High-res multi-view results

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

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

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

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

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

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

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

MCST-MVS		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 44	96.26 27	72.84 25	99.38 1	92.64 5	95.93 10	97.08 9

DVP-MVS++.		90.53 3	91.09 4	88.87 14	97.31 4	69.91 38	93.96 67	94.37 49	72.48 160	92.07 6	96.85 12	83.82 2	99.15 2	91.53 15	97.42 4	97.55 4

OPU-MVS				89.97 3	97.52 3	73.15 13	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

test_0728_SECOND				88.70 16	96.45 14	70.43 28	96.64 8	94.37 49					99.15 2	91.91 13	94.90 21	96.51 20

PC_three_1452									80.91 36	94.07 2	96.83 14	83.57 4	99.12 5	95.70 2	97.42 4	97.55 4

SED-MVS		89.94 8	90.36 8	88.70 16	96.45 14	69.38 48	96.89 4	94.44 42	71.65 191	92.11 4	97.21 5	76.79 9	99.11 6	92.34 7	95.36 14	97.62 2

test_241102_TWO								94.41 45	71.65 191	92.07 6	97.21 5	74.58 16	99.11 6	92.34 7	95.36 14	96.59 15

test_241102_ONE					96.45 14	69.38 48		94.44 42	71.65 191	92.11 4	97.05 8	76.79 9	99.11 6

DPM-MVS		90.70 2	90.52 6	91.24 1	89.68 157	76.68 2	97.29 1	95.35 10	82.87 14	91.58 10	97.22 4	79.93 5	99.10 9	83.12 79	97.64 2	97.94 1

CANet		89.61 10	89.99 10	88.46 20	94.39 42	69.71 44	96.53 11	93.78 64	86.89 4	89.68 18	95.78 34	65.94 63	99.10 9	92.99 3	93.91 44	96.58 17

DVP-MVS		89.41 11	89.73 12	88.45 21	96.40 17	69.99 34	96.64 8	94.52 38	71.92 177	90.55 15	96.93 10	73.77 20	99.08 11	91.91 13	94.90 21	96.29 30
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test_0728_THIRD									72.48 160	90.55 15	96.93 10	76.24 11	99.08 11	91.53 15	94.99 17	96.43 26

MSC_two_6792asdad				89.60 9	97.31 4	73.22 11		95.05 20					99.07 13	92.01 11	94.77 25	96.51 20

No_MVS				89.60 9	97.31 4	73.22 11		95.05 20					99.07 13	92.01 11	94.77 25	96.51 20

CNVR-MVS		90.32 5	90.89 5	88.61 19	96.76 10	70.65 25	96.47 12	94.83 25	84.83 8	89.07 22	96.80 15	70.86 32	99.06 15	92.64 5	95.71 11	96.12 34

QAPM		79.95 147	77.39 176	87.64 29	89.63 158	71.41 18	93.30 98	93.70 71	65.34 267	67.39 243	91.75 142	47.83 253	98.96 16	57.71 269	89.81 98	92.54 158

DELS-MVS		90.05 7	90.09 9	89.94 4	93.14 75	73.88 8	97.01 3	94.40 47	88.32 2	85.71 46	94.91 67	74.11 19	98.91 17	87.26 50	95.94 9	97.03 10
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
MVS		84.66 68	82.86 91	90.06 2	90.93 132	74.56 6	87.91 260	95.54 9	68.55 242	72.35 180	94.71 72	59.78 130	98.90 18	81.29 99	94.69 32	96.74 12

API-MVS		82.28 108	80.53 124	87.54 34	96.13 24	70.59 26	93.63 86	91.04 186	65.72 264	75.45 148	92.83 121	56.11 173	98.89 19	64.10 234	89.75 101	93.15 141

MAR-MVS		84.18 78	83.43 78	86.44 69	96.25 23	65.93 138	94.28 53	94.27 53	74.41 116	79.16 110	95.61 40	53.99 197	98.88 20	69.62 183	93.26 56	94.50 94
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
PHI-MVS		86.83 39	86.85 39	86.78 54	93.47 66	65.55 148	95.39 29	95.10 17	71.77 188	85.69 47	96.52 18	62.07 108	98.77 21	86.06 58	95.60 12	96.03 37

NCCC		89.07 13	89.46 13	87.91 24	96.60 12	69.05 55	96.38 13	94.64 35	84.42 9	86.74 33	96.20 28	66.56 59	98.76 22	89.03 33	94.56 33	95.92 41

DeepPCF-MVS	81.17 1	89.72 9	91.38 3	84.72 127	93.00 78	58.16 288	96.72 7	94.41 45	86.50 5	90.25 17	97.83 1	75.46 14	98.67 23	92.78 4	95.49 13	97.32 6

HPM-MVS++		89.37 12	89.95 11	87.64 29	95.10 33	68.23 76	95.24 32	94.49 40	82.43 17	88.90 23	96.35 24	71.89 31	98.63 24	88.76 35	96.40 6	96.06 35

CHOSEN 1792x2688		84.98 63	83.45 77	89.57 11	89.94 152	75.14 5	92.07 143	92.32 126	81.87 24	75.68 143	88.27 184	60.18 123	98.60 25	80.46 104	90.27 97	94.96 77

3Dnovator	73.91 6	82.69 104	80.82 118	88.31 22	89.57 159	71.26 19	92.60 125	94.39 48	78.84 60	67.89 235	92.48 128	48.42 247	98.52 26	68.80 193	94.40 37	95.15 68

DPE-MVS		88.77 14	89.21 14	87.45 36	96.26 22	67.56 91	94.17 54	94.15 57	68.77 240	90.74 13	97.27 3	76.09 12	98.49 27	90.58 21	94.91 20	96.30 29
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
ETH3 D test6400		90.27 6	90.44 7	89.75 6	96.82 9	74.33 7	95.89 17	94.80 28	77.13 83	89.13 21	97.38 2	74.49 17	98.48 28	92.32 10	95.98 8	96.46 25

CSCG		86.87 37	86.26 42	88.72 15	95.05 34	70.79 24	93.83 81	95.33 11	68.48 244	77.63 126	94.35 84	73.04 23	98.45 29	84.92 67	93.71 50	96.92 11

DeepC-MVS	77.85 3	85.52 54	85.24 57	86.37 73	88.80 179	66.64 120	92.15 137	93.68 72	81.07 34	76.91 136	93.64 101	62.59 104	98.44 30	85.50 61	92.84 62	94.03 114
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DeepC-MVS_fast	79.48 2	87.95 19	88.00 19	87.79 27	95.86 28	68.32 71	95.74 21	94.11 59	83.82 11	83.49 68	96.19 29	64.53 80	98.44 30	83.42 78	94.88 24	96.61 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SMA-MVS		88.14 15	88.29 18	87.67 28	93.21 72	68.72 63	93.85 77	94.03 60	74.18 122	91.74 9	96.67 16	65.61 68	98.42 32	89.24 28	96.08 7	95.88 43
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
Regformer-1		87.24 29	87.60 27	86.15 80	95.14 31	65.83 141	93.95 70	95.12 15	82.11 21	84.25 59	95.73 36	67.88 46	98.35 33	85.60 60	88.64 106	94.26 99

Regformer-3		85.80 52	85.92 47	85.46 102	94.17 46	65.09 164	92.95 111	95.11 16	81.13 33	81.68 80	95.04 58	65.82 65	98.32 34	83.02 80	84.36 140	92.97 148

TSAR-MVS + GP.		87.96 18	88.37 17	86.70 59	93.51 65	65.32 152	95.15 35	93.84 63	78.17 69	85.93 43	94.80 70	75.80 13	98.21 35	89.38 25	88.78 104	96.59 15

DP-MVS Recon		82.73 101	81.65 108	85.98 83	97.31 4	67.06 107	95.15 35	91.99 141	69.08 237	76.50 139	93.89 98	54.48 192	98.20 36	70.76 174	85.66 131	92.69 153

Regformer-2		87.00 36	87.43 29	85.71 95	95.14 31	64.73 171	93.95 70	94.95 22	81.69 26	84.03 64	95.73 36	67.35 50	98.19 37	85.40 62	88.64 106	94.20 101

MVS_111021_HR		86.19 47	85.80 50	87.37 38	93.17 74	69.79 41	93.99 66	93.76 67	79.08 57	78.88 115	93.99 96	62.25 107	98.15 38	85.93 59	91.15 87	94.15 106

OpenMVS	70.45 11	78.54 176	75.92 195	86.41 72	85.93 235	71.68 16	92.74 117	92.51 123	66.49 258	64.56 264	91.96 137	43.88 276	98.10 39	54.61 278	90.65 93	89.44 207

Regformer-4		85.45 55	85.69 52	84.73 125	94.17 46	63.23 209	92.95 111	94.83 25	80.66 38	81.29 83	95.04 58	65.12 72	98.08 40	82.74 82	84.36 140	92.88 152

ZNCC-MVS		85.33 58	85.08 59	86.06 81	93.09 77	65.65 145	93.89 75	93.41 87	73.75 133	79.94 99	94.68 73	60.61 120	98.03 41	82.63 86	93.72 49	94.52 93

ETH3D cwj APD-0.16		87.06 33	87.18 34	86.71 57	91.99 104	67.48 96	92.97 109	94.21 54	71.48 202	85.72 45	96.32 26	68.13 41	98.00 42	89.06 31	94.70 31	94.65 87

ETH3D-3000-0.1		87.61 23	87.89 20	86.75 55	93.58 62	67.21 102	94.31 52	94.14 58	72.92 151	87.13 29	96.62 17	67.81 47	97.94 43	90.13 22	94.42 36	95.09 71

SteuartSystems-ACMMP		86.82 40	86.90 37	86.58 63	90.42 143	66.38 126	96.09 16	93.87 62	77.73 76	84.01 65	95.66 38	63.39 96	97.94 43	87.40 47	93.55 53	95.42 51
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ACMMP_NAP		86.05 48	85.80 50	86.80 53	91.58 118	67.53 93	91.79 156	93.49 82	74.93 112	84.61 55	95.30 47	59.42 134	97.92 45	86.13 57	94.92 19	94.94 78

testtj		86.62 42	86.66 40	86.50 67	96.95 8	65.70 143	94.41 50	93.45 83	67.74 246	86.19 38	96.39 23	64.38 81	97.91 46	87.33 48	93.14 58	95.90 42

EI-MVSNet-Vis-set		83.77 87	83.67 73	84.06 144	92.79 86	63.56 205	91.76 159	94.81 27	79.65 47	77.87 122	94.09 93	63.35 97	97.90 47	79.35 109	79.36 173	90.74 191

PS-MVSNAJ		88.14 15	87.61 26	89.71 7	92.06 100	76.72 1	95.75 20	93.26 91	83.86 10	89.55 19	96.06 31	53.55 202	97.89 48	91.10 17	93.31 55	94.54 91

9.14			87.63 25		93.86 53		94.41 50	94.18 55	72.76 154	86.21 37	96.51 19	66.64 57	97.88 49	90.08 23	94.04 41

GST-MVS		84.63 69	84.29 69	85.66 97	92.82 83	65.27 154	93.04 106	93.13 99	73.20 143	78.89 112	94.18 92	59.41 135	97.85 50	81.45 95	92.48 68	93.86 122

xxxxxxxxxxxxxcwj		87.14 31	87.19 33	86.99 48	93.84 54	67.89 83	95.05 39	84.72 313	78.19 67	86.25 35	96.44 21	66.98 52	97.79 51	88.68 36	94.56 33	95.28 61

SF-MVS		87.03 34	87.09 35	86.84 50	92.70 87	67.45 97	93.64 85	93.76 67	70.78 215	86.25 35	96.44 21	66.98 52	97.79 51	88.68 36	94.56 33	95.28 61

EI-MVSNet-UG-set		83.14 95	82.96 88	83.67 154	92.28 95	63.19 211	91.38 176	94.68 33	79.22 52	76.60 137	93.75 99	62.64 103	97.76 53	78.07 123	78.01 184	90.05 199

xiu_mvs_v2_base		87.92 20	87.38 31	89.55 12	91.41 125	76.43 3	95.74 21	93.12 100	83.53 12	89.55 19	95.95 32	53.45 206	97.68 54	91.07 18	92.62 64	94.54 91

HFP-MVS		84.73 66	84.40 68	85.72 93	93.75 58	65.01 165	93.50 92	93.19 95	72.19 171	79.22 108	94.93 64	59.04 139	97.67 55	81.55 93	92.21 69	94.49 95

#test#		84.98 63	84.74 64	85.72 93	93.75 58	65.01 165	94.09 60	93.19 95	73.55 139	79.22 108	94.93 64	59.04 139	97.67 55	82.66 84	92.21 69	94.49 95

IB-MVS	77.80 4	82.18 109	80.46 126	87.35 39	89.14 171	70.28 30	95.59 26	95.17 14	78.85 59	70.19 203	85.82 214	70.66 34	97.67 55	72.19 163	66.52 261	94.09 109
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
APDe-MVS		87.54 24	87.84 21	86.65 60	96.07 25	66.30 129	94.84 45	93.78 64	69.35 231	88.39 24	96.34 25	67.74 48	97.66 58	90.62 20	93.44 54	96.01 38

3Dnovator+	73.60 7	82.10 113	80.60 123	86.60 61	90.89 135	66.80 117	95.20 33	93.44 85	74.05 124	67.42 241	92.49 127	49.46 237	97.65 59	70.80 173	91.68 77	95.33 55

SD-MVS		87.49 26	87.49 28	87.50 35	93.60 61	68.82 61	93.90 74	92.63 119	76.86 87	87.90 26	95.76 35	66.17 60	97.63 60	89.06 31	91.48 81	96.05 36
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
WTY-MVS		86.32 44	85.81 49	87.85 25	92.82 83	69.37 50	95.20 33	95.25 12	82.71 15	81.91 78	94.73 71	67.93 45	97.63 60	79.55 108	82.25 154	96.54 18

PAPR		85.15 60	84.47 65	87.18 42	96.02 26	68.29 72	91.85 154	93.00 105	76.59 91	79.03 111	95.00 60	61.59 111	97.61 62	78.16 122	89.00 103	95.63 47

test12				87.09 45	94.60 39	68.86 59		92.91 107		82.67 75		65.44 70	97.55 63		93.69 51	94.84 80

region2R		84.36 72	84.03 71	85.36 107	93.54 64	64.31 185	93.43 95	92.95 106	72.16 174	78.86 116	94.84 69	56.97 161	97.53 64	81.38 97	92.11 73	94.24 100

PAPM_NR		82.97 98	81.84 105	86.37 73	94.10 50	66.76 118	87.66 264	92.84 109	69.96 224	74.07 159	93.57 103	63.10 101	97.50 65	70.66 176	90.58 94	94.85 79

ACMMPR		84.37 71	84.06 70	85.28 109	93.56 63	64.37 183	93.50 92	93.15 98	72.19 171	78.85 117	94.86 68	56.69 166	97.45 66	81.55 93	92.20 71	94.02 115

test_yl		84.28 74	83.16 85	87.64 29	94.52 40	69.24 51	95.78 18	95.09 18	69.19 234	81.09 86	92.88 119	57.00 159	97.44 67	81.11 100	81.76 158	96.23 32

DCV-MVSNet		84.28 74	83.16 85	87.64 29	94.52 40	69.24 51	95.78 18	95.09 18	69.19 234	81.09 86	92.88 119	57.00 159	97.44 67	81.11 100	81.76 158	96.23 32

XVS		83.87 85	83.47 76	85.05 114	93.22 70	63.78 195	92.92 113	92.66 116	73.99 125	78.18 120	94.31 88	55.25 180	97.41 69	79.16 111	91.58 79	93.95 117

X-MVStestdata		76.86 199	74.13 219	85.05 114	93.22 70	63.78 195	92.92 113	92.66 116	73.99 125	78.18 120	10.19 369	55.25 180	97.41 69	79.16 111	91.58 79	93.95 117

gm-plane-assit					88.42 187	67.04 109			78.62 64		91.83 139		97.37 71	76.57 131

CDPH-MVS		85.71 53	85.46 55	86.46 68	94.75 36	67.19 103	93.89 75	92.83 110	70.90 211	83.09 71	95.28 48	63.62 92	97.36 72	80.63 102	94.18 39	94.84 80

AdaColmap		78.94 164	77.00 181	84.76 123	96.34 19	65.86 139	92.66 124	87.97 285	62.18 290	70.56 196	92.37 131	43.53 277	97.35 73	64.50 232	82.86 150	91.05 189

EPNet		87.84 21	88.38 16	86.23 78	93.30 68	66.05 133	95.26 31	94.84 24	87.09 3	88.06 25	94.53 75	66.79 56	97.34 74	83.89 75	91.68 77	95.29 59
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
Anonymous20240529		76.84 201	74.15 218	84.88 120	91.02 130	64.95 168	93.84 80	91.09 181	53.57 331	73.00 165	87.42 198	35.91 315	97.32 75	69.14 189	72.41 224	92.36 161

PGM-MVS		83.25 93	82.70 95	84.92 118	92.81 85	64.07 191	90.44 207	92.20 135	71.28 204	77.23 132	94.43 78	55.17 184	97.31 76	79.33 110	91.38 83	93.37 132

ZD-MVS					96.63 11	65.50 150		93.50 81	70.74 216	85.26 52	95.19 56	64.92 77	97.29 77	87.51 44	93.01 59

Anonymous202405211		77.96 185	75.33 201	85.87 87	93.73 60	64.52 173	94.85 44	85.36 308	62.52 288	76.11 140	90.18 163	29.43 336	97.29 77	68.51 194	77.24 197	95.81 44

PVSNet_BlendedMVS		83.38 92	83.43 78	83.22 163	93.76 56	67.53 93	94.06 62	93.61 75	79.13 55	81.00 89	85.14 220	63.19 99	97.29 77	87.08 51	73.91 212	84.83 281

PVSNet_Blended		86.73 41	86.86 38	86.31 76	93.76 56	67.53 93	96.33 15	93.61 75	82.34 18	81.00 89	93.08 110	63.19 99	97.29 77	87.08 51	91.38 83	94.13 107

TEST9					94.18 44	67.28 100	94.16 55	93.51 79	71.75 189	85.52 48	95.33 45	68.01 42	97.27 81

train_agg		87.21 30	87.42 30	86.60 61	94.18 44	67.28 100	94.16 55	93.51 79	71.87 182	85.52 48	95.33 45	68.19 39	97.27 81	89.09 29	94.90 21	95.25 66

MSP-MVS		90.38 4	91.87 1	85.88 86	92.83 81	64.03 192	93.06 104	94.33 51	82.19 19	93.65 3	96.15 30	85.89 1	97.19 83	91.02 19	97.75 1	96.43 26
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
MP-MVS		85.02 61	84.97 61	85.17 113	92.60 89	64.27 188	93.24 99	92.27 129	73.13 145	79.63 104	94.43 78	61.90 109	97.17 84	85.00 65	92.56 65	94.06 112
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
zzz-MVS		84.73 66	84.47 65	85.50 100	91.89 109	65.16 158	91.55 167	92.23 130	75.32 106	80.53 93	95.21 54	56.06 174	97.16 85	84.86 68	92.55 66	94.18 102

MTAPA		83.91 84	83.38 82	85.50 100	91.89 109	65.16 158	81.75 303	92.23 130	75.32 106	80.53 93	95.21 54	56.06 174	97.16 85	84.86 68	92.55 66	94.18 102

h-mvs33		83.01 97	82.56 97	84.35 138	89.34 164	62.02 232	92.72 118	93.76 67	81.45 27	82.73 73	92.25 134	60.11 124	97.13 87	87.69 42	62.96 285	93.91 119

VDD-MVS		83.06 96	81.81 107	86.81 52	90.86 136	67.70 88	95.40 28	91.50 164	75.46 101	81.78 79	92.34 132	40.09 288	97.13 87	86.85 54	82.04 156	95.60 48

LFMVS		84.34 73	82.73 94	89.18 13	94.76 35	73.25 10	94.99 42	91.89 146	71.90 179	82.16 77	93.49 105	47.98 252	97.05 89	82.55 87	84.82 136	97.25 7

sss		82.71 103	82.38 100	83.73 151	89.25 168	59.58 273	92.24 135	94.89 23	77.96 72	79.86 101	92.38 130	56.70 165	97.05 89	77.26 128	80.86 166	94.55 89

1314		80.70 132	78.95 150	85.94 85	87.77 203	67.56 91	87.91 260	92.55 122	72.17 173	67.44 240	93.09 109	50.27 230	97.04 91	71.68 169	87.64 114	93.23 139

无先验							92.71 119	92.61 120	62.03 292				97.01 92	66.63 208		93.97 116

1121		81.25 123	80.05 128	84.87 121	92.30 94	64.31 185	87.91 260	91.39 168	59.44 310	79.94 99	92.91 116	57.09 155	97.01 92	66.63 208	92.81 63	93.29 136

MP-MVS-pluss		85.24 59	85.13 58	85.56 99	91.42 123	65.59 147	91.54 168	92.51 123	74.56 115	80.62 92	95.64 39	59.15 138	97.00 94	86.94 53	93.80 46	94.07 111
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
VNet		86.20 46	85.65 53	87.84 26	93.92 52	69.99 34	95.73 23	95.94 6	78.43 65	86.00 42	93.07 112	58.22 145	97.00 94	85.22 64	84.33 143	96.52 19

APD-MVS		85.93 50	85.99 46	85.76 92	95.98 27	65.21 156	93.59 88	92.58 121	66.54 257	86.17 39	95.88 33	63.83 88	97.00 94	86.39 56	92.94 60	95.06 72
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mPP-MVS		82.96 99	82.44 99	84.52 133	92.83 81	62.92 218	92.76 116	91.85 148	71.52 199	75.61 146	94.24 90	53.48 205	96.99 97	78.97 114	90.73 91	93.64 128

CANet_DTU		84.09 80	83.52 74	85.81 89	90.30 146	66.82 115	91.87 152	89.01 258	85.27 6	86.09 41	93.74 100	47.71 255	96.98 98	77.90 125	89.78 100	93.65 127

PVSNet_Blended_VisFu		83.97 82	83.50 75	85.39 106	90.02 150	66.59 123	93.77 82	91.73 152	77.43 82	77.08 135	89.81 169	63.77 90	96.97 99	79.67 107	88.21 109	92.60 156

ACMMP		81.49 120	80.67 121	83.93 146	91.71 115	62.90 219	92.13 138	92.22 134	71.79 187	71.68 189	93.49 105	50.32 228	96.96 100	78.47 119	84.22 147	91.93 172
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
test_8					94.19 43	67.19 103	94.15 57	93.42 86	71.87 182	85.38 50	95.35 44	68.19 39	96.95 101

HY-MVS	76.49 5	84.28 74	83.36 83	87.02 47	92.22 97	67.74 87	84.65 283	94.50 39	79.15 54	82.23 76	87.93 191	66.88 54	96.94 102	80.53 103	82.20 155	96.39 28

MG-MVS		87.11 32	86.27 41	89.62 8	97.79 1	76.27 4	94.96 43	94.49 40	78.74 63	83.87 66	92.94 115	64.34 82	96.94 102	75.19 138	94.09 40	95.66 46

canonicalmvs		86.85 38	86.25 43	88.66 18	91.80 113	71.92 15	93.54 90	91.71 154	80.26 41	87.55 27	95.25 52	63.59 94	96.93 104	88.18 38	84.34 142	97.11 8

alignmvs		87.28 28	86.97 36	88.24 23	91.30 126	71.14 22	95.61 25	93.56 77	79.30 50	87.07 32	95.25 52	68.43 37	96.93 104	87.87 40	84.33 143	96.65 13

test_prior3		87.38 27	87.70 24	86.42 70	94.71 37	67.35 98	95.10 37	93.10 101	75.40 104	85.25 53	95.61 40	67.94 43	96.84 106	87.47 45	94.77 25	95.05 73

test_prior				86.42 70	94.71 37	67.35 98		93.10 101					96.84 106			95.05 73

MSLP-MVS++		86.27 45	85.91 48	87.35 39	92.01 103	68.97 58	95.04 41	92.70 113	79.04 58	81.50 82	96.50 20	58.98 141	96.78 108	83.49 77	93.93 43	96.29 30

abl_6		79.82 149	79.20 147	81.70 203	89.85 153	58.34 286	88.47 253	90.07 217	62.56 287	77.71 124	93.08 110	47.65 256	96.78 108	77.94 124	85.45 133	89.99 200

agg_prior1		87.02 35	87.26 32	86.28 77	94.16 48	66.97 111	94.08 61	93.31 89	71.85 184	84.49 57	95.39 43	68.91 36	96.75 110	88.84 34	94.32 38	95.13 69

agg_prior					94.16 48	66.97 111		93.31 89		84.49 57			96.75 110

原ACMM1				84.42 136	93.21 72	64.27 188		93.40 88	65.39 265	79.51 105	92.50 125	58.11 147	96.69 112	65.27 228	93.96 42	92.32 163

ab-mvs		80.18 141	78.31 156	85.80 90	88.44 186	65.49 151	83.00 298	92.67 115	71.82 186	77.36 130	85.01 221	54.50 189	96.59 113	76.35 134	75.63 205	95.32 57

PCF-MVS	73.15 9	79.29 158	77.63 169	84.29 140	86.06 230	65.96 137	87.03 270	91.10 180	69.86 226	69.79 211	90.64 152	57.54 152	96.59 113	64.37 233	82.29 153	90.32 195
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
新几何1				84.73 125	92.32 93	64.28 187		91.46 166	59.56 309	79.77 102	92.90 117	56.95 162	96.57 115	63.40 238	92.91 61	93.34 133

VDDNet		80.50 136	78.26 157	87.21 41	86.19 228	69.79 41	94.48 48	91.31 170	60.42 302	79.34 106	90.91 149	38.48 295	96.56 116	82.16 88	81.05 164	95.27 63

thisisatest0515		83.41 91	82.49 98	86.16 79	89.46 163	68.26 74	93.54 90	94.70 32	74.31 119	75.75 142	90.92 148	72.62 26	96.52 117	69.64 181	81.50 160	93.71 125

cascas		78.18 181	75.77 197	85.41 105	87.14 214	69.11 53	92.96 110	91.15 178	66.71 256	70.47 197	86.07 211	37.49 305	96.48 118	70.15 179	79.80 170	90.65 192

EIA-MVS		84.84 65	84.88 62	84.69 128	91.30 126	62.36 227	93.85 77	92.04 139	79.45 48	79.33 107	94.28 89	62.42 106	96.35 119	80.05 105	91.25 86	95.38 53

baseline2		83.68 90	83.42 80	84.48 135	87.37 210	66.00 135	90.06 219	95.93 7	79.71 46	69.08 216	90.39 159	77.92 6	96.28 120	78.91 115	81.38 161	91.16 187

HPM-MVS		83.25 93	82.95 89	84.17 142	92.25 96	62.88 220	90.91 193	91.86 147	70.30 220	77.12 133	93.96 97	56.75 164	96.28 120	82.04 89	91.34 85	93.34 133
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DWT-MVSNet_test		83.95 83	82.80 92	87.41 37	92.90 80	70.07 33	89.12 242	94.42 44	82.15 20	77.64 125	91.77 140	70.81 33	96.22 122	65.03 229	81.36 162	95.94 39

CP-MVS		83.71 89	83.40 81	84.65 129	93.14 75	63.84 193	94.59 47	92.28 128	71.03 209	77.41 129	94.92 66	55.21 183	96.19 123	81.32 98	90.70 92	93.91 119

UGNet		79.87 148	78.68 151	83.45 161	89.96 151	61.51 241	92.13 138	90.79 191	76.83 88	78.85 117	86.33 209	38.16 297	96.17 124	67.93 198	87.17 117	92.67 154
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
APD-MVS_3200maxsize		81.64 119	81.32 111	82.59 176	92.36 92	58.74 282	91.39 174	91.01 187	63.35 278	79.72 103	94.62 74	51.82 215	96.14 125	79.71 106	87.93 112	92.89 151

BH-RMVSNet		79.46 157	77.65 168	84.89 119	91.68 116	65.66 144	93.55 89	88.09 282	72.93 150	73.37 163	91.12 147	46.20 265	96.12 126	56.28 274	85.61 132	92.91 150

testdata2													96.09 127	61.26 253

MVS_Test		84.16 79	83.20 84	87.05 46	91.56 119	69.82 40	89.99 224	92.05 138	77.77 75	82.84 72	86.57 206	63.93 87	96.09 127	74.91 144	89.18 102	95.25 66

baseline		85.01 62	84.44 67	86.71 57	88.33 189	68.73 62	90.24 215	91.82 150	81.05 35	81.18 85	92.50 125	63.69 91	96.08 129	84.45 70	86.71 124	95.32 57

casdiffmvs		85.37 56	84.87 63	86.84 50	88.25 192	69.07 54	93.04 106	91.76 151	81.27 31	80.84 91	92.07 136	64.23 83	96.06 130	84.98 66	87.43 116	95.39 52

thisisatest0530		81.15 124	80.07 127	84.39 137	88.26 191	65.63 146	91.40 172	94.62 36	71.27 205	70.93 194	89.18 173	72.47 27	96.04 131	65.62 223	76.89 199	91.49 177

TSAR-MVS + MP.		88.11 17	88.64 15	86.54 65	91.73 114	68.04 79	90.36 211	93.55 78	82.89 13	91.29 11	92.89 118	72.27 28	96.03 132	87.99 39	94.77 25	95.54 50
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSDG		69.54 269	65.73 278	80.96 223	85.11 246	63.71 199	84.19 285	83.28 328	56.95 321	54.50 316	84.03 231	31.50 329	96.03 132	42.87 325	69.13 243	83.14 300

Effi-MVS+		83.82 86	82.76 93	86.99 48	89.56 160	69.40 47	91.35 178	86.12 302	72.59 156	83.22 70	92.81 122	59.60 132	96.01 134	81.76 91	87.80 113	95.56 49

CS-MVS		87.54 24	87.81 22	86.74 56	90.46 142	70.23 31	96.34 14	92.31 127	81.40 30	86.14 40	95.17 57	65.49 69	95.92 135	89.09 29	93.91 44	94.06 112

UA-Net		80.02 145	79.65 135	81.11 217	89.33 166	57.72 292	86.33 277	89.00 259	77.44 81	81.01 88	89.15 174	59.33 136	95.90 136	61.01 254	84.28 145	89.73 203

SR-MVS		82.81 100	82.58 96	83.50 159	93.35 67	61.16 247	92.23 136	91.28 173	64.48 270	81.27 84	95.28 48	53.71 201	95.86 137	82.87 81	88.77 105	93.49 131

CS-MVS-test		85.35 57	85.55 54	84.75 124	90.77 139	65.29 153	95.38 30	91.54 160	78.03 71	83.67 67	94.32 87	62.47 105	95.81 138	82.73 83	91.00 89	93.15 141

lupinMVS		87.74 22	87.77 23	87.63 33	89.24 169	71.18 20	96.57 10	92.90 108	82.70 16	87.13 29	95.27 50	64.99 74	95.80 139	89.34 26	91.80 75	95.93 40

MS-PatchMatch		77.90 188	76.50 187	82.12 192	85.99 231	69.95 37	91.75 161	92.70 113	73.97 127	62.58 283	84.44 229	41.11 285	95.78 140	63.76 237	92.17 72	80.62 325

CLD-MVS		82.73 101	82.35 101	83.86 147	87.90 201	67.65 90	95.45 27	92.18 137	85.06 7	72.58 173	92.27 133	52.46 212	95.78 140	84.18 71	79.06 176	88.16 223
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
HPM-MVS_fast		80.25 140	79.55 140	82.33 182	91.55 120	59.95 268	91.32 180	89.16 250	65.23 268	74.71 152	93.07 112	47.81 254	95.74 142	74.87 146	88.23 108	91.31 185

xiu_mvs_v1_base_debu		82.16 110	81.12 113	85.26 110	86.42 223	68.72 63	92.59 127	90.44 201	73.12 146	84.20 60	94.36 80	38.04 299	95.73 143	84.12 72	86.81 119	91.33 181

xiu_mvs_v1_base		82.16 110	81.12 113	85.26 110	86.42 223	68.72 63	92.59 127	90.44 201	73.12 146	84.20 60	94.36 80	38.04 299	95.73 143	84.12 72	86.81 119	91.33 181

xiu_mvs_v1_base_debi		82.16 110	81.12 113	85.26 110	86.42 223	68.72 63	92.59 127	90.44 201	73.12 146	84.20 60	94.36 80	38.04 299	95.73 143	84.12 72	86.81 119	91.33 181

DP-MVS		69.90 267	66.48 273	80.14 236	95.36 29	62.93 216	89.56 229	76.11 342	50.27 340	57.69 309	85.23 219	39.68 289	95.73 143	33.35 351	71.05 233	81.78 317

114514_t		79.17 160	77.67 167	83.68 153	95.32 30	65.53 149	92.85 115	91.60 159	63.49 277	67.92 233	90.63 154	46.65 260	95.72 147	67.01 206	83.54 148	89.79 201

TR-MVS		78.77 170	77.37 177	82.95 167	90.49 141	60.88 251	93.67 84	90.07 217	70.08 223	74.51 153	91.37 145	45.69 266	95.70 148	60.12 260	80.32 168	92.29 164

ETV-MVS		86.01 49	86.11 45	85.70 96	90.21 148	67.02 110	93.43 95	91.92 144	81.21 32	84.13 63	94.07 95	60.93 117	95.63 149	89.28 27	89.81 98	94.46 97

tttt0517		79.50 155	78.53 154	82.41 181	87.22 212	61.43 243	89.75 228	94.76 29	69.29 232	67.91 234	88.06 190	72.92 24	95.63 149	62.91 243	73.90 213	90.16 197

test1172		81.90 116	81.83 106	82.13 191	93.23 69	57.52 296	91.61 166	90.98 188	64.32 272	80.20 97	95.00 60	51.26 222	95.61 151	81.73 92	88.13 110	93.26 137

SR-MVS-dyc-post		81.06 128	80.70 120	82.15 189	92.02 101	58.56 284	90.90 194	90.45 198	62.76 284	78.89 112	94.46 76	51.26 222	95.61 151	78.77 117	86.77 122	92.28 165

thres200		79.66 151	78.33 155	83.66 155	92.54 90	65.82 142	93.06 104	96.31 3	74.90 113	73.30 164	88.66 177	59.67 131	95.61 151	47.84 305	78.67 180	89.56 206

HQP4-MVS										74.18 155			95.61 151			88.63 213

BH-w/o		80.49 137	79.30 145	84.05 145	90.83 137	64.36 184	93.60 87	89.42 240	74.35 118	69.09 215	90.15 164	55.23 182	95.61 151	64.61 231	86.43 128	92.17 170

HQP-MVS		81.14 125	80.64 122	82.64 174	87.54 205	63.66 202	94.06 62	91.70 155	79.80 43	74.18 155	90.30 161	51.63 219	95.61 151	77.63 126	78.90 177	88.63 213

HQP_MVS		80.34 139	79.75 134	82.12 192	86.94 216	62.42 225	93.13 102	91.31 170	78.81 61	72.53 174	89.14 175	50.66 226	95.55 157	76.74 129	78.53 182	88.39 219

plane_prior5								91.31 170					95.55 157	76.74 129	78.53 182	88.39 219

jason		86.40 43	86.17 44	87.11 44	86.16 229	70.54 27	95.71 24	92.19 136	82.00 23	84.58 56	94.34 85	61.86 110	95.53 159	87.76 41	90.89 90	95.27 63
jason: jason.
RRT_test8_iter05		80.61 133	79.62 136	83.60 156	91.87 112	66.90 113	93.42 97	93.68 72	77.09 85	68.83 222	85.63 217	66.82 55	95.42 160	76.46 133	62.74 288	88.48 216

DROMVSNet		84.53 70	85.04 60	83.01 166	89.34 164	61.37 244	94.42 49	91.09 181	77.91 74	83.24 69	94.20 91	58.37 144	95.40 161	85.35 63	91.41 82	92.27 168

BH-untuned		78.68 172	77.08 178	83.48 160	89.84 154	63.74 197	92.70 120	88.59 271	71.57 197	66.83 249	88.65 178	51.75 217	95.39 162	59.03 265	84.77 137	91.32 184

MVS_111021_LR		82.02 114	81.52 109	83.51 158	88.42 187	62.88 220	89.77 227	88.93 260	76.78 89	75.55 147	93.10 108	50.31 229	95.38 163	83.82 76	87.02 118	92.26 169

thres100view900		78.37 178	77.01 180	82.46 177	91.89 109	63.21 210	91.19 187	96.33 1	72.28 168	70.45 199	87.89 192	60.31 121	95.32 164	45.16 315	77.58 189	88.83 209

tfpn200view9		78.79 169	77.43 172	82.88 168	92.21 98	64.49 174	92.05 144	96.28 4	73.48 140	71.75 187	88.26 185	60.07 126	95.32 164	45.16 315	77.58 189	88.83 209

thres400		78.68 172	77.43 172	82.43 178	92.21 98	64.49 174	92.05 144	96.28 4	73.48 140	71.75 187	88.26 185	60.07 126	95.32 164	45.16 315	77.58 189	87.48 229

RPMNet		70.42 263	65.68 279	84.63 131	83.15 274	67.96 81	70.25 339	90.45 198	46.83 349	69.97 207	65.10 347	56.48 170	95.30 167	35.79 346	73.13 216	90.64 193

OPM-MVS		79.00 162	78.09 159	81.73 200	83.52 270	63.83 194	91.64 165	90.30 208	76.36 94	71.97 184	89.93 168	46.30 264	95.17 168	75.10 139	77.70 187	86.19 255
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test_part1		79.63 152	77.86 166	84.93 117	92.50 91	71.43 17	94.15 57	91.08 183	72.51 159	70.66 195	84.98 222	59.84 128	95.07 169	72.07 164	62.94 286	88.30 222

PAPM		85.89 51	85.46 55	87.18 42	88.20 194	72.42 14	92.41 131	92.77 111	82.11 21	80.34 95	93.07 112	68.27 38	95.02 170	78.39 120	93.59 52	94.09 109

PMMVS		81.98 115	82.04 103	81.78 199	89.76 156	56.17 306	91.13 189	90.69 193	77.96 72	80.09 98	93.57 103	46.33 263	94.99 171	81.41 96	87.46 115	94.17 104

CostFormer		82.33 107	81.15 112	85.86 88	89.01 174	68.46 68	82.39 301	93.01 103	75.59 99	80.25 96	81.57 261	72.03 30	94.96 172	79.06 113	77.48 193	94.16 105

EPP-MVSNet		81.79 118	81.52 109	82.61 175	88.77 180	60.21 265	93.02 108	93.66 74	68.52 243	72.90 168	90.39 159	72.19 29	94.96 172	74.93 143	79.29 175	92.67 154

ACMH	63.93 17	68.62 276	64.81 284	80.03 239	85.22 242	63.25 208	87.72 263	84.66 315	60.83 300	51.57 328	79.43 293	27.29 341	94.96 172	41.76 328	64.84 272	81.88 315
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
thres600view7		78.00 183	76.66 185	82.03 197	91.93 106	63.69 200	91.30 181	96.33 1	72.43 163	70.46 198	87.89 192	60.31 121	94.92 175	42.64 327	76.64 200	87.48 229

baseline1		81.84 117	81.03 117	84.28 141	91.60 117	66.62 121	91.08 190	91.66 157	81.87 24	74.86 151	91.67 144	69.98 35	94.92 175	71.76 167	64.75 274	91.29 186

XXY-MVS		77.94 186	76.44 188	82.43 178	82.60 278	64.44 178	92.01 146	91.83 149	73.59 138	70.00 206	85.82 214	54.43 193	94.76 177	69.63 182	68.02 251	88.10 224

Vis-MVSNet		80.92 131	79.98 131	83.74 149	88.48 184	61.80 235	93.44 94	88.26 280	73.96 128	77.73 123	91.76 141	49.94 233	94.76 177	65.84 220	90.37 96	94.65 87
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
nrg030		80.93 130	79.86 132	84.13 143	83.69 267	68.83 60	93.23 100	91.20 174	75.55 100	75.06 150	88.22 188	63.04 102	94.74 179	81.88 90	66.88 258	88.82 211

GA-MVS		78.33 180	76.23 191	84.65 129	83.65 268	66.30 129	91.44 169	90.14 215	76.01 96	70.32 201	84.02 232	42.50 280	94.72 180	70.98 171	77.00 198	92.94 149

EI-MVSNet		78.97 163	78.22 158	81.25 211	85.33 240	62.73 223	89.53 232	93.21 92	72.39 165	72.14 181	90.13 165	60.99 115	94.72 180	67.73 200	72.49 222	86.29 252

MVSTER		82.47 105	82.05 102	83.74 149	92.68 88	69.01 56	91.90 151	93.21 92	79.83 42	72.14 181	85.71 216	74.72 15	94.72 180	75.72 135	72.49 222	87.50 228

hse-mvs2		81.12 127	81.11 116	81.16 214	86.52 222	57.48 297	89.40 235	91.16 176	81.45 27	82.73 73	90.49 157	60.11 124	94.58 183	87.69 42	60.41 312	91.41 180

AUN-MVS		78.37 178	77.43 172	81.17 213	86.60 221	57.45 298	89.46 234	91.16 176	74.11 123	74.40 154	90.49 157	55.52 179	94.57 184	74.73 147	60.43 311	91.48 178

PLC	68.80 14	75.23 226	73.68 225	79.86 244	92.93 79	58.68 283	90.64 204	88.30 276	60.90 299	64.43 268	90.53 155	42.38 281	94.57 184	56.52 272	76.54 201	86.33 250
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
GG-mvs-BLEND				86.53 66	91.91 108	69.67 46	75.02 335	94.75 30		78.67 119	90.85 150	77.91 7	94.56 186	72.25 160	93.74 48	95.36 54

OMC-MVS		78.67 174	77.91 165	80.95 224	85.76 236	57.40 299	88.49 252	88.67 268	73.85 130	72.43 178	92.10 135	49.29 240	94.55 187	72.73 155	77.89 185	90.91 190

mvs-test1		78.74 171	77.95 163	81.14 215	83.22 272	57.13 301	93.96 67	87.78 286	75.42 102	72.68 170	90.80 151	45.08 270	94.54 188	75.08 140	77.49 192	91.74 174

Fast-Effi-MVS+		81.14 125	80.01 129	84.51 134	90.24 147	65.86 139	94.12 59	89.15 251	73.81 132	75.37 149	88.26 185	57.26 153	94.53 189	66.97 207	84.92 135	93.15 141

diffmvs		84.28 74	83.83 72	85.61 98	87.40 209	68.02 80	90.88 196	89.24 245	80.54 39	81.64 81	92.52 124	59.83 129	94.52 190	87.32 49	85.11 134	94.29 98

HyFIR lowres test		81.03 129	79.56 138	85.43 104	87.81 202	68.11 78	90.18 216	90.01 222	70.65 217	72.95 167	86.06 212	63.61 93	94.50 191	75.01 142	79.75 171	93.67 126

v2v482		77.42 193	75.65 199	82.73 171	80.38 295	67.13 106	91.85 154	90.23 212	75.09 110	69.37 212	83.39 240	53.79 200	94.44 192	71.77 166	65.00 271	86.63 247

v1144		76.73 204	74.88 204	82.27 184	80.23 300	66.60 122	91.68 163	90.21 214	73.69 135	69.06 217	81.89 254	52.73 210	94.40 193	69.21 188	65.23 268	85.80 266

TAPA-MVS	70.22 12	74.94 230	73.53 226	79.17 258	90.40 144	52.07 326	89.19 240	89.61 235	62.69 286	70.07 204	92.67 123	48.89 246	94.32 194	38.26 341	79.97 169	91.12 188
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
LPG-MVS_test		75.82 218	74.58 209	79.56 253	84.31 259	59.37 276	90.44 207	89.73 231	69.49 229	64.86 260	88.42 180	38.65 293	94.30 195	72.56 157	72.76 219	85.01 279

LGP-MVS_train				79.56 253	84.31 259	59.37 276		89.73 231	69.49 229	64.86 260	88.42 180	38.65 293	94.30 195	72.56 157	72.76 219	85.01 279

v1192		75.98 214	73.92 222	82.15 189	79.73 302	66.24 131	91.22 184	89.75 228	72.67 155	68.49 228	81.42 264	49.86 234	94.27 197	67.08 205	65.02 270	85.95 263

tpmvs		72.88 248	69.76 260	82.22 187	90.98 131	67.05 108	78.22 328	88.30 276	63.10 282	64.35 269	74.98 322	55.09 185	94.27 197	43.25 321	69.57 239	85.34 276

tpm2		79.80 150	77.95 163	85.34 108	88.28 190	68.26 74	81.56 306	91.42 167	70.11 222	77.59 128	80.50 279	67.40 49	94.26 199	67.34 203	77.35 194	93.51 130

PVSNet_0	68.08 15	71.81 255	68.32 267	82.27 184	84.68 250	62.31 229	88.68 249	90.31 207	75.84 97	57.93 308	80.65 278	37.85 302	94.19 200	69.94 180	29.05 361	90.31 196

MVP-Stereo		77.12 198	76.23 191	79.79 246	81.72 284	66.34 128	89.29 236	90.88 190	70.56 218	62.01 286	82.88 243	49.34 238	94.13 201	65.55 225	93.80 46	78.88 338
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
ACMM	69.62 13	74.34 233	72.73 234	79.17 258	84.25 261	57.87 290	90.36 211	89.93 223	63.17 281	65.64 255	86.04 213	37.79 303	94.10 202	65.89 219	71.52 229	85.55 272
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
V42		76.46 206	74.55 210	82.19 188	79.14 312	67.82 85	90.26 214	89.42 240	73.75 133	68.63 226	81.89 254	51.31 221	94.09 203	71.69 168	64.84 272	84.66 282

TESTMET0.1,1		82.41 106	81.98 104	83.72 152	88.08 195	63.74 197	92.70 120	93.77 66	79.30 50	77.61 127	87.57 196	58.19 146	94.08 204	73.91 148	86.68 125	93.33 135

Anonymous20231211		73.08 243	70.39 254	81.13 216	90.62 140	63.33 207	91.40 172	90.06 220	51.84 335	64.46 267	80.67 277	36.49 313	94.07 205	63.83 236	64.17 279	85.98 262

v8		75.35 224	73.26 229	81.61 204	80.67 292	66.82 115	89.54 231	89.27 244	71.65 191	63.30 277	80.30 283	54.99 186	94.06 206	67.33 204	62.33 292	83.94 287

EG-PatchMatch MVS		68.55 277	65.41 282	77.96 271	78.69 318	62.93 216	89.86 226	89.17 249	60.55 301	50.27 333	77.73 303	22.60 350	94.06 206	47.18 308	72.65 221	76.88 345

PVSNet	73.49 8	80.05 144	78.63 152	84.31 139	90.92 133	64.97 167	92.47 130	91.05 185	79.18 53	72.43 178	90.51 156	37.05 311	94.06 206	68.06 196	86.00 129	93.90 121

GeoE		78.90 165	77.43 172	83.29 162	88.95 175	62.02 232	92.31 132	86.23 300	70.24 221	71.34 192	89.27 172	54.43 193	94.04 209	63.31 239	80.81 167	93.81 124

v10		74.77 231	72.54 238	81.46 206	80.33 298	66.71 119	89.15 241	89.08 255	70.94 210	63.08 278	79.86 288	52.52 211	94.04 209	65.70 222	62.17 293	83.64 289

v144192		76.05 212	74.03 220	82.12 192	79.50 306	66.55 124	91.39 174	89.71 234	72.30 167	68.17 230	81.33 266	51.75 217	94.03 211	67.94 197	64.19 278	85.77 267

tpm cat1		75.30 225	72.21 241	84.58 132	88.52 182	67.77 86	78.16 329	88.02 283	61.88 295	68.45 229	76.37 315	60.65 118	94.03 211	53.77 283	74.11 209	91.93 172

gg-mvs-nofinetune		77.18 197	74.31 214	85.80 90	91.42 123	68.36 70	71.78 337	94.72 31	49.61 341	77.12 133	45.92 357	77.41 8	93.98 213	67.62 201	93.16 57	95.05 73

PS-MVSNAJss		77.26 196	76.31 190	80.13 237	80.64 293	59.16 279	90.63 206	91.06 184	72.80 153	68.58 227	84.57 228	53.55 202	93.96 214	72.97 151	71.96 226	87.27 236

OpenMVS_ROB	61.12 18	66.39 292	62.92 298	76.80 287	76.51 332	57.77 291	89.22 238	83.41 326	55.48 328	53.86 320	77.84 302	26.28 344	93.95 215	34.90 348	68.76 245	78.68 340

MDTV_nov1_ep13			72.61 236		89.06 172	68.48 67	80.33 314	90.11 216	71.84 185	71.81 186	75.92 319	53.01 208	93.92 216	48.04 302	73.38 214

v1921920		75.63 222	73.49 227	82.06 196	79.38 307	66.35 127	91.07 192	89.48 237	71.98 176	67.99 231	81.22 269	49.16 243	93.90 217	66.56 210	64.56 277	85.92 265

v1240		75.21 227	72.98 231	81.88 198	79.20 309	66.00 135	90.75 201	89.11 254	71.63 195	67.41 242	81.22 269	47.36 257	93.87 218	65.46 226	64.72 275	85.77 267

ACMP	71.68 10	75.58 223	74.23 216	79.62 251	84.97 248	59.64 271	90.80 199	89.07 256	70.39 219	62.95 279	87.30 200	38.28 296	93.87 218	72.89 152	71.45 230	85.36 275
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
v148		76.19 207	74.47 212	81.36 208	80.05 301	64.44 178	91.75 161	90.23 212	73.68 136	67.13 245	80.84 274	55.92 177	93.86 220	68.95 191	61.73 300	85.76 269

LS3D		69.17 271	66.40 274	77.50 275	91.92 107	56.12 307	85.12 280	80.37 336	46.96 347	56.50 313	87.51 197	37.25 306	93.71 221	32.52 356	79.40 172	82.68 307

EPMVS		78.49 177	75.98 194	86.02 82	91.21 128	69.68 45	80.23 316	91.20 174	75.25 108	72.48 176	78.11 300	54.65 188	93.69 222	57.66 270	83.04 149	94.69 83

IS-MVSNet		80.14 142	79.41 142	82.33 182	87.91 200	60.08 267	91.97 149	88.27 278	72.90 152	71.44 191	91.73 143	61.44 112	93.66 223	62.47 247	86.53 126	93.24 138

v7n		71.31 258	68.65 263	79.28 256	76.40 333	60.77 254	86.71 275	89.45 238	64.17 273	58.77 302	78.24 298	44.59 273	93.54 224	57.76 268	61.75 299	83.52 292

VPA-MVSNet		79.03 161	78.00 161	82.11 195	85.95 232	64.48 176	93.22 101	94.66 34	75.05 111	74.04 160	84.95 223	52.17 214	93.52 225	74.90 145	67.04 257	88.32 221

tfpnnormal		70.10 264	67.36 270	78.32 266	83.45 271	60.97 250	88.85 246	92.77 111	64.85 269	60.83 290	78.53 296	43.52 278	93.48 226	31.73 357	61.70 301	80.52 326

旧先验2							92.00 148		59.37 311	87.54 28			93.47 227	75.39 137

1112_ss		80.56 135	79.83 133	82.77 170	88.65 181	60.78 253	92.29 133	88.36 275	72.58 157	72.46 177	94.95 62	65.09 73	93.42 228	66.38 214	77.71 186	94.10 108

testdata				81.34 209	89.02 173	57.72 292		89.84 226	58.65 314	85.32 51	94.09 93	57.03 157	93.28 229	69.34 186	90.56 95	93.03 146

LTVRE_ROB	59.60 19	66.27 293	63.54 294	74.45 300	84.00 264	51.55 328	67.08 349	83.53 324	58.78 313	54.94 315	80.31 282	34.54 319	93.23 230	40.64 334	68.03 250	78.58 341
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
VPNet		78.82 167	77.53 171	82.70 172	84.52 254	66.44 125	93.93 72	92.23 130	80.46 40	72.60 172	88.38 182	49.18 241	93.13 231	72.47 159	63.97 282	88.55 215

Test_1112_low_res		79.56 154	78.60 153	82.43 178	88.24 193	60.39 262	92.09 141	87.99 284	72.10 175	71.84 185	87.42 198	64.62 79	93.04 232	65.80 221	77.30 195	93.85 123

PatchMatch-RL		72.06 254	69.98 255	78.28 267	89.51 162	55.70 310	83.49 290	83.39 327	61.24 298	63.72 273	82.76 244	34.77 318	93.03 233	53.37 285	77.59 188	86.12 259

Fast-Effi-MVS+-dtu		75.04 228	73.37 228	80.07 238	80.86 289	59.52 274	91.20 186	85.38 307	71.90 179	65.20 258	84.84 224	41.46 284	92.97 234	66.50 213	72.96 218	87.73 226

cl____		76.07 209	74.67 205	80.28 233	85.15 243	61.76 237	90.12 217	88.73 266	71.16 206	65.43 256	81.57 261	61.15 113	92.95 235	66.54 211	62.17 293	86.13 258

pm-mvs1		72.89 247	71.09 250	78.26 268	79.10 313	57.62 294	90.80 199	89.30 243	67.66 248	62.91 280	81.78 256	49.11 244	92.95 235	60.29 259	58.89 317	84.22 285

TAMVS		80.37 138	79.45 141	83.13 165	85.14 244	63.37 206	91.23 183	90.76 192	74.81 114	72.65 171	88.49 179	60.63 119	92.95 235	69.41 185	81.95 157	93.08 145

ACMH+	65.35 16	67.65 285	64.55 287	76.96 285	84.59 253	57.10 302	88.08 257	80.79 334	58.59 315	53.00 322	81.09 273	26.63 343	92.95 235	46.51 309	61.69 302	80.82 322

DIV-MVS_self_test		76.07 209	74.67 205	80.28 233	85.14 244	61.75 238	90.12 217	88.73 266	71.16 206	65.42 257	81.60 260	61.15 113	92.94 239	66.54 211	62.16 295	86.14 256

cl22		77.94 186	76.78 183	81.42 207	87.57 204	64.93 169	90.67 202	88.86 263	72.45 162	67.63 239	82.68 246	64.07 84	92.91 240	71.79 165	65.30 265	86.44 249

CDS-MVSNet		81.43 121	80.74 119	83.52 157	86.26 227	64.45 177	92.09 141	90.65 196	75.83 98	73.95 161	89.81 169	63.97 86	92.91 240	71.27 170	82.82 151	93.20 140
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
miper_enhance_ethall		78.86 166	77.97 162	81.54 205	88.00 199	65.17 157	91.41 170	89.15 251	75.19 109	68.79 223	83.98 233	67.17 51	92.82 242	72.73 155	65.30 265	86.62 248

eth_miper_zixun_eth		75.96 215	74.40 213	80.66 227	84.66 251	63.02 213	89.28 237	88.27 278	71.88 181	65.73 254	81.65 258	59.45 133	92.81 243	68.13 195	60.53 309	86.14 256

CPTT-MVS		79.59 153	79.16 148	80.89 226	91.54 121	59.80 270	92.10 140	88.54 273	60.42 302	72.96 166	93.28 107	48.27 248	92.80 244	78.89 116	86.50 127	90.06 198

PatchmatchNet		77.46 192	74.63 207	85.96 84	89.55 161	70.35 29	79.97 320	89.55 236	72.23 170	70.94 193	76.91 311	57.03 157	92.79 245	54.27 280	81.17 163	94.74 82
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
jajsoiax		73.05 244	71.51 248	77.67 273	77.46 327	54.83 314	88.81 247	90.04 221	69.13 236	62.85 281	83.51 237	31.16 331	92.75 246	70.83 172	69.80 236	85.43 274

mvs_tets		72.71 251	71.11 249	77.52 274	77.41 328	54.52 316	88.45 254	89.76 227	68.76 241	62.70 282	83.26 241	29.49 335	92.71 247	70.51 178	69.62 238	85.34 276

tpmrst		80.57 134	79.14 149	84.84 122	90.10 149	68.28 73	81.70 304	89.72 233	77.63 78	75.96 141	79.54 292	64.94 76	92.71 247	75.43 136	77.28 196	93.55 129

D2MVS		73.80 239	72.02 243	79.15 260	79.15 311	62.97 214	88.58 251	90.07 217	72.94 149	59.22 297	78.30 297	42.31 282	92.70 249	65.59 224	72.00 225	81.79 316

test_post											23.01 364	56.49 169	92.67 250

MVSFormer		83.75 88	82.88 90	86.37 73	89.24 169	71.18 20	89.07 243	90.69 193	65.80 262	87.13 29	94.34 85	64.99 74	92.67 250	72.83 153	91.80 75	95.27 63

test_djsdf		73.76 241	72.56 237	77.39 278	77.00 330	53.93 318	89.07 243	90.69 193	65.80 262	63.92 270	82.03 253	43.14 279	92.67 250	72.83 153	68.53 247	85.57 271

miper_ehance_all_eth		77.60 190	76.44 188	81.09 221	85.70 237	64.41 181	90.65 203	88.64 270	72.31 166	67.37 244	82.52 247	64.77 78	92.64 253	70.67 175	65.30 265	86.24 254

c3_l		76.83 202	75.47 200	80.93 225	85.02 247	64.18 190	90.39 210	88.11 281	71.66 190	66.65 251	81.64 259	63.58 95	92.56 254	69.31 187	62.86 287	86.04 260

dp		75.01 229	72.09 242	83.76 148	89.28 167	66.22 132	79.96 321	89.75 228	71.16 206	67.80 237	77.19 308	51.81 216	92.54 255	50.39 291	71.44 231	92.51 159

Effi-MVS+-dtu		76.14 208	75.28 202	78.72 263	83.22 272	55.17 313	89.87 225	87.78 286	75.42 102	67.98 232	81.43 263	45.08 270	92.52 256	75.08 140	71.63 227	88.48 216

F-COLMAP		70.66 260	68.44 265	77.32 279	86.37 226	55.91 308	88.00 258	86.32 297	56.94 322	57.28 311	88.07 189	33.58 321	92.49 257	51.02 289	68.37 248	83.55 290

USDC		67.43 289	64.51 288	76.19 290	77.94 325	55.29 312	78.38 326	85.00 311	73.17 144	48.36 339	80.37 281	21.23 352	92.48 258	52.15 287	64.02 281	80.81 323

bset_n11_16_dypcd		75.95 216	74.16 217	81.30 210	76.91 331	65.14 160	88.89 245	87.48 289	74.30 120	69.90 210	83.40 239	42.16 283	92.42 259	78.39 120	66.03 262	86.32 251

pmmvs6		67.57 286	64.76 285	76.00 292	72.82 344	53.37 320	88.71 248	86.78 295	53.19 332	57.58 310	78.03 301	35.33 317	92.41 260	55.56 276	54.88 329	82.21 313

test-LLR		80.10 143	79.56 138	81.72 201	86.93 218	61.17 245	92.70 120	91.54 160	71.51 200	75.62 144	86.94 203	53.83 198	92.38 261	72.21 161	84.76 138	91.60 175

test-mter		79.96 146	79.38 144	81.72 201	86.93 218	61.17 245	92.70 120	91.54 160	73.85 130	75.62 144	86.94 203	49.84 235	92.38 261	72.21 161	84.76 138	91.60 175

UniMVSNet (Re)		77.58 191	76.78 183	79.98 240	84.11 262	60.80 252	91.76 159	93.17 97	76.56 92	69.93 209	84.78 225	63.32 98	92.36 263	64.89 230	62.51 291	86.78 242

ET-MVSNet_ETH3D		84.01 81	83.15 87	86.58 63	90.78 138	70.89 23	94.74 46	94.62 36	81.44 29	58.19 303	93.64 101	73.64 22	92.35 264	82.66 84	78.66 181	96.50 23

mvs_anonymous		81.36 122	79.99 130	85.46 102	90.39 145	68.40 69	86.88 274	90.61 197	74.41 116	70.31 202	84.67 226	63.79 89	92.32 265	73.13 150	85.70 130	95.67 45

IterMVS-LS		76.49 205	75.18 203	80.43 230	84.49 255	62.74 222	90.64 204	88.80 264	72.40 164	65.16 259	81.72 257	60.98 116	92.27 266	67.74 199	64.65 276	86.29 252
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet3		77.73 189	76.04 193	82.80 169	91.20 129	68.99 57	91.87 152	91.99 141	73.35 142	67.04 246	83.19 242	56.62 167	92.14 267	59.80 262	69.34 240	87.28 235

UniMVSNet_NR-MVSNet		78.15 182	77.55 170	79.98 240	84.46 256	60.26 263	92.25 134	93.20 94	77.50 80	68.88 220	86.61 205	66.10 61	92.13 268	66.38 214	62.55 289	87.54 227

DU-MVS		76.86 199	75.84 196	79.91 242	82.96 276	60.26 263	91.26 182	91.54 160	76.46 93	68.88 220	86.35 207	56.16 171	92.13 268	66.38 214	62.55 289	87.35 233

tpm		78.58 175	77.03 179	83.22 163	85.94 234	64.56 172	83.21 296	91.14 179	78.31 66	73.67 162	79.68 290	64.01 85	92.09 270	66.07 218	71.26 232	93.03 146

Baseline_NR-MVSNet		73.99 237	72.83 232	77.48 276	80.78 290	59.29 278	91.79 156	84.55 316	68.85 238	68.99 218	80.70 275	56.16 171	92.04 271	62.67 245	60.98 306	81.11 319

FMVSNet2		76.07 209	74.01 221	82.26 186	88.85 176	67.66 89	91.33 179	91.61 158	70.84 212	65.98 253	82.25 250	48.03 249	92.00 272	58.46 267	68.73 246	87.10 237

TransMVSNet (Re)		70.07 265	67.66 269	77.31 280	80.62 294	59.13 280	91.78 158	84.94 312	65.97 261	60.08 293	80.44 280	50.78 225	91.87 273	48.84 298	45.46 345	80.94 321

UniMVSNet_ETH3D		72.74 250	70.53 253	79.36 255	78.62 320	56.64 304	85.01 281	89.20 247	63.77 276	64.84 262	84.44 229	34.05 320	91.86 274	63.94 235	70.89 234	89.57 205

NR-MVSNet		76.05 212	74.59 208	80.44 229	82.96 276	62.18 231	90.83 198	91.73 152	77.12 84	60.96 289	86.35 207	59.28 137	91.80 275	60.74 255	61.34 304	87.35 233

FIs		79.47 156	79.41 142	79.67 249	85.95 232	59.40 275	91.68 163	93.94 61	78.06 70	68.96 219	88.28 183	66.61 58	91.77 276	66.20 217	74.99 206	87.82 225

XVG-OURS		74.25 235	72.46 239	79.63 250	78.45 321	57.59 295	80.33 314	87.39 290	63.86 275	68.76 224	89.62 171	40.50 287	91.72 277	69.00 190	74.25 208	89.58 204

test_0402		64.54 300	61.09 306	74.92 297	84.10 263	60.75 255	87.95 259	79.71 338	52.03 334	52.41 324	77.20 307	32.21 327	91.64 278	23.14 360	61.03 305	72.36 352

XVG-OURS-SEG-HR		74.70 232	73.08 230	79.57 252	78.25 322	57.33 300	80.49 312	87.32 291	63.22 280	68.76 224	90.12 167	44.89 272	91.59 279	70.55 177	74.09 210	89.79 201

TranMVSNet+NR-MVSNet		75.86 217	74.52 211	79.89 243	82.44 279	60.64 259	91.37 177	91.37 169	76.63 90	67.65 238	86.21 210	52.37 213	91.55 280	61.84 250	60.81 307	87.48 229

GBi-Net		75.65 220	73.83 223	81.10 218	88.85 176	65.11 161	90.01 221	90.32 204	70.84 212	67.04 246	80.25 284	48.03 249	91.54 281	59.80 262	69.34 240	86.64 244

test1		75.65 220	73.83 223	81.10 218	88.85 176	65.11 161	90.01 221	90.32 204	70.84 212	67.04 246	80.25 284	48.03 249	91.54 281	59.80 262	69.34 240	86.64 244

FMVSNet1		72.71 251	69.91 258	81.10 218	83.60 269	65.11 161	90.01 221	90.32 204	63.92 274	63.56 274	80.25 284	36.35 314	91.54 281	54.46 279	66.75 259	86.64 244

pmmvs4		73.92 238	71.81 245	80.25 235	79.17 310	65.24 155	87.43 267	87.26 293	67.64 250	63.46 275	83.91 234	48.96 245	91.53 284	62.94 242	65.49 264	83.96 286

test_post1							78.95 322				20.70 367	53.05 207	91.50 285	60.43 257

anonymousdsp		71.14 259	69.37 261	76.45 288	72.95 342	54.71 315	84.19 285	88.88 261	61.92 294	62.15 285	79.77 289	38.14 298	91.44 286	68.90 192	67.45 256	83.21 298

XVG-ACMP-BASELINE		68.04 282	65.53 281	75.56 293	74.06 340	52.37 324	78.43 325	85.88 304	62.03 292	58.91 301	81.21 271	20.38 354	91.15 287	60.69 256	68.18 249	83.16 299

CNLPA		74.31 234	72.30 240	80.32 231	91.49 122	61.66 239	90.85 197	80.72 335	56.67 324	63.85 272	90.64 152	46.75 259	90.84 288	53.79 282	75.99 204	88.47 218

ppachtmachnet_test		67.72 284	63.70 293	79.77 247	78.92 314	66.04 134	88.68 249	82.90 330	60.11 306	55.45 314	75.96 318	39.19 290	90.55 289	39.53 336	52.55 334	82.71 305

pmmvs5		73.35 242	71.52 247	78.86 262	78.64 319	60.61 260	91.08 190	86.90 294	67.69 247	63.32 276	83.64 235	44.33 274	90.53 290	62.04 249	66.02 263	85.46 273

SixPastTwentyTwo		64.92 298	61.78 305	74.34 302	78.74 317	49.76 337	83.42 293	79.51 339	62.86 283	50.27 333	77.35 304	30.92 333	90.49 291	45.89 313	47.06 343	82.78 301

COLMAP_ROB	57.96 20	62.98 308	59.65 310	72.98 311	81.44 286	53.00 322	83.75 288	75.53 346	48.34 345	48.81 338	81.40 265	24.14 346	90.30 292	32.95 353	60.52 310	75.65 348
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
patchmatchnet-post											67.62 343	57.62 151	90.25 293

SCA		75.82 218	72.76 233	85.01 116	86.63 220	70.08 32	81.06 310	89.19 248	71.60 196	70.01 205	77.09 309	45.53 267	90.25 293	60.43 257	73.27 215	94.68 84

JIA-IIPM		66.06 294	62.45 301	76.88 286	81.42 287	54.45 317	57.49 357	88.67 268	49.36 342	63.86 271	46.86 356	56.06 174	90.25 293	49.53 295	68.83 244	85.95 263

WR-MVS		76.76 203	75.74 198	79.82 245	84.60 252	62.27 230	92.60 125	92.51 123	76.06 95	67.87 236	85.34 218	56.76 163	90.24 296	62.20 248	63.69 284	86.94 240

FC-MVSNet-test		77.99 184	78.08 160	77.70 272	84.89 249	55.51 311	90.27 213	93.75 70	76.87 86	66.80 250	87.59 195	65.71 67	90.23 297	62.89 244	73.94 211	87.37 232

EPNet_dtu		78.80 168	79.26 146	77.43 277	88.06 196	49.71 338	91.96 150	91.95 143	77.67 77	76.56 138	91.28 146	58.51 143	90.20 298	56.37 273	80.95 165	92.39 160
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CMPMVS	48.56 21	66.77 291	64.41 290	73.84 305	70.65 349	50.31 335	77.79 330	85.73 306	45.54 350	44.76 348	82.14 252	35.40 316	90.14 299	63.18 241	74.54 207	81.07 320
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet (Re-imp)		79.24 159	79.57 137	78.24 269	88.46 185	52.29 325	90.41 209	89.12 253	74.24 121	69.13 214	91.91 138	65.77 66	90.09 300	59.00 266	88.09 111	92.33 162

lessismore_v0				73.72 306	72.93 343	47.83 345		61.72 365		45.86 345	73.76 325	28.63 339	89.81 301	47.75 307	31.37 360	83.53 291

MVS-HIRNet		60.25 315	55.55 321	74.35 301	84.37 258	56.57 305	71.64 338	74.11 348	34.44 358	45.54 347	42.24 360	31.11 332	89.81 301	40.36 335	76.10 203	76.67 346

our_test_3		68.29 280	64.69 286	79.11 261	78.92 314	64.85 170	88.40 255	85.06 310	60.32 304	52.68 323	76.12 317	40.81 286	89.80 303	44.25 320	55.65 325	82.67 308

CR-MVSNet		73.79 240	70.82 252	82.70 172	83.15 274	67.96 81	70.25 339	84.00 321	73.67 137	69.97 207	72.41 330	57.82 149	89.48 304	52.99 286	73.13 216	90.64 193

Patchmtry		67.53 287	63.93 292	78.34 265	82.12 281	64.38 182	68.72 343	84.00 321	48.23 346	59.24 296	72.41 330	57.82 149	89.27 305	46.10 312	56.68 324	81.36 318

ADS-MVSNet		68.54 278	64.38 291	81.03 222	88.06 196	66.90 113	68.01 346	84.02 320	57.57 316	64.48 265	69.87 339	38.68 291	89.21 306	40.87 332	67.89 252	86.97 238

RRT_MVS		77.38 194	76.59 186	79.77 247	90.91 134	63.61 204	91.15 188	90.91 189	72.28 168	72.06 183	87.28 201	43.92 275	89.04 307	73.32 149	67.47 255	86.67 243

Patchmatch-RL test		68.17 281	64.49 289	79.19 257	71.22 346	53.93 318	70.07 341	71.54 354	69.22 233	56.79 312	62.89 349	56.58 168	88.61 308	69.53 184	52.61 333	95.03 76

UnsupCasMVSNet_bld		61.60 311	57.71 315	73.29 309	68.73 353	51.64 327	78.61 324	89.05 257	57.20 320	46.11 342	61.96 350	28.70 338	88.60 309	50.08 293	38.90 353	79.63 333

OurMVSNet-221017-0		64.68 299	62.17 303	72.21 318	76.08 336	47.35 347	80.67 311	81.02 333	56.19 325	51.60 327	79.66 291	27.05 342	88.56 310	53.60 284	53.63 332	80.71 324

PatchT		69.11 272	65.37 283	80.32 231	82.07 282	63.68 201	67.96 348	87.62 288	50.86 338	69.37 212	65.18 346	57.09 155	88.53 311	41.59 330	66.60 260	88.74 212

TinyColmap		60.32 314	56.42 320	72.00 321	78.78 316	53.18 321	78.36 327	75.64 344	52.30 333	41.59 354	75.82 320	14.76 360	88.35 312	35.84 344	54.71 330	74.46 349

LCM-MVSNet-Re		72.93 246	71.84 244	76.18 291	88.49 183	48.02 343	80.07 319	70.17 355	73.96 128	52.25 325	80.09 287	49.98 232	88.24 313	67.35 202	84.23 146	92.28 165

ambc				69.61 326	61.38 361	41.35 355	49.07 360	85.86 305		50.18 335	66.40 344	10.16 363	88.14 314	45.73 314	44.20 346	79.32 336

MVS_0304		68.99 275	67.23 272	74.28 303	80.36 296	52.54 323	87.01 272	86.36 296	59.89 308	66.22 252	73.56 326	24.25 345	88.03 315	57.34 271	70.11 235	82.27 312

Patchmatch-test		65.86 295	60.94 307	80.62 228	83.75 266	58.83 281	58.91 356	75.26 347	44.50 353	50.95 332	77.09 309	58.81 142	87.90 316	35.13 347	64.03 280	95.12 70

MIMVSNet		71.64 256	68.44 265	81.23 212	81.97 283	64.44 178	73.05 336	88.80 264	69.67 228	64.59 263	74.79 323	32.79 323	87.82 317	53.99 281	76.35 202	91.42 179

K. test v3		63.09 307	59.61 311	73.53 307	76.26 334	49.38 340	83.27 294	77.15 341	64.35 271	47.77 341	72.32 332	28.73 337	87.79 318	49.93 294	36.69 355	83.41 295

CL-MVSNet_self_test		69.92 266	68.09 268	75.41 294	73.25 341	55.90 309	90.05 220	89.90 224	69.96 224	61.96 287	76.54 312	51.05 224	87.64 319	49.51 296	50.59 338	82.70 306

KD-MVS_2432*1600		69.03 273	66.37 275	77.01 283	85.56 238	61.06 248	81.44 307	90.25 210	67.27 252	58.00 306	76.53 313	54.49 190	87.63 320	48.04 302	35.77 356	82.34 310

miper_refine_blended		69.03 273	66.37 275	77.01 283	85.56 238	61.06 248	81.44 307	90.25 210	67.27 252	58.00 306	76.53 313	54.49 190	87.63 320	48.04 302	35.77 356	82.34 310

miper_lstm_enhance		73.05 244	71.73 246	77.03 282	83.80 265	58.32 287	81.76 302	88.88 261	69.80 227	61.01 288	78.23 299	57.19 154	87.51 322	65.34 227	59.53 314	85.27 278

UnsupCasMVSNet_eth		65.79 296	63.10 296	73.88 304	70.71 348	50.29 336	81.09 309	89.88 225	72.58 157	49.25 337	74.77 324	32.57 325	87.43 323	55.96 275	41.04 351	83.90 288

Anonymous20231206		67.53 287	65.78 277	72.79 313	74.95 337	47.59 346	88.23 256	87.32 291	61.75 297	58.07 305	77.29 306	37.79 303	87.29 324	42.91 323	63.71 283	83.48 293

pmmvs-eth3d		65.53 297	62.32 302	75.19 295	69.39 352	59.59 272	82.80 299	83.43 325	62.52 288	51.30 330	72.49 328	32.86 322	87.16 325	55.32 277	50.73 337	78.83 339

IterMVS		72.65 253	70.83 251	78.09 270	82.17 280	62.96 215	87.64 265	86.28 298	71.56 198	60.44 291	78.85 295	45.42 269	86.66 326	63.30 240	61.83 297	84.65 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		61.66 310	58.06 314	72.46 315	79.57 303	51.42 330	80.17 317	68.61 357	51.25 336	45.88 343	81.23 267	19.86 355	86.58 327	38.98 338	57.01 322	79.39 334

TestCases				72.46 315	79.57 303	51.42 330		68.61 357	51.25 336	45.88 343	81.23 267	19.86 355	86.58 327	38.98 338	57.01 322	79.39 334

MDA-MVSNet-bldmvs		61.54 312	57.70 316	73.05 310	79.53 305	57.00 303	83.08 297	81.23 332	57.57 316	34.91 358	72.45 329	32.79 323	86.26 329	35.81 345	41.95 349	75.89 347

Anonymous20240521		62.09 309	59.08 312	71.10 323	67.19 354	48.72 342	83.91 287	85.23 309	50.38 339	47.84 340	71.22 338	20.74 353	85.51 330	46.47 310	58.75 318	79.06 337

FMVSNet5		68.04 282	65.66 280	75.18 296	84.43 257	57.89 289	83.54 289	86.26 299	61.83 296	53.64 321	73.30 327	37.15 309	85.08 331	48.99 297	61.77 298	82.56 309

test0.0.03 1		72.76 249	72.71 235	72.88 312	80.25 299	47.99 344	91.22 184	89.45 238	71.51 200	62.51 284	87.66 194	53.83 198	85.06 332	50.16 292	67.84 254	85.58 270

testgi		64.48 301	62.87 299	69.31 327	71.24 345	40.62 357	85.49 279	79.92 337	65.36 266	54.18 318	83.49 238	23.74 348	84.55 333	41.60 329	60.79 308	82.77 302

ADS-MVSNet2		66.90 290	63.44 295	77.26 281	88.06 196	60.70 257	68.01 346	75.56 345	57.57 316	64.48 265	69.87 339	38.68 291	84.10 334	40.87 332	67.89 252	86.97 238

CVMVSNet		74.04 236	74.27 215	73.33 308	85.33 240	43.94 353	89.53 232	88.39 274	54.33 330	70.37 200	90.13 165	49.17 242	84.05 335	61.83 251	79.36 173	91.99 171

ITE_SJBPF				70.43 325	74.44 338	47.06 348		77.32 340	60.16 305	54.04 319	83.53 236	23.30 349	84.01 336	43.07 322	61.58 303	80.21 331

CHOSEN 280x420		77.35 195	76.95 182	78.55 264	87.07 215	62.68 224	69.71 342	82.95 329	68.80 239	71.48 190	87.27 202	66.03 62	84.00 337	76.47 132	82.81 152	88.95 208

DTE-MVSNet		68.46 279	67.33 271	71.87 322	77.94 325	49.00 341	86.16 278	88.58 272	66.36 259	58.19 303	82.21 251	46.36 262	83.87 338	44.97 318	55.17 327	82.73 303

IterMVS-SCA-FT		71.55 257	69.97 256	76.32 289	81.48 285	60.67 258	87.64 265	85.99 303	66.17 260	59.50 295	78.88 294	45.53 267	83.65 339	62.58 246	61.93 296	84.63 284

PEN-MVS		69.46 270	68.56 264	72.17 319	79.27 308	49.71 338	86.90 273	89.24 245	67.24 255	59.08 299	82.51 248	47.23 258	83.54 340	48.42 300	57.12 320	83.25 297

WR-MVS_H		70.59 261	69.94 257	72.53 314	81.03 288	51.43 329	87.35 268	92.03 140	67.38 251	60.23 292	80.70 275	55.84 178	83.45 341	46.33 311	58.58 319	82.72 304

YYNet1		63.76 306	60.14 309	74.62 299	78.06 324	60.19 266	83.46 292	83.99 323	56.18 326	39.25 355	71.56 337	37.18 308	83.34 342	42.90 324	48.70 341	80.32 328

PM-MVS		59.40 317	56.59 318	67.84 329	63.63 357	41.86 354	76.76 331	63.22 363	59.01 312	51.07 331	72.27 333	11.72 362	83.25 343	61.34 252	50.28 339	78.39 342

MDA-MVSNet_test_wron		63.78 305	60.16 308	74.64 298	78.15 323	60.41 261	83.49 290	84.03 319	56.17 327	39.17 356	71.59 336	37.22 307	83.24 344	42.87 325	48.73 340	80.26 329

KD-MVS_self_test		60.87 313	58.60 313	67.68 331	66.13 355	39.93 359	75.63 334	84.70 314	57.32 319	49.57 336	68.45 342	29.55 334	82.87 345	48.09 301	47.94 342	80.25 330

N_pmnet		50.55 323	49.11 326	54.88 340	77.17 329	4.02 376	84.36 284	2.00 375	48.59 343	45.86 345	68.82 341	32.22 326	82.80 346	31.58 358	51.38 336	77.81 343

test20.03		63.83 304	62.65 300	67.38 333	70.58 350	39.94 358	86.57 276	84.17 318	63.29 279	51.86 326	77.30 305	37.09 310	82.47 347	38.87 340	54.13 331	79.73 332

TDRefinement		55.28 321	51.58 324	66.39 335	59.53 362	46.15 349	76.23 332	72.80 350	44.60 352	42.49 353	76.28 316	15.29 358	82.39 348	33.20 352	43.75 347	70.62 354

CP-MVSNet		70.50 262	69.91 258	72.26 317	80.71 291	51.00 332	87.23 269	90.30 208	67.84 245	59.64 294	82.69 245	50.23 231	82.30 349	51.28 288	59.28 315	83.46 294

PS-CasMVS		69.86 268	69.13 262	72.07 320	80.35 297	50.57 334	87.02 271	89.75 228	67.27 252	59.19 298	82.28 249	46.58 261	82.24 350	50.69 290	59.02 316	83.39 296

RPSCF		64.24 302	61.98 304	71.01 324	76.10 335	45.00 350	75.83 333	75.94 343	46.94 348	58.96 300	84.59 227	31.40 330	82.00 351	47.76 306	60.33 313	86.04 260

new-patchmatchnet		59.30 318	56.48 319	67.79 330	65.86 356	44.19 351	82.47 300	81.77 331	59.94 307	43.65 352	66.20 345	27.67 340	81.68 352	39.34 337	41.40 350	77.50 344

MIMVSNet1		60.16 316	57.33 317	68.67 328	69.71 351	44.13 352	78.92 323	84.21 317	55.05 329	44.63 349	71.85 334	23.91 347	81.54 353	32.63 355	55.03 328	80.35 327

EU-MVSNet		64.01 303	63.01 297	67.02 334	74.40 339	38.86 362	83.27 294	86.19 301	45.11 351	54.27 317	81.15 272	36.91 312	80.01 354	48.79 299	57.02 321	82.19 314

pmmvs3		55.51 320	51.50 325	67.53 332	57.90 363	50.93 333	80.37 313	73.66 349	40.63 356	44.15 351	64.75 348	16.30 357	78.97 355	44.77 319	40.98 352	72.69 350

DSMNet-mixed		56.78 319	54.44 322	63.79 336	63.21 358	29.44 366	64.43 351	64.10 362	42.12 355	51.32 329	71.60 335	31.76 328	75.04 356	36.23 343	65.20 269	86.87 241

new_pmnet		49.31 324	46.44 327	57.93 338	62.84 359	40.74 356	68.47 345	62.96 364	36.48 357	35.09 357	57.81 352	14.97 359	72.18 357	32.86 354	46.44 344	60.88 358

Gipuma		34.91 330	31.44 333	45.30 345	70.99 347	39.64 361	19.85 365	72.56 351	20.10 364	16.16 366	21.47 366	5.08 370	71.16 358	13.07 364	43.70 348	25.08 363
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LF4IMVS		54.01 322	52.12 323	59.69 337	62.41 360	39.91 360	68.59 344	68.28 359	42.96 354	44.55 350	75.18 321	14.09 361	68.39 359	41.36 331	51.68 335	70.78 353

PMVS	26.43 22	31.84 331	28.16 334	42.89 346	25.87 374	27.58 367	50.92 359	49.78 368	21.37 363	14.17 367	40.81 362	2.01 373	66.62 360	9.61 366	38.88 354	34.49 362
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
LCM-MVSNet		40.54 326	35.79 329	54.76 341	36.92 371	30.81 365	51.41 358	69.02 356	22.07 362	24.63 361	45.37 358	4.56 371	65.81 361	33.67 350	34.50 358	67.67 355

PMMVS2		37.93 329	33.61 332	50.92 343	46.31 368	24.76 369	60.55 355	50.05 367	28.94 361	20.93 362	47.59 355	4.41 372	65.13 362	25.14 359	18.55 363	62.87 357

FPMVS		45.64 325	43.10 328	53.23 342	51.42 366	36.46 363	64.97 350	71.91 352	29.13 360	27.53 360	61.55 351	9.83 364	65.01 363	16.00 363	55.58 326	58.22 359

ANet_high		40.27 327	35.20 330	55.47 339	34.74 372	34.47 364	63.84 352	71.56 353	48.42 344	18.80 364	41.08 361	9.52 365	64.45 364	20.18 361	8.66 368	67.49 356

test_method		38.59 328	35.16 331	48.89 344	54.33 364	21.35 371	45.32 361	53.71 366	7.41 368	28.74 359	51.62 354	8.70 366	52.87 365	33.73 349	32.89 359	72.47 351

MVE	24.84 23	24.35 333	19.77 339	38.09 347	34.56 373	26.92 368	26.57 363	38.87 371	11.73 367	11.37 368	27.44 363	1.37 374	50.42 366	11.41 365	14.60 364	36.93 360
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
E-PMN		24.61 332	24.00 336	26.45 349	43.74 369	18.44 373	60.86 353	39.66 369	15.11 365	9.53 369	22.10 365	6.52 368	46.94 367	8.31 367	10.14 365	13.98 365

EMVS		23.76 334	23.20 338	25.46 350	41.52 370	16.90 374	60.56 354	38.79 372	14.62 366	8.99 370	20.24 368	7.35 367	45.82 368	7.25 368	9.46 366	13.64 366

DeepMVS_CX				34.71 348	51.45 365	24.73 370		28.48 374	31.46 359	17.49 365	52.75 353	5.80 369	42.60 369	18.18 362	19.42 362	36.81 361

tmp_tt		22.26 335	23.75 337	17.80 351	5.23 375	12.06 375	35.26 362	39.48 370	2.82 370	18.94 363	44.20 359	22.23 351	24.64 370	36.30 342	9.31 367	16.69 364

wuyk23d		11.30 337	10.95 340	12.33 352	48.05 367	19.89 372	25.89 364	1.92 376	3.58 369	3.12 371	1.37 370	0.64 375	15.77 371	6.23 369	7.77 369	1.35 367

testmvs		7.23 339	9.62 342	0.06 354	0.04 376	0.02 378	84.98 282	0.02 377	0.03 371	0.18 372	1.21 371	0.01 377	0.02 372	0.14 370	0.01 370	0.13 369

test123		6.92 340	9.21 343	0.08 353	0.03 377	0.05 377	81.65 305	0.01 378	0.02 372	0.14 373	0.85 372	0.03 376	0.02 372	0.12 371	0.00 371	0.16 368

test_blank		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 371	0.00 370

uanet_test		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 371	0.00 370

cdsmvs_eth3d_5k		19.86 336	26.47 335	0.00 355	0.00 378	0.00 379	0.00 366	93.45 83	0.00 373	0.00 374	95.27 50	49.56 236	0.00 374	0.00 372	0.00 371	0.00 370

pcd_1.5k_mvsjas		4.46 341	5.95 344	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	53.55 202	0.00 374	0.00 372	0.00 371	0.00 370

sosnet-low-res		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 371	0.00 370

sosnet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 371	0.00 370

uncertanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 371	0.00 370

Regformer		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 371	0.00 370

ab-mvs-re		7.91 338	10.55 341	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	94.95 62	0.00 378	0.00 374	0.00 372	0.00 371	0.00 370

uanet		0.00 342	0.00 345	0.00 355	0.00 378	0.00 379	0.00 366	0.00 379	0.00 373	0.00 374	0.00 373	0.00 378	0.00 374	0.00 372	0.00 371	0.00 370

FOURS1					93.95 51	61.77 236	93.96 67	91.92 144	62.14 291	86.57 34

test_one_0601					96.32 20	69.74 43		94.18 55	71.42 203	90.67 14	96.85 12	74.45 18

eth-test2					0.00 378

eth-test					0.00 378

RE-MVS-def			80.48 125		92.02 101	58.56 284	90.90 194	90.45 198	62.76 284	78.89 112	94.46 76	49.30 239		78.77 117	86.77 122	92.28 165

IU-MVS					96.46 13	69.91 38		95.18 13	80.75 37	95.28 1				92.34 7	95.36 14	96.47 24

save fliter					93.84 54	67.89 83	95.05 39	92.66 116	78.19 67

test0726					96.40 17	69.99 34	96.76 6	94.33 51	71.92 177	91.89 8	97.11 7	73.77 20

GSMVS																94.68 84

test_part2					96.29 21	68.16 77				90.78 12

sam_mvs1												57.85 148				94.68 84

sam_mvs												54.91 187

MTGPA								92.23 130

MTMP							93.77 82	32.52 373

test9_res														89.41 24	94.96 18	95.29 59

agg_prior2														86.41 55	94.75 30	95.33 55

test_prior4						67.18 105	93.92 73

test_prior2							95.10 37		75.40 104	85.25 53	95.61 40	67.94 43		87.47 45	94.77 25

新几何2							91.41 170

旧先验1					91.94 105	60.74 256		91.50 164			94.36 80	65.23 71			91.84 74	94.55 89

原ACMM2							92.01 146

test222					89.77 155	61.60 240	89.55 230	89.42 240	56.83 323	77.28 131	92.43 129	52.76 209			91.14 88	93.09 144

segment_acmp												65.94 63

testdata1							89.21 239		77.55 79

plane_prior7					86.94 216	61.51 241

plane_prior6					87.23 211	62.32 228						50.66 226

plane_prior4											89.14 175

plane_prior3						61.95 234			79.09 56	72.53 174

plane_prior2							93.13 102		78.81 61

plane_prior1					87.15 213

plane_prior						62.42 225	93.85 77		79.38 49						78.80 179

n2								0.00 379

nn								0.00 379

door-mid								66.01 361

test11								93.01 103

door								66.57 360

HQP5-MVS						63.66 202

HQP-NCC					87.54 205		94.06 62		79.80 43	74.18 155

ACMP_Plane					87.54 205		94.06 62		79.80 43	74.18 155

BP-MVS														77.63 126

HQP3-MVS								91.70 155							78.90 177

HQP2-MVS												51.63 219

NP-MVS					87.41 208	63.04 212					90.30 161

MDTV_nov1_ep13_2view						59.90 269	80.13 318		67.65 249	72.79 169		54.33 195		59.83 261		92.58 157

ACMMP++_ref															71.63 227

ACMMP++															69.72 237

Test By Simon												54.21 196

High-resolution multi-view benchmark