High-res multi-view results

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

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

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

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

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

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

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

MG-MVS		87.11 35	86.27 45	89.62 8	97.79 1	76.27 4	94.96 43	94.49 43	78.74 87	83.87 75	92.94 119	64.34 88	96.94 104	75.19 155	94.09 39	95.66 52

MCST-MVS		91.08 1	91.46 3	89.94 4	97.66 2	73.37 12	97.13 2	95.58 10	89.33 1	85.77 54	96.26 30	72.84 26	99.38 1	92.64 20	95.93 9	97.08 11

OPU-MVS				89.97 3	97.52 3	73.15 16	96.89 6				97.00 9	83.82 2	99.15 2	95.72 5	97.63 3	97.62 2

DVP-MVS++		90.53 4	91.09 5	88.87 17	97.31 4	69.91 44	93.96 69	94.37 51	72.48 183	92.07 9	96.85 16	83.82 2	99.15 2	91.53 30	97.42 4	97.55 4

MSC_two_6792asdad				89.60 9	97.31 4	73.22 14		95.05 25					99.07 13	92.01 25	94.77 26	96.51 24

No_MVS				89.60 9	97.31 4	73.22 14		95.05 25					99.07 13	92.01 25	94.77 26	96.51 24

DP-MVS Recon		82.73 115	81.65 122	85.98 86	97.31 4	67.06 114	95.15 36	91.99 141	69.08 259	76.50 154	93.89 101	54.48 209	98.20 35	70.76 194	85.66 138	92.69 167

CNVR-MVS		90.32 6	90.89 8	88.61 23	96.76 8	70.65 32	96.47 14	94.83 29	84.83 11	89.07 31	96.80 19	70.86 38	99.06 15	92.64 20	95.71 11	96.12 41

ZD-MVS					96.63 9	65.50 155		93.50 82	70.74 238	85.26 62	95.19 62	64.92 81	97.29 76	87.51 58	93.01 58

NCCC		89.07 16	89.46 15	87.91 29	96.60 10	69.05 63	96.38 15	94.64 38	84.42 12	86.74 45	96.20 31	66.56 64	98.76 24	89.03 48	94.56 35	95.92 47

IU-MVS					96.46 11	69.91 44		95.18 19	80.75 48	95.28 1				92.34 22	95.36 14	96.47 28

SED-MVS		89.94 9	90.36 10	88.70 19	96.45 12	69.38 55	96.89 6	94.44 45	71.65 213	92.11 7	97.21 4	76.79 9	99.11 6	92.34 22	95.36 14	97.62 2

test_241102_ONE					96.45 12	69.38 55		94.44 45	71.65 213	92.11 7	97.05 7	76.79 9	99.11 6

test_0728_SECOND				88.70 19	96.45 12	70.43 35	96.64 10	94.37 51					99.15 2	91.91 28	94.90 22	96.51 24

DVP-MVS		89.41 13	89.73 14	88.45 26	96.40 15	69.99 40	96.64 10	94.52 41	71.92 199	90.55 20	96.93 11	73.77 21	99.08 11	91.91 28	94.90 22	96.29 36
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					96.40 15	69.99 40	96.76 8	94.33 53	71.92 199	91.89 11	97.11 6	73.77 21

AdaColmap		78.94 181	77.00 197	84.76 131	96.34 17	65.86 145	92.66 129	87.97 305	62.18 314	70.56 218	92.37 134	43.53 300	97.35 72	64.50 256	82.86 159	91.05 208

test_one_0601					96.32 18	69.74 50		94.18 56	71.42 224	90.67 19	96.85 16	74.45 18

test_part2					96.29 19	68.16 87				90.78 17

DPE-MVS		88.77 17	89.21 16	87.45 45	96.26 20	67.56 101	94.17 57	94.15 58	68.77 262	90.74 18	97.27 2	76.09 12	98.49 29	90.58 38	94.91 21	96.30 35
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
MAR-MVS		84.18 89	83.43 91	86.44 75	96.25 21	65.93 144	94.28 55	94.27 55	74.41 141	79.16 123	95.61 45	53.99 214	98.88 22	69.62 203	93.26 56	94.50 111
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
API-MVS		82.28 122	80.53 141	87.54 43	96.13 22	70.59 33	93.63 90	91.04 192	65.72 286	75.45 164	92.83 124	56.11 190	98.89 21	64.10 258	89.75 101	93.15 154

APDe-MVS		87.54 27	87.84 26	86.65 66	96.07 23	66.30 135	94.84 45	93.78 65	69.35 253	88.39 33	96.34 28	67.74 55	97.66 54	90.62 37	93.44 53	96.01 45
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
patch_mono-2		89.71 11	90.99 6	85.85 92	96.04 24	63.70 202	95.04 40	95.19 18	86.74 7	91.53 15	95.15 63	73.86 20	97.58 59	93.38 14	92.00 71	96.28 38

PAPR		85.15 70	84.47 75	87.18 50	96.02 25	68.29 80	91.85 166	93.00 104	76.59 118	79.03 124	95.00 65	61.59 126	97.61 58	78.16 139	89.00 105	95.63 53

APD-MVS		85.93 55	85.99 53	85.76 96	95.98 26	65.21 160	93.59 92	92.58 122	66.54 279	86.17 50	95.88 39	63.83 94	97.00 94	86.39 72	92.94 59	95.06 81
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	79.48 2	87.95 22	88.00 25	87.79 32	95.86 27	68.32 79	95.74 21	94.11 59	83.82 16	83.49 76	96.19 32	64.53 87	98.44 31	83.42 99	94.88 25	96.61 18
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DP-MVS		69.90 291	66.48 299	80.14 253	95.36 28	62.93 224	89.56 243	76.11 365	50.27 370	57.69 336	85.23 247	39.68 313	95.73 154	33.35 380	71.05 258	81.78 341

114514_t		79.17 176	77.67 182	83.68 168	95.32 29	65.53 154	92.85 119	91.60 164	63.49 300	67.92 254	90.63 166	46.65 280	95.72 158	67.01 231	83.54 154	89.79 223

HPM-MVS++		89.37 14	89.95 13	87.64 35	95.10 30	68.23 85	95.24 33	94.49 43	82.43 25	88.90 32	96.35 27	71.89 34	98.63 26	88.76 49	96.40 6	96.06 42

CSCG		86.87 37	86.26 46	88.72 18	95.05 31	70.79 31	93.83 81	95.33 15	68.48 266	77.63 140	94.35 88	73.04 24	98.45 30	84.92 85	93.71 48	96.92 14

dcpmvs_2		87.37 31	87.55 30	86.85 58	95.04 32	68.20 86	90.36 224	90.66 200	79.37 72	81.20 94	93.67 105	74.73 15	96.55 121	90.88 35	92.00 71	95.82 49

LFMVS		84.34 83	82.73 107	89.18 13	94.76 33	73.25 13	94.99 42	91.89 147	71.90 201	82.16 86	93.49 110	47.98 271	97.05 89	82.55 104	84.82 142	97.25 8

CDPH-MVS		85.71 60	85.46 62	86.46 74	94.75 34	67.19 110	93.89 74	92.83 109	70.90 233	83.09 79	95.28 54	63.62 99	97.36 71	80.63 118	94.18 38	94.84 91

test_prior				86.42 76	94.71 35	67.35 107		93.10 100					96.84 110			95.05 82

test12				87.09 53	94.60 36	68.86 67		92.91 106		82.67 84		65.44 74	97.55 62		93.69 49	94.84 91

test_yl		84.28 84	83.16 98	87.64 35	94.52 37	69.24 59	95.78 18	95.09 22	69.19 256	81.09 96	92.88 122	57.00 175	97.44 66	81.11 116	81.76 173	96.23 39

DCV-MVSNet		84.28 84	83.16 98	87.64 35	94.52 37	69.24 59	95.78 18	95.09 22	69.19 256	81.09 96	92.88 122	57.00 175	97.44 66	81.11 116	81.76 173	96.23 39

CANet		89.61 12	89.99 12	88.46 25	94.39 39	69.71 51	96.53 13	93.78 65	86.89 6	89.68 28	95.78 40	65.94 69	99.10 9	92.99 17	93.91 43	96.58 21

test_8					94.19 40	67.19 110	94.15 60	93.42 87	71.87 204	85.38 60	95.35 50	68.19 50	96.95 103

TEST9					94.18 41	67.28 108	94.16 58	93.51 80	71.75 210	85.52 57	95.33 51	68.01 52	97.27 80

train_agg		87.21 33	87.42 32	86.60 68	94.18 41	67.28 108	94.16 58	93.51 80	71.87 204	85.52 57	95.33 51	68.19 50	97.27 80	89.09 46	94.90 22	95.25 75

agg_prior					94.16 43	66.97 118		93.31 90		84.49 68			96.75 113

PAPM_NR		82.97 112	81.84 119	86.37 78	94.10 44	66.76 124	87.66 280	92.84 108	69.96 246	74.07 178	93.57 108	63.10 111	97.50 64	70.66 196	90.58 92	94.85 88

MVS_0304		90.32 6	90.90 7	88.55 24	94.05 45	70.23 38	97.00 5	93.73 72	87.30 4	92.15 6	96.15 34	66.38 65	98.94 17	96.71 2	94.67 34	96.47 28

FOURS1					93.95 46	61.77 249	93.96 69	91.92 144	62.14 317	86.57 46

VNet		86.20 49	85.65 60	87.84 31	93.92 47	69.99 40	95.73 23	95.94 7	78.43 90	86.00 52	93.07 116	58.22 162	97.00 94	85.22 79	84.33 148	96.52 23

9.14			87.63 28		93.86 48		94.41 52	94.18 56	72.76 178	86.21 48	96.51 24	66.64 62	97.88 44	90.08 39	94.04 40

save fliter					93.84 49	67.89 93	95.05 39	92.66 117	78.19 92

PVSNet_BlendedMVS		83.38 104	83.43 91	83.22 180	93.76 50	67.53 103	94.06 62	93.61 76	79.13 78	81.00 99	85.14 248	63.19 108	97.29 76	87.08 66	73.91 237	84.83 304

PVSNet_Blended		86.73 42	86.86 40	86.31 81	93.76 50	67.53 103	96.33 16	93.61 76	82.34 27	81.00 99	93.08 115	63.19 108	97.29 76	87.08 66	91.38 82	94.13 122

HFP-MVS		84.73 77	84.40 77	85.72 98	93.75 52	65.01 166	93.50 97	93.19 95	72.19 193	79.22 122	94.93 68	59.04 154	97.67 51	81.55 109	92.21 66	94.49 112

Anonymous202405211		77.96 201	75.33 219	85.87 90	93.73 53	64.52 172	94.85 44	85.36 330	62.52 312	76.11 155	90.18 176	29.43 365	97.29 76	68.51 216	77.24 216	95.81 50

balanced_conf03		89.08 15	88.84 17	89.81 6	93.66 54	75.15 5	90.61 219	93.43 86	84.06 14	86.20 49	90.17 177	72.42 29	96.98 98	93.09 16	95.92 10	97.29 7

testing99		86.01 53	85.47 61	87.63 39	93.62 55	71.25 25	93.47 100	95.23 17	80.42 53	80.60 104	91.95 143	71.73 35	96.50 124	80.02 123	82.22 167	95.13 78

SD-MVS		87.49 28	87.49 31	87.50 44	93.60 56	68.82 69	93.90 73	92.63 120	76.86 112	87.90 35	95.76 41	66.17 66	97.63 56	89.06 47	91.48 80	96.05 43
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
testing91		85.93 55	85.31 64	87.78 33	93.59 57	71.47 21	93.50 97	95.08 24	80.26 55	80.53 105	91.93 144	70.43 40	96.51 123	80.32 121	82.13 169	95.37 62

ACMMPR		84.37 81	84.06 79	85.28 112	93.56 58	64.37 182	93.50 97	93.15 97	72.19 193	78.85 130	94.86 71	56.69 182	97.45 65	81.55 109	92.20 67	94.02 129

testing11		86.71 43	86.44 44	87.55 42	93.54 59	71.35 23	93.65 88	95.58 10	81.36 41	80.69 102	92.21 139	72.30 30	96.46 126	85.18 81	83.43 155	94.82 94

region2R		84.36 82	84.03 80	85.36 109	93.54 59	64.31 185	93.43 102	92.95 105	72.16 196	78.86 129	94.84 72	56.97 177	97.53 63	81.38 113	92.11 69	94.24 116

TSAR-MVS + GP.		87.96 21	88.37 21	86.70 65	93.51 61	65.32 157	95.15 36	93.84 64	78.17 93	85.93 53	94.80 73	75.80 13	98.21 34	89.38 42	88.78 106	96.59 19

PHI-MVS		86.83 40	86.85 41	86.78 63	93.47 62	65.55 153	95.39 30	95.10 21	71.77 209	85.69 56	96.52 23	62.07 121	98.77 23	86.06 75	95.60 12	96.03 44

SR-MVS		82.81 114	82.58 109	83.50 174	93.35 63	61.16 261	92.23 145	91.28 178	64.48 293	81.27 93	95.28 54	53.71 218	95.86 148	82.87 101	88.77 107	93.49 145

EPNet		87.84 24	88.38 20	86.23 82	93.30 64	66.05 139	95.26 32	94.84 28	87.09 5	88.06 34	94.53 79	66.79 61	97.34 73	83.89 96	91.68 76	95.29 69
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
XVS		83.87 95	83.47 89	85.05 119	93.22 65	63.78 196	92.92 116	92.66 117	73.99 149	78.18 134	94.31 91	55.25 197	97.41 68	79.16 129	91.58 78	93.95 131

X-MVStestdata		76.86 217	74.13 236	85.05 119	93.22 65	63.78 196	92.92 116	92.66 117	73.99 149	78.18 134	10.19 410	55.25 197	97.41 68	79.16 129	91.58 78	93.95 131

SMA-MVS		88.14 18	88.29 22	87.67 34	93.21 67	68.72 71	93.85 76	94.03 61	74.18 146	91.74 12	96.67 21	65.61 73	98.42 33	89.24 45	96.08 7	95.88 48
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
原ACMM1				84.42 146	93.21 67	64.27 187		93.40 89	65.39 287	79.51 117	92.50 128	58.11 164	96.69 114	65.27 252	93.96 41	92.32 179

MVS_111021_HR		86.19 50	85.80 57	87.37 46	93.17 69	69.79 48	93.99 68	93.76 68	79.08 80	78.88 128	93.99 99	62.25 120	98.15 36	85.93 76	91.15 86	94.15 121

CP-MVS		83.71 100	83.40 94	84.65 137	93.14 70	63.84 194	94.59 49	92.28 128	71.03 231	77.41 143	94.92 69	55.21 200	96.19 133	81.32 114	90.70 90	93.91 133

DELS-MVS		90.05 8	90.09 11	89.94 4	93.14 70	73.88 11	97.01 4	94.40 49	88.32 3	85.71 55	94.91 70	74.11 19	98.91 18	87.26 63	95.94 8	97.03 12
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
ZNCC-MVS		85.33 67	85.08 68	86.06 84	93.09 72	65.65 149	93.89 74	93.41 88	73.75 157	79.94 112	94.68 76	60.61 136	98.03 38	82.63 103	93.72 47	94.52 109

bld_raw_conf03		87.15 34	86.52 43	89.06 14	93.04 73	74.04 8	87.84 276	92.69 114	80.90 47	81.47 91	89.48 187	69.08 46	96.67 116	89.42 40	94.74 31	96.47 28

DeepPCF-MVS	81.17 1	89.72 10	91.38 4	84.72 133	93.00 74	58.16 305	96.72 9	94.41 47	86.50 8	90.25 22	97.83 1	75.46 14	98.67 25	92.78 19	95.49 13	97.32 6

PLC	68.80 14	75.23 244	73.68 243	79.86 264	92.93 75	58.68 301	90.64 216	88.30 294	60.90 326	64.43 291	90.53 167	42.38 305	94.57 200	56.52 298	76.54 220	86.33 273
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
testing222		85.18 69	84.69 74	86.63 67	92.91 76	69.91 44	92.61 131	95.80 9	80.31 54	80.38 107	92.27 136	68.73 47	95.19 179	75.94 150	83.27 157	94.81 95

MSP-MVS		90.38 5	91.87 1	85.88 89	92.83 77	64.03 192	93.06 110	94.33 53	82.19 28	93.65 3	96.15 34	85.89 1	97.19 82	91.02 34	97.75 1	96.43 32
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
mPP-MVS		82.96 113	82.44 112	84.52 143	92.83 77	62.92 226	92.76 121	91.85 151	71.52 221	75.61 162	94.24 93	53.48 222	96.99 97	78.97 132	90.73 89	93.64 142

GST-MVS		84.63 79	84.29 78	85.66 100	92.82 79	65.27 158	93.04 112	93.13 98	73.20 166	78.89 125	94.18 95	59.41 150	97.85 45	81.45 111	92.48 65	93.86 136

WTY-MVS		86.32 47	85.81 56	87.85 30	92.82 79	69.37 57	95.20 34	95.25 16	82.71 22	81.91 87	94.73 74	67.93 54	97.63 56	79.55 126	82.25 166	96.54 22

PGM-MVS		83.25 106	82.70 108	84.92 122	92.81 81	64.07 191	90.44 220	92.20 134	71.28 225	77.23 146	94.43 82	55.17 201	97.31 75	79.33 128	91.38 82	93.37 147

EI-MVSNet-Vis-set		83.77 98	83.67 83	84.06 157	92.79 82	63.56 208	91.76 171	94.81 30	79.65 66	77.87 137	94.09 96	63.35 106	97.90 42	79.35 127	79.36 193	90.74 210

SF-MVS		87.03 36	87.09 35	86.84 59	92.70 83	67.45 106	93.64 89	93.76 68	70.78 237	86.25 47	96.44 26	66.98 59	97.79 47	88.68 50	94.56 35	95.28 71

MVSTER		82.47 119	82.05 115	83.74 164	92.68 84	69.01 64	91.90 163	93.21 92	79.83 61	72.14 202	85.71 244	74.72 16	94.72 193	75.72 151	72.49 247	87.50 251

CS-MVS-test		86.14 51	87.01 36	83.52 171	92.63 85	59.36 294	95.49 27	91.92 144	80.09 59	85.46 59	95.53 47	61.82 125	95.77 152	86.77 70	93.37 54	95.41 59

MP-MVS		85.02 71	84.97 70	85.17 117	92.60 86	64.27 187	93.24 105	92.27 129	73.13 168	79.63 116	94.43 82	61.90 122	97.17 83	85.00 83	92.56 63	94.06 127
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
ETVMVS		84.22 88	83.71 82	85.76 96	92.58 87	68.25 84	92.45 139	95.53 13	79.54 68	79.46 118	91.64 151	70.29 41	94.18 217	69.16 209	82.76 163	94.84 91

thres200		79.66 168	78.33 172	83.66 170	92.54 88	65.82 147	93.06 110	96.31 3	74.90 138	73.30 185	88.66 195	59.67 146	95.61 162	47.84 333	78.67 200	89.56 228

APD-MVS_3200maxsize		81.64 133	81.32 125	82.59 193	92.36 89	58.74 300	91.39 184	91.01 193	63.35 302	79.72 115	94.62 78	51.82 233	96.14 135	79.71 124	87.93 114	92.89 165

新几何1				84.73 132	92.32 90	64.28 186		91.46 170	59.56 336	79.77 114	92.90 120	56.95 178	96.57 119	63.40 262	92.91 60	93.34 148

EI-MVSNet-UG-set		83.14 109	82.96 101	83.67 169	92.28 91	63.19 218	91.38 186	94.68 36	79.22 75	76.60 152	93.75 102	62.64 115	97.76 48	78.07 140	78.01 204	90.05 219

HPM-MVS		83.25 106	82.95 102	84.17 155	92.25 92	62.88 228	90.91 204	91.86 149	70.30 242	77.12 147	93.96 100	56.75 180	96.28 129	82.04 106	91.34 84	93.34 148
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
HY-MVS	76.49 5	84.28 84	83.36 96	87.02 56	92.22 93	67.74 96	84.65 301	94.50 42	79.15 77	82.23 85	87.93 211	66.88 60	96.94 104	80.53 119	82.20 168	96.39 34

tfpn200view9		78.79 186	77.43 187	82.88 185	92.21 94	64.49 173	92.05 154	96.28 4	73.48 163	71.75 207	88.26 203	60.07 142	95.32 174	45.16 344	77.58 209	88.83 233

thres400		78.68 188	77.43 187	82.43 195	92.21 94	64.49 173	92.05 154	96.28 4	73.48 163	71.75 207	88.26 203	60.07 142	95.32 174	45.16 344	77.58 209	87.48 252

MM		90.87 2	91.52 2	88.92 16	92.12 96	71.10 29	97.02 3	96.04 6	88.70 2	91.57 14	96.19 32	70.12 42	98.91 18	96.83 1	95.06 17	96.76 15

PS-MVSNAJ		88.14 18	87.61 29	89.71 7	92.06 97	76.72 1	95.75 20	93.26 91	83.86 15	89.55 29	96.06 36	53.55 219	97.89 43	91.10 32	93.31 55	94.54 107

SR-MVS-dyc-post		81.06 143	80.70 136	82.15 207	92.02 98	58.56 302	90.90 205	90.45 204	62.76 309	78.89 125	94.46 80	51.26 242	95.61 162	78.77 135	86.77 128	92.28 181

RE-MVS-def			80.48 142		92.02 98	58.56 302	90.90 205	90.45 204	62.76 309	78.89 125	94.46 80	49.30 258		78.77 135	86.77 128	92.28 181

MSLP-MVS++		86.27 48	85.91 55	87.35 47	92.01 100	68.97 66	95.04 40	92.70 112	79.04 82	81.50 90	96.50 25	58.98 156	96.78 112	83.49 98	93.93 42	96.29 36

CS-MVS		85.80 58	86.65 42	83.27 179	92.00 101	58.92 298	95.31 31	91.86 149	79.97 60	84.82 65	95.40 49	62.26 119	95.51 170	86.11 74	92.08 70	95.37 62

旧先验1					91.94 102	60.74 271		91.50 168			94.36 84	65.23 76			91.84 73	94.55 105

thres600view7		78.00 199	76.66 201	82.03 214	91.93 103	63.69 203	91.30 192	96.33 1	72.43 186	70.46 220	87.89 212	60.31 137	94.92 188	42.64 356	76.64 219	87.48 252

LS3D		69.17 296	66.40 301	77.50 294	91.92 104	56.12 325	85.12 298	80.37 359	46.96 378	56.50 340	87.51 220	37.25 332	93.71 238	32.52 386	79.40 192	82.68 332

GG-mvs-BLEND				86.53 73	91.91 105	69.67 53	75.02 367	94.75 32		78.67 132	90.85 163	77.91 7	94.56 202	72.25 180	93.74 46	95.36 64

thres100view900		78.37 194	77.01 196	82.46 194	91.89 106	63.21 217	91.19 199	96.33 1	72.28 191	70.45 221	87.89 212	60.31 137	95.32 174	45.16 344	77.58 209	88.83 233

MTAPA		83.91 94	83.38 95	85.50 103	91.89 106	65.16 162	81.75 326	92.23 130	75.32 132	80.53 105	95.21 61	56.06 191	97.16 85	84.86 86	92.55 64	94.18 118

sasdasda		86.85 38	86.25 47	88.66 21	91.80 108	71.92 18	93.54 94	91.71 157	80.26 55	87.55 37	95.25 58	63.59 101	96.93 106	88.18 51	84.34 146	97.11 9

canonicalmvs		86.85 38	86.25 47	88.66 21	91.80 108	71.92 18	93.54 94	91.71 157	80.26 55	87.55 37	95.25 58	63.59 101	96.93 106	88.18 51	84.34 146	97.11 9

TSAR-MVS + MP.		88.11 20	88.64 18	86.54 72	91.73 110	68.04 89	90.36 224	93.55 79	82.89 20	91.29 16	92.89 121	72.27 31	96.03 144	87.99 53	94.77 26	95.54 57
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
ACMMP		81.49 135	80.67 137	83.93 161	91.71 111	62.90 227	92.13 148	92.22 133	71.79 208	71.68 209	93.49 110	50.32 247	96.96 102	78.47 137	84.22 152	91.93 191
Qingshan Xu, Weihang Kong, Wenbing Tao, Marc Pollefeys: Multi-Scale Geometric Consistency Guided and Planar Prior Assisted Multi-View Stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence
BH-RMVSNet		79.46 173	77.65 183	84.89 123	91.68 112	65.66 148	93.55 93	88.09 301	72.93 173	73.37 184	91.12 160	46.20 287	96.12 136	56.28 300	85.61 139	92.91 163

baseline1		81.84 130	81.03 131	84.28 153	91.60 113	66.62 127	91.08 201	91.66 162	81.87 31	74.86 169	91.67 150	69.98 43	94.92 188	71.76 186	64.75 301	91.29 204

ACMMP_NAP		86.05 52	85.80 57	86.80 62	91.58 114	67.53 103	91.79 168	93.49 83	74.93 137	84.61 66	95.30 53	59.42 149	97.92 41	86.13 73	94.92 20	94.94 87

MVS_Test		84.16 90	83.20 97	87.05 55	91.56 115	69.82 47	89.99 238	92.05 138	77.77 99	82.84 80	86.57 233	63.93 93	96.09 138	74.91 160	89.18 104	95.25 75

HPM-MVS_fast		80.25 158	79.55 157	82.33 199	91.55 116	59.95 284	91.32 191	89.16 259	65.23 290	74.71 171	93.07 116	47.81 274	95.74 153	74.87 162	88.23 110	91.31 203

CPTT-MVS		79.59 169	79.16 164	80.89 241	91.54 117	59.80 286	92.10 150	88.54 289	60.42 329	72.96 187	93.28 112	48.27 267	92.80 262	78.89 134	86.50 133	90.06 218

CNLPA		74.31 252	72.30 260	80.32 247	91.49 118	61.66 253	90.85 208	80.72 358	56.67 351	63.85 295	90.64 164	46.75 279	90.84 307	53.79 309	75.99 224	88.47 242

MP-MVS-pluss		85.24 68	85.13 67	85.56 102	91.42 119	65.59 151	91.54 178	92.51 124	74.56 140	80.62 103	95.64 44	59.15 153	97.00 94	86.94 68	93.80 44	94.07 126
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
gg-mvs-nofinetune		77.18 212	74.31 232	85.80 94	91.42 119	68.36 78	71.78 370	94.72 33	49.61 371	77.12 147	45.92 395	77.41 8	93.98 230	67.62 224	93.16 57	95.05 82

mvsmamba		81.55 134	80.72 135	84.03 159	91.42 119	66.93 119	83.08 317	89.13 262	78.55 89	67.50 262	87.02 228	51.79 235	90.07 320	87.48 59	90.49 94	95.10 80

MGCFI-Net		85.59 64	85.73 59	85.17 117	91.41 122	62.44 234	92.87 118	91.31 174	79.65 66	86.99 44	95.14 64	62.90 114	96.12 136	87.13 65	84.13 153	96.96 13

xiu_mvs_v2_base		87.92 23	87.38 33	89.55 12	91.41 122	76.43 3	95.74 21	93.12 99	83.53 18	89.55 29	95.95 38	53.45 223	97.68 50	91.07 33	92.62 62	94.54 107

EIA-MVS		84.84 75	84.88 71	84.69 135	91.30 124	62.36 237	93.85 76	92.04 139	79.45 69	79.33 121	94.28 92	62.42 117	96.35 127	80.05 122	91.25 85	95.38 61

alignmvs		87.28 32	86.97 37	88.24 28	91.30 124	71.14 28	95.61 25	93.56 78	79.30 73	87.07 42	95.25 58	68.43 48	96.93 106	87.87 54	84.33 148	96.65 17

EPMVS		78.49 193	75.98 210	86.02 85	91.21 126	69.68 52	80.23 341	91.20 179	75.25 133	72.48 197	78.11 329	54.65 205	93.69 239	57.66 296	83.04 158	94.69 97

FMVSNet3		77.73 205	76.04 209	82.80 186	91.20 127	68.99 65	91.87 164	91.99 141	73.35 165	67.04 269	83.19 269	56.62 183	92.14 284	59.80 287	69.34 264	87.28 258

Anonymous20240529		76.84 219	74.15 235	84.88 124	91.02 128	64.95 168	93.84 79	91.09 186	53.57 359	73.00 186	87.42 221	35.91 341	97.32 74	69.14 210	72.41 249	92.36 177

tpmvs		72.88 268	69.76 284	82.22 204	90.98 129	67.05 115	78.22 354	88.30 294	63.10 307	64.35 292	74.98 351	55.09 202	94.27 213	43.25 350	69.57 263	85.34 299

MVS		84.66 78	82.86 105	90.06 2	90.93 130	74.56 7	87.91 274	95.54 12	68.55 264	72.35 201	94.71 75	59.78 145	98.90 20	81.29 115	94.69 33	96.74 16

PVSNet	73.49 8	80.05 162	78.63 169	84.31 151	90.92 131	64.97 167	92.47 138	91.05 191	79.18 76	72.43 199	90.51 168	37.05 337	94.06 223	68.06 218	86.00 135	93.90 135

3Dnovator+	73.60 7	82.10 127	80.60 140	86.60 68	90.89 132	66.80 123	95.20 34	93.44 85	74.05 148	67.42 264	92.49 130	49.46 256	97.65 55	70.80 193	91.68 76	95.33 65

VDD-MVS		83.06 110	81.81 120	86.81 61	90.86 133	67.70 97	95.40 29	91.50 168	75.46 129	81.78 88	92.34 135	40.09 312	97.13 87	86.85 69	82.04 170	95.60 54

BH-w/o		80.49 153	79.30 162	84.05 158	90.83 134	64.36 184	93.60 91	89.42 248	74.35 143	69.09 236	90.15 179	55.23 199	95.61 162	64.61 255	86.43 134	92.17 187

ET-MVSNet_ETH3D		84.01 92	83.15 100	86.58 70	90.78 135	70.89 30	94.74 47	94.62 39	81.44 38	58.19 329	93.64 106	73.64 23	92.35 281	82.66 102	78.66 201	96.50 27

Anonymous20231211		73.08 262	70.39 278	81.13 231	90.62 136	63.33 214	91.40 182	90.06 225	51.84 364	64.46 290	80.67 305	36.49 339	94.07 222	63.83 260	64.17 307	85.98 284

FA-MVS(test-final)		79.12 177	77.23 193	84.81 129	90.54 137	63.98 193	81.35 332	91.71 157	71.09 230	74.85 170	82.94 270	52.85 226	97.05 89	67.97 219	81.73 175	93.41 146

TR-MVS		78.77 187	77.37 192	82.95 184	90.49 138	60.88 265	93.67 87	90.07 223	70.08 245	74.51 172	91.37 157	45.69 289	95.70 159	60.12 285	80.32 185	92.29 180

SteuartSystems-ACMMP		86.82 41	86.90 39	86.58 70	90.42 139	66.38 132	96.09 17	93.87 63	77.73 100	84.01 74	95.66 43	63.39 104	97.94 40	87.40 61	93.55 51	95.42 58
Skip Steuart: Steuart Systems R&D Blog.
TAPA-MVS	70.22 12	74.94 248	73.53 244	79.17 277	90.40 140	52.07 344	89.19 254	89.61 242	62.69 311	70.07 226	92.67 126	48.89 265	94.32 209	38.26 370	79.97 187	91.12 207
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
mvs_anonymous		81.36 137	79.99 148	85.46 104	90.39 141	68.40 77	86.88 291	90.61 202	74.41 141	70.31 224	84.67 253	63.79 95	92.32 282	73.13 169	85.70 137	95.67 51

CANet_DTU		84.09 91	83.52 85	85.81 93	90.30 142	66.82 121	91.87 164	89.01 269	85.27 9	86.09 51	93.74 103	47.71 275	96.98 98	77.90 141	89.78 100	93.65 141

Fast-Effi-MVS+		81.14 140	80.01 147	84.51 144	90.24 143	65.86 145	94.12 61	89.15 260	73.81 156	75.37 165	88.26 203	57.26 170	94.53 204	66.97 232	84.92 141	93.15 154

ETV-MVS		86.01 53	86.11 50	85.70 99	90.21 144	67.02 117	93.43 102	91.92 144	81.21 43	84.13 73	94.07 98	60.93 133	95.63 160	89.28 44	89.81 98	94.46 113

MVSMamba_PlusPlus		84.97 74	83.65 84	88.93 15	90.17 145	74.04 8	87.84 276	92.69 114	62.18 314	81.47 91	87.64 216	71.47 36	96.28 129	84.69 87	94.74 31	96.47 28

iter_conf05		83.18 108	81.72 121	87.58 41	90.17 145	73.92 10	83.37 312	88.63 286	62.18 314	73.79 181	87.64 216	71.47 36	96.28 129	84.69 87	93.54 52	92.54 172

tpmrst		80.57 150	79.14 165	84.84 125	90.10 147	68.28 81	81.70 327	89.72 240	77.63 104	75.96 156	79.54 321	64.94 80	92.71 265	75.43 153	77.28 215	93.55 143

PVSNet_Blended_VisFu		83.97 93	83.50 87	85.39 107	90.02 148	66.59 129	93.77 83	91.73 155	77.43 108	77.08 149	89.81 184	63.77 96	96.97 101	79.67 125	88.21 111	92.60 170

UGNet		79.87 166	78.68 168	83.45 176	89.96 149	61.51 255	92.13 148	90.79 195	76.83 113	78.85 130	86.33 237	38.16 323	96.17 134	67.93 221	87.17 122	92.67 168
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
CHOSEN 1792x2688		84.98 73	83.45 90	89.57 11	89.94 150	75.14 6	92.07 153	92.32 127	81.87 31	75.68 159	88.27 202	60.18 139	98.60 27	80.46 120	90.27 96	94.96 85

BH-untuned		78.68 188	77.08 194	83.48 175	89.84 151	63.74 198	92.70 125	88.59 287	71.57 219	66.83 273	88.65 196	51.75 236	95.39 172	59.03 290	84.77 143	91.32 202

FE-MVS		75.97 233	73.02 249	84.82 126	89.78 152	65.56 152	77.44 357	91.07 189	64.55 292	72.66 191	79.85 317	46.05 288	96.69 114	54.97 304	80.82 182	92.21 186

test222					89.77 153	61.60 254	89.55 244	89.42 248	56.83 350	77.28 145	92.43 132	52.76 227			91.14 87	93.09 156

PMMVS		81.98 129	82.04 116	81.78 216	89.76 154	56.17 324	91.13 200	90.69 197	77.96 95	80.09 111	93.57 108	46.33 285	94.99 184	81.41 112	87.46 119	94.17 119

DPM-MVS		90.70 3	90.52 9	91.24 1	89.68 155	76.68 2	97.29 1	95.35 14	82.87 21	91.58 13	97.22 3	79.93 5	99.10 9	83.12 100	97.64 2	97.94 1

QAPM		79.95 165	77.39 191	87.64 35	89.63 156	71.41 22	93.30 104	93.70 73	65.34 289	67.39 266	91.75 148	47.83 273	98.96 16	57.71 295	89.81 98	92.54 172

3Dnovator	73.91 6	82.69 118	80.82 133	88.31 27	89.57 157	71.26 24	92.60 132	94.39 50	78.84 84	67.89 257	92.48 131	48.42 266	98.52 28	68.80 214	94.40 37	95.15 77

Effi-MVS+		83.82 96	82.76 106	86.99 57	89.56 158	69.40 54	91.35 189	86.12 323	72.59 180	83.22 78	92.81 125	59.60 147	96.01 146	81.76 108	87.80 115	95.56 56

PatchmatchNet		77.46 208	74.63 225	85.96 87	89.55 159	70.35 36	79.97 346	89.55 243	72.23 192	70.94 214	76.91 340	57.03 173	92.79 263	54.27 307	81.17 178	94.74 96
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.06 276	69.98 279	78.28 286	89.51 160	55.70 328	83.49 308	83.39 350	61.24 324	63.72 296	82.76 272	34.77 345	93.03 250	53.37 312	77.59 208	86.12 281

thisisatest0515		83.41 103	82.49 111	86.16 83	89.46 161	68.26 82	93.54 94	94.70 35	74.31 144	75.75 157	90.92 161	72.62 27	96.52 122	69.64 201	81.50 176	93.71 139

h-mvs33		83.01 111	82.56 110	84.35 150	89.34 162	62.02 244	92.72 123	93.76 68	81.45 36	82.73 82	92.25 138	60.11 140	97.13 87	87.69 56	62.96 314	93.91 133

EC-MVSNet		84.53 80	85.04 69	83.01 183	89.34 162	61.37 258	94.42 51	91.09 186	77.91 97	83.24 77	94.20 94	58.37 160	95.40 171	85.35 78	91.41 81	92.27 184

UWE-MVS		80.81 148	81.01 132	80.20 252	89.33 164	57.05 318	91.91 162	94.71 34	75.67 126	75.01 168	89.37 189	63.13 110	91.44 304	67.19 229	82.80 162	92.12 189

UA-Net		80.02 163	79.65 153	81.11 232	89.33 164	57.72 309	86.33 294	89.00 272	77.44 107	81.01 98	89.15 192	59.33 151	95.90 147	61.01 279	84.28 150	89.73 225

dp		75.01 247	72.09 262	83.76 163	89.28 166	66.22 138	79.96 347	89.75 235	71.16 227	67.80 259	77.19 337	51.81 234	92.54 273	50.39 318	71.44 256	92.51 175

SDMVSNet		80.26 157	78.88 167	84.40 147	89.25 167	67.63 100	85.35 297	93.02 101	76.77 115	70.84 216	87.12 226	47.95 272	96.09 138	85.04 82	74.55 228	89.48 229

sd_testset		77.08 215	75.37 217	82.20 205	89.25 167	62.11 243	82.06 324	89.09 265	76.77 115	70.84 216	87.12 226	41.43 308	95.01 183	67.23 228	74.55 228	89.48 229

sss		82.71 117	82.38 113	83.73 166	89.25 167	59.58 289	92.24 144	94.89 27	77.96 95	79.86 113	92.38 133	56.70 181	97.05 89	77.26 144	80.86 181	94.55 105

MVSFormer		83.75 99	82.88 104	86.37 78	89.24 170	71.18 26	89.07 256	90.69 197	65.80 284	87.13 40	94.34 89	64.99 78	92.67 268	72.83 172	91.80 74	95.27 72

lupinMVS		87.74 25	87.77 27	87.63 39	89.24 170	71.18 26	96.57 12	92.90 107	82.70 23	87.13 40	95.27 56	64.99 78	95.80 149	89.34 43	91.80 74	95.93 46

IB-MVS	77.80 4	82.18 123	80.46 143	87.35 47	89.14 172	70.28 37	95.59 26	95.17 20	78.85 83	70.19 225	85.82 242	70.66 39	97.67 51	72.19 183	66.52 287	94.09 124
Christian Sormann, Mattia Rossi, Andreas Kuhn and Friedrich Fraundorfer: IB-MVS: An Iterative Algorithm for Deep Multi-View Stereo based on Binary Decisions. BMVC 2021
MDTV_nov1_ep13			72.61 256		89.06 173	68.48 75	80.33 339	90.11 222	71.84 206	71.81 206	75.92 348	53.01 225	93.92 233	48.04 330	73.38 239

testdata				81.34 226	89.02 174	57.72 309		89.84 232	58.65 340	85.32 61	94.09 96	57.03 173	93.28 246	69.34 206	90.56 93	93.03 159

CostFormer		82.33 121	81.15 126	85.86 91	89.01 175	68.46 76	82.39 323	93.01 102	75.59 127	80.25 109	81.57 289	72.03 33	94.96 185	79.06 131	77.48 212	94.16 120

GeoE		78.90 182	77.43 187	83.29 178	88.95 176	62.02 244	92.31 141	86.23 321	70.24 243	71.34 213	89.27 190	54.43 210	94.04 226	63.31 264	80.81 183	93.81 138

GBi-Net		75.65 238	73.83 240	81.10 233	88.85 177	65.11 163	90.01 235	90.32 210	70.84 234	67.04 269	80.25 312	48.03 268	91.54 299	59.80 287	69.34 264	86.64 267

test1		75.65 238	73.83 240	81.10 233	88.85 177	65.11 163	90.01 235	90.32 210	70.84 234	67.04 269	80.25 312	48.03 268	91.54 299	59.80 287	69.34 264	86.64 267

FMVSNet2		76.07 227	74.01 238	82.26 203	88.85 177	67.66 98	91.33 190	91.61 163	70.84 234	65.98 276	82.25 278	48.03 268	92.00 289	58.46 292	68.73 272	87.10 261

DeepC-MVS	77.85 3	85.52 65	85.24 65	86.37 78	88.80 180	66.64 126	92.15 147	93.68 74	81.07 44	76.91 150	93.64 106	62.59 116	98.44 31	85.50 77	92.84 61	94.03 128
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EPP-MVSNet		81.79 131	81.52 123	82.61 192	88.77 181	60.21 281	93.02 114	93.66 75	68.52 265	72.90 189	90.39 171	72.19 32	94.96 185	74.93 159	79.29 195	92.67 168

1112_ss		80.56 151	79.83 151	82.77 187	88.65 182	60.78 267	92.29 142	88.36 292	72.58 181	72.46 198	94.95 66	65.09 77	93.42 245	66.38 238	77.71 206	94.10 123

tpm cat1		75.30 243	72.21 261	84.58 141	88.52 183	67.77 95	78.16 355	88.02 302	61.88 321	68.45 250	76.37 344	60.65 134	94.03 228	53.77 310	74.11 234	91.93 191

LCM-MVSNet-Re		72.93 266	71.84 265	76.18 310	88.49 184	48.02 364	80.07 344	70.17 383	73.96 152	52.25 354	80.09 315	49.98 251	88.24 332	67.35 225	84.23 151	92.28 181

Vis-MVSNet		80.92 146	79.98 149	83.74 164	88.48 185	61.80 248	93.44 101	88.26 298	73.96 152	77.73 138	91.76 147	49.94 252	94.76 190	65.84 244	90.37 95	94.65 101
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
Vis-MVSNet (Re-imp)		79.24 175	79.57 154	78.24 288	88.46 186	52.29 343	90.41 222	89.12 263	74.24 145	69.13 235	91.91 145	65.77 71	90.09 319	59.00 291	88.09 112	92.33 178

ab-mvs		80.18 159	78.31 173	85.80 94	88.44 187	65.49 156	83.00 320	92.67 116	71.82 207	77.36 144	85.01 249	54.50 206	96.59 117	76.35 149	75.63 225	95.32 67

gm-plane-assit					88.42 188	67.04 116			78.62 88		91.83 146		97.37 70	76.57 147

MVS_111021_LR		82.02 128	81.52 123	83.51 173	88.42 188	62.88 228	89.77 241	88.93 273	76.78 114	75.55 163	93.10 113	50.31 248	95.38 173	83.82 97	87.02 123	92.26 185

test2506		83.29 105	82.92 103	84.37 149	88.39 190	63.18 219	92.01 156	91.35 173	77.66 102	78.49 133	91.42 154	64.58 86	95.09 181	73.19 168	89.23 102	94.85 88

ECVR-MVS		81.29 138	80.38 144	84.01 160	88.39 190	61.96 246	92.56 137	86.79 316	77.66 102	76.63 151	91.42 154	46.34 284	95.24 178	74.36 164	89.23 102	94.85 88

baseline		85.01 72	84.44 76	86.71 64	88.33 192	68.73 70	90.24 229	91.82 153	81.05 45	81.18 95	92.50 128	63.69 97	96.08 141	84.45 91	86.71 130	95.32 67

tpm2		79.80 167	77.95 180	85.34 110	88.28 193	68.26 82	81.56 329	91.42 171	70.11 244	77.59 142	80.50 307	67.40 57	94.26 215	67.34 226	77.35 213	93.51 144

thisisatest0530		81.15 139	80.07 145	84.39 148	88.26 194	65.63 150	91.40 182	94.62 39	71.27 226	70.93 215	89.18 191	72.47 28	96.04 143	65.62 247	76.89 218	91.49 195

casdiffmvs		85.37 66	84.87 72	86.84 59	88.25 195	69.07 62	93.04 112	91.76 154	81.27 42	80.84 101	92.07 141	64.23 89	96.06 142	84.98 84	87.43 120	95.39 60
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
Test_1112_low_res		79.56 170	78.60 170	82.43 195	88.24 196	60.39 278	92.09 151	87.99 303	72.10 197	71.84 205	87.42 221	64.62 85	93.04 249	65.80 245	77.30 214	93.85 137

casdiffmvs_mvg		85.66 62	85.18 66	87.09 53	88.22 197	69.35 58	93.74 85	91.89 147	81.47 35	80.10 110	91.45 153	64.80 83	96.35 127	87.23 64	87.69 116	95.58 55
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
PAPM		85.89 57	85.46 62	87.18 50	88.20 198	72.42 17	92.41 140	92.77 110	82.11 29	80.34 108	93.07 116	68.27 49	95.02 182	78.39 138	93.59 50	94.09 124

TESTMET0.1,1		82.41 120	81.98 118	83.72 167	88.08 199	63.74 198	92.70 125	93.77 67	79.30 73	77.61 141	87.57 219	58.19 163	94.08 221	73.91 166	86.68 131	93.33 150

ADS-MVSNet2		66.90 315	63.44 323	77.26 300	88.06 200	60.70 273	68.01 381	75.56 369	57.57 343	64.48 288	69.87 368	38.68 315	84.10 358	40.87 361	67.89 278	86.97 262

ADS-MVSNet		68.54 303	64.38 319	81.03 237	88.06 200	66.90 120	68.01 381	84.02 342	57.57 343	64.48 288	69.87 368	38.68 315	89.21 326	40.87 361	67.89 278	86.97 262

EPNet_dtu		78.80 185	79.26 163	77.43 296	88.06 200	49.71 356	91.96 161	91.95 143	77.67 101	76.56 153	91.28 158	58.51 158	90.20 317	56.37 299	80.95 180	92.39 176
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
miper_enhance_ethall		78.86 183	77.97 179	81.54 222	88.00 203	65.17 161	91.41 180	89.15 260	75.19 134	68.79 244	83.98 261	67.17 58	92.82 260	72.73 175	65.30 292	86.62 271

IS-MVSNet		80.14 160	79.41 159	82.33 199	87.91 204	60.08 283	91.97 160	88.27 296	72.90 176	71.44 212	91.73 149	61.44 127	93.66 240	62.47 272	86.53 132	93.24 151

CLD-MVS		82.73 115	82.35 114	83.86 162	87.90 205	67.65 99	95.45 28	92.18 136	85.06 10	72.58 194	92.27 136	52.46 230	95.78 150	84.18 92	79.06 196	88.16 246
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Syy-MVS		69.65 293	69.52 285	70.03 348	87.87 206	43.21 383	88.07 270	89.01 269	72.91 174	63.11 301	88.10 207	45.28 293	85.54 351	22.07 396	69.23 267	81.32 343

myMVS_eth3d		72.58 275	72.74 253	72.10 341	87.87 206	49.45 358	88.07 270	89.01 269	72.91 174	63.11 301	88.10 207	63.63 98	85.54 351	32.73 384	69.23 267	81.32 343

test1111		80.84 147	80.02 146	83.33 177	87.87 206	60.76 269	92.62 130	86.86 315	77.86 98	75.73 158	91.39 156	46.35 283	94.70 196	72.79 174	88.68 108	94.52 109

HyFIR lowres test		81.03 144	79.56 155	85.43 105	87.81 209	68.11 88	90.18 230	90.01 228	70.65 239	72.95 188	86.06 240	63.61 100	94.50 206	75.01 158	79.75 190	93.67 140

dmvs_re		76.93 216	75.36 218	81.61 220	87.78 210	60.71 272	80.00 345	87.99 303	79.42 70	69.02 239	89.47 188	46.77 278	94.32 209	63.38 263	74.45 231	89.81 222

1314		80.70 149	78.95 166	85.94 88	87.77 211	67.56 101	87.91 274	92.55 123	72.17 195	67.44 263	93.09 114	50.27 249	97.04 92	71.68 188	87.64 117	93.23 152

cl22		77.94 202	76.78 199	81.42 224	87.57 212	64.93 169	90.67 214	88.86 276	72.45 185	67.63 261	82.68 274	64.07 90	92.91 258	71.79 184	65.30 292	86.44 272

HQP-NCC					87.54 213		94.06 62		79.80 62	74.18 174

ACMP_Plane					87.54 213		94.06 62		79.80 62	74.18 174

HQP-MVS		81.14 140	80.64 138	82.64 191	87.54 213	63.66 205	94.06 62	91.70 160	79.80 62	74.18 174	90.30 173	51.63 238	95.61 162	77.63 142	78.90 197	88.63 237

NP-MVS					87.41 216	63.04 220					90.30 173

diffmvs		84.28 84	83.83 81	85.61 101	87.40 217	68.02 90	90.88 207	89.24 254	80.54 49	81.64 89	92.52 127	59.83 144	94.52 205	87.32 62	85.11 140	94.29 114
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
baseline2		83.68 102	83.42 93	84.48 145	87.37 218	66.00 141	90.06 233	95.93 8	79.71 65	69.08 237	90.39 171	77.92 6	96.28 129	78.91 133	81.38 177	91.16 206

fmvsm_s_conf0.5_n		86.39 46	86.91 38	84.82 126	87.36 219	63.54 210	94.74 47	90.02 227	82.52 24	90.14 25	96.92 13	62.93 113	97.84 46	95.28 8	82.26 165	93.07 158

plane_prior6					87.23 220	62.32 239						50.66 245

tttt0517		79.50 171	78.53 171	82.41 198	87.22 221	61.43 257	89.75 242	94.76 31	69.29 254	67.91 255	88.06 210	72.92 25	95.63 160	62.91 268	73.90 238	90.16 217

plane_prior1					87.15 222

cascas		78.18 197	75.77 213	85.41 106	87.14 223	69.11 61	92.96 115	91.15 183	66.71 278	70.47 219	86.07 239	37.49 331	96.48 125	70.15 199	79.80 189	90.65 211

fmvsm_l_conf0.5_n_a		87.44 30	88.15 24	85.30 111	87.10 224	64.19 189	94.41 52	88.14 299	80.24 58	92.54 5	96.97 10	69.52 45	97.17 83	95.89 3	88.51 109	94.56 104

CHOSEN 280x420		77.35 210	76.95 198	78.55 283	87.07 225	62.68 232	69.71 376	82.95 352	68.80 261	71.48 211	87.27 225	66.03 68	84.00 361	76.47 148	82.81 161	88.95 232

test_fmvsm_n_1920		87.69 26	88.50 19	85.27 113	87.05 226	63.55 209	93.69 86	91.08 188	84.18 13	90.17 24	97.04 8	67.58 56	97.99 39	95.72 5	90.03 97	94.26 115

fmvsm_l_conf0.5_n		87.49 28	88.19 23	85.39 107	86.95 227	64.37 182	94.30 54	88.45 290	80.51 50	92.70 4	96.86 15	69.98 43	97.15 86	95.83 4	88.08 113	94.65 101

HQP_MVS		80.34 156	79.75 152	82.12 209	86.94 228	62.42 235	93.13 108	91.31 174	78.81 85	72.53 195	89.14 193	50.66 245	95.55 167	76.74 145	78.53 202	88.39 243

plane_prior7					86.94 228	61.51 255

test-LLR		80.10 161	79.56 155	81.72 218	86.93 230	61.17 259	92.70 125	91.54 165	71.51 222	75.62 160	86.94 229	53.83 215	92.38 278	72.21 181	84.76 144	91.60 193

test-mter		79.96 164	79.38 161	81.72 218	86.93 230	61.17 259	92.70 125	91.54 165	73.85 154	75.62 160	86.94 229	49.84 254	92.38 278	72.21 181	84.76 144	91.60 193

SCA		75.82 236	72.76 252	85.01 121	86.63 232	70.08 39	81.06 334	89.19 257	71.60 218	70.01 227	77.09 338	45.53 290	90.25 312	60.43 282	73.27 240	94.68 98

AUN-MVS		78.37 194	77.43 187	81.17 229	86.60 233	57.45 314	89.46 248	91.16 181	74.11 147	74.40 173	90.49 169	55.52 196	94.57 200	74.73 163	60.43 340	91.48 196

hse-mvs2		81.12 142	81.11 130	81.16 230	86.52 234	57.48 313	89.40 249	91.16 181	81.45 36	82.73 82	90.49 169	60.11 140	94.58 198	87.69 56	60.41 341	91.41 198

xiu_mvs_v1_base_debu		82.16 124	81.12 127	85.26 114	86.42 235	68.72 71	92.59 134	90.44 207	73.12 169	84.20 70	94.36 84	38.04 325	95.73 154	84.12 93	86.81 125	91.33 199

xiu_mvs_v1_base		82.16 124	81.12 127	85.26 114	86.42 235	68.72 71	92.59 134	90.44 207	73.12 169	84.20 70	94.36 84	38.04 325	95.73 154	84.12 93	86.81 125	91.33 199

xiu_mvs_v1_base_debi		82.16 124	81.12 127	85.26 114	86.42 235	68.72 71	92.59 134	90.44 207	73.12 169	84.20 70	94.36 84	38.04 325	95.73 154	84.12 93	86.81 125	91.33 199

F-COLMAP		70.66 283	68.44 291	77.32 298	86.37 238	55.91 326	88.00 272	86.32 318	56.94 349	57.28 338	88.07 209	33.58 349	92.49 275	51.02 316	68.37 274	83.55 314

CDS-MVSNet		81.43 136	80.74 134	83.52 171	86.26 239	64.45 176	92.09 151	90.65 201	75.83 125	73.95 180	89.81 184	63.97 92	92.91 258	71.27 189	82.82 160	93.20 153
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
VDDNet		80.50 152	78.26 174	87.21 49	86.19 240	69.79 48	94.48 50	91.31 174	60.42 329	79.34 120	90.91 162	38.48 320	96.56 120	82.16 105	81.05 179	95.27 72

WB-MVSnew		77.14 213	76.18 208	80.01 258	86.18 241	63.24 216	91.26 193	94.11 59	71.72 211	73.52 183	87.29 224	45.14 294	93.00 251	56.98 297	79.42 191	83.80 312

jason		86.40 45	86.17 49	87.11 52	86.16 242	70.54 34	95.71 24	92.19 135	82.00 30	84.58 67	94.34 89	61.86 123	95.53 169	87.76 55	90.89 88	95.27 72
jason: jason.
PCF-MVS	73.15 9	79.29 174	77.63 184	84.29 152	86.06 243	65.96 143	87.03 287	91.10 185	69.86 248	69.79 232	90.64 164	57.54 169	96.59 117	64.37 257	82.29 164	90.32 215
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MS-PatchMatch		77.90 204	76.50 202	82.12 209	85.99 244	69.95 43	91.75 173	92.70 112	73.97 151	62.58 308	84.44 257	41.11 309	95.78 150	63.76 261	92.17 68	80.62 351

FIs		79.47 172	79.41 159	79.67 268	85.95 245	59.40 291	91.68 175	93.94 62	78.06 94	68.96 241	88.28 201	66.61 63	91.77 293	66.20 241	74.99 227	87.82 248

VPA-MVSNet		79.03 178	78.00 178	82.11 212	85.95 245	64.48 175	93.22 107	94.66 37	75.05 136	74.04 179	84.95 250	52.17 232	93.52 242	74.90 161	67.04 283	88.32 245

tpm		78.58 191	77.03 195	83.22 180	85.94 247	64.56 171	83.21 316	91.14 184	78.31 91	73.67 182	79.68 319	64.01 91	92.09 287	66.07 242	71.26 257	93.03 159

OpenMVS	70.45 11	78.54 192	75.92 211	86.41 77	85.93 248	71.68 20	92.74 122	92.51 124	66.49 280	64.56 287	91.96 142	43.88 299	98.10 37	54.61 305	90.65 91	89.44 231

testing3		70.38 287	70.83 272	69.03 352	85.82 249	43.93 382	90.72 213	90.56 203	68.06 267	60.24 317	86.82 231	64.83 82	84.12 357	26.33 392	64.10 308	79.04 364

OMC-MVS		78.67 190	77.91 181	80.95 239	85.76 250	57.40 315	88.49 265	88.67 283	73.85 154	72.43 199	92.10 140	49.29 259	94.55 203	72.73 175	77.89 205	90.91 209

fmvsm_s_conf0.5_n_a		85.75 59	86.09 51	84.72 133	85.73 251	63.58 207	93.79 82	89.32 251	81.42 39	90.21 23	96.91 14	62.41 118	97.67 51	94.48 10	80.56 184	92.90 164

miper_ehance_all_eth		77.60 206	76.44 203	81.09 236	85.70 252	64.41 180	90.65 215	88.64 285	72.31 189	67.37 267	82.52 275	64.77 84	92.64 271	70.67 195	65.30 292	86.24 276

KD-MVS_2432*1600		69.03 298	66.37 302	77.01 302	85.56 253	61.06 262	81.44 330	90.25 216	67.27 274	58.00 332	76.53 342	54.49 207	87.63 340	48.04 330	35.77 391	82.34 335

miper_refine_blended		69.03 298	66.37 302	77.01 302	85.56 253	61.06 262	81.44 330	90.25 216	67.27 274	58.00 332	76.53 342	54.49 207	87.63 340	48.04 330	35.77 391	82.34 335

EI-MVSNet		78.97 180	78.22 175	81.25 227	85.33 255	62.73 231	89.53 246	93.21 92	72.39 188	72.14 202	90.13 180	60.99 130	94.72 193	67.73 223	72.49 247	86.29 274

CVMVSNet		74.04 255	74.27 233	73.33 329	85.33 255	43.94 381	89.53 246	88.39 291	54.33 358	70.37 222	90.13 180	49.17 261	84.05 359	61.83 276	79.36 193	91.99 190

test_fmvsmconf_n		86.58 44	87.17 34	84.82 126	85.28 257	62.55 233	94.26 56	89.78 233	83.81 17	87.78 36	96.33 29	65.33 75	96.98 98	94.40 11	87.55 118	94.95 86

ACMH	63.93 17	68.62 301	64.81 312	80.03 257	85.22 258	63.25 215	87.72 279	84.66 336	60.83 327	51.57 357	79.43 322	27.29 370	94.96 185	41.76 357	64.84 299	81.88 339
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cl____		76.07 227	74.67 223	80.28 249	85.15 259	61.76 250	90.12 231	88.73 280	71.16 227	65.43 279	81.57 289	61.15 128	92.95 253	66.54 235	62.17 322	86.13 280

DIV-MVS_self_test		76.07 227	74.67 223	80.28 249	85.14 260	61.75 251	90.12 231	88.73 280	71.16 227	65.42 280	81.60 288	61.15 128	92.94 257	66.54 235	62.16 324	86.14 278

TAMVS		80.37 155	79.45 158	83.13 182	85.14 260	63.37 213	91.23 195	90.76 196	74.81 139	72.65 192	88.49 197	60.63 135	92.95 253	69.41 205	81.95 172	93.08 157

MSDG		69.54 294	65.73 305	80.96 238	85.11 262	63.71 201	84.19 303	83.28 351	56.95 348	54.50 345	84.03 259	31.50 357	96.03 144	42.87 354	69.13 269	83.14 324

c3_l		76.83 220	75.47 216	80.93 240	85.02 263	64.18 190	90.39 223	88.11 300	71.66 212	66.65 275	81.64 287	63.58 103	92.56 272	69.31 207	62.86 315	86.04 282

ACMP	71.68 10	75.58 241	74.23 234	79.62 270	84.97 264	59.64 287	90.80 210	89.07 267	70.39 241	62.95 304	87.30 223	38.28 321	93.87 235	72.89 171	71.45 255	85.36 298
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
FC-MVSNet-test		77.99 200	78.08 177	77.70 291	84.89 265	55.51 329	90.27 227	93.75 71	76.87 111	66.80 274	87.59 218	65.71 72	90.23 316	62.89 269	73.94 236	87.37 255

PVSNet_0	68.08 15	71.81 277	68.32 293	82.27 201	84.68 266	62.31 240	88.68 262	90.31 213	75.84 124	57.93 334	80.65 306	37.85 328	94.19 216	69.94 200	29.05 399	90.31 216

eth_miper_zixun_eth		75.96 234	74.40 231	80.66 242	84.66 267	63.02 221	89.28 251	88.27 296	71.88 203	65.73 277	81.65 286	59.45 148	92.81 261	68.13 217	60.53 338	86.14 278

WR-MVS		76.76 221	75.74 214	79.82 265	84.60 268	62.27 241	92.60 132	92.51 124	76.06 122	67.87 258	85.34 246	56.76 179	90.24 315	62.20 273	63.69 313	86.94 264

ACMH+	65.35 16	67.65 310	64.55 315	76.96 304	84.59 269	57.10 317	88.08 269	80.79 357	58.59 341	53.00 351	81.09 301	26.63 372	92.95 253	46.51 338	61.69 331	80.82 348

VPNet		78.82 184	77.53 186	82.70 189	84.52 270	66.44 131	93.93 71	92.23 130	80.46 51	72.60 193	88.38 200	49.18 260	93.13 248	72.47 179	63.97 311	88.55 240

IterMVS-LS		76.49 223	75.18 221	80.43 246	84.49 271	62.74 230	90.64 216	88.80 278	72.40 187	65.16 282	81.72 285	60.98 131	92.27 283	67.74 222	64.65 303	86.29 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		78.15 198	77.55 185	79.98 259	84.46 272	60.26 279	92.25 143	93.20 94	77.50 106	68.88 242	86.61 232	66.10 67	92.13 285	66.38 238	62.55 318	87.54 250

FMVSNet5		68.04 307	65.66 307	75.18 316	84.43 273	57.89 306	83.54 307	86.26 320	61.83 322	53.64 350	73.30 355	37.15 335	85.08 354	48.99 325	61.77 327	82.56 334

MVS-HIRNet		60.25 344	55.55 351	74.35 322	84.37 274	56.57 323	71.64 371	74.11 373	34.44 393	45.54 378	42.24 400	31.11 361	89.81 321	40.36 364	76.10 223	76.67 374

LPG-MVS_test		75.82 236	74.58 227	79.56 272	84.31 275	59.37 292	90.44 220	89.73 238	69.49 251	64.86 283	88.42 198	38.65 317	94.30 211	72.56 177	72.76 244	85.01 302

LGP-MVS_train				79.56 272	84.31 275	59.37 292		89.73 238	69.49 251	64.86 283	88.42 198	38.65 317	94.30 211	72.56 177	72.76 244	85.01 302

ACMM	69.62 13	74.34 251	72.73 254	79.17 277	84.25 277	57.87 307	90.36 224	89.93 229	63.17 306	65.64 278	86.04 241	37.79 329	94.10 219	65.89 243	71.52 254	85.55 294
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
UniMVSNet (Re)		77.58 207	76.78 199	79.98 259	84.11 278	60.80 266	91.76 171	93.17 96	76.56 119	69.93 231	84.78 252	63.32 107	92.36 280	64.89 254	62.51 320	86.78 266

test_0402		64.54 328	61.09 334	74.92 318	84.10 279	60.75 270	87.95 273	79.71 361	52.03 362	52.41 353	77.20 336	32.21 355	91.64 295	23.14 394	61.03 334	72.36 382

LTVRE_ROB	59.60 19	66.27 318	63.54 322	74.45 321	84.00 280	51.55 346	67.08 384	83.53 347	58.78 339	54.94 344	80.31 310	34.54 346	93.23 247	40.64 363	68.03 276	78.58 368
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
miper_lstm_enhance		73.05 264	71.73 267	77.03 301	83.80 281	58.32 304	81.76 325	88.88 274	69.80 249	61.01 313	78.23 328	57.19 171	87.51 342	65.34 251	59.53 343	85.27 301

Patchmatch-test		65.86 320	60.94 335	80.62 244	83.75 282	58.83 299	58.91 395	75.26 371	44.50 385	50.95 361	77.09 338	58.81 157	87.90 334	35.13 376	64.03 309	95.12 79

nrg030		80.93 145	79.86 150	84.13 156	83.69 283	68.83 68	93.23 106	91.20 179	75.55 128	75.06 167	88.22 206	63.04 112	94.74 192	81.88 107	66.88 284	88.82 235

GA-MVS		78.33 196	76.23 206	84.65 137	83.65 284	66.30 135	91.44 179	90.14 221	76.01 123	70.32 223	84.02 260	42.50 304	94.72 193	70.98 191	77.00 217	92.94 162

FMVSNet1		72.71 271	69.91 282	81.10 233	83.60 285	65.11 163	90.01 235	90.32 210	63.92 296	63.56 297	80.25 312	36.35 340	91.54 299	54.46 306	66.75 285	86.64 267

OPM-MVS		79.00 179	78.09 176	81.73 217	83.52 286	63.83 195	91.64 177	90.30 214	76.36 121	71.97 204	89.93 183	46.30 286	95.17 180	75.10 156	77.70 207	86.19 277
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
tfpnnormal		70.10 288	67.36 297	78.32 285	83.45 287	60.97 264	88.85 259	92.77 110	64.85 291	60.83 315	78.53 325	43.52 301	93.48 243	31.73 387	61.70 330	80.52 352

Effi-MVS+-dtu		76.14 226	75.28 220	78.72 282	83.22 288	55.17 331	89.87 239	87.78 306	75.42 130	67.98 253	81.43 291	45.08 295	92.52 274	75.08 157	71.63 252	88.48 241

CR-MVSNet		73.79 259	70.82 274	82.70 189	83.15 289	67.96 91	70.25 373	84.00 343	73.67 161	69.97 229	72.41 358	57.82 166	89.48 324	52.99 313	73.13 241	90.64 212

RPMNet		70.42 286	65.68 306	84.63 139	83.15 289	67.96 91	70.25 373	90.45 204	46.83 380	69.97 229	65.10 378	56.48 187	95.30 177	35.79 375	73.13 241	90.64 212

DU-MVS		76.86 217	75.84 212	79.91 262	82.96 291	60.26 279	91.26 193	91.54 165	76.46 120	68.88 242	86.35 235	56.16 188	92.13 285	66.38 238	62.55 318	87.35 256

NR-MVSNet		76.05 230	74.59 226	80.44 245	82.96 291	62.18 242	90.83 209	91.73 155	77.12 110	60.96 314	86.35 235	59.28 152	91.80 292	60.74 280	61.34 333	87.35 256

fmvsm_s_conf0.1_n		85.61 63	85.93 54	84.68 136	82.95 293	63.48 212	94.03 67	89.46 245	81.69 33	89.86 26	96.74 20	61.85 124	97.75 49	94.74 9	82.01 171	92.81 166

XXY-MVS		77.94 202	76.44 203	82.43 195	82.60 294	64.44 177	92.01 156	91.83 152	73.59 162	70.00 228	85.82 242	54.43 210	94.76 190	69.63 202	68.02 277	88.10 247

test_fmvsmvis_n_1920		83.80 97	83.48 88	84.77 130	82.51 295	63.72 200	91.37 187	83.99 345	81.42 39	77.68 139	95.74 42	58.37 160	97.58 59	93.38 14	86.87 124	93.00 161

TranMVSNet+NR-MVSNet		75.86 235	74.52 229	79.89 263	82.44 296	60.64 275	91.37 187	91.37 172	76.63 117	67.65 260	86.21 238	52.37 231	91.55 298	61.84 275	60.81 336	87.48 252

test_vis1_n_1920		81.66 132	82.01 117	80.64 243	82.24 297	55.09 332	94.76 46	86.87 314	81.67 34	84.40 69	94.63 77	38.17 322	94.67 197	91.98 27	83.34 156	92.16 188

IterMVS		72.65 274	70.83 272	78.09 289	82.17 298	62.96 223	87.64 281	86.28 319	71.56 220	60.44 316	78.85 324	45.42 292	86.66 346	63.30 265	61.83 326	84.65 306
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmtry		67.53 312	63.93 320	78.34 284	82.12 299	64.38 181	68.72 378	84.00 343	48.23 377	59.24 322	72.41 358	57.82 166	89.27 325	46.10 341	56.68 353	81.36 342

PatchT		69.11 297	65.37 310	80.32 247	82.07 300	63.68 204	67.96 383	87.62 307	50.86 368	69.37 233	65.18 377	57.09 172	88.53 330	41.59 359	66.60 286	88.74 236

MIMVSNet		71.64 278	68.44 291	81.23 228	81.97 301	64.44 177	73.05 369	88.80 278	69.67 250	64.59 286	74.79 352	32.79 351	87.82 336	53.99 308	76.35 221	91.42 197

MVP-Stereo		77.12 214	76.23 206	79.79 266	81.72 302	66.34 134	89.29 250	90.88 194	70.56 240	62.01 311	82.88 271	49.34 257	94.13 218	65.55 249	93.80 44	78.88 365
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
kuosan		60.86 342	60.24 336	62.71 365	81.57 303	46.43 374	75.70 365	85.88 325	57.98 342	48.95 368	69.53 370	58.42 159	76.53 383	28.25 391	35.87 390	65.15 389

IterMVS-SCA-FT		71.55 280	69.97 280	76.32 308	81.48 304	60.67 274	87.64 281	85.99 324	66.17 282	59.50 321	78.88 323	45.53 290	83.65 363	62.58 271	61.93 325	84.63 307

COLMAP_ROB	57.96 20	62.98 336	59.65 339	72.98 332	81.44 305	53.00 341	83.75 306	75.53 370	48.34 375	48.81 369	81.40 293	24.14 375	90.30 311	32.95 382	60.52 339	75.65 376
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
JIA-IIPM		66.06 319	62.45 329	76.88 305	81.42 306	54.45 336	57.49 396	88.67 283	49.36 372	63.86 294	46.86 394	56.06 191	90.25 312	49.53 323	68.83 270	85.95 285

WR-MVS_H		70.59 284	69.94 281	72.53 335	81.03 307	51.43 347	87.35 284	92.03 140	67.38 273	60.23 318	80.70 303	55.84 194	83.45 365	46.33 340	58.58 348	82.72 329

Fast-Effi-MVS+-dtu		75.04 246	73.37 246	80.07 255	80.86 308	59.52 290	91.20 198	85.38 329	71.90 201	65.20 281	84.84 251	41.46 307	92.97 252	66.50 237	72.96 243	87.73 249

test_fmvsmconf0.1_n		85.71 60	86.08 52	84.62 140	80.83 309	62.33 238	93.84 79	88.81 277	83.50 19	87.00 43	96.01 37	63.36 105	96.93 106	94.04 12	87.29 121	94.61 103

Baseline_NR-MVSNet		73.99 256	72.83 251	77.48 295	80.78 310	59.29 295	91.79 168	84.55 338	68.85 260	68.99 240	80.70 303	56.16 188	92.04 288	62.67 270	60.98 335	81.11 345

CP-MVSNet		70.50 285	69.91 282	72.26 338	80.71 311	51.00 350	87.23 286	90.30 214	67.84 268	59.64 320	82.69 273	50.23 250	82.30 373	51.28 315	59.28 344	83.46 318

v8		75.35 242	73.26 247	81.61 220	80.67 312	66.82 121	89.54 245	89.27 253	71.65 213	63.30 300	80.30 311	54.99 203	94.06 223	67.33 227	62.33 321	83.94 310

PS-MVSNAJss		77.26 211	76.31 205	80.13 254	80.64 313	59.16 296	90.63 218	91.06 190	72.80 177	68.58 248	84.57 255	53.55 219	93.96 231	72.97 170	71.96 251	87.27 259

TransMVSNet (Re)		70.07 289	67.66 295	77.31 299	80.62 314	59.13 297	91.78 170	84.94 334	65.97 283	60.08 319	80.44 308	50.78 244	91.87 290	48.84 326	45.46 376	80.94 347

v2v482		77.42 209	75.65 215	82.73 188	80.38 315	67.13 113	91.85 166	90.23 218	75.09 135	69.37 233	83.39 267	53.79 217	94.44 207	71.77 185	65.00 298	86.63 270

PS-CasMVS		69.86 292	69.13 287	72.07 342	80.35 316	50.57 352	87.02 288	89.75 235	67.27 274	59.19 324	82.28 277	46.58 281	82.24 374	50.69 317	59.02 345	83.39 320

v10		74.77 249	72.54 258	81.46 223	80.33 317	66.71 125	89.15 255	89.08 266	70.94 232	63.08 303	79.86 316	52.52 229	94.04 226	65.70 246	62.17 322	83.64 313

test0.0.03 1		72.76 269	72.71 255	72.88 333	80.25 318	47.99 365	91.22 196	89.45 246	71.51 222	62.51 309	87.66 215	53.83 215	85.06 355	50.16 320	67.84 280	85.58 292

fmvsm_s_conf0.1_n_a		84.76 76	84.84 73	84.53 142	80.23 319	63.50 211	92.79 120	88.73 280	80.46 51	89.84 27	96.65 22	60.96 132	97.57 61	93.80 13	80.14 186	92.53 174

v1144		76.73 222	74.88 222	82.27 201	80.23 319	66.60 128	91.68 175	90.21 220	73.69 159	69.06 238	81.89 282	52.73 228	94.40 208	69.21 208	65.23 295	85.80 288

v148		76.19 225	74.47 230	81.36 225	80.05 321	64.44 177	91.75 173	90.23 218	73.68 160	67.13 268	80.84 302	55.92 193	93.86 237	68.95 212	61.73 329	85.76 291

dmvs_testset		65.55 323	66.45 300	62.86 364	79.87 322	22.35 410	76.55 359	71.74 379	77.42 109	55.85 341	87.77 214	51.39 240	80.69 379	31.51 390	65.92 290	85.55 294

v1192		75.98 232	73.92 239	82.15 207	79.73 323	66.24 137	91.22 196	89.75 235	72.67 179	68.49 249	81.42 292	49.86 253	94.27 213	67.08 230	65.02 297	85.95 285

AllTest		61.66 338	58.06 343	72.46 336	79.57 324	51.42 348	80.17 342	68.61 386	51.25 366	45.88 374	81.23 295	19.86 386	86.58 347	38.98 367	57.01 351	79.39 360

TestCases				72.46 336	79.57 324	51.42 348		68.61 386	51.25 366	45.88 374	81.23 295	19.86 386	86.58 347	38.98 367	57.01 351	79.39 360

MDA-MVSNet-bldmvs		61.54 340	57.70 345	73.05 331	79.53 326	57.00 321	83.08 317	81.23 355	57.57 343	34.91 393	72.45 357	32.79 351	86.26 349	35.81 374	41.95 381	75.89 375

v144192		76.05 230	74.03 237	82.12 209	79.50 327	66.55 130	91.39 184	89.71 241	72.30 190	68.17 251	81.33 294	51.75 236	94.03 228	67.94 220	64.19 306	85.77 289

v1921920		75.63 240	73.49 245	82.06 213	79.38 328	66.35 133	91.07 203	89.48 244	71.98 198	67.99 252	81.22 297	49.16 262	93.90 234	66.56 234	64.56 304	85.92 287

PEN-MVS		69.46 295	68.56 289	72.17 340	79.27 329	49.71 356	86.90 290	89.24 254	67.24 277	59.08 325	82.51 276	47.23 277	83.54 364	48.42 328	57.12 349	83.25 321

v1240		75.21 245	72.98 250	81.88 215	79.20 330	66.00 141	90.75 212	89.11 264	71.63 217	67.41 265	81.22 297	47.36 276	93.87 235	65.46 250	64.72 302	85.77 289

pmmvs4		73.92 257	71.81 266	80.25 251	79.17 331	65.24 159	87.43 283	87.26 311	67.64 272	63.46 298	83.91 262	48.96 264	91.53 302	62.94 267	65.49 291	83.96 309

D2MVS		73.80 258	72.02 263	79.15 279	79.15 332	62.97 222	88.58 264	90.07 223	72.94 172	59.22 323	78.30 326	42.31 306	92.70 267	65.59 248	72.00 250	81.79 340

V42		76.46 224	74.55 228	82.19 206	79.14 333	67.82 94	90.26 228	89.42 248	73.75 157	68.63 247	81.89 282	51.31 241	94.09 220	71.69 187	64.84 299	84.66 305

pm-mvs1		72.89 267	71.09 271	78.26 287	79.10 334	57.62 311	90.80 210	89.30 252	67.66 270	62.91 305	81.78 284	49.11 263	92.95 253	60.29 284	58.89 346	84.22 308

our_test_3		68.29 305	64.69 314	79.11 280	78.92 335	64.85 170	88.40 267	85.06 332	60.32 331	52.68 352	76.12 346	40.81 310	89.80 323	44.25 349	55.65 354	82.67 333

ppachtmachnet_test		67.72 309	63.70 321	79.77 267	78.92 335	66.04 140	88.68 262	82.90 353	60.11 333	55.45 342	75.96 347	39.19 314	90.55 308	39.53 365	52.55 364	82.71 330

test_fmvs1		74.07 254	73.69 242	75.22 314	78.91 337	47.34 369	89.06 258	74.69 372	63.68 299	79.41 119	91.59 152	24.36 374	87.77 338	85.22 79	76.26 222	90.55 214

TinyColmap		60.32 343	56.42 350	72.00 343	78.78 338	53.18 340	78.36 353	75.64 368	52.30 361	41.59 388	75.82 349	14.76 393	88.35 331	35.84 373	54.71 359	74.46 377

SixPastTwentyTwo		64.92 326	61.78 333	74.34 323	78.74 339	49.76 355	83.42 311	79.51 362	62.86 308	50.27 362	77.35 333	30.92 362	90.49 310	45.89 342	47.06 373	82.78 326

EG-PatchMatch MVS		68.55 302	65.41 309	77.96 290	78.69 340	62.93 224	89.86 240	89.17 258	60.55 328	50.27 362	77.73 332	22.60 379	94.06 223	47.18 336	72.65 246	76.88 373

pmmvs5		73.35 261	71.52 268	78.86 281	78.64 341	60.61 276	91.08 201	86.90 313	67.69 269	63.32 299	83.64 263	44.33 298	90.53 309	62.04 274	66.02 289	85.46 296

UniMVSNet_ETH3D		72.74 270	70.53 277	79.36 274	78.62 342	56.64 322	85.01 299	89.20 256	63.77 298	64.84 285	84.44 257	34.05 348	91.86 291	63.94 259	70.89 259	89.57 227

XVG-OURS		74.25 253	72.46 259	79.63 269	78.45 343	57.59 312	80.33 339	87.39 308	63.86 297	68.76 245	89.62 186	40.50 311	91.72 294	69.00 211	74.25 233	89.58 226

tt0805		73.07 263	70.73 275	80.07 255	78.37 344	57.05 318	87.78 278	92.18 136	61.23 325	67.04 269	86.49 234	31.35 359	94.58 198	65.06 253	67.12 282	88.57 239

test_cas_vis1_n_1920		80.45 154	80.61 139	79.97 261	78.25 345	57.01 320	94.04 66	88.33 293	79.06 81	82.81 81	93.70 104	38.65 317	91.63 296	90.82 36	79.81 188	91.27 205

XVG-OURS-SEG-HR		74.70 250	73.08 248	79.57 271	78.25 345	57.33 316	80.49 337	87.32 309	63.22 304	68.76 245	90.12 182	44.89 296	91.59 297	70.55 197	74.09 235	89.79 223

MDA-MVSNet_test_wron		63.78 333	60.16 337	74.64 319	78.15 347	60.41 277	83.49 308	84.03 341	56.17 354	39.17 390	71.59 364	37.22 333	83.24 368	42.87 354	48.73 370	80.26 355

YYNet1		63.76 334	60.14 338	74.62 320	78.06 348	60.19 282	83.46 310	83.99 345	56.18 353	39.25 389	71.56 365	37.18 334	83.34 366	42.90 353	48.70 371	80.32 354

DTE-MVSNet		68.46 304	67.33 298	71.87 344	77.94 349	49.00 362	86.16 295	88.58 288	66.36 281	58.19 329	82.21 279	46.36 282	83.87 362	44.97 347	55.17 356	82.73 328

USDC		67.43 314	64.51 316	76.19 309	77.94 349	55.29 330	78.38 352	85.00 333	73.17 167	48.36 370	80.37 309	21.23 381	92.48 276	52.15 314	64.02 310	80.81 349

mamv4		65.18 325	67.43 296	58.44 368	77.88 351	49.36 361	69.40 377	70.99 382	48.31 376	57.78 335	85.53 245	59.01 155	51.88 406	73.67 167	64.32 305	74.07 378

jajsoiax		73.05 264	71.51 269	77.67 292	77.46 352	54.83 333	88.81 260	90.04 226	69.13 258	62.85 306	83.51 265	31.16 360	92.75 264	70.83 192	69.80 260	85.43 297

mvs_tets		72.71 271	71.11 270	77.52 293	77.41 353	54.52 335	88.45 266	89.76 234	68.76 263	62.70 307	83.26 268	29.49 364	92.71 265	70.51 198	69.62 262	85.34 299

N_pmnet		50.55 355	49.11 358	54.88 374	77.17 354	4.02 418	84.36 302	2.00 416	48.59 373	45.86 376	68.82 371	32.22 354	82.80 370	31.58 388	51.38 366	77.81 371

test_djsdf		73.76 260	72.56 257	77.39 297	77.00 355	53.93 337	89.07 256	90.69 197	65.80 284	63.92 293	82.03 281	43.14 303	92.67 268	72.83 172	68.53 273	85.57 293

OpenMVS_ROB	61.12 18	66.39 317	62.92 326	76.80 306	76.51 356	57.77 308	89.22 252	83.41 349	55.48 355	53.86 349	77.84 331	26.28 373	93.95 232	34.90 377	68.76 271	78.68 367

v7n		71.31 281	68.65 288	79.28 275	76.40 357	60.77 268	86.71 292	89.45 246	64.17 295	58.77 328	78.24 327	44.59 297	93.54 241	57.76 294	61.75 328	83.52 316

K. test v3		63.09 335	59.61 340	73.53 328	76.26 358	49.38 360	83.27 313	77.15 364	64.35 294	47.77 372	72.32 360	28.73 366	87.79 337	49.93 322	36.69 389	83.41 319

RPSCF		64.24 330	61.98 332	71.01 346	76.10 359	45.00 378	75.83 364	75.94 366	46.94 379	58.96 326	84.59 254	31.40 358	82.00 375	47.76 334	60.33 342	86.04 282

OurMVSNet-221017-0		64.68 327	62.17 331	72.21 339	76.08 360	47.35 368	80.67 336	81.02 356	56.19 352	51.60 356	79.66 320	27.05 371	88.56 329	53.60 311	53.63 361	80.71 350

dongtai		55.18 353	55.46 352	54.34 376	76.03 361	36.88 394	76.07 362	84.61 337	51.28 365	43.41 385	64.61 380	56.56 185	67.81 394	18.09 399	28.50 400	58.32 392

test_fmvsmconf0.01_n		83.70 101	83.52 85	84.25 154	75.26 362	61.72 252	92.17 146	87.24 312	82.36 26	84.91 64	95.41 48	55.60 195	96.83 111	92.85 18	85.87 136	94.21 117

Anonymous20231206		67.53 312	65.78 304	72.79 334	74.95 363	47.59 367	88.23 268	87.32 309	61.75 323	58.07 331	77.29 335	37.79 329	87.29 344	42.91 352	63.71 312	83.48 317

EGC-MVSNET		42.35 362	38.09 365	55.11 373	74.57 364	46.62 373	71.63 372	55.77 397	0.04 411	0.24 412	62.70 383	14.24 394	74.91 386	17.59 400	46.06 375	43.80 397

ITE_SJBPF				70.43 347	74.44 365	47.06 372		77.32 363	60.16 332	54.04 348	83.53 264	23.30 378	84.01 360	43.07 351	61.58 332	80.21 357

EU-MVSNet		64.01 331	63.01 325	67.02 360	74.40 366	38.86 393	83.27 313	86.19 322	45.11 383	54.27 346	81.15 300	36.91 338	80.01 381	48.79 327	57.02 350	82.19 338

XVG-ACMP-BASELINE		68.04 307	65.53 308	75.56 312	74.06 367	52.37 342	78.43 351	85.88 325	62.03 318	58.91 327	81.21 299	20.38 384	91.15 306	60.69 281	68.18 275	83.16 323

mvsany_test1		68.77 300	68.56 289	69.39 350	73.57 368	45.88 377	80.93 335	60.88 396	59.65 335	71.56 210	90.26 175	43.22 302	75.05 384	74.26 165	62.70 317	87.25 260

CL-MVSNet_self_test		69.92 290	68.09 294	75.41 313	73.25 369	55.90 327	90.05 234	89.90 230	69.96 246	61.96 312	76.54 341	51.05 243	87.64 339	49.51 324	50.59 368	82.70 331

anonymousdsp		71.14 282	69.37 286	76.45 307	72.95 370	54.71 334	84.19 303	88.88 274	61.92 320	62.15 310	79.77 318	38.14 324	91.44 304	68.90 213	67.45 281	83.21 322

lessismore_v0				73.72 327	72.93 371	47.83 366		61.72 395		45.86 376	73.76 354	28.63 368	89.81 321	47.75 335	31.37 396	83.53 315

pmmvs6		67.57 311	64.76 313	76.00 311	72.82 372	53.37 339	88.71 261	86.78 317	53.19 360	57.58 337	78.03 330	35.33 344	92.41 277	55.56 302	54.88 358	82.21 337

testgi		64.48 329	62.87 327	69.31 351	71.24 373	40.62 388	85.49 296	79.92 360	65.36 288	54.18 347	83.49 266	23.74 377	84.55 356	41.60 358	60.79 337	82.77 327

Patchmatch-RL test		68.17 306	64.49 317	79.19 276	71.22 374	53.93 337	70.07 375	71.54 381	69.22 255	56.79 339	62.89 382	56.58 184	88.61 327	69.53 204	52.61 363	95.03 84

test_fmvs1_n		72.69 273	71.92 264	74.99 317	71.15 375	47.08 371	87.34 285	75.67 367	63.48 301	78.08 136	91.17 159	20.16 385	87.87 335	84.65 89	75.57 226	90.01 220

Gipuma		34.91 369	31.44 372	45.30 384	70.99 376	39.64 392	19.85 406	72.56 376	20.10 402	16.16 406	21.47 407	5.08 407	71.16 390	13.07 404	43.70 379	25.08 404
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		65.79 321	63.10 324	73.88 325	70.71 377	50.29 354	81.09 333	89.88 231	72.58 181	49.25 367	74.77 353	32.57 353	87.43 343	55.96 301	41.04 383	83.90 311

CMPMVS	48.56 21	66.77 316	64.41 318	73.84 326	70.65 378	50.31 353	77.79 356	85.73 328	45.54 382	44.76 380	82.14 280	35.40 343	90.14 318	63.18 266	74.54 230	81.07 346
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test20.03		63.83 332	62.65 328	67.38 359	70.58 379	39.94 389	86.57 293	84.17 340	63.29 303	51.86 355	77.30 334	37.09 336	82.47 371	38.87 369	54.13 360	79.73 358

MIMVSNet1		60.16 345	57.33 346	68.67 353	69.71 380	44.13 380	78.92 349	84.21 339	55.05 356	44.63 381	71.85 362	23.91 376	81.54 377	32.63 385	55.03 357	80.35 353

test_vis1_n		71.63 279	70.73 275	74.31 324	69.63 381	47.29 370	86.91 289	72.11 377	63.21 305	75.18 166	90.17 177	20.40 383	85.76 350	84.59 90	74.42 232	89.87 221

pmmvs-eth3d		65.53 324	62.32 330	75.19 315	69.39 382	59.59 288	82.80 321	83.43 348	62.52 312	51.30 359	72.49 356	32.86 350	87.16 345	55.32 303	50.73 367	78.83 366

UnsupCasMVSNet_bld		61.60 339	57.71 344	73.29 330	68.73 383	51.64 345	78.61 350	89.05 268	57.20 347	46.11 373	61.96 385	28.70 367	88.60 328	50.08 321	38.90 387	79.63 359

test_vis1_rt		59.09 348	57.31 347	64.43 362	68.44 384	46.02 376	83.05 319	48.63 405	51.96 363	49.57 365	63.86 381	16.30 388	80.20 380	71.21 190	62.79 316	67.07 388

Anonymous20240521		62.09 337	59.08 341	71.10 345	67.19 385	48.72 363	83.91 305	85.23 331	50.38 369	47.84 371	71.22 367	20.74 382	85.51 353	46.47 339	58.75 347	79.06 363

test_fmvs2		65.78 322	64.84 311	68.60 354	66.54 386	41.71 385	83.27 313	69.81 384	54.38 357	67.91 255	84.54 256	15.35 390	81.22 378	75.65 152	66.16 288	82.88 325

KD-MVS_self_test		60.87 341	58.60 342	67.68 357	66.13 387	39.93 390	75.63 366	84.70 335	57.32 346	49.57 365	68.45 372	29.55 363	82.87 369	48.09 329	47.94 372	80.25 356

new-patchmatchnet		59.30 347	56.48 349	67.79 356	65.86 388	44.19 379	82.47 322	81.77 354	59.94 334	43.65 384	66.20 376	27.67 369	81.68 376	39.34 366	41.40 382	77.50 372

PM-MVS		59.40 346	56.59 348	67.84 355	63.63 389	41.86 384	76.76 358	63.22 393	59.01 338	51.07 360	72.27 361	11.72 396	83.25 367	61.34 277	50.28 369	78.39 369

DSMNet-mixed		56.78 350	54.44 354	63.79 363	63.21 390	29.44 405	64.43 387	64.10 392	42.12 390	51.32 358	71.60 363	31.76 356	75.04 385	36.23 372	65.20 296	86.87 265

new_pmnet		49.31 356	46.44 359	57.93 369	62.84 391	40.74 387	68.47 380	62.96 394	36.48 392	35.09 392	57.81 389	14.97 392	72.18 389	32.86 383	46.44 374	60.88 391

LF4IMVS		54.01 354	52.12 355	59.69 367	62.41 392	39.91 391	68.59 379	68.28 388	42.96 389	44.55 382	75.18 350	14.09 395	68.39 393	41.36 360	51.68 365	70.78 383

WB-MVS		46.23 359	44.94 361	50.11 379	62.13 393	21.23 412	76.48 360	55.49 398	45.89 381	35.78 391	61.44 387	35.54 342	72.83 388	9.96 406	21.75 401	56.27 394

ambc				69.61 349	61.38 394	41.35 386	49.07 401	85.86 327		50.18 364	66.40 375	10.16 398	88.14 333	45.73 343	44.20 377	79.32 362

SSC-MVS		44.51 361	43.35 363	47.99 383	61.01 395	18.90 414	74.12 368	54.36 399	43.42 388	34.10 394	60.02 388	34.42 347	70.39 391	9.14 408	19.57 402	54.68 395

TDRefinement		55.28 352	51.58 356	66.39 361	59.53 396	46.15 375	76.23 361	72.80 375	44.60 384	42.49 386	76.28 345	15.29 391	82.39 372	33.20 381	43.75 378	70.62 384

pmmvs3		55.51 351	51.50 357	67.53 358	57.90 397	50.93 351	80.37 338	73.66 374	40.63 391	44.15 383	64.75 379	16.30 388	78.97 382	44.77 348	40.98 385	72.69 380

test_method		38.59 367	35.16 370	48.89 381	54.33 398	21.35 411	45.32 402	53.71 400	7.41 408	28.74 396	51.62 392	8.70 401	52.87 405	33.73 378	32.89 395	72.47 381

test_fmvs3		56.82 349	54.86 353	62.69 366	53.59 399	35.47 396	75.87 363	65.64 391	43.91 386	55.10 343	71.43 366	6.91 404	74.40 387	68.64 215	52.63 362	78.20 370

APD_test1		40.50 364	37.31 367	50.09 380	51.88 400	35.27 397	59.45 394	52.59 401	21.64 400	26.12 398	57.80 390	4.56 408	66.56 396	22.64 395	39.09 386	48.43 396

DeepMVS_CX				34.71 389	51.45 401	24.73 409		28.48 415	31.46 396	17.49 405	52.75 391	5.80 406	42.60 410	18.18 398	19.42 403	36.81 402

FPMVS		45.64 360	43.10 364	53.23 377	51.42 402	36.46 395	64.97 386	71.91 378	29.13 397	27.53 397	61.55 386	9.83 399	65.01 400	16.00 403	55.58 355	58.22 393

wuyk23d		11.30 378	10.95 381	12.33 393	48.05 403	19.89 413	25.89 405	1.92 417	3.58 409	3.12 411	1.37 411	0.64 416	15.77 412	6.23 411	7.77 410	1.35 408

PMMVS2		37.93 368	33.61 371	50.92 378	46.31 404	24.76 408	60.55 393	50.05 402	28.94 398	20.93 400	47.59 393	4.41 410	65.13 399	25.14 393	18.55 404	62.87 390

mvsany_test3		48.86 357	46.35 360	56.41 370	46.00 405	31.67 401	62.26 389	47.25 406	43.71 387	45.54 378	68.15 373	10.84 397	64.44 402	57.95 293	35.44 393	73.13 379

test_f		46.58 358	43.45 362	55.96 371	45.18 406	32.05 400	61.18 390	49.49 404	33.39 394	42.05 387	62.48 384	7.00 403	65.56 398	47.08 337	43.21 380	70.27 385

test_vis3_rt		40.46 365	37.79 366	48.47 382	44.49 407	33.35 399	66.56 385	32.84 413	32.39 395	29.65 395	39.13 403	3.91 411	68.65 392	50.17 319	40.99 384	43.40 398

E-PMN		24.61 373	24.00 377	26.45 390	43.74 408	18.44 415	60.86 391	39.66 409	15.11 405	9.53 409	22.10 406	6.52 405	46.94 408	8.31 409	10.14 406	13.98 406

testf1		32.77 370	29.47 373	42.67 386	41.89 409	30.81 402	52.07 397	43.45 407	15.45 403	18.52 403	44.82 397	2.12 412	58.38 403	16.05 401	30.87 397	38.83 399

APD_test2		32.77 370	29.47 373	42.67 386	41.89 409	30.81 402	52.07 397	43.45 407	15.45 403	18.52 403	44.82 397	2.12 412	58.38 403	16.05 401	30.87 397	38.83 399

EMVS		23.76 375	23.20 379	25.46 391	41.52 411	16.90 416	60.56 392	38.79 412	14.62 406	8.99 410	20.24 409	7.35 402	45.82 409	7.25 410	9.46 407	13.64 407

LCM-MVSNet		40.54 363	35.79 368	54.76 375	36.92 412	30.81 402	51.41 399	69.02 385	22.07 399	24.63 399	45.37 396	4.56 408	65.81 397	33.67 379	34.50 394	67.67 386

ANet_high		40.27 366	35.20 369	55.47 372	34.74 413	34.47 398	63.84 388	71.56 380	48.42 374	18.80 402	41.08 401	9.52 400	64.45 401	20.18 397	8.66 409	67.49 387

MVE	24.84 23	24.35 374	19.77 380	38.09 388	34.56 414	26.92 407	26.57 404	38.87 411	11.73 407	11.37 408	27.44 404	1.37 415	50.42 407	11.41 405	14.60 405	36.93 401
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMVS	26.43 22	31.84 372	28.16 375	42.89 385	25.87 415	27.58 406	50.92 400	49.78 403	21.37 401	14.17 407	40.81 402	2.01 414	66.62 395	9.61 407	38.88 388	34.49 403
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
tmp_tt		22.26 376	23.75 378	17.80 392	5.23 416	12.06 417	35.26 403	39.48 410	2.82 410	18.94 401	44.20 399	22.23 380	24.64 411	36.30 371	9.31 408	16.69 405

testmvs		7.23 380	9.62 383	0.06 395	0.04 417	0.02 420	84.98 300	0.02 418	0.03 412	0.18 413	1.21 412	0.01 418	0.02 413	0.14 412	0.01 411	0.13 410

test123		6.92 381	9.21 384	0.08 394	0.03 418	0.05 419	81.65 328	0.01 419	0.02 413	0.14 414	0.85 413	0.03 417	0.02 413	0.12 413	0.00 412	0.16 409

test_blank		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

eth-test2					0.00 419

eth-test					0.00 419

uanet_test		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

DCPMVS		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

cdsmvs_eth3d_5k		19.86 377	26.47 376	0.00 396	0.00 419	0.00 421	0.00 407	93.45 84	0.00 414	0.00 415	95.27 56	49.56 255	0.00 415	0.00 414	0.00 412	0.00 411

pcd_1.5k_mvsjas		4.46 382	5.95 385	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	53.55 219	0.00 415	0.00 414	0.00 412	0.00 411

sosnet-low-res		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

sosnet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

uncertanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

Regformer		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

ab-mvs-re		7.91 379	10.55 382	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	94.95 66	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

uanet		0.00 383	0.00 386	0.00 396	0.00 419	0.00 421	0.00 407	0.00 420	0.00 414	0.00 415	0.00 414	0.00 419	0.00 415	0.00 414	0.00 412	0.00 411

WAC-MVS						49.45 358								31.56 389

PC_three_1452									80.91 46	94.07 2	96.83 18	83.57 4	99.12 5	95.70 7	97.42 4	97.55 4

test_241102_TWO								94.41 47	71.65 213	92.07 9	97.21 4	74.58 17	99.11 6	92.34 22	95.36 14	96.59 19

test_0728_THIRD									72.48 183	90.55 20	96.93 11	76.24 11	99.08 11	91.53 30	94.99 18	96.43 32

GSMVS																94.68 98

sam_mvs1												57.85 165				94.68 98

sam_mvs												54.91 204

MTGPA								92.23 130

test_post1							78.95 348				20.70 408	53.05 224	91.50 303	60.43 282

test_post											23.01 405	56.49 186	92.67 268

patchmatchnet-post											67.62 374	57.62 168	90.25 312

MTMP							93.77 83	32.52 414

test9_res														89.41 41	94.96 19	95.29 69

agg_prior2														86.41 71	94.75 30	95.33 65

test_prior4						67.18 112	93.92 72

test_prior2							95.10 38		75.40 131	85.25 63	95.61 45	67.94 53		87.47 60	94.77 26

旧先验2							92.00 159		59.37 337	87.54 39			93.47 244	75.39 154

新几何2							91.41 180

无先验							92.71 124	92.61 121	62.03 318				97.01 93	66.63 233		93.97 130

原ACMM2							92.01 156

testdata2													96.09 138	61.26 278

segment_acmp												65.94 69

testdata1							89.21 253		77.55 105

plane_prior5								91.31 174					95.55 167	76.74 145	78.53 202	88.39 243

plane_prior4											89.14 193

plane_prior3						61.95 247			79.09 79	72.53 195

plane_prior2							93.13 108		78.81 85

plane_prior						62.42 235	93.85 76		79.38 71						78.80 199

n2								0.00 420

nn								0.00 420

door-mid								66.01 390

test11								93.01 102

door								66.57 389

HQP5-MVS						63.66 205

BP-MVS														77.63 142

HQP4-MVS										74.18 174			95.61 162			88.63 237

HQP3-MVS								91.70 160							78.90 197

HQP2-MVS												51.63 238

MDTV_nov1_ep13_2view						59.90 285	80.13 343		67.65 271	72.79 190		54.33 212		59.83 286		92.58 171

ACMMP++_ref															71.63 252

ACMMP++															69.72 261

Test By Simon												54.21 213

High-resolution multi-view benchmark