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		78.42 21	76.99 37	82.73 2	93.17 1	64.46 1	89.93 29	88.51 43	64.83 72	73.52 49	88.09 120	48.07 60	92.19 51	62.24 139	84.53 49	91.53 48

MCST-MVS		83.01 2	83.30 3	82.15 10	92.84 2	57.58 16	93.77 1	91.10 6	75.95 2	77.10 24	93.09 17	54.15 23	95.57 10	85.80 3	85.87 34	93.31 9

OPU-MVS				81.71 12	92.05 3	55.97 42	92.48 3				94.01 6	67.21 2	95.10 13	89.82 1	92.55 3	94.06 3

ETH3 D test6400		83.28 1	83.47 1	82.72 3	91.48 4	59.33 6	92.10 9	90.95 7	65.68 59	80.67 15	94.42 3	59.41 7	95.89 9	86.74 2	89.75 5	92.94 16

MAR-MVS		76.76 48	75.60 53	80.21 23	90.87 5	54.68 76	89.14 41	89.11 22	62.95 97	70.54 81	92.33 29	41.05 147	94.95 15	57.90 181	86.55 28	91.00 64
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
DP-MVS Recon		71.99 114	70.31 119	77.01 102	90.65 6	53.44 105	89.37 36	82.97 159	56.33 223	63.56 151	89.47 94	34.02 226	92.15 54	54.05 206	72.41 149	85.43 179

CNVR-MVS		81.76 7	81.90 6	81.33 15	90.04 7	57.70 14	91.71 10	88.87 30	70.31 16	77.64 23	93.87 8	52.58 30	93.91 24	84.17 4	87.92 14	92.39 26

API-MVS		74.17 81	72.07 98	80.49 19	90.02 8	58.55 9	87.30 73	84.27 129	57.51 202	65.77 118	87.77 127	41.61 144	95.97 8	51.71 223	82.63 59	86.94 146

testtj		76.96 43	76.48 43	78.40 65	89.89 9	53.67 95	88.72 48	86.15 83	54.56 244	74.86 34	92.31 30	44.38 107	91.97 57	75.19 55	82.24 63	89.54 96

LFMVS		78.52 19	77.14 35	82.67 4	89.58 10	58.90 8	91.27 18	88.05 47	63.22 94	74.63 36	90.83 63	41.38 146	94.40 19	75.42 53	79.90 89	94.72 2

ZD-MVS					89.55 11	53.46 101		84.38 125	57.02 209	73.97 43	91.03 54	44.57 105	91.17 72	75.41 54	81.78 69

test_0728_SECOND				82.20 8	89.50 12	57.73 13	92.34 5	88.88 29					96.39 4	81.68 13	87.13 19	92.47 24

NCCC		79.57 16	79.23 16	80.59 18	89.50 12	56.99 24	91.38 15	88.17 46	67.71 38	73.81 44	92.75 21	46.88 73	93.28 28	78.79 28	84.07 53	91.50 50

SED-MVS		81.92 6	81.75 7	82.44 7	89.48 14	56.89 26	92.48 3	88.94 27	57.50 203	84.61 3	94.09 4	58.81 9	96.37 5	82.28 11	87.60 16	94.06 3

IU-MVS					89.48 14	57.49 17		91.38 5	66.22 51	88.26 1				82.83 7	87.60 16	92.44 25

test_241102_ONE					89.48 14	56.89 26		88.94 27	57.53 201	84.61 3	93.29 13	58.81 9	96.45 1

DVP-MVS		81.30 8	81.00 10	82.20 8	89.40 17	57.45 18	92.34 5	89.99 14	57.71 197	81.91 9	93.64 10	55.17 17	96.44 2	81.68 13	87.13 19	92.72 21
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					89.40 17	57.45 18	92.32 7	88.63 37	57.71 197	83.14 7	93.96 7	55.17 17

test_part2					89.33 19	55.48 48				82.27 8

DPE-MVS		79.82 15	79.66 13	80.29 21	89.27 20	55.08 63	88.70 49	87.92 50	55.55 231	81.21 12	93.69 9	56.51 14	94.27 21	78.36 31	85.70 36	91.51 49
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CSCG		80.41 12	79.72 12	82.49 5	89.12 21	57.67 15	89.29 40	91.54 3	59.19 163	71.82 69	90.05 84	59.72 6	96.04 7	78.37 30	88.40 12	93.75 5

APDe-MVS		78.44 20	78.20 21	79.19 37	88.56 22	54.55 80	89.76 33	87.77 54	55.91 226	78.56 20	92.49 25	48.20 59	92.65 40	79.49 22	83.04 57	90.39 77

APD-MVS		76.15 56	75.68 51	77.54 87	88.52 23	53.44 105	87.26 76	85.03 111	53.79 248	74.91 33	91.68 45	43.80 112	90.31 95	74.36 60	81.82 67	88.87 112
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	67.50 3	78.00 29	77.63 29	79.13 39	88.52 23	55.12 60	89.95 28	85.98 86	68.31 27	71.33 75	92.75 21	45.52 92	90.37 92	71.15 80	85.14 43	91.91 38
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
114514_t		69.87 146	67.88 153	75.85 128	88.38 25	52.35 138	86.94 83	83.68 142	53.70 249	55.68 248	85.60 153	30.07 263	91.20 71	55.84 196	71.02 160	83.99 198

WTY-MVS		77.47 36	77.52 31	77.30 93	88.33 26	46.25 261	88.46 52	90.32 10	71.40 10	72.32 64	91.72 43	53.44 25	92.37 46	66.28 109	75.42 122	93.28 10

PAPR		75.20 70	74.13 71	78.41 64	88.31 27	55.10 62	84.31 147	85.66 90	63.76 85	67.55 97	90.73 64	43.48 121	89.40 116	66.36 108	77.03 107	90.73 69

DP-MVS		59.24 260	56.12 271	68.63 261	88.24 28	50.35 181	82.51 196	64.43 330	41.10 323	46.70 301	78.77 227	24.75 298	88.57 142	22.26 340	56.29 271	66.96 338

AdaColmap		67.86 182	65.48 202	75.00 142	88.15 29	54.99 65	86.10 100	76.63 269	49.30 277	57.80 219	86.65 142	29.39 266	88.94 132	45.10 260	70.21 166	81.06 250

test_yl		75.85 61	74.83 65	78.91 42	88.08 30	51.94 144	91.30 16	89.28 18	57.91 191	71.19 77	89.20 100	42.03 138	92.77 35	69.41 87	75.07 127	92.01 36

DCV-MVSNet		75.85 61	74.83 65	78.91 42	88.08 30	51.94 144	91.30 16	89.28 18	57.91 191	71.19 77	89.20 100	42.03 138	92.77 35	69.41 87	75.07 127	92.01 36

Regformer-1		77.80 32	77.44 32	78.88 44	87.78 32	52.44 134	87.60 62	90.08 12	68.86 24	72.49 62	91.79 40	47.69 66	94.90 16	73.57 67	77.05 105	89.31 99

Regformer-2		77.15 40	76.82 39	78.14 72	87.78 32	51.84 148	87.60 62	89.12 21	67.23 42	71.93 68	91.79 40	46.03 84	93.53 27	72.85 73	77.05 105	89.05 108

CANet		80.90 9	81.17 9	80.09 28	87.62 34	54.21 85	91.60 13	86.47 75	73.13 5	79.89 18	93.10 15	49.88 51	92.98 31	84.09 5	84.75 47	93.08 14

VNet		77.99 30	77.92 27	78.19 71	87.43 35	50.12 189	90.93 21	91.41 4	67.48 41	75.12 32	90.15 82	46.77 75	91.00 76	73.52 68	78.46 98	93.44 7

HPM-MVS++		80.50 11	80.71 11	79.88 30	87.34 36	55.20 58	89.93 29	87.55 59	66.04 58	79.46 19	93.00 19	53.10 27	91.76 59	80.40 20	89.56 7	92.68 22

DWT-MVSNet_test		75.47 67	73.87 75	80.29 21	87.33 37	57.05 23	82.86 188	87.96 49	72.59 6	67.29 99	87.79 125	51.61 35	91.52 64	54.75 203	72.63 147	92.29 28

RRT_test8_iter05		72.74 100	71.20 110	77.36 91	87.25 38	53.51 99	88.68 50	89.53 16	65.20 69	61.32 170	81.27 207	45.89 86	92.48 44	65.99 110	55.65 280	86.10 164

Anonymous202405211		70.11 138	67.88 153	76.79 110	87.20 39	47.24 248	89.49 35	77.38 255	54.88 240	66.14 111	86.84 138	20.93 318	91.54 63	56.45 194	71.62 155	91.59 44

test12				79.24 36	86.89 40	56.08 41		85.16 107		72.27 65		47.15 71	91.10 74		85.93 33	90.54 75

DELS-MVS		82.32 4	82.50 4	81.79 11	86.80 41	56.89 26	92.77 2	86.30 79	77.83 1	77.88 21	92.13 32	60.24 4	94.78 18	78.97 25	89.61 6	93.69 6
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
GG-mvs-BLEND				77.77 81	86.68 42	50.61 170	68.67 314	88.45 44		68.73 88	87.45 131	59.15 8	90.67 84	54.83 200	87.67 15	92.03 35

CDPH-MVS		76.05 58	75.19 58	78.62 58	86.51 43	54.98 66	87.32 71	84.59 122	58.62 180	70.75 79	90.85 62	43.10 127	90.63 87	70.50 83	84.51 50	90.24 80

test_prior3		77.59 34	77.33 34	78.39 66	86.35 44	54.91 69	89.04 43	85.45 92	61.88 114	73.55 47	91.46 50	48.01 62	89.70 110	74.73 57	85.46 38	90.55 72

test_prior				78.39 66	86.35 44	54.91 69		85.45 92					89.70 110			90.55 72

gg-mvs-nofinetune		67.43 192	64.53 216	76.13 121	85.95 46	47.79 240	64.38 321	88.28 45	39.34 325	66.62 104	41.27 347	58.69 11	89.00 126	49.64 234	86.62 27	91.59 44

test_part1		73.80 87	72.13 95	78.79 51	85.92 47	58.26 10	90.60 23	86.85 68	63.98 80	63.95 143	81.54 205	52.08 34	92.24 50	64.93 125	59.32 239	85.87 170

PVSNet_BlendedMVS		73.42 93	73.30 78	73.76 170	85.91 48	51.83 149	86.18 98	84.24 132	65.40 63	69.09 86	80.86 211	46.70 76	88.13 157	75.43 51	65.92 195	81.33 245

PVSNet_Blended		76.53 51	76.54 41	76.50 112	85.91 48	51.83 149	88.89 46	84.24 132	67.82 36	69.09 86	89.33 99	46.70 76	88.13 157	75.43 51	81.48 71	89.55 95

test_8					85.72 50	55.31 53	87.60 62	83.88 139	57.84 194	72.84 56	90.99 55	44.99 97	88.34 149

TEST9					85.68 51	55.42 49	87.59 65	84.00 136	57.72 196	72.99 53	90.98 56	44.87 100	88.58 139

train_agg		76.91 44	76.40 45	78.45 63	85.68 51	55.42 49	87.59 65	84.00 136	57.84 194	72.99 53	90.98 56	44.99 97	88.58 139	78.19 33	85.32 41	91.34 56

9.14			78.19 22		85.67 53		88.32 54	88.84 31	59.89 146	74.58 39	92.62 24	46.80 74	92.66 39	81.40 17	85.62 37

agg_prior1		76.68 50	76.24 48	78.00 76	85.64 54	54.92 67	87.55 68	83.61 145	57.99 190	72.53 60	91.05 53	45.36 93	88.10 159	77.76 39	84.68 48	90.99 65

agg_prior					85.64 54	54.92 67		83.61 145		72.53 60			88.10 159

ETH3D-3000-0.1		78.73 18	78.71 18	78.78 52	85.58 56	52.40 135	88.42 53	89.03 24	60.01 144	76.06 29	92.80 20	48.34 57	92.88 33	81.66 15	86.48 29	91.04 62

PS-MVSNAJ		80.06 13	79.52 14	81.68 13	85.58 56	60.97 3	91.69 11	87.02 64	70.62 13	80.75 14	93.22 14	37.77 177	92.50 42	82.75 8	86.25 31	91.57 46

MVSTER		73.25 95	72.33 89	76.01 125	85.54 58	53.76 92	83.52 165	87.16 62	67.06 44	63.88 146	81.66 203	52.77 28	90.44 90	64.66 126	64.69 200	83.84 205

Regformer-3		76.02 59	75.47 55	77.70 83	85.49 59	51.47 157	85.12 127	90.19 11	68.52 26	69.36 84	90.66 65	46.45 79	94.81 17	70.25 85	73.16 139	86.81 154

Regformer-4		75.06 73	74.59 68	76.47 113	85.49 59	50.33 182	85.12 127	88.61 39	66.42 47	68.48 90	90.66 65	44.15 108	92.68 38	69.24 89	73.16 139	86.39 161

EPNet		78.36 23	78.49 19	77.97 78	85.49 59	52.04 142	89.36 37	84.07 135	73.22 4	77.03 25	91.72 43	49.32 55	90.17 101	73.46 69	82.77 58	91.69 41
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
DeepPCF-MVS	69.37 1	80.65 10	81.56 8	77.94 80	85.46 62	49.56 199	90.99 20	86.66 73	70.58 14	80.07 17	95.30 1	56.18 15	90.97 78	82.57 10	86.22 32	93.28 10

SD-MVS		76.18 55	74.85 64	80.18 24	85.39 63	56.90 25	85.75 108	82.45 165	56.79 215	74.48 40	91.81 39	43.72 116	90.75 83	74.61 59	78.65 96	92.91 17
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
xxxxxxxxxxxxxcwj		77.31 38	76.54 41	79.61 31	85.35 64	56.34 36	89.31 38	72.84 301	61.55 119	74.63 36	92.38 27	47.75 64	91.35 68	78.18 35	86.85 24	91.15 58

save fliter					85.35 64	56.34 36	89.31 38	81.46 178	61.55 119

MVS_111021_HR		76.39 53	75.38 57	79.42 34	85.33 66	56.47 33	88.15 55	84.97 112	65.15 70	66.06 113	89.88 87	43.79 113	92.16 52	75.03 56	80.03 87	89.64 94

PHI-MVS		77.49 35	77.00 36	78.95 41	85.33 66	50.69 169	88.57 51	88.59 41	58.14 186	73.60 45	93.31 12	43.14 125	93.79 25	73.81 64	88.53 11	92.37 27

TSAR-MVS + GP.		77.82 31	77.59 30	78.49 60	85.25 68	50.27 186	90.02 26	90.57 9	56.58 220	74.26 41	91.60 46	54.26 21	92.16 52	75.87 47	79.91 88	93.05 15

EIA-MVS		75.92 60	75.18 59	78.13 73	85.14 69	51.60 153	87.17 77	85.32 99	64.69 73	68.56 89	90.53 70	45.79 89	91.58 62	67.21 101	82.18 65	91.20 57

baseline1		72.51 106	72.12 97	73.69 173	85.05 70	44.46 278	83.51 169	86.13 84	71.61 9	64.64 131	87.97 123	55.00 20	89.48 115	59.07 165	56.05 274	87.13 145

FMVSNet3		68.84 162	67.40 165	73.19 184	85.05 70	48.53 222	85.71 111	85.36 96	60.90 133	57.58 225	79.15 224	42.16 134	86.77 193	47.25 249	63.40 208	84.27 193

xiu_mvs_v2_base		79.86 14	79.31 15	81.53 14	85.03 72	60.73 4	91.65 12	86.86 67	70.30 17	80.77 13	93.07 18	37.63 182	92.28 49	82.73 9	85.71 35	91.57 46

EPMVS		68.45 171	65.44 205	77.47 89	84.91 73	56.17 39	71.89 302	81.91 172	61.72 117	60.85 173	72.49 291	36.21 207	87.06 185	47.32 248	71.62 155	89.17 105

原ACMM1				76.13 121	84.89 74	54.59 79		85.26 103	51.98 261	66.70 102	87.07 136	40.15 158	89.70 110	51.23 225	85.06 45	84.10 194

thres200		68.71 168	67.27 168	73.02 185	84.73 75	46.76 252	85.03 132	87.73 55	62.34 106	59.87 180	83.45 176	43.15 124	88.32 151	31.25 315	67.91 181	83.98 200

HY-MVS	67.03 5	73.90 85	73.14 79	76.18 120	84.70 76	47.36 245	75.56 272	86.36 78	66.27 50	70.66 80	83.91 168	51.05 40	89.31 117	67.10 102	72.61 148	91.88 39

MVS		76.91 44	75.48 54	81.23 16	84.56 77	55.21 57	80.23 241	91.64 2	58.65 179	65.37 121	91.48 49	45.72 90	95.05 14	72.11 77	89.52 8	93.44 7

ET-MVSNet_ETH3D		75.23 69	74.08 72	78.67 56	84.52 78	55.59 45	88.92 45	89.21 20	68.06 33	53.13 266	90.22 78	49.71 52	87.62 173	72.12 76	70.82 162	92.82 19

SMA-MVS		79.10 17	78.76 17	80.12 26	84.42 79	55.87 43	87.58 67	86.76 70	61.48 123	80.26 16	93.10 15	46.53 78	92.41 45	79.97 21	88.77 9	92.08 33
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
PVSNet	62.49 8	69.27 156	67.81 156	73.64 175	84.41 80	51.85 147	84.63 142	77.80 246	66.42 47	59.80 182	84.95 160	22.14 313	80.44 276	55.03 199	75.11 126	88.62 118

canonicalmvs		78.17 27	77.86 28	79.12 40	84.30 81	54.22 84	87.71 60	84.57 123	67.70 39	77.70 22	92.11 35	50.90 42	89.95 104	78.18 35	77.54 103	93.20 12

HFP-MVS		74.37 78	73.13 81	78.10 74	84.30 81	53.68 93	85.58 113	84.36 126	56.82 213	65.78 116	90.56 67	40.70 152	90.90 79	69.18 90	80.88 73	89.71 91

#test#		74.86 76	73.78 76	78.10 74	84.30 81	53.68 93	86.95 82	84.36 126	59.00 173	65.78 116	90.56 67	40.70 152	90.90 79	71.48 78	80.88 73	89.71 91

VDD-MVS		76.08 57	74.97 62	79.44 33	84.27 84	53.33 111	91.13 19	85.88 87	65.33 66	72.37 63	89.34 97	32.52 241	92.76 37	77.90 38	75.96 116	92.22 31

ETH3D cwj APD-0.16		78.36 23	78.19 22	78.86 46	84.21 85	52.68 128	86.70 88	89.02 25	59.13 169	75.37 31	92.49 25	49.06 56	93.20 29	80.67 19	87.08 21	90.71 70

BH-RMVSNet		70.08 140	68.01 150	76.27 116	84.21 85	51.22 165	87.29 74	79.33 220	58.96 175	63.63 150	86.77 139	33.29 235	90.30 97	44.63 262	73.96 133	87.30 143

MVS_Test		75.85 61	74.93 63	78.62 58	84.08 87	55.20 58	83.99 157	85.17 106	68.07 32	73.38 50	82.76 185	50.44 46	89.00 126	65.90 112	80.61 77	91.64 42

tfpn200view9		67.57 188	66.13 188	71.89 216	84.05 88	45.07 273	83.40 174	87.71 57	60.79 134	57.79 220	82.76 185	43.53 119	87.80 166	28.80 321	66.36 190	82.78 224

thres400		67.40 195	66.13 188	71.19 226	84.05 88	45.07 273	83.40 174	87.71 57	60.79 134	57.79 220	82.76 185	43.53 119	87.80 166	28.80 321	66.36 190	80.71 255

tpmvs		62.45 245	59.42 250	71.53 221	83.93 90	54.32 82	70.03 309	77.61 250	51.91 262	53.48 265	68.29 315	37.91 175	86.66 197	33.36 305	58.27 248	73.62 318

ACMMPR		73.76 88	72.61 84	77.24 98	83.92 91	52.96 124	85.58 113	84.29 128	56.82 213	65.12 123	90.45 72	37.24 192	90.18 100	69.18 90	80.84 75	88.58 119

region2R		73.75 89	72.55 86	77.33 92	83.90 92	52.98 123	85.54 116	84.09 134	56.83 212	65.10 124	90.45 72	37.34 190	90.24 98	68.89 92	80.83 76	88.77 115

ZNCC-MVS		75.82 64	75.02 61	78.23 70	83.88 93	53.80 91	86.91 85	86.05 85	59.71 148	67.85 96	90.55 69	42.23 133	91.02 75	72.66 75	85.29 42	89.87 90

Anonymous20240529		69.71 148	67.28 167	77.00 103	83.78 94	50.36 180	88.87 47	85.10 110	47.22 287	64.03 141	83.37 177	27.93 274	92.10 55	57.78 183	67.44 183	88.53 121

SF-MVS		77.64 33	77.42 33	78.32 69	83.75 95	52.47 133	86.63 90	87.80 51	58.78 177	74.63 36	92.38 27	47.75 64	91.35 68	78.18 35	86.85 24	91.15 58

PMMVS		72.98 97	72.05 99	75.78 129	83.57 96	48.60 219	84.08 152	82.85 161	61.62 118	68.24 93	90.33 76	28.35 269	87.78 169	72.71 74	76.69 110	90.95 66

alignmvs		78.08 28	77.98 25	78.39 66	83.53 97	53.22 114	89.77 32	85.45 92	66.11 53	76.59 28	91.99 38	54.07 24	89.05 122	77.34 41	77.00 108	92.89 18

CS-MVS		78.19 26	77.97 26	78.82 47	83.52 98	53.08 119	89.10 42	86.30 79	68.01 34	73.57 46	91.26 52	47.28 69	92.35 47	78.21 32	84.51 50	91.05 61

XVS		72.92 98	71.62 102	76.81 107	83.41 99	52.48 131	84.88 136	83.20 154	58.03 187	63.91 144	89.63 92	35.50 215	89.78 107	65.50 114	80.50 79	88.16 124

X-MVStestdata		65.85 220	62.20 229	76.81 107	83.41 99	52.48 131	84.88 136	83.20 154	58.03 187	63.91 144	4.82 362	35.50 215	89.78 107	65.50 114	80.50 79	88.16 124

thres600view7		66.46 212	65.12 210	70.47 235	83.41 99	43.80 286	82.15 203	87.78 52	59.37 157	56.02 245	82.21 196	43.73 114	86.90 191	26.51 332	64.94 197	80.71 255

3Dnovator+	62.71 7	72.29 110	70.50 116	77.65 85	83.40 102	51.29 163	87.32 71	86.40 77	59.01 172	58.49 210	88.32 115	32.40 242	91.27 70	57.04 189	82.15 66	90.38 78

SR-MVS		70.92 128	69.73 129	74.50 149	83.38 103	50.48 175	84.27 148	79.35 218	48.96 280	66.57 107	90.45 72	33.65 232	87.11 183	66.42 106	74.56 130	85.91 168

GST-MVS		74.87 75	73.90 74	77.77 81	83.30 104	53.45 104	85.75 108	85.29 101	59.22 162	66.50 108	89.85 88	40.94 148	90.76 82	70.94 81	83.35 56	89.10 107

thres100view900		66.87 207	65.42 206	71.24 224	83.29 105	43.15 290	81.67 214	87.78 52	59.04 171	55.92 246	82.18 197	43.73 114	87.80 166	28.80 321	66.36 190	82.78 224

gm-plane-assit					83.24 106	54.21 85			70.91 12		88.23 118		95.25 12	66.37 107

tpmrst		71.04 125	69.77 128	74.86 144	83.19 107	55.86 44	75.64 271	78.73 230	67.88 35	64.99 128	73.73 277	49.96 50	79.56 287	65.92 111	67.85 182	89.14 106

新几何1				73.30 183	83.10 108	53.48 100		71.43 312	45.55 300	66.14 111	87.17 134	33.88 230	80.54 274	48.50 241	80.33 82	85.88 169

PatchmatchNet		67.07 203	63.63 223	77.40 90	83.10 108	58.03 11	72.11 300	77.77 247	58.85 176	59.37 190	70.83 304	37.84 176	84.93 240	42.96 270	69.83 169	89.26 100
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
CHOSEN 1792x2688		76.24 54	74.03 73	82.88 1	83.09 110	62.84 2	85.73 110	85.39 95	69.79 19	64.87 129	83.49 175	41.52 145	93.69 26	70.55 82	81.82 67	92.12 32

1121		68.79 166	66.77 174	74.82 145	83.08 111	53.46 101	80.23 241	71.53 311	45.47 302	66.31 110	87.19 133	34.02 226	85.13 236	52.78 216	80.36 81	85.87 170

Anonymous20231211		66.08 218	63.67 222	73.31 182	83.07 112	48.75 217	86.01 103	84.67 121	45.27 303	56.54 240	76.67 254	28.06 273	88.95 130	52.78 216	59.95 232	82.23 227

IB-MVS	68.87 2	74.01 83	72.03 100	79.94 29	83.04 113	55.50 47	90.24 25	88.65 35	67.14 43	61.38 169	81.74 202	53.21 26	94.28 20	60.45 158	62.41 222	90.03 86
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
MVSFormer		73.53 92	72.19 94	77.57 86	83.02 114	55.24 55	81.63 215	81.44 179	50.28 271	76.67 26	90.91 60	44.82 102	86.11 210	60.83 150	80.09 84	91.36 54

lupinMVS		78.38 22	78.11 24	79.19 37	83.02 114	55.24 55	91.57 14	84.82 116	69.12 23	76.67 26	92.02 36	44.82 102	90.23 99	80.83 18	80.09 84	92.08 33

MSP-MVS		82.30 5	83.47 1	78.80 49	82.99 116	52.71 127	85.04 131	88.63 37	66.08 55	86.77 2	92.75 21	72.05 1	91.46 66	83.35 6	93.53 1	92.23 29
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
PGM-MVS		72.60 103	71.20 110	76.80 109	82.95 117	52.82 126	83.07 183	82.14 166	56.51 221	63.18 153	89.81 89	35.68 214	89.76 109	67.30 100	80.19 83	87.83 132

TR-MVS		69.71 148	67.85 155	75.27 138	82.94 118	48.48 225	87.40 70	80.86 188	57.15 208	64.61 132	87.08 135	32.67 240	89.64 113	46.38 254	71.55 157	87.68 136

CP-MVS		72.59 105	71.46 105	76.00 126	82.93 119	52.32 139	86.93 84	82.48 164	55.15 235	63.65 149	90.44 75	35.03 219	88.53 143	68.69 93	77.83 101	87.15 144

MP-MVS		74.99 74	74.33 70	76.95 105	82.89 120	53.05 121	85.63 112	83.50 147	57.86 193	67.25 100	90.24 77	43.38 122	88.85 134	76.03 45	82.23 64	88.96 110
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mvs_anonymous		72.29 110	70.74 113	76.94 106	82.85 121	54.72 74	78.43 259	81.54 177	63.77 84	61.69 168	79.32 221	51.11 39	85.31 229	62.15 141	75.79 118	90.79 68

3Dnovator	64.70 6	74.46 77	72.48 87	80.41 20	82.84 122	55.40 52	83.08 182	88.61 39	67.61 40	59.85 181	88.66 109	34.57 222	93.97 22	58.42 172	88.70 10	91.85 40

test1172		69.64 152	68.38 146	73.41 181	82.77 123	48.84 215	82.79 190	78.34 239	47.02 290	65.27 122	90.07 83	31.17 255	86.09 214	64.51 127	73.49 137	85.31 180

BH-w/o		70.02 142	68.51 143	74.56 148	82.77 123	50.39 178	86.60 91	78.14 241	59.77 147	59.65 184	85.57 154	39.27 166	87.30 180	49.86 232	74.94 129	85.99 165

Fast-Effi-MVS+		72.73 101	71.15 112	77.48 88	82.75 125	54.76 71	86.77 87	80.64 191	63.05 96	65.93 114	84.01 166	44.42 106	89.03 123	56.45 194	76.36 115	88.64 117

GBi-Net		67.09 201	65.47 203	71.96 209	82.71 126	46.36 257	83.52 165	83.31 149	58.55 181	57.58 225	76.23 260	36.72 202	86.20 206	47.25 249	63.40 208	83.32 211

test1		67.09 201	65.47 203	71.96 209	82.71 126	46.36 257	83.52 165	83.31 149	58.55 181	57.58 225	76.23 260	36.72 202	86.20 206	47.25 249	63.40 208	83.32 211

FMVSNet2		67.57 188	65.79 195	72.90 187	82.71 126	47.97 239	85.15 123	84.93 113	58.55 181	56.71 238	78.26 231	36.72 202	86.67 196	46.15 256	62.94 219	84.07 195

mPP-MVS		71.79 117	70.38 118	76.04 124	82.65 129	52.06 141	84.45 143	81.78 174	55.59 230	62.05 166	89.68 91	33.48 233	88.28 154	65.45 119	78.24 100	87.77 134

CANet_DTU		73.71 90	73.14 79	75.40 134	82.61 130	50.05 190	84.67 141	79.36 217	69.72 20	75.39 30	90.03 85	29.41 265	85.93 222	67.99 97	79.11 93	90.22 81

EI-MVSNet-Vis-set		73.19 96	72.60 85	74.99 143	82.56 131	49.80 195	82.55 195	89.00 26	66.17 52	65.89 115	88.98 103	43.83 111	92.29 48	65.38 123	69.01 173	82.87 223

dp		64.41 226	61.58 233	72.90 187	82.40 132	54.09 88	72.53 292	76.59 270	60.39 140	55.68 248	70.39 308	35.18 218	76.90 309	39.34 278	61.71 226	87.73 135

MS-PatchMatch		72.34 108	71.26 108	75.61 130	82.38 133	55.55 46	88.00 56	89.95 15	65.38 64	56.51 242	80.74 213	32.28 244	92.89 32	57.95 180	88.10 13	78.39 278

CostFormer		73.89 86	72.30 91	78.66 57	82.36 134	56.58 29	75.56 272	85.30 100	66.06 56	70.50 82	76.88 251	57.02 12	89.06 120	68.27 96	68.74 175	90.33 79

QAPM		71.88 115	69.33 135	79.52 32	82.20 135	54.30 83	86.30 96	88.77 33	56.61 219	59.72 183	87.48 130	33.90 229	95.36 11	47.48 247	81.49 70	88.90 111

HPM-MVS		72.60 103	71.50 104	75.89 127	82.02 136	51.42 159	80.70 235	83.05 156	56.12 225	64.03 141	89.53 93	37.55 184	88.37 147	70.48 84	80.04 86	87.88 131
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TESTMET0.1,1		72.86 99	72.33 89	74.46 150	81.98 137	50.77 167	85.13 124	85.47 91	66.09 54	67.30 98	83.69 173	37.27 191	83.57 253	65.06 124	78.97 95	89.05 108

ACMMP_NAP		76.43 52	75.66 52	78.73 53	81.92 138	54.67 77	84.06 154	85.35 97	61.10 128	72.99 53	91.50 48	40.25 155	91.00 76	76.84 43	86.98 22	90.51 76

Effi-MVS+		75.24 68	73.61 77	80.16 25	81.92 138	57.42 20	85.21 121	76.71 267	60.68 137	73.32 51	89.34 97	47.30 68	91.63 61	68.28 95	79.72 90	91.42 51

ETV-MVS		77.17 39	76.74 40	78.48 61	81.80 140	54.55 80	86.13 99	85.33 98	68.20 29	73.10 52	90.52 71	45.23 95	90.66 85	79.37 23	80.95 72	90.22 81

PLC	52.38 18	60.89 252	58.97 255	66.68 279	81.77 141	45.70 268	78.96 255	74.04 290	43.66 315	47.63 295	83.19 181	23.52 304	77.78 304	37.47 281	60.46 231	76.55 299
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MDTV_nov1_ep13			61.56 234		81.68 142	55.12 60	72.41 294	78.18 240	59.19 163	58.85 203	69.29 312	34.69 221	86.16 209	36.76 290	62.96 218

baseline2		75.15 71	74.54 69	76.98 104	81.67 143	51.74 151	83.84 160	91.94 1	69.97 18	58.98 197	86.02 147	59.73 5	91.73 60	68.37 94	70.40 165	87.48 138

thisisatest0515		73.64 91	72.20 93	77.97 78	81.63 144	53.01 122	86.69 89	88.81 32	62.53 103	64.06 140	85.65 152	52.15 33	92.50 42	58.43 170	69.84 168	88.39 123

BH-untuned		68.28 175	66.40 180	73.91 164	81.62 145	50.01 191	85.56 115	77.39 254	57.63 199	57.47 230	83.69 173	36.36 206	87.08 184	44.81 261	73.08 144	84.65 188

EI-MVSNet-UG-set		72.37 107	71.73 101	74.29 156	81.60 146	49.29 205	81.85 210	88.64 36	65.29 68	65.05 125	88.29 116	43.18 123	91.83 58	63.74 130	67.97 180	81.75 233

sss		70.49 134	70.13 124	71.58 220	81.59 147	39.02 315	80.78 234	84.71 120	59.34 158	66.61 105	88.09 120	37.17 193	85.52 225	61.82 144	71.02 160	90.20 83

APD-MVS_3200maxsize		69.62 153	68.23 148	73.80 169	81.58 148	48.22 231	81.91 208	79.50 212	48.21 282	64.24 139	89.75 90	31.91 250	87.55 175	63.08 133	73.85 135	85.64 175

旧先验1					81.57 149	47.48 242		71.83 306			88.66 109	36.94 196			78.34 99	88.67 116

zzz-MVS		74.15 82	73.11 82	77.27 95	81.54 150	53.57 97	84.02 156	81.31 181	59.41 155	68.39 91	90.96 58	36.07 209	89.01 124	73.80 65	82.45 61	89.23 101

MTAPA		72.73 101	71.22 109	77.27 95	81.54 150	53.57 97	67.06 317	81.31 181	59.41 155	68.39 91	90.96 58	36.07 209	89.01 124	73.80 65	82.45 61	89.23 101

RRT_MVS		65.43 224	64.01 221	69.68 248	81.54 150	50.15 187	82.31 201	76.78 265	55.25 234	60.64 177	82.00 200	25.18 294	79.00 288	60.96 148	51.45 302	79.89 265

PAPM_NR		71.80 116	69.98 126	77.26 97	81.54 150	53.34 110	78.60 258	85.25 104	53.46 250	60.53 178	88.66 109	45.69 91	89.24 118	56.49 191	79.62 91	89.19 104

ACMMP		70.81 130	69.29 136	75.39 135	81.52 154	51.92 146	83.43 172	83.03 157	56.67 218	58.80 204	88.91 105	31.92 249	88.58 139	65.89 113	73.39 138	85.67 173
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
MSLP-MVS++		74.21 80	72.25 92	80.11 27	81.45 155	56.47 33	86.32 95	79.65 209	58.19 185	66.36 109	92.29 31	36.11 208	90.66 85	67.39 99	82.49 60	93.18 13

tpm cat1		66.28 214	62.78 225	76.77 111	81.40 156	57.14 22	70.03 309	77.19 257	53.00 254	58.76 205	70.73 307	46.17 80	86.73 195	43.27 267	64.46 202	86.44 159

MP-MVS-pluss		75.54 66	75.03 60	77.04 100	81.37 157	52.65 130	84.34 146	84.46 124	61.16 126	69.14 85	91.76 42	39.98 161	88.99 128	78.19 33	84.89 46	89.48 97
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TSAR-MVS + MP.		78.31 25	78.26 20	78.48 61	81.33 158	56.31 38	81.59 218	86.41 76	69.61 21	81.72 11	88.16 119	55.09 19	88.04 161	74.12 62	86.31 30	91.09 60
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
PVSNet_Blended_VisFu		73.40 94	72.44 88	76.30 115	81.32 159	54.70 75	85.81 104	78.82 227	63.70 86	64.53 133	85.38 156	47.11 72	87.38 179	67.75 98	77.55 102	86.81 154

LS3D		56.40 284	53.82 284	64.12 293	81.12 160	45.69 269	73.42 287	66.14 326	35.30 341	43.24 314	79.88 216	22.18 312	79.62 286	19.10 348	64.00 204	67.05 337

SteuartSystems-ACMMP		77.08 41	76.33 46	79.34 35	80.98 161	55.31 53	89.76 33	86.91 66	62.94 98	71.65 70	91.56 47	42.33 131	92.56 41	77.14 42	83.69 55	90.15 84
Skip Steuart: Steuart Systems R&D Blog.
SR-MVS-dyc-post		68.27 176	66.87 171	72.48 197	80.96 162	48.14 234	81.54 219	76.98 261	46.42 295	62.75 158	89.42 95	31.17 255	86.09 214	60.52 156	72.06 153	83.19 216

RE-MVS-def			66.66 178		80.96 162	48.14 234	81.54 219	76.98 261	46.42 295	62.75 158	89.42 95	29.28 267		60.52 156	72.06 153	83.19 216

CPTT-MVS		67.15 200	65.84 194	71.07 228	80.96 162	50.32 183	81.94 207	74.10 288	46.18 298	57.91 217	87.64 129	29.57 264	81.31 267	64.10 128	70.18 167	81.56 236

Vis-MVSNet		70.61 133	69.34 134	74.42 152	80.95 165	48.49 224	86.03 102	77.51 252	58.74 178	65.55 120	87.78 126	34.37 223	85.95 221	52.53 221	80.61 77	88.80 113
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ab-mvs		70.65 132	69.11 138	75.29 137	80.87 166	46.23 262	73.48 286	85.24 105	59.99 145	66.65 103	80.94 210	43.13 126	88.69 135	63.58 131	68.07 178	90.95 66

tpm2		70.82 129	68.44 144	77.98 77	80.78 167	56.11 40	74.21 282	81.28 184	60.24 142	68.04 94	75.27 269	52.26 32	88.50 144	55.82 197	68.03 179	89.33 98

1112_ss		70.05 141	69.37 133	72.10 203	80.77 168	42.78 294	85.12 127	76.75 266	59.69 149	61.19 172	92.12 33	47.48 67	83.84 248	53.04 212	68.21 177	89.66 93

DeepC-MVS	67.15 4	76.90 46	76.27 47	78.80 49	80.70 169	55.02 64	86.39 93	86.71 71	66.96 45	67.91 95	89.97 86	48.03 61	91.41 67	75.60 50	84.14 52	89.96 87
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
CLD-MVS		75.60 65	75.39 56	76.24 117	80.69 170	52.40 135	90.69 22	86.20 82	74.40 3	65.01 127	88.93 104	42.05 137	90.58 88	76.57 44	73.96 133	85.73 172
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		67.86 182	66.28 184	72.61 192	80.67 171	48.34 229	81.18 227	75.95 275	50.81 270	59.55 188	88.05 122	27.86 275	85.98 218	58.83 167	73.58 136	83.51 208

ADS-MVSNet2		55.21 291	51.44 295	66.51 280	80.60 172	49.56 199	55.03 339	65.44 327	44.72 306	51.00 279	61.19 333	22.83 305	75.41 314	28.54 324	53.63 291	74.57 312

ADS-MVSNet		56.17 285	51.95 294	68.84 255	80.60 172	53.07 120	55.03 339	70.02 319	44.72 306	51.00 279	61.19 333	22.83 305	78.88 290	28.54 324	53.63 291	74.57 312

UGNet		68.71 168	67.11 170	73.50 179	80.55 174	47.61 241	84.08 152	78.51 235	59.45 153	65.68 119	82.73 188	23.78 301	85.08 238	52.80 215	76.40 111	87.80 133
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
baseline		76.86 47	76.24 48	78.71 54	80.47 175	54.20 87	83.90 159	84.88 115	71.38 11	71.51 73	89.15 102	50.51 45	90.55 89	75.71 48	78.65 96	91.39 52

casdiffmvs		77.36 37	76.85 38	78.88 44	80.40 176	54.66 78	87.06 79	85.88 87	72.11 8	71.57 72	88.63 113	50.89 44	90.35 93	76.00 46	79.11 93	91.63 43

PAPM		76.76 48	76.07 50	78.81 48	80.20 177	59.11 7	86.86 86	86.23 81	68.60 25	70.18 83	88.84 107	51.57 36	87.16 182	65.48 116	86.68 26	90.15 84

hse-mvs3		73.95 84	72.89 83	77.15 99	80.17 178	50.37 179	84.68 139	83.33 148	68.08 30	71.97 66	88.65 112	42.50 129	91.15 73	78.82 26	57.78 260	89.91 89

DPM-MVS		82.39 3	82.36 5	82.49 5	80.12 179	59.50 5	92.24 8	90.72 8	69.37 22	83.22 6	94.47 2	63.81 3	93.18 30	74.02 63	93.25 2	94.80 1

tpm		68.36 172	67.48 164	70.97 230	79.93 180	51.34 161	76.58 268	78.75 229	67.73 37	63.54 152	74.86 271	48.33 58	72.36 330	53.93 207	63.71 206	89.21 103

thisisatest0530		70.47 136	68.56 142	76.20 119	79.78 181	51.52 156	83.49 171	88.58 42	57.62 200	58.60 206	82.79 184	51.03 41	91.48 65	52.84 214	62.36 224	85.59 177

jason		77.01 42	76.45 44	78.69 55	79.69 182	54.74 72	90.56 24	83.99 138	68.26 28	74.10 42	90.91 60	42.14 135	89.99 103	79.30 24	79.12 92	91.36 54
jason: jason.
VDDNet		74.37 78	72.13 95	81.09 17	79.58 183	56.52 32	90.02 26	86.70 72	52.61 257	71.23 76	87.20 132	31.75 251	93.96 23	74.30 61	75.77 119	92.79 20

test222					79.36 184	50.97 166	77.99 260	67.84 323	42.54 320	62.84 157	86.53 143	30.26 261			76.91 109	85.23 181

cascas		69.01 160	66.13 188	77.66 84	79.36 184	55.41 51	86.99 80	83.75 141	56.69 217	58.92 200	81.35 206	24.31 299	92.10 55	53.23 209	70.61 163	85.46 178

1314		71.11 124	69.41 132	76.22 118	79.32 186	50.49 174	80.23 241	85.14 109	59.44 154	58.93 199	88.89 106	33.83 231	89.60 114	61.49 145	77.42 104	88.57 120

LCM-MVSNet-Re		58.82 268	56.54 266	65.68 283	79.31 187	29.09 346	61.39 331	45.79 349	60.73 136	37.65 331	72.47 292	31.42 253	81.08 269	49.66 233	70.41 164	86.87 148

CNLPA		60.59 254	58.44 257	67.05 274	79.21 188	47.26 247	79.75 248	64.34 331	42.46 321	51.90 275	83.94 167	27.79 277	75.41 314	37.12 284	59.49 237	78.47 275

EPP-MVSNet		71.14 122	70.07 125	74.33 155	79.18 189	46.52 255	83.81 161	86.49 74	56.32 224	57.95 216	84.90 161	54.23 22	89.14 119	58.14 176	69.65 170	87.33 141

KD-MVS_2432*1600		59.04 265	56.44 268	66.86 275	79.07 190	45.87 265	72.13 298	80.42 195	55.03 237	48.15 291	71.01 302	36.73 200	78.05 297	35.21 296	30.18 348	76.67 294

miper_refine_blended		59.04 265	56.44 268	66.86 275	79.07 190	45.87 265	72.13 298	80.42 195	55.03 237	48.15 291	71.01 302	36.73 200	78.05 297	35.21 296	30.18 348	76.67 294

HQP-NCC					79.02 192		88.00 56		65.45 60	64.48 134

ACMP_Plane					79.02 192		88.00 56		65.45 60	64.48 134

HQP-MVS		72.34 108	71.44 106	75.03 141	79.02 192	51.56 154	88.00 56	83.68 142	65.45 60	64.48 134	85.13 157	37.35 188	88.62 137	66.70 104	73.12 141	84.91 186

miper_enhance_ethall		69.77 147	68.90 140	72.38 199	78.93 195	49.91 193	83.29 178	78.85 225	64.90 71	59.37 190	79.46 219	52.77 28	85.16 235	63.78 129	58.72 243	82.08 228

UA-Net		67.32 196	66.23 185	70.59 234	78.85 196	41.23 307	73.60 284	75.45 279	61.54 121	66.61 105	84.53 162	38.73 170	86.57 202	42.48 273	74.24 131	83.98 200

NP-MVS					78.76 197	50.43 176					85.12 158

VPA-MVSNet		71.12 123	70.66 115	72.49 196	78.75 198	44.43 280	87.64 61	90.02 13	63.97 81	65.02 126	81.58 204	42.14 135	87.42 178	63.42 132	63.38 211	85.63 176

Test_1112_low_res		67.18 199	66.23 185	70.02 246	78.75 198	41.02 308	83.43 172	73.69 293	57.29 206	58.45 212	82.39 194	45.30 94	80.88 271	50.50 228	66.26 193	88.16 124

test-LLR		69.65 151	69.01 139	71.60 218	78.67 200	48.17 232	85.13 124	79.72 206	59.18 165	63.13 154	82.58 190	36.91 197	80.24 278	60.56 154	75.17 124	86.39 161

test-mter		68.36 172	67.29 166	71.60 218	78.67 200	48.17 232	85.13 124	79.72 206	53.38 251	63.13 154	82.58 190	27.23 280	80.24 278	60.56 154	75.17 124	86.39 161

EPNet_dtu		66.25 215	66.71 176	64.87 291	78.66 202	34.12 329	82.80 189	75.51 277	61.75 116	64.47 137	86.90 137	37.06 194	72.46 329	43.65 266	69.63 171	88.02 130
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VPNet		72.07 113	71.42 107	74.04 161	78.64 203	47.17 249	89.91 31	87.97 48	72.56 7	64.66 130	85.04 159	41.83 142	88.33 150	61.17 147	60.97 229	86.62 156

SCA		63.84 230	60.01 247	75.32 136	78.58 204	57.92 12	61.61 329	77.53 251	56.71 216	57.75 222	70.77 305	31.97 247	79.91 284	48.80 238	56.36 267	88.13 127

abl_6		68.03 179	66.15 187	73.66 174	78.54 205	48.48 225	79.77 246	78.04 242	47.39 286	63.70 148	88.25 117	28.21 270	89.06 120	60.17 162	71.25 158	83.45 209

diffmvs		75.11 72	74.65 67	76.46 114	78.52 206	53.35 109	83.28 179	79.94 201	70.51 15	71.64 71	88.72 108	46.02 85	86.08 216	77.52 40	75.75 120	89.96 87

IS-MVSNet		68.80 165	67.55 162	72.54 194	78.50 207	43.43 288	81.03 229	79.35 218	59.12 170	57.27 233	86.71 140	46.05 83	87.70 171	44.32 263	75.60 121	86.49 158

TAPA-MVS	56.12 14	61.82 249	60.18 246	66.71 277	78.48 208	37.97 320	75.19 277	76.41 272	46.82 291	57.04 234	86.52 144	27.67 278	77.03 307	26.50 333	67.02 186	85.14 182
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
plane_prior6					78.42 209	49.39 203						36.04 211

OpenMVS	61.00 11	69.99 144	67.55 162	77.30 93	78.37 210	54.07 89	84.36 145	85.76 89	57.22 207	56.71 238	87.67 128	30.79 258	92.83 34	43.04 268	84.06 54	85.01 184

plane_prior1					78.31 211

tttt0517		68.33 174	66.29 183	74.46 150	78.08 212	49.06 207	80.88 232	89.08 23	54.40 246	54.75 252	80.77 212	51.31 38	90.33 94	49.35 236	58.01 254	83.99 198

cl-mvsnet2		68.85 161	67.69 157	72.35 200	78.07 213	49.98 192	82.45 198	78.48 236	62.50 104	58.46 211	77.95 232	49.99 49	85.17 234	62.55 136	58.72 243	81.90 230

HQP_MVS		70.96 127	69.91 127	74.12 159	77.95 214	49.57 197	85.76 106	82.59 162	63.60 89	62.15 163	83.28 179	36.04 211	88.30 152	65.46 117	72.34 150	84.49 189

plane_prior7					77.95 214	48.46 227

FIs		70.00 143	70.24 123	69.30 251	77.93 216	38.55 317	83.99 157	87.72 56	66.86 46	57.66 223	84.17 165	52.28 31	85.31 229	52.72 220	68.80 174	84.02 196

PatchMatch-RL		56.66 280	53.75 285	65.37 288	77.91 217	45.28 271	69.78 311	60.38 336	41.35 322	47.57 296	73.73 277	16.83 333	76.91 308	36.99 287	59.21 240	73.92 316

XXY-MVS		70.18 137	69.28 137	72.89 189	77.64 218	42.88 293	85.06 130	87.50 60	62.58 102	62.66 160	82.34 195	43.64 118	89.83 106	58.42 172	63.70 207	85.96 167

testdata				67.08 273	77.59 219	45.46 270		69.20 321	44.47 308	71.50 74	88.34 114	31.21 254	70.76 335	52.20 222	75.88 117	85.03 183

CDS-MVSNet		70.48 135	69.43 131	73.64 175	77.56 220	48.83 216	83.51 169	77.45 253	63.27 93	62.33 162	85.54 155	43.85 110	83.29 257	57.38 188	74.00 132	88.79 114
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
Vis-MVSNet (Re-imp)		65.52 221	65.63 199	65.17 289	77.49 221	30.54 341	75.49 275	77.73 248	59.34 158	52.26 273	86.69 141	49.38 54	80.53 275	37.07 286	75.28 123	84.42 191

PVSNet_0	57.04 13	61.19 251	57.24 262	73.02 185	77.45 222	50.31 184	79.43 253	77.36 256	63.96 82	47.51 298	72.45 293	25.03 296	83.78 250	52.76 219	19.22 353	84.96 185

FMVSNet1		64.57 225	62.11 230	71.96 209	77.32 223	46.36 257	83.52 165	83.31 149	52.43 259	54.42 255	76.23 260	27.80 276	86.20 206	42.59 272	61.34 228	83.32 211

MVS_111021_LR		69.07 157	67.91 151	72.54 194	77.27 224	49.56 199	79.77 246	73.96 291	59.33 160	60.73 175	87.82 124	30.19 262	81.53 265	69.94 86	72.19 152	86.53 157

xiu_mvs_v1_base_debu		71.60 118	70.29 120	75.55 131	77.26 225	53.15 115	85.34 117	79.37 214	55.83 227	72.54 57	90.19 79	22.38 309	86.66 197	73.28 70	76.39 112	86.85 150

xiu_mvs_v1_base		71.60 118	70.29 120	75.55 131	77.26 225	53.15 115	85.34 117	79.37 214	55.83 227	72.54 57	90.19 79	22.38 309	86.66 197	73.28 70	76.39 112	86.85 150

xiu_mvs_v1_base_debi		71.60 118	70.29 120	75.55 131	77.26 225	53.15 115	85.34 117	79.37 214	55.83 227	72.54 57	90.19 79	22.38 309	86.66 197	73.28 70	76.39 112	86.85 150

FMVSNet5		58.61 270	56.45 267	65.10 290	77.20 228	39.74 312	74.77 278	77.12 259	50.27 273	43.28 313	67.71 317	26.15 287	76.90 309	36.78 289	54.78 285	78.65 273

PCF-MVS	61.03 10	70.10 139	68.40 145	75.22 140	77.15 229	51.99 143	79.30 254	82.12 167	56.47 222	61.88 167	86.48 145	43.98 109	87.24 181	55.37 198	72.79 146	86.43 160
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
HyFIR lowres test		69.94 145	67.58 159	77.04 100	77.11 230	57.29 21	81.49 223	79.11 223	58.27 184	58.86 202	80.41 214	42.33 131	86.96 188	61.91 142	68.68 176	86.87 148

miper_ehance_all_eth		68.70 170	67.58 159	72.08 204	76.91 231	49.48 202	82.47 197	78.45 237	62.68 101	58.28 215	77.88 234	50.90 42	85.01 239	61.91 142	58.72 243	81.75 233

test_0402		56.45 283	53.03 287	66.69 278	76.78 232	50.31 184	81.76 212	69.61 320	42.79 319	43.88 308	72.13 297	22.82 307	86.46 203	16.57 350	50.94 303	63.31 344

ACMH	53.70 16	59.78 256	55.94 273	71.28 223	76.59 233	48.35 228	80.15 244	76.11 273	49.74 275	41.91 318	73.45 284	16.50 336	90.31 95	31.42 313	57.63 261	75.17 308
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AUN-MVS		68.20 178	66.35 181	73.76 170	76.37 234	47.45 243	79.52 251	79.52 211	60.98 131	62.34 161	86.02 147	36.59 205	86.94 189	62.32 138	53.47 295	86.89 147

hse-mvs2		71.44 121	70.68 114	73.73 172	76.34 235	47.44 244	79.45 252	79.47 213	68.08 30	71.97 66	86.01 149	42.50 129	86.93 190	78.82 26	53.46 296	86.83 153

cl-mvsnet_		67.43 192	65.93 192	71.95 212	76.33 236	48.02 237	82.58 192	79.12 222	61.30 125	56.72 237	76.92 249	46.12 81	86.44 204	57.98 178	56.31 269	81.38 244

cl-mvsnet1		67.43 192	65.93 192	71.94 213	76.33 236	48.01 238	82.57 193	79.11 223	61.31 124	56.73 236	76.92 249	46.09 82	86.43 205	57.98 178	56.31 269	81.39 243

TAMVS		69.51 155	68.16 149	73.56 178	76.30 238	48.71 218	82.57 193	77.17 258	62.10 109	61.32 170	84.23 164	41.90 140	83.46 255	54.80 202	73.09 143	88.50 122

tfpnnormal		61.47 250	59.09 253	68.62 262	76.29 239	41.69 302	81.14 228	85.16 107	54.48 245	51.32 277	73.63 281	32.32 243	86.89 192	21.78 342	55.71 279	77.29 290

cl_fuxian		67.97 180	66.66 178	71.91 215	76.20 240	49.31 204	82.13 204	78.00 244	61.99 111	57.64 224	76.94 248	49.41 53	84.93 240	60.62 153	57.01 264	81.49 237

FC-MVSNet-test		67.49 190	67.91 151	66.21 281	76.06 241	33.06 334	80.82 233	87.18 61	64.44 75	54.81 250	82.87 182	50.40 47	82.60 259	48.05 244	66.55 188	82.98 221

MVS-HIRNet		49.01 309	44.71 313	61.92 305	76.06 241	46.61 254	63.23 324	54.90 342	24.77 348	33.56 341	36.60 350	21.28 317	75.88 312	29.49 318	62.54 221	63.26 345

MVP-Stereo		70.97 126	70.44 117	72.59 193	76.03 243	51.36 160	85.02 133	86.99 65	60.31 141	56.53 241	78.92 226	40.11 159	90.00 102	60.00 163	90.01 4	76.41 300
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
nrg030		72.27 112	71.56 103	74.42 152	75.93 244	50.60 171	86.97 81	83.21 153	62.75 100	67.15 101	84.38 163	50.07 48	86.66 197	71.19 79	62.37 223	85.99 165

WR-MVS		67.58 187	66.76 175	70.04 245	75.92 245	45.06 276	86.23 97	85.28 102	64.31 76	58.50 209	81.00 208	44.80 104	82.00 264	49.21 237	55.57 281	83.06 219

MIMVSNet		63.12 236	60.29 245	71.61 217	75.92 245	46.65 253	65.15 318	81.94 169	59.14 168	54.65 253	69.47 311	25.74 289	80.63 273	41.03 275	69.56 172	87.55 137

UniMVSNet_NR-MVSNet		68.82 163	68.29 147	70.40 238	75.71 247	42.59 296	84.23 149	86.78 69	66.31 49	58.51 207	82.45 192	51.57 36	84.64 244	53.11 210	55.96 275	83.96 202

eth_miper_zixun_eth		66.98 205	65.28 208	72.06 205	75.61 248	50.40 177	81.00 230	76.97 264	62.00 110	56.99 235	76.97 247	44.84 101	85.58 224	58.75 168	54.42 287	80.21 261

OPM-MVS		70.75 131	69.58 130	74.26 157	75.55 249	51.34 161	86.05 101	83.29 152	61.94 113	62.95 156	85.77 151	34.15 225	88.44 145	65.44 120	71.07 159	82.99 220

Effi-MVS+-dtu		66.24 216	64.96 213	70.08 243	75.17 250	49.64 196	82.01 205	74.48 285	62.15 107	57.83 218	76.08 264	30.59 259	83.79 249	65.40 121	60.93 230	76.81 293

mvs-test1		69.04 158	67.57 161	73.44 180	75.17 250	51.68 152	86.57 92	74.48 285	62.15 107	62.07 165	85.79 150	30.59 259	87.48 176	65.40 121	65.94 194	81.18 249

GA-MVS		69.04 158	66.70 177	76.06 123	75.11 252	52.36 137	83.12 181	80.23 197	63.32 92	60.65 176	79.22 223	30.98 257	88.37 147	61.25 146	66.41 189	87.46 139

IterMVS-LS		66.63 209	65.36 207	70.42 237	75.10 253	48.90 213	81.45 224	76.69 268	61.05 129	55.71 247	77.10 246	45.86 88	83.65 252	57.44 186	57.88 258	78.70 271
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
miper_lstm_enhance		63.91 229	62.30 228	68.75 259	75.06 254	46.78 251	69.02 313	81.14 185	59.68 150	52.76 268	72.39 294	40.71 151	77.99 299	56.81 190	53.09 297	81.48 239

EI-MVSNet		69.70 150	68.70 141	72.68 191	75.00 255	48.90 213	79.54 249	87.16 62	61.05 129	63.88 146	83.74 171	45.87 87	90.44 90	57.42 187	64.68 201	78.70 271

CVMVSNet		60.85 253	60.44 244	62.07 302	75.00 255	32.73 336	79.54 249	73.49 296	36.98 333	56.28 244	83.74 171	29.28 267	69.53 338	46.48 253	63.23 213	83.94 203

ACMP	61.11 9	66.24 216	64.33 217	72.00 208	74.89 257	49.12 206	83.18 180	79.83 204	55.41 233	52.29 271	82.68 189	25.83 288	86.10 212	60.89 149	63.94 205	80.78 253
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MSDG		59.44 258	55.14 278	72.32 201	74.69 258	50.71 168	74.39 281	73.58 294	44.44 309	43.40 312	77.52 238	19.45 322	90.87 81	31.31 314	57.49 262	75.38 306

ACMH+	54.58 15	58.55 272	55.24 275	68.50 264	74.68 259	45.80 267	80.27 239	70.21 318	47.15 288	42.77 315	75.48 268	16.73 335	85.98 218	35.10 300	54.78 285	73.72 317

UniMVSNet (Re)		67.71 185	66.80 173	70.45 236	74.44 260	42.93 292	82.42 199	84.90 114	63.69 87	59.63 185	80.99 209	47.18 70	85.23 233	51.17 226	56.75 266	83.19 216

LPG-MVS_test		66.44 213	64.58 215	72.02 206	74.42 261	48.60 219	83.07 183	80.64 191	54.69 242	53.75 262	83.83 169	25.73 290	86.98 186	60.33 160	64.71 198	80.48 257

LGP-MVS_train				72.02 206	74.42 261	48.60 219		80.64 191	54.69 242	53.75 262	83.83 169	25.73 290	86.98 186	60.33 160	64.71 198	80.48 257

Baseline_NR-MVSNet		65.49 223	64.27 218	69.13 252	74.37 263	41.65 303	83.39 176	78.85 225	59.56 151	59.62 186	76.88 251	40.75 149	87.44 177	49.99 230	55.05 282	78.28 280

ACMM	58.35 12	64.35 227	62.01 231	71.38 222	74.21 264	48.51 223	82.25 202	79.66 208	47.61 284	54.54 254	80.11 215	25.26 293	86.00 217	51.26 224	63.16 215	79.64 267
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CHOSEN 280x420		57.53 277	56.38 270	60.97 312	74.01 265	48.10 236	46.30 345	54.31 343	48.18 283	50.88 282	77.43 241	38.37 173	59.16 346	54.83 200	63.14 216	75.66 304

TransMVSNet (Re)		62.82 239	60.76 241	69.02 253	73.98 266	41.61 304	86.36 94	79.30 221	56.90 210	52.53 269	76.44 256	41.85 141	87.60 174	38.83 279	40.61 332	77.86 284

CR-MVSNet		62.47 244	59.04 254	72.77 190	73.97 267	56.57 30	60.52 332	71.72 307	60.04 143	57.49 228	65.86 322	38.94 167	80.31 277	42.86 271	59.93 233	81.42 240

RPMNet		59.29 259	54.25 282	74.42 152	73.97 267	56.57 30	60.52 332	76.98 261	35.72 337	57.49 228	58.87 340	37.73 180	85.26 232	27.01 331	59.93 233	81.42 240

TranMVSNet+NR-MVSNet		66.94 206	65.61 200	70.93 231	73.45 269	43.38 289	83.02 185	84.25 130	65.31 67	58.33 214	81.90 201	39.92 162	85.52 225	49.43 235	54.89 284	83.89 204

Patchmatch-test		53.33 299	48.17 306	68.81 257	73.31 270	42.38 300	42.98 348	58.23 338	32.53 342	38.79 329	70.77 305	39.66 163	73.51 324	25.18 335	52.06 300	90.55 72

EG-PatchMatch MVS		62.40 246	59.59 248	70.81 232	73.29 271	49.05 208	85.81 104	84.78 118	51.85 264	44.19 307	73.48 283	15.52 339	89.85 105	40.16 276	67.24 184	73.54 319

DU-MVS		66.84 208	65.74 197	70.16 241	73.27 272	42.59 296	81.50 221	82.92 160	63.53 91	58.51 207	82.11 198	40.75 149	84.64 244	53.11 210	55.96 275	83.24 214

NR-MVSNet		67.25 197	65.99 191	71.04 229	73.27 272	43.91 284	85.32 120	84.75 119	66.05 57	53.65 264	82.11 198	45.05 96	85.97 220	47.55 246	56.18 272	83.24 214

PS-MVSNAJss		68.78 167	67.17 169	73.62 177	73.01 274	48.33 230	84.95 134	84.81 117	59.30 161	58.91 201	79.84 217	37.77 177	88.86 133	62.83 135	63.12 217	83.67 207

OMC-MVS		65.97 219	65.06 211	68.71 260	72.97 275	42.58 298	78.61 257	75.35 280	54.72 241	59.31 192	86.25 146	33.30 234	77.88 301	57.99 177	67.05 185	85.66 174

PatchT		56.60 281	52.97 288	67.48 269	72.94 276	46.16 263	57.30 337	73.78 292	38.77 327	54.37 256	57.26 343	37.52 185	78.06 296	32.02 310	52.79 298	78.23 282

v8		67.25 197	64.99 212	74.04 161	72.89 277	53.31 112	82.37 200	80.11 199	61.54 121	54.29 257	76.02 265	42.89 128	88.41 146	58.43 170	56.36 267	80.39 259

F-COLMAP		55.96 288	53.65 286	62.87 300	72.76 278	42.77 295	74.70 280	70.37 317	40.03 324	41.11 322	79.36 220	17.77 330	73.70 323	32.80 309	53.96 290	72.15 325

IterMVS		63.77 232	61.67 232	70.08 243	72.68 279	51.24 164	80.44 237	75.51 277	60.51 139	51.41 276	73.70 280	32.08 246	78.91 289	54.30 205	54.35 288	80.08 263
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v10		66.61 210	64.20 219	73.83 168	72.59 280	53.37 108	81.88 209	79.91 203	61.11 127	54.09 259	75.60 267	40.06 160	88.26 155	56.47 192	56.10 273	79.86 266

Patchmtry		56.56 282	52.95 289	67.42 270	72.53 281	50.59 172	59.05 334	71.72 307	37.86 331	46.92 299	65.86 322	38.94 167	80.06 281	36.94 288	46.72 319	71.60 329

Fast-Effi-MVS+-dtu		66.53 211	64.10 220	73.84 167	72.41 282	52.30 140	84.73 138	75.66 276	59.51 152	56.34 243	79.11 225	28.11 272	85.85 223	57.74 184	63.29 212	83.35 210

v1144		68.81 164	66.82 172	74.80 146	72.34 283	53.46 101	84.68 139	81.77 175	64.25 77	60.28 179	77.91 233	40.23 156	88.95 130	60.37 159	59.52 235	81.97 229

v2v482		69.55 154	67.64 158	75.26 139	72.32 284	53.83 90	84.93 135	81.94 169	65.37 65	60.80 174	79.25 222	41.62 143	88.98 129	63.03 134	59.51 236	82.98 221

test0.0.03 1		62.54 241	62.44 227	62.86 301	72.28 285	29.51 343	82.93 186	78.78 228	59.18 165	53.07 267	82.41 193	36.91 197	77.39 305	37.45 282	58.96 241	81.66 235

v1192		67.96 181	65.74 197	74.63 147	71.79 286	53.43 107	84.06 154	80.99 187	63.19 95	59.56 187	77.46 240	37.50 187	88.65 136	58.20 175	58.93 242	81.79 232

v148		68.24 177	66.35 181	73.88 165	71.76 287	51.47 157	84.23 149	81.90 173	63.69 87	58.94 198	76.44 256	43.72 116	87.78 169	60.63 152	55.86 277	82.39 226

v144192		67.86 182	65.76 196	74.16 158	71.68 288	53.09 118	84.14 151	80.83 189	62.85 99	59.21 194	77.28 243	39.30 165	88.00 162	58.67 169	57.88 258	81.40 242

pm-mvs1		64.12 228	62.56 226	68.78 258	71.68 288	38.87 316	82.89 187	81.57 176	55.54 232	53.89 261	77.82 235	37.73 180	86.74 194	48.46 242	53.49 294	80.72 254

MDA-MVSNet-bldmvs		51.56 305	47.75 310	63.00 299	71.60 290	47.32 246	69.70 312	72.12 305	43.81 314	27.65 350	63.38 328	21.97 314	75.96 311	27.30 330	32.19 345	65.70 341

v1921920		67.45 191	65.23 209	74.10 160	71.51 291	52.90 125	83.75 163	80.44 194	62.48 105	59.12 196	77.13 244	36.98 195	87.90 163	57.53 185	58.14 252	81.49 237

our_test_3		59.11 263	55.08 279	71.18 227	71.42 292	53.29 113	81.96 206	74.52 284	48.32 281	42.08 316	69.28 313	28.14 271	82.15 261	34.35 302	45.68 322	78.11 283

ppachtmachnet_test		58.56 271	54.34 280	71.24 224	71.42 292	54.74 72	81.84 211	72.27 304	49.02 279	45.86 306	68.99 314	26.27 285	83.30 256	30.12 317	43.23 327	75.69 303

v1240		66.99 204	64.68 214	73.93 163	71.38 294	52.66 129	83.39 176	79.98 200	61.97 112	58.44 213	77.11 245	35.25 217	87.81 165	56.46 193	58.15 250	81.33 245

JIA-IIPM		52.33 303	47.77 309	66.03 282	71.20 295	46.92 250	40.00 352	76.48 271	37.10 332	46.73 300	37.02 349	32.96 236	77.88 301	35.97 291	52.45 299	73.29 321

OpenMVS_ROB	53.19 17	59.20 261	56.00 272	68.83 256	71.13 296	44.30 281	83.64 164	75.02 282	46.42 295	46.48 303	73.03 286	18.69 326	88.14 156	27.74 328	61.80 225	74.05 315

MVS_0304		56.72 279	55.17 276	61.37 309	70.71 297	36.80 325	75.74 269	68.75 322	44.11 313	52.53 269	68.20 316	15.05 340	74.53 317	42.98 269	58.44 246	72.79 324

SixPastTwentyTwo		54.37 292	50.10 299	67.21 271	70.70 298	41.46 305	74.73 279	64.69 329	47.56 285	39.12 327	69.49 310	18.49 328	84.69 243	31.87 311	34.20 343	75.48 305

V42		67.66 186	65.60 201	73.86 166	70.69 299	53.63 96	81.50 221	78.61 233	63.85 83	59.49 189	77.49 239	37.98 174	87.65 172	62.33 137	58.43 247	80.29 260

IterMVS-SCA-FT		59.12 262	58.81 256	60.08 314	70.68 300	45.07 273	80.42 238	74.25 287	43.54 316	50.02 284	73.73 277	31.97 247	56.74 347	51.06 227	53.60 293	78.42 277

pmmvs4		63.34 234	61.07 239	70.16 241	70.14 301	50.53 173	79.97 245	71.41 313	55.08 236	54.12 258	78.58 229	32.79 239	82.09 263	50.33 229	57.22 263	77.86 284

MDA-MVSNet_test_wron		53.82 297	49.95 301	65.43 286	70.13 302	49.05 208	72.30 295	71.65 310	44.23 312	31.85 346	63.13 329	23.68 303	74.01 319	33.25 307	39.35 335	73.23 322

YYNet1		53.82 297	49.96 300	65.41 287	70.09 303	48.95 210	72.30 295	71.66 309	44.25 311	31.89 345	63.07 330	23.73 302	73.95 320	33.26 306	39.40 334	73.34 320

LTVRE_ROB	45.45 19	52.73 300	49.74 302	61.69 306	69.78 304	34.99 326	44.52 346	67.60 325	43.11 318	43.79 309	74.03 275	18.54 327	81.45 266	28.39 326	57.94 255	68.62 335
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
pmmvs5		62.80 240	61.18 237	67.66 268	69.53 305	42.37 301	82.65 191	75.19 281	54.30 247	52.03 274	78.51 230	31.64 252	80.67 272	48.60 240	58.15 250	79.95 264

D2MVS		63.49 233	61.39 236	69.77 247	69.29 306	48.93 212	78.89 256	77.71 249	60.64 138	49.70 285	72.10 299	27.08 281	83.48 254	54.48 204	62.65 220	76.90 292

WR-MVS_H		58.91 267	58.04 258	61.54 307	69.07 307	33.83 331	76.91 265	81.99 168	51.40 267	48.17 290	74.67 272	40.23 156	74.15 318	31.78 312	48.10 308	76.64 297

bset_n11_16_dypcd		65.51 222	63.21 224	72.41 198	68.84 308	50.15 187	81.25 225	72.40 303	59.17 167	59.20 195	78.66 228	25.69 292	85.27 231	66.80 103	56.88 265	81.80 231

test_djsdf		63.84 230	61.56 234	70.70 233	68.78 309	44.69 277	81.63 215	81.44 179	50.28 271	52.27 272	76.26 259	26.72 283	86.11 210	60.83 150	55.84 278	81.29 248

Anonymous20231206		59.08 264	57.59 260	63.55 296	68.77 310	32.14 339	80.26 240	79.78 205	50.00 274	49.39 286	72.39 294	26.64 284	78.36 292	33.12 308	57.94 255	80.14 262

K. test v3		54.04 295	49.42 303	67.92 267	68.55 311	42.57 299	75.51 274	63.07 333	52.07 260	39.21 326	64.59 326	19.34 323	82.21 260	37.11 285	25.31 350	78.97 268

CP-MVSNet		58.54 273	57.57 261	61.46 308	68.50 312	33.96 330	76.90 266	78.60 234	51.67 266	47.83 293	76.60 255	34.99 220	72.79 327	35.45 293	47.58 311	77.64 288

N_pmnet		41.25 316	39.77 319	45.66 332	68.50 312	0.82 367	72.51 293	0.38 367	35.61 338	35.26 337	61.51 332	20.07 321	67.74 339	23.51 339	40.63 331	68.42 336

jajsoiax		63.21 235	60.84 240	70.32 239	68.33 314	44.45 279	81.23 226	81.05 186	53.37 252	50.96 281	77.81 236	17.49 331	85.49 227	59.31 164	58.05 253	81.02 251

UniMVSNet_ETH3D		62.51 242	60.49 243	68.57 263	68.30 315	40.88 310	73.89 283	79.93 202	51.81 265	54.77 251	79.61 218	24.80 297	81.10 268	49.93 231	61.35 227	83.73 206

PS-CasMVS		58.12 275	57.03 265	61.37 309	68.24 316	33.80 332	76.73 267	78.01 243	51.20 268	47.54 297	76.20 263	32.85 237	72.76 328	35.17 298	47.37 313	77.55 289

mvs_tets		62.96 238	60.55 242	70.19 240	68.22 317	44.24 283	80.90 231	80.74 190	52.99 255	50.82 283	77.56 237	16.74 334	85.44 228	59.04 166	57.94 255	80.89 252

COLMAP_ROB	43.60 20	50.90 306	48.05 307	59.47 315	67.81 318	40.57 311	71.25 304	62.72 335	36.49 336	36.19 334	73.51 282	13.48 342	73.92 321	20.71 344	50.26 304	63.92 343
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		58.35 274	57.15 263	61.94 304	67.55 319	34.39 328	77.01 264	78.35 238	51.87 263	47.72 294	76.73 253	33.91 228	73.75 322	34.03 303	47.17 315	77.68 286

v7n		62.50 243	59.27 252	72.20 202	67.25 320	49.83 194	77.87 261	80.12 198	52.50 258	48.80 289	73.07 285	32.10 245	87.90 163	46.83 252	54.92 283	78.86 269

pmmvs6		59.64 257	57.15 263	67.09 272	66.01 321	36.86 324	80.50 236	78.64 231	45.05 305	49.05 288	73.94 276	27.28 279	86.10 212	43.96 265	49.94 305	78.31 279

DTE-MVSNet		57.03 278	55.73 274	60.95 313	65.94 322	32.57 337	75.71 270	77.09 260	51.16 269	46.65 302	76.34 258	32.84 238	73.22 326	30.94 316	44.87 323	77.06 291

CL-MVSNet_2432*1600		62.98 237	61.14 238	68.50 264	65.86 323	42.96 291	84.37 144	82.98 158	60.98 131	53.95 260	72.70 290	40.43 154	83.71 251	41.10 274	47.93 310	78.83 270

TinyColmap		48.15 311	44.49 315	59.13 317	65.73 324	38.04 319	63.34 323	62.86 334	38.78 326	29.48 348	67.23 320	6.46 355	73.30 325	24.59 336	41.90 330	66.04 339

XVG-OURS		61.88 248	59.34 251	69.49 249	65.37 325	46.27 260	64.80 320	73.49 296	47.04 289	57.41 232	82.85 183	25.15 295	78.18 293	53.00 213	64.98 196	84.01 197

XVG-OURS-SEG-HR		62.02 247	59.54 249	69.46 250	65.30 326	45.88 264	65.06 319	73.57 295	46.45 294	57.42 231	83.35 178	26.95 282	78.09 295	53.77 208	64.03 203	84.42 191

OurMVSNet-221017-0		52.39 302	48.73 304	63.35 298	65.21 327	38.42 318	68.54 315	64.95 328	38.19 328	39.57 325	71.43 301	13.23 343	79.92 282	37.16 283	40.32 333	71.72 328

AllTest		47.32 312	44.66 314	55.32 324	65.08 328	37.50 322	62.96 326	54.25 344	35.45 339	33.42 342	72.82 287	9.98 347	59.33 344	24.13 337	43.84 325	69.13 333

TestCases				55.32 324	65.08 328	37.50 322		54.25 344	35.45 339	33.42 342	72.82 287	9.98 347	59.33 344	24.13 337	43.84 325	69.13 333

lessismore_v0				67.98 266	64.76 330	41.25 306		45.75 350		36.03 335	65.63 324	19.29 324	84.11 246	35.67 292	21.24 352	78.59 274

UnsupCasMVSNet_eth		57.56 276	55.15 277	64.79 292	64.57 331	33.12 333	73.17 289	83.87 140	58.98 174	41.75 319	70.03 309	22.54 308	79.92 282	46.12 257	35.31 338	81.32 247

USDC		54.36 293	51.23 296	63.76 295	64.29 332	37.71 321	62.84 327	73.48 298	56.85 211	35.47 336	71.94 300	9.23 349	78.43 291	38.43 280	48.57 307	75.13 309

Patchmatch-RL test		58.72 269	54.32 281	71.92 214	63.91 333	44.25 282	61.73 328	55.19 341	57.38 205	49.31 287	54.24 344	37.60 183	80.89 270	62.19 140	47.28 314	90.63 71

anonymousdsp		60.46 255	57.65 259	68.88 254	63.63 334	45.09 272	72.93 290	78.63 232	46.52 293	51.12 278	72.80 289	21.46 316	83.07 258	57.79 182	53.97 289	78.47 275

UnsupCasMVSNet_bld		53.86 296	50.53 298	63.84 294	63.52 335	34.75 327	71.38 303	81.92 171	46.53 292	38.95 328	57.93 341	20.55 319	80.20 280	39.91 277	34.09 344	76.57 298

test20.03		55.22 290	54.07 283	58.68 318	63.14 336	25.00 350	77.69 262	74.78 283	52.64 256	43.43 311	72.39 294	26.21 286	74.76 316	29.31 319	47.05 317	76.28 301

testgi		54.25 294	52.57 293	59.29 316	62.76 337	21.65 355	72.21 297	70.47 316	53.25 253	41.94 317	77.33 242	14.28 341	77.95 300	29.18 320	51.72 301	78.28 280

EU-MVSNet		52.63 301	50.72 297	58.37 319	62.69 338	28.13 348	72.60 291	75.97 274	30.94 343	40.76 324	72.11 298	20.16 320	70.80 334	35.11 299	46.11 320	76.19 302

XVG-ACMP-BASELINE		56.03 286	52.85 290	65.58 284	61.91 339	40.95 309	63.36 322	72.43 302	45.20 304	46.02 304	74.09 274	9.20 350	78.12 294	45.13 259	58.27 248	77.66 287

MIMVSNet1		50.35 307	47.81 308	57.96 320	61.53 340	27.80 349	67.40 316	74.06 289	43.25 317	33.31 344	65.38 325	16.03 337	71.34 332	21.80 341	47.55 312	74.75 310

pmmvs-eth3d		55.97 287	52.78 291	65.54 285	61.02 341	46.44 256	75.36 276	67.72 324	49.61 276	43.65 310	67.58 318	21.63 315	77.04 306	44.11 264	44.33 324	73.15 323

CMPMVS	40.41 21	55.34 289	52.64 292	63.46 297	60.88 342	43.84 285	61.58 330	71.06 314	30.43 344	36.33 333	74.63 273	24.14 300	75.44 313	48.05 244	66.62 187	71.12 332
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Gipuma		27.47 324	24.26 327	37.12 337	60.55 343	29.17 345	11.68 358	60.00 337	14.18 355	10.52 358	15.12 359	2.20 363	63.01 342	8.39 355	35.65 337	19.18 354
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
ITE_SJBPF				51.84 327	58.03 344	31.94 340		53.57 346	36.67 334	41.32 321	75.23 270	11.17 345	51.57 351	25.81 334	48.04 309	72.02 327

new-patchmatchnet		48.21 310	46.55 312	53.18 326	57.73 345	18.19 359	70.24 307	71.02 315	45.70 299	33.70 340	60.23 335	18.00 329	69.86 337	27.97 327	34.35 341	71.49 331

RPSCF		45.77 314	44.13 316	50.68 328	57.67 346	29.66 342	54.92 341	45.25 351	26.69 347	45.92 305	75.92 266	17.43 332	45.70 355	27.44 329	45.95 321	76.67 294

Anonymous20240521		51.65 304	48.42 305	61.34 311	56.43 347	39.65 314	73.57 285	73.47 299	36.64 335	36.59 332	63.98 327	10.75 346	72.25 331	35.35 294	49.01 306	72.11 326

DIV-MVS_2432*1600		49.24 308	46.85 311	56.44 322	54.32 348	22.87 352	57.39 336	73.36 300	44.36 310	37.98 330	59.30 339	18.97 325	71.17 333	33.48 304	42.44 328	75.26 307

ambc				62.06 303	53.98 349	29.38 344	35.08 354	79.65 209		41.37 320	59.96 336	6.27 356	82.15 261	35.34 295	38.22 336	74.65 311

PM-MVS		46.92 313	43.76 317	56.41 323	52.18 350	32.26 338	63.21 325	38.18 354	37.99 330	40.78 323	66.20 321	5.09 358	65.42 341	48.19 243	41.99 329	71.54 330

TDRefinement		40.91 317	38.37 320	48.55 330	50.45 351	33.03 335	58.98 335	50.97 347	28.50 345	29.89 347	67.39 319	6.21 357	54.51 348	17.67 349	35.25 339	58.11 346

new_pmnet		33.56 321	31.89 324	38.59 335	49.01 352	20.42 356	51.01 342	37.92 355	20.58 349	23.45 351	46.79 346	6.66 354	49.28 353	20.00 347	31.57 347	46.09 351

pmmvs3		45.53 315	41.55 318	57.44 321	48.97 353	39.68 313	70.06 308	57.66 339	28.32 346	34.06 339	57.29 342	8.50 351	66.85 340	34.86 301	34.26 342	65.80 340

DSMNet-mixed		38.35 318	35.36 321	47.33 331	48.11 354	14.91 361	37.87 353	36.60 356	19.18 352	34.37 338	59.56 338	15.53 338	53.01 350	20.14 346	46.89 318	74.07 314

FPMVS		35.40 319	33.67 322	40.57 334	46.34 355	28.74 347	41.05 350	57.05 340	20.37 351	22.27 352	53.38 345	6.87 353	44.94 356	8.62 354	47.11 316	48.01 350

LF4IMVS		33.04 322	32.55 323	34.52 338	40.96 356	22.03 354	44.45 347	35.62 357	20.42 350	28.12 349	62.35 331	5.03 359	31.88 360	21.61 343	34.42 340	49.63 349

wuyk23d		9.11 332	8.77 336	10.15 344	40.18 357	16.76 360	20.28 356	1.01 366	2.58 361	2.66 363	0.98 363	0.23 368	12.49 362	4.08 361	6.90 359	1.19 359

PMMVS2		26.71 325	22.98 329	37.87 336	36.89 358	8.51 365	42.51 349	29.32 361	19.09 353	13.01 355	37.54 348	2.23 362	53.11 349	14.54 351	11.71 354	51.99 348

E-PMN		19.16 327	18.40 331	21.44 341	36.19 359	13.63 362	47.59 343	30.89 359	10.73 357	5.91 361	16.59 357	3.66 361	39.77 357	5.95 358	8.14 356	10.92 356

EMVS		18.42 328	17.66 332	20.71 342	34.13 360	12.64 363	46.94 344	29.94 360	10.46 359	5.58 362	14.93 360	4.23 360	38.83 358	5.24 360	7.51 358	10.67 357

ANet_high		34.39 320	29.59 325	48.78 329	30.34 361	22.28 353	55.53 338	63.79 332	38.11 329	15.47 354	36.56 351	6.94 352	59.98 343	13.93 352	5.64 361	64.08 342

MVE	16.60 23	17.34 330	13.39 333	29.16 340	28.43 362	19.72 357	13.73 357	23.63 362	7.23 360	7.96 359	21.41 355	0.80 367	36.08 359	6.97 356	10.39 355	31.69 352
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
LCM-MVSNet		28.07 323	23.85 328	40.71 333	27.46 363	18.93 358	30.82 355	46.19 348	12.76 356	16.40 353	34.70 353	1.90 364	48.69 354	20.25 345	24.22 351	54.51 347

PMVS	19.57 22	25.07 326	22.43 330	32.99 339	23.12 364	22.98 351	40.98 351	35.19 358	15.99 354	11.95 357	35.87 352	1.47 366	49.29 352	5.41 359	31.90 346	26.70 353
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
DeepMVS_CX				13.10 343	21.34 365	8.99 364		10.02 365	10.59 358	7.53 360	30.55 354	1.82 365	14.55 361	6.83 357	7.52 357	15.75 355

tmp_tt		9.44 331	10.68 334	5.73 345	2.49 366	4.21 366	10.48 359	18.04 363	0.34 362	12.59 356	20.49 356	11.39 344	7.03 363	13.84 353	6.46 360	5.95 358

testmvs		6.14 334	8.18 337	0.01 346	0.01 367	0.00 369	73.40 288	0.00 368	0.00 363	0.02 364	0.15 364	0.00 369	0.00 364	0.02 362	0.00 362	0.02 360

uanet_test		0.00 337	0.00 340	0.00 348	0.00 368	0.00 369	0.00 360	0.00 368	0.00 363	0.00 366	0.00 366	0.00 369	0.00 364	0.00 364	0.00 362	0.00 362

cdsmvs_eth3d_5k		18.33 329	24.44 326	0.00 348	0.00 368	0.00 369	0.00 360	89.40 17	0.00 363	0.00 366	92.02 36	38.55 171	0.00 364	0.00 364	0.00 362	0.00 362

pcd_1.5k_mvsjas		3.15 336	4.20 339	0.00 348	0.00 368	0.00 369	0.00 360	0.00 368	0.00 363	0.00 366	0.00 366	37.77 177	0.00 364	0.00 364	0.00 362	0.00 362

sosnet-low-res		0.00 337	0.00 340	0.00 348	0.00 368	0.00 369	0.00 360	0.00 368	0.00 363	0.00 366	0.00 366	0.00 369	0.00 364	0.00 364	0.00 362	0.00 362

sosnet		0.00 337	0.00 340	0.00 348	0.00 368	0.00 369	0.00 360	0.00 368	0.00 363	0.00 366	0.00 366	0.00 369	0.00 364	0.00 364	0.00 362	0.00 362

uncertanet		0.00 337	0.00 340	0.00 348	0.00 368	0.00 369	0.00 360	0.00 368	0.00 363	0.00 366	0.00 366	0.00 369	0.00 364	0.00 364	0.00 362	0.00 362

Regformer		0.00 337	0.00 340	0.00 348	0.00 368	0.00 369	0.00 360	0.00 368	0.00 363	0.00 366	0.00 366	0.00 369	0.00 364	0.00 364	0.00 362	0.00 362

test123		6.01 335	8.01 338	0.01 346	0.00 368	0.01 368	71.93 301	0.00 368	0.00 363	0.02 364	0.11 365	0.00 369	0.00 364	0.02 362	0.00 362	0.02 360

ab-mvs-re		7.68 333	10.24 335	0.00 348	0.00 368	0.00 369	0.00 360	0.00 368	0.00 363	0.00 366	92.12 33	0.00 369	0.00 364	0.00 364	0.00 362	0.00 362

uanet		0.00 337	0.00 340	0.00 348	0.00 368	0.00 369	0.00 360	0.00 368	0.00 363	0.00 366	0.00 366	0.00 369	0.00 364	0.00 364	0.00 362	0.00 362

test_241102_TWO								88.76 34	57.50 203	83.60 5	94.09 4	56.14 16	96.37 5	82.28 11	87.43 18	92.55 23

test_0728_THIRD									58.00 189	81.91 9	93.64 10	56.54 13	96.44 2	81.64 16	86.86 23	92.23 29

GSMVS																88.13 127

sam_mvs1												38.86 169				88.13 127

sam_mvs												35.99 213

MTGPA								81.31 181

test_post1							70.84 306				14.72 361	34.33 224	83.86 247	48.80 238

test_post											16.22 358	37.52 185	84.72 242

patchmatchnet-post											59.74 337	38.41 172	79.91 284

MTMP							87.27 75	15.34 364

test9_res														78.72 29	85.44 40	91.39 52

agg_prior2														75.65 49	85.11 44	91.01 63

test_prior4						56.39 35	87.15 78

test_prior2							89.04 43		61.88 114	73.55 47	91.46 50	48.01 62		74.73 57	85.46 38

旧先验2							81.73 213		45.53 301	74.66 35			70.48 336	58.31 174

新几何2							81.61 217

无先验							85.19 122	78.00 244	49.08 278				85.13 236	52.78 216		87.45 140

原ACMM2							83.77 162

testdata2													77.81 303	45.64 258

segment_acmp												44.97 99

testdata1							77.55 263		64.14 78

plane_prior5								82.59 162					88.30 152	65.46 117	72.34 150	84.49 189

plane_prior4											83.28 179

plane_prior3						48.95 210			64.01 79	62.15 163

plane_prior2							85.76 106		63.60 89

plane_prior						49.57 197	87.43 69		64.57 74						72.84 145

n2								0.00 368

nn								0.00 368

door-mid								41.31 353

test11								84.25 130

door								43.27 352

HQP5-MVS						51.56 154

BP-MVS														66.70 104

HQP4-MVS										64.47 137			88.61 138			84.91 186

HQP3-MVS								83.68 142							73.12 141

HQP2-MVS												37.35 188

MDTV_nov1_ep13_2view						43.62 287	71.13 305		54.95 239	59.29 193		36.76 199		46.33 255		87.32 142

ACMMP++_ref															63.20 214

ACMMP++															59.38 238

Test By Simon												39.38 164

High-resolution multi-view benchmark