	81.84 118	38.30 291	79.26 227	83.57 131	53.69 228	52.86 230	84.51 128	46.96 50	84.79 215	24.28 306	63.09 195	80.87 223

AllTest		47.32 299	44.66 300	55.32 312	65.08 306	37.50 295	62.96 312	54.25 333	35.45 323	33.42 326	72.82 262	9.98 332	59.33 334	24.13 307	43.84 311	69.13 318

TestCases				55.32 312	65.08 306	37.50 295		54.25 333	35.45 323	33.42 326	72.82 262	9.98 332	59.33 334	24.13 307	43.84 311	69.13 318

N_pmnet		41.25 307	39.77 309	45.66 325	68.50 290	0.82 357	72.51 276	0.38 358	35.61 321	35.26 317	61.51 313	20.07 306	67.74 327	23.51 309	40.63 315	68.42 321

no-one		37.21 313	31.48 317	54.40 314	39.62 346	31.91 320	45.68 334	67.42 310	35.54 322	14.59 342	35.91 340	7.35 338	73.20 311	22.98 310	14.23 342	58.09 336

view600		64.79 196	63.45 194	68.82 236	82.13 109	40.75 280	79.41 222	88.29 27	56.54 200	53.26 225	81.30 178	44.26 82	85.01 210	22.97 311	62.85 198	80.71 226

view800		64.79 196	63.45 194	68.82 236	82.13 109	40.75 280	79.41 222	88.29 27	56.54 200	53.26 225	81.30 178	44.26 82	85.01 210	22.97 311	62.85 198	80.71 226

conf0.05thres1000		64.79 196	63.45 194	68.82 236	82.13 109	40.75 280	79.41 222	88.29 27	56.54 200	53.26 225	81.30 178	44.26 82	85.01 210	22.97 311	62.85 198	80.71 226

tfpn		64.79 196	63.45 194	68.82 236	82.13 109	40.75 280	79.41 222	88.29 27	56.54 200	53.26 225	81.30 178	44.26 82	85.01 210	22.97 311	62.85 198	80.71 226

test1235678		47.09 300	43.82 303	56.91 309	53.18 334	24.90 334	71.93 282	70.31 302	39.54 306	31.44 331	56.59 328	9.50 334	71.55 317	22.63 315	39.24 320	74.28 297

conf0.01		63.04 221	61.74 206	66.95 256	80.60 145	35.92 299	76.01 245	84.09 109	52.62 237	50.87 246	83.60 141	46.49 55	83.04 239	22.59 316	58.77 228	81.89 202

conf0.002		63.04 221	61.74 206	66.95 256	80.60 145	35.92 299	76.01 245	84.09 109	52.62 237	50.87 246	83.60 141	46.49 55	83.04 239	22.59 316	58.77 228	81.89 202

thresconf0.02		62.84 224	61.74 206	66.14 263	80.60 145	35.92 299	76.01 245	84.09 109	52.62 237	50.87 246	83.60 141	46.49 55	83.04 239	22.59 316	58.77 228	79.44 241

tfpn_n400		62.84 224	61.74 206	66.14 263	80.60 145	35.92 299	76.01 245	84.09 109	52.62 237	50.87 246	83.60 141	46.49 55	83.04 239	22.59 316	58.77 228	79.44 241

tfpnconf		62.84 224	61.74 206	66.14 263	80.60 145	35.92 299	76.01 245	84.09 109	52.62 237	50.87 246	83.60 141	46.49 55	83.04 239	22.59 316	58.77 228	79.44 241

tfpnview11		62.84 224	61.74 206	66.14 263	80.60 145	35.92 299	76.01 245	84.09 109	52.62 237	50.87 246	83.60 141	46.49 55	83.04 239	22.59 316	58.77 228	79.44 241

DP-MVS		59.24 251	56.12 258	68.63 243	88.24 18	50.35 167	82.51 163	64.43 317	41.10 304	46.70 278	78.77 199	24.75 280	88.57 119	22.26 322	56.29 252	66.96 323

tfpn1000		62.79 230	61.74 206	65.95 268	80.50 154	35.93 298	76.53 244	83.99 120	51.24 255	49.82 254	83.44 149	47.32 45	83.02 245	21.84 323	60.99 208	78.89 246

MIMVSNet1		50.35 293	47.81 292	57.96 306	61.53 320	27.80 331	67.40 301	74.06 275	43.25 298	33.31 328	65.38 306	16.03 323	71.34 319	21.80 324	47.55 291	74.75 293

tfpnnormal		61.47 239	59.09 240	68.62 244	76.29 204	41.69 272	81.14 192	85.16 84	54.48 224	51.32 239	73.63 256	32.32 232	86.89 169	21.78 325	55.71 259	77.29 276

LF4IMVS		33.04 317	32.55 315	34.52 332	40.96 343	22.03 338	44.45 336	35.62 347	20.42 338	28.12 334	62.35 312	5.03 346	31.88 352	21.61 326	34.42 328	49.63 339

1111		48.00 298	46.30 297	53.08 316	55.68 331	20.86 342	70.41 289	76.03 256	36.88 316	34.86 318	59.55 320	23.72 285	68.13 325	20.82 327	38.76 321	70.25 317

.test1245		38.91 310	41.99 307	29.67 335	55.68 331	20.86 342	70.41 289	76.03 256	36.88 316	34.86 318	59.55 320	23.72 285	68.13 325	20.82 327	0.00 353	0.02 353

COLMAP_ROB	43.60 20	50.90 292	48.05 291	59.47 300	67.81 295	40.57 284	71.25 286	62.72 323	36.49 319	36.19 313	73.51 257	13.48 327	73.92 305	20.71 329	50.26 282	63.92 330
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet		28.07 318	23.85 322	40.71 326	27.46 352	18.93 347	30.82 344	46.19 337	12.76 345	16.40 340	34.70 342	1.90 354	48.69 344	20.25 330	24.22 338	54.51 337

DSMNet-mixed		38.35 311	35.36 312	47.33 322	48.11 339	14.91 350	37.87 342	36.60 346	19.18 340	34.37 320	59.56 319	15.53 324	53.01 340	20.14 331	46.89 297	74.07 299

new_pmnet		33.56 316	31.89 316	38.59 328	49.01 337	20.42 344	51.01 329	37.92 345	20.58 337	23.45 336	46.79 334	6.66 341	49.28 343	20.00 332	31.57 335	46.09 341

Anonymous20231211		46.87 303	43.27 305	57.67 307	57.88 327	30.12 322	73.14 271	64.16 319	33.43 327	34.34 321	59.42 322	12.15 329	77.99 282	19.64 333	35.23 327	64.90 328

LS3D		56.40 270	53.82 267	64.12 279	81.12 135	45.69 239	73.42 267	66.14 312	35.30 325	43.24 294	79.88 190	22.18 296	79.62 271	19.10 334	64.00 180	67.05 322

testmv		39.64 309	36.01 311	50.55 319	42.18 342	21.56 340	64.81 305	66.88 311	32.22 329	22.25 339	47.47 333	4.33 349	64.81 331	17.71 335	26.22 336	65.29 327

TDRefinement		40.91 308	38.37 310	48.55 321	50.45 336	33.03 314	58.98 321	50.97 336	28.50 333	29.89 332	67.39 298	6.21 344	54.51 338	17.67 336	35.25 326	58.11 335

LP		47.05 301	42.23 306	61.53 294	72.04 267	49.37 185	49.48 330	65.50 313	34.57 326	34.29 322	52.30 331	17.73 316	75.32 299	17.56 337	36.57 323	59.91 334

test12356		37.84 312	35.07 313	46.18 324	45.03 341	8.02 355	57.70 323	62.67 324	31.83 330	22.78 337	50.25 332	4.46 348	66.95 328	17.25 338	23.62 339	63.57 331

test_0402		56.45 269	53.03 271	66.69 259	76.78 201	50.31 170	81.76 174	69.61 305	42.79 300	43.88 288	72.13 270	22.82 291	86.46 181	16.57 339	50.94 281	63.31 332

PMMVS2		26.71 321	22.98 324	37.87 329	36.89 347	8.51 354	42.51 338	29.32 352	19.09 341	13.01 343	37.54 336	2.23 352	53.11 339	14.54 340	11.71 343	51.99 338

ANet_high		34.39 315	29.59 318	48.78 320	30.34 350	22.28 337	55.53 325	63.79 320	38.11 311	15.47 341	36.56 339	6.94 339	59.98 333	13.93 341	5.64 352	64.08 329

tmp_tt		9.44 329	10.68 330	5.73 342	2.49 356	4.21 356	10.48 350	18.04 354	0.34 352	12.59 344	20.49 346	11.39 330	7.03 355	13.84 342	6.46 351	5.95 351

PNet_i23d		25.11 322	23.09 323	31.17 334	40.18 344	21.30 341	57.99 322	33.28 349	13.77 344	9.94 347	30.29 344	0.45 358	43.74 348	13.61 343	8.28 345	28.46 345

wuykxyi23d		19.94 324	14.87 328	35.13 331	22.47 354	19.80 345	25.80 345	38.64 343	7.61 349	4.88 352	13.58 352	0.23 359	48.42 345	13.11 344	7.53 347	37.18 343

FPMVS		35.40 314	33.67 314	40.57 327	46.34 340	28.74 328	41.05 339	57.05 329	20.37 339	22.27 338	53.38 330	6.87 340	44.94 347	8.62 345	47.11 295	48.01 340

Gipuma		27.47 320	24.26 321	37.12 330	60.55 325	29.17 326	11.68 349	60.00 326	14.18 343	10.52 346	15.12 349	2.20 353	63.01 332	8.39 346	35.65 324	19.18 347
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	16.60 23	17.34 328	13.39 329	29.16 336	28.43 351	19.72 346	13.73 348	23.63 353	7.23 350	7.96 348	21.41 345	0.80 357	36.08 351	6.97 347	10.39 344	31.69 344
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				13.10 340	21.34 355	8.99 353		10.02 356	10.59 347	7.53 349	30.55 343	1.82 355	14.55 353	6.83 348	7.52 348	15.75 348

E-PMN		19.16 325	18.40 326	21.44 338	36.19 348	13.63 351	47.59 331	30.89 350	10.73 346	5.91 350	16.59 347	3.66 351	39.77 349	5.95 349	8.14 346	10.92 349

PMVS	19.57 22	25.07 323	22.43 325	32.99 333	23.12 353	22.98 336	40.98 340	35.19 348	15.99 342	11.95 345	35.87 341	1.47 356	49.29 342	5.41 350	31.90 334	26.70 346
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
EMVS		18.42 326	17.66 327	20.71 339	34.13 349	12.64 352	46.94 332	29.94 351	10.46 348	5.58 351	14.93 350	4.23 350	38.83 350	5.24 351	7.51 349	10.67 350

wuyk23d		9.11 330	8.77 332	10.15 341	40.18 344	16.76 349	20.28 347	1.01 357	2.58 351	2.66 353	0.98 354	0.23 359	12.49 354	4.08 352	6.90 350	1.19 352

testmvs		6.14 332	8.18 333	0.01 343	0.01 357	0.00 359	73.40 268	0.00 359	0.00 353	0.02 354	0.15 355	0.00 361	0.00 356	0.02 353	0.00 353	0.02 353

test123		6.01 333	8.01 334	0.01 343	0.00 358	0.01 358	71.93 282	0.00 359	0.00 353	0.02 354	0.11 356	0.00 361	0.00 356	0.02 353	0.00 353	0.02 353

cdsmvs_eth3d_5k		18.33 327	24.44 320	0.00 345	0.00 358	0.00 359	0.00 351	89.40 12	0.00 353	0.00 356	92.02 22	38.55 161	0.00 356	0.00 355	0.00 353	0.00 356

pcd_1.5k_mvsjas		3.15 334	4.20 335	0.00 345	0.00 358	0.00 359	0.00 351	0.00 359	0.00 353	0.00 356	0.00 357	37.77 167	0.00 356	0.00 355	0.00 353	0.00 356

pcd1.5k->3k		27.74 319	27.68 319	27.93 337	73.75 231	0.00 359	0.00 351	85.50 70	0.00 353	0.00 356	0.00 357	26.52 269	0.00 356	0.00 355	63.37 188	83.79 175

sosnet-low-res		0.00 335	0.00 336	0.00 345	0.00 358	0.00 359	0.00 351	0.00 359	0.00 353	0.00 356	0.00 357	0.00 361	0.00 356	0.00 355	0.00 353	0.00 356

sosnet		0.00 335	0.00 336	0.00 345	0.00 358	0.00 359	0.00 351	0.00 359	0.00 353	0.00 356	0.00 357	0.00 361	0.00 356	0.00 355	0.00 353	0.00 356

uncertanet		0.00 335	0.00 336	0.00 345	0.00 358	0.00 359	0.00 351	0.00 359	0.00 353	0.00 356	0.00 357	0.00 361	0.00 356	0.00 355	0.00 353	0.00 356

Regformer		0.00 335	0.00 336	0.00 345	0.00 358	0.00 359	0.00 351	0.00 359	0.00 353	0.00 356	0.00 357	0.00 361	0.00 356	0.00 355	0.00 353	0.00 356

ab-mvs-re		7.68 331	10.24 331	0.00 345	0.00 358	0.00 359	0.00 351	0.00 359	0.00 353	0.00 356	92.12 19	0.00 361	0.00 356	0.00 355	0.00 353	0.00 356

uanet		0.00 335	0.00 336	0.00 345	0.00 358	0.00 359	0.00 351	0.00 359	0.00 353	0.00 356	0.00 357	0.00 361	0.00 356	0.00 355	0.00 353	0.00 356

GSMVS																88.13 106

test_part2					89.33 9	55.48 37				82.27 2

test_part1								88.42 25				58.18 6			86.59 15	91.53 35

sam_mvs1												38.86 159				88.13 106

sam_mvs												35.99 202

MTGPA								81.31 170

test_post											16.22 348	37.52 175	84.72 216

patchmatchnet-post											59.74 318	38.41 162	79.91 270

MTMP								15.34 355

TEST9					85.68 37	55.42 39	87.59 47	84.00 118	57.72 181	72.99 35	90.98 40	44.87 76	88.58 116

test_8					85.72 36	55.31 43	87.60 44	83.88 123	57.84 178	72.84 38	90.99 39	44.99 73	88.34 127

agg_prior					85.64 40	54.92 56		83.61 129		72.53 42			88.10 138

test_prior4						56.39 28	87.15 58

test_prior				78.39 51	86.35 31	54.91 58		85.45 72					89.70 80			90.55 54

新几何2							81.61 183

旧先验1					81.57 125	47.48 217		71.83 289			88.66 81	36.94 186			78.34 78	88.67 97

原ACMM2							83.77 136

test222					79.36 159	50.97 156	77.99 234	67.84 307	42.54 301	62.84 126	86.53 112	30.26 248			76.91 91	85.23 151

segment_acmp												44.97 75

testdata1							77.55 237		64.14 73

test12				79.24 30	86.89 26	56.08 32		85.16 84		72.27 47		47.15 48	91.10 47		85.93 21	90.54 57

plane_prior7					77.95 184	48.46 206

plane_prior6					78.42 181	49.39 184						36.04 199

plane_prior4											83.28 151

plane_prior3						48.95 190			64.01 74	62.15 129

plane_prior2							85.76 79		63.60 84

plane_prior1					78.31 183

plane_prior						49.57 179	87.43 51		64.57 66						72.84 122

n2								0.00 359

nn								0.00 359

door-mid								41.31 342

test11								84.25 105

door								43.27 341

HQP5-MVS						51.56 144

HQP-NCC					79.02 166		88.00 38		65.45 52	64.48 106

ACMP_Plane					79.02 166		88.00 38		65.45 52	64.48 106

HQP4-MVS										64.47 109			88.61 114			84.91 156

HQP3-MVS								83.68 126							73.12 118

HQP2-MVS												37.35 178

NP-MVS					78.76 170	50.43 165					85.12 123

ACMMP++_ref															63.20 191

ACMMP++															59.38 224

Test By Simon												39.38 154

High-resolution multi-view benchmark