High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

HPM-MVS++		79.88 3	80.14 3	79.10 11	88.17 1	64.80 1	86.59 4	83.70 40	65.37 16	78.78 8	90.64 7	58.63 12	87.24 31	79.00 4	90.37 2	85.26 92

CNVR-MVS		79.84 4	79.97 4	79.45 4	87.90 2	62.17 20	84.37 20	85.03 14	66.96 6	77.58 11	90.06 20	59.47 9	89.13 8	78.67 6	89.73 4	87.03 33

test_part2					87.58 3	60.47 37				83.42 2

NCCC		78.58 8	78.31 9	79.39 5	87.51 4	62.61 16	85.20 17	84.42 21	66.73 10	74.67 29	89.38 30	55.30 24	89.18 7	74.19 20	87.34 28	86.38 42

region2R		77.67 18	77.18 20	79.15 7	86.76 5	62.95 8	86.29 5	84.16 27	62.81 46	73.30 45	90.58 9	49.90 70	88.21 18	73.78 23	87.03 31	86.29 51

ACMMPR		77.71 16	77.23 19	79.16 6	86.75 6	62.93 9	86.29 5	84.24 25	62.82 44	73.55 43	90.56 10	49.80 72	88.24 17	74.02 21	87.03 31	86.32 49

HFP-MVS		78.01 14	77.65 14	79.10 11	86.71 7	62.81 10	86.29 5	84.32 23	62.82 44	73.96 33	90.50 12	53.20 46	88.35 14	74.02 21	87.05 29	86.13 53

#test#		77.83 15	77.41 17	79.10 11	86.71 7	62.81 10	85.69 14	84.32 23	61.61 62	73.96 33	90.50 12	53.20 46	88.35 14	73.68 24	87.05 29	86.13 53

MCST-MVS		77.48 20	77.45 16	77.54 34	86.67 9	58.36 60	83.22 35	86.93 1	56.91 141	74.91 25	88.19 44	59.15 10	87.68 27	73.67 25	87.45 27	86.57 41

APDe-MVS		80.16 2	80.59 2	78.86 18	86.64 10	60.02 40	88.12 1	86.42 6	62.94 40	82.40 3	92.12 1	59.64 7	89.76 3	78.70 5	88.32 16	86.79 38

DP-MVS Recon		72.15 73	70.73 80	76.40 49	86.57 11	57.99 64	81.15 71	82.96 58	57.03 138	66.78 134	85.56 83	44.50 149	88.11 20	51.77 171	80.23 85	83.10 160

MP-MVS		78.35 11	78.26 11	78.64 21	86.54 12	63.47 5	86.02 9	83.55 43	63.89 31	73.60 42	90.60 8	54.85 29	86.72 44	77.20 10	88.06 21	85.74 67

mPP-MVS		76.54 30	75.93 32	78.34 26	86.47 13	63.50 4	85.74 13	82.28 66	62.90 41	71.77 62	90.26 17	46.61 128	86.55 51	71.71 35	85.66 44	84.97 101

APD-MVS		78.02 13	78.04 13	77.98 31	86.44 14	60.81 34	85.52 15	84.36 22	60.61 73	79.05 7	90.30 16	55.54 23	88.32 16	73.48 28	87.03 31	84.83 104
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
XVS		77.17 23	76.56 26	79.00 14	86.32 15	62.62 14	85.83 10	83.92 32	64.55 22	72.17 58	90.01 22	47.95 110	88.01 22	71.55 36	86.74 36	86.37 45

X-MVStestdata		70.21 104	67.28 140	79.00 14	86.32 15	62.62 14	85.83 10	83.92 32	64.55 22	72.17 58	6.49 345	47.95 110	88.01 22	71.55 36	86.74 36	86.37 45

HSP-MVS		80.69 1	81.20 1	79.14 8	86.21 17	62.73 12	86.09 8	85.03 14	65.51 15	83.81 1	90.51 11	63.71 2	89.23 6	81.51 1	88.44 12	85.45 79

114514_t		70.83 85	69.56 95	74.64 76	86.21 17	54.63 111	82.34 51	81.81 75	48.22 245	63.01 177	85.83 79	40.92 187	87.10 37	57.91 135	79.79 90	82.18 174

DeepC-MVS_fast	68.24 3	77.25 22	76.63 25	79.12 10	86.15 19	60.86 33	84.71 18	84.85 18	61.98 59	73.06 49	88.88 38	53.72 40	89.06 9	68.27 47	88.04 22	87.42 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PGM-MVS		76.77 28	76.06 29	78.88 17	86.14 20	62.73 12	82.55 47	83.74 39	61.71 60	72.45 57	90.34 15	48.48 105	88.13 19	72.32 31	86.85 34	85.78 62

MPTG		77.61 19	77.36 18	78.35 24	86.08 21	63.57 2	83.37 33	80.97 98	65.13 18	75.77 17	90.88 5	48.63 101	86.66 45	77.23 8	88.17 18	84.81 105

MTAPA		76.90 26	76.42 27	78.35 24	86.08 21	63.57 2	74.92 184	80.97 98	65.13 18	75.77 17	90.88 5	48.63 101	86.66 45	77.23 8	88.17 18	84.81 105

CP-MVS		77.12 24	76.68 24	78.43 23	86.05 23	63.18 7	87.55 3	83.45 46	62.44 50	72.68 53	90.50 12	48.18 108	87.34 30	73.59 26	85.71 43	84.76 109

agg_prior3		76.13 34	75.89 34	76.85 42	85.76 24	62.02 21	81.65 61	81.01 97	55.51 172	73.73 39	88.60 43	53.23 45	84.90 88	75.24 15	88.33 14	83.65 148

新几何1				70.76 173	85.66 25	61.13 30		66.43 263	44.68 276	70.29 71	86.64 61	41.29 181	75.23 251	49.72 185	81.75 68	75.93 253

MG-MVS		73.96 51	73.89 47	74.16 82	85.65 26	49.69 193	81.59 65	81.29 88	61.45 63	71.05 66	88.11 45	51.77 56	87.73 26	61.05 121	83.09 55	85.05 98

1121		68.53 138	67.16 145	72.63 133	85.64 27	61.14 29	73.95 195	66.46 262	44.61 277	70.28 72	86.68 60	41.42 179	80.78 179	53.62 158	81.79 66	75.97 250

TEST9					85.58 28	61.59 25	81.62 63	81.26 89	55.65 169	74.93 23	88.81 39	53.70 41	84.68 95

train_agg		76.27 33	76.15 28	76.64 47	85.58 28	61.59 25	81.62 63	81.26 89	55.86 163	74.93 23	88.81 39	53.70 41	84.68 95	75.24 15	88.33 14	83.65 148

ACMMP_Plus		78.77 7	78.78 7	78.74 20	85.44 30	61.04 31	83.84 28	85.16 12	62.88 42	78.10 9	91.26 3	52.51 49	88.39 13	79.34 3	90.52 1	86.78 39

test_8					85.40 31	60.96 32	81.54 66	81.18 92	55.86 163	74.81 26	88.80 41	53.70 41	84.45 100

原ACMM1				74.69 73	85.39 32	59.40 46		83.42 47	51.47 217	70.27 73	86.61 63	48.61 103	86.51 52	53.85 157	87.96 23	78.16 230

CDPH-MVS		76.31 32	75.67 35	78.22 27	85.35 33	59.14 50	81.31 69	84.02 29	56.32 156	74.05 32	88.98 36	53.34 44	87.92 24	69.23 44	88.42 13	87.59 18

ACMMP		76.02 35	75.33 37	78.07 28	85.20 34	61.91 22	85.49 16	84.44 20	63.04 38	69.80 84	89.74 27	45.43 139	87.16 35	72.01 34	82.87 60	85.14 94
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
agg_prior1		75.94 36	76.01 31	75.72 57	85.04 35	59.96 41	81.44 67	81.04 95	56.14 161	74.68 27	88.90 37	53.91 37	84.04 107	75.01 17	87.92 25	83.16 159

agg_prior					85.04 35	59.96 41		81.04 95		74.68 27			84.04 107

HPM-MVS		77.28 21	76.85 22	78.54 22	85.00 37	60.81 34	82.91 40	85.08 13	62.57 47	73.09 48	89.97 23	50.90 66	87.48 29	75.30 13	86.85 34	87.33 28

MP-MVS-pluss		78.35 11	78.46 8	78.03 30	84.96 38	59.52 45	82.93 39	85.39 10	62.15 53	76.41 15	91.51 2	52.47 51	86.78 43	80.66 2	89.64 6	87.80 12
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		78.44 10	78.28 10	78.90 16	84.96 38	61.41 27	84.03 26	83.82 38	59.34 113	79.37 6	89.76 26	59.84 5	87.62 28	76.69 11	86.74 36	87.68 16

AdaColmap		69.99 108	68.66 112	73.97 85	84.94 40	57.83 65	82.63 45	78.71 151	56.28 158	64.34 166	84.14 104	41.57 174	87.06 38	46.45 205	78.88 103	77.02 243

DP-MVS		65.68 185	63.66 192	71.75 154	84.93 41	56.87 82	80.74 75	73.16 218	53.06 196	59.09 237	82.35 131	36.79 228	85.94 65	32.82 284	69.96 210	72.45 289

DeepPCF-MVS	69.58 1	79.03 6	79.00 6	79.13 9	84.92 42	60.32 38	83.03 37	85.33 11	62.86 43	80.17 4	90.03 21	61.76 3	88.95 10	74.21 19	88.67 11	88.12 7

CPTT-MVS		72.78 61	72.08 63	74.87 71	84.88 43	61.41 27	84.15 25	77.86 167	55.27 174	67.51 127	88.08 47	41.93 168	81.85 159	69.04 46	80.01 86	81.35 192

test12				77.76 33	84.52 44	58.41 59		83.36 50		72.93 51		54.61 31	88.05 21		88.12 20	86.81 37

SD-MVS		77.70 17	77.62 15	77.93 32	84.47 45	61.88 23	84.55 19	83.87 36	60.37 78	79.89 5	89.38 30	54.97 26	85.58 70	76.12 12	84.94 46	86.33 48

HPM-MVS_fast		74.30 48	73.46 52	76.80 43	84.45 46	59.04 51	83.65 30	81.05 94	60.15 84	70.43 69	89.84 25	41.09 184	85.59 69	67.61 54	82.90 59	85.77 64

test_prior3		76.89 27	76.96 21	76.69 44	84.20 47	57.27 72	81.75 59	84.88 16	60.37 78	75.01 21	89.06 33	56.22 18	86.43 54	72.19 32	88.96 9	86.38 42

test_prior				76.69 44	84.20 47	57.27 72		84.88 16					86.43 54			86.38 42

CSCG		76.92 25	76.75 23	77.41 36	83.96 49	59.60 44	82.95 38	86.50 5	60.78 71	75.27 20	84.83 91	60.76 4	86.56 50	67.86 51	87.87 26	86.06 56

DeepC-MVS	69.38 2	78.56 9	78.14 12	79.83 2	83.60 50	61.62 24	84.17 24	86.85 2	63.23 35	73.84 38	90.25 18	57.68 13	89.96 2	74.62 18	89.03 7	87.89 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
UA-Net		73.13 58	72.93 56	73.76 91	83.58 51	51.66 146	78.75 96	77.66 171	67.75 4	72.61 54	89.42 28	49.82 71	83.29 124	53.61 160	83.14 54	86.32 49

LFMVS		71.78 76	71.59 66	72.32 148	83.40 52	46.38 222	79.75 87	71.08 225	64.18 28	72.80 52	88.64 42	42.58 163	83.72 115	57.41 138	84.49 49	86.86 36

test222					83.14 53	58.68 57	72.57 215	63.45 282	41.78 295	67.56 126	86.12 72	37.13 218			78.73 107	74.98 264

旧先验1					83.04 54	53.15 126		67.52 255			87.85 48	44.08 153			80.76 73	78.03 232

MSLP-MVS++		73.77 54	73.47 51	74.66 74	83.02 55	59.29 49	82.30 55	81.88 72	59.34 113	71.59 64	86.83 56	45.94 132	83.65 117	65.09 71	85.22 45	81.06 198

SteuartSystems-ACMMP		79.48 5	79.31 5	79.98 1	83.01 56	62.18 19	87.60 2	85.83 8	66.69 11	78.03 10	90.98 4	54.26 33	90.06 1	78.42 7	89.02 8	87.69 15
Skip Steuart: Steuart Systems R&D Blog.
MVS_111021_HR		74.02 50	73.46 52	75.69 59	83.01 56	60.63 36	77.29 138	78.40 163	61.18 67	70.58 68	85.97 76	54.18 35	84.00 111	67.52 55	82.98 58	82.45 171

VDDNet		71.81 75	71.33 72	73.26 119	82.80 58	47.60 214	78.74 97	75.27 197	59.59 106	72.94 50	89.40 29	41.51 178	83.91 112	58.75 133	82.99 57	88.26 5

abl_6		74.34 46	73.50 49	76.86 41	82.43 59	60.16 39	83.48 32	81.86 73	58.81 119	73.95 35	89.86 24	41.87 169	86.62 47	67.98 50	81.23 71	83.80 141

3Dnovator+	66.72 4	75.84 38	74.57 41	79.66 3	82.40 60	59.92 43	85.83 10	86.32 7	66.92 9	67.80 123	89.24 32	42.03 166	89.38 5	64.07 86	86.50 39	89.69 1

APD-MVS_3200maxsize		74.96 41	74.39 43	76.67 46	82.20 61	58.24 62	83.67 29	83.29 53	58.41 125	73.71 40	90.14 19	45.62 134	85.99 62	69.64 42	82.85 61	85.78 62

PVSNet_Blended_VisFu		71.45 81	70.39 83	74.65 75	82.01 62	58.82 55	79.93 83	80.35 117	55.09 177	65.82 148	82.16 138	49.17 93	82.64 149	60.34 125	78.62 109	82.50 170

TSAR-MVS + GP.		74.90 42	74.15 45	77.17 39	82.00 63	58.77 56	81.80 58	78.57 154	58.58 121	74.32 31	84.51 100	55.94 20	87.22 32	67.11 57	84.48 50	85.52 74

API-MVS		72.17 71	71.41 69	74.45 80	81.95 64	57.22 74	84.03 26	80.38 115	59.89 92	68.40 108	82.33 132	49.64 73	87.83 25	51.87 169	84.16 52	78.30 228

MAR-MVS		71.51 79	70.15 87	75.60 62	81.84 65	59.39 47	81.38 68	82.90 61	54.90 181	68.08 116	78.70 217	47.73 112	85.51 73	51.68 173	84.17 51	81.88 179
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_NR		72.63 63	71.80 64	75.13 68	81.72 66	53.42 123	79.91 84	83.28 54	59.14 115	66.31 141	85.90 77	51.86 55	86.06 59	57.45 137	80.62 74	85.91 59

VDD-MVS		72.50 64	72.09 62	73.75 93	81.58 67	49.69 193	77.76 125	77.63 172	63.21 36	73.21 46	89.02 35	42.14 165	83.32 123	61.72 118	82.50 62	88.25 6

PS-MVSNAJ		70.51 94	69.70 91	72.93 124	81.52 68	55.79 97	74.92 184	79.00 146	55.04 179	69.88 80	78.66 218	47.05 121	82.19 154	61.61 119	79.58 92	80.83 202

testdata				64.66 246	81.52 68	52.93 129		65.29 268	46.09 264	73.88 37	87.46 50	38.08 209	66.26 284	53.31 163	78.48 110	74.78 268

CHOSEN 1792x2688		65.08 195	62.84 200	71.82 153	81.49 70	56.26 88	66.32 268	74.20 211	40.53 305	63.16 176	78.65 219	41.30 180	77.80 225	45.80 212	74.09 142	81.40 185

HQP_MVS		74.31 47	73.73 48	76.06 51	81.41 71	56.31 85	84.22 22	84.01 30	64.52 24	69.27 96	86.10 73	45.26 143	87.21 33	68.16 48	80.58 76	84.65 110

plane_prior7					81.41 71	55.96 94

MVS_0304		76.73 29	76.04 30	78.78 19	81.32 73	58.89 54	82.50 49	84.07 28	67.73 5	72.08 60	87.28 54	49.49 74	89.57 4	73.52 27	86.40 40	87.87 11

CANet		76.46 31	75.93 32	78.06 29	81.29 74	57.53 69	82.35 50	83.31 52	67.78 3	70.09 74	86.34 69	54.92 27	88.90 11	72.68 30	84.55 48	87.76 14

Vis-MVSNet		72.18 70	71.37 71	74.61 77	81.29 74	55.41 105	80.90 72	78.28 165	60.73 72	69.23 99	88.09 46	44.36 152	82.65 148	57.68 136	81.75 68	85.77 64
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
plane_prior1					81.27 76

xiu_mvs_v2_base		70.52 93	69.75 89	72.84 128	81.21 77	55.63 101	75.11 179	78.92 147	54.92 180	69.96 79	79.68 203	47.00 125	82.09 156	61.60 120	79.37 95	80.81 203

plane_prior6					81.20 78	56.24 89						45.26 143

PAPR		71.72 77	70.82 79	74.41 81	81.20 78	51.17 150	79.55 91	83.33 51	55.81 166	66.93 133	84.61 96	50.95 64	86.06 59	55.79 146	79.20 100	86.00 57

PLC	56.13 14	65.09 194	63.21 196	70.72 175	81.04 80	54.87 110	78.57 101	77.47 174	48.51 241	55.71 264	81.89 146	33.71 253	79.71 190	41.66 246	70.37 198	77.58 234
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
NP-MVS					80.98 81	56.05 93					85.54 85

OPM-MVS		74.73 44	74.25 44	76.19 50	80.81 82	59.01 52	82.60 46	83.64 41	63.74 33	72.52 55	87.49 49	47.18 120	85.88 66	69.47 43	80.78 72	83.66 147

HQP-NCC					80.66 83		82.31 52		62.10 54	67.85 118

ACMP_Plane					80.66 83		82.31 52		62.10 54	67.85 118

HQP-MVS		73.45 55	72.80 57	75.40 64	80.66 83	54.94 107	82.31 52	83.90 34	62.10 54	67.85 118	85.54 85	45.46 137	86.93 39	67.04 58	80.35 82	84.32 117

PHI-MVS		75.87 37	75.36 36	77.41 36	80.62 86	55.91 96	84.28 21	85.78 9	56.08 162	73.41 44	86.58 65	50.94 65	88.54 12	70.79 40	89.71 5	87.79 13

ACMM	61.98 7	70.80 87	69.73 90	74.02 83	80.59 87	58.59 58	82.68 44	82.02 71	55.46 173	67.18 130	84.39 102	38.51 202	83.17 127	60.65 122	76.10 129	80.30 208
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-1		75.47 40	74.93 40	77.09 40	80.43 88	57.70 67	79.50 92	82.13 67	67.84 1	75.73 19	80.75 180	56.50 15	86.07 58	71.07 39	80.38 80	87.50 20

Regformer-2		75.63 39	74.99 38	77.54 34	80.43 88	58.32 61	79.50 92	82.92 59	67.84 1	75.94 16	80.75 180	55.73 21	86.80 41	71.44 38	80.38 80	87.50 20

ACMP	63.53 6	72.30 68	71.20 75	75.59 63	80.28 90	57.54 68	82.74 43	82.84 63	60.58 74	65.24 154	86.18 71	39.25 196	86.03 61	66.95 60	76.79 126	83.22 154
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LPG-MVS_test		72.74 62	71.74 65	75.76 55	80.22 91	57.51 70	82.55 47	83.40 48	61.32 64	66.67 135	87.33 52	39.15 197	86.59 48	67.70 52	77.30 120	83.19 156

LGP-MVS_train				75.76 55	80.22 91	57.51 70		83.40 48	61.32 64	66.67 135	87.33 52	39.15 197	86.59 48	67.70 52	77.30 120	83.19 156

WR-MVS		68.47 139	68.47 115	68.44 203	80.20 93	39.84 264	73.75 200	76.07 189	64.68 21	68.11 115	83.63 111	50.39 69	79.14 204	49.78 182	69.66 217	86.34 47

BH-RMVSNet		68.81 128	67.42 134	72.97 123	80.11 94	52.53 135	74.26 192	76.29 186	58.48 124	68.38 109	84.20 103	42.59 162	83.83 114	46.53 204	75.91 130	82.56 167

test_0402		63.25 210	61.01 224	69.96 184	80.00 95	54.37 113	76.86 146	72.02 223	54.58 184	58.71 240	80.79 178	35.00 239	84.36 101	26.41 321	64.71 253	71.15 302

HyFIR lowres test		65.67 186	63.01 198	73.67 96	79.97 96	55.65 100	69.07 255	75.52 194	42.68 293	63.53 172	77.95 226	40.43 188	81.64 162	46.01 210	71.91 178	83.73 142

Regformer-3		73.89 52	73.28 54	75.71 58	79.75 97	55.48 104	78.54 103	79.93 119	66.58 12	73.62 41	80.30 190	54.87 28	84.54 98	69.09 45	76.84 124	87.10 32

Regformer-4		74.25 49	73.48 50	76.57 48	79.75 97	56.54 84	78.54 103	81.49 81	66.93 8	73.90 36	80.30 190	53.84 39	85.98 63	69.76 41	76.84 124	87.17 30

BH-untuned		68.27 145	67.29 139	71.21 165	79.74 99	53.22 125	76.06 161	77.46 176	57.19 135	66.10 142	81.61 154	45.37 141	83.50 119	45.42 220	76.68 128	76.91 246

VNet		69.68 112	70.19 86	68.16 204	79.73 100	41.63 257	70.53 241	77.38 177	60.37 78	70.69 67	86.63 62	51.08 62	77.09 233	53.61 160	81.69 70	85.75 66

LS3D		64.71 197	62.50 204	71.34 163	79.72 101	55.71 98	79.82 85	74.72 206	48.50 242	56.62 259	84.62 95	33.59 255	82.34 153	29.65 308	75.23 134	75.97 250

BH-w/o		66.85 171	65.83 170	69.90 186	79.29 102	52.46 137	74.66 188	76.65 184	54.51 186	64.85 161	78.12 224	45.59 136	82.95 133	43.26 234	75.54 133	74.27 272

1112_ss		64.00 203	63.36 195	65.93 230	79.28 103	42.58 250	71.35 231	72.36 222	46.41 261	60.55 220	77.89 229	46.27 131	73.28 257	46.18 207	69.97 209	81.92 178

UniMVSNet_NR-MVSNet		71.11 83	71.00 77	71.44 159	79.20 104	44.13 237	76.02 164	82.60 64	66.48 14	68.20 111	84.60 97	56.82 14	82.82 139	54.62 152	70.43 193	87.36 27

VPNet		67.52 158	68.11 121	65.74 233	79.18 105	36.80 292	72.17 221	72.83 219	62.04 57	67.79 124	85.83 79	48.88 100	76.60 239	51.30 174	72.97 162	83.81 138

TR-MVS		66.59 178	65.07 181	71.17 167	79.18 105	49.63 195	73.48 203	75.20 199	52.95 197	67.90 117	80.33 189	39.81 191	83.68 116	43.20 235	73.56 151	80.20 209

TAMVS		66.78 173	65.27 177	71.33 164	79.16 107	53.67 117	73.84 199	69.59 237	52.32 204	65.28 151	81.72 149	44.49 150	77.40 230	42.32 241	78.66 108	82.92 162

Test_1112_low_res		62.32 222	61.77 216	64.00 250	79.08 108	39.53 268	68.17 261	70.17 230	43.25 288	59.03 238	79.90 196	44.08 153	71.24 264	43.79 230	68.42 226	81.25 193

CDS-MVSNet		66.80 172	65.37 174	71.10 169	78.98 109	53.13 128	73.27 205	71.07 226	52.15 205	64.72 162	80.23 193	43.56 158	77.10 232	45.48 218	78.88 103	83.05 161
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
canonicalmvs		74.67 45	74.98 39	73.71 95	78.94 110	50.56 165	80.23 79	83.87 36	60.30 82	77.15 12	86.56 66	59.65 6	82.00 157	66.01 64	82.12 64	88.58 4

IS-MVSNet		71.57 78	71.00 77	73.27 118	78.86 111	45.63 226	80.22 80	78.69 152	64.14 29	66.46 137	87.36 51	49.30 78	85.60 68	50.26 180	83.71 53	88.59 3

CLD-MVS		73.33 56	72.68 58	75.29 67	78.82 112	53.33 124	78.23 109	84.79 19	61.30 66	70.41 70	81.04 168	52.41 52	87.12 36	64.61 76	82.49 63	85.41 86
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
pcd1.5k->3k		30.06 316	30.56 317	28.55 331	78.81 113	0.00 354	0.00 345	82.07 70	0.00 349	0.00 351	0.00 351	39.61 193	0.00 350	0.00 349	74.56 137	85.66 70

MVSFormer		71.50 80	70.38 84	74.88 70	78.76 114	57.15 79	82.79 41	78.48 158	51.26 220	69.49 91	83.22 117	43.99 155	83.24 125	66.06 62	79.37 95	84.23 124

lupinMVS		69.57 115	68.28 118	73.44 108	78.76 114	57.15 79	76.57 149	73.29 217	46.19 263	69.49 91	82.18 135	43.99 155	79.23 197	64.66 74	79.37 95	83.93 133

CNLPA		65.43 189	64.02 186	69.68 187	78.73 116	58.07 63	77.82 124	70.71 228	51.49 216	61.57 206	83.58 113	38.23 207	70.82 265	43.90 228	70.10 207	80.16 210

EPP-MVSNet		72.16 72	71.31 73	74.71 72	78.68 117	49.70 191	82.10 56	81.65 77	60.40 77	65.94 144	85.84 78	51.74 57	86.37 56	55.93 143	79.55 94	88.07 8

TranMVSNet+NR-MVSNet		70.36 101	70.10 88	71.17 167	78.64 118	42.97 248	76.53 150	81.16 93	66.95 7	68.53 107	85.42 87	51.61 58	83.07 130	52.32 167	69.70 216	87.46 22

UniMVSNet (Re)		70.63 91	70.20 85	71.89 151	78.55 119	45.29 227	75.94 165	82.92 59	63.68 34	68.16 113	83.59 112	53.89 38	83.49 120	53.97 156	71.12 185	86.89 35

Fast-Effi-MVS+		70.28 103	69.12 106	73.73 94	78.50 120	51.50 149	75.01 181	79.46 137	56.16 160	68.59 104	79.55 207	53.97 36	84.05 106	53.34 162	77.53 116	85.65 71

PS-MVSNAJss		72.24 69	71.21 74	75.31 66	78.50 120	55.93 95	81.63 62	82.12 68	56.24 159	70.02 78	85.68 82	47.05 121	84.34 102	65.27 70	74.41 140	85.67 69

EI-MVSNet-Vis-set		72.42 67	71.59 66	74.91 69	78.47 122	54.02 114	77.05 142	79.33 141	65.03 20	71.68 63	79.35 211	52.75 48	84.89 89	66.46 61	74.23 141	85.83 61

MVS_111021_LR		69.50 117	68.78 110	71.65 156	78.38 123	59.33 48	74.82 186	70.11 231	58.08 129	67.83 122	84.68 93	41.96 167	76.34 242	65.62 68	77.54 115	79.30 222

FIs		70.82 86	71.43 68	68.98 196	78.33 124	38.14 280	76.96 144	83.59 42	61.02 68	67.33 129	86.73 57	55.07 25	81.64 162	54.61 154	79.22 99	87.14 31

UGNet		68.81 128	67.39 135	73.06 122	78.33 124	54.47 112	79.77 86	75.40 196	60.45 76	63.22 174	84.40 101	32.71 266	80.91 176	51.71 172	80.56 78	83.81 138
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
jason		69.65 113	68.39 117	73.43 109	78.27 126	56.88 81	77.12 140	73.71 215	46.53 259	69.34 95	83.22 117	43.37 159	79.18 198	64.77 73	79.20 100	84.23 124
jason: jason.
alignmvs		73.86 53	73.99 46	73.45 107	78.20 127	50.50 167	78.57 101	82.43 65	59.40 111	76.57 13	86.71 59	56.42 17	81.23 170	65.84 66	81.79 66	88.62 2

xiu_mvs_v1_base_debu		68.58 134	67.28 140	72.48 139	78.19 128	57.19 76	75.28 174	75.09 201	51.61 212	70.04 75	81.41 162	32.79 262	79.02 206	63.81 91	77.31 117	81.22 194

xiu_mvs_v1_base		68.58 134	67.28 140	72.48 139	78.19 128	57.19 76	75.28 174	75.09 201	51.61 212	70.04 75	81.41 162	32.79 262	79.02 206	63.81 91	77.31 117	81.22 194

xiu_mvs_v1_base_debi		68.58 134	67.28 140	72.48 139	78.19 128	57.19 76	75.28 174	75.09 201	51.61 212	70.04 75	81.41 162	32.79 262	79.02 206	63.81 91	77.31 117	81.22 194

PAPM		67.92 155	66.69 157	71.63 157	78.09 131	49.02 200	77.09 141	81.24 91	51.04 223	60.91 216	83.98 108	47.71 113	84.99 80	40.81 250	79.32 98	80.90 201

ACMH	55.70 15	65.20 193	63.57 193	70.07 183	78.07 132	52.01 145	79.48 94	79.69 121	55.75 167	56.59 260	80.98 171	27.12 298	80.94 174	42.90 239	71.58 181	77.25 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DU-MVS		70.01 107	69.53 96	71.44 159	78.05 133	44.13 237	75.01 181	81.51 80	64.37 27	68.20 111	84.52 98	49.12 96	82.82 139	54.62 152	70.43 193	87.37 25

NR-MVSNet		69.54 116	68.85 108	71.59 158	78.05 133	43.81 241	74.20 193	80.86 101	65.18 17	62.76 179	84.52 98	52.35 53	83.59 118	50.96 176	70.78 187	87.37 25

EI-MVSNet-UG-set		71.92 74	71.06 76	74.52 79	77.98 135	53.56 120	76.62 148	79.16 143	64.40 26	71.18 65	78.95 216	52.19 54	84.66 97	65.47 69	73.57 150	85.32 89

WR-MVS_H		67.02 168	66.92 149	67.33 212	77.95 136	37.75 283	77.57 131	82.11 69	62.03 58	62.65 182	82.48 129	50.57 67	79.46 193	42.91 238	64.01 259	84.79 107

Effi-MVS+		73.31 57	72.54 59	75.62 61	77.87 137	53.64 118	79.62 90	79.61 124	61.63 61	72.02 61	82.61 126	56.44 16	85.97 64	63.99 89	79.07 102	87.25 29

DELS-MVS		74.76 43	74.46 42	75.65 60	77.84 138	52.25 140	75.59 168	84.17 26	63.76 32	73.15 47	82.79 121	59.58 8	86.80 41	67.24 56	86.04 42	87.89 9
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
ACMH+	57.40 11	66.12 182	64.06 185	72.30 149	77.79 139	52.83 130	80.39 78	78.03 166	57.30 134	57.47 254	82.55 128	27.68 294	84.17 104	45.54 216	69.78 213	79.90 213

3Dnovator	64.47 5	72.49 65	71.39 70	75.79 54	77.70 140	58.99 53	80.66 76	83.15 56	62.24 52	65.46 149	86.59 64	42.38 164	85.52 72	59.59 131	84.72 47	82.85 165

EG-PatchMatch MVS		64.71 197	62.87 199	70.22 180	77.68 141	53.48 121	77.99 119	78.82 148	53.37 194	56.03 263	77.41 245	24.75 312	84.04 107	46.37 206	73.42 153	73.14 282

CP-MVSNet		66.49 179	66.41 163	66.72 215	77.67 142	36.33 296	76.83 147	79.52 135	62.45 49	62.54 185	83.47 116	46.32 129	78.37 217	45.47 219	63.43 264	85.45 79

GBi-Net		67.21 162	66.55 158	69.19 193	77.63 143	43.33 244	77.31 135	77.83 168	56.62 150	65.04 157	82.70 122	41.85 170	80.33 185	47.18 199	72.76 164	83.92 134

test1		67.21 162	66.55 158	69.19 193	77.63 143	43.33 244	77.31 135	77.83 168	56.62 150	65.04 157	82.70 122	41.85 170	80.33 185	47.18 199	72.76 164	83.92 134

FMVSNet2		66.93 170	66.31 167	68.79 199	77.63 143	42.98 247	76.11 159	77.47 174	56.62 150	65.22 156	82.17 137	41.85 170	80.18 188	47.05 202	72.72 167	83.20 155

PCF-MVS	61.88 8	70.95 84	69.49 100	75.35 65	77.63 143	55.71 98	76.04 163	81.81 75	50.30 228	69.66 85	85.40 88	52.51 49	84.89 89	51.82 170	80.24 84	85.45 79
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MVP-Stereo		65.41 190	63.80 189	70.22 180	77.62 147	55.53 102	76.30 154	78.53 156	50.59 227	56.47 261	78.65 219	39.84 190	82.68 147	44.10 227	72.12 177	72.44 290
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
FC-MVSNet-test		69.80 110	70.58 82	67.46 209	77.61 148	34.73 305	76.05 162	83.19 55	60.84 69	65.88 146	86.46 67	54.52 32	80.76 181	52.52 166	78.12 112	86.91 34

PS-CasMVS		66.42 180	66.32 166	66.70 217	77.60 149	36.30 298	76.94 145	79.61 124	62.36 51	62.43 193	83.66 110	45.69 133	78.37 217	45.35 221	63.26 265	85.42 82

FMVSNet1		66.70 174	65.87 169	69.19 193	77.49 150	43.33 244	77.31 135	77.83 168	56.45 154	64.60 164	82.70 122	38.08 209	80.33 185	46.08 209	72.31 175	83.92 134

VPA-MVSNet		69.02 125	69.47 102	67.69 208	77.42 151	41.00 261	74.04 194	79.68 122	60.06 85	69.26 98	84.81 92	51.06 63	77.58 227	54.44 155	74.43 139	84.48 115

conf200view11		63.38 206	62.41 205	66.29 222	77.31 152	38.66 275	72.65 211	69.11 244	57.07 136	62.45 189	81.03 169	37.01 220	79.17 199	31.84 288	73.25 156	81.03 199

thres100view900		63.28 209	62.41 205	65.89 231	77.31 152	38.66 275	72.65 211	69.11 244	57.07 136	62.45 189	81.03 169	37.01 220	79.17 199	31.84 288	73.25 156	79.83 214

view600		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

view800		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

conf0.05thres1000		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

tfpn		62.77 215	61.84 212	65.55 235	77.28 154	36.87 288	72.15 222	67.78 251	56.79 142	61.46 207	81.92 142	36.88 223	78.42 213	29.86 303	72.46 168	81.36 186

cascas		65.98 184	63.42 194	73.64 99	77.26 158	52.58 134	72.26 220	77.21 179	48.56 240	61.21 212	74.60 272	32.57 270	85.82 67	50.38 179	76.75 127	82.52 169

thres600view7		63.30 208	62.27 207	66.41 219	77.18 159	38.87 272	72.35 218	69.11 244	56.98 139	62.37 194	80.96 172	37.01 220	79.00 210	31.43 296	73.05 161	81.36 186

PEN-MVS		66.60 176	66.45 160	67.04 213	77.11 160	36.56 294	77.03 143	80.42 114	62.95 39	62.51 187	84.03 107	46.69 127	79.07 205	44.22 224	63.08 267	85.51 75

PatchMatch-RL		56.25 264	54.55 268	61.32 266	77.06 161	56.07 92	65.57 271	54.10 325	44.13 283	53.49 288	71.27 290	25.20 309	66.78 281	36.52 272	63.66 261	61.12 322

PVSNet_BlendedMVS		68.56 137	67.72 126	71.07 170	77.03 162	50.57 163	74.50 190	81.52 78	53.66 193	64.22 170	79.72 202	49.13 94	82.87 137	55.82 144	73.92 145	79.77 217

PVSNet_Blended		68.59 133	67.72 126	71.19 166	77.03 162	50.57 163	72.51 216	81.52 78	51.91 207	64.22 170	77.77 232	49.13 94	82.87 137	55.82 144	79.58 92	80.14 211

F-COLMAP		63.05 213	60.87 225	69.58 191	76.99 164	53.63 119	78.12 111	76.16 187	47.97 249	52.41 290	81.61 154	27.87 292	78.11 221	40.07 253	66.66 240	77.00 244

tfpn200view9		63.18 211	62.18 209	66.21 224	76.85 165	39.62 266	71.96 228	69.44 240	56.63 148	62.61 183	79.83 198	37.18 215	79.17 199	31.84 288	73.25 156	79.83 214

thres400		63.31 207	62.18 209	66.72 215	76.85 165	39.62 266	71.96 228	69.44 240	56.63 148	62.61 183	79.83 198	37.18 215	79.17 199	31.84 288	73.25 156	81.36 186

TAPA-MVS	59.36 10	66.60 176	65.20 178	70.81 172	76.63 167	48.75 202	76.52 151	80.04 118	50.64 226	65.24 154	84.93 90	39.15 197	78.54 212	36.77 267	76.88 123	85.14 94
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
OMC-MVS		71.40 82	70.60 81	73.78 89	76.60 168	53.15 126	79.74 88	79.78 120	58.37 126	68.75 103	86.45 68	45.43 139	80.60 182	62.58 101	77.73 114	87.58 19

LTVRE_ROB	55.42 16	63.15 212	61.23 222	68.92 197	76.57 169	47.80 210	59.92 297	76.39 185	54.35 188	58.67 241	82.46 130	29.44 285	81.49 166	42.12 242	71.14 184	77.46 235
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
QAPM		70.05 106	68.81 109	73.78 89	76.54 170	53.43 122	83.23 34	83.48 44	52.89 198	65.90 145	86.29 70	41.55 177	86.49 53	51.01 175	78.40 111	81.42 184

FMVSNet3		66.32 181	65.61 172	68.46 202	76.48 171	42.34 251	74.98 183	77.15 180	55.83 165	65.04 157	81.16 165	39.91 189	80.14 189	47.18 199	72.76 164	82.90 164

ab-mvs		66.65 175	66.42 162	67.37 210	76.17 172	41.73 255	70.41 244	76.14 188	53.99 191	65.98 143	83.51 114	49.48 75	76.24 243	48.60 193	73.46 152	84.14 131

Effi-MVS+-dtu		69.64 114	67.53 131	75.95 52	76.10 173	62.29 18	80.20 81	76.06 190	59.83 94	65.26 153	77.09 246	41.56 175	84.02 110	60.60 123	71.09 186	81.53 182

mvs-test1		70.44 99	68.19 119	77.18 38	76.10 173	63.22 6	80.59 77	76.06 190	59.83 94	66.32 140	79.87 197	41.56 175	85.53 71	60.60 123	72.77 163	82.80 166

DTE-MVSNet		65.58 187	65.34 175	66.31 220	76.06 175	34.79 303	76.43 152	79.38 140	62.55 48	61.66 204	83.83 109	45.60 135	79.15 203	41.64 248	60.88 282	85.00 99

EPNet		73.09 59	72.16 61	75.90 53	75.95 176	56.28 87	83.05 36	72.39 221	66.53 13	65.27 152	87.00 55	50.40 68	85.47 74	62.48 103	86.32 41	85.94 58
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
SixPastTwentyTwo		61.65 230	58.80 239	70.20 182	75.80 177	47.22 217	75.59 168	69.68 235	54.61 183	54.11 281	79.26 212	27.07 299	82.96 132	43.27 233	49.79 319	80.41 207

Baseline_NR-MVSNet		67.05 167	67.56 129	65.50 239	75.65 178	37.70 284	75.42 171	74.65 207	59.90 90	68.14 114	83.15 120	49.12 96	77.20 231	52.23 168	69.78 213	81.60 181

jajsoiax		68.25 147	66.45 160	73.66 97	75.62 179	55.49 103	80.82 73	78.51 157	52.33 203	64.33 167	84.11 105	28.28 290	81.81 161	63.48 96	70.62 189	83.67 146

mvs_tets		68.18 151	66.36 164	73.63 100	75.61 180	55.35 106	80.77 74	78.56 155	52.48 202	64.27 169	84.10 106	27.45 296	81.84 160	63.45 97	70.56 192	83.69 143

PVSNet	50.76 19	58.40 251	57.39 249	61.42 264	75.53 181	44.04 239	61.43 290	63.45 282	47.04 257	56.91 257	73.61 278	27.00 300	64.76 289	39.12 257	72.40 172	75.47 258

MVS		67.37 159	66.33 165	70.51 178	75.46 182	50.94 151	73.95 195	81.85 74	41.57 299	62.54 185	78.57 222	47.98 109	85.47 74	52.97 164	82.05 65	75.14 260

nrg030		72.96 60	73.01 55	72.84 128	75.41 183	50.24 174	80.02 82	82.89 62	58.36 127	74.44 30	86.73 57	58.90 11	80.83 177	65.84 66	74.46 138	87.44 23

thres200		62.20 223	61.16 223	65.34 241	75.38 184	39.99 263	69.60 250	69.29 242	55.64 170	61.87 199	76.99 247	37.07 219	78.96 211	31.28 297	73.28 155	77.06 242

TransMVSNet (Re)		64.72 196	64.33 184	65.87 232	75.22 185	38.56 277	74.66 188	75.08 204	58.90 118	61.79 201	82.63 125	51.18 61	78.07 222	43.63 231	55.87 301	80.99 200

MS-PatchMatch		62.42 221	61.46 219	65.31 242	75.21 186	52.10 141	72.05 226	74.05 212	46.41 261	57.42 255	74.36 273	34.35 247	77.57 228	45.62 215	73.67 147	66.26 313

IB-MVS	56.42 12	65.40 191	62.73 202	73.40 113	74.89 187	52.78 131	73.09 207	75.13 200	55.69 168	58.48 245	73.73 277	32.86 261	86.32 57	50.63 177	70.11 206	81.10 197
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
MVS_Test		72.45 66	72.46 60	72.42 145	74.88 188	48.50 204	76.28 155	83.14 57	59.40 111	72.46 56	84.68 93	55.66 22	81.12 171	65.98 65	79.66 91	87.63 17

CANet_DTU		68.18 151	67.71 128	69.59 189	74.83 189	46.24 223	78.66 99	76.85 183	59.60 103	63.45 173	82.09 141	35.25 238	77.41 229	59.88 128	78.76 106	85.14 94

tfpnnormal		62.47 220	61.63 218	64.99 244	74.81 190	39.01 271	71.22 233	73.72 214	55.22 176	60.21 222	80.09 195	41.26 183	76.98 235	30.02 302	68.09 231	78.97 226

Vis-MVSNet (Re-imp)		63.69 204	63.88 188	63.14 254	74.75 191	31.04 323	71.16 235	63.64 281	56.32 156	59.80 228	84.99 89	44.51 148	75.46 246	39.12 257	80.62 74	82.92 162

HY-MVS	56.14 13	64.55 200	63.89 187	66.55 218	74.73 192	41.02 259	69.96 248	74.43 208	49.29 234	61.66 204	80.92 173	47.43 119	76.68 238	44.91 223	71.69 180	81.94 177

COLMAP_ROB	52.97 17	61.27 233	58.81 235	68.64 200	74.63 193	52.51 136	78.42 106	73.30 216	49.92 232	50.96 295	81.51 157	23.06 315	79.40 194	31.63 293	65.85 244	74.01 278
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
LCM-MVSNet-Re		61.88 228	61.35 220	63.46 251	74.58 194	31.48 322	61.42 291	58.14 304	58.71 120	53.02 289	79.55 207	43.07 161	76.80 236	45.69 213	77.96 113	82.11 176

test_djsdf		69.45 119	67.74 125	74.58 78	74.57 195	54.92 109	82.79 41	78.48 158	51.26 220	65.41 150	83.49 115	38.37 204	83.24 125	66.06 62	69.25 220	85.56 72

EI-MVSNet		69.27 121	68.44 116	71.73 155	74.47 196	49.39 197	75.20 177	78.45 160	59.60 103	69.16 100	76.51 256	51.29 59	82.50 150	59.86 130	71.45 183	83.30 152

CVMVSNet		59.63 241	59.14 233	61.08 271	74.47 196	38.84 273	75.20 177	68.74 247	31.15 327	58.24 246	76.51 256	32.39 271	68.58 275	49.77 183	65.84 245	75.81 254

IterMVS-LS		69.22 124	68.48 114	71.43 161	74.44 198	49.40 196	76.23 157	77.55 173	59.60 103	65.85 147	81.59 156	51.28 60	81.58 165	59.87 129	69.90 211	83.30 152
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
XVG-OURS-SEG-HR		68.81 128	67.47 133	72.82 130	74.40 199	56.87 82	70.59 240	79.04 145	54.77 182	66.99 132	86.01 75	39.57 194	78.21 220	62.54 102	73.33 154	83.37 151

XVG-OURS		68.76 131	67.37 136	72.90 125	74.32 200	57.22 74	70.09 247	78.81 149	55.24 175	67.79 124	85.81 81	36.54 230	78.28 219	62.04 113	75.74 131	83.19 156

OpenMVS	61.03 9	68.85 127	67.56 129	72.70 132	74.26 201	53.99 115	81.21 70	81.34 86	52.70 199	62.75 180	85.55 84	38.86 200	84.14 105	48.41 195	83.01 56	79.97 212

MIMVSNet		57.35 257	57.07 251	58.22 280	74.21 202	37.18 285	62.46 286	60.88 296	48.88 238	55.29 270	75.99 262	31.68 273	62.04 298	31.87 287	72.35 173	75.43 259

tfpn_n400		59.40 243	58.81 235	61.17 268	74.15 203	33.83 311	68.32 258	64.22 275	51.79 208	58.04 248	79.57 204	35.41 234	75.41 247	29.57 309	68.26 227	74.25 273

tfpnconf		59.40 243	58.81 235	61.17 268	74.15 203	33.83 311	68.32 258	64.22 275	51.79 208	58.04 248	79.57 204	35.41 234	75.41 247	29.57 309	68.26 227	74.25 273

tfpnview11		59.40 243	58.81 235	61.17 268	74.15 203	33.83 311	68.32 258	64.22 275	51.79 208	58.04 248	79.57 204	35.41 234	75.41 247	29.57 309	68.26 227	74.25 273

Patchmatch-test1		59.75 239	58.00 247	64.98 245	74.14 206	48.06 208	63.35 283	63.23 284	49.13 236	59.33 234	71.46 287	37.45 213	69.59 270	41.39 249	62.57 270	77.30 237

tfpn_ndepth		59.57 242	59.02 234	61.23 267	73.81 207	35.60 300	69.40 253	65.59 266	50.96 224	57.96 251	77.72 233	34.81 240	75.91 245	30.36 300	70.57 191	72.18 295

tfpn1000		59.24 247	58.70 240	60.86 272	73.75 208	33.99 309	68.86 256	63.98 279	51.25 222	57.29 256	79.51 209	34.58 242	75.26 250	29.08 313	69.99 208	73.32 281

K. test v3		60.47 235	57.11 250	70.56 177	73.74 209	48.22 206	75.10 180	62.55 289	58.27 128	53.62 285	76.31 258	27.81 293	81.59 164	47.42 197	39.18 331	81.88 179

v10		70.21 104	69.02 107	73.81 88	73.51 210	50.92 153	78.74 97	81.39 84	60.05 86	66.39 139	81.83 147	47.58 114	85.41 77	62.80 100	68.86 223	85.09 97

v13		68.29 143	66.84 150	72.63 133	73.50 211	50.83 156	78.25 108	79.58 131	60.05 86	60.76 218	77.68 235	49.11 99	82.77 141	62.17 110	60.45 290	84.30 119

v7		70.57 92	69.48 101	73.85 86	73.50 211	50.92 153	78.27 107	81.43 82	58.93 116	69.61 86	81.49 158	47.56 115	85.43 76	63.94 90	70.62 189	85.21 93

v12		68.28 144	66.83 152	72.60 135	73.43 213	50.74 158	78.18 110	79.59 129	60.01 88	60.89 217	77.66 236	49.12 96	82.77 141	62.18 108	60.46 289	84.29 120

v11		68.15 153	66.73 155	72.42 145	73.43 213	50.28 173	77.94 121	79.65 123	59.88 93	61.11 214	77.55 241	48.25 107	82.75 146	61.88 117	60.85 283	84.23 124

tpmp4_e23		62.71 219	60.13 228	70.45 179	73.40 215	48.39 205	72.82 210	69.49 239	44.88 273	59.91 225	74.99 268	37.79 211	81.47 167	40.22 252	67.71 236	81.48 183

V9		68.27 145	66.84 150	72.56 136	73.39 216	50.63 161	78.10 115	79.60 126	59.94 89	61.05 215	77.62 237	49.18 92	82.77 141	62.17 110	60.48 288	84.27 121

V14		68.25 147	66.82 153	72.52 138	73.33 217	50.53 166	78.02 118	79.60 126	59.83 94	61.16 213	77.57 240	49.19 91	82.77 141	62.18 108	60.50 287	84.26 122

v15		68.22 150	66.81 154	72.47 143	73.25 218	50.40 169	77.92 122	79.60 126	59.77 97	61.28 211	77.52 242	49.25 87	82.77 141	62.16 112	60.51 286	84.24 123

v1144		70.42 100	69.31 104	73.76 91	73.22 219	50.64 160	77.83 123	81.43 82	58.58 121	69.40 94	81.16 165	47.53 116	85.29 79	64.01 88	70.64 188	85.34 88

v17		68.37 141	67.00 147	72.48 139	73.22 219	50.31 171	78.10 115	79.58 131	59.71 98	61.67 203	77.60 238	49.31 77	82.89 135	62.37 105	61.48 279	84.23 124

v16		68.38 140	67.01 146	72.47 143	73.22 219	50.29 172	78.10 115	79.59 129	59.71 98	61.72 202	77.60 238	49.28 83	82.89 135	62.36 106	61.54 276	84.23 124

v1192		69.97 109	68.68 111	73.85 86	73.19 222	50.94 151	77.68 129	81.36 85	57.51 133	68.95 102	80.85 176	45.28 142	85.33 78	62.97 99	70.37 198	85.27 91

v1neww		70.66 88	69.70 91	73.53 102	73.15 223	50.22 175	78.11 112	80.68 103	59.65 100	69.83 81	81.67 150	49.29 80	84.96 84	64.55 77	70.38 196	85.42 82

v7new		70.66 88	69.70 91	73.53 102	73.15 223	50.22 175	78.11 112	80.68 103	59.65 100	69.83 81	81.67 150	49.29 80	84.96 84	64.55 77	70.38 196	85.42 82

v8		70.33 102	69.28 105	73.49 105	73.15 223	50.22 175	78.62 100	80.78 102	60.79 70	66.45 138	82.11 140	49.35 76	84.98 82	63.58 95	68.71 224	85.28 90

v6		70.66 88	69.70 91	73.53 102	73.14 226	50.21 178	78.11 112	80.67 105	59.65 100	69.82 83	81.65 152	49.29 80	84.96 84	64.55 77	70.39 195	85.42 82

v18		68.33 142	66.96 148	72.42 145	73.13 227	50.16 180	77.97 120	79.57 133	59.57 107	61.80 200	77.50 243	49.30 78	82.90 134	62.31 107	61.50 277	84.20 130

v144192		69.71 111	68.51 113	73.33 115	73.10 228	50.13 182	77.54 132	80.64 106	56.65 147	68.57 106	80.55 183	46.87 126	84.96 84	62.98 98	69.66 217	84.89 103

v1921920		69.47 118	68.17 120	73.36 114	73.06 229	50.10 183	77.39 134	80.56 110	56.58 153	68.59 104	80.37 186	44.72 145	84.98 82	62.47 104	69.82 212	85.00 99

PatchmatchNet		59.84 238	58.24 243	64.65 247	73.05 230	46.70 221	69.42 252	62.18 291	47.55 252	58.88 239	71.96 285	34.49 245	69.16 272	42.99 237	63.60 262	78.07 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
v1240		69.24 123	67.91 123	73.25 120	73.02 231	49.82 188	77.21 139	80.54 111	56.43 155	68.34 110	80.51 184	43.33 160	84.99 80	62.03 114	69.77 215	84.95 102

Fast-Effi-MVS+-dtu		67.37 159	65.33 176	73.48 106	72.94 232	57.78 66	77.47 133	76.88 182	57.60 132	61.97 197	76.85 250	39.31 195	80.49 183	54.72 151	70.28 204	82.17 175

v1141		70.50 95	69.53 96	73.41 111	72.92 233	50.00 185	77.69 126	80.60 107	59.50 108	69.60 87	81.43 159	49.24 90	84.77 92	64.48 81	70.30 202	85.46 78

divwei89l23v2f112		70.50 95	69.53 96	73.41 111	72.91 234	50.00 185	77.69 126	80.59 108	59.50 108	69.60 87	81.43 159	49.26 85	84.77 92	64.48 81	70.31 201	85.47 76

v1		70.50 95	69.53 96	73.42 110	72.91 234	50.00 185	77.69 126	80.59 108	59.50 108	69.59 89	81.42 161	49.26 85	84.77 92	64.49 80	70.30 202	85.47 76

EPNet_dtu		61.90 226	61.97 211	61.68 262	72.89 236	39.78 265	75.85 166	65.62 265	55.09 177	54.56 276	79.36 210	37.59 212	67.02 280	39.80 256	76.95 122	78.25 229
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm2		62.07 225	60.10 229	67.99 205	72.79 237	43.86 240	71.05 236	66.85 260	43.14 290	62.77 178	75.39 266	38.32 205	80.80 178	41.69 245	68.88 222	79.32 221

MDTV_nov1_ep13			57.00 252		72.73 238	38.26 279	65.02 278	64.73 272	44.74 275	55.46 266	72.48 282	32.61 269	70.47 268	37.47 263	67.75 235

MSDG		61.81 229	59.23 232	69.55 192	72.64 239	52.63 133	70.45 243	75.81 192	51.38 218	53.70 283	76.11 259	29.52 283	81.08 173	37.70 262	65.79 246	74.93 265

gg-mvs-nofinetune		57.86 255	56.43 257	62.18 260	72.62 240	35.35 302	66.57 265	56.33 313	50.65 225	57.64 253	57.10 327	30.65 276	76.36 241	37.38 264	78.88 103	74.82 267

v2v482		70.50 95	69.45 103	73.66 97	72.62 240	50.03 184	77.58 130	80.51 112	59.90 90	69.52 90	82.14 139	47.53 116	84.88 91	65.07 72	70.17 205	86.09 55

v7n		69.01 126	67.36 137	73.98 84	72.51 242	52.65 132	78.54 103	81.30 87	60.26 83	62.67 181	81.62 153	43.61 157	84.49 99	57.01 139	68.70 225	84.79 107

pm-mvs1		65.24 192	64.97 182	66.04 227	72.38 243	39.40 269	72.62 214	75.63 193	55.53 171	62.35 195	83.18 119	47.45 118	76.47 240	49.06 190	66.54 241	82.24 173

XVG-ACMP-BASELINE		64.36 201	62.23 208	70.74 174	72.35 244	52.45 138	70.80 239	78.45 160	53.84 192	59.87 226	81.10 167	16.24 325	79.32 196	55.64 148	71.76 179	80.47 205

WTY-MVS		59.75 239	60.39 227	57.85 283	72.32 245	37.83 282	61.05 295	64.18 278	45.95 268	61.91 198	79.11 214	47.01 124	60.88 301	42.50 240	69.49 219	74.83 266

tpm cat1		59.25 246	56.95 253	66.15 225	72.19 246	46.96 218	68.09 262	65.76 264	40.03 308	57.81 252	70.56 293	38.32 205	74.51 255	38.26 260	61.50 277	77.00 244

PatchFormer-LS_test		62.20 223	60.59 226	67.04 213	72.18 247	46.82 220	70.36 245	68.62 248	51.92 206	59.19 235	70.23 295	36.86 227	75.07 252	50.23 181	65.68 247	79.23 223

mvs_anonymous		68.03 154	67.51 132	69.59 189	72.08 248	44.57 234	71.99 227	75.23 198	51.67 211	67.06 131	82.57 127	54.68 30	77.94 223	56.56 140	75.71 132	86.26 52

OurMVSNet-221017-0		61.37 232	58.63 242	69.61 188	72.05 249	48.06 208	73.93 198	72.51 220	47.23 255	54.74 273	80.92 173	21.49 319	81.24 169	48.57 194	56.22 300	79.53 219

semantic-postprocess				65.40 240	71.99 250	50.80 157		69.63 236	45.71 270	60.61 219	77.93 227	36.56 229	65.99 286	55.67 147	63.50 263	79.42 220

DWT-MVSNet_test		61.90 226	59.93 230	67.83 206	71.98 251	46.09 224	71.03 237	69.71 233	50.09 229	58.51 244	70.62 292	30.21 280	77.63 226	49.28 188	67.91 232	79.78 216

CostFormer		64.04 202	62.51 203	68.61 201	71.88 252	45.77 225	71.30 232	70.60 229	47.55 252	64.31 168	76.61 254	41.63 173	79.62 192	49.74 184	69.00 221	80.42 206

1314		64.61 199	63.21 196	68.80 198	71.87 253	47.46 215	73.95 195	78.39 164	42.88 292	59.97 224	76.60 255	38.11 208	79.39 195	54.84 150	72.32 174	79.55 218

tpm		57.34 258	58.16 244	54.86 295	71.80 254	34.77 304	67.47 264	56.04 316	48.20 246	60.10 223	76.92 248	37.17 217	53.41 329	40.76 251	65.01 251	76.40 249

pmmvs4		61.48 231	59.39 231	67.76 207	71.57 255	53.86 116	71.42 230	65.34 267	44.20 281	59.46 230	77.92 228	35.90 231	74.71 254	43.87 229	64.87 252	74.71 269

AllTest		57.08 260	54.65 267	64.39 248	71.44 256	49.03 198	69.92 249	67.30 256	45.97 266	47.16 306	79.77 200	17.47 322	67.56 277	33.65 281	59.16 294	76.57 247

TestCases				64.39 248	71.44 256	49.03 198		67.30 256	45.97 266	47.16 306	79.77 200	17.47 322	67.56 277	33.65 281	59.16 294	76.57 247

lessismore_v0				69.91 185	71.42 258	47.80 210		50.90 330		50.39 299	75.56 265	27.43 297	81.33 168	45.91 211	34.10 334	80.59 204

gm-plane-assit					71.40 259	41.72 256			48.85 239		73.31 280		82.48 152	48.90 191

GG-mvs-BLEND				62.34 259	71.36 260	37.04 287	69.20 254	57.33 308		54.73 274	65.48 313	30.37 277	77.82 224	34.82 277	74.93 135	72.17 296

DI_MVS_plusplus_test		69.35 120	68.03 122	73.30 117	71.11 261	50.14 181	75.49 170	79.16 143	54.57 185	62.45 189	80.76 179	44.67 147	84.20 103	64.23 84	79.81 89	85.54 73

FMVSNet5		55.86 267	54.93 265	58.66 279	71.05 262	36.35 295	64.18 282	62.48 290	46.76 258	50.66 298	74.73 271	25.80 306	64.04 291	33.11 283	65.57 248	75.59 257

GA-MVS		65.53 188	63.70 191	71.02 171	70.87 263	48.10 207	70.48 242	74.40 209	56.69 146	64.70 163	76.77 251	33.66 254	81.10 172	55.42 149	70.32 200	83.87 137

test_normal		69.26 122	67.90 124	73.32 116	70.84 264	50.38 170	75.30 173	79.17 142	54.23 189	62.00 196	80.61 182	44.69 146	83.89 113	64.33 83	79.95 88	85.69 68

pmmvs6		63.69 204	62.82 201	66.27 223	70.63 265	39.27 270	73.13 206	75.47 195	52.69 200	59.75 229	82.30 133	39.71 192	77.03 234	47.40 198	64.35 258	82.53 168

OpenMVS_ROB	52.78 18	60.03 236	58.14 245	65.69 234	70.47 266	44.82 229	75.33 172	70.86 227	45.04 272	56.06 262	76.00 260	26.89 301	79.65 191	35.36 276	67.29 238	72.60 286

v148		68.24 149	67.19 144	71.40 162	70.43 267	47.77 212	75.76 167	77.03 181	58.91 117	67.36 128	80.10 194	48.60 104	81.89 158	60.01 127	66.52 242	84.53 113

XXY-MVS		60.68 234	61.67 217	57.70 285	70.43 267	38.45 278	64.19 281	66.47 261	48.05 248	63.22 174	80.86 175	49.28 83	60.47 302	45.25 222	67.28 239	74.19 276

MVSTER		67.16 164	65.58 173	71.88 152	70.37 269	49.70 191	70.25 246	78.45 160	51.52 215	69.16 100	80.37 186	38.45 203	82.50 150	60.19 126	71.46 182	83.44 150

tpmvs		58.47 250	56.95 253	63.03 256	70.20 270	41.21 258	67.90 263	67.23 258	49.62 233	54.73 274	70.84 291	34.14 248	76.24 243	36.64 270	61.29 280	71.64 298

anonymousdsp		67.00 169	64.82 183	73.57 101	70.09 271	56.13 90	76.35 153	77.35 178	48.43 243	64.99 160	80.84 177	33.01 259	80.34 184	64.66 74	67.64 237	84.23 124

MIMVSNet1		55.17 271	54.31 270	57.77 284	70.03 272	32.01 320	65.68 270	64.81 270	49.19 235	46.75 309	76.00 260	25.53 308	64.04 291	28.65 315	62.13 273	77.26 240

CR-MVSNet		59.91 237	57.90 248	65.96 228	69.96 273	52.07 142	65.31 275	63.15 285	42.48 294	59.36 231	74.84 269	35.83 232	70.75 266	45.50 217	64.65 256	75.06 261

RPMNet		58.70 249	56.29 259	65.96 228	69.96 273	52.07 142	65.31 275	62.15 292	43.20 289	59.36 231	70.15 297	35.37 237	70.75 266	36.42 273	64.65 256	75.06 261

v748		67.26 161	65.67 171	72.02 150	69.90 275	49.77 190	76.24 156	79.57 133	58.58 121	60.49 221	80.38 185	44.47 151	82.17 155	56.16 142	65.26 250	84.12 132

Anonymous20231206		55.10 272	55.30 264	54.48 297	69.81 276	33.94 310	62.91 285	62.13 293	41.08 300	55.18 271	75.65 264	32.75 265	56.59 318	30.32 301	67.86 233	72.91 283

diffmvs		67.72 157	66.73 155	70.70 176	69.74 277	47.69 213	73.33 204	74.74 205	53.30 195	64.51 165	81.80 148	49.25 87	79.02 206	59.15 132	74.75 136	85.39 87

IterMVS		62.79 214	61.27 221	67.35 211	69.37 278	52.04 144	71.17 234	68.24 250	52.63 201	59.82 227	76.91 249	37.32 214	72.36 260	52.80 165	63.19 266	77.66 233
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		57.16 259	56.47 256	59.23 274	69.17 279	34.58 306	62.98 284	63.15 285	44.53 278	56.83 258	74.84 269	35.83 232	68.71 274	40.03 254	60.91 281	74.39 271

V42		68.65 132	67.35 138	72.56 136	68.93 280	50.18 179	72.90 209	79.47 136	56.92 140	69.45 93	80.26 192	46.29 130	82.99 131	64.07 86	67.82 234	84.53 113

Test4		67.77 156	65.97 168	73.19 121	68.64 281	50.58 162	74.80 187	80.48 113	54.13 190	59.11 236	79.07 215	33.89 252	83.12 129	63.61 94	79.98 87	85.87 60

test-LLR		58.15 254	58.13 246	58.22 280	68.57 282	44.80 230	65.46 272	57.92 305	50.08 230	55.44 267	69.82 298	32.62 267	57.44 312	49.66 186	73.62 148	72.41 291

test-mter		56.42 262	55.82 261	58.22 280	68.57 282	44.80 230	65.46 272	57.92 305	39.94 309	55.44 267	69.82 298	21.92 318	57.44 312	49.66 186	73.62 148	72.41 291

MVS-HIRNet		45.52 298	44.48 300	48.65 313	68.49 284	34.05 308	59.41 300	44.50 340	27.03 331	37.96 330	50.47 334	26.16 305	64.10 290	26.74 320	59.52 292	47.82 332

dp		51.89 284	51.60 282	52.77 304	68.44 285	32.45 318	62.36 287	54.57 321	44.16 282	49.31 301	67.91 302	28.87 289	56.61 317	33.89 280	54.89 304	69.24 310

PatchT		53.17 280	53.44 277	52.33 306	68.29 286	25.34 337	58.21 302	54.41 322	44.46 279	54.56 276	69.05 300	33.32 257	60.94 300	36.93 266	61.76 275	70.73 304

Anonymous20231211		55.92 266	53.63 275	62.77 257	68.22 287	35.56 301	74.48 191	69.89 232	46.42 260	49.07 302	73.45 279	21.13 320	76.77 237	28.74 314	51.30 315	75.97 250

Patchmatch-RL test		58.16 253	55.49 263	66.15 225	67.92 288	48.89 201	60.66 296	51.07 329	47.86 250	59.36 231	62.71 320	34.02 250	72.27 261	56.41 141	59.40 293	77.30 237

pmmvs-eth3d		58.81 248	56.31 258	66.30 221	67.61 289	52.42 139	72.30 219	64.76 271	43.55 286	54.94 272	74.19 275	28.95 287	72.60 259	43.31 232	57.21 298	73.88 279

PVSNet_0	43.31 20	47.46 295	45.64 295	52.92 303	67.60 290	44.65 232	54.06 312	54.64 320	41.59 298	46.15 310	58.75 326	30.99 274	58.66 308	32.18 285	24.81 337	55.46 329

CHOSEN 280x420		47.83 293	46.36 294	52.24 307	67.37 291	49.78 189	38.91 337	43.11 341	35.00 321	43.27 321	63.30 319	28.95 287	49.19 334	36.53 271	60.80 284	57.76 327

tpmrst		58.24 252	58.70 240	56.84 287	66.97 292	34.32 307	69.57 251	61.14 295	47.17 256	58.58 243	71.60 286	41.28 182	60.41 303	49.20 189	62.84 268	75.78 255

sss		56.17 265	56.57 255	54.96 294	66.93 293	36.32 297	57.94 303	61.69 294	41.67 297	58.64 242	75.32 267	38.72 201	56.25 321	42.04 243	66.19 243	72.31 294

TinyColmap		54.14 273	51.72 281	61.40 265	66.84 294	41.97 252	66.52 266	68.51 249	44.81 274	42.69 323	75.77 263	11.66 335	72.94 258	31.96 286	56.77 299	69.27 309

v52		67.09 165	65.16 179	72.87 126	66.77 295	51.60 147	73.69 201	79.45 138	57.88 130	62.46 188	78.57 222	40.95 186	83.34 121	61.99 115	64.70 255	83.68 144

V4		67.09 165	65.16 179	72.87 126	66.76 296	51.60 147	73.69 201	79.45 138	57.88 130	62.45 189	78.58 221	40.96 185	83.34 121	61.99 115	64.71 253	83.68 144

TESTMET0.1,1		55.28 270	54.90 266	56.42 288	66.56 297	43.67 242	65.46 272	56.27 314	39.18 311	53.83 282	67.44 305	24.21 313	55.46 326	48.04 196	73.11 160	70.13 305

MDA-MVSNet-bldmvs		53.87 276	50.81 283	63.05 255	66.25 298	48.58 203	56.93 306	63.82 280	48.09 247	41.22 324	70.48 294	30.34 278	68.00 276	34.24 279	45.92 326	72.57 287

ITE_SJBPF				62.09 261	66.16 299	44.55 235		64.32 274	47.36 254	55.31 269	80.34 188	19.27 321	62.68 296	36.29 274	62.39 272	79.04 224

EPMVS		53.96 274	53.69 274	54.79 296	66.12 300	31.96 321	62.34 288	49.05 332	44.42 280	55.54 265	71.33 289	30.22 279	56.70 316	41.65 247	62.54 271	75.71 256

testing_2		66.02 183	63.77 190	72.76 131	66.03 301	50.48 168	72.93 208	80.36 116	54.41 187	54.25 280	76.76 252	30.89 275	83.16 128	64.19 85	74.08 143	84.65 110

ADS-MVSNet2		51.33 286	48.76 289	59.07 276	66.02 302	44.60 233	50.90 321	59.76 299	36.90 316	50.74 296	66.18 311	26.38 302	63.11 293	27.17 317	54.76 305	69.50 307

ADS-MVSNet		48.48 292	47.77 291	50.63 309	66.02 302	29.92 325	50.90 321	50.87 331	36.90 316	50.74 296	66.18 311	26.38 302	52.47 331	27.17 317	54.76 305	69.50 307

EU-MVSNet		55.61 269	54.41 269	59.19 275	65.41 304	33.42 315	72.44 217	71.91 224	28.81 329	51.27 293	73.87 276	24.76 311	69.08 273	43.04 236	58.20 297	75.06 261

RPSCF		55.80 268	54.22 272	60.53 273	65.13 305	42.91 249	64.30 280	57.62 307	36.84 318	58.05 247	82.28 134	28.01 291	56.24 322	37.14 265	58.61 296	82.44 172

USDC		56.35 263	54.24 271	62.69 258	64.74 306	40.31 262	65.05 277	73.83 213	43.93 284	47.58 304	77.71 234	15.36 327	75.05 253	38.19 261	61.81 274	72.70 285

JIA-IIPM		51.56 285	47.68 293	63.21 253	64.61 307	50.73 159	47.71 326	58.77 302	42.90 291	48.46 303	51.72 331	24.97 310	70.24 269	36.06 275	53.89 308	68.64 311

Patchmatch-test		49.08 290	48.28 290	51.50 308	64.40 308	30.85 324	45.68 329	48.46 335	35.60 320	46.10 312	72.10 284	34.47 246	46.37 335	27.08 319	60.65 285	77.27 239

TDRefinement		53.44 278	50.72 284	61.60 263	64.31 309	46.96 218	70.89 238	65.27 269	41.78 295	44.61 315	77.98 225	11.52 336	66.36 283	28.57 316	51.59 313	71.49 299

N_pmnet		39.35 309	40.28 306	36.54 326	63.76 310	1.62 352	49.37 324	0.76 354	34.62 322	43.61 320	66.38 310	26.25 304	42.57 341	26.02 323	51.77 312	65.44 314

ambc				65.13 243	63.72 311	37.07 286	47.66 327	78.78 150		54.37 279	71.42 288	11.24 337	80.94 174	45.64 214	53.85 309	77.38 236

LP		48.51 291	45.51 296	57.52 286	62.86 312	44.53 236	52.38 318	59.84 298	38.11 313	42.81 322	61.02 321	23.23 314	63.02 294	24.10 324	45.24 327	65.02 316

test0.0.03 1		53.32 279	53.59 276	52.50 305	62.81 313	29.45 326	59.51 298	54.11 324	50.08 230	54.40 278	74.31 274	32.62 267	55.92 323	30.50 299	63.95 260	72.15 297

PMMVS		53.96 274	53.26 278	56.04 289	62.60 314	50.92 153	61.17 294	56.09 315	32.81 324	53.51 287	66.84 307	34.04 249	59.93 305	44.14 226	68.18 230	57.27 328

PM-MVS		52.33 282	50.19 285	58.75 278	62.10 315	45.14 228	65.75 269	40.38 342	43.60 285	53.52 286	72.65 281	9.16 341	65.87 287	50.41 178	54.18 307	65.24 315

Gipuma		34.77 314	31.91 316	43.33 321	62.05 316	37.87 281	20.39 342	67.03 259	23.23 335	18.41 340	25.84 340	4.24 347	62.73 295	14.71 338	51.32 314	29.38 340
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test20.03		53.87 276	54.02 273	53.41 301	61.47 317	28.11 329	61.30 292	59.21 300	51.34 219	52.09 291	77.43 244	33.29 258	58.55 309	29.76 307	60.27 291	73.58 280

pmmvs5		56.47 261	55.68 262	58.86 277	61.41 318	36.71 293	66.37 267	62.75 288	40.38 306	53.70 283	76.62 253	34.56 243	67.05 279	40.02 255	65.27 249	72.83 284

MDA-MVSNet_test_wron		50.71 288	48.95 287	56.00 291	61.17 319	41.84 253	51.90 320	56.45 311	40.96 301	44.79 314	67.84 303	30.04 282	55.07 328	36.71 269	50.69 317	71.11 303

YYNet1		50.73 287	48.96 286	56.03 290	61.10 320	41.78 254	51.94 319	56.44 312	40.94 302	44.84 313	67.80 304	30.08 281	55.08 327	36.77 267	50.71 316	71.22 300

CMPMVS	42.80 21	57.81 256	55.97 260	63.32 252	60.98 321	47.38 216	64.66 279	69.50 238	32.06 326	46.83 308	77.80 231	29.50 284	71.36 263	48.68 192	73.75 146	71.21 301
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_bld		50.07 289	48.87 288	53.66 299	60.97 322	33.67 314	57.62 304	64.56 273	39.47 310	47.38 305	64.02 316	27.47 295	59.32 306	34.69 278	43.68 329	67.98 312

testpf		44.11 302	45.40 297	40.26 324	60.52 323	27.34 331	33.26 339	54.33 323	45.87 269	41.08 325	60.26 323	16.46 324	59.14 307	46.09 208	50.68 318	34.31 338

testgi		51.90 283	52.37 280	50.51 310	60.39 324	23.55 340	58.42 301	58.15 303	49.03 237	51.83 292	79.21 213	22.39 316	55.59 324	29.24 312	62.64 269	72.40 293

test2356		45.61 297	44.66 299	48.47 314	60.15 325	28.08 330	52.44 317	52.83 328	38.01 314	46.13 311	60.98 322	15.08 328	55.54 325	20.43 333	55.85 302	61.78 320

test1235678		45.66 296	44.46 301	49.26 311	59.88 326	28.68 328	56.36 308	55.54 319	39.12 312	40.89 326	63.40 318	14.41 329	57.32 314	21.05 330	49.47 321	61.78 320

testus		44.59 300	43.87 302	46.76 316	59.85 327	24.65 338	53.86 313	55.82 317	36.26 319	43.97 319	63.42 317	8.39 342	53.14 330	20.70 332	52.52 311	62.51 318

UnsupCasMVSNet_eth		53.16 281	52.47 279	55.23 292	59.45 328	33.39 316	59.43 299	69.13 243	45.98 265	50.35 300	72.32 283	29.30 286	58.26 310	42.02 244	44.30 328	74.05 277

new-patchmatchnet		47.56 294	47.73 292	47.06 315	58.81 329	9.37 348	48.78 325	59.21 300	43.28 287	44.22 316	68.66 301	25.67 307	57.20 315	31.57 295	49.35 322	74.62 270

FPMVS		42.18 305	41.11 305	45.39 317	58.03 330	41.01 260	49.50 323	53.81 326	30.07 328	33.71 331	64.03 314	11.69 334	52.08 332	14.01 339	55.11 303	43.09 335

1111		44.40 301	45.00 298	42.61 322	57.55 331	17.33 345	53.82 315	57.05 309	40.78 303	44.11 317	66.57 308	13.37 330	45.77 336	22.15 326	49.58 320	64.73 317

.test1245		34.88 313	39.49 308	21.04 334	57.55 331	17.33 345	53.82 315	57.05 309	40.78 303	44.11 317	66.57 308	13.37 330	45.77 336	22.15 326	0.00 348	0.03 347

testmv		42.25 304	40.11 307	48.66 312	53.23 333	27.02 332	56.62 307	55.74 318	37.25 315	33.10 332	59.52 325	7.78 343	56.58 319	19.61 334	38.13 333	62.40 319

new_pmnet		34.13 315	34.29 314	33.64 327	52.63 334	18.23 344	44.43 333	33.90 345	22.81 336	30.89 333	53.18 329	10.48 339	35.72 345	20.77 331	39.51 330	46.98 333

pmmvs3		44.92 299	41.95 304	53.86 298	52.58 335	43.55 243	62.11 289	46.90 339	26.05 333	40.63 327	60.19 324	11.08 338	57.91 311	31.83 292	46.15 325	60.11 324

DSMNet-mixed		39.30 310	38.72 309	41.03 323	51.22 336	19.66 342	45.53 330	31.35 347	15.83 341	39.80 329	67.42 306	22.19 317	45.13 338	22.43 325	52.69 310	58.31 326

test12356		36.16 312	35.94 312	36.83 325	50.82 337	8.52 349	44.84 332	53.49 327	32.72 325	30.11 334	55.08 328	7.11 345	49.47 333	16.60 336	32.68 335	52.50 330

no-one		40.85 307	36.09 311	55.14 293	48.55 338	38.72 274	42.15 335	62.92 287	34.60 323	23.55 337	49.74 335	12.21 333	66.16 285	26.27 322	24.84 336	60.54 323

LF4IMVS		42.95 303	42.26 303	45.04 318	48.30 339	32.50 317	54.80 310	48.49 334	28.03 330	40.51 328	70.16 296	9.24 340	43.89 339	31.63 293	49.18 323	58.72 325

PNet_i23d		27.88 318	25.99 318	33.55 328	47.54 340	25.89 334	47.24 328	32.91 346	21.44 338	15.90 341	38.09 337	0.85 353	42.76 340	16.90 335	13.03 343	32.00 339

wuyk23d		13.32 324	12.52 325	15.71 335	47.54 340	26.27 333	31.06 341	1.98 353	4.93 345	5.18 348	1.94 348	0.45 354	18.54 347	6.81 346	12.83 344	2.33 345

LCM-MVSNet		40.30 308	35.88 313	53.57 300	42.24 342	29.15 327	45.21 331	60.53 297	22.23 337	28.02 335	50.98 333	3.72 349	61.78 299	31.22 298	38.76 332	69.78 306

E-PMN		23.77 320	22.73 321	26.90 332	42.02 343	20.67 341	42.66 334	35.70 343	17.43 339	10.28 345	25.05 341	6.42 346	42.39 342	10.28 342	14.71 340	17.63 341

EMVS		22.97 321	21.84 323	26.36 333	40.20 344	19.53 343	41.95 336	34.64 344	17.09 340	9.73 346	22.83 343	7.29 344	42.22 343	9.18 344	13.66 342	17.32 342

ANet_high		41.38 306	37.47 310	53.11 302	39.73 345	24.45 339	56.94 305	69.69 234	47.65 251	26.04 336	52.32 330	12.44 332	62.38 297	21.80 329	10.61 345	72.49 288

PMMVS2		27.40 319	25.91 319	31.87 330	39.46 346	6.57 350	31.17 340	28.52 348	23.96 334	20.45 339	48.94 336	4.20 348	37.94 344	16.51 337	19.97 338	51.09 331

PMVS	28.69 22	36.22 311	33.29 315	45.02 319	36.82 347	35.98 299	54.68 311	48.74 333	26.31 332	21.02 338	51.61 332	2.88 351	60.10 304	9.99 343	47.58 324	38.99 337
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
wuykxyi23d		28.12 317	22.54 322	44.87 320	34.97 348	32.11 319	37.96 338	47.31 337	13.32 342	9.29 347	23.72 342	0.45 354	56.58 319	21.85 328	13.98 341	45.93 334

MVE	17.77 23	21.41 322	17.77 324	32.34 329	34.34 349	25.44 336	16.11 343	24.11 349	11.19 343	13.22 343	31.92 338	1.58 352	30.95 346	10.47 341	17.03 339	40.62 336
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 336	17.97 350	10.91 347		10.60 352	7.46 344	11.07 344	28.36 339	3.28 350	11.29 348	8.01 345	9.74 347	13.89 343

tmp_tt		9.43 325	11.14 326	4.30 337	2.38 351	4.40 351	13.62 344	16.08 351	0.39 346	15.89 342	13.06 344	15.80 326	5.54 349	12.63 340	10.46 346	2.95 344

testmvs		4.52 328	6.03 329	0.01 339	0.01 352	0.00 354	53.86 313	0.00 355	0.01 347	0.04 349	0.27 349	0.00 357	0.00 350	0.04 347	0.00 348	0.03 347

test123		4.73 327	6.30 328	0.02 338	0.01 352	0.01 353	56.36 308	0.00 355	0.01 347	0.04 349	0.21 350	0.01 356	0.00 350	0.03 348	0.00 348	0.04 346

cdsmvs_eth3d_5k		17.50 323	23.34 320	0.00 340	0.00 354	0.00 354	0.00 345	78.63 153	0.00 349	0.00 351	82.18 135	49.25 87	0.00 350	0.00 349	0.00 348	0.00 349

pcd_1.5k_mvsjas		3.92 329	5.23 330	0.00 340	0.00 354	0.00 354	0.00 345	0.00 355	0.00 349	0.00 351	0.00 351	47.05 121	0.00 350	0.00 349	0.00 348	0.00 349

sosnet-low-res		0.00 330	0.00 331	0.00 340	0.00 354	0.00 354	0.00 345	0.00 355	0.00 349	0.00 351	0.00 351	0.00 357	0.00 350	0.00 349	0.00 348	0.00 349

sosnet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 354	0.00 345	0.00 355	0.00 349	0.00 351	0.00 351	0.00 357	0.00 350	0.00 349	0.00 348	0.00 349

uncertanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 354	0.00 345	0.00 355	0.00 349	0.00 351	0.00 351	0.00 357	0.00 350	0.00 349	0.00 348	0.00 349

Regformer		0.00 330	0.00 331	0.00 340	0.00 354	0.00 354	0.00 345	0.00 355	0.00 349	0.00 351	0.00 351	0.00 357	0.00 350	0.00 349	0.00 348	0.00 349

ab-mvs-re		6.49 326	8.65 327	0.00 340	0.00 354	0.00 354	0.00 345	0.00 355	0.00 349	0.00 351	77.89 229	0.00 357	0.00 350	0.00 349	0.00 348	0.00 349

uanet		0.00 330	0.00 331	0.00 340	0.00 354	0.00 354	0.00 345	0.00 355	0.00 349	0.00 351	0.00 351	0.00 357	0.00 350	0.00 349	0.00 348	0.00 349

test_part1								86.64 4				65.59 1			90.06 3	86.78 39

test_all								86.76 3

sam_mvs1												34.74 241

sam_mvs												33.43 256

MTGPA								80.97 98

test_post1							68.67 257				3.64 346	32.39 271	69.49 271	44.17 225

test_post											3.55 347	33.90 251	66.52 282

patchmatchnet-post											64.03 314	34.50 244	74.27 256

MTMP								17.08 350

test9_res														75.28 14	88.31 17	83.81 138

agg_prior2														73.09 29	87.93 24	84.33 116

test_prior4						62.51 17	82.08 57

test_prior2							81.75 59		60.37 78	75.01 21	89.06 33	56.22 18		72.19 32	88.96 9

旧先验2							76.08 160		45.32 271	76.55 14			65.56 288	58.75 133

新几何2							76.12 158

无先验							79.66 89	74.30 210	48.40 244				80.78 179	53.62 158		79.03 225

原ACMM2							79.02 95

testdata2													72.18 262	46.95 203

segment_acmp												54.23 34

testdata1							72.65 211		60.50 75

plane_prior5								84.01 30					87.21 33	68.16 48	80.58 76	84.65 110

plane_prior4											86.10 73

plane_prior3						56.09 91			63.92 30	69.27 96

plane_prior2							84.22 22		64.52 24

plane_prior						56.31 85	83.58 31		63.19 37						80.48 79

n2								0.00 355

nn								0.00 355

door-mid								47.19 338

test11								83.47 45

door								47.60 336

HQP5-MVS						54.94 107

BP-MVS														67.04 58

HQP4-MVS										67.85 118			86.93 39			84.32 117

HQP3-MVS								83.90 34							80.35 82

HQP2-MVS												45.46 137

MDTV_nov1_ep13_2view						25.89 334	61.22 293		40.10 307	51.10 294		32.97 260		38.49 259		78.61 227

ACMMP++_ref															74.07 144

ACMMP++															72.16 176

Test By Simon												48.33 106

High-resolution multi-view benchmark