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.

MG-MVS		87.11 30	86.27 40	89.62 8	97.79 1	76.27 4	94.96 42	94.49 38	78.74 60	83.87 62	92.94 110	64.34 78	96.94 97	75.19 130	94.09 36	95.66 42

MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 39	96.26 24	72.84 22	99.38 1	92.64 4	95.93 8	97.08 7

OPU-MVS				89.97 3	97.52 3	73.15 11	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

DP-MVS Recon		82.73 97	81.65 104	85.98 79	97.31 4	67.06 101	95.15 34	91.99 136	69.08 228	76.50 131	93.89 93	54.48 184	98.20 32	70.76 165	85.66 125	92.69 146

testtj		86.62 40	86.66 39	86.50 63	96.95 5	65.70 139	94.41 48	93.45 78	67.74 237	86.19 34	96.39 20	64.38 77	97.91 42	87.33 42	93.14 53	95.90 38

ETH3 D test6400		90.27 5	90.44 6	89.75 6	96.82 6	74.33 7	95.89 16	94.80 26	77.13 78	89.13 18	97.38 2	74.49 15	98.48 24	92.32 9	95.98 6	96.46 21

CNVR-MVS		90.32 4	90.89 4	88.61 16	96.76 7	70.65 22	96.47 12	94.83 23	84.83 8	89.07 19	96.80 12	70.86 29	99.06 11	92.64 4	95.71 9	96.12 30

ZD-MVS					96.63 8	65.50 146		93.50 76	70.74 208	85.26 47	95.19 53	64.92 73	97.29 73	87.51 38	93.01 54

NCCC		89.07 12	89.46 12	87.91 21	96.60 9	69.05 49	96.38 13	94.64 33	84.42 9	86.74 30	96.20 25	66.56 56	98.76 18	89.03 28	94.56 29	95.92 37

IU-MVS					96.46 10	69.91 34		95.18 13	80.75 33	95.28 1				92.34 6	95.36 12	96.47 20

SED-MVS		89.94 7	90.36 7	88.70 13	96.45 11	69.38 42	96.89 4	94.44 40	71.65 184	92.11 3	97.21 5	76.79 7	99.11 4	92.34 6	95.36 12	97.62 2

test_241102_ONE					96.45 11	69.38 42		94.44 40	71.65 184	92.11 3	97.05 8	76.79 7	99.11 4

test_0728_SECOND				88.70 13	96.45 11	70.43 25	96.64 8	94.37 47					99.15 2	91.91 10	94.90 19	96.51 18

DVP-MVS		89.41 10	89.73 11	88.45 18	96.40 14	69.99 30	96.64 8	94.52 36	71.92 170	90.55 12	96.93 10	73.77 17	99.08 9	91.91 10	94.90 19	96.29 26

test0726					96.40 14	69.99 30	96.76 6	94.33 48	71.92 170	91.89 6	97.11 7	73.77 17

AdaColmap		78.94 158	77.00 174	84.76 119	96.34 16	65.86 135	92.66 118	87.97 277	62.18 281	70.56 186	92.37 126	43.53 268	97.35 69	64.50 224	82.86 144	91.05 180

test_part2					96.29 17	68.16 71				90.78 10

DPE-MVS		88.77 13	89.21 13	87.45 33	96.26 18	67.56 85	94.17 52	94.15 53	68.77 231	90.74 11	97.27 3	76.09 10	98.49 23	90.58 17	94.91 18	96.30 25

MAR-MVS		84.18 75	83.43 75	86.44 65	96.25 19	65.93 134	94.28 51	94.27 50	74.41 111	79.16 102	95.61 37	53.99 188	98.88 16	69.62 174	93.26 51	94.50 91
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
API-MVS		82.28 104	80.53 119	87.54 31	96.13 20	70.59 23	93.63 81	91.04 176	65.72 255	75.45 140	92.83 116	56.11 165	98.89 15	64.10 226	89.75 95	93.15 135

APDe-MVS		87.54 23	87.84 20	86.65 56	96.07 21	66.30 124	94.84 44	93.78 60	69.35 222	88.39 21	96.34 22	67.74 45	97.66 54	90.62 16	93.44 49	96.01 34

PAPR		85.15 58	84.47 62	87.18 39	96.02 22	68.29 66	91.85 147	93.00 100	76.59 86	79.03 103	95.00 56	61.59 107	97.61 58	78.16 113	89.00 97	95.63 43

APD-MVS		85.93 49	85.99 45	85.76 89	95.98 23	65.21 151	93.59 83	92.58 116	66.54 248	86.17 35	95.88 30	63.83 84	97.00 89	86.39 50	92.94 55	95.06 69
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	79.48 2	87.95 18	88.00 18	87.79 24	95.86 24	68.32 65	95.74 20	94.11 55	83.82 11	83.49 63	96.19 26	64.53 76	98.44 26	83.42 71	94.88 22	96.61 12
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DP-MVS		69.90 261	66.48 267	80.14 228	95.36 25	62.93 211	89.56 223	76.11 331	50.27 330	57.69 299	85.23 211	39.68 280	95.73 137	33.35 340	71.05 226	81.78 308

114514_t		79.17 154	77.67 161	83.68 147	95.32 26	65.53 145	92.85 110	91.60 153	63.49 268	67.92 222	90.63 148	46.65 251	95.72 141	67.01 198	83.54 142	89.79 192

Regformer-1		87.24 27	87.60 25	86.15 76	95.14 27	65.83 137	93.95 65	95.12 15	82.11 21	84.25 54	95.73 33	67.88 43	98.35 29	85.60 54	88.64 100	94.26 96

Regformer-2		87.00 34	87.43 27	85.71 92	95.14 27	64.73 166	93.95 65	94.95 20	81.69 26	84.03 60	95.73 33	67.35 47	98.19 33	85.40 56	88.64 100	94.20 98

HPM-MVS++		89.37 11	89.95 10	87.64 26	95.10 29	68.23 70	95.24 31	94.49 38	82.43 17	88.90 20	96.35 21	71.89 28	98.63 20	88.76 30	96.40 4	96.06 31

CSCG		86.87 35	86.26 41	88.72 12	95.05 30	70.79 21	93.83 76	95.33 11	68.48 235	77.63 118	94.35 81	73.04 20	98.45 25	84.92 60	93.71 45	96.92 9

LFMVS		84.34 70	82.73 91	89.18 11	94.76 31	73.25 10	94.99 41	91.89 140	71.90 172	82.16 69	93.49 100	47.98 243	97.05 84	82.55 79	84.82 130	97.25 5

CDPH-MVS		85.71 52	85.46 53	86.46 64	94.75 32	67.19 97	93.89 70	92.83 105	70.90 203	83.09 65	95.28 45	63.62 88	97.36 68	80.63 94	94.18 35	94.84 77

test_prior3		87.38 25	87.70 22	86.42 66	94.71 33	67.35 92	95.10 36	93.10 96	75.40 99	85.25 48	95.61 37	67.94 40	96.84 101	87.47 39	94.77 23	95.05 70

test_prior				86.42 66	94.71 33	67.35 92		93.10 96					96.84 101			95.05 70

test12				87.09 42	94.60 35	68.86 53		92.91 102		82.67 67		65.44 66	97.55 59		93.69 46	94.84 77

test_yl		84.28 71	83.16 82	87.64 26	94.52 36	69.24 45	95.78 17	95.09 18	69.19 225	81.09 78	92.88 114	57.00 151	97.44 63	81.11 92	81.76 152	96.23 28

DCV-MVSNet		84.28 71	83.16 82	87.64 26	94.52 36	69.24 45	95.78 17	95.09 18	69.19 225	81.09 78	92.88 114	57.00 151	97.44 63	81.11 92	81.76 152	96.23 28

CANet		89.61 9	89.99 9	88.46 17	94.39 38	69.71 38	96.53 11	93.78 60	86.89 4	89.68 15	95.78 31	65.94 60	99.10 7	92.99 2	93.91 40	96.58 15

test_8					94.19 39	67.19 97	94.15 55	93.42 81	71.87 175	85.38 45	95.35 41	68.19 36	96.95 96

TEST9					94.18 40	67.28 94	94.16 53	93.51 74	71.75 182	85.52 43	95.33 42	68.01 39	97.27 77

train_agg		87.21 28	87.42 28	86.60 57	94.18 40	67.28 94	94.16 53	93.51 74	71.87 175	85.52 43	95.33 42	68.19 36	97.27 77	89.09 25	94.90 19	95.25 63

Regformer-3		85.80 51	85.92 46	85.46 99	94.17 42	65.09 158	92.95 106	95.11 16	81.13 30	81.68 72	95.04 54	65.82 62	98.32 30	83.02 73	84.36 134	92.97 141

Regformer-4		85.45 54	85.69 51	84.73 120	94.17 42	63.23 204	92.95 106	94.83 23	80.66 34	81.29 75	95.04 54	65.12 68	98.08 36	82.74 75	84.36 134	92.88 145

agg_prior1		87.02 33	87.26 30	86.28 73	94.16 44	66.97 105	94.08 58	93.31 84	71.85 177	84.49 52	95.39 40	68.91 33	96.75 105	88.84 29	94.32 34	95.13 66

agg_prior					94.16 44	66.97 105		93.31 84		84.49 52			96.75 105

PAPM_NR		82.97 94	81.84 101	86.37 69	94.10 46	66.76 112	87.66 256	92.84 104	69.96 215	74.07 151	93.57 98	63.10 97	97.50 61	70.66 167	90.58 88	94.85 76

VNet		86.20 45	85.65 52	87.84 23	93.92 47	69.99 30	95.73 22	95.94 6	78.43 62	86.00 37	93.07 107	58.22 137	97.00 89	85.22 57	84.33 137	96.52 17

9.14			87.63 23		93.86 48		94.41 48	94.18 52	72.76 148	86.21 33	96.51 16	66.64 54	97.88 45	90.08 19	94.04 37

xxxxxxxxxxxxxcwj		87.14 29	87.19 31	86.99 45	93.84 49	67.89 77	95.05 38	84.72 302	78.19 64	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 58

save fliter					93.84 49	67.89 77	95.05 38	92.66 111	78.19 64

PVSNet_BlendedMVS		83.38 89	83.43 75	83.22 156	93.76 51	67.53 87	94.06 59	93.61 70	79.13 52	81.00 81	85.14 212	63.19 95	97.29 73	87.08 45	73.91 205	84.83 272

PVSNet_Blended		86.73 39	86.86 36	86.31 72	93.76 51	67.53 87	96.33 14	93.61 70	82.34 18	81.00 81	93.08 105	63.19 95	97.29 73	87.08 45	91.38 78	94.13 104

HFP-MVS		84.73 64	84.40 65	85.72 90	93.75 53	65.01 160	93.50 87	93.19 90	72.19 164	79.22 100	94.93 60	59.04 132	97.67 51	81.55 85	92.21 64	94.49 92

#test#		84.98 61	84.74 61	85.72 90	93.75 53	65.01 160	94.09 57	93.19 90	73.55 133	79.22 100	94.93 60	59.04 132	97.67 51	82.66 76	92.21 64	94.49 92

Anonymous202405211		77.96 178	75.33 194	85.87 84	93.73 55	64.52 168	94.85 43	85.36 298	62.52 279	76.11 132	90.18 156	29.43 327	97.29 73	68.51 185	77.24 190	95.81 40

SD-MVS		87.49 24	87.49 26	87.50 32	93.60 56	68.82 55	93.90 69	92.63 114	76.86 82	87.90 23	95.76 32	66.17 57	97.63 56	89.06 26	91.48 77	96.05 32
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
ETH3D-3000-0.1		87.61 22	87.89 19	86.75 52	93.58 57	67.21 96	94.31 50	94.14 54	72.92 145	87.13 26	96.62 14	67.81 44	97.94 39	90.13 18	94.42 32	95.09 68

ACMMPR		84.37 68	84.06 67	85.28 106	93.56 58	64.37 178	93.50 87	93.15 93	72.19 164	78.85 109	94.86 64	56.69 158	97.45 62	81.55 85	92.20 66	94.02 111

region2R		84.36 69	84.03 68	85.36 104	93.54 59	64.31 180	93.43 90	92.95 101	72.16 167	78.86 108	94.84 65	56.97 153	97.53 60	81.38 89	92.11 68	94.24 97

TSAR-MVS + GP.		87.96 17	88.37 16	86.70 55	93.51 60	65.32 148	95.15 34	93.84 59	78.17 66	85.93 38	94.80 67	75.80 11	98.21 31	89.38 21	88.78 98	96.59 13

PHI-MVS		86.83 37	86.85 37	86.78 51	93.47 61	65.55 144	95.39 29	95.10 17	71.77 181	85.69 42	96.52 15	62.07 104	98.77 17	86.06 52	95.60 10	96.03 33

SR-MVS		82.81 96	82.58 93	83.50 153	93.35 62	61.16 238	92.23 129	91.28 166	64.48 261	81.27 76	95.28 45	53.71 192	95.86 132	82.87 74	88.77 99	93.49 125

EPNet		87.84 20	88.38 15	86.23 74	93.30 63	66.05 129	95.26 30	94.84 22	87.09 3	88.06 22	94.53 72	66.79 53	97.34 70	83.89 68	91.68 73	95.29 56
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test1172		81.90 112	81.83 102	82.13 184	93.23 64	57.52 287	91.61 159	90.98 178	64.32 263	80.20 89	95.00 56	51.26 213	95.61 145	81.73 84	88.13 104	93.26 131

XVS		83.87 82	83.47 73	85.05 111	93.22 65	63.78 190	92.92 108	92.66 111	73.99 119	78.18 112	94.31 84	55.25 172	97.41 65	79.16 103	91.58 75	93.95 113

X-MVStestdata		76.86 192	74.13 211	85.05 111	93.22 65	63.78 190	92.92 108	92.66 111	73.99 119	78.18 112	10.19 359	55.25 172	97.41 65	79.16 103	91.58 75	93.95 113

SMA-MVS		88.14 14	88.29 17	87.67 25	93.21 67	68.72 57	93.85 72	94.03 56	74.18 116	91.74 7	96.67 13	65.61 65	98.42 28	89.24 24	96.08 5	95.88 39
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
原ACMM1				84.42 131	93.21 67	64.27 183		93.40 83	65.39 256	79.51 97	92.50 120	58.11 139	96.69 107	65.27 220	93.96 38	92.32 156

MVS_111021_HR		86.19 46	85.80 49	87.37 35	93.17 69	69.79 36	93.99 63	93.76 63	79.08 54	78.88 107	93.99 91	62.25 103	98.15 34	85.93 53	91.15 82	94.15 103

CP-MVS		83.71 86	83.40 78	84.65 124	93.14 70	63.84 188	94.59 46	92.28 122	71.03 201	77.41 121	94.92 62	55.21 175	96.19 118	81.32 90	90.70 86	93.91 115

DELS-MVS		90.05 6	90.09 8	89.94 4	93.14 70	73.88 8	97.01 3	94.40 45	88.32 2	85.71 41	94.91 63	74.11 16	98.91 13	87.26 44	95.94 7	97.03 8
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
ZNCC-MVS		85.33 56	85.08 57	86.06 77	93.09 72	65.65 141	93.89 70	93.41 82	73.75 127	79.94 91	94.68 70	60.61 116	98.03 37	82.63 78	93.72 44	94.52 90

DeepPCF-MVS	81.17 1	89.72 8	91.38 3	84.72 122	93.00 73	58.16 279	96.72 7	94.41 43	86.50 5	90.25 14	97.83 1	75.46 12	98.67 19	92.78 3	95.49 11	97.32 4

PLC	68.80 14	75.23 218	73.68 217	79.86 236	92.93 74	58.68 274	90.64 198	88.30 268	60.90 289	64.43 257	90.53 149	42.38 272	94.57 175	56.52 263	76.54 194	86.33 241
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
DWT-MVSNet_test		83.95 80	82.80 89	87.41 34	92.90 75	70.07 29	89.12 235	94.42 42	82.15 20	77.64 117	91.77 134	70.81 30	96.22 117	65.03 221	81.36 156	95.94 35

MSP-MVS		90.38 3	91.87 1	85.88 82	92.83 76	64.03 187	93.06 99	94.33 48	82.19 19	93.65 2	96.15 27	85.89 1	97.19 79	91.02 15	97.75 1	96.43 22

mPP-MVS		82.96 95	82.44 95	84.52 128	92.83 76	62.92 213	92.76 111	91.85 142	71.52 192	75.61 138	94.24 86	53.48 196	96.99 92	78.97 106	90.73 85	93.64 122

GST-MVS		84.63 67	84.29 66	85.66 94	92.82 78	65.27 149	93.04 101	93.13 94	73.20 137	78.89 104	94.18 87	59.41 128	97.85 46	81.45 87	92.48 63	93.86 117

WTY-MVS		86.32 43	85.81 48	87.85 22	92.82 78	69.37 44	95.20 32	95.25 12	82.71 15	81.91 70	94.73 68	67.93 42	97.63 56	79.55 100	82.25 148	96.54 16

PGM-MVS		83.25 90	82.70 92	84.92 114	92.81 80	64.07 186	90.44 201	92.20 130	71.28 196	77.23 124	94.43 75	55.17 176	97.31 72	79.33 102	91.38 78	93.37 126

EI-MVSNet-Vis-set		83.77 84	83.67 70	84.06 138	92.79 81	63.56 200	91.76 152	94.81 25	79.65 44	77.87 114	94.09 88	63.35 93	97.90 43	79.35 101	79.36 166	90.74 182

SF-MVS		87.03 32	87.09 33	86.84 47	92.70 82	67.45 91	93.64 80	93.76 63	70.78 207	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 58

MVSTER		82.47 101	82.05 98	83.74 143	92.68 83	69.01 50	91.90 144	93.21 87	79.83 39	72.14 173	85.71 208	74.72 13	94.72 172	75.72 126	72.49 215	87.50 218

MP-MVS		85.02 59	84.97 58	85.17 110	92.60 84	64.27 183	93.24 94	92.27 123	73.13 139	79.63 96	94.43 75	61.90 105	97.17 80	85.00 58	92.56 60	94.06 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
thres200		79.66 146	78.33 150	83.66 149	92.54 85	65.82 138	93.06 99	96.31 3	74.90 108	73.30 156	88.66 169	59.67 124	95.61 145	47.84 296	78.67 173	89.56 197

APD-MVS_3200maxsize		81.64 115	81.32 107	82.59 168	92.36 86	58.74 273	91.39 167	91.01 177	63.35 269	79.72 95	94.62 71	51.82 206	96.14 120	79.71 98	87.93 106	92.89 144

新几何1				84.73 120	92.32 87	64.28 182		91.46 159	59.56 299	79.77 94	92.90 112	56.95 154	96.57 110	63.40 230	92.91 56	93.34 127

1121		81.25 119	80.05 123	84.87 117	92.30 88	64.31 180	87.91 252	91.39 161	59.44 300	79.94 91	92.91 111	57.09 147	97.01 87	66.63 200	92.81 58	93.29 130

EI-MVSNet-UG-set		83.14 92	82.96 85	83.67 148	92.28 89	63.19 206	91.38 169	94.68 31	79.22 49	76.60 129	93.75 94	62.64 100	97.76 49	78.07 114	78.01 177	90.05 190

HPM-MVS		83.25 90	82.95 86	84.17 136	92.25 90	62.88 215	90.91 186	91.86 141	70.30 212	77.12 125	93.96 92	56.75 156	96.28 115	82.04 81	91.34 80	93.34 127
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HY-MVS	76.49 5	84.28 71	83.36 80	87.02 44	92.22 91	67.74 81	84.65 275	94.50 37	79.15 51	82.23 68	87.93 183	66.88 51	96.94 97	80.53 95	82.20 149	96.39 24

tfpn200view9		78.79 162	77.43 166	82.88 160	92.21 92	64.49 169	92.05 137	96.28 4	73.48 134	71.75 179	88.26 177	60.07 120	95.32 157	45.16 305	77.58 182	88.83 200

thres400		78.68 165	77.43 166	82.43 170	92.21 92	64.49 169	92.05 137	96.28 4	73.48 134	71.75 179	88.26 177	60.07 120	95.32 157	45.16 305	77.58 182	87.48 219

PS-MVSNAJ		88.14 14	87.61 24	89.71 7	92.06 94	76.72 1	95.75 19	93.26 86	83.86 10	89.55 16	96.06 28	53.55 193	97.89 44	91.10 13	93.31 50	94.54 88

SR-MVS-dyc-post		81.06 123	80.70 115	82.15 182	92.02 95	58.56 275	90.90 187	90.45 189	62.76 275	78.89 104	94.46 73	51.26 213	95.61 145	78.77 109	86.77 116	92.28 158

RE-MVS-def			80.48 120		92.02 95	58.56 275	90.90 187	90.45 189	62.76 275	78.89 104	94.46 73	49.30 230		78.77 109	86.77 116	92.28 158

MSLP-MVS++		86.27 44	85.91 47	87.35 36	92.01 97	68.97 52	95.04 40	92.70 108	79.04 55	81.50 74	96.50 17	58.98 134	96.78 103	83.49 70	93.93 39	96.29 26

ETH3D cwj APD-0.16		87.06 31	87.18 32	86.71 53	91.99 98	67.48 90	92.97 104	94.21 51	71.48 195	85.72 40	96.32 23	68.13 38	98.00 38	89.06 26	94.70 27	94.65 84

旧先验1					91.94 99	60.74 247		91.50 157			94.36 77	65.23 67			91.84 70	94.55 86

thres600view7		78.00 176	76.66 178	82.03 190	91.93 100	63.69 195	91.30 174	96.33 1	72.43 155	70.46 188	87.89 184	60.31 117	94.92 167	42.64 317	76.64 193	87.48 219

LS3D		69.17 265	66.40 268	77.50 267	91.92 101	56.12 297	85.12 272	80.37 325	46.96 337	56.50 303	87.51 189	37.25 297	93.71 213	32.52 345	79.40 165	82.68 298

GG-mvs-BLEND				86.53 62	91.91 102	69.67 40	75.02 327	94.75 28		78.67 111	90.85 144	77.91 5	94.56 177	72.25 152	93.74 43	95.36 50

thres100view900		78.37 171	77.01 173	82.46 169	91.89 103	63.21 205	91.19 180	96.33 1	72.28 160	70.45 189	87.89 184	60.31 117	95.32 157	45.16 305	77.58 182	88.83 200

zzz-MVS		84.73 64	84.47 62	85.50 97	91.89 103	65.16 153	91.55 160	92.23 125	75.32 101	80.53 85	95.21 51	56.06 166	97.16 81	84.86 61	92.55 61	94.18 99

MTAPA		83.91 81	83.38 79	85.50 97	91.89 103	65.16 153	81.75 295	92.23 125	75.32 101	80.53 85	95.21 51	56.06 166	97.16 81	84.86 61	92.55 61	94.18 99

RRT_test8_iter05		80.61 128	79.62 131	83.60 150	91.87 106	66.90 107	93.42 92	93.68 67	77.09 80	68.83 211	85.63 209	66.82 52	95.42 154	76.46 124	62.74 279	88.48 207

canonicalmvs		86.85 36	86.25 42	88.66 15	91.80 107	71.92 13	93.54 85	91.71 148	80.26 38	87.55 24	95.25 49	63.59 90	96.93 99	88.18 34	84.34 136	97.11 6

TSAR-MVS + MP.		88.11 16	88.64 14	86.54 61	91.73 108	68.04 73	90.36 205	93.55 73	82.89 13	91.29 9	92.89 113	72.27 25	96.03 128	87.99 35	94.77 23	95.54 46
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		81.49 116	80.67 116	83.93 140	91.71 109	62.90 214	92.13 131	92.22 129	71.79 180	71.68 181	93.49 100	50.32 219	96.96 95	78.47 111	84.22 141	91.93 164
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
BH-RMVSNet		79.46 151	77.65 162	84.89 115	91.68 110	65.66 140	93.55 84	88.09 274	72.93 144	73.37 155	91.12 141	46.20 256	96.12 121	56.28 265	85.61 126	92.91 143

baseline1		81.84 113	81.03 112	84.28 135	91.60 111	66.62 115	91.08 183	91.66 151	81.87 24	74.86 143	91.67 138	69.98 32	94.92 167	71.76 158	64.75 267	91.29 177

ACMMP_NAP		86.05 47	85.80 49	86.80 50	91.58 112	67.53 87	91.79 149	93.49 77	74.93 107	84.61 50	95.30 44	59.42 127	97.92 41	86.13 51	94.92 17	94.94 75

MVS_Test		84.16 76	83.20 81	87.05 43	91.56 113	69.82 35	89.99 218	92.05 133	77.77 70	82.84 66	86.57 198	63.93 83	96.09 122	74.91 136	89.18 96	95.25 63

HPM-MVS_fast		80.25 135	79.55 135	82.33 175	91.55 114	59.95 259	91.32 173	89.16 242	65.23 259	74.71 144	93.07 107	47.81 245	95.74 136	74.87 138	88.23 102	91.31 176

CPTT-MVS		79.59 147	79.16 143	80.89 218	91.54 115	59.80 261	92.10 133	88.54 265	60.42 292	72.96 158	93.28 102	48.27 239	92.80 236	78.89 108	86.50 121	90.06 189

CS-MVS		86.61 41	86.85 37	85.88 82	91.52 116	66.25 126	95.42 27	92.25 124	80.36 37	84.10 59	94.82 66	62.88 99	96.08 124	88.25 33	92.07 69	95.30 55

CNLPA		74.31 226	72.30 232	80.32 223	91.49 117	61.66 231	90.85 190	80.72 324	56.67 314	63.85 261	90.64 146	46.75 250	90.84 279	53.79 273	75.99 197	88.47 209

MP-MVS-pluss		85.24 57	85.13 56	85.56 96	91.42 118	65.59 143	91.54 161	92.51 118	74.56 110	80.62 84	95.64 36	59.15 131	97.00 89	86.94 47	93.80 41	94.07 108
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
gg-mvs-nofinetune		77.18 190	74.31 207	85.80 87	91.42 118	68.36 64	71.78 329	94.72 29	49.61 331	77.12 125	45.92 347	77.41 6	93.98 204	67.62 192	93.16 52	95.05 70

xiu_mvs_v2_base		87.92 19	87.38 29	89.55 10	91.41 120	76.43 3	95.74 20	93.12 95	83.53 12	89.55 16	95.95 29	53.45 197	97.68 50	91.07 14	92.62 59	94.54 88

EIA-MVS		84.84 63	84.88 59	84.69 123	91.30 121	62.36 222	93.85 72	92.04 134	79.45 45	79.33 99	94.28 85	62.42 102	96.35 114	80.05 97	91.25 81	95.38 49

alignmvs		87.28 26	86.97 34	88.24 20	91.30 121	71.14 19	95.61 24	93.56 72	79.30 47	87.07 29	95.25 49	68.43 34	96.93 99	87.87 36	84.33 137	96.65 11

EPMVS		78.49 170	75.98 187	86.02 78	91.21 123	69.68 39	80.23 308	91.20 167	75.25 103	72.48 168	78.11 291	54.65 180	93.69 214	57.66 261	83.04 143	94.69 80

FMVSNet3		77.73 182	76.04 186	82.80 161	91.20 124	68.99 51	91.87 145	91.99 136	73.35 136	67.04 235	83.19 232	56.62 159	92.14 258	59.80 253	69.34 234	87.28 225

Anonymous20240529		76.84 194	74.15 210	84.88 116	91.02 125	64.95 163	93.84 75	91.09 173	53.57 321	73.00 157	87.42 190	35.91 306	97.32 71	69.14 180	72.41 217	92.36 154

tpmvs		72.88 240	69.76 252	82.22 180	90.98 126	67.05 102	78.22 320	88.30 268	63.10 273	64.35 258	74.98 313	55.09 177	94.27 188	43.25 311	69.57 233	85.34 267

MVS		84.66 66	82.86 88	90.06 2	90.93 127	74.56 6	87.91 252	95.54 9	68.55 233	72.35 172	94.71 69	59.78 123	98.90 14	81.29 91	94.69 28	96.74 10

PVSNet	73.49 8	80.05 139	78.63 147	84.31 133	90.92 128	64.97 162	92.47 124	91.05 175	79.18 50	72.43 170	90.51 150	37.05 302	94.06 197	68.06 187	86.00 123	93.90 116

RRT_MVS		77.38 187	76.59 179	79.77 239	90.91 129	63.61 199	91.15 181	90.91 179	72.28 160	72.06 175	87.28 193	43.92 266	89.04 298	73.32 141	67.47 249	86.67 234

3Dnovator+	73.60 7	82.10 109	80.60 118	86.60 57	90.89 130	66.80 111	95.20 32	93.44 80	74.05 118	67.42 230	92.49 122	49.46 228	97.65 55	70.80 164	91.68 73	95.33 51

VDD-MVS		83.06 93	81.81 103	86.81 49	90.86 131	67.70 82	95.40 28	91.50 157	75.46 96	81.78 71	92.34 127	40.09 279	97.13 83	86.85 48	82.04 150	95.60 44

BH-w/o		80.49 132	79.30 140	84.05 139	90.83 132	64.36 179	93.60 82	89.42 232	74.35 113	69.09 204	90.15 157	55.23 174	95.61 145	64.61 223	86.43 122	92.17 162

ET-MVSNet_ETH3D		84.01 78	83.15 84	86.58 59	90.78 133	70.89 20	94.74 45	94.62 34	81.44 27	58.19 293	93.64 96	73.64 19	92.35 255	82.66 76	78.66 174	96.50 19

Anonymous20231211		73.08 235	70.39 246	81.13 208	90.62 134	63.33 202	91.40 165	90.06 211	51.84 326	64.46 256	80.67 267	36.49 304	94.07 196	63.83 228	64.17 272	85.98 252

TR-MVS		78.77 163	77.37 170	82.95 159	90.49 135	60.88 242	93.67 79	90.07 208	70.08 214	74.51 145	91.37 139	45.69 257	95.70 142	60.12 251	80.32 161	92.29 157

SteuartSystems-ACMMP		86.82 38	86.90 35	86.58 59	90.42 136	66.38 121	96.09 15	93.87 58	77.73 71	84.01 61	95.66 35	63.39 92	97.94 39	87.40 41	93.55 48	95.42 47
Skip Steuart: Steuart Systems R&D Blog.
TAPA-MVS	70.22 12	74.94 222	73.53 218	79.17 250	90.40 137	52.07 316	89.19 233	89.61 226	62.69 277	70.07 194	92.67 118	48.89 237	94.32 185	38.26 331	79.97 162	91.12 179
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_anonymous		81.36 118	79.99 125	85.46 99	90.39 138	68.40 63	86.88 266	90.61 188	74.41 111	70.31 192	84.67 217	63.79 85	92.32 256	73.13 142	85.70 124	95.67 41

CANet_DTU		84.09 77	83.52 71	85.81 86	90.30 139	66.82 109	91.87 145	89.01 250	85.27 6	86.09 36	93.74 95	47.71 246	96.98 93	77.90 116	89.78 94	93.65 121

Fast-Effi-MVS+		81.14 121	80.01 124	84.51 129	90.24 140	65.86 135	94.12 56	89.15 243	73.81 126	75.37 141	88.26 177	57.26 145	94.53 180	66.97 199	84.92 129	93.15 135

ETV-MVS		86.01 48	86.11 44	85.70 93	90.21 141	67.02 104	93.43 90	91.92 139	81.21 29	84.13 58	94.07 90	60.93 113	95.63 143	89.28 23	89.81 92	94.46 94

tpmrst		80.57 129	79.14 144	84.84 118	90.10 142	68.28 67	81.70 296	89.72 224	77.63 73	75.96 133	79.54 282	64.94 72	92.71 239	75.43 128	77.28 189	93.55 123

PVSNet_Blended_VisFu		83.97 79	83.50 72	85.39 103	90.02 143	66.59 117	93.77 77	91.73 146	77.43 77	77.08 127	89.81 162	63.77 86	96.97 94	79.67 99	88.21 103	92.60 149

UGNet		79.87 143	78.68 146	83.45 155	89.96 144	61.51 233	92.13 131	90.79 181	76.83 83	78.85 109	86.33 201	38.16 288	96.17 119	67.93 189	87.17 111	92.67 147
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
CHOSEN 1792x2688		84.98 61	83.45 74	89.57 9	89.94 145	75.14 5	92.07 136	92.32 121	81.87 24	75.68 135	88.27 176	60.18 119	98.60 21	80.46 96	90.27 91	94.96 74

abl_6		79.82 144	79.20 142	81.70 196	89.85 146	58.34 277	88.47 245	90.07 208	62.56 278	77.71 116	93.08 105	47.65 247	96.78 103	77.94 115	85.45 127	89.99 191

BH-untuned		78.68 165	77.08 171	83.48 154	89.84 147	63.74 192	92.70 114	88.59 263	71.57 190	66.83 238	88.65 170	51.75 208	95.39 155	59.03 256	84.77 131	91.32 175

test222					89.77 148	61.60 232	89.55 224	89.42 232	56.83 313	77.28 123	92.43 124	52.76 200			91.14 83	93.09 137

PMMVS		81.98 111	82.04 99	81.78 192	89.76 149	56.17 296	91.13 182	90.69 184	77.96 68	80.09 90	93.57 98	46.33 254	94.99 163	81.41 88	87.46 109	94.17 101

DPM-MVS		90.70 2	90.52 5	91.24 1	89.68 150	76.68 2	97.29 1	95.35 10	82.87 14	91.58 8	97.22 4	79.93 3	99.10 7	83.12 72	97.64 2	97.94 1

QAPM		79.95 142	77.39 169	87.64 26	89.63 151	71.41 15	93.30 93	93.70 66	65.34 258	67.39 232	91.75 136	47.83 244	98.96 12	57.71 260	89.81 92	92.54 151

3Dnovator	73.91 6	82.69 100	80.82 113	88.31 19	89.57 152	71.26 16	92.60 119	94.39 46	78.84 57	67.89 224	92.48 123	48.42 238	98.52 22	68.80 184	94.40 33	95.15 65

Effi-MVS+		83.82 83	82.76 90	86.99 45	89.56 153	69.40 41	91.35 171	86.12 292	72.59 150	83.22 64	92.81 117	59.60 125	96.01 130	81.76 83	87.80 107	95.56 45

PatchmatchNet		77.46 185	74.63 200	85.96 80	89.55 154	70.35 26	79.97 312	89.55 227	72.23 162	70.94 184	76.91 302	57.03 149	92.79 237	54.27 271	81.17 157	94.74 79
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.06 247	69.98 247	78.28 259	89.51 155	55.70 300	83.49 282	83.39 316	61.24 288	63.72 262	82.76 234	34.77 309	93.03 225	53.37 276	77.59 181	86.12 249

thisisatest0515		83.41 88	82.49 94	86.16 75	89.46 156	68.26 68	93.54 85	94.70 30	74.31 114	75.75 134	90.92 142	72.62 23	96.52 112	69.64 172	81.50 154	93.71 119

UA-Net		80.02 140	79.65 130	81.11 209	89.33 157	57.72 283	86.33 269	89.00 251	77.44 76	81.01 80	89.15 166	59.33 129	95.90 131	61.01 245	84.28 139	89.73 194

dp		75.01 221	72.09 234	83.76 142	89.28 158	66.22 128	79.96 313	89.75 219	71.16 198	67.80 226	77.19 299	51.81 207	92.54 247	50.39 282	71.44 224	92.51 152

sss		82.71 99	82.38 96	83.73 145	89.25 159	59.58 264	92.24 128	94.89 21	77.96 68	79.86 93	92.38 125	56.70 157	97.05 84	77.26 119	80.86 160	94.55 86

MVSFormer		83.75 85	82.88 87	86.37 69	89.24 160	71.18 17	89.07 236	90.69 184	65.80 253	87.13 26	94.34 82	64.99 70	92.67 242	72.83 145	91.80 71	95.27 60

lupinMVS		87.74 21	87.77 21	87.63 30	89.24 160	71.18 17	96.57 10	92.90 103	82.70 16	87.13 26	95.27 47	64.99 70	95.80 133	89.34 22	91.80 71	95.93 36

IB-MVS	77.80 4	82.18 105	80.46 121	87.35 36	89.14 162	70.28 27	95.59 25	95.17 14	78.85 56	70.19 193	85.82 206	70.66 31	97.67 51	72.19 155	66.52 255	94.09 106
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
MDTV_nov1_ep13			72.61 228		89.06 163	68.48 61	80.33 306	90.11 207	71.84 178	71.81 178	75.92 310	53.01 199	93.92 207	48.04 293	73.38 207

testdata				81.34 203	89.02 164	57.72 283		89.84 217	58.65 304	85.32 46	94.09 88	57.03 149	93.28 221	69.34 177	90.56 89	93.03 139

CostFormer		82.33 103	81.15 108	85.86 85	89.01 165	68.46 62	82.39 293	93.01 98	75.59 94	80.25 88	81.57 251	72.03 27	94.96 164	79.06 105	77.48 186	94.16 102

GBi-Net		75.65 212	73.83 215	81.10 210	88.85 166	65.11 155	90.01 215	90.32 195	70.84 204	67.04 235	80.25 274	48.03 240	91.54 272	59.80 253	69.34 234	86.64 235

test1		75.65 212	73.83 215	81.10 210	88.85 166	65.11 155	90.01 215	90.32 195	70.84 204	67.04 235	80.25 274	48.03 240	91.54 272	59.80 253	69.34 234	86.64 235

FMVSNet2		76.07 202	74.01 213	82.26 179	88.85 166	67.66 83	91.33 172	91.61 152	70.84 204	65.98 242	82.25 240	48.03 240	92.00 263	58.46 258	68.73 240	87.10 227

DeepC-MVS	77.85 3	85.52 53	85.24 55	86.37 69	88.80 169	66.64 114	92.15 130	93.68 67	81.07 31	76.91 128	93.64 96	62.59 101	98.44 26	85.50 55	92.84 57	94.03 110
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		81.79 114	81.52 105	82.61 167	88.77 170	60.21 256	93.02 103	93.66 69	68.52 234	72.90 160	90.39 152	72.19 26	94.96 164	74.93 135	79.29 168	92.67 147

1112_ss		80.56 130	79.83 128	82.77 162	88.65 171	60.78 244	92.29 126	88.36 267	72.58 151	72.46 169	94.95 58	65.09 69	93.42 220	66.38 206	77.71 179	94.10 105

tpm cat1		75.30 217	72.21 233	84.58 127	88.52 172	67.77 80	78.16 321	88.02 275	61.88 285	68.45 218	76.37 306	60.65 114	94.03 201	53.77 274	74.11 202	91.93 164

LCM-MVSNet-Re		72.93 238	71.84 236	76.18 283	88.49 173	48.02 332	80.07 311	70.17 344	73.96 122	52.25 315	80.09 277	49.98 223	88.24 304	67.35 193	84.23 140	92.28 158

Vis-MVSNet		80.92 126	79.98 126	83.74 143	88.48 174	61.80 228	93.44 89	88.26 272	73.96 122	77.73 115	91.76 135	49.94 224	94.76 169	65.84 212	90.37 90	94.65 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Vis-MVSNet (Re-imp)		79.24 153	79.57 132	78.24 261	88.46 175	52.29 315	90.41 203	89.12 245	74.24 115	69.13 203	91.91 132	65.77 63	90.09 291	59.00 257	88.09 105	92.33 155

ab-mvs		80.18 136	78.31 151	85.80 87	88.44 176	65.49 147	83.00 290	92.67 110	71.82 179	77.36 122	85.01 213	54.50 181	96.59 108	76.35 125	75.63 198	95.32 53

gm-plane-assit					88.42 177	67.04 103			78.62 61		91.83 133		97.37 67	76.57 122

MVS_111021_LR		82.02 110	81.52 105	83.51 152	88.42 177	62.88 215	89.77 221	88.93 252	76.78 84	75.55 139	93.10 103	50.31 220	95.38 156	83.82 69	87.02 112	92.26 161

baseline		85.01 60	84.44 64	86.71 53	88.33 179	68.73 56	90.24 209	91.82 144	81.05 32	81.18 77	92.50 120	63.69 87	96.08 124	84.45 63	86.71 118	95.32 53

tpm2		79.80 145	77.95 158	85.34 105	88.28 180	68.26 68	81.56 298	91.42 160	70.11 213	77.59 120	80.50 269	67.40 46	94.26 190	67.34 194	77.35 187	93.51 124

thisisatest0530		81.15 120	80.07 122	84.39 132	88.26 181	65.63 142	91.40 165	94.62 34	71.27 197	70.93 185	89.18 165	72.47 24	96.04 127	65.62 215	76.89 192	91.49 169

casdiffmvs		85.37 55	84.87 60	86.84 47	88.25 182	69.07 48	93.04 101	91.76 145	81.27 28	80.84 83	92.07 130	64.23 79	96.06 126	84.98 59	87.43 110	95.39 48

Test_1112_low_res		79.56 148	78.60 148	82.43 170	88.24 183	60.39 253	92.09 134	87.99 276	72.10 168	71.84 177	87.42 190	64.62 75	93.04 224	65.80 213	77.30 188	93.85 118

PAPM		85.89 50	85.46 53	87.18 39	88.20 184	72.42 12	92.41 125	92.77 106	82.11 21	80.34 87	93.07 107	68.27 35	95.02 162	78.39 112	93.59 47	94.09 106

TESTMET0.1,1		82.41 102	81.98 100	83.72 146	88.08 185	63.74 192	92.70 114	93.77 62	79.30 47	77.61 119	87.57 188	58.19 138	94.08 195	73.91 140	86.68 119	93.33 129

ADS-MVSNet2		66.90 284	63.44 289	77.26 273	88.06 186	60.70 248	68.01 338	75.56 334	57.57 306	64.48 254	69.87 330	38.68 282	84.10 324	40.87 322	67.89 246	86.97 228

ADS-MVSNet		68.54 272	64.38 285	81.03 214	88.06 186	66.90 107	68.01 338	84.02 309	57.57 306	64.48 254	69.87 330	38.68 282	89.21 297	40.87 322	67.89 246	86.97 228

EPNet_dtu		78.80 161	79.26 141	77.43 269	88.06 186	49.71 328	91.96 143	91.95 138	77.67 72	76.56 130	91.28 140	58.51 136	90.20 289	56.37 264	80.95 159	92.39 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_enhance_ethall		78.86 159	77.97 157	81.54 199	88.00 189	65.17 152	91.41 163	89.15 243	75.19 104	68.79 212	83.98 224	67.17 48	92.82 234	72.73 147	65.30 258	86.62 239

IS-MVSNet		80.14 137	79.41 137	82.33 175	87.91 190	60.08 258	91.97 142	88.27 270	72.90 146	71.44 183	91.73 137	61.44 108	93.66 215	62.47 238	86.53 120	93.24 132

CLD-MVS		82.73 97	82.35 97	83.86 141	87.90 191	67.65 84	95.45 26	92.18 132	85.06 7	72.58 165	92.27 128	52.46 203	95.78 134	84.18 64	79.06 169	88.16 213
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HyFIR lowres test		81.03 124	79.56 133	85.43 101	87.81 192	68.11 72	90.18 210	90.01 213	70.65 209	72.95 159	86.06 204	63.61 89	94.50 182	75.01 134	79.75 164	93.67 120

1314		80.70 127	78.95 145	85.94 81	87.77 193	67.56 85	87.91 252	92.55 117	72.17 166	67.44 229	93.09 104	50.27 221	97.04 86	71.68 160	87.64 108	93.23 133

cl-mvsnet2		77.94 179	76.78 176	81.42 201	87.57 194	64.93 164	90.67 196	88.86 255	72.45 154	67.63 228	82.68 236	64.07 80	92.91 232	71.79 156	65.30 258	86.44 240

HQP-NCC					87.54 195		94.06 59		79.80 40	74.18 147

ACMP_Plane					87.54 195		94.06 59		79.80 40	74.18 147

HQP-MVS		81.14 121	80.64 117	82.64 166	87.54 195	63.66 197	94.06 59	91.70 149	79.80 40	74.18 147	90.30 154	51.63 210	95.61 145	77.63 117	78.90 170	88.63 204

NP-MVS					87.41 198	63.04 207					90.30 154

diffmvs		84.28 71	83.83 69	85.61 95	87.40 199	68.02 74	90.88 189	89.24 237	80.54 35	81.64 73	92.52 119	59.83 122	94.52 181	87.32 43	85.11 128	94.29 95

baseline2		83.68 87	83.42 77	84.48 130	87.37 200	66.00 131	90.06 213	95.93 7	79.71 43	69.08 205	90.39 152	77.92 4	96.28 115	78.91 107	81.38 155	91.16 178

plane_prior6					87.23 201	62.32 223						50.66 217

tttt0517		79.50 149	78.53 149	82.41 173	87.22 202	61.43 235	89.75 222	94.76 27	69.29 223	67.91 223	88.06 182	72.92 21	95.63 143	62.91 234	73.90 206	90.16 188

plane_prior1					87.15 203

cascas		78.18 174	75.77 190	85.41 102	87.14 204	69.11 47	92.96 105	91.15 170	66.71 247	70.47 187	86.07 203	37.49 296	96.48 113	70.15 170	79.80 163	90.65 183

CHOSEN 280x420		77.35 188	76.95 175	78.55 256	87.07 205	62.68 219	69.71 334	82.95 318	68.80 230	71.48 182	87.27 194	66.03 59	84.00 327	76.47 123	82.81 146	88.95 199

HQP_MVS		80.34 134	79.75 129	82.12 185	86.94 206	62.42 220	93.13 97	91.31 163	78.81 58	72.53 166	89.14 167	50.66 217	95.55 151	76.74 120	78.53 175	88.39 210

plane_prior7					86.94 206	61.51 233

test-LLR		80.10 138	79.56 133	81.72 194	86.93 208	61.17 236	92.70 114	91.54 154	71.51 193	75.62 136	86.94 195	53.83 189	92.38 252	72.21 153	84.76 132	91.60 167

test-mter		79.96 141	79.38 139	81.72 194	86.93 208	61.17 236	92.70 114	91.54 154	73.85 124	75.62 136	86.94 195	49.84 226	92.38 252	72.21 153	84.76 132	91.60 167

SCA		75.82 210	72.76 225	85.01 113	86.63 210	70.08 28	81.06 302	89.19 240	71.60 189	70.01 195	77.09 300	45.53 258	90.25 284	60.43 248	73.27 208	94.68 81

AUN-MVS		78.37 171	77.43 166	81.17 206	86.60 211	57.45 288	89.46 228	91.16 169	74.11 117	74.40 146	90.49 151	55.52 171	94.57 175	74.73 139	60.43 303	91.48 170

xiu_mvs_v1_base_debu		82.16 106	81.12 109	85.26 107	86.42 212	68.72 57	92.59 121	90.44 192	73.12 140	84.20 55	94.36 77	38.04 290	95.73 137	84.12 65	86.81 113	91.33 172

xiu_mvs_v1_base		82.16 106	81.12 109	85.26 107	86.42 212	68.72 57	92.59 121	90.44 192	73.12 140	84.20 55	94.36 77	38.04 290	95.73 137	84.12 65	86.81 113	91.33 172

xiu_mvs_v1_base_debi		82.16 106	81.12 109	85.26 107	86.42 212	68.72 57	92.59 121	90.44 192	73.12 140	84.20 55	94.36 77	38.04 290	95.73 137	84.12 65	86.81 113	91.33 172

F-COLMAP		70.66 254	68.44 258	77.32 271	86.37 215	55.91 298	88.00 250	86.32 288	56.94 312	57.28 301	88.07 181	33.58 312	92.49 249	51.02 280	68.37 242	83.55 281

CDS-MVSNet		81.43 117	80.74 114	83.52 151	86.26 216	64.45 172	92.09 134	90.65 187	75.83 93	73.95 153	89.81 162	63.97 82	92.91 232	71.27 161	82.82 145	93.20 134
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
VDDNet		80.50 131	78.26 152	87.21 38	86.19 217	69.79 36	94.48 47	91.31 163	60.42 292	79.34 98	90.91 143	38.48 286	96.56 111	82.16 80	81.05 158	95.27 60

jason		86.40 42	86.17 43	87.11 41	86.16 218	70.54 24	95.71 23	92.19 131	82.00 23	84.58 51	94.34 82	61.86 106	95.53 153	87.76 37	90.89 84	95.27 60
jason: jason.
PCF-MVS	73.15 9	79.29 152	77.63 163	84.29 134	86.06 219	65.96 133	87.03 262	91.10 172	69.86 217	69.79 200	90.64 146	57.54 144	96.59 108	64.37 225	82.29 147	90.32 186
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		77.90 181	76.50 180	82.12 185	85.99 220	69.95 33	91.75 154	92.70 108	73.97 121	62.58 273	84.44 220	41.11 275	95.78 134	63.76 229	92.17 67	80.62 316

FIs		79.47 150	79.41 137	79.67 241	85.95 221	59.40 266	91.68 156	93.94 57	78.06 67	68.96 208	88.28 175	66.61 55	91.77 267	66.20 209	74.99 199	87.82 215

VPA-MVSNet		79.03 155	78.00 156	82.11 188	85.95 221	64.48 171	93.22 96	94.66 32	75.05 106	74.04 152	84.95 214	52.17 205	93.52 217	74.90 137	67.04 251	88.32 212

tpm		78.58 168	77.03 172	83.22 156	85.94 223	64.56 167	83.21 288	91.14 171	78.31 63	73.67 154	79.68 280	64.01 81	92.09 261	66.07 210	71.26 225	93.03 139

OpenMVS	70.45 11	78.54 169	75.92 188	86.41 68	85.93 224	71.68 14	92.74 112	92.51 118	66.49 249	64.56 253	91.96 131	43.88 267	98.10 35	54.61 269	90.65 87	89.44 198

OMC-MVS		78.67 167	77.91 160	80.95 216	85.76 225	57.40 289	88.49 244	88.67 260	73.85 124	72.43 170	92.10 129	49.29 231	94.55 178	72.73 147	77.89 178	90.91 181

miper_ehance_all_eth		77.60 183	76.44 181	81.09 213	85.70 226	64.41 176	90.65 197	88.64 262	72.31 158	67.37 233	82.52 237	64.77 74	92.64 245	70.67 166	65.30 258	86.24 244

KD-MVS_2432*1600		69.03 267	66.37 269	77.01 275	85.56 227	61.06 239	81.44 299	90.25 201	67.27 243	58.00 296	76.53 304	54.49 182	87.63 311	48.04 293	35.77 346	82.34 301

miper_refine_blended		69.03 267	66.37 269	77.01 275	85.56 227	61.06 239	81.44 299	90.25 201	67.27 243	58.00 296	76.53 304	54.49 182	87.63 311	48.04 293	35.77 346	82.34 301

EI-MVSNet		78.97 157	78.22 153	81.25 204	85.33 229	62.73 218	89.53 226	93.21 87	72.39 157	72.14 173	90.13 158	60.99 111	94.72 172	67.73 191	72.49 215	86.29 242

CVMVSNet		74.04 228	74.27 208	73.33 300	85.33 229	43.94 342	89.53 226	88.39 266	54.33 320	70.37 190	90.13 158	49.17 233	84.05 325	61.83 242	79.36 166	91.99 163

ACMH	63.93 17	68.62 270	64.81 278	80.03 231	85.22 231	63.25 203	87.72 255	84.66 304	60.83 290	51.57 318	79.43 283	27.29 333	94.96 164	41.76 318	64.84 265	81.88 306
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cl-mvsnet_		76.07 202	74.67 198	80.28 225	85.15 232	61.76 229	90.12 211	88.73 258	71.16 198	65.43 245	81.57 251	61.15 109	92.95 227	66.54 203	62.17 284	86.13 248

cl-mvsnet1		76.07 202	74.67 198	80.28 225	85.14 233	61.75 230	90.12 211	88.73 258	71.16 198	65.42 246	81.60 250	61.15 109	92.94 231	66.54 203	62.16 286	86.14 246

TAMVS		80.37 133	79.45 136	83.13 158	85.14 233	63.37 201	91.23 176	90.76 183	74.81 109	72.65 163	88.49 171	60.63 115	92.95 227	69.41 176	81.95 151	93.08 138

MSDG		69.54 263	65.73 272	80.96 215	85.11 235	63.71 194	84.19 277	83.28 317	56.95 311	54.50 306	84.03 222	31.50 320	96.03 128	42.87 315	69.13 237	83.14 291

cl_fuxian		76.83 195	75.47 193	80.93 217	85.02 236	64.18 185	90.39 204	88.11 273	71.66 183	66.65 240	81.64 249	63.58 91	92.56 246	69.31 178	62.86 278	86.04 250

ACMP	71.68 10	75.58 215	74.23 209	79.62 243	84.97 237	59.64 262	90.80 192	89.07 248	70.39 211	62.95 269	87.30 192	38.28 287	93.87 209	72.89 144	71.45 223	85.36 266
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FC-MVSNet-test		77.99 177	78.08 155	77.70 264	84.89 238	55.51 301	90.27 207	93.75 65	76.87 81	66.80 239	87.59 187	65.71 64	90.23 288	62.89 235	73.94 204	87.37 222

PVSNet_0	68.08 15	71.81 248	68.32 260	82.27 177	84.68 239	62.31 224	88.68 241	90.31 198	75.84 92	57.93 298	80.65 268	37.85 293	94.19 191	69.94 171	29.05 350	90.31 187

eth_miper_zixun_eth		75.96 208	74.40 206	80.66 219	84.66 240	63.02 208	89.28 230	88.27 270	71.88 174	65.73 243	81.65 248	59.45 126	92.81 235	68.13 186	60.53 301	86.14 246

WR-MVS		76.76 196	75.74 191	79.82 237	84.60 241	62.27 225	92.60 119	92.51 118	76.06 90	67.87 225	85.34 210	56.76 155	90.24 287	62.20 239	63.69 277	86.94 231

ACMH+	65.35 16	67.65 279	64.55 281	76.96 277	84.59 242	57.10 292	88.08 249	80.79 323	58.59 305	53.00 312	81.09 263	26.63 335	92.95 227	46.51 300	61.69 293	80.82 313

VPNet		78.82 160	77.53 165	82.70 164	84.52 243	66.44 120	93.93 67	92.23 125	80.46 36	72.60 164	88.38 174	49.18 232	93.13 223	72.47 151	63.97 275	88.55 206

IterMVS-LS		76.49 198	75.18 196	80.43 222	84.49 244	62.74 217	90.64 198	88.80 256	72.40 156	65.16 248	81.72 247	60.98 112	92.27 257	67.74 190	64.65 269	86.29 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		78.15 175	77.55 164	79.98 232	84.46 245	60.26 254	92.25 127	93.20 89	77.50 75	68.88 209	86.61 197	66.10 58	92.13 259	66.38 206	62.55 280	87.54 217

FMVSNet5		68.04 276	65.66 274	75.18 288	84.43 246	57.89 280	83.54 281	86.26 290	61.83 286	53.64 311	73.30 318	37.15 300	85.08 321	48.99 288	61.77 289	82.56 300

MVS-HIRNet		60.25 308	55.55 314	74.35 293	84.37 247	56.57 295	71.64 330	74.11 337	34.44 348	45.54 336	42.24 350	31.11 323	89.81 292	40.36 325	76.10 196	76.67 336

LPG-MVS_test		75.82 210	74.58 202	79.56 245	84.31 248	59.37 267	90.44 201	89.73 222	69.49 220	64.86 249	88.42 172	38.65 284	94.30 186	72.56 149	72.76 212	85.01 270

LGP-MVS_train				79.56 245	84.31 248	59.37 267		89.73 222	69.49 220	64.86 249	88.42 172	38.65 284	94.30 186	72.56 149	72.76 212	85.01 270

ACMM	69.62 13	74.34 225	72.73 226	79.17 250	84.25 250	57.87 281	90.36 205	89.93 214	63.17 272	65.64 244	86.04 205	37.79 294	94.10 193	65.89 211	71.52 222	85.55 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		77.58 184	76.78 176	79.98 232	84.11 251	60.80 243	91.76 152	93.17 92	76.56 87	69.93 199	84.78 216	63.32 94	92.36 254	64.89 222	62.51 282	86.78 233

test_0402		64.54 294	61.09 300	74.92 289	84.10 252	60.75 246	87.95 251	79.71 327	52.03 325	52.41 314	77.20 298	32.21 318	91.64 269	23.14 349	61.03 296	72.36 341

LTVRE_ROB	59.60 19	66.27 287	63.54 288	74.45 292	84.00 253	51.55 318	67.08 341	83.53 313	58.78 303	54.94 305	80.31 272	34.54 310	93.23 222	40.64 324	68.03 244	78.58 331
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
miper_lstm_enhance		73.05 236	71.73 238	77.03 274	83.80 254	58.32 278	81.76 294	88.88 253	69.80 218	61.01 278	78.23 290	57.19 146	87.51 313	65.34 219	59.53 305	85.27 269

Patchmatch-test		65.86 289	60.94 301	80.62 220	83.75 255	58.83 272	58.91 348	75.26 336	44.50 343	50.95 322	77.09 300	58.81 135	87.90 307	35.13 337	64.03 273	95.12 67

nrg030		80.93 125	79.86 127	84.13 137	83.69 256	68.83 54	93.23 95	91.20 167	75.55 95	75.06 142	88.22 180	63.04 98	94.74 171	81.88 82	66.88 252	88.82 202

GA-MVS		78.33 173	76.23 184	84.65 124	83.65 257	66.30 124	91.44 162	90.14 206	76.01 91	70.32 191	84.02 223	42.50 271	94.72 172	70.98 162	77.00 191	92.94 142

FMVSNet1		72.71 243	69.91 250	81.10 210	83.60 258	65.11 155	90.01 215	90.32 195	63.92 265	63.56 263	80.25 274	36.35 305	91.54 272	54.46 270	66.75 253	86.64 235

OPM-MVS		79.00 156	78.09 154	81.73 193	83.52 259	63.83 189	91.64 158	90.30 199	76.36 89	71.97 176	89.93 161	46.30 255	95.17 161	75.10 131	77.70 180	86.19 245

tfpnnormal		70.10 258	67.36 263	78.32 258	83.45 260	60.97 241	88.85 238	92.77 106	64.85 260	60.83 280	78.53 287	43.52 269	93.48 218	31.73 346	61.70 292	80.52 317

Effi-MVS+-dtu		76.14 201	75.28 195	78.72 255	83.22 261	55.17 303	89.87 219	87.78 278	75.42 97	67.98 221	81.43 253	45.08 261	92.52 248	75.08 132	71.63 220	88.48 207

mvs-test1		78.74 164	77.95 158	81.14 207	83.22 261	57.13 291	93.96 64	87.78 278	75.42 97	72.68 162	90.80 145	45.08 261	94.54 179	75.08 132	77.49 185	91.74 166

CR-MVSNet		73.79 232	70.82 244	82.70 164	83.15 263	67.96 75	70.25 331	84.00 310	73.67 131	69.97 197	72.41 321	57.82 141	89.48 295	52.99 277	73.13 209	90.64 184

RPMNet		70.42 257	65.68 273	84.63 126	83.15 263	67.96 75	70.25 331	90.45 189	46.83 339	69.97 197	65.10 338	56.48 162	95.30 160	35.79 336	73.13 209	90.64 184

DU-MVS		76.86 192	75.84 189	79.91 234	82.96 265	60.26 254	91.26 175	91.54 154	76.46 88	68.88 209	86.35 199	56.16 163	92.13 259	66.38 206	62.55 280	87.35 223

NR-MVSNet		76.05 205	74.59 201	80.44 221	82.96 265	62.18 226	90.83 191	91.73 146	77.12 79	60.96 279	86.35 199	59.28 130	91.80 266	60.74 246	61.34 295	87.35 223

XXY-MVS		77.94 179	76.44 181	82.43 170	82.60 267	64.44 173	92.01 139	91.83 143	73.59 132	70.00 196	85.82 206	54.43 185	94.76 169	69.63 173	68.02 245	88.10 214

TranMVSNet+NR-MVSNet		75.86 209	74.52 204	79.89 235	82.44 268	60.64 250	91.37 170	91.37 162	76.63 85	67.65 227	86.21 202	52.37 204	91.55 271	61.84 241	60.81 298	87.48 219

IterMVS		72.65 245	70.83 243	78.09 262	82.17 269	62.96 210	87.64 257	86.28 289	71.56 191	60.44 281	78.85 286	45.42 260	86.66 317	63.30 231	61.83 288	84.65 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		67.53 281	63.93 286	78.34 257	82.12 270	64.38 177	68.72 335	84.00 310	48.23 336	59.24 286	72.41 321	57.82 141	89.27 296	46.10 302	56.68 314	81.36 309

PatchT		69.11 266	65.37 277	80.32 223	82.07 271	63.68 196	67.96 340	87.62 280	50.86 329	69.37 201	65.18 337	57.09 147	88.53 302	41.59 320	66.60 254	88.74 203

MIMVSNet		71.64 249	68.44 258	81.23 205	81.97 272	64.44 173	73.05 328	88.80 256	69.67 219	64.59 252	74.79 314	32.79 314	87.82 308	53.99 272	76.35 195	91.42 171

MVP-Stereo		77.12 191	76.23 184	79.79 238	81.72 273	66.34 123	89.29 229	90.88 180	70.56 210	62.01 276	82.88 233	49.34 229	94.13 192	65.55 217	93.80 41	78.88 328
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
IterMVS-SCA-FT		71.55 250	69.97 248	76.32 281	81.48 274	60.67 249	87.64 257	85.99 293	66.17 251	59.50 285	78.88 285	45.53 258	83.65 329	62.58 237	61.93 287	84.63 275

COLMAP_ROB	57.96 20	62.98 302	59.65 304	72.98 303	81.44 275	53.00 312	83.75 279	75.53 335	48.34 335	48.81 328	81.40 255	24.14 338	90.30 283	32.95 342	60.52 302	75.65 338
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
JIA-IIPM		66.06 288	62.45 295	76.88 278	81.42 276	54.45 307	57.49 349	88.67 260	49.36 332	63.86 260	46.86 346	56.06 166	90.25 284	49.53 286	68.83 238	85.95 254

WR-MVS_H		70.59 255	69.94 249	72.53 306	81.03 277	51.43 319	87.35 260	92.03 135	67.38 242	60.23 282	80.70 265	55.84 170	83.45 331	46.33 301	58.58 309	82.72 295

Fast-Effi-MVS+-dtu		75.04 220	73.37 220	80.07 230	80.86 278	59.52 265	91.20 179	85.38 297	71.90 172	65.20 247	84.84 215	41.46 274	92.97 226	66.50 205	72.96 211	87.73 216

Baseline_NR-MVSNet		73.99 229	72.83 224	77.48 268	80.78 279	59.29 269	91.79 149	84.55 305	68.85 229	68.99 207	80.70 265	56.16 163	92.04 262	62.67 236	60.98 297	81.11 310

CP-MVSNet		70.50 256	69.91 250	72.26 309	80.71 280	51.00 322	87.23 261	90.30 199	67.84 236	59.64 284	82.69 235	50.23 222	82.30 339	51.28 279	59.28 306	83.46 285

v8		75.35 216	73.26 221	81.61 198	80.67 281	66.82 109	89.54 225	89.27 236	71.65 184	63.30 267	80.30 273	54.99 178	94.06 197	67.33 195	62.33 283	83.94 278

PS-MVSNAJss		77.26 189	76.31 183	80.13 229	80.64 282	59.16 270	90.63 200	91.06 174	72.80 147	68.58 216	84.57 219	53.55 193	93.96 205	72.97 143	71.96 219	87.27 226

TransMVSNet (Re)		70.07 259	67.66 262	77.31 272	80.62 283	59.13 271	91.78 151	84.94 301	65.97 252	60.08 283	80.44 270	50.78 216	91.87 264	48.84 289	45.46 335	80.94 312

v2v482		77.42 186	75.65 192	82.73 163	80.38 284	67.13 100	91.85 147	90.23 203	75.09 105	69.37 201	83.39 230	53.79 191	94.44 183	71.77 157	65.00 264	86.63 238

MVS_0304		68.99 269	67.23 266	74.28 295	80.36 285	52.54 313	87.01 264	86.36 287	59.89 298	66.22 241	73.56 317	24.25 337	88.03 306	57.34 262	70.11 228	82.27 303

PS-CasMVS		69.86 262	69.13 255	72.07 312	80.35 286	50.57 324	87.02 263	89.75 219	67.27 243	59.19 288	82.28 239	46.58 252	82.24 340	50.69 281	59.02 307	83.39 287

v10		74.77 223	72.54 230	81.46 200	80.33 287	66.71 113	89.15 234	89.08 247	70.94 202	63.08 268	79.86 278	52.52 202	94.04 200	65.70 214	62.17 284	83.64 280

test0.0.03 1		72.76 241	72.71 227	72.88 304	80.25 288	47.99 333	91.22 177	89.45 230	71.51 193	62.51 274	87.66 186	53.83 189	85.06 322	50.16 283	67.84 248	85.58 261

v1144		76.73 197	74.88 197	82.27 177	80.23 289	66.60 116	91.68 156	90.21 205	73.69 129	69.06 206	81.89 244	52.73 201	94.40 184	69.21 179	65.23 261	85.80 257

v148		76.19 200	74.47 205	81.36 202	80.05 290	64.44 173	91.75 154	90.23 203	73.68 130	67.13 234	80.84 264	55.92 169	93.86 211	68.95 182	61.73 291	85.76 260

test_part1		72.36 246	69.25 254	81.68 197	79.94 291	65.07 159	90.68 195	89.53 228	52.32 323	63.42 265	79.21 284	40.43 278	94.01 203	67.14 196	60.59 300	85.96 253

v1192		75.98 207	73.92 214	82.15 182	79.73 292	66.24 127	91.22 177	89.75 219	72.67 149	68.49 217	81.42 254	49.86 225	94.27 188	67.08 197	65.02 263	85.95 254

AllTest		61.66 303	58.06 307	72.46 307	79.57 293	51.42 320	80.17 309	68.61 346	51.25 327	45.88 332	81.23 257	19.86 346	86.58 318	38.98 328	57.01 312	79.39 325

TestCases				72.46 307	79.57 293	51.42 320		68.61 346	51.25 327	45.88 332	81.23 257	19.86 346	86.58 318	38.98 328	57.01 312	79.39 325

MDA-MVSNet-bldmvs		61.54 305	57.70 309	73.05 302	79.53 295	57.00 293	83.08 289	81.23 321	57.57 306	34.91 348	72.45 320	32.79 314	86.26 320	35.81 335	41.95 339	75.89 337

v144192		76.05 205	74.03 212	82.12 185	79.50 296	66.55 118	91.39 167	89.71 225	72.30 159	68.17 219	81.33 256	51.75 208	94.03 201	67.94 188	64.19 271	85.77 258

v1921920		75.63 214	73.49 219	82.06 189	79.38 297	66.35 122	91.07 185	89.48 229	71.98 169	67.99 220	81.22 259	49.16 234	93.90 208	66.56 202	64.56 270	85.92 256

PEN-MVS		69.46 264	68.56 257	72.17 311	79.27 298	49.71 328	86.90 265	89.24 237	67.24 246	59.08 289	82.51 238	47.23 249	83.54 330	48.42 291	57.12 310	83.25 288

v1240		75.21 219	72.98 223	81.88 191	79.20 299	66.00 131	90.75 194	89.11 246	71.63 188	67.41 231	81.22 259	47.36 248	93.87 209	65.46 218	64.72 268	85.77 258

pmmvs4		73.92 230	71.81 237	80.25 227	79.17 300	65.24 150	87.43 259	87.26 284	67.64 241	63.46 264	83.91 225	48.96 236	91.53 275	62.94 233	65.49 257	83.96 277

D2MVS		73.80 231	72.02 235	79.15 252	79.15 301	62.97 209	88.58 243	90.07 208	72.94 143	59.22 287	78.30 288	42.31 273	92.70 241	65.59 216	72.00 218	81.79 307

V42		76.46 199	74.55 203	82.19 181	79.14 302	67.82 79	90.26 208	89.42 232	73.75 127	68.63 215	81.89 244	51.31 212	94.09 194	71.69 159	64.84 265	84.66 273

pm-mvs1		72.89 239	71.09 242	78.26 260	79.10 303	57.62 285	90.80 192	89.30 235	67.66 239	62.91 270	81.78 246	49.11 235	92.95 227	60.29 250	58.89 308	84.22 276

our_test_3		68.29 274	64.69 280	79.11 253	78.92 304	64.85 165	88.40 247	85.06 299	60.32 294	52.68 313	76.12 308	40.81 276	89.80 294	44.25 310	55.65 315	82.67 299

ppachtmachnet_test		67.72 278	63.70 287	79.77 239	78.92 304	66.04 130	88.68 241	82.90 319	60.11 296	55.45 304	75.96 309	39.19 281	90.55 280	39.53 326	52.55 324	82.71 296

TinyColmap		60.32 307	56.42 313	72.00 313	78.78 306	53.18 311	78.36 319	75.64 333	52.30 324	41.59 344	75.82 311	14.76 351	88.35 303	35.84 334	54.71 320	74.46 339

SixPastTwentyTwo		64.92 292	61.78 299	74.34 294	78.74 307	49.76 327	83.42 285	79.51 328	62.86 274	50.27 323	77.35 295	30.92 324	90.49 282	45.89 303	47.06 333	82.78 292

EG-PatchMatch MVS		68.55 271	65.41 276	77.96 263	78.69 308	62.93 211	89.86 220	89.17 241	60.55 291	50.27 323	77.73 294	22.60 342	94.06 197	47.18 299	72.65 214	76.88 335

pmmvs5		73.35 234	71.52 239	78.86 254	78.64 309	60.61 251	91.08 183	86.90 285	67.69 238	63.32 266	83.64 226	44.33 265	90.53 281	62.04 240	66.02 256	85.46 264

UniMVSNet_ETH3D		72.74 242	70.53 245	79.36 247	78.62 310	56.64 294	85.01 273	89.20 239	63.77 267	64.84 251	84.44 220	34.05 311	91.86 265	63.94 227	70.89 227	89.57 196

XVG-OURS		74.25 227	72.46 231	79.63 242	78.45 311	57.59 286	80.33 306	87.39 281	63.86 266	68.76 213	89.62 164	40.50 277	91.72 268	69.00 181	74.25 201	89.58 195

XVG-OURS-SEG-HR		74.70 224	73.08 222	79.57 244	78.25 312	57.33 290	80.49 304	87.32 282	63.22 271	68.76 213	90.12 160	44.89 263	91.59 270	70.55 168	74.09 203	89.79 192

MDA-MVSNet_test_wron		63.78 299	60.16 302	74.64 290	78.15 313	60.41 252	83.49 282	84.03 308	56.17 317	39.17 346	71.59 327	37.22 298	83.24 334	42.87 315	48.73 330	80.26 320

YYNet1		63.76 300	60.14 303	74.62 291	78.06 314	60.19 257	83.46 284	83.99 312	56.18 316	39.25 345	71.56 328	37.18 299	83.34 332	42.90 314	48.70 331	80.32 319

DTE-MVSNet		68.46 273	67.33 264	71.87 314	77.94 315	49.00 331	86.16 270	88.58 264	66.36 250	58.19 293	82.21 241	46.36 253	83.87 328	44.97 308	55.17 317	82.73 294

USDC		67.43 283	64.51 282	76.19 282	77.94 315	55.29 302	78.38 318	85.00 300	73.17 138	48.36 329	80.37 271	21.23 344	92.48 250	52.15 278	64.02 274	80.81 314

jajsoiax		73.05 236	71.51 240	77.67 265	77.46 317	54.83 304	88.81 239	90.04 212	69.13 227	62.85 271	83.51 228	31.16 322	92.75 238	70.83 163	69.80 230	85.43 265

mvs_tets		72.71 243	71.11 241	77.52 266	77.41 318	54.52 306	88.45 246	89.76 218	68.76 232	62.70 272	83.26 231	29.49 326	92.71 239	70.51 169	69.62 232	85.34 267

N_pmnet		50.55 316	49.11 319	54.88 331	77.17 319	4.02 364	84.36 276	2.00 363	48.59 333	45.86 334	68.82 332	32.22 317	82.80 336	31.58 347	51.38 326	77.81 333

test_djsdf		73.76 233	72.56 229	77.39 270	77.00 320	53.93 308	89.07 236	90.69 184	65.80 253	63.92 259	82.03 243	43.14 270	92.67 242	72.83 145	68.53 241	85.57 262

OpenMVS_ROB	61.12 18	66.39 286	62.92 292	76.80 279	76.51 321	57.77 282	89.22 231	83.41 315	55.48 318	53.86 310	77.84 293	26.28 336	93.95 206	34.90 338	68.76 239	78.68 330

v7n		71.31 251	68.65 256	79.28 248	76.40 322	60.77 245	86.71 267	89.45 230	64.17 264	58.77 292	78.24 289	44.59 264	93.54 216	57.76 259	61.75 290	83.52 283

K. test v3		63.09 301	59.61 305	73.53 299	76.26 323	49.38 330	83.27 286	77.15 330	64.35 262	47.77 330	72.32 323	28.73 328	87.79 309	49.93 285	36.69 345	83.41 286

RPSCF		64.24 296	61.98 298	71.01 315	76.10 324	45.00 339	75.83 325	75.94 332	46.94 338	58.96 290	84.59 218	31.40 321	82.00 341	47.76 297	60.33 304	86.04 250

OurMVSNet-221017-0		64.68 293	62.17 297	72.21 310	76.08 325	47.35 336	80.67 303	81.02 322	56.19 315	51.60 317	79.66 281	27.05 334	88.56 301	53.60 275	53.63 322	80.71 315

Anonymous20231206		67.53 281	65.78 271	72.79 305	74.95 326	47.59 335	88.23 248	87.32 282	61.75 287	58.07 295	77.29 297	37.79 294	87.29 315	42.91 313	63.71 276	83.48 284

ITE_SJBPF				70.43 316	74.44 327	47.06 337		77.32 329	60.16 295	54.04 309	83.53 227	23.30 341	84.01 326	43.07 312	61.58 294	80.21 322

EU-MVSNet		64.01 297	63.01 291	67.02 325	74.40 328	38.86 351	83.27 286	86.19 291	45.11 341	54.27 307	81.15 262	36.91 303	80.01 344	48.79 290	57.02 311	82.19 305

XVG-ACMP-BASELINE		68.04 276	65.53 275	75.56 285	74.06 329	52.37 314	78.43 317	85.88 294	62.03 282	58.91 291	81.21 261	20.38 345	91.15 278	60.69 247	68.18 243	83.16 290

CL-MVSNet_2432*1600		69.92 260	68.09 261	75.41 286	73.25 330	55.90 299	90.05 214	89.90 215	69.96 215	61.96 277	76.54 303	51.05 215	87.64 310	49.51 287	50.59 328	82.70 297

anonymousdsp		71.14 252	69.37 253	76.45 280	72.95 331	54.71 305	84.19 277	88.88 253	61.92 284	62.15 275	79.77 279	38.14 289	91.44 277	68.90 183	67.45 250	83.21 289

lessismore_v0				73.72 298	72.93 332	47.83 334		61.72 354		45.86 334	73.76 316	28.63 330	89.81 292	47.75 298	31.37 349	83.53 282

pmmvs6		67.57 280	64.76 279	76.00 284	72.82 333	53.37 310	88.71 240	86.78 286	53.19 322	57.58 300	78.03 292	35.33 308	92.41 251	55.56 267	54.88 319	82.21 304

testgi		64.48 295	62.87 293	69.31 318	71.24 334	40.62 346	85.49 271	79.92 326	65.36 257	54.18 308	83.49 229	23.74 340	84.55 323	41.60 319	60.79 299	82.77 293

Patchmatch-RL test		68.17 275	64.49 283	79.19 249	71.22 335	53.93 308	70.07 333	71.54 343	69.22 224	56.79 302	62.89 340	56.58 160	88.61 299	69.53 175	52.61 323	95.03 73

Gipuma		34.91 322	31.44 325	45.30 335	70.99 336	39.64 350	19.85 356	72.56 340	20.10 354	16.16 355	21.47 356	5.08 360	71.16 348	13.07 353	43.70 338	25.08 352
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		65.79 290	63.10 290	73.88 296	70.71 337	50.29 326	81.09 301	89.88 216	72.58 151	49.25 327	74.77 315	32.57 316	87.43 314	55.96 266	41.04 341	83.90 279

CMPMVS	48.56 21	66.77 285	64.41 284	73.84 297	70.65 338	50.31 325	77.79 322	85.73 296	45.54 340	44.76 338	82.14 242	35.40 307	90.14 290	63.18 232	74.54 200	81.07 311
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		63.83 298	62.65 294	67.38 324	70.58 339	39.94 347	86.57 268	84.17 307	63.29 270	51.86 316	77.30 296	37.09 301	82.47 337	38.87 330	54.13 321	79.73 323

testing_2		71.09 253	67.32 265	82.40 174	69.82 340	66.52 119	83.64 280	90.77 182	72.21 163	45.12 337	71.07 329	27.60 332	93.74 212	75.71 127	69.96 229	86.95 230

MIMVSNet1		60.16 309	57.33 310	68.67 319	69.71 341	44.13 341	78.92 315	84.21 306	55.05 319	44.63 339	71.85 325	23.91 339	81.54 343	32.63 344	55.03 318	80.35 318

pmmvs-eth3d		65.53 291	62.32 296	75.19 287	69.39 342	59.59 263	82.80 291	83.43 314	62.52 279	51.30 320	72.49 319	32.86 313	87.16 316	55.32 268	50.73 327	78.83 329

UnsupCasMVSNet_bld		61.60 304	57.71 308	73.29 301	68.73 343	51.64 317	78.61 316	89.05 249	57.20 310	46.11 331	61.96 341	28.70 329	88.60 300	50.08 284	38.90 343	79.63 324

DIV-MVS_2432*1600		60.87 306	58.60 306	67.68 322	66.13 344	39.93 348	75.63 326	84.70 303	57.32 309	49.57 326	68.45 333	29.55 325	82.87 335	48.09 292	47.94 332	80.25 321

new-patchmatchnet		59.30 311	56.48 312	67.79 321	65.86 345	44.19 340	82.47 292	81.77 320	59.94 297	43.65 342	66.20 336	27.67 331	81.68 342	39.34 327	41.40 340	77.50 334

PM-MVS		59.40 310	56.59 311	67.84 320	63.63 346	41.86 343	76.76 323	63.22 352	59.01 302	51.07 321	72.27 324	11.72 353	83.25 333	61.34 243	50.28 329	78.39 332

DSMNet-mixed		56.78 312	54.44 315	63.79 327	63.21 347	29.44 355	64.43 343	64.10 351	42.12 345	51.32 319	71.60 326	31.76 319	75.04 346	36.23 333	65.20 262	86.87 232

new_pmnet		49.31 317	46.44 320	57.93 329	62.84 348	40.74 345	68.47 337	62.96 353	36.48 347	35.09 347	57.81 343	14.97 350	72.18 347	32.86 343	46.44 334	60.88 347

LF4IMVS		54.01 315	52.12 316	59.69 328	62.41 349	39.91 349	68.59 336	68.28 348	42.96 344	44.55 340	75.18 312	14.09 352	68.39 349	41.36 321	51.68 325	70.78 342

ambc				69.61 317	61.38 350	41.35 344	49.07 352	85.86 295		50.18 325	66.40 335	10.16 354	88.14 305	45.73 304	44.20 336	79.32 327

TDRefinement		55.28 314	51.58 317	66.39 326	59.53 351	46.15 338	76.23 324	72.80 339	44.60 342	42.49 343	76.28 307	15.29 349	82.39 338	33.20 341	43.75 337	70.62 343

pmmvs3		55.51 313	51.50 318	67.53 323	57.90 352	50.93 323	80.37 305	73.66 338	40.63 346	44.15 341	64.75 339	16.30 348	78.97 345	44.77 309	40.98 342	72.69 340

DeepMVS_CX				34.71 338	51.45 353	24.73 359		28.48 362	31.46 349	17.49 354	52.75 344	5.80 359	42.60 358	18.18 351	19.42 351	36.81 350

FPMVS		45.64 318	43.10 321	53.23 333	51.42 354	36.46 352	64.97 342	71.91 341	29.13 350	27.53 349	61.55 342	9.83 355	65.01 353	16.00 352	55.58 316	58.22 348

wuyk23d		11.30 329	10.95 332	12.33 342	48.05 355	19.89 360	25.89 355	1.92 364	3.58 358	3.12 360	1.37 360	0.64 365	15.77 360	6.23 358	7.77 358	1.35 356

PMMVS2		37.93 321	33.61 324	50.92 334	46.31 356	24.76 358	60.55 347	50.05 355	28.94 351	20.93 351	47.59 345	4.41 362	65.13 352	25.14 348	18.55 352	62.87 346

E-PMN		24.61 324	24.00 328	26.45 339	43.74 357	18.44 361	60.86 345	39.66 357	15.11 355	9.53 358	22.10 355	6.52 358	46.94 356	8.31 356	10.14 354	13.98 354

EMVS		23.76 326	23.20 330	25.46 340	41.52 358	16.90 362	60.56 346	38.79 360	14.62 356	8.99 359	20.24 358	7.35 357	45.82 357	7.25 357	9.46 355	13.64 355

LCM-MVSNet		40.54 319	35.79 322	54.76 332	36.92 359	30.81 354	51.41 350	69.02 345	22.07 352	24.63 350	45.37 348	4.56 361	65.81 351	33.67 339	34.50 348	67.67 344

ANet_high		40.27 320	35.20 323	55.47 330	34.74 360	34.47 353	63.84 344	71.56 342	48.42 334	18.80 353	41.08 351	9.52 356	64.45 354	20.18 350	8.66 357	67.49 345

MVE	24.84 23	24.35 325	19.77 331	38.09 337	34.56 361	26.92 357	26.57 354	38.87 359	11.73 357	11.37 357	27.44 353	1.37 364	50.42 355	11.41 354	14.60 353	36.93 349
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	26.43 22	31.84 323	28.16 326	42.89 336	25.87 362	27.58 356	50.92 351	49.78 356	21.37 353	14.17 356	40.81 352	2.01 363	66.62 350	9.61 355	38.88 344	34.49 351
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		22.26 327	23.75 329	17.80 341	5.23 363	12.06 363	35.26 353	39.48 358	2.82 359	18.94 352	44.20 349	22.23 343	24.64 359	36.30 332	9.31 356	16.69 353

testmvs		7.23 331	9.62 334	0.06 344	0.04 364	0.02 366	84.98 274	0.02 365	0.03 360	0.18 361	1.21 361	0.01 367	0.02 361	0.14 359	0.01 359	0.13 358

test123		6.92 332	9.21 335	0.08 343	0.03 365	0.05 365	81.65 297	0.01 366	0.02 361	0.14 362	0.85 362	0.03 366	0.02 361	0.12 360	0.00 360	0.16 357

uanet_test		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

cdsmvs_eth3d_5k		19.86 328	26.47 327	0.00 345	0.00 366	0.00 367	0.00 357	93.45 78	0.00 362	0.00 363	95.27 47	49.56 227	0.00 363	0.00 361	0.00 360	0.00 359

pcd_1.5k_mvsjas		4.46 333	5.95 336	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	53.55 193	0.00 363	0.00 361	0.00 360	0.00 359

sosnet-low-res		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

sosnet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

uncertanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

Regformer		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

ab-mvs-re		7.91 330	10.55 333	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	94.95 58	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

uanet		0.00 334	0.00 337	0.00 345	0.00 366	0.00 367	0.00 357	0.00 367	0.00 362	0.00 363	0.00 363	0.00 368	0.00 363	0.00 361	0.00 360	0.00 359

test_241102_TWO								94.41 43	71.65 184	92.07 5	97.21 5	74.58 14	99.11 4	92.34 6	95.36 12	96.59 13

test_0728_THIRD									72.48 153	90.55 12	96.93 10	76.24 9	99.08 9	91.53 12	94.99 15	96.43 22

GSMVS																94.68 81

sam_mvs1												57.85 140				94.68 81

sam_mvs												54.91 179

MTGPA								92.23 125

test_post1							78.95 314				20.70 357	53.05 198	91.50 276	60.43 248

test_post											23.01 354	56.49 161	92.67 242

patchmatchnet-post											67.62 334	57.62 143	90.25 284

MTMP							93.77 77	32.52 361

test9_res														89.41 20	94.96 16	95.29 56

agg_prior2														86.41 49	94.75 26	95.33 51

test_prior4						67.18 99	93.92 68

test_prior2							95.10 36		75.40 99	85.25 48	95.61 37	67.94 40		87.47 39	94.77 23

旧先验2							92.00 141		59.37 301	87.54 25			93.47 219	75.39 129

新几何2							91.41 163

无先验							92.71 113	92.61 115	62.03 282				97.01 87	66.63 200		93.97 112

原ACMM2							92.01 139

testdata2													96.09 122	61.26 244

segment_acmp												65.94 60

testdata1							89.21 232		77.55 74

plane_prior5								91.31 163					95.55 151	76.74 120	78.53 175	88.39 210

plane_prior4											89.14 167

plane_prior3						61.95 227			79.09 53	72.53 166

plane_prior2							93.13 97		78.81 58

plane_prior						62.42 220	93.85 72		79.38 46						78.80 172

n2								0.00 367

nn								0.00 367

door-mid								66.01 350

test11								93.01 98

door								66.57 349

HQP5-MVS						63.66 197

BP-MVS														77.63 117

HQP4-MVS										74.18 147			95.61 145			88.63 204

HQP3-MVS								91.70 149							78.90 170

HQP2-MVS												51.63 210

MDTV_nov1_ep13_2view						59.90 260	80.13 310		67.65 240	72.79 161		54.33 186		59.83 252		92.58 150

ACMMP++_ref															71.63 220

ACMMP++															69.72 231

Test By Simon												54.21 187

High-resolution multi-view benchmark