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.

IU-MVS					89.48 17	57.49 15		91.38 5	66.22 60	88.26 1				82.83 21	87.60 17	92.44 29

PC_three_1452									66.58 52	87.27 2	93.70 9	66.82 4	94.95 17	89.74 3	91.98 4	93.98 5

MSP-MVS		82.30 5	83.47 1	78.80 50	82.99 111	52.71 126	85.04 125	88.63 36	66.08 64	86.77 3	92.75 32	72.05 1	91.46 63	83.35 19	93.53 1	92.23 34
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
SED-MVS		81.92 6	81.75 8	82.44 7	89.48 17	56.89 25	92.48 3	88.94 24	57.50 220	84.61 4	94.09 3	58.81 11	96.37 6	82.28 25	87.60 17	94.06 3

test_241102_ONE					89.48 17	56.89 25		88.94 24	57.53 218	84.61 4	93.29 22	58.81 11	96.45 1

DVP-MVS++		82.44 2	82.38 4	82.62 4	91.77 4	57.49 15	84.98 128	88.88 26	58.00 206	83.60 6	93.39 18	67.21 2	96.39 4	81.64 30	91.98 4	93.98 5

test_241102_TWO								88.76 32	57.50 220	83.60 6	94.09 3	56.14 18	96.37 6	82.28 25	87.43 19	92.55 27

DPM-MVS		82.39 3	82.36 5	82.49 5	80.12 181	59.50 5	92.24 8	90.72 9	69.37 26	83.22 8	94.47 2	63.81 5	93.18 31	74.02 81	93.25 2	94.80 1

test0726					89.40 20	57.45 17	92.32 7	88.63 36	57.71 214	83.14 9	93.96 6	55.17 20

MM				80.89 20		55.40 54	92.16 9	89.85 15	75.28 4	82.41 10	93.86 8	54.30 25	93.98 23	90.29 1	87.13 20	93.30 12

test_part2					89.33 23	55.48 50				82.27 11

test_one_0601					89.39 22	57.29 20		88.09 46	57.21 226	82.06 12	93.39 18	54.94 24

DVP-MVS		81.30 9	81.00 12	82.20 8	89.40 20	57.45 17	92.34 5	89.99 13	57.71 214	81.91 13	93.64 11	55.17 20	96.44 2	81.68 28	87.13 20	92.72 24
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									58.00 206	81.91 13	93.64 11	56.54 15	96.44 2	81.64 30	86.86 24	92.23 34

TSAR-MVS + MP.		78.31 26	78.26 22	78.48 61	81.33 156	56.31 37	81.59 227	86.41 74	69.61 24	81.72 15	88.16 126	55.09 22	88.04 170	74.12 80	86.31 32	91.09 67
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
fmvsm_l_conf0.5_n		75.95 56	76.16 47	75.31 141	76.01 251	48.44 225	84.98 128	71.08 322	63.50 104	81.70 16	93.52 15	50.00 54	87.18 198	87.80 5	76.87 112	90.32 83

fmvsm_l_conf0.5_n_a		75.88 58	76.07 48	75.31 141	76.08 247	48.34 228	85.24 116	70.62 326	63.13 112	81.45 17	93.62 14	49.98 56	87.40 194	87.76 6	76.77 113	90.20 88

DPE-MVS		79.82 17	79.66 15	80.29 25	89.27 24	55.08 66	88.70 46	87.92 49	55.55 250	81.21 18	93.69 10	56.51 16	94.27 22	78.36 50	85.70 38	91.51 56
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
xiu_mvs_v2_base		79.86 16	79.31 17	81.53 14	85.03 67	60.73 4	91.65 12	86.86 66	70.30 20	80.77 19	93.07 29	37.63 194	92.28 47	82.73 23	85.71 37	91.57 53

PS-MVSNAJ		80.06 15	79.52 16	81.68 13	85.58 55	60.97 3	91.69 11	87.02 63	70.62 16	80.75 20	93.22 24	37.77 189	92.50 42	82.75 22	86.25 33	91.57 53

test_fmvsm_n_1920		75.56 64	75.54 53	75.61 129	74.60 270	49.51 196	81.82 219	74.08 297	66.52 55	80.40 21	93.46 17	46.95 78	89.72 112	86.69 7	75.30 127	87.61 149

SMA-MVS		79.10 20	78.76 20	80.12 30	84.42 75	55.87 45	87.58 64	86.76 68	61.48 140	80.26 22	93.10 25	46.53 84	92.41 44	79.97 37	88.77 10	92.08 38
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
DeepPCF-MVS	69.37 1	80.65 12	81.56 10	77.94 75	85.46 58	49.56 193	90.99 21	86.66 71	70.58 17	80.07 23	95.30 1	56.18 17	90.97 78	82.57 24	86.22 34	93.28 13

MVS_0304		81.58 8	82.05 6	80.20 27	82.36 128	54.70 76	91.13 19	88.95 23	74.49 5	80.04 24	93.64 11	52.40 36	93.27 30	88.85 4	86.56 30	92.61 26

CANet		80.90 10	81.17 11	80.09 32	87.62 37	54.21 88	91.60 13	86.47 73	73.13 8	79.89 25	93.10 25	49.88 58	92.98 32	84.09 16	84.75 48	93.08 17

HPM-MVS++		80.50 13	80.71 13	79.88 34	87.34 39	55.20 61	89.93 29	87.55 58	66.04 67	79.46 26	93.00 30	53.10 32	91.76 57	80.40 36	89.56 8	92.68 25

patch_mono-2		80.84 11	81.59 9	78.62 57	90.34 9	53.77 95	88.08 52	88.36 43	76.17 2	79.40 27	91.09 62	55.43 19	90.09 103	85.01 12	80.40 80	91.99 43

fmvsm_s_conf0.5_n		74.48 74	74.12 71	75.56 131	76.96 236	47.85 245	85.32 114	69.80 333	64.16 88	78.74 28	93.48 16	45.51 98	89.29 121	86.48 8	66.62 198	89.55 104

fmvsm_s_conf0.5_n_a		73.68 90	73.15 79	75.29 144	75.45 258	48.05 238	83.88 163	68.84 338	63.43 106	78.60 29	93.37 20	45.32 99	88.92 138	85.39 11	64.04 218	88.89 120

APDe-MVS		78.44 22	78.20 23	79.19 40	88.56 26	54.55 82	89.76 33	87.77 53	55.91 245	78.56 30	92.49 37	48.20 65	92.65 40	79.49 38	83.04 57	90.39 80
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
fmvsm_s_conf0.1_n		73.80 85	73.26 78	75.43 136	73.28 285	47.80 246	84.57 145	69.43 335	63.34 107	78.40 31	93.29 22	44.73 113	89.22 123	85.99 9	66.28 205	89.26 109

fmvsm_s_conf0.1_n_a		72.82 102	72.05 101	75.12 149	70.95 312	47.97 241	82.72 196	68.43 340	62.52 123	78.17 32	93.08 28	44.21 116	88.86 139	84.82 13	63.54 224	88.54 131

DELS-MVS		82.32 4	82.50 3	81.79 11	86.80 42	56.89 25	92.77 2	86.30 77	77.83 1	77.88 33	92.13 41	60.24 6	94.78 19	78.97 43	89.61 7	93.69 8
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
canonicalmvs		78.17 27	77.86 28	79.12 44	84.30 77	54.22 87	87.71 59	84.57 124	67.70 42	77.70 34	92.11 44	50.90 47	89.95 106	78.18 53	77.54 107	93.20 15

CNVR-MVS		81.76 7	81.90 7	81.33 17	90.04 10	57.70 12	91.71 10	88.87 28	70.31 19	77.64 35	93.87 7	52.58 35	93.91 26	84.17 14	87.92 15	92.39 30

MCST-MVS		83.01 1	83.30 2	82.15 10	92.84 2	57.58 14	93.77 1	91.10 6	75.95 3	77.10 36	93.09 27	54.15 28	95.57 12	85.80 10	85.87 36	93.31 11

EPNet		78.36 25	78.49 21	77.97 73	85.49 57	52.04 138	89.36 38	84.07 135	73.22 7	77.03 37	91.72 52	49.32 62	90.17 102	73.46 85	82.77 58	91.69 48
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MVSFormer		73.53 92	72.19 97	77.57 80	83.02 109	55.24 58	81.63 224	81.44 183	50.28 292	76.67 38	90.91 68	44.82 110	86.11 228	60.83 160	80.09 84	91.36 61

lupinMVS		78.38 24	78.11 25	79.19 40	83.02 109	55.24 58	91.57 14	84.82 115	69.12 27	76.67 38	92.02 45	44.82 110	90.23 100	80.83 35	80.09 84	92.08 38

alignmvs		78.08 28	77.98 26	78.39 65	83.53 92	53.22 114	89.77 32	85.45 91	66.11 62	76.59 40	91.99 47	54.07 29	89.05 128	77.34 58	77.00 110	92.89 20

CANet_DTU		73.71 88	73.14 81	75.40 137	82.61 124	50.05 182	84.67 142	79.36 224	69.72 23	75.39 41	90.03 91	29.41 282	85.93 239	67.99 110	79.11 96	90.22 86

VNet		77.99 30	77.92 27	78.19 69	87.43 38	50.12 181	90.93 22	91.41 4	67.48 44	75.12 42	90.15 88	46.77 81	91.00 75	73.52 84	78.46 101	93.44 9

test_fmvsmconf_n		74.41 76	74.05 73	75.49 135	74.16 276	48.38 226	82.66 197	72.57 310	67.05 48	75.11 43	92.88 31	46.35 85	87.81 175	83.93 17	71.71 157	90.28 84

APD-MVS		76.15 53	75.68 50	77.54 81	88.52 27	53.44 105	87.26 73	85.03 110	53.79 267	74.91 44	91.68 54	43.80 120	90.31 96	74.36 77	81.82 67	88.87 121
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
旧先验2							81.73 222		45.53 323	74.66 45			70.48 356	58.31 185

SF-MVS		77.64 33	77.42 32	78.32 67	83.75 89	52.47 131	86.63 85	87.80 50	58.78 194	74.63 46	92.38 38	47.75 70	91.35 65	78.18 53	86.85 25	91.15 66

LFMVS		78.52 21	77.14 35	82.67 3	89.58 13	58.90 7	91.27 18	88.05 47	63.22 110	74.63 46	90.83 71	41.38 156	94.40 20	75.42 70	79.90 89	94.72 2

9.14			78.19 24		85.67 53		88.32 50	88.84 29	59.89 165	74.58 48	92.62 35	46.80 80	92.66 39	81.40 34	85.62 39

SD-MVS		76.18 52	74.85 64	80.18 28	85.39 59	56.90 24	85.75 102	82.45 166	56.79 234	74.48 49	91.81 50	43.72 124	90.75 84	74.61 75	78.65 99	92.91 19
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
test_fmvsmconf0.1_n		73.69 89	73.15 79	75.34 139	70.71 313	48.26 231	82.15 209	71.83 314	66.75 51	74.47 50	92.59 36	44.89 107	87.78 180	83.59 18	71.35 161	89.97 95

TSAR-MVS + GP.		77.82 31	77.59 30	78.49 60	85.25 63	50.27 180	90.02 26	90.57 10	56.58 239	74.26 51	91.60 57	54.26 26	92.16 49	75.87 64	79.91 88	93.05 18

jason		77.01 40	76.45 42	78.69 54	79.69 186	54.74 73	90.56 24	83.99 138	68.26 31	74.10 52	90.91 68	42.14 144	89.99 105	79.30 40	79.12 95	91.36 61
jason: jason.
ZD-MVS					89.55 14	53.46 102		84.38 126	57.02 228	73.97 53	91.03 63	44.57 114	91.17 70	75.41 71	81.78 69

NCCC		79.57 18	79.23 18	80.59 21	89.50 15	56.99 23	91.38 15	88.17 45	67.71 41	73.81 54	92.75 32	46.88 79	93.28 29	78.79 46	84.07 53	91.50 57

PHI-MVS		77.49 34	77.00 36	78.95 45	85.33 61	50.69 164	88.57 48	88.59 39	58.14 203	73.60 55	93.31 21	43.14 133	93.79 27	73.81 82	88.53 12	92.37 31

test_prior2							89.04 42		61.88 133	73.55 56	91.46 61	48.01 68		74.73 74	85.46 40

MG-MVS		78.42 23	76.99 37	82.73 2	93.17 1	64.46 1	89.93 29	88.51 41	64.83 81	73.52 57	88.09 127	48.07 66	92.19 48	62.24 149	84.53 50	91.53 55

FOURS1					83.24 101	49.90 186	84.98 128	78.76 236	47.71 307	73.42 58

MVS_Test		75.85 59	74.93 63	78.62 57	84.08 81	55.20 61	83.99 160	85.17 105	68.07 35	73.38 59	82.76 198	50.44 51	89.00 131	65.90 123	80.61 76	91.64 49

Effi-MVS+		75.24 67	73.61 76	80.16 29	81.92 133	57.42 19	85.21 117	76.71 274	60.68 156	73.32 60	89.34 103	47.30 74	91.63 59	68.28 108	79.72 91	91.42 58

test_vis1_n_1920		68.59 178	68.31 154	69.44 264	69.16 324	41.51 320	84.63 143	68.58 339	58.80 193	73.26 61	88.37 120	25.30 309	80.60 291	79.10 41	67.55 191	86.23 176

ETV-MVS		77.17 38	76.74 39	78.48 61	81.80 136	54.55 82	86.13 93	85.33 96	68.20 32	73.10 62	90.52 76	45.23 101	90.66 86	79.37 39	80.95 72	90.22 86

ACMMP_NAP		76.43 49	75.66 51	78.73 52	81.92 133	54.67 79	84.06 158	85.35 95	61.10 145	72.99 63	91.50 59	40.25 165	91.00 75	76.84 60	86.98 23	90.51 79

TEST9					85.68 51	55.42 51	87.59 62	84.00 136	57.72 213	72.99 63	90.98 65	44.87 108	88.58 147

train_agg		76.91 41	76.40 43	78.45 63	85.68 51	55.42 51	87.59 62	84.00 136	57.84 211	72.99 63	90.98 65	44.99 104	88.58 147	78.19 51	85.32 42	91.34 63

CS-MVS-test		77.20 37	77.25 34	77.05 93	84.60 72	49.04 205	89.42 36	85.83 85	65.90 68	72.85 66	91.98 49	45.10 102	91.27 66	75.02 73	84.56 49	90.84 72

test_8					85.72 50	55.31 56	87.60 61	83.88 139	57.84 211	72.84 67	90.99 64	44.99 104	88.34 158

test_fmvsmconf0.01_n		71.97 117	70.95 116	75.04 150	66.21 339	47.87 244	80.35 250	70.08 330	65.85 69	72.69 68	91.68 54	39.99 171	87.67 184	82.03 27	69.66 176	89.58 103

xiu_mvs_v1_base_debu		71.60 123	70.29 127	75.55 132	77.26 230	53.15 115	85.34 111	79.37 221	55.83 246	72.54 69	90.19 85	22.38 329	86.66 214	73.28 86	76.39 116	86.85 163

xiu_mvs_v1_base		71.60 123	70.29 127	75.55 132	77.26 230	53.15 115	85.34 111	79.37 221	55.83 246	72.54 69	90.19 85	22.38 329	86.66 214	73.28 86	76.39 116	86.85 163

xiu_mvs_v1_base_debi		71.60 123	70.29 127	75.55 132	77.26 230	53.15 115	85.34 111	79.37 221	55.83 246	72.54 69	90.19 85	22.38 329	86.66 214	73.28 86	76.39 116	86.85 163

agg_prior					85.64 54	54.92 70		83.61 146		72.53 72			88.10 168

CS-MVS		76.77 45	76.70 40	76.99 98	83.55 91	48.75 214	88.60 47	85.18 104	66.38 57	72.47 73	91.62 56	45.53 96	90.99 77	74.48 76	82.51 60	91.23 64

VDD-MVS		76.08 54	74.97 62	79.44 36	84.27 79	53.33 111	91.13 19	85.88 83	65.33 76	72.37 74	89.34 103	32.52 258	92.76 38	77.90 55	75.96 120	92.22 36

WTY-MVS		77.47 35	77.52 31	77.30 87	88.33 30	46.25 270	88.46 49	90.32 11	71.40 13	72.32 75	91.72 52	53.44 30	92.37 45	66.28 121	75.42 126	93.28 13

test12				79.24 39	86.89 41	56.08 40		85.16 106		72.27 76		47.15 76	91.10 73		85.93 35	90.54 78

h-mvs33		73.95 82	72.89 84	77.15 92	80.17 180	50.37 174	84.68 140	83.33 148	68.08 33	71.97 77	88.65 118	42.50 138	91.15 71	78.82 44	57.78 281	89.91 98

hse-mvs2		71.44 127	70.68 118	73.73 184	76.34 241	47.44 251	79.45 262	79.47 220	68.08 33	71.97 77	86.01 158	42.50 138	86.93 207	78.82 44	53.46 318	86.83 166

CSCG		80.41 14	79.72 14	82.49 5	89.12 25	57.67 13	89.29 40	91.54 3	59.19 182	71.82 79	90.05 90	59.72 9	96.04 10	78.37 49	88.40 13	93.75 7

SteuartSystems-ACMMP		77.08 39	76.33 44	79.34 38	80.98 160	55.31 56	89.76 33	86.91 65	62.94 115	71.65 80	91.56 58	42.33 140	92.56 41	77.14 59	83.69 55	90.15 90
Skip Steuart: Steuart Systems R&D Blog.
diffmvs		75.11 71	74.65 67	76.46 109	78.52 210	53.35 109	83.28 184	79.94 208	70.51 18	71.64 81	88.72 114	46.02 90	86.08 233	77.52 56	75.75 124	89.96 96
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
casdiffmvs		77.36 36	76.85 38	78.88 48	80.40 178	54.66 80	87.06 76	85.88 83	72.11 10	71.57 82	88.63 119	50.89 49	90.35 94	76.00 63	79.11 96	91.63 50
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline		76.86 44	76.24 46	78.71 53	80.47 176	54.20 90	83.90 162	84.88 114	71.38 14	71.51 83	89.15 108	50.51 50	90.55 90	75.71 65	78.65 99	91.39 59

testdata				67.08 289	77.59 224	45.46 280		69.20 337	44.47 329	71.50 84	88.34 122	31.21 271	70.76 355	52.20 238	75.88 121	85.03 198

casdiffmvs_mvg		77.75 32	77.28 33	79.16 42	80.42 177	54.44 84	87.76 58	85.46 90	71.67 11	71.38 85	88.35 121	51.58 40	91.22 68	79.02 42	79.89 90	91.83 47
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
DeepC-MVS_fast	67.50 3	78.00 29	77.63 29	79.13 43	88.52 27	55.12 63	89.95 28	85.98 82	68.31 30	71.33 86	92.75 32	45.52 97	90.37 93	71.15 94	85.14 44	91.91 44
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDDNet		74.37 77	72.13 98	81.09 19	79.58 187	56.52 32	90.02 26	86.70 70	52.61 277	71.23 87	87.20 142	31.75 268	93.96 25	74.30 79	75.77 123	92.79 23

test_yl		75.85 59	74.83 65	78.91 46	88.08 34	51.94 140	91.30 16	89.28 17	57.91 208	71.19 88	89.20 106	42.03 147	92.77 36	69.41 101	75.07 132	92.01 41

DCV-MVSNet		75.85 59	74.83 65	78.91 46	88.08 34	51.94 140	91.30 16	89.28 17	57.91 208	71.19 88	89.20 106	42.03 147	92.77 36	69.41 101	75.07 132	92.01 41

EC-MVSNet		75.30 66	75.20 57	75.62 128	80.98 160	49.00 206	87.43 65	84.68 121	63.49 105	70.97 90	90.15 88	42.86 137	91.14 72	74.33 78	81.90 66	86.71 168

CDPH-MVS		76.05 55	75.19 58	78.62 57	86.51 44	54.98 69	87.32 68	84.59 123	58.62 197	70.75 91	90.85 70	43.10 135	90.63 88	70.50 98	84.51 51	90.24 85

test_cas_vis1_n_1920		67.10 210	66.60 187	68.59 277	65.17 347	43.23 304	83.23 185	69.84 332	55.34 253	70.67 92	87.71 134	24.70 316	76.66 330	78.57 48	64.20 217	85.89 185

HY-MVS	67.03 5	73.90 83	73.14 81	76.18 117	84.70 71	47.36 252	75.56 284	86.36 76	66.27 59	70.66 93	83.91 180	51.05 45	89.31 120	67.10 115	72.61 150	91.88 45

MAR-MVS		76.76 46	75.60 52	80.21 26	90.87 7	54.68 78	89.14 41	89.11 20	62.95 114	70.54 94	92.33 39	41.05 157	94.95 17	57.90 193	86.55 31	91.00 69
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
CostFormer		73.89 84	72.30 93	78.66 56	82.36 128	56.58 28	75.56 284	85.30 98	66.06 65	70.50 95	76.88 273	57.02 14	89.06 127	68.27 109	68.74 182	90.33 82

PAPM		76.76 46	76.07 48	78.81 49	80.20 179	59.11 6	86.86 82	86.23 78	68.60 29	70.18 96	88.84 113	51.57 41	87.16 199	65.48 127	86.68 28	90.15 90

dcpmvs_2		79.33 19	78.94 19	80.49 22	89.75 12	56.54 31	84.83 135	83.68 142	67.85 38	69.36 97	90.24 82	60.20 7	92.10 52	84.14 15	80.40 80	92.82 21

MP-MVS-pluss		75.54 65	75.03 60	77.04 94	81.37 155	52.65 128	84.34 149	84.46 125	61.16 143	69.14 98	91.76 51	39.98 172	88.99 133	78.19 51	84.89 47	89.48 107
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PVSNet_BlendedMVS		73.42 94	73.30 77	73.76 182	85.91 48	51.83 144	86.18 92	84.24 132	65.40 73	69.09 99	80.86 229	46.70 82	88.13 166	75.43 68	65.92 207	81.33 264

PVSNet_Blended		76.53 48	76.54 41	76.50 108	85.91 48	51.83 144	88.89 44	84.24 132	67.82 39	69.09 99	89.33 105	46.70 82	88.13 166	75.43 68	81.48 71	89.55 104

GG-mvs-BLEND				77.77 76	86.68 43	50.61 165	68.67 330	88.45 42		68.73 101	87.45 138	59.15 10	90.67 85	54.83 216	87.67 16	92.03 40

EIA-MVS		75.92 57	75.18 59	78.13 70	85.14 64	51.60 149	87.17 74	85.32 97	64.69 82	68.56 102	90.53 75	45.79 93	91.58 60	67.21 114	82.18 64	91.20 65

MTAPA		72.73 103	71.22 112	77.27 89	81.54 150	53.57 99	67.06 336	81.31 185	59.41 175	68.39 103	90.96 67	36.07 223	89.01 130	73.80 83	82.45 62	89.23 111

PMMVS		72.98 98	72.05 101	75.78 126	83.57 90	48.60 217	84.08 156	82.85 161	61.62 136	68.24 104	90.33 81	28.35 286	87.78 180	72.71 89	76.69 114	90.95 70

tpm2		70.82 137	68.44 152	77.98 72	80.78 168	56.11 39	74.21 295	81.28 187	60.24 162	68.04 105	75.27 291	52.26 38	88.50 152	55.82 213	68.03 186	89.33 108

DeepC-MVS	67.15 4	76.90 43	76.27 45	78.80 50	80.70 170	55.02 67	86.39 87	86.71 69	66.96 49	67.91 106	89.97 92	48.03 67	91.41 64	75.60 67	84.14 52	89.96 96
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZNCC-MVS		75.82 62	75.02 61	78.23 68	83.88 87	53.80 94	86.91 81	86.05 81	59.71 168	67.85 107	90.55 74	42.23 142	91.02 74	72.66 90	85.29 43	89.87 99

PAPR		75.20 69	74.13 70	78.41 64	88.31 31	55.10 65	84.31 150	85.66 87	63.76 97	67.55 108	90.73 72	43.48 129	89.40 119	66.36 120	77.03 109	90.73 74

TESTMET0.1,1		72.86 101	72.33 91	74.46 159	81.98 132	50.77 162	85.13 120	85.47 89	66.09 63	67.30 109	83.69 185	37.27 204	83.57 268	65.06 135	78.97 98	89.05 117

MP-MVS		74.99 72	74.33 69	76.95 100	82.89 115	53.05 120	85.63 106	83.50 147	57.86 210	67.25 110	90.24 82	43.38 130	88.85 141	76.03 62	82.23 63	88.96 118
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
nrg030		72.27 114	71.56 106	74.42 161	75.93 252	50.60 166	86.97 78	83.21 153	62.75 117	67.15 111	84.38 172	50.07 53	86.66 214	71.19 93	62.37 241	85.99 181

test_fmvsmvis_n_1920		71.29 128	70.38 124	74.00 174	71.04 311	48.79 213	79.19 265	64.62 348	62.75 117	66.73 112	91.99 47	40.94 158	88.35 157	83.00 20	73.18 143	84.85 203

原ACMM1				76.13 118	84.89 69	54.59 81		85.26 101	51.98 281	66.70 113	87.07 145	40.15 168	89.70 113	51.23 243	85.06 46	84.10 211

ab-mvs		70.65 140	69.11 146	75.29 144	80.87 166	46.23 271	73.48 300	85.24 103	59.99 164	66.65 114	80.94 228	43.13 134	88.69 143	63.58 140	68.07 185	90.95 70

gg-mvs-nofinetune		67.43 201	64.53 227	76.13 118	85.95 47	47.79 247	64.38 342	88.28 44	39.34 345	66.62 115	41.27 379	58.69 13	89.00 131	49.64 252	86.62 29	91.59 51

UA-Net		67.32 205	66.23 194	70.59 247	78.85 201	41.23 324	73.60 298	75.45 287	61.54 138	66.61 116	84.53 171	38.73 182	86.57 219	42.48 295	74.24 136	83.98 217

sss		70.49 142	70.13 131	71.58 233	81.59 147	39.02 332	80.78 245	84.71 120	59.34 177	66.61 116	88.09 127	37.17 206	85.52 242	61.82 154	71.02 164	90.20 88

SR-MVS		70.92 136	69.73 136	74.50 158	83.38 98	50.48 170	84.27 151	79.35 225	48.96 302	66.57 118	90.45 77	33.65 249	87.11 200	66.42 118	74.56 135	85.91 184

GST-MVS		74.87 73	73.90 75	77.77 76	83.30 99	53.45 104	85.75 102	85.29 99	59.22 181	66.50 119	89.85 94	40.94 158	90.76 83	70.94 96	83.35 56	89.10 116

MSLP-MVS++		74.21 79	72.25 94	80.11 31	81.45 153	56.47 33	86.32 89	79.65 216	58.19 202	66.36 120	92.29 40	36.11 221	90.66 86	67.39 112	82.49 61	93.18 16

iter_conf_final		71.46 126	69.68 137	76.81 102	86.03 46	53.49 100	84.73 137	74.37 294	60.27 161	66.28 121	84.36 174	35.14 233	90.87 80	65.41 132	70.51 169	86.05 178

Anonymous202405211		70.11 146	67.88 161	76.79 106	87.20 40	47.24 256	89.49 35	77.38 262	54.88 259	66.14 122	86.84 147	20.93 339	91.54 61	56.45 209	71.62 158	91.59 51

新几何1				73.30 193	83.10 104	53.48 101		71.43 320	45.55 322	66.14 122	87.17 143	33.88 247	80.54 292	48.50 261	80.33 82	85.88 186

MVS_111021_HR		76.39 50	75.38 56	79.42 37	85.33 61	56.47 33	88.15 51	84.97 111	65.15 79	66.06 124	89.88 93	43.79 121	92.16 49	75.03 72	80.03 87	89.64 102

test2506		72.91 100	72.43 90	74.32 165	80.12 181	44.18 295	83.19 186	84.77 118	64.02 90	65.97 125	87.43 139	47.67 71	88.72 142	59.08 174	79.66 92	90.08 92

Fast-Effi-MVS+		72.73 103	71.15 114	77.48 82	82.75 119	54.76 72	86.77 83	80.64 196	63.05 113	65.93 126	84.01 178	44.42 115	89.03 129	56.45 209	76.36 119	88.64 127

EI-MVSNet-Vis-set		73.19 97	72.60 86	74.99 153	82.56 125	49.80 189	82.55 202	89.00 22	66.17 61	65.89 127	88.98 109	43.83 119	92.29 46	65.38 134	69.01 180	82.87 240

HFP-MVS		74.37 77	73.13 83	78.10 71	84.30 77	53.68 97	85.58 107	84.36 127	56.82 232	65.78 128	90.56 73	40.70 163	90.90 79	69.18 103	80.88 73	89.71 100

API-MVS		74.17 80	72.07 100	80.49 22	90.02 11	58.55 8	87.30 70	84.27 129	57.51 219	65.77 129	87.77 133	41.61 153	95.97 11	51.71 239	82.63 59	86.94 159

UGNet		68.71 175	67.11 178	73.50 190	80.55 175	47.61 248	84.08 156	78.51 243	59.45 173	65.68 130	82.73 201	23.78 320	85.08 253	52.80 232	76.40 115	87.80 144
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
Vis-MVSNet		70.61 141	69.34 142	74.42 161	80.95 165	48.49 222	86.03 96	77.51 259	58.74 195	65.55 131	87.78 132	34.37 241	85.95 238	52.53 237	80.61 76	88.80 123
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
MVS		76.91 41	75.48 54	81.23 18	84.56 73	55.21 60	80.23 253	91.64 2	58.65 196	65.37 132	91.48 60	45.72 94	95.05 16	72.11 92	89.52 9	93.44 9

ACMMPR		73.76 86	72.61 85	77.24 91	83.92 85	52.96 123	85.58 107	84.29 128	56.82 232	65.12 133	90.45 77	37.24 205	90.18 101	69.18 103	80.84 74	88.58 129

region2R		73.75 87	72.55 87	77.33 85	83.90 86	52.98 122	85.54 110	84.09 134	56.83 231	65.10 134	90.45 77	37.34 203	90.24 99	68.89 105	80.83 75	88.77 125

EI-MVSNet-UG-set		72.37 109	71.73 104	74.29 166	81.60 146	49.29 200	81.85 217	88.64 35	65.29 78	65.05 135	88.29 124	43.18 131	91.83 56	63.74 139	67.97 187	81.75 251

VPA-MVSNet		71.12 130	70.66 119	72.49 208	78.75 203	44.43 291	87.64 60	90.02 12	63.97 93	65.02 136	81.58 223	42.14 144	87.42 193	63.42 141	63.38 229	85.63 191

CLD-MVS		75.60 63	75.39 55	76.24 112	80.69 171	52.40 132	90.69 23	86.20 79	74.40 6	65.01 137	88.93 110	42.05 146	90.58 89	76.57 61	73.96 138	85.73 187
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
tpmrst		71.04 133	69.77 135	74.86 154	83.19 103	55.86 46	75.64 283	78.73 238	67.88 37	64.99 138	73.73 302	49.96 57	79.56 305	65.92 122	67.85 189	89.14 115

CHOSEN 1792x2688		76.24 51	74.03 74	82.88 1	83.09 106	62.84 2	85.73 104	85.39 93	69.79 22	64.87 139	83.49 188	41.52 155	93.69 28	70.55 97	81.82 67	92.12 37

VPNet		72.07 115	71.42 110	74.04 172	78.64 208	47.17 257	89.91 31	87.97 48	72.56 9	64.66 140	85.04 167	41.83 151	88.33 159	61.17 158	60.97 247	86.62 169

ECVR-MVS		71.81 120	71.00 115	74.26 167	80.12 181	43.49 300	84.69 139	82.16 167	64.02 90	64.64 141	87.43 139	35.04 235	89.21 124	61.24 157	79.66 92	90.08 92

baseline1		72.51 108	72.12 99	73.69 185	85.05 65	44.46 289	83.51 173	86.13 80	71.61 12	64.64 141	87.97 130	55.00 23	89.48 117	59.07 175	56.05 294	87.13 158

TR-MVS		69.71 157	67.85 164	75.27 146	82.94 113	48.48 223	87.40 67	80.86 193	57.15 227	64.61 143	87.08 144	32.67 257	89.64 115	46.38 274	71.55 160	87.68 148

PVSNet_Blended_VisFu		73.40 95	72.44 89	76.30 110	81.32 157	54.70 76	85.81 98	78.82 234	63.70 98	64.53 144	85.38 164	47.11 77	87.38 195	67.75 111	77.55 106	86.81 167

HQP-NCC					79.02 197		88.00 53		65.45 70	64.48 145

ACMP_Plane					79.02 197		88.00 53		65.45 70	64.48 145

HQP-MVS		72.34 110	71.44 109	75.03 151	79.02 197	51.56 150	88.00 53	83.68 142	65.45 70	64.48 145	85.13 165	37.35 201	88.62 145	66.70 116	73.12 144	84.91 201

EPNet_dtu		66.25 226	66.71 183	64.87 307	78.66 207	34.12 349	82.80 195	75.51 285	61.75 134	64.47 148	86.90 146	37.06 207	72.46 349	43.65 288	69.63 178	88.02 141
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
HQP4-MVS										64.47 148			88.61 146			84.91 201

APD-MVS_3200maxsize		69.62 161	68.23 156	73.80 181	81.58 148	48.22 232	81.91 215	79.50 219	48.21 305	64.24 150	89.75 96	31.91 267	87.55 191	63.08 143	73.85 140	85.64 190

thisisatest0515		73.64 91	72.20 96	77.97 73	81.63 144	53.01 121	86.69 84	88.81 30	62.53 122	64.06 151	85.65 160	52.15 39	92.50 42	58.43 181	69.84 174	88.39 134

iter_conf05		73.51 93	72.24 95	77.33 85	87.93 36	55.97 43	87.90 57	70.81 325	68.72 28	64.04 152	84.36 174	47.54 72	90.87 80	71.11 95	67.75 190	85.13 197

Anonymous20240529		69.71 157	67.28 175	77.00 97	83.78 88	50.36 175	88.87 45	85.10 109	47.22 310	64.03 153	83.37 190	27.93 290	92.10 52	57.78 196	67.44 192	88.53 132

HPM-MVS		72.60 105	71.50 107	75.89 124	82.02 131	51.42 154	80.70 246	83.05 156	56.12 244	64.03 153	89.53 99	37.55 197	88.37 155	70.48 99	80.04 86	87.88 142
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
XVS		72.92 99	71.62 105	76.81 102	83.41 94	52.48 129	84.88 133	83.20 154	58.03 204	63.91 155	89.63 98	35.50 228	89.78 109	65.50 125	80.50 78	88.16 135

X-MVStestdata		65.85 231	62.20 239	76.81 102	83.41 94	52.48 129	84.88 133	83.20 154	58.03 204	63.91 155	4.82 398	35.50 228	89.78 109	65.50 125	80.50 78	88.16 135

EI-MVSNet		69.70 159	68.70 149	72.68 203	75.00 264	48.90 210	79.54 259	87.16 61	61.05 146	63.88 157	83.74 183	45.87 91	90.44 91	57.42 200	64.68 215	78.70 292

MVSTER		73.25 96	72.33 91	76.01 122	85.54 56	53.76 96	83.52 169	87.16 61	67.06 47	63.88 157	81.66 221	52.77 33	90.44 91	64.66 136	64.69 214	83.84 222

CP-MVS		72.59 107	71.46 108	76.00 123	82.93 114	52.32 135	86.93 80	82.48 165	55.15 254	63.65 159	90.44 80	35.03 236	88.53 151	68.69 106	77.83 105	87.15 157

BH-RMVSNet		70.08 148	68.01 158	76.27 111	84.21 80	51.22 160	87.29 71	79.33 227	58.96 191	63.63 160	86.77 148	33.29 252	90.30 98	44.63 283	73.96 138	87.30 156

test_fmvs1		53.60 315	52.54 310	56.78 340	58.07 365	30.26 362	68.95 329	42.19 376	32.46 364	63.59 161	82.56 207	11.55 366	60.81 365	58.25 186	55.27 302	79.28 286

DP-MVS Recon		71.99 116	70.31 126	77.01 96	90.65 8	53.44 105	89.37 37	82.97 159	56.33 242	63.56 162	89.47 100	34.02 244	92.15 51	54.05 222	72.41 151	85.43 194

tpm		68.36 180	67.48 172	70.97 243	79.93 184	51.34 156	76.58 281	78.75 237	67.73 40	63.54 163	74.86 293	48.33 64	72.36 350	53.93 223	63.71 222	89.21 112

PGM-MVS		72.60 105	71.20 113	76.80 105	82.95 112	52.82 125	83.07 190	82.14 168	56.51 240	63.18 164	89.81 95	35.68 227	89.76 111	67.30 113	80.19 83	87.83 143

test-LLR		69.65 160	69.01 147	71.60 231	78.67 205	48.17 233	85.13 120	79.72 213	59.18 184	63.13 165	82.58 205	36.91 210	80.24 296	60.56 164	75.17 129	86.39 174

test-mter		68.36 180	67.29 174	71.60 231	78.67 205	48.17 233	85.13 120	79.72 213	53.38 271	63.13 165	82.58 205	27.23 296	80.24 296	60.56 164	75.17 129	86.39 174

test1111		71.06 132	70.42 123	72.97 198	79.48 188	41.49 321	84.82 136	82.74 162	64.20 87	62.98 167	87.43 139	35.20 231	87.92 172	58.54 180	78.42 102	89.49 106

OPM-MVS		70.75 139	69.58 138	74.26 167	75.55 257	51.34 156	86.05 95	83.29 152	61.94 132	62.95 168	85.77 159	34.15 243	88.44 153	65.44 131	71.07 163	82.99 237
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
test222					79.36 189	50.97 161	77.99 272	67.84 341	42.54 340	62.84 169	86.53 152	30.26 277			76.91 111	85.23 195

SR-MVS-dyc-post		68.27 184	66.87 179	72.48 209	80.96 162	48.14 235	81.54 228	76.98 268	46.42 317	62.75 170	89.42 101	31.17 272	86.09 232	60.52 166	72.06 155	83.19 233

RE-MVS-def			66.66 185		80.96 162	48.14 235	81.54 228	76.98 268	46.42 317	62.75 170	89.42 101	29.28 284		60.52 166	72.06 155	83.19 233

XXY-MVS		70.18 145	69.28 145	72.89 201	77.64 222	42.88 308	85.06 124	87.50 59	62.58 121	62.66 172	82.34 213	43.64 126	89.83 108	58.42 183	63.70 223	85.96 183

AUN-MVS		68.20 186	66.35 190	73.76 182	76.37 240	47.45 250	79.52 261	79.52 218	60.98 148	62.34 173	86.02 156	36.59 218	86.94 206	62.32 148	53.47 317	86.89 160

CDS-MVSNet		70.48 143	69.43 139	73.64 186	77.56 225	48.83 212	83.51 173	77.45 260	63.27 109	62.33 174	85.54 163	43.85 118	83.29 272	57.38 201	74.00 137	88.79 124
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
test_fmvs1_n		52.55 319	51.19 314	56.65 341	51.90 375	30.14 363	67.66 333	42.84 375	32.27 365	62.30 175	82.02 218	9.12 374	60.84 364	57.82 194	54.75 308	78.99 288

FA-MVS(test-final)		69.00 168	66.60 187	76.19 116	83.48 93	47.96 243	74.73 291	82.07 170	57.27 224	62.18 176	78.47 251	36.09 222	92.89 33	53.76 225	71.32 162	87.73 146

HQP_MVS		70.96 135	69.91 134	74.12 170	77.95 218	49.57 191	85.76 100	82.59 163	63.60 101	62.15 177	83.28 192	36.04 224	88.30 161	65.46 128	72.34 152	84.49 205

plane_prior3						48.95 207			64.01 92	62.15 177

mPP-MVS		71.79 122	70.38 124	76.04 121	82.65 123	52.06 137	84.45 146	81.78 178	55.59 249	62.05 179	89.68 97	33.48 250	88.28 163	65.45 130	78.24 104	87.77 145

PCF-MVS	61.03 10	70.10 147	68.40 153	75.22 148	77.15 234	51.99 139	79.30 264	82.12 169	56.47 241	61.88 180	86.48 154	43.98 117	87.24 197	55.37 214	72.79 149	86.43 173
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs_anonymous		72.29 112	70.74 117	76.94 101	82.85 116	54.72 75	78.43 270	81.54 181	63.77 96	61.69 181	79.32 241	51.11 44	85.31 246	62.15 151	75.79 122	90.79 73

SDMVSNet		71.89 118	70.62 120	75.70 127	81.70 140	51.61 148	73.89 296	88.72 33	66.58 52	61.64 182	82.38 211	37.63 194	89.48 117	77.44 57	65.60 208	86.01 179

sd_testset		67.79 192	65.95 201	73.32 191	81.70 140	46.33 268	68.99 328	80.30 202	66.58 52	61.64 182	82.38 211	30.45 276	87.63 187	55.86 211	65.60 208	86.01 179

IB-MVS	68.87 2	74.01 81	72.03 103	79.94 33	83.04 108	55.50 49	90.24 25	88.65 34	67.14 46	61.38 184	81.74 220	53.21 31	94.28 21	60.45 168	62.41 240	90.03 94
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
TAMVS		69.51 163	68.16 157	73.56 189	76.30 244	48.71 216	82.57 200	77.17 265	62.10 128	61.32 185	84.23 176	41.90 149	83.46 270	54.80 218	73.09 146	88.50 133

1112_ss		70.05 149	69.37 141	72.10 215	80.77 169	42.78 309	85.12 123	76.75 272	59.69 169	61.19 186	92.12 42	47.48 73	83.84 263	53.04 229	68.21 184	89.66 101

GeoE		69.96 153	67.88 161	76.22 113	81.11 159	51.71 147	84.15 154	76.74 273	59.83 166	60.91 187	84.38 172	41.56 154	88.10 168	51.67 240	70.57 168	88.84 122

EPMVS		68.45 179	65.44 215	77.47 83	84.91 68	56.17 38	71.89 316	81.91 175	61.72 135	60.85 188	72.49 316	36.21 220	87.06 202	47.32 268	71.62 158	89.17 114

v2v482		69.55 162	67.64 167	75.26 147	72.32 298	53.83 93	84.93 132	81.94 172	65.37 75	60.80 189	79.25 243	41.62 152	88.98 134	63.03 144	59.51 256	82.98 238

MVS_111021_LR		69.07 165	67.91 159	72.54 206	77.27 229	49.56 193	79.77 257	73.96 300	59.33 179	60.73 190	87.82 131	30.19 278	81.53 280	69.94 100	72.19 154	86.53 170

GA-MVS		69.04 166	66.70 184	76.06 120	75.11 260	52.36 133	83.12 188	80.23 203	63.32 108	60.65 191	79.22 244	30.98 273	88.37 155	61.25 156	66.41 201	87.46 152

PAPM_NR		71.80 121	69.98 133	77.26 90	81.54 150	53.34 110	78.60 269	85.25 102	53.46 270	60.53 192	88.66 115	45.69 95	89.24 122	56.49 206	79.62 94	89.19 113

v1144		68.81 172	66.82 180	74.80 155	72.34 297	53.46 102	84.68 140	81.77 179	64.25 86	60.28 193	77.91 254	40.23 166	88.95 135	60.37 169	59.52 255	81.97 247

dmvs_re		67.61 195	66.00 199	72.42 210	81.86 135	43.45 301	64.67 341	80.00 206	69.56 25	60.07 194	85.00 168	34.71 238	87.63 187	51.48 241	66.68 196	86.17 177

thres200		68.71 175	67.27 176	73.02 196	84.73 70	46.76 260	85.03 126	87.73 54	62.34 126	59.87 195	83.45 189	43.15 132	88.32 160	31.25 338	67.91 188	83.98 217

3Dnovator	64.70 6	74.46 75	72.48 88	80.41 24	82.84 117	55.40 54	83.08 189	88.61 38	67.61 43	59.85 196	88.66 115	34.57 240	93.97 24	58.42 183	88.70 11	91.85 46

PVSNet	62.49 8	69.27 164	67.81 165	73.64 186	84.41 76	51.85 143	84.63 143	77.80 253	66.42 56	59.80 197	84.95 169	22.14 334	80.44 294	55.03 215	75.11 131	88.62 128

QAPM		71.88 119	69.33 143	79.52 35	82.20 130	54.30 86	86.30 90	88.77 31	56.61 238	59.72 198	87.48 137	33.90 246	95.36 13	47.48 267	81.49 70	88.90 119

BH-w/o		70.02 150	68.51 151	74.56 157	82.77 118	50.39 173	86.60 86	78.14 249	59.77 167	59.65 199	85.57 162	39.27 177	87.30 196	49.86 250	74.94 134	85.99 181

UniMVSNet (Re)		67.71 193	66.80 181	70.45 249	74.44 271	42.93 307	82.42 206	84.90 113	63.69 99	59.63 200	80.99 227	47.18 75	85.23 249	51.17 244	56.75 286	83.19 233

Baseline_NR-MVSNet		65.49 233	64.27 229	69.13 266	74.37 274	41.65 318	83.39 180	78.85 232	59.56 171	59.62 201	76.88 273	40.75 160	87.44 192	49.99 248	55.05 303	78.28 301

v1192		67.96 188	65.74 207	74.63 156	71.79 300	53.43 107	84.06 158	80.99 192	63.19 111	59.56 202	77.46 261	37.50 200	88.65 144	58.20 187	58.93 262	81.79 250

HPM-MVS_fast		67.86 189	66.28 193	72.61 204	80.67 172	48.34 228	81.18 236	75.95 283	50.81 290	59.55 203	88.05 129	27.86 291	85.98 235	58.83 177	73.58 141	83.51 226

test_vis1_n		51.19 324	49.66 321	55.76 345	51.26 376	29.85 367	67.20 335	38.86 380	32.12 366	59.50 204	79.86 236	8.78 375	58.23 372	56.95 203	52.46 321	79.19 287

V42		67.66 194	65.60 211	73.86 178	70.69 315	53.63 98	81.50 230	78.61 241	63.85 95	59.49 205	77.49 260	37.98 186	87.65 185	62.33 147	58.43 266	80.29 278

miper_enhance_ethall		69.77 156	68.90 148	72.38 211	78.93 200	49.91 185	83.29 183	78.85 232	64.90 80	59.37 206	79.46 239	52.77 33	85.16 251	63.78 138	58.72 263	82.08 246

PatchmatchNet		67.07 213	63.63 233	77.40 84	83.10 104	58.03 9	72.11 314	77.77 254	58.85 192	59.37 206	70.83 329	37.84 188	84.93 255	42.96 291	69.83 175	89.26 109
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
FE-MVS		64.15 237	60.43 258	75.30 143	80.85 167	49.86 187	68.28 332	78.37 246	50.26 295	59.31 208	73.79 301	26.19 303	91.92 55	40.19 298	66.67 197	84.12 210

OMC-MVS		65.97 230	65.06 221	68.71 274	72.97 289	42.58 313	78.61 268	75.35 288	54.72 260	59.31 208	86.25 155	33.30 251	77.88 319	57.99 189	67.05 194	85.66 189

MDTV_nov1_ep13_2view						43.62 299	71.13 319		54.95 258	59.29 210		36.76 212		46.33 275		87.32 155

v144192		67.86 189	65.76 206	74.16 169	71.68 302	53.09 118	84.14 155	80.83 194	62.85 116	59.21 211	77.28 264	39.30 176	88.00 171	58.67 179	57.88 279	81.40 261

v1921920		67.45 200	65.23 219	74.10 171	71.51 305	52.90 124	83.75 167	80.44 199	62.48 125	59.12 212	77.13 265	36.98 208	87.90 173	57.53 198	58.14 273	81.49 255

baseline2		75.15 70	74.54 68	76.98 99	81.67 143	51.74 146	83.84 164	91.94 1	69.97 21	58.98 213	86.02 156	59.73 8	91.73 58	68.37 107	70.40 171	87.48 151

v148		68.24 185	66.35 190	73.88 177	71.76 301	51.47 153	84.23 152	81.90 176	63.69 99	58.94 214	76.44 278	43.72 124	87.78 180	60.63 162	55.86 297	82.39 244

1314		71.11 131	69.41 140	76.22 113	79.32 191	50.49 169	80.23 253	85.14 108	59.44 174	58.93 215	88.89 112	33.83 248	89.60 116	61.49 155	77.42 108	88.57 130

cascas		69.01 167	66.13 196	77.66 78	79.36 189	55.41 53	86.99 77	83.75 141	56.69 236	58.92 216	81.35 225	24.31 318	92.10 52	53.23 226	70.61 167	85.46 193

PS-MVSNAJss		68.78 174	67.17 177	73.62 188	73.01 288	48.33 230	84.95 131	84.81 116	59.30 180	58.91 217	79.84 237	37.77 189	88.86 139	62.83 145	63.12 235	83.67 225

HyFIR lowres test		69.94 154	67.58 168	77.04 94	77.11 235	57.29 20	81.49 232	79.11 230	58.27 201	58.86 218	80.41 232	42.33 140	86.96 205	61.91 152	68.68 183	86.87 161

MDTV_nov1_ep13			61.56 244		81.68 142	55.12 63	72.41 308	78.18 248	59.19 182	58.85 219	69.29 337	34.69 239	86.16 227	36.76 313	62.96 236

ACMMP		70.81 138	69.29 144	75.39 138	81.52 152	51.92 142	83.43 176	83.03 157	56.67 237	58.80 220	88.91 111	31.92 266	88.58 147	65.89 124	73.39 142	85.67 188
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
tpm cat1		66.28 225	62.78 235	76.77 107	81.40 154	57.14 22	70.03 323	77.19 264	53.00 274	58.76 221	70.73 332	46.17 86	86.73 212	43.27 289	64.46 216	86.44 172

thisisatest0530		70.47 144	68.56 150	76.20 115	79.78 185	51.52 152	83.49 175	88.58 40	57.62 217	58.60 222	82.79 197	51.03 46	91.48 62	52.84 231	62.36 242	85.59 192

UniMVSNet_NR-MVSNet		68.82 171	68.29 155	70.40 251	75.71 255	42.59 311	84.23 152	86.78 67	66.31 58	58.51 223	82.45 208	51.57 41	84.64 259	53.11 227	55.96 295	83.96 219

DU-MVS		66.84 219	65.74 207	70.16 254	73.27 286	42.59 311	81.50 230	82.92 160	63.53 103	58.51 223	82.11 216	40.75 160	84.64 259	53.11 227	55.96 295	83.24 231

WR-MVS		67.58 196	66.76 182	70.04 258	75.92 253	45.06 287	86.23 91	85.28 100	64.31 85	58.50 225	81.00 226	44.80 112	82.00 279	49.21 256	55.57 300	83.06 236

3Dnovator+	62.71 7	72.29 112	70.50 121	77.65 79	83.40 97	51.29 158	87.32 68	86.40 75	59.01 189	58.49 226	88.32 123	32.40 259	91.27 66	57.04 202	82.15 65	90.38 81

cl22		68.85 169	67.69 166	72.35 212	78.07 217	49.98 184	82.45 205	78.48 244	62.50 124	58.46 227	77.95 253	49.99 55	85.17 250	62.55 146	58.72 263	81.90 249

Test_1112_low_res		67.18 208	66.23 194	70.02 259	78.75 203	41.02 325	83.43 176	73.69 302	57.29 223	58.45 228	82.39 210	45.30 100	80.88 286	50.50 246	66.26 206	88.16 135

v1240		66.99 214	64.68 225	73.93 175	71.38 308	52.66 127	83.39 180	79.98 207	61.97 131	58.44 229	77.11 266	35.25 230	87.81 175	56.46 208	58.15 271	81.33 264

TranMVSNet+NR-MVSNet		66.94 216	65.61 210	70.93 244	73.45 282	43.38 303	83.02 192	84.25 130	65.31 77	58.33 230	81.90 219	39.92 173	85.52 242	49.43 253	54.89 305	83.89 221

miper_ehance_all_eth		68.70 177	67.58 168	72.08 216	76.91 237	49.48 197	82.47 204	78.45 245	62.68 119	58.28 231	77.88 255	50.90 47	85.01 254	61.91 152	58.72 263	81.75 251

EPP-MVSNet		71.14 129	70.07 132	74.33 164	79.18 194	46.52 263	83.81 165	86.49 72	56.32 243	57.95 232	84.90 170	54.23 27	89.14 126	58.14 188	69.65 177	87.33 154

CPTT-MVS		67.15 209	65.84 204	71.07 241	80.96 162	50.32 177	81.94 214	74.10 296	46.18 320	57.91 233	87.64 136	29.57 281	81.31 282	64.10 137	70.18 173	81.56 254

Effi-MVS+-dtu		66.24 227	64.96 223	70.08 256	75.17 259	49.64 190	82.01 212	74.48 293	62.15 127	57.83 234	76.08 286	30.59 275	83.79 264	65.40 133	60.93 248	76.81 314

AdaColmap		67.86 189	65.48 212	75.00 152	88.15 33	54.99 68	86.10 94	76.63 276	49.30 299	57.80 235	86.65 151	29.39 283	88.94 137	45.10 280	70.21 172	81.06 268

tfpn200view9		67.57 197	66.13 196	71.89 228	84.05 82	45.07 284	83.40 178	87.71 56	60.79 153	57.79 236	82.76 198	43.53 127	87.80 177	28.80 345	66.36 202	82.78 242

thres400		67.40 204	66.13 196	71.19 239	84.05 82	45.07 284	83.40 178	87.71 56	60.79 153	57.79 236	82.76 198	43.53 127	87.80 177	28.80 345	66.36 202	80.71 273

SCA		63.84 240	60.01 262	75.32 140	78.58 209	57.92 10	61.61 352	77.53 258	56.71 235	57.75 238	70.77 330	31.97 264	79.91 302	48.80 258	56.36 287	88.13 138

FIs		70.00 151	70.24 130	69.30 265	77.93 220	38.55 335	83.99 160	87.72 55	66.86 50	57.66 239	84.17 177	52.28 37	85.31 246	52.72 236	68.80 181	84.02 213

c3_l		67.97 187	66.66 185	71.91 227	76.20 246	49.31 199	82.13 211	78.00 251	61.99 130	57.64 240	76.94 270	49.41 60	84.93 255	60.62 163	57.01 285	81.49 255

GBi-Net		67.09 211	65.47 213	71.96 221	82.71 120	46.36 265	83.52 169	83.31 149	58.55 198	57.58 241	76.23 282	36.72 215	86.20 224	47.25 269	63.40 226	83.32 228

test1		67.09 211	65.47 213	71.96 221	82.71 120	46.36 265	83.52 169	83.31 149	58.55 198	57.58 241	76.23 282	36.72 215	86.20 224	47.25 269	63.40 226	83.32 228

FMVSNet3		68.84 170	67.40 173	73.19 194	85.05 65	48.53 220	85.71 105	85.36 94	60.90 152	57.58 241	79.15 245	42.16 143	86.77 210	47.25 269	63.40 226	84.27 209

CR-MVSNet		62.47 257	59.04 269	72.77 202	73.97 279	56.57 29	60.52 355	71.72 316	60.04 163	57.49 244	65.86 346	38.94 179	80.31 295	42.86 292	59.93 251	81.42 259

RPMNet		59.29 275	54.25 298	74.42 161	73.97 279	56.57 29	60.52 355	76.98 268	35.72 357	57.49 244	58.87 365	37.73 192	85.26 248	27.01 356	59.93 251	81.42 259

BH-untuned		68.28 183	66.40 189	73.91 176	81.62 145	50.01 183	85.56 109	77.39 261	57.63 216	57.47 246	83.69 185	36.36 219	87.08 201	44.81 281	73.08 147	84.65 204

XVG-OURS-SEG-HR		62.02 260	59.54 264	69.46 263	65.30 345	45.88 274	65.06 339	73.57 304	46.45 316	57.42 247	83.35 191	26.95 298	78.09 313	53.77 224	64.03 219	84.42 207

XVG-OURS		61.88 261	59.34 266	69.49 262	65.37 344	46.27 269	64.80 340	73.49 305	47.04 312	57.41 248	82.85 196	25.15 311	78.18 311	53.00 230	64.98 210	84.01 214

IS-MVSNet		68.80 173	67.55 170	72.54 206	78.50 211	43.43 302	81.03 238	79.35 225	59.12 187	57.27 249	86.71 149	46.05 89	87.70 183	44.32 285	75.60 125	86.49 171

TAPA-MVS	56.12 14	61.82 262	60.18 261	66.71 293	78.48 212	37.97 338	75.19 289	76.41 279	46.82 313	57.04 250	86.52 153	27.67 294	77.03 325	26.50 358	67.02 195	85.14 196
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
eth_miper_zixun_eth		66.98 215	65.28 218	72.06 217	75.61 256	50.40 172	81.00 239	76.97 271	62.00 129	56.99 251	76.97 269	44.84 109	85.58 241	58.75 178	54.42 309	80.21 279

DIV-MVS_self_test		67.43 201	65.93 202	71.94 225	76.33 242	48.01 240	82.57 200	79.11 230	61.31 141	56.73 252	76.92 271	46.09 88	86.43 222	57.98 190	56.31 289	81.39 262

cl____		67.43 201	65.93 202	71.95 224	76.33 242	48.02 239	82.58 199	79.12 229	61.30 142	56.72 253	76.92 271	46.12 87	86.44 221	57.98 190	56.31 289	81.38 263

FMVSNet2		67.57 197	65.79 205	72.90 199	82.71 120	47.97 241	85.15 119	84.93 112	58.55 198	56.71 254	78.26 252	36.72 215	86.67 213	46.15 276	62.94 237	84.07 212

OpenMVS	61.00 11	69.99 152	67.55 170	77.30 87	78.37 214	54.07 92	84.36 148	85.76 86	57.22 225	56.71 254	87.67 135	30.79 274	92.83 35	43.04 290	84.06 54	85.01 199

Anonymous20231211		66.08 229	63.67 232	73.31 192	83.07 107	48.75 214	86.01 97	84.67 122	45.27 324	56.54 256	76.67 276	28.06 289	88.95 135	52.78 233	59.95 250	82.23 245

MVP-Stereo		70.97 134	70.44 122	72.59 205	76.03 250	51.36 155	85.02 127	86.99 64	60.31 160	56.53 257	78.92 247	40.11 169	90.00 104	60.00 172	90.01 6	76.41 321
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
MS-PatchMatch		72.34 110	71.26 111	75.61 129	82.38 127	55.55 48	88.00 53	89.95 14	65.38 74	56.51 258	80.74 231	32.28 261	92.89 33	57.95 192	88.10 14	78.39 299

Fast-Effi-MVS+-dtu		66.53 222	64.10 231	73.84 179	72.41 296	52.30 136	84.73 137	75.66 284	59.51 172	56.34 259	79.11 246	28.11 288	85.85 240	57.74 197	63.29 230	83.35 227

CVMVSNet		60.85 267	60.44 257	62.07 319	75.00 264	32.73 356	79.54 259	73.49 305	36.98 353	56.28 260	83.74 183	29.28 284	69.53 358	46.48 273	63.23 231	83.94 220

thres600view7		66.46 223	65.12 220	70.47 248	83.41 94	43.80 298	82.15 209	87.78 51	59.37 176	56.02 261	82.21 214	43.73 122	86.90 208	26.51 357	64.94 211	80.71 273

thres100view900		66.87 218	65.42 216	71.24 237	83.29 100	43.15 305	81.67 223	87.78 51	59.04 188	55.92 262	82.18 215	43.73 122	87.80 177	28.80 345	66.36 202	82.78 242

IterMVS-LS		66.63 220	65.36 217	70.42 250	75.10 261	48.90 210	81.45 233	76.69 275	61.05 146	55.71 263	77.10 267	45.86 92	83.65 267	57.44 199	57.88 279	78.70 292
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvsmamba		66.93 217	64.88 224	73.09 195	75.06 262	47.26 254	83.36 182	69.21 336	62.64 120	55.68 264	81.43 224	29.72 280	89.20 125	63.35 142	63.50 225	82.79 241

114514_t		69.87 155	67.88 161	75.85 125	88.38 29	52.35 134	86.94 79	83.68 142	53.70 268	55.68 264	85.60 161	30.07 279	91.20 69	55.84 212	71.02 164	83.99 215

dp		64.41 235	61.58 243	72.90 199	82.40 126	54.09 91	72.53 306	76.59 277	60.39 159	55.68 264	70.39 333	35.18 232	76.90 328	39.34 301	61.71 244	87.73 146

tt0805		63.39 246	61.31 248	69.64 261	69.36 322	38.87 333	78.00 271	85.48 88	48.82 303	55.66 267	81.66 221	24.38 317	86.37 223	49.04 257	59.36 259	83.68 224

Syy-MVS		61.51 263	61.35 247	62.00 321	81.73 138	30.09 364	80.97 240	81.02 190	60.93 150	55.06 268	82.64 203	35.09 234	80.81 287	16.40 382	58.32 267	75.10 331

myMVS_eth3d		63.52 244	63.56 234	63.40 314	81.73 138	34.28 347	80.97 240	81.02 190	60.93 150	55.06 268	82.64 203	48.00 69	80.81 287	23.42 367	58.32 267	75.10 331

FC-MVSNet-test		67.49 199	67.91 159	66.21 297	76.06 248	33.06 354	80.82 244	87.18 60	64.44 84	54.81 270	82.87 195	50.40 52	82.60 274	48.05 264	66.55 200	82.98 238

UniMVSNet_ETH3D		62.51 255	60.49 256	68.57 278	68.30 332	40.88 327	73.89 296	79.93 209	51.81 285	54.77 271	79.61 238	24.80 314	81.10 283	49.93 249	61.35 245	83.73 223

tttt0517		68.33 182	66.29 192	74.46 159	78.08 216	49.06 202	80.88 243	89.08 21	54.40 264	54.75 272	80.77 230	51.31 43	90.33 95	49.35 254	58.01 275	83.99 215

MIMVSNet		63.12 249	60.29 259	71.61 230	75.92 253	46.65 261	65.15 338	81.94 172	59.14 186	54.65 273	69.47 336	25.74 306	80.63 290	41.03 297	69.56 179	87.55 150

ACMM	58.35 12	64.35 236	62.01 241	71.38 235	74.21 275	48.51 221	82.25 208	79.66 215	47.61 308	54.54 274	80.11 233	25.26 310	86.00 234	51.26 242	63.16 233	79.64 284
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet1		64.57 234	62.11 240	71.96 221	77.32 228	46.36 265	83.52 169	83.31 149	52.43 279	54.42 275	76.23 282	27.80 292	86.20 224	42.59 294	61.34 246	83.32 228

PatchT		56.60 297	52.97 304	67.48 285	72.94 290	46.16 272	57.30 363	73.78 301	38.77 347	54.37 276	57.26 368	37.52 198	78.06 314	32.02 333	52.79 320	78.23 303

v8		67.25 206	64.99 222	74.04 172	72.89 291	53.31 112	82.37 207	80.11 205	61.54 138	54.29 277	76.02 287	42.89 136	88.41 154	58.43 181	56.36 287	80.39 277

pmmvs4		63.34 247	61.07 251	70.16 254	70.14 317	50.53 168	79.97 256	71.41 321	55.08 255	54.12 278	78.58 249	32.79 256	82.09 278	50.33 247	57.22 284	77.86 305

v10		66.61 221	64.20 230	73.83 180	72.59 294	53.37 108	81.88 216	79.91 210	61.11 144	54.09 279	75.60 289	40.06 170	88.26 164	56.47 207	56.10 293	79.86 283

CL-MVSNet_self_test		62.98 250	61.14 250	68.50 279	65.86 342	42.96 306	84.37 147	82.98 158	60.98 148	53.95 280	72.70 315	40.43 164	83.71 266	41.10 296	47.93 333	78.83 291

pm-mvs1		64.12 238	62.56 236	68.78 272	71.68 302	38.87 333	82.89 194	81.57 180	55.54 251	53.89 281	77.82 256	37.73 192	86.74 211	48.46 262	53.49 316	80.72 272

LPG-MVS_test		66.44 224	64.58 226	72.02 218	74.42 272	48.60 217	83.07 190	80.64 196	54.69 261	53.75 282	83.83 181	25.73 307	86.98 203	60.33 170	64.71 212	80.48 275

LGP-MVS_train				72.02 218	74.42 272	48.60 217		80.64 196	54.69 261	53.75 282	83.83 181	25.73 307	86.98 203	60.33 170	64.71 212	80.48 275

NR-MVSNet		67.25 206	65.99 200	71.04 242	73.27 286	43.91 296	85.32 114	84.75 119	66.05 66	53.65 284	82.11 216	45.05 103	85.97 237	47.55 266	56.18 292	83.24 231

tpmvs		62.45 258	59.42 265	71.53 234	83.93 84	54.32 85	70.03 323	77.61 257	51.91 282	53.48 285	68.29 340	37.91 187	86.66 214	33.36 328	58.27 269	73.62 341

ET-MVSNet_ETH3D		75.23 68	74.08 72	78.67 55	84.52 74	55.59 47	88.92 43	89.21 19	68.06 36	53.13 286	90.22 84	49.71 59	87.62 189	72.12 91	70.82 166	92.82 21

test0.0.03 1		62.54 254	62.44 237	62.86 318	72.28 299	29.51 369	82.93 193	78.78 235	59.18 184	53.07 287	82.41 209	36.91 210	77.39 323	37.45 305	58.96 261	81.66 253

RRT_MVS		63.68 243	61.01 252	71.70 229	73.48 281	45.98 273	81.19 235	76.08 281	54.33 265	52.84 288	79.27 242	22.21 332	87.65 185	54.13 221	55.54 301	81.46 258

miper_lstm_enhance		63.91 239	62.30 238	68.75 273	75.06 262	46.78 259	69.02 327	81.14 188	59.68 170	52.76 289	72.39 319	40.71 162	77.99 317	56.81 205	53.09 319	81.48 257

TransMVSNet (Re)		62.82 252	60.76 254	69.02 267	73.98 278	41.61 319	86.36 88	79.30 228	56.90 229	52.53 290	76.44 278	41.85 150	87.60 190	38.83 302	40.61 357	77.86 305

test_fmvs2		45.89 333	44.32 335	50.62 351	45.85 384	24.70 378	58.87 361	37.84 383	25.22 374	52.46 291	74.56 296	7.07 378	54.69 374	49.28 255	47.70 334	72.48 347

ACMP	61.11 9	66.24 227	64.33 228	72.00 220	74.89 266	49.12 201	83.18 187	79.83 211	55.41 252	52.29 292	82.68 202	25.83 305	86.10 230	60.89 159	63.94 221	80.78 271
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_djsdf		63.84 240	61.56 244	70.70 246	68.78 326	44.69 288	81.63 224	81.44 183	50.28 292	52.27 293	76.26 281	26.72 299	86.11 228	60.83 160	55.84 298	81.29 267

Vis-MVSNet (Re-imp)		65.52 232	65.63 209	65.17 305	77.49 226	30.54 361	75.49 287	77.73 255	59.34 177	52.26 294	86.69 150	49.38 61	80.53 293	37.07 309	75.28 128	84.42 207

pmmvs5		62.80 253	61.18 249	67.66 284	69.53 321	42.37 316	82.65 198	75.19 289	54.30 266	52.03 295	78.51 250	31.64 269	80.67 289	48.60 260	58.15 271	79.95 282

CNLPA		60.59 268	58.44 272	67.05 290	79.21 193	47.26 254	79.75 258	64.34 350	42.46 341	51.90 296	83.94 179	27.79 293	75.41 335	37.12 307	59.49 257	78.47 296

IterMVS		63.77 242	61.67 242	70.08 256	72.68 293	51.24 159	80.44 248	75.51 285	60.51 158	51.41 297	73.70 305	32.08 263	78.91 307	54.30 220	54.35 310	80.08 281
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpnnormal		61.47 264	59.09 268	68.62 276	76.29 245	41.69 317	81.14 237	85.16 106	54.48 263	51.32 298	73.63 306	32.32 260	86.89 209	21.78 371	55.71 299	77.29 311

anonymousdsp		60.46 269	57.65 275	68.88 268	63.63 355	45.09 283	72.93 304	78.63 240	46.52 315	51.12 299	72.80 314	21.46 337	83.07 273	57.79 195	53.97 311	78.47 296

ADS-MVSNet2		55.21 307	51.44 312	66.51 296	80.60 173	49.56 193	55.03 366	65.44 345	44.72 327	51.00 300	61.19 358	22.83 325	75.41 335	28.54 348	53.63 313	74.57 335

ADS-MVSNet		56.17 301	51.95 311	68.84 269	80.60 173	53.07 119	55.03 366	70.02 331	44.72 327	51.00 300	61.19 358	22.83 325	78.88 308	28.54 348	53.63 313	74.57 335

jajsoiax		63.21 248	60.84 253	70.32 252	68.33 331	44.45 290	81.23 234	81.05 189	53.37 272	50.96 302	77.81 257	17.49 353	85.49 244	59.31 173	58.05 274	81.02 269

CHOSEN 280x420		57.53 294	56.38 287	60.97 329	74.01 277	48.10 237	46.30 373	54.31 364	48.18 306	50.88 303	77.43 262	38.37 185	59.16 371	54.83 216	63.14 234	75.66 325

mvs_tets		62.96 251	60.55 255	70.19 253	68.22 334	44.24 294	80.90 242	80.74 195	52.99 275	50.82 304	77.56 258	16.74 357	85.44 245	59.04 176	57.94 276	80.89 270

testing3		59.97 270	60.19 260	59.32 333	77.60 223	30.01 366	81.75 221	81.79 177	53.54 269	50.34 305	79.94 234	48.99 63	76.91 326	17.19 380	50.59 326	71.03 356

IterMVS-SCA-FT		59.12 278	58.81 271	60.08 331	70.68 316	45.07 284	80.42 249	74.25 295	43.54 336	50.02 306	73.73 302	31.97 264	56.74 373	51.06 245	53.60 315	78.42 298

D2MVS		63.49 245	61.39 246	69.77 260	69.29 323	48.93 209	78.89 267	77.71 256	60.64 157	49.70 307	72.10 324	27.08 297	83.48 269	54.48 219	62.65 238	76.90 313

Anonymous20231206		59.08 280	57.59 276	63.55 312	68.77 327	32.14 359	80.26 252	79.78 212	50.00 296	49.39 308	72.39 319	26.64 300	78.36 310	33.12 331	57.94 276	80.14 280

Patchmatch-RL test		58.72 285	54.32 297	71.92 226	63.91 354	44.25 293	61.73 351	55.19 362	57.38 222	49.31 309	54.24 370	37.60 196	80.89 285	62.19 150	47.28 338	90.63 75

pmmvs6		59.64 273	57.15 279	67.09 288	66.01 340	36.86 342	80.50 247	78.64 239	45.05 326	49.05 310	73.94 300	27.28 295	86.10 230	43.96 287	49.94 328	78.31 300

dmvs_testset		57.65 292	58.21 273	55.97 344	74.62 269	9.82 400	63.75 343	63.34 352	67.23 45	48.89 311	83.68 187	39.12 178	76.14 331	23.43 366	59.80 253	81.96 248

v7n		62.50 256	59.27 267	72.20 214	67.25 337	49.83 188	77.87 273	80.12 204	52.50 278	48.80 312	73.07 310	32.10 262	87.90 173	46.83 272	54.92 304	78.86 290

WR-MVS_H		58.91 283	58.04 274	61.54 325	69.07 325	33.83 351	76.91 278	81.99 171	51.40 287	48.17 313	74.67 294	40.23 166	74.15 338	31.78 335	48.10 331	76.64 318

KD-MVS_2432*1600		59.04 281	56.44 285	66.86 291	79.07 195	45.87 275	72.13 312	80.42 200	55.03 256	48.15 314	71.01 327	36.73 213	78.05 315	35.21 319	30.18 376	76.67 315

miper_refine_blended		59.04 281	56.44 285	66.86 291	79.07 195	45.87 275	72.13 312	80.42 200	55.03 256	48.15 314	71.01 327	36.73 213	78.05 315	35.21 319	30.18 376	76.67 315

CP-MVSNet		58.54 289	57.57 277	61.46 326	68.50 329	33.96 350	76.90 279	78.60 242	51.67 286	47.83 316	76.60 277	34.99 237	72.79 347	35.45 316	47.58 335	77.64 309

PEN-MVS		58.35 290	57.15 279	61.94 322	67.55 336	34.39 346	77.01 277	78.35 247	51.87 283	47.72 317	76.73 275	33.91 245	73.75 342	34.03 326	47.17 339	77.68 307

PLC	52.38 18	60.89 266	58.97 270	66.68 295	81.77 137	45.70 278	78.96 266	74.04 299	43.66 335	47.63 318	83.19 194	23.52 323	77.78 322	37.47 304	60.46 249	76.55 320
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchMatch-RL		56.66 296	53.75 301	65.37 304	77.91 221	45.28 282	69.78 325	60.38 356	41.35 342	47.57 319	73.73 302	16.83 356	76.91 326	36.99 310	59.21 260	73.92 339

PS-CasMVS		58.12 291	57.03 281	61.37 327	68.24 333	33.80 352	76.73 280	78.01 250	51.20 288	47.54 320	76.20 285	32.85 254	72.76 348	35.17 321	47.37 337	77.55 310

PVSNet_0	57.04 13	61.19 265	57.24 278	73.02 196	77.45 227	50.31 178	79.43 263	77.36 263	63.96 94	47.51 321	72.45 318	25.03 312	83.78 265	52.76 235	19.22 388	84.96 200

mvsany_test1		43.38 336	42.57 338	45.82 355	50.96 377	26.10 376	55.80 364	27.74 393	27.15 372	47.41 322	74.39 297	18.67 348	44.95 385	44.66 282	36.31 363	66.40 364

bld_raw_dy_0_64		59.75 272	57.01 282	67.96 282	66.73 338	45.30 281	77.59 275	59.97 358	50.49 291	47.15 323	77.03 268	17.45 354	79.06 306	56.92 204	59.76 254	79.51 285

Patchmtry		56.56 298	52.95 305	67.42 286	72.53 295	50.59 167	59.05 359	71.72 316	37.86 351	46.92 324	65.86 346	38.94 179	80.06 299	36.94 311	46.72 343	71.60 352

JIA-IIPM		52.33 321	47.77 328	66.03 298	71.20 309	46.92 258	40.00 381	76.48 278	37.10 352	46.73 325	37.02 381	32.96 253	77.88 319	35.97 314	52.45 322	73.29 344

DP-MVS		59.24 276	56.12 288	68.63 275	88.24 32	50.35 176	82.51 203	64.43 349	41.10 343	46.70 326	78.77 248	24.75 315	88.57 150	22.26 369	56.29 291	66.96 362

DTE-MVSNet		57.03 295	55.73 291	60.95 330	65.94 341	32.57 357	75.71 282	77.09 267	51.16 289	46.65 327	76.34 280	32.84 255	73.22 346	30.94 339	44.87 348	77.06 312

OpenMVS_ROB	53.19 17	59.20 277	56.00 289	68.83 270	71.13 310	44.30 292	83.64 168	75.02 290	46.42 317	46.48 328	73.03 311	18.69 347	88.14 165	27.74 353	61.80 243	74.05 338

XVG-ACMP-BASELINE		56.03 302	52.85 306	65.58 300	61.91 360	40.95 326	63.36 344	72.43 311	45.20 325	46.02 329	74.09 298	9.20 373	78.12 312	45.13 279	58.27 269	77.66 308

RPSCF		45.77 334	44.13 336	50.68 350	57.67 368	29.66 368	54.92 368	45.25 372	26.69 373	45.92 330	75.92 288	17.43 355	45.70 384	27.44 354	45.95 346	76.67 315

ppachtmachnet_test		58.56 287	54.34 296	71.24 237	71.42 306	54.74 73	81.84 218	72.27 312	49.02 301	45.86 331	68.99 339	26.27 301	83.30 271	30.12 340	43.23 352	75.69 324

EG-PatchMatch MVS		62.40 259	59.59 263	70.81 245	73.29 284	49.05 203	85.81 98	84.78 117	51.85 284	44.19 332	73.48 308	15.52 362	89.85 107	40.16 299	67.24 193	73.54 342

test_0402		56.45 299	53.03 303	66.69 294	76.78 238	50.31 178	81.76 220	69.61 334	42.79 339	43.88 333	72.13 322	22.82 327	86.46 220	16.57 381	50.94 325	63.31 370

LTVRE_ROB	45.45 19	52.73 317	49.74 320	61.69 324	69.78 320	34.99 344	44.52 374	67.60 343	43.11 338	43.79 334	74.03 299	18.54 349	81.45 281	28.39 350	57.94 276	68.62 359
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
pmmvs-eth3d		55.97 303	52.78 307	65.54 301	61.02 362	46.44 264	75.36 288	67.72 342	49.61 298	43.65 335	67.58 342	21.63 336	77.04 324	44.11 286	44.33 349	73.15 346

test20.03		55.22 306	54.07 299	58.68 336	63.14 357	25.00 377	77.69 274	74.78 291	52.64 276	43.43 336	72.39 319	26.21 302	74.76 337	29.31 343	47.05 341	76.28 322

MSDG		59.44 274	55.14 294	72.32 213	74.69 267	50.71 163	74.39 294	73.58 303	44.44 330	43.40 337	77.52 259	19.45 343	90.87 80	31.31 337	57.49 283	75.38 327

FMVSNet5		58.61 286	56.45 284	65.10 306	77.20 233	39.74 329	74.77 290	77.12 266	50.27 294	43.28 338	67.71 341	26.15 304	76.90 328	36.78 312	54.78 306	78.65 294

LS3D		56.40 300	53.82 300	64.12 309	81.12 158	45.69 279	73.42 301	66.14 344	35.30 361	43.24 339	79.88 235	22.18 333	79.62 304	19.10 377	64.00 220	67.05 361

ACMH+	54.58 15	58.55 288	55.24 292	68.50 279	74.68 268	45.80 277	80.27 251	70.21 329	47.15 311	42.77 340	75.48 290	16.73 358	85.98 235	35.10 323	54.78 306	73.72 340

our_test_3		59.11 279	55.08 295	71.18 240	71.42 306	53.29 113	81.96 213	74.52 292	48.32 304	42.08 341	69.28 338	28.14 287	82.15 276	34.35 325	45.68 347	78.11 304

testgi		54.25 310	52.57 309	59.29 334	62.76 358	21.65 384	72.21 311	70.47 327	53.25 273	41.94 342	77.33 263	14.28 363	77.95 318	29.18 344	51.72 324	78.28 301

ACMH	53.70 16	59.78 271	55.94 290	71.28 236	76.59 239	48.35 227	80.15 255	76.11 280	49.74 297	41.91 343	73.45 309	16.50 359	90.31 96	31.42 336	57.63 282	75.17 329
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UnsupCasMVSNet_eth		57.56 293	55.15 293	64.79 308	64.57 352	33.12 353	73.17 303	83.87 140	58.98 190	41.75 344	70.03 334	22.54 328	79.92 300	46.12 277	35.31 365	81.32 266

ambc				62.06 320	53.98 372	29.38 370	35.08 384	79.65 216		41.37 345	59.96 361	6.27 384	82.15 276	35.34 318	38.22 361	74.65 334

ITE_SJBPF				51.84 349	58.03 366	31.94 360		53.57 367	36.67 354	41.32 346	75.23 292	11.17 368	51.57 378	25.81 359	48.04 332	72.02 350

test_fmvs3		37.95 341	35.75 344	44.55 358	35.50 390	18.92 388	48.32 370	34.00 388	18.36 382	41.31 347	61.58 356	2.29 393	48.06 383	42.72 293	37.71 362	66.66 363

F-COLMAP		55.96 304	53.65 302	62.87 317	72.76 292	42.77 310	74.70 293	70.37 328	40.03 344	41.11 348	79.36 240	17.77 352	73.70 343	32.80 332	53.96 312	72.15 348

PM-MVS		46.92 332	43.76 337	56.41 343	52.18 374	32.26 358	63.21 347	38.18 381	37.99 350	40.78 349	66.20 345	5.09 386	65.42 361	48.19 263	41.99 354	71.54 353

EU-MVSNet		52.63 318	50.72 315	58.37 337	62.69 359	28.13 374	72.60 305	75.97 282	30.94 368	40.76 350	72.11 323	20.16 341	70.80 354	35.11 322	46.11 345	76.19 323

OurMVSNet-221017-0		52.39 320	48.73 323	63.35 315	65.21 346	38.42 336	68.54 331	64.95 346	38.19 348	39.57 351	71.43 326	13.23 365	79.92 300	37.16 306	40.32 358	71.72 351

K. test v3		54.04 311	49.42 322	67.92 283	68.55 328	42.57 314	75.51 286	63.07 353	52.07 280	39.21 352	64.59 350	19.34 344	82.21 275	37.11 308	25.31 381	78.97 289

SixPastTwentyTwo		54.37 308	50.10 317	67.21 287	70.70 314	41.46 322	74.73 291	64.69 347	47.56 309	39.12 353	69.49 335	18.49 350	84.69 258	31.87 334	34.20 371	75.48 326

UnsupCasMVSNet_bld		53.86 312	50.53 316	63.84 310	63.52 356	34.75 345	71.38 317	81.92 174	46.53 314	38.95 354	57.93 366	20.55 340	80.20 298	39.91 300	34.09 372	76.57 319

Patchmatch-test		53.33 316	48.17 325	68.81 271	73.31 283	42.38 315	42.98 376	58.23 359	32.53 363	38.79 355	70.77 330	39.66 174	73.51 344	25.18 360	52.06 323	90.55 76

test_vis1_rt		40.29 339	38.64 341	45.25 357	48.91 381	30.09 364	59.44 358	27.07 394	24.52 376	38.48 356	51.67 375	6.71 381	49.44 379	44.33 284	46.59 344	56.23 373

KD-MVS_self_test		49.24 327	46.85 330	56.44 342	54.32 370	22.87 380	57.39 362	73.36 309	44.36 331	37.98 357	59.30 364	18.97 346	71.17 353	33.48 327	42.44 353	75.26 328

LCM-MVSNet-Re		58.82 284	56.54 283	65.68 299	79.31 192	29.09 372	61.39 354	45.79 370	60.73 155	37.65 358	72.47 317	31.42 270	81.08 284	49.66 251	70.41 170	86.87 161

Anonymous20240521		51.65 322	48.42 324	61.34 328	56.43 369	39.65 331	73.57 299	73.47 308	36.64 355	36.59 359	63.98 351	10.75 369	72.25 351	35.35 317	49.01 329	72.11 349

CMPMVS	40.41 21	55.34 305	52.64 308	63.46 313	60.88 363	43.84 297	61.58 353	71.06 323	30.43 369	36.33 360	74.63 295	24.14 319	75.44 334	48.05 264	66.62 198	71.12 355
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
COLMAP_ROB	43.60 20	50.90 325	48.05 326	59.47 332	67.81 335	40.57 328	71.25 318	62.72 355	36.49 356	36.19 361	73.51 307	13.48 364	73.92 341	20.71 373	50.26 327	63.92 369
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
lessismore_v0				67.98 281	64.76 351	41.25 323		45.75 371		36.03 362	65.63 348	19.29 345	84.11 261	35.67 315	21.24 386	78.59 295

USDC		54.36 309	51.23 313	63.76 311	64.29 353	37.71 339	62.84 349	73.48 307	56.85 230	35.47 363	71.94 325	9.23 372	78.43 309	38.43 303	48.57 330	75.13 330

N_pmnet		41.25 337	39.77 340	45.66 356	68.50 329	0.82 406	72.51 307	0.38 405	35.61 358	35.26 364	61.51 357	20.07 342	67.74 359	23.51 365	40.63 356	68.42 360

DSMNet-mixed		38.35 340	35.36 345	47.33 354	48.11 382	14.91 396	37.87 382	36.60 384	19.18 380	34.37 365	59.56 363	15.53 361	53.01 377	20.14 375	46.89 342	74.07 337

pmmvs3		45.53 335	41.55 339	57.44 339	48.97 380	39.68 330	70.06 322	57.66 360	28.32 371	34.06 366	57.29 367	8.50 376	66.85 360	34.86 324	34.26 370	65.80 366

new-patchmatchnet		48.21 329	46.55 331	53.18 348	57.73 367	18.19 392	70.24 321	71.02 324	45.70 321	33.70 367	60.23 360	18.00 351	69.86 357	27.97 352	34.35 369	71.49 354

MVS-HIRNet		49.01 328	44.71 332	61.92 323	76.06 248	46.61 262	63.23 346	54.90 363	24.77 375	33.56 368	36.60 383	21.28 338	75.88 333	29.49 342	62.54 239	63.26 371

AllTest		47.32 331	44.66 333	55.32 346	65.08 348	37.50 340	62.96 348	54.25 365	35.45 359	33.42 369	72.82 312	9.98 370	59.33 368	24.13 363	43.84 350	69.13 357

TestCases				55.32 346	65.08 348	37.50 340		54.25 365	35.45 359	33.42 369	72.82 312	9.98 370	59.33 368	24.13 363	43.84 350	69.13 357

MIMVSNet1		50.35 326	47.81 327	57.96 338	61.53 361	27.80 375	67.40 334	74.06 298	43.25 337	33.31 371	65.38 349	16.03 360	71.34 352	21.80 370	47.55 336	74.75 333

YYNet1		53.82 313	49.96 318	65.41 303	70.09 319	48.95 207	72.30 309	71.66 318	44.25 332	31.89 372	63.07 354	23.73 321	73.95 340	33.26 329	39.40 359	73.34 343

MDA-MVSNet_test_wron		53.82 313	49.95 319	65.43 302	70.13 318	49.05 203	72.30 309	71.65 319	44.23 333	31.85 373	63.13 353	23.68 322	74.01 339	33.25 330	39.35 360	73.23 345

TDRefinement		40.91 338	38.37 342	48.55 353	50.45 378	33.03 355	58.98 360	50.97 368	28.50 370	29.89 374	67.39 343	6.21 385	54.51 375	17.67 379	35.25 366	58.11 372

TinyColmap		48.15 330	44.49 334	59.13 335	65.73 343	38.04 337	63.34 345	62.86 354	38.78 346	29.48 375	67.23 344	6.46 383	73.30 345	24.59 362	41.90 355	66.04 365

mvsany_test3		28.00 350	25.98 352	34.05 368	28.97 395	15.31 394	34.54 385	18.17 399	16.24 383	29.30 376	53.37 373	2.79 391	33.38 396	30.01 341	20.41 387	53.45 376

test_f		27.12 352	24.85 353	33.93 369	26.17 400	15.25 395	30.24 389	22.38 398	12.53 388	28.23 377	49.43 376	2.59 392	34.34 395	25.12 361	26.99 379	52.20 377

LF4IMVS		33.04 348	32.55 348	34.52 367	40.96 385	22.03 382	44.45 375	35.62 385	20.42 378	28.12 378	62.35 355	5.03 387	31.88 397	21.61 372	34.42 368	49.63 379

WB-MVS		37.41 342	36.37 343	40.54 362	54.23 371	10.43 399	65.29 337	43.75 373	34.86 362	27.81 379	54.63 369	24.94 313	63.21 362	6.81 394	15.00 389	47.98 381

MDA-MVSNet-bldmvs		51.56 323	47.75 329	63.00 316	71.60 304	47.32 253	69.70 326	72.12 313	43.81 334	27.65 380	63.38 352	21.97 335	75.96 332	27.30 355	32.19 373	65.70 367

SSC-MVS		35.20 344	34.30 346	37.90 364	52.58 373	8.65 402	61.86 350	41.64 377	31.81 367	25.54 381	52.94 374	23.39 324	59.28 370	6.10 395	12.86 390	45.78 383

new_pmnet		33.56 347	31.89 349	38.59 363	49.01 379	20.42 385	51.01 369	37.92 382	20.58 377	23.45 382	46.79 377	6.66 382	49.28 381	20.00 376	31.57 375	46.09 382

FPMVS		35.40 343	33.67 347	40.57 361	46.34 383	28.74 373	41.05 378	57.05 361	20.37 379	22.27 383	53.38 372	6.87 380	44.94 386	8.62 388	47.11 340	48.01 380

test_vis3_rt		24.79 356	22.95 359	30.31 373	28.59 396	18.92 388	37.43 383	17.27 401	12.90 386	21.28 384	29.92 390	1.02 400	36.35 390	28.28 351	29.82 378	35.65 384

LCM-MVSNet		28.07 349	23.85 357	40.71 360	27.46 399	18.93 387	30.82 388	46.19 369	12.76 387	16.40 385	34.70 386	1.90 396	48.69 382	20.25 374	24.22 382	54.51 375

APD_test1		26.46 354	24.41 355	32.62 372	37.58 387	21.74 383	40.50 380	30.39 390	11.45 389	16.33 386	43.76 378	1.63 398	41.62 387	11.24 386	26.82 380	34.51 386

ANet_high		34.39 345	29.59 351	48.78 352	30.34 394	22.28 381	55.53 365	63.79 351	38.11 349	15.47 387	36.56 384	6.94 379	59.98 367	13.93 384	5.64 398	64.08 368

test_method		24.09 357	21.07 361	33.16 370	27.67 398	8.35 404	26.63 390	35.11 387	3.40 396	14.35 388	36.98 382	3.46 390	35.31 392	19.08 378	22.95 383	55.81 374

PMMVS2		26.71 353	22.98 358	37.87 365	36.89 388	8.51 403	42.51 377	29.32 392	19.09 381	13.01 389	37.54 380	2.23 394	53.11 376	14.54 383	11.71 391	51.99 378

tmp_tt		9.44 364	10.68 367	5.73 381	2.49 403	4.21 405	10.48 394	18.04 400	0.34 398	12.59 390	20.49 392	11.39 367	7.03 400	13.84 385	6.46 397	5.95 395

testf1		21.11 358	19.08 362	27.18 375	30.56 392	18.28 390	33.43 386	24.48 395	8.02 393	12.02 391	33.50 387	0.75 402	35.09 393	7.68 390	21.32 384	28.17 388

APD_test2		21.11 358	19.08 362	27.18 375	30.56 392	18.28 390	33.43 386	24.48 395	8.02 393	12.02 391	33.50 387	0.75 402	35.09 393	7.68 390	21.32 384	28.17 388

PMVS	19.57 22	25.07 355	22.43 360	32.99 371	23.12 401	22.98 379	40.98 379	35.19 386	15.99 384	11.95 393	35.87 385	1.47 399	49.29 380	5.41 397	31.90 374	26.70 390
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		27.47 351	24.26 356	37.12 366	60.55 364	29.17 371	11.68 393	60.00 357	14.18 385	10.52 394	15.12 395	2.20 395	63.01 363	8.39 389	35.65 364	19.18 391
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	16.60 23	17.34 363	13.39 366	29.16 374	28.43 397	19.72 386	13.73 392	23.63 397	7.23 395	7.96 395	21.41 391	0.80 401	36.08 391	6.97 392	10.39 392	31.69 387
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				13.10 379	21.34 402	8.99 401		10.02 403	10.59 391	7.53 396	30.55 389	1.82 397	14.55 398	6.83 393	7.52 394	15.75 392

E-PMN		19.16 360	18.40 364	21.44 377	36.19 389	13.63 397	47.59 371	30.89 389	10.73 390	5.91 397	16.59 393	3.66 389	39.77 388	5.95 396	8.14 393	10.92 393

EMVS		18.42 361	17.66 365	20.71 378	34.13 391	12.64 398	46.94 372	29.94 391	10.46 392	5.58 398	14.93 396	4.23 388	38.83 389	5.24 398	7.51 395	10.67 394

wuyk23d		9.11 365	8.77 369	10.15 380	40.18 386	16.76 393	20.28 391	1.01 404	2.58 397	2.66 399	0.98 399	0.23 404	12.49 399	4.08 399	6.90 396	1.19 396

EGC-MVSNET		33.75 346	30.42 350	43.75 359	64.94 350	36.21 343	60.47 357	40.70 379	0.02 399	0.10 400	53.79 371	7.39 377	60.26 366	11.09 387	35.23 367	34.79 385

testmvs		6.14 367	8.18 370	0.01 382	0.01 404	0.00 408	73.40 302	0.00 406	0.00 400	0.02 401	0.15 400	0.00 405	0.00 401	0.02 400	0.00 399	0.02 397

test123		6.01 368	8.01 371	0.01 382	0.00 405	0.01 407	71.93 315	0.00 406	0.00 400	0.02 401	0.11 401	0.00 405	0.00 401	0.02 400	0.00 399	0.02 397

test_blank		0.00 370	0.00 373	0.00 384	0.00 405	0.00 408	0.00 395	0.00 406	0.00 400	0.00 403	0.00 402	0.00 405	0.00 401	0.00 402	0.00 399	0.00 399

uanet_test		0.00 370	0.00 373	0.00 384	0.00 405	0.00 408	0.00 395	0.00 406	0.00 400	0.00 403	0.00 402	0.00 405	0.00 401	0.00 402	0.00 399	0.00 399

DCPMVS		0.00 370	0.00 373	0.00 384	0.00 405	0.00 408	0.00 395	0.00 406	0.00 400	0.00 403	0.00 402	0.00 405	0.00 401	0.00 402	0.00 399	0.00 399

cdsmvs_eth3d_5k		18.33 362	24.44 354	0.00 384	0.00 405	0.00 408	0.00 395	89.40 16	0.00 400	0.00 403	92.02 45	38.55 183	0.00 401	0.00 402	0.00 399	0.00 399

pcd_1.5k_mvsjas		3.15 369	4.20 372	0.00 384	0.00 405	0.00 408	0.00 395	0.00 406	0.00 400	0.00 403	0.00 402	37.77 189	0.00 401	0.00 402	0.00 399	0.00 399

sosnet-low-res		0.00 370	0.00 373	0.00 384	0.00 405	0.00 408	0.00 395	0.00 406	0.00 400	0.00 403	0.00 402	0.00 405	0.00 401	0.00 402	0.00 399	0.00 399

sosnet		0.00 370	0.00 373	0.00 384	0.00 405	0.00 408	0.00 395	0.00 406	0.00 400	0.00 403	0.00 402	0.00 405	0.00 401	0.00 402	0.00 399	0.00 399

uncertanet		0.00 370	0.00 373	0.00 384	0.00 405	0.00 408	0.00 395	0.00 406	0.00 400	0.00 403	0.00 402	0.00 405	0.00 401	0.00 402	0.00 399	0.00 399

Regformer		0.00 370	0.00 373	0.00 384	0.00 405	0.00 408	0.00 395	0.00 406	0.00 400	0.00 403	0.00 402	0.00 405	0.00 401	0.00 402	0.00 399	0.00 399

ab-mvs-re		7.68 366	10.24 368	0.00 384	0.00 405	0.00 408	0.00 395	0.00 406	0.00 400	0.00 403	92.12 42	0.00 405	0.00 401	0.00 402	0.00 399	0.00 399

uanet		0.00 370	0.00 373	0.00 384	0.00 405	0.00 408	0.00 395	0.00 406	0.00 400	0.00 403	0.00 402	0.00 405	0.00 401	0.00 402	0.00 399	0.00 399

WAC-MVS						34.28 347								22.56 368

MSC_two_6792asdad				81.53 14	91.77 4	56.03 41		91.10 6					96.22 8	81.46 32	86.80 26	92.34 32

No_MVS				81.53 14	91.77 4	56.03 41		91.10 6					96.22 8	81.46 32	86.80 26	92.34 32

eth-test2					0.00 405

eth-test					0.00 405

OPU-MVS				81.71 12	92.05 3	55.97 43	92.48 3				94.01 5	67.21 2	95.10 15	89.82 2	92.55 3	94.06 3

save fliter					85.35 60	56.34 36	89.31 39	81.46 182	61.55 137

test_0728_SECOND				82.20 8	89.50 15	57.73 11	92.34 5	88.88 26					96.39 4	81.68 28	87.13 20	92.47 28

GSMVS																88.13 138

sam_mvs1												38.86 181				88.13 138

sam_mvs												35.99 226

MTGPA								81.31 185

test_post1							70.84 320				14.72 397	34.33 242	83.86 262	48.80 258

test_post											16.22 394	37.52 198	84.72 257

patchmatchnet-post											59.74 362	38.41 184	79.91 302

MTMP							87.27 72	15.34 402

gm-plane-assit					83.24 101	54.21 88			70.91 15		88.23 125		95.25 14	66.37 119

test9_res														78.72 47	85.44 41	91.39 59

agg_prior2														75.65 66	85.11 45	91.01 68

test_prior4						56.39 35	87.15 75

test_prior				78.39 65	86.35 45	54.91 71		85.45 91					89.70 113			90.55 76

新几何2							81.61 226

旧先验1					81.57 149	47.48 249		71.83 314			88.66 115	36.94 209			78.34 103	88.67 126

无先验							85.19 118	78.00 251	49.08 300				85.13 252	52.78 233		87.45 153

原ACMM2							83.77 166

testdata2													77.81 321	45.64 278

segment_acmp												44.97 106

testdata1							77.55 276		64.14 89

plane_prior7					77.95 218	48.46 224

plane_prior6					78.42 213	49.39 198						36.04 224

plane_prior5								82.59 163					88.30 161	65.46 128	72.34 152	84.49 205

plane_prior4											83.28 192

plane_prior2							85.76 100		63.60 101

plane_prior1					78.31 215

plane_prior						49.57 191	87.43 65		64.57 83						72.84 148

n2								0.00 406

nn								0.00 406

door-mid								41.31 378

test11								84.25 130

door								43.27 374

HQP5-MVS						51.56 150

BP-MVS														66.70 116

HQP3-MVS								83.68 142							73.12 144

HQP2-MVS												37.35 201

NP-MVS					78.76 202	50.43 171					85.12 166

ACMMP++_ref															63.20 232

ACMMP++															59.38 258

Test By Simon												39.38 175

High-resolution multi-view benchmark