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.

MM		80.20 7	80.28 8	79.99 2	82.19 79	60.01 46	86.19 17	83.93 51	73.19 1	77.08 30	91.21 15	57.23 31	90.73 10	83.35 1	88.12 35	89.22 5

MVS_0304		78.73 16	78.75 15	78.66 30	80.82 101	57.62 83	85.31 30	81.31 112	70.51 2	74.17 58	91.24 14	54.99 45	89.56 17	82.29 2	88.13 34	88.80 7

CANet		76.46 37	75.93 40	78.06 39	81.29 93	57.53 85	82.35 69	83.31 74	67.78 3	70.09 109	86.34 101	54.92 47	88.90 25	72.68 57	84.55 65	87.76 32

UA-Net		73.13 68	72.93 68	73.76 117	83.58 64	51.66 187	78.75 118	77.66 184	67.75 4	72.61 87	89.42 47	49.82 108	83.29 143	53.61 199	83.14 75	86.32 80

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 36	85.03 34	66.96 5	77.58 27	90.06 36	59.47 20	89.13 22	78.67 14	89.73 16	87.03 53

TranMVSNet+NR-MVSNet		70.36 116	70.10 112	71.17 188	78.64 149	42.97 289	76.53 173	81.16 119	66.95 6	68.53 138	85.42 128	51.61 92	83.07 147	52.32 207	69.70 255	87.46 41

3Dnovator+	66.72 4	75.84 45	74.57 53	79.66 9	82.40 76	59.92 48	85.83 22	86.32 16	66.92 7	67.80 157	89.24 51	42.03 197	89.38 19	64.07 116	86.50 55	89.69 2

NCCC		78.58 17	78.31 19	79.39 12	87.51 12	62.61 13	85.20 31	84.42 42	66.73 8	74.67 51	89.38 49	55.30 42	89.18 21	74.19 46	87.34 43	86.38 72

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 72	62.18 16	87.60 9	85.83 19	66.69 9	78.03 26	90.98 16	54.26 53	90.06 13	78.42 19	89.02 23	87.69 33
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 69	72.16 75	75.90 65	75.95 219	56.28 104	83.05 56	72.39 256	66.53 10	65.27 206	87.00 81	50.40 104	85.47 102	62.48 133	86.32 56	85.94 92
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 100	71.00 95	71.44 177	79.20 134	44.13 277	76.02 186	82.60 87	66.48 11	68.20 142	84.60 140	56.82 33	82.82 158	54.62 190	70.43 235	87.36 48

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 23	63.71 12	89.23 20	81.51 3	88.44 27	88.09 21
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
HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 61	65.37 13	78.78 22	90.64 19	58.63 24	87.24 51	79.00 12	90.37 14	85.26 127

NR-MVSNet		69.54 138	68.85 130	71.59 174	78.05 167	43.81 281	74.20 220	80.86 125	65.18 14	62.76 242	84.52 141	52.35 80	83.59 139	50.96 222	70.78 230	87.37 46

MTAPA		76.90 34	76.42 35	78.35 35	86.08 37	63.57 2	74.92 208	80.97 123	65.13 15	75.77 35	90.88 17	48.63 122	86.66 70	77.23 24	88.17 33	84.81 140

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 60	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 6	91.38 2	88.42 11

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 6	90.63 10	88.09 21

EI-MVSNet-Vis-set		72.42 80	71.59 80	74.91 84	78.47 153	54.02 141	77.05 162	79.33 147	65.03 18	71.68 97	79.35 251	52.75 72	84.89 114	66.46 96	74.23 180	85.83 98

casdiffmvs_mvg		76.14 41	76.30 36	75.66 71	76.46 213	51.83 186	79.67 109	85.08 31	65.02 19	75.84 34	88.58 60	59.42 21	85.08 108	72.75 56	83.93 72	90.08 1
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_one_0601					87.58 9	59.30 57		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

ETV-MVS		74.46 58	73.84 61	76.33 60	79.27 132	55.24 129	79.22 115	85.00 36	64.97 21	72.65 86	79.46 248	53.65 66	87.87 42	67.45 90	82.91 81	85.89 96

WR-MVS		68.47 164	68.47 141	68.44 234	80.20 113	39.84 311	73.75 232	76.07 207	64.68 22	68.11 147	83.63 160	50.39 105	79.14 232	49.78 227	69.66 256	86.34 76

XVS		77.17 31	76.56 34	79.00 23	86.32 29	62.62 11	85.83 22	83.92 52	64.55 23	72.17 92	90.01 40	47.95 129	88.01 38	71.55 65	86.74 52	86.37 74

X-MVStestdata		70.21 119	67.28 171	79.00 23	86.32 29	62.62 11	85.83 22	83.92 52	64.55 23	72.17 92	6.49 399	47.95 129	88.01 38	71.55 65	86.74 52	86.37 74

HQP_MVS		74.31 59	73.73 62	76.06 62	81.41 90	56.31 102	84.22 40	84.01 49	64.52 25	69.27 127	86.10 108	45.26 170	87.21 53	68.16 81	80.58 103	84.65 144

plane_prior2							84.22 40		64.52 25

EI-MVSNet-UG-set		71.92 88	71.06 94	74.52 99	77.98 170	53.56 149	76.62 171	79.16 148	64.40 27	71.18 100	78.95 256	52.19 82	84.66 120	65.47 107	73.57 191	85.32 123

DU-MVS		70.01 122	69.53 119	71.44 177	78.05 167	44.13 277	75.01 205	81.51 102	64.37 28	68.20 142	84.52 141	49.12 119	82.82 158	54.62 190	70.43 235	87.37 46

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 64	87.85 5	85.03 34	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 15	90.61 11	85.45 116
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					87.75 7	59.07 64	87.86 4	86.83 8	64.26 29	84.19 7	91.92 5	64.82 8

test_241102_ONE					87.77 4	58.90 69		86.78 10	64.20 31	85.97 1	91.34 12	66.87 3	90.78 7

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 69	87.82 7	86.78 10	64.18 32	85.97 1	91.84 8	66.87 3	90.83 5	78.63 17	90.87 5	88.23 16

test_241102_TWO								86.73 12	64.18 32	84.26 5	91.84 8	65.19 6	90.83 5	78.63 17	90.70 7	87.65 35

LFMVS		71.78 90	71.59 80	72.32 158	83.40 67	46.38 254	79.75 107	71.08 265	64.18 32	72.80 83	88.64 59	42.58 192	83.72 135	57.41 168	84.49 66	86.86 58

IS-MVSNet		71.57 94	71.00 95	73.27 139	78.86 142	45.63 265	80.22 97	78.69 159	64.14 35	66.46 182	87.36 76	49.30 113	85.60 95	50.26 226	83.71 74	88.59 9

plane_prior3						56.09 108			63.92 36	69.27 127

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 65	63.89 37	73.60 65	90.60 20	54.85 48	86.72 68	77.20 25	88.06 37	85.74 105
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DELS-MVS		74.76 52	74.46 54	75.65 72	77.84 174	52.25 178	75.59 192	84.17 46	63.76 38	73.15 73	82.79 174	59.58 19	86.80 66	67.24 91	86.04 57	87.89 24
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
OPM-MVS		74.73 53	74.25 56	76.19 61	80.81 102	59.01 67	82.60 66	83.64 62	63.74 39	72.52 88	87.49 74	47.18 144	85.88 90	69.47 74	80.78 99	83.66 179
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 110	70.20 108	71.89 163	78.55 150	45.29 268	75.94 187	82.92 82	63.68 40	68.16 145	83.59 161	53.89 58	83.49 141	53.97 195	71.12 228	86.89 57

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 21	85.21 29	63.56 41	74.29 57	90.03 38	52.56 74	88.53 29	74.79 42	88.34 29	86.63 68

EC-MVSNet		75.84 45	75.87 42	75.74 69	78.86 142	52.65 168	83.73 50	86.08 17	63.47 42	72.77 84	87.25 80	53.13 69	87.93 40	71.97 61	85.57 60	86.66 66

ZNCC-MVS		78.82 13	78.67 17	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 43	75.08 40	90.47 26	53.96 57	88.68 27	76.48 28	89.63 20	87.16 51

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 26	86.42 14	63.28 44	83.27 13	91.83 10	64.96 7	90.47 11	76.41 29	89.67 18	86.84 59
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS		76.25 40	75.98 39	77.06 50	80.15 116	55.63 120	84.51 35	83.90 54	63.24 45	73.30 68	87.27 79	55.06 44	86.30 83	71.78 62	84.58 64	89.25 4

DeepC-MVS	69.38 2	78.56 18	78.14 22	79.83 7	83.60 63	61.62 23	84.17 42	86.85 6	63.23 46	73.84 63	90.25 32	57.68 27	89.96 14	74.62 43	89.03 22	87.89 24
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		72.50 77	72.09 76	73.75 119	81.58 86	49.69 217	77.76 143	77.63 185	63.21 47	73.21 71	89.02 53	42.14 196	83.32 142	61.72 140	82.50 87	88.25 15

plane_prior						56.31 102	83.58 53		63.19 48						80.48 106

ACMMP		76.02 43	75.33 46	78.07 38	85.20 49	61.91 20	85.49 29	84.44 41	63.04 49	69.80 119	89.74 46	45.43 166	87.16 55	72.01 60	82.87 83	85.14 129
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
PEN-MVS		66.60 204	66.45 184	67.04 248	77.11 199	36.56 343	77.03 163	80.42 131	62.95 50	62.51 250	84.03 151	46.69 152	79.07 233	44.22 274	63.08 316	85.51 113

APDe-MVS		80.16 8	80.59 6	78.86 28	86.64 21	60.02 45	88.12 3	86.42 14	62.94 51	82.40 14	92.12 2	59.64 18	89.76 15	78.70 13	88.32 31	86.79 61
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mPP-MVS		76.54 36	75.93 40	78.34 36	86.47 26	63.50 3	85.74 25	82.28 90	62.90 52	71.77 95	90.26 31	46.61 153	86.55 74	71.71 63	85.66 59	84.97 136

ACMMP_NAP		78.77 15	78.78 14	78.74 29	85.44 45	61.04 31	83.84 49	85.16 30	62.88 53	78.10 24	91.26 13	52.51 75	88.39 30	79.34 8	90.52 13	86.78 62

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 44	83.03 57	85.33 27	62.86 54	80.17 17	90.03 38	61.76 14	88.95 24	74.21 45	88.67 26	88.12 20

HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 44	62.82 55	73.96 61	90.50 24	53.20 68	88.35 31	74.02 48	87.05 44	86.13 87

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 45	62.82 55	73.55 66	90.56 22	49.80 109	88.24 33	74.02 48	87.03 45	86.32 80

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 47	62.81 57	73.30 68	90.58 21	49.90 107	88.21 34	73.78 50	87.03 45	86.29 83

casdiffmvs		74.80 51	74.89 51	74.53 98	75.59 225	50.37 204	78.17 131	85.06 33	62.80 58	74.40 54	87.86 70	57.88 26	83.61 138	69.46 75	82.79 85	89.59 3
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		74.61 55	74.70 52	74.34 102	75.70 221	49.99 212	77.54 148	84.63 40	62.73 59	73.98 60	87.79 73	57.67 28	83.82 134	69.49 73	82.74 86	89.20 6

HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 60	85.08 31	62.57 60	73.09 77	89.97 41	50.90 102	87.48 49	75.30 36	86.85 50	87.33 49
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 216	65.34 207	66.31 255	76.06 218	34.79 354	76.43 175	79.38 146	62.55 61	61.66 260	83.83 156	45.60 160	79.15 231	41.64 301	60.88 331	85.00 134

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 21	62.49 62	82.20 15	92.28 1	56.53 34	89.70 16	79.85 5	91.48 1	88.19 18
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
CP-MVSNet		66.49 207	66.41 188	66.72 250	77.67 179	36.33 346	76.83 170	79.52 143	62.45 63	62.54 248	83.47 167	46.32 154	78.37 241	45.47 269	63.43 313	85.45 116

CP-MVS		77.12 32	76.68 32	78.43 33	86.05 38	63.18 5	87.55 10	83.45 68	62.44 64	72.68 85	90.50 24	48.18 127	87.34 50	73.59 52	85.71 58	84.76 143

PS-CasMVS		66.42 208	66.32 192	66.70 252	77.60 187	36.30 348	76.94 165	79.61 141	62.36 65	62.43 252	83.66 159	45.69 158	78.37 241	45.35 271	63.26 314	85.42 119

3Dnovator	64.47 5	72.49 78	71.39 86	75.79 66	77.70 177	58.99 68	80.66 94	83.15 79	62.24 66	65.46 202	86.59 93	42.38 195	85.52 98	59.59 158	84.72 63	82.85 200

MP-MVS-pluss		78.35 20	78.46 18	78.03 40	84.96 52	59.52 53	82.93 59	85.39 26	62.15 67	76.41 33	91.51 11	52.47 77	86.78 67	80.66 4	89.64 19	87.80 30
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 103		82.31 71		62.10 68	67.85 152

ACMP_Plane					80.66 103		82.31 71		62.10 68	67.85 152

HQP-MVS		73.45 65	72.80 69	75.40 76	80.66 103	54.94 131	82.31 71	83.90 54	62.10 68	67.85 152	85.54 126	45.46 164	86.93 62	67.04 93	80.35 107	84.32 151

CS-MVS-test		75.62 47	75.31 47	76.56 57	80.63 106	55.13 130	83.88 48	85.22 28	62.05 71	71.49 99	86.03 111	53.83 59	86.36 81	67.74 85	86.91 49	88.19 18

VPNet		67.52 183	68.11 148	65.74 268	79.18 135	36.80 341	72.17 253	72.83 253	62.04 72	67.79 158	85.83 119	48.88 121	76.60 270	51.30 218	72.97 204	83.81 169

WR-MVS_H		67.02 195	66.92 180	67.33 247	77.95 171	37.75 330	77.57 146	82.11 93	62.03 73	62.65 245	82.48 185	50.57 103	79.46 222	42.91 290	64.01 306	84.79 141

DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 33	84.85 38	61.98 74	73.06 78	88.88 55	53.72 62	89.06 23	68.27 78	88.04 38	87.42 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		78.82 13	79.22 12	77.60 44	82.88 74	57.83 80	84.99 32	88.13 2	61.86 75	79.16 20	90.75 18	57.96 25	87.09 60	77.08 26	90.18 15	87.87 26

PGM-MVS		76.77 35	76.06 38	78.88 27	86.14 35	62.73 9	82.55 67	83.74 60	61.71 76	72.45 91	90.34 29	48.48 125	88.13 35	72.32 58	86.85 50	85.78 99

Effi-MVS+		73.31 67	72.54 72	75.62 73	77.87 172	53.64 147	79.62 111	79.61 141	61.63 77	72.02 94	82.61 179	56.44 35	85.97 88	63.99 119	79.07 127	87.25 50

MG-MVS		73.96 62	73.89 60	74.16 107	85.65 42	49.69 217	81.59 84	81.29 114	61.45 78	71.05 101	88.11 63	51.77 89	87.73 45	61.05 146	83.09 76	85.05 133

LPG-MVS_test		72.74 74	71.74 79	75.76 67	80.22 111	57.51 86	82.55 67	83.40 70	61.32 79	66.67 179	87.33 77	39.15 229	86.59 71	67.70 86	77.30 153	83.19 191

LGP-MVS_train				75.76 67	80.22 111	57.51 86		83.40 70	61.32 79	66.67 179	87.33 77	39.15 229	86.59 71	67.70 86	77.30 153	83.19 191

CLD-MVS		73.33 66	72.68 70	75.29 80	78.82 144	53.33 156	78.23 128	84.79 39	61.30 81	70.41 106	81.04 216	52.41 78	87.12 58	64.61 115	82.49 88	85.41 120
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		74.02 61	73.46 65	75.69 70	83.01 72	60.63 40	77.29 156	78.40 173	61.18 82	70.58 104	85.97 113	54.18 55	84.00 131	67.52 89	82.98 80	82.45 207

FIs		70.82 107	71.43 84	68.98 227	78.33 158	38.14 326	76.96 164	83.59 64	61.02 83	67.33 165	86.73 87	55.07 43	81.64 179	54.61 192	79.22 123	87.14 52

FOURS1					86.12 36	60.82 37	88.18 1	83.61 63	60.87 84	81.50 16

FC-MVSNet-test		69.80 129	70.58 102	67.46 243	77.61 185	34.73 357	76.05 184	83.19 78	60.84 85	65.88 195	86.46 98	54.52 52	80.76 203	52.52 206	78.12 140	86.91 56

v8		70.33 117	69.28 124	73.49 131	73.15 260	50.22 206	78.62 122	80.78 126	60.79 86	66.45 183	82.11 197	49.35 112	84.98 111	63.58 124	68.71 270	85.28 125

CSCG		76.92 33	76.75 31	77.41 46	83.96 62	59.60 51	82.95 58	86.50 13	60.78 87	75.27 37	84.83 133	60.76 15	86.56 73	67.86 84	87.87 41	86.06 89

Vis-MVSNet		72.18 83	71.37 87	74.61 94	81.29 93	55.41 126	80.90 90	78.28 175	60.73 88	69.23 130	88.09 64	44.36 178	82.65 162	57.68 165	81.75 96	85.77 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 27	84.36 43	60.61 89	79.05 21	90.30 30	55.54 41	88.32 32	73.48 53	87.03 45	84.83 139
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 81	71.20 91	75.59 75	80.28 109	57.54 84	82.74 63	82.84 85	60.58 90	65.24 210	86.18 105	39.25 227	86.03 86	66.95 95	76.79 161	83.22 189
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testdata1							72.65 243		60.50 91

UGNet		68.81 154	67.39 166	73.06 142	78.33 158	54.47 137	79.77 106	75.40 217	60.45 92	63.22 236	84.40 144	32.71 298	80.91 199	51.71 216	80.56 105	83.81 169
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
h-mvs33		72.71 75	71.49 83	76.40 58	81.99 82	59.58 52	76.92 166	76.74 200	60.40 93	74.81 47	85.95 115	45.54 162	85.76 93	70.41 70	70.61 233	83.86 168

hse-mvs2		71.04 101	69.86 113	74.60 95	79.58 124	57.12 96	73.96 224	75.25 220	60.40 93	74.81 47	81.95 199	45.54 162	82.90 151	70.41 70	66.83 285	83.77 173

EPP-MVSNet		72.16 86	71.31 89	74.71 88	78.68 148	49.70 215	82.10 76	81.65 99	60.40 93	65.94 191	85.84 118	51.74 90	86.37 80	55.93 176	79.55 118	88.07 23

UniMVSNet_ETH3D		67.60 182	67.07 179	69.18 226	77.39 192	42.29 293	74.18 221	75.59 213	60.37 96	66.77 176	86.06 110	37.64 243	78.93 239	52.16 209	73.49 193	86.32 80

test_prior2							81.75 80		60.37 96	75.01 41	89.06 52	56.22 37		72.19 59	88.96 24

SD-MVS		77.70 26	77.62 26	77.93 42	84.47 59	61.88 21	84.55 34	83.87 57	60.37 96	79.89 18	89.38 49	54.97 46	85.58 97	76.12 31	84.94 62	86.33 78
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
VNet		69.68 133	70.19 109	68.16 237	79.73 122	41.63 302	70.53 275	77.38 190	60.37 96	70.69 103	86.63 91	51.08 98	77.09 261	53.61 199	81.69 98	85.75 104

canonicalmvs		74.67 54	74.98 50	73.71 121	78.94 141	50.56 202	80.23 96	83.87 57	60.30 100	77.15 29	86.56 95	59.65 17	82.00 174	66.01 101	82.12 89	88.58 10

v7n		69.01 152	67.36 168	73.98 110	72.51 274	52.65 168	78.54 125	81.30 113	60.26 101	62.67 244	81.62 205	43.61 183	84.49 121	57.01 169	68.70 271	84.79 141

HPM-MVS_fast		74.30 60	73.46 65	76.80 52	84.45 60	59.04 66	83.65 52	81.05 120	60.15 102	70.43 105	89.84 43	41.09 213	85.59 96	67.61 88	82.90 82	85.77 102

VPA-MVSNet		69.02 151	69.47 121	67.69 241	77.42 191	41.00 307	74.04 222	79.68 139	60.06 103	69.26 129	84.81 134	51.06 99	77.58 253	54.44 193	74.43 178	84.48 148

v10		70.21 119	69.02 128	73.81 114	73.51 257	50.92 194	78.74 119	81.39 105	60.05 104	66.39 184	81.83 202	47.58 136	85.41 105	62.80 130	68.86 269	85.09 132

SR-MVS		76.13 42	75.70 43	77.40 48	85.87 40	61.20 29	85.52 27	82.19 91	59.99 105	75.10 39	90.35 28	47.66 134	86.52 75	71.64 64	82.99 78	84.47 149

9.14			78.75 15		83.10 69		84.15 43	88.26 1	59.90 106	78.57 23	90.36 27	57.51 30	86.86 64	77.39 23	89.52 21

v2v482		70.50 113	69.45 122	73.66 123	72.62 270	50.03 211	77.58 145	80.51 130	59.90 106	69.52 121	82.14 195	47.53 137	84.88 116	65.07 111	70.17 242	86.09 88

Baseline_NR-MVSNet		67.05 194	67.56 157	65.50 270	75.65 222	37.70 332	75.42 195	74.65 233	59.90 106	68.14 146	83.15 171	49.12 119	77.20 259	52.23 208	69.78 252	81.60 219

API-MVS		72.17 84	71.41 85	74.45 100	81.95 83	57.22 89	84.03 45	80.38 132	59.89 109	68.40 139	82.33 188	49.64 110	87.83 44	51.87 213	84.16 71	78.30 267

Effi-MVS+-dtu		69.64 135	67.53 160	75.95 64	76.10 217	62.29 15	80.20 98	76.06 208	59.83 110	65.26 209	77.09 280	41.56 205	84.02 130	60.60 149	71.09 229	81.53 220

CANet_DTU		68.18 170	67.71 155	69.59 217	74.83 236	46.24 256	78.66 121	76.85 197	59.60 111	63.45 235	82.09 198	35.25 267	77.41 256	59.88 155	78.76 132	85.14 129

EI-MVSNet		69.27 148	68.44 143	71.73 169	74.47 246	49.39 222	75.20 200	78.45 169	59.60 111	69.16 131	76.51 291	51.29 94	82.50 166	59.86 157	71.45 225	83.30 186

IterMVS-LS		69.22 150	68.48 139	71.43 179	74.44 248	49.40 221	76.23 179	77.55 186	59.60 111	65.85 196	81.59 208	51.28 95	81.58 182	59.87 156	69.90 250	83.30 186
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
VDDNet		71.81 89	71.33 88	73.26 140	82.80 75	47.60 245	78.74 119	75.27 219	59.59 114	72.94 80	89.40 48	41.51 207	83.91 132	58.75 162	82.99 78	88.26 14

alignmvs		73.86 63	73.99 58	73.45 133	78.20 161	50.50 203	78.57 123	82.43 88	59.40 115	76.57 31	86.71 89	56.42 36	81.23 190	65.84 103	81.79 93	88.62 8

MVS_Test		72.45 79	72.46 73	72.42 157	74.88 234	48.50 233	76.28 178	83.14 80	59.40 115	72.46 89	84.68 135	55.66 40	81.12 191	65.98 102	79.66 115	87.63 36

TSAR-MVS + MP.		78.44 19	78.28 20	78.90 26	84.96 52	61.41 26	84.03 45	83.82 59	59.34 117	79.37 19	89.76 45	59.84 16	87.62 47	76.69 27	86.74 52	87.68 34
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSLP-MVS++		73.77 64	73.47 64	74.66 91	83.02 71	59.29 58	82.30 74	81.88 95	59.34 117	71.59 98	86.83 83	45.94 157	83.65 137	65.09 110	85.22 61	81.06 234

PAPM_NR		72.63 76	71.80 78	75.13 83	81.72 85	53.42 154	79.91 104	83.28 76	59.14 119	66.31 186	85.90 116	51.86 87	86.06 84	57.45 167	80.62 101	85.91 94

save fliter					86.17 33	61.30 28	83.98 47	79.66 140	59.00 120

v148		68.24 169	67.19 177	71.40 180	70.43 306	47.77 242	75.76 190	77.03 195	58.91 121	67.36 164	80.10 235	48.60 124	81.89 175	60.01 153	66.52 288	84.53 146

TransMVSNet (Re)		64.72 227	64.33 216	65.87 267	75.22 230	38.56 322	74.66 214	75.08 229	58.90 122	61.79 258	82.63 178	51.18 96	78.07 246	43.63 283	55.87 352	80.99 236

Anonymous202405211		66.84 199	65.99 198	69.40 221	80.19 114	42.21 295	71.11 269	71.31 264	58.80 123	67.90 150	86.39 100	29.83 319	79.65 219	49.60 233	78.78 131	86.33 78

test2506		65.33 221	64.61 214	67.50 242	79.46 127	34.19 361	74.43 218	51.92 367	58.72 124	66.75 177	88.05 66	25.99 349	80.92 198	51.94 212	84.25 68	87.39 44

ECVR-MVS		67.72 180	67.51 161	68.35 235	79.46 127	36.29 349	74.79 211	66.93 296	58.72 124	67.19 167	88.05 66	36.10 260	81.38 185	52.07 210	84.25 68	87.39 44

test1111		67.21 187	67.14 178	67.42 244	79.24 133	34.76 356	73.89 229	65.65 304	58.71 126	66.96 172	87.95 69	36.09 261	80.53 205	52.03 211	83.79 73	86.97 54

LCM-MVSNet-Re		61.88 258	61.35 251	63.46 285	74.58 244	31.48 373	61.42 335	58.14 346	58.71 126	53.02 341	79.55 246	43.07 187	76.80 265	45.69 263	77.96 142	82.11 213

v1144		70.42 115	69.31 123	73.76 117	73.22 258	50.64 199	77.83 141	81.43 104	58.58 128	69.40 125	81.16 213	47.53 137	85.29 107	64.01 118	70.64 231	85.34 122

TSAR-MVS + GP.		74.90 50	74.15 57	77.17 49	82.00 81	58.77 72	81.80 79	78.57 162	58.58 128	74.32 56	84.51 143	55.94 39	87.22 52	67.11 92	84.48 67	85.52 112

BH-RMVSNet		68.81 154	67.42 165	72.97 143	80.11 117	52.53 173	74.26 219	76.29 203	58.48 130	68.38 140	84.20 146	42.59 191	83.83 133	46.53 255	75.91 168	82.56 202

APD-MVS_3200maxsize		74.96 49	74.39 55	76.67 54	82.20 78	58.24 77	83.67 51	83.29 75	58.41 131	73.71 64	90.14 33	45.62 159	85.99 87	69.64 72	82.85 84	85.78 99

OMC-MVS		71.40 98	70.60 100	73.78 115	76.60 209	53.15 159	79.74 108	79.78 137	58.37 132	68.75 134	86.45 99	45.43 166	80.60 204	62.58 131	77.73 144	87.58 39

nrg030		72.96 71	73.01 67	72.84 146	75.41 228	50.24 205	80.02 100	82.89 84	58.36 133	74.44 53	86.73 87	58.90 23	80.83 200	65.84 103	74.46 177	87.44 42

K. test v3		60.47 268	57.11 282	70.56 199	73.74 256	48.22 236	75.10 204	62.55 325	58.27 134	53.62 337	76.31 294	27.81 336	81.59 181	47.42 246	39.18 382	81.88 217

FA-MVS(test-final)		69.82 127	68.48 139	73.84 113	78.44 154	50.04 210	75.58 194	78.99 152	58.16 135	67.59 161	82.14 195	42.66 190	85.63 94	56.60 171	76.19 166	85.84 97

MVS_111021_LR		69.50 140	68.78 133	71.65 172	78.38 155	59.33 56	74.82 210	70.11 273	58.08 136	67.83 156	84.68 135	41.96 198	76.34 275	65.62 106	77.54 146	79.30 261

SR-MVS-dyc-post		74.57 56	73.90 59	76.58 56	83.49 65	59.87 49	84.29 37	81.36 107	58.07 137	73.14 74	90.07 34	44.74 173	85.84 91	68.20 79	81.76 94	84.03 159

RE-MVS-def			73.71 63		83.49 65	59.87 49	84.29 37	81.36 107	58.07 137	73.14 74	90.07 34	43.06 188		68.20 79	81.76 94	84.03 159

SDMVSNet		68.03 172	68.10 149	67.84 239	77.13 197	48.72 231	65.32 314	79.10 149	58.02 139	65.08 213	82.55 181	47.83 131	73.40 288	63.92 120	73.92 184	81.41 222

sd_testset		64.46 232	64.45 215	64.51 280	77.13 197	42.25 294	62.67 328	72.11 259	58.02 139	65.08 213	82.55 181	41.22 212	69.88 308	47.32 248	73.92 184	81.41 222

GeoE		71.01 102	70.15 110	73.60 128	79.57 125	52.17 179	78.93 117	78.12 177	58.02 139	67.76 160	83.87 155	52.36 79	82.72 160	56.90 170	75.79 169	85.92 93

ZD-MVS					86.64 21	60.38 43		82.70 86	57.95 142	78.10 24	90.06 36	56.12 38	88.84 26	74.05 47	87.00 48

EIA-MVS		71.78 90	70.60 100	75.30 79	79.85 120	53.54 150	77.27 157	83.26 77	57.92 143	66.49 181	79.39 249	52.07 84	86.69 69	60.05 152	79.14 126	85.66 107

test_yl		69.69 131	69.13 125	71.36 181	78.37 156	45.74 261	74.71 212	80.20 134	57.91 144	70.01 114	83.83 156	42.44 193	82.87 154	54.97 186	79.72 113	85.48 114

DCV-MVSNet		69.69 131	69.13 125	71.36 181	78.37 156	45.74 261	74.71 212	80.20 134	57.91 144	70.01 114	83.83 156	42.44 193	82.87 154	54.97 186	79.72 113	85.48 114

dcpmvs_2		74.55 57	75.23 48	72.48 153	82.34 77	53.34 155	77.87 138	81.46 103	57.80 146	75.49 36	86.81 84	62.22 13	77.75 251	71.09 67	82.02 91	86.34 76

mvsmamba		71.15 99	69.54 118	75.99 63	77.61 185	53.46 152	81.95 78	75.11 225	57.73 147	66.95 173	85.96 114	37.14 252	87.56 48	67.94 83	75.49 173	86.97 54

Fast-Effi-MVS+-dtu		67.37 185	65.33 208	73.48 132	72.94 265	57.78 82	77.47 150	76.88 196	57.60 148	61.97 255	76.85 284	39.31 225	80.49 208	54.72 189	70.28 240	82.17 212

v1192		69.97 124	68.68 135	73.85 112	73.19 259	50.94 192	77.68 144	81.36 107	57.51 149	68.95 133	80.85 223	45.28 169	85.33 106	62.97 129	70.37 237	85.27 126

ACMH+	57.40 11	66.12 210	64.06 217	72.30 159	77.79 176	52.83 166	80.39 95	78.03 178	57.30 150	57.47 300	82.55 181	27.68 337	84.17 125	45.54 266	69.78 252	79.90 252

diffmvs		70.69 109	70.43 103	71.46 176	69.45 321	48.95 227	72.93 240	78.46 168	57.27 151	71.69 96	83.97 154	51.48 93	77.92 248	70.70 69	77.95 143	87.53 40
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-untuned		68.27 167	67.29 170	71.21 185	79.74 121	53.22 158	76.06 183	77.46 189	57.19 152	66.10 188	81.61 206	45.37 168	83.50 140	45.42 270	76.68 163	76.91 290

thres100view900		63.28 242	62.41 240	65.89 266	77.31 194	38.66 321	72.65 243	69.11 284	57.07 153	62.45 251	81.03 217	37.01 256	79.17 228	31.84 352	73.25 199	79.83 254

DP-MVS Recon		72.15 87	70.73 99	76.40 58	86.57 24	57.99 79	81.15 89	82.96 81	57.03 154	66.78 175	85.56 123	44.50 176	88.11 36	51.77 215	80.23 110	83.10 195

thres600view7		63.30 241	62.27 241	66.41 254	77.18 196	38.87 319	72.35 250	69.11 284	56.98 155	62.37 253	80.96 219	37.01 256	79.00 237	31.43 359	73.05 203	81.36 225

V42		68.65 158	67.35 169	72.56 151	68.93 327	50.18 207	72.90 241	79.47 144	56.92 156	69.45 124	80.26 232	46.29 155	82.99 148	64.07 116	67.82 277	84.53 146

MCST-MVS		77.48 28	77.45 27	77.54 45	86.67 20	58.36 76	83.22 55	86.93 5	56.91 157	74.91 45	88.19 62	59.15 22	87.68 46	73.67 51	87.45 42	86.57 69

GA-MVS		65.53 217	63.70 223	71.02 192	70.87 301	48.10 237	70.48 276	74.40 235	56.69 158	64.70 221	76.77 285	33.66 285	81.10 192	55.42 185	70.32 239	83.87 167

v144192		69.71 130	68.51 138	73.33 138	73.10 261	50.13 208	77.54 148	80.64 127	56.65 159	68.57 137	80.55 226	46.87 151	84.96 113	62.98 128	69.66 256	84.89 138

tfpn200view9		63.18 244	62.18 243	66.21 258	76.85 204	39.62 313	71.96 257	69.44 280	56.63 160	62.61 246	79.83 238	37.18 249	79.17 228	31.84 352	73.25 199	79.83 254

thres400		63.31 240	62.18 243	66.72 250	76.85 204	39.62 313	71.96 257	69.44 280	56.63 160	62.61 246	79.83 238	37.18 249	79.17 228	31.84 352	73.25 199	81.36 225

GBi-Net		67.21 187	66.55 182	69.19 223	77.63 180	43.33 284	77.31 153	77.83 181	56.62 162	65.04 215	82.70 175	41.85 200	80.33 210	47.18 250	72.76 206	83.92 164

test1		67.21 187	66.55 182	69.19 223	77.63 180	43.33 284	77.31 153	77.83 181	56.62 162	65.04 215	82.70 175	41.85 200	80.33 210	47.18 250	72.76 206	83.92 164

FMVSNet2		66.93 197	66.31 193	68.79 230	77.63 180	42.98 288	76.11 181	77.47 187	56.62 162	65.22 212	82.17 193	41.85 200	80.18 216	47.05 253	72.72 209	83.20 190

DPM-MVS		75.47 48	75.00 49	76.88 51	81.38 92	59.16 59	79.94 102	85.71 22	56.59 165	72.46 89	86.76 85	56.89 32	87.86 43	66.36 97	88.91 25	83.64 181

v1921920		69.47 141	68.17 147	73.36 137	73.06 262	50.10 209	77.39 151	80.56 128	56.58 166	68.59 135	80.37 228	44.72 174	84.98 111	62.47 134	69.82 251	85.00 134

FMVSNet1		66.70 202	65.87 199	69.19 223	77.49 189	43.33 284	77.31 153	77.83 181	56.45 167	64.60 223	82.70 175	38.08 241	80.33 210	46.08 259	72.31 215	83.92 164

v1240		69.24 149	67.91 151	73.25 141	73.02 264	49.82 213	77.21 158	80.54 129	56.43 168	68.34 141	80.51 227	43.33 186	84.99 109	62.03 138	69.77 254	84.95 137

testing222		62.29 253	61.31 252	65.25 275	77.87 172	38.53 323	68.34 292	66.31 301	56.37 169	63.15 239	77.58 277	28.47 331	76.18 278	37.04 322	76.65 164	81.05 235

CDPH-MVS		76.31 38	75.67 44	78.22 37	85.35 48	59.14 62	81.31 87	84.02 48	56.32 170	74.05 59	88.98 54	53.34 67	87.92 41	69.23 76	88.42 28	87.59 38

Vis-MVSNet (Re-imp)		63.69 237	63.88 220	63.14 289	74.75 239	31.04 374	71.16 267	63.64 318	56.32 170	59.80 277	84.99 131	44.51 175	75.46 279	39.12 311	80.62 101	82.92 197

AdaColmap		69.99 123	68.66 136	73.97 111	84.94 54	57.83 80	82.63 65	78.71 158	56.28 172	64.34 224	84.14 148	41.57 204	87.06 61	46.45 256	78.88 128	77.02 286

PS-MVSNAJss		72.24 82	71.21 90	75.31 78	78.50 151	55.93 112	81.63 81	82.12 92	56.24 173	70.02 113	85.68 122	47.05 146	84.34 124	65.27 109	74.41 179	85.67 106

c3_l		68.33 166	67.56 157	70.62 198	70.87 301	46.21 257	74.47 217	78.80 156	56.22 174	66.19 187	78.53 263	51.88 86	81.40 184	62.08 135	69.04 265	84.25 153

Fast-Effi-MVS+		70.28 118	69.12 127	73.73 120	78.50 151	51.50 188	75.01 205	79.46 145	56.16 175	68.59 135	79.55 246	53.97 56	84.05 127	53.34 201	77.53 147	85.65 108

PHI-MVS		75.87 44	75.36 45	77.41 46	80.62 107	55.91 113	84.28 39	85.78 20	56.08 176	73.41 67	86.58 94	50.94 101	88.54 28	70.79 68	89.71 17	87.79 31

baseline1		63.81 236	63.87 221	63.62 284	76.29 214	36.36 344	71.78 259	67.29 293	56.05 177	64.23 228	82.95 173	47.11 145	74.41 284	47.30 249	61.85 325	80.10 250

train_agg		76.27 39	76.15 37	76.64 55	85.58 43	61.59 24	81.62 82	81.26 115	55.86 178	74.93 43	88.81 56	53.70 63	84.68 118	75.24 38	88.33 30	83.65 180

test_8					85.40 46	60.96 34	81.54 85	81.18 118	55.86 178	74.81 47	88.80 58	53.70 63	84.45 122

RRT_MVS		69.42 143	67.49 163	75.21 82	78.01 169	52.56 172	82.23 75	78.15 176	55.84 180	65.65 198	85.07 130	30.86 310	86.83 65	61.56 144	70.00 246	86.24 85

FMVSNet3		66.32 209	65.61 204	68.46 233	76.48 212	42.34 292	74.98 207	77.15 194	55.83 181	65.04 215	81.16 213	39.91 218	80.14 217	47.18 250	72.76 206	82.90 199

PAPR		71.72 93	70.82 97	74.41 101	81.20 97	51.17 189	79.55 112	83.33 73	55.81 182	66.93 174	84.61 139	50.95 100	86.06 84	55.79 179	79.20 124	86.00 90

eth_miper_zixun_eth		67.63 181	66.28 194	71.67 171	71.60 287	48.33 235	73.68 233	77.88 179	55.80 183	65.91 192	78.62 261	47.35 143	82.88 153	59.45 159	66.25 289	83.81 169

ACMH	55.70 15	65.20 223	63.57 225	70.07 207	78.07 166	52.01 184	79.48 113	79.69 138	55.75 184	56.59 306	80.98 218	27.12 341	80.94 196	42.90 291	71.58 223	77.25 284
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 220	62.73 237	73.40 136	74.89 233	52.78 167	73.09 239	75.13 224	55.69 185	58.48 294	73.73 317	32.86 293	86.32 82	50.63 223	70.11 243	81.10 233
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
CL-MVSNet_self_test		61.53 261	60.94 258	63.30 287	68.95 326	36.93 340	67.60 297	72.80 254	55.67 186	59.95 274	76.63 287	45.01 172	72.22 295	39.74 309	62.09 324	80.74 240

TEST9					85.58 43	61.59 24	81.62 82	81.26 115	55.65 187	74.93 43	88.81 56	53.70 63	84.68 118

thres200		62.20 254	61.16 256	65.34 273	75.38 229	39.99 310	69.60 285	69.29 282	55.64 188	61.87 257	76.99 281	37.07 255	78.96 238	31.28 360	73.28 198	77.06 285

pm-mvs1		65.24 222	64.97 212	66.04 263	72.38 276	39.40 316	72.62 245	75.63 212	55.53 189	62.35 254	83.18 170	47.45 139	76.47 273	49.06 237	66.54 287	82.24 209

ACMM	61.98 7	70.80 108	69.73 115	74.02 109	80.59 108	58.59 74	82.68 64	82.02 94	55.46 190	67.18 168	84.39 145	38.51 234	83.17 146	60.65 148	76.10 167	80.30 246
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		69.91 125	69.02 128	72.56 151	80.19 114	47.65 243	77.56 147	80.99 122	55.45 191	69.88 117	86.76 85	39.24 228	82.18 172	54.04 194	77.10 157	87.85 27

tt0805		67.77 179	67.24 175	69.34 222	74.87 235	40.08 309	77.36 152	81.37 106	55.31 192	66.33 185	84.65 137	37.35 247	82.55 165	55.65 182	72.28 216	85.39 121

CPTT-MVS		72.78 73	72.08 77	74.87 86	84.88 57	61.41 26	84.15 43	77.86 180	55.27 193	67.51 163	88.08 65	41.93 199	81.85 176	69.04 77	80.01 111	81.35 227

XVG-OURS		68.76 157	67.37 167	72.90 145	74.32 251	57.22 89	70.09 281	78.81 155	55.24 194	67.79 158	85.81 121	36.54 259	78.28 243	62.04 137	75.74 170	83.19 191

tfpnnormal		62.47 249	61.63 248	64.99 277	74.81 237	39.01 318	71.22 265	73.72 245	55.22 195	60.21 269	80.09 236	41.26 211	76.98 263	30.02 365	68.09 275	78.97 264

cl____		67.18 190	66.26 195	69.94 209	70.20 309	45.74 261	73.30 235	76.83 198	55.10 196	65.27 206	79.57 245	47.39 141	80.53 205	59.41 161	69.22 263	83.53 183

DIV-MVS_self_test		67.18 190	66.26 195	69.94 209	70.20 309	45.74 261	73.29 236	76.83 198	55.10 196	65.27 206	79.58 244	47.38 142	80.53 205	59.43 160	69.22 263	83.54 182

PC_three_1452									55.09 198	84.46 4	89.84 43	66.68 5	89.41 18	74.24 44	91.38 2	88.42 11

EPNet_dtu		61.90 257	61.97 245	61.68 297	72.89 266	39.78 312	75.85 189	65.62 305	55.09 198	54.56 327	79.36 250	37.59 244	67.02 322	39.80 308	76.95 158	78.25 268
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 97	70.39 104	74.65 92	82.01 80	58.82 71	79.93 103	80.35 133	55.09 198	65.82 197	82.16 194	49.17 116	82.64 163	60.34 150	78.62 135	82.50 206

cl22		67.47 184	66.45 184	70.54 200	69.85 317	46.49 253	73.85 230	77.35 191	55.07 201	65.51 201	77.92 268	47.64 135	81.10 192	61.58 143	69.32 259	84.01 161

miper_ehance_all_eth		68.03 172	67.24 175	70.40 202	70.54 304	46.21 257	73.98 223	78.68 160	55.07 201	66.05 189	77.80 272	52.16 83	81.31 187	61.53 145	69.32 259	83.67 177

PS-MVSNAJ		70.51 112	69.70 116	72.93 144	81.52 87	55.79 116	74.92 208	79.00 151	55.04 203	69.88 117	78.66 258	47.05 146	82.19 171	61.61 141	79.58 116	80.83 238

iter_conf_final		69.82 127	68.02 150	75.23 81	79.38 129	52.91 163	80.11 99	73.96 243	54.99 204	68.04 149	83.59 161	29.05 325	87.16 55	65.41 108	77.62 145	85.63 109

xiu_mvs_v2_base		70.52 111	69.75 114	72.84 146	81.21 96	55.63 120	75.11 202	78.92 153	54.92 205	69.96 116	79.68 243	47.00 150	82.09 173	61.60 142	79.37 119	80.81 239

MAR-MVS		71.51 95	70.15 110	75.60 74	81.84 84	59.39 55	81.38 86	82.90 83	54.90 206	68.08 148	78.70 257	47.73 132	85.51 99	51.68 217	84.17 70	81.88 217
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
XVG-OURS-SEG-HR		68.81 154	67.47 164	72.82 148	74.40 249	56.87 99	70.59 274	79.04 150	54.77 207	66.99 171	86.01 112	39.57 223	78.21 244	62.54 132	73.33 197	83.37 185

testing3		56.54 293	55.92 295	58.41 315	77.52 188	27.93 382	69.72 284	56.36 355	54.75 208	58.63 292	77.80 272	20.88 368	71.75 298	25.31 380	62.25 322	75.53 300

iter_conf05		69.40 145	67.62 156	74.73 87	77.84 174	51.13 190	79.28 114	73.71 246	54.62 209	68.17 144	83.59 161	28.68 330	87.16 55	65.74 105	76.95 158	85.91 94

Anonymous20231211		69.28 147	68.47 141	71.73 169	80.28 109	47.18 249	79.98 101	82.37 89	54.61 210	67.24 166	84.01 152	39.43 224	82.41 169	55.45 184	72.83 205	85.62 110

SixPastTwentyTwo		61.65 260	58.80 271	70.20 205	75.80 220	47.22 248	75.59 192	69.68 276	54.61 210	54.11 331	79.26 252	27.07 342	82.96 149	43.27 285	49.79 369	80.41 244

test_0402		63.25 243	61.01 257	69.96 208	80.00 118	54.37 139	76.86 169	72.02 260	54.58 212	58.71 289	80.79 225	35.00 270	84.36 123	26.41 378	64.71 300	71.15 348

tttt0517		67.83 178	65.66 203	74.33 103	76.69 206	50.82 196	77.86 139	73.99 242	54.54 213	64.64 222	82.53 184	35.06 269	85.50 100	55.71 180	69.91 249	86.67 65

BH-w/o		66.85 198	65.83 200	69.90 212	79.29 130	52.46 175	74.66 214	76.65 201	54.51 214	64.85 219	78.12 264	45.59 161	82.95 150	43.26 286	75.54 172	74.27 316

AUN-MVS		68.45 165	66.41 188	74.57 97	79.53 126	57.08 97	73.93 227	75.23 221	54.44 215	66.69 178	81.85 201	37.10 254	82.89 152	62.07 136	66.84 284	83.75 174

LTVRE_ROB	55.42 16	63.15 245	61.23 255	68.92 228	76.57 210	47.80 240	59.92 344	76.39 202	54.35 216	58.67 290	82.46 186	29.44 323	81.49 183	42.12 295	71.14 227	77.46 279
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
test_fmvsmconf_n		73.01 70	72.59 71	74.27 105	71.28 296	55.88 114	78.21 130	75.56 214	54.31 217	74.86 46	87.80 72	54.72 49	80.23 214	78.07 21	78.48 136	86.70 63

test_fmvsmconf0.01_n		72.17 84	71.50 82	74.16 107	67.96 333	55.58 123	78.06 135	74.67 232	54.19 218	74.54 52	88.23 61	50.35 106	80.24 213	78.07 21	77.46 149	86.65 67

test_fmvsmconf0.1_n		72.81 72	72.33 74	74.24 106	69.89 316	55.81 115	78.22 129	75.40 217	54.17 219	75.00 42	88.03 68	53.82 60	80.23 214	78.08 20	78.34 139	86.69 64

ETVMVS		59.51 275	58.81 269	61.58 299	77.46 190	34.87 353	64.94 319	59.35 341	54.06 220	61.08 265	76.67 286	29.54 320	71.87 297	32.16 348	74.07 182	78.01 275

ab-mvs		66.65 203	66.42 187	67.37 245	76.17 216	41.73 299	70.41 278	76.14 206	53.99 221	65.98 190	83.51 165	49.48 111	76.24 276	48.60 240	73.46 195	84.14 157

IU-MVS					87.77 4	59.15 60		85.53 25	53.93 222	84.64 3				79.07 11	90.87 5	88.37 13

XVG-ACMP-BASELINE		64.36 233	62.23 242	70.74 196	72.35 277	52.45 176	70.80 273	78.45 169	53.84 223	59.87 275	81.10 215	16.24 374	79.32 225	55.64 183	71.76 220	80.47 242

FE-MVS		65.91 212	63.33 229	73.63 126	77.36 193	51.95 185	72.62 245	75.81 209	53.70 224	65.31 204	78.96 255	28.81 329	86.39 79	43.93 279	73.48 194	82.55 203

thisisatest0530		67.92 176	65.78 201	74.33 103	76.29 214	51.03 191	76.89 167	74.25 239	53.67 225	65.59 200	81.76 203	35.15 268	85.50 100	55.94 175	72.47 210	86.47 71

PVSNet_BlendedMVS		68.56 163	67.72 153	71.07 191	77.03 201	50.57 200	74.50 216	81.52 100	53.66 226	64.22 229	79.72 242	49.13 117	82.87 154	55.82 177	73.92 184	79.77 256

patch_mono-2		69.85 126	71.09 93	66.16 259	79.11 138	54.80 135	71.97 256	74.31 237	53.50 227	70.90 102	84.17 147	57.63 29	63.31 336	66.17 98	82.02 91	80.38 245

EG-PatchMatch MVS		64.71 228	62.87 234	70.22 203	77.68 178	53.48 151	77.99 136	78.82 154	53.37 228	56.03 311	77.41 279	24.75 356	84.04 128	46.37 257	73.42 196	73.14 322

DP-MVS		65.68 214	63.66 224	71.75 168	84.93 55	56.87 99	80.74 93	73.16 251	53.06 229	59.09 286	82.35 187	36.79 258	85.94 89	32.82 346	69.96 248	72.45 330

TR-MVS		66.59 206	65.07 211	71.17 188	79.18 135	49.63 219	73.48 234	75.20 223	52.95 230	67.90 150	80.33 231	39.81 221	83.68 136	43.20 287	73.56 192	80.20 247

ET-MVSNet_ETH3D		67.96 175	65.72 202	74.68 90	76.67 207	55.62 122	75.11 202	74.74 230	52.91 231	60.03 272	80.12 234	33.68 284	82.64 163	61.86 139	76.34 165	85.78 99

QAPM		70.05 121	68.81 132	73.78 115	76.54 211	53.43 153	83.23 54	83.48 66	52.89 232	65.90 193	86.29 102	41.55 206	86.49 77	51.01 220	78.40 138	81.42 221

OpenMVS	61.03 9	68.85 153	67.56 157	72.70 150	74.26 252	53.99 142	81.21 88	81.34 111	52.70 233	62.75 243	85.55 125	38.86 232	84.14 126	48.41 242	83.01 77	79.97 251

pmmvs6		63.69 237	62.82 236	66.27 257	70.63 303	39.27 317	73.13 238	75.47 216	52.69 234	59.75 279	82.30 189	39.71 222	77.03 262	47.40 247	64.35 305	82.53 204

IterMVS		62.79 247	61.27 253	67.35 246	69.37 322	52.04 183	71.17 266	68.24 289	52.63 235	59.82 276	76.91 283	37.32 248	72.36 292	52.80 205	63.19 315	77.66 277
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 170	66.36 190	73.63 126	75.61 224	55.35 128	80.77 92	78.56 163	52.48 236	64.27 227	84.10 150	27.45 339	81.84 177	63.45 126	70.56 234	83.69 176

jajsoiax		68.25 168	66.45 184	73.66 123	75.62 223	55.49 125	80.82 91	78.51 165	52.33 237	64.33 225	84.11 149	28.28 333	81.81 178	63.48 125	70.62 232	83.67 177

TAMVS		66.78 201	65.27 209	71.33 184	79.16 137	53.67 146	73.84 231	69.59 278	52.32 238	65.28 205	81.72 204	44.49 177	77.40 257	42.32 294	78.66 134	82.92 197

CDS-MVSNet		66.80 200	65.37 206	71.10 190	78.98 140	53.13 161	73.27 237	71.07 266	52.15 239	64.72 220	80.23 233	43.56 184	77.10 260	45.48 268	78.88 128	83.05 196
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PVSNet_Blended		68.59 159	67.72 153	71.19 186	77.03 201	50.57 200	72.51 248	81.52 100	51.91 240	64.22 229	77.77 275	49.13 117	82.87 154	55.82 177	79.58 116	80.14 249

mvs_anonymous		68.03 172	67.51 161	69.59 217	72.08 281	44.57 275	71.99 255	75.23 221	51.67 241	67.06 170	82.57 180	54.68 50	77.94 247	56.56 172	75.71 171	86.26 84

xiu_mvs_v1_base_debu		68.58 160	67.28 171	72.48 153	78.19 162	57.19 91	75.28 197	75.09 226	51.61 242	70.04 110	81.41 210	32.79 294	79.02 234	63.81 121	77.31 150	81.22 229

xiu_mvs_v1_base		68.58 160	67.28 171	72.48 153	78.19 162	57.19 91	75.28 197	75.09 226	51.61 242	70.04 110	81.41 210	32.79 294	79.02 234	63.81 121	77.31 150	81.22 229

xiu_mvs_v1_base_debi		68.58 160	67.28 171	72.48 153	78.19 162	57.19 91	75.28 197	75.09 226	51.61 242	70.04 110	81.41 210	32.79 294	79.02 234	63.81 121	77.31 150	81.22 229

MVSTER		67.16 192	65.58 205	71.88 164	70.37 308	49.70 215	70.25 280	78.45 169	51.52 245	69.16 131	80.37 228	38.45 235	82.50 166	60.19 151	71.46 224	83.44 184

CNLPA		65.43 218	64.02 218	69.68 215	78.73 147	58.07 78	77.82 142	70.71 269	51.49 246	61.57 262	83.58 164	38.23 239	70.82 301	43.90 280	70.10 244	80.16 248

原ACMM1				74.69 89	85.39 47	59.40 54		83.42 69	51.47 247	70.27 108	86.61 92	48.61 123	86.51 76	53.85 197	87.96 39	78.16 269

miper_enhance_ethall		67.11 193	66.09 197	70.17 206	69.21 324	45.98 259	72.85 242	78.41 172	51.38 248	65.65 198	75.98 299	51.17 97	81.25 188	60.82 147	69.32 259	83.29 188

MSDG		61.81 259	59.23 266	69.55 220	72.64 269	52.63 170	70.45 277	75.81 209	51.38 248	53.70 334	76.11 295	29.52 321	81.08 194	37.70 317	65.79 293	74.93 308

test20.03		53.87 313	54.02 312	53.41 345	61.47 367	28.11 381	61.30 336	59.21 342	51.34 250	52.09 343	77.43 278	33.29 289	58.55 356	29.76 366	60.27 336	73.58 321

MVSFormer		71.50 96	70.38 105	74.88 85	78.76 145	57.15 94	82.79 61	78.48 166	51.26 251	69.49 122	83.22 168	43.99 181	83.24 144	66.06 99	79.37 119	84.23 154

test_djsdf		69.45 142	67.74 152	74.58 96	74.57 245	54.92 133	82.79 61	78.48 166	51.26 251	65.41 203	83.49 166	38.37 236	83.24 144	66.06 99	69.25 262	85.56 111

bld_raw_dy_0_64		64.87 226	63.22 230	69.83 214	74.79 238	53.32 157	78.15 132	62.02 333	51.20 253	60.17 270	83.12 172	24.15 358	74.20 287	63.08 127	72.33 213	81.96 214

dmvs_testset		50.16 330	51.90 320	44.94 363	66.49 343	11.78 401	61.01 341	51.50 368	51.17 254	50.30 355	67.44 357	39.28 226	60.29 347	22.38 383	57.49 345	62.76 368

PAPM		67.92 176	66.69 181	71.63 173	78.09 165	49.02 225	77.09 161	81.24 117	51.04 255	60.91 266	83.98 153	47.71 133	84.99 109	40.81 302	79.32 122	80.90 237

Syy-MVS		56.00 300	56.23 293	55.32 332	74.69 241	26.44 388	65.52 309	57.49 350	50.97 256	56.52 307	72.18 324	39.89 219	68.09 315	24.20 381	64.59 303	71.44 344

myMVS_eth3d		54.86 309	54.61 304	55.61 331	74.69 241	27.31 385	65.52 309	57.49 350	50.97 256	56.52 307	72.18 324	21.87 366	68.09 315	27.70 373	64.59 303	71.44 344

miper_lstm_enhance		62.03 256	60.88 259	65.49 271	66.71 341	46.25 255	56.29 360	75.70 211	50.68 258	61.27 263	75.48 305	40.21 217	68.03 317	56.31 174	65.25 296	82.18 210

gg-mvs-nofinetune		57.86 285	56.43 291	62.18 295	72.62 270	35.35 352	66.57 300	56.33 356	50.65 259	57.64 299	57.10 379	30.65 311	76.36 274	37.38 319	78.88 128	74.82 310

TAPA-MVS	59.36 10	66.60 204	65.20 210	70.81 194	76.63 208	48.75 229	76.52 174	80.04 136	50.64 260	65.24 210	84.93 132	39.15 229	78.54 240	36.77 324	76.88 160	85.14 129
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 292	56.83 287	56.61 326	69.23 323	41.02 304	58.37 349	64.18 315	50.59 261	57.45 301	71.42 332	35.54 265	58.94 354	37.23 320	67.45 280	69.87 357

MVP-Stereo		65.41 219	63.80 222	70.22 203	77.62 184	55.53 124	76.30 177	78.53 164	50.59 261	56.47 309	78.65 259	39.84 220	82.68 161	44.10 278	72.12 218	72.44 331
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 104	69.49 120	75.35 77	77.63 180	55.71 117	76.04 185	81.81 97	50.30 263	69.66 120	85.40 129	52.51 75	84.89 114	51.82 214	80.24 109	85.45 116
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
baseline2		63.42 239	61.26 254	69.89 213	72.55 272	47.62 244	71.54 260	68.38 288	50.11 264	54.82 323	75.55 304	43.06 188	80.96 195	48.13 243	67.16 283	81.11 232

test-LLR		58.15 283	58.13 279	58.22 317	68.57 328	44.80 271	65.46 311	57.92 347	50.08 265	55.44 315	69.82 345	32.62 300	57.44 360	49.66 231	73.62 189	72.41 332

test0.0.03 1		53.32 318	53.59 315	52.50 349	62.81 362	29.45 377	59.51 345	54.11 363	50.08 265	54.40 329	74.31 314	32.62 300	55.92 369	30.50 363	63.95 308	72.15 337

fmvsm_s_conf0.5_n		69.58 136	68.84 131	71.79 167	72.31 279	52.90 164	77.90 137	62.43 328	49.97 267	72.85 82	85.90 116	52.21 81	76.49 271	75.75 33	70.26 241	85.97 91

COLMAP_ROB	52.97 17	61.27 265	58.81 269	68.64 231	74.63 243	52.51 174	78.42 126	73.30 249	49.92 268	50.96 347	81.51 209	23.06 360	79.40 223	31.63 356	65.85 291	74.01 319
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_a		69.54 138	68.74 134	71.93 162	72.47 275	53.82 144	78.25 127	62.26 330	49.78 269	73.12 76	86.21 104	52.66 73	76.79 266	75.02 39	68.88 267	85.18 128

tpmvs		58.47 279	56.95 285	63.03 291	70.20 309	41.21 303	67.90 296	67.23 294	49.62 270	54.73 325	70.84 336	34.14 278	76.24 276	36.64 328	61.29 329	71.64 340

fmvsm_s_conf0.1_n		69.41 144	68.60 137	71.83 165	71.07 298	52.88 165	77.85 140	62.44 327	49.58 271	72.97 79	86.22 103	51.68 91	76.48 272	75.53 34	70.10 244	86.14 86

thisisatest0515		65.83 213	63.50 226	72.82 148	73.75 255	49.50 220	71.32 263	73.12 252	49.39 272	63.82 231	76.50 293	34.95 271	84.84 117	53.20 203	75.49 173	84.13 158

fmvsm_s_conf0.1_n_a		69.32 146	68.44 143	71.96 161	70.91 300	53.78 145	78.12 133	62.30 329	49.35 273	73.20 72	86.55 96	51.99 85	76.79 266	74.83 41	68.68 272	85.32 123

HY-MVS	56.14 13	64.55 231	63.89 219	66.55 253	74.73 240	41.02 304	69.96 282	74.43 234	49.29 274	61.66 260	80.92 220	47.43 140	76.68 269	44.91 273	71.69 221	81.94 215

MIMVSNet1		55.17 307	54.31 309	57.77 322	70.03 313	32.01 371	65.68 307	64.81 309	49.19 275	46.75 364	76.00 296	25.53 352	64.04 334	28.65 370	62.13 323	77.26 283

SCA		60.49 267	58.38 275	66.80 249	74.14 254	48.06 238	63.35 325	63.23 321	49.13 276	59.33 285	72.10 326	37.45 245	74.27 285	44.17 275	62.57 319	78.05 271

test_fmvsmvis_n_1920		70.84 105	70.38 105	72.22 160	71.16 297	55.39 127	75.86 188	72.21 258	49.03 277	73.28 70	86.17 106	51.83 88	77.29 258	75.80 32	78.05 141	83.98 162

testgi		51.90 322	52.37 319	50.51 355	60.39 374	23.55 395	58.42 348	58.15 345	49.03 277	51.83 344	79.21 253	22.39 361	55.59 370	29.24 369	62.64 318	72.40 334

MIMVSNet		57.35 287	57.07 283	58.22 317	74.21 253	37.18 335	62.46 329	60.88 338	48.88 279	55.29 318	75.99 298	31.68 307	62.04 341	31.87 351	72.35 212	75.43 302

gm-plane-assit					71.40 293	41.72 301			48.85 280		73.31 319		82.48 168	48.90 238

fmvsm_l_conf0.5_n		70.99 103	70.82 97	71.48 175	71.45 289	54.40 138	77.18 159	70.46 271	48.67 281	75.17 38	86.86 82	53.77 61	76.86 264	76.33 30	77.51 148	83.17 194

cascas		65.98 211	63.42 227	73.64 125	77.26 195	52.58 171	72.26 252	77.21 193	48.56 282	61.21 264	74.60 312	32.57 303	85.82 92	50.38 225	76.75 162	82.52 205

PLC	56.13 14	65.09 224	63.21 231	70.72 197	81.04 99	54.87 134	78.57 123	77.47 187	48.51 283	55.71 312	81.89 200	33.71 283	79.71 218	41.66 299	70.37 237	77.58 278
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 228	62.50 239	71.34 183	79.72 123	55.71 117	79.82 105	74.72 231	48.50 284	56.62 305	84.62 138	33.59 286	82.34 170	29.65 367	75.23 175	75.97 294

anonymousdsp		67.00 196	64.82 213	73.57 129	70.09 312	56.13 107	76.35 176	77.35 191	48.43 285	64.99 218	80.84 224	33.01 291	80.34 209	64.66 113	67.64 279	84.23 154

无先验							79.66 110	74.30 238	48.40 286				80.78 202	53.62 198		79.03 263

114514_t		70.83 106	69.56 117	74.64 93	86.21 31	54.63 136	82.34 70	81.81 97	48.22 287	63.01 240	85.83 119	40.92 214	87.10 59	57.91 164	79.79 112	82.18 210

tpm		57.34 288	58.16 277	54.86 335	71.80 286	34.77 355	67.47 299	56.04 359	48.20 288	60.10 271	76.92 282	37.17 251	53.41 376	40.76 303	65.01 297	76.40 293

test_fmvsm_n_1920		71.73 92	71.14 92	73.50 130	72.52 273	56.53 101	75.60 191	76.16 204	48.11 289	77.22 28	85.56 123	53.10 70	77.43 255	74.86 40	77.14 155	86.55 70

MDA-MVSNet-bldmvs		53.87 313	50.81 325	63.05 290	66.25 345	48.58 232	56.93 358	63.82 317	48.09 290	41.22 376	70.48 341	30.34 314	68.00 318	34.24 339	45.92 374	72.57 328

XXY-MVS		60.68 266	61.67 247	57.70 323	70.43 306	38.45 324	64.19 322	66.47 298	48.05 291	63.22 236	80.86 222	49.28 114	60.47 345	45.25 272	67.28 282	74.19 317

F-COLMAP		63.05 246	60.87 260	69.58 219	76.99 203	53.63 148	78.12 133	76.16 204	47.97 292	52.41 342	81.61 206	27.87 335	78.11 245	40.07 305	66.66 286	77.00 287

fmvsm_l_conf0.5_n_a		70.50 113	70.27 107	71.18 187	71.30 295	54.09 140	76.89 167	69.87 274	47.90 293	74.37 55	86.49 97	53.07 71	76.69 268	75.41 35	77.11 156	82.76 201

Patchmatch-RL test		58.16 282	55.49 298	66.15 260	67.92 334	48.89 228	60.66 342	51.07 371	47.86 294	59.36 282	62.71 373	34.02 280	72.27 294	56.41 173	59.40 338	77.30 281

D2MVS		62.30 252	60.29 262	68.34 236	66.46 344	48.42 234	65.70 306	73.42 248	47.71 295	58.16 296	75.02 308	30.51 312	77.71 252	53.96 196	71.68 222	78.90 265

ANet_high		41.38 347	37.47 354	53.11 346	39.73 398	24.45 393	56.94 357	69.69 275	47.65 296	26.04 390	52.32 382	12.44 380	62.38 340	21.80 384	10.61 399	72.49 329

CostFormer		64.04 234	62.51 238	68.61 232	71.88 284	45.77 260	71.30 264	70.60 270	47.55 297	64.31 226	76.61 289	41.63 203	79.62 221	49.74 229	69.00 266	80.42 243

PatchmatchNet		59.84 271	58.24 276	64.65 279	73.05 263	46.70 252	69.42 287	62.18 331	47.55 297	58.88 288	71.96 328	34.49 275	69.16 310	42.99 289	63.60 310	78.07 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 306	53.89 313	59.21 309	57.80 379	27.47 384	57.75 354	74.32 236	47.38 299	50.90 348	70.00 344	28.45 332	70.30 306	40.44 304	57.92 343	79.87 253

ITE_SJBPF				62.09 296	66.16 346	44.55 276		64.32 313	47.36 300	55.31 317	80.34 230	19.27 369	62.68 339	36.29 332	62.39 321	79.04 262

KD-MVS_2432*1600		53.45 315	51.50 323	59.30 306	62.82 360	37.14 336	55.33 361	71.79 262	47.34 301	55.09 320	70.52 339	21.91 364	70.45 304	35.72 334	42.97 377	70.31 353

miper_refine_blended		53.45 315	51.50 323	59.30 306	62.82 360	37.14 336	55.33 361	71.79 262	47.34 301	55.09 320	70.52 339	21.91 364	70.45 304	35.72 334	42.97 377	70.31 353

OurMVSNet-221017-0		61.37 264	58.63 273	69.61 216	72.05 282	48.06 238	73.93 227	72.51 255	47.23 303	54.74 324	80.92 220	21.49 367	81.24 189	48.57 241	56.22 351	79.53 258

tpmrst		58.24 281	58.70 272	56.84 325	66.97 338	34.32 359	69.57 286	61.14 337	47.17 304	58.58 293	71.60 331	41.28 210	60.41 346	49.20 235	62.84 317	75.78 297

PVSNet	50.76 19	58.40 280	57.39 281	61.42 300	75.53 226	44.04 279	61.43 334	63.45 319	47.04 305	56.91 303	73.61 318	27.00 343	64.76 332	39.12 311	72.40 211	75.47 301

WB-MVSnew		59.66 273	59.69 264	59.56 305	75.19 232	35.78 351	69.34 288	64.28 314	46.88 306	61.76 259	75.79 300	40.61 215	65.20 331	32.16 348	71.21 226	77.70 276

FMVSNet5		55.86 301	54.93 301	58.66 314	71.05 299	36.35 345	64.18 323	62.48 326	46.76 307	50.66 352	74.73 311	25.80 350	64.04 334	33.11 344	65.57 294	75.59 299

jason		69.65 134	68.39 145	73.43 135	78.27 160	56.88 98	77.12 160	73.71 246	46.53 308	69.34 126	83.22 168	43.37 185	79.18 227	64.77 112	79.20 124	84.23 154
jason: jason.
MS-PatchMatch		62.42 250	61.46 250	65.31 274	75.21 231	52.10 180	72.05 254	74.05 241	46.41 309	57.42 302	74.36 313	34.35 277	77.57 254	45.62 265	73.67 188	66.26 365

1112_ss		64.00 235	63.36 228	65.93 265	79.28 131	42.58 291	71.35 262	72.36 257	46.41 309	60.55 268	77.89 270	46.27 156	73.28 289	46.18 258	69.97 247	81.92 216

lupinMVS		69.57 137	68.28 146	73.44 134	78.76 145	57.15 94	76.57 172	73.29 250	46.19 311	69.49 122	82.18 191	43.99 181	79.23 226	64.66 113	79.37 119	83.93 163

testdata				64.66 278	81.52 87	52.93 162		65.29 307	46.09 312	73.88 62	87.46 75	38.08 241	66.26 327	53.31 202	78.48 136	74.78 311

UnsupCasMVSNet_eth		53.16 320	52.47 318	55.23 333	59.45 375	33.39 366	59.43 346	69.13 283	45.98 313	50.35 354	72.32 323	29.30 324	58.26 358	42.02 297	44.30 375	74.05 318

AllTest		57.08 290	54.65 303	64.39 281	71.44 290	49.03 223	69.92 283	67.30 291	45.97 314	47.16 361	79.77 240	17.47 370	67.56 319	33.65 341	59.16 339	76.57 291

TestCases				64.39 281	71.44 290	49.03 223		67.30 291	45.97 314	47.16 361	79.77 240	17.47 370	67.56 319	33.65 341	59.16 339	76.57 291

WTY-MVS		59.75 272	60.39 261	57.85 321	72.32 278	37.83 329	61.05 340	64.18 315	45.95 316	61.91 256	79.11 254	47.01 149	60.88 344	42.50 293	69.49 258	74.83 309

IterMVS-SCA-FT		62.49 248	61.52 249	65.40 272	71.99 283	50.80 197	71.15 268	69.63 277	45.71 317	60.61 267	77.93 267	37.45 245	65.99 328	55.67 181	63.50 312	79.42 259

WB-MVS		43.26 342	43.41 343	42.83 367	63.32 359	10.32 403	58.17 351	45.20 383	45.42 318	40.44 379	67.26 360	34.01 281	58.98 353	11.96 395	24.88 390	59.20 371

旧先验2							76.08 182		45.32 319	76.55 32			65.56 330	58.75 162

OpenMVS_ROB	52.78 18	60.03 269	58.14 278	65.69 269	70.47 305	44.82 270	75.33 196	70.86 268	45.04 320	56.06 310	76.00 296	26.89 344	79.65 219	35.36 336	67.29 281	72.60 327

TinyColmap		54.14 310	51.72 321	61.40 301	66.84 340	41.97 296	66.52 301	68.51 287	44.81 321	42.69 375	75.77 301	11.66 382	72.94 290	31.96 350	56.77 349	69.27 361

MDTV_nov1_ep13			57.00 284		72.73 268	38.26 325	65.02 318	64.73 311	44.74 322	55.46 314	72.48 322	32.61 302	70.47 303	37.47 318	67.75 278

新几何1				70.76 195	85.66 41	61.13 30		66.43 299	44.68 323	70.29 107	86.64 90	41.29 209	75.23 280	49.72 230	81.75 96	75.93 295

Patchmtry		57.16 289	56.47 290	59.23 308	69.17 325	34.58 358	62.98 326	63.15 322	44.53 324	56.83 304	74.84 309	35.83 263	68.71 312	40.03 306	60.91 330	74.39 315

ppachtmachnet_test		58.06 284	55.38 299	66.10 262	69.51 319	48.99 226	68.01 295	66.13 302	44.50 325	54.05 332	70.74 337	32.09 306	72.34 293	36.68 327	56.71 350	76.99 289

PatchT		53.17 319	53.44 316	52.33 350	68.29 332	25.34 392	58.21 350	54.41 362	44.46 326	54.56 327	69.05 351	33.32 288	60.94 343	36.93 323	61.76 327	70.73 351

EPMVS		53.96 311	53.69 314	54.79 336	66.12 347	31.96 372	62.34 331	49.05 374	44.42 327	55.54 313	71.33 334	30.22 315	56.70 363	41.65 300	62.54 320	75.71 298

pmmvs4		61.48 263	59.39 265	67.76 240	71.57 288	53.86 143	71.42 261	65.34 306	44.20 328	59.46 281	77.92 268	35.90 262	74.71 282	43.87 281	64.87 299	74.71 312

dp		51.89 323	51.60 322	52.77 348	68.44 331	32.45 370	62.36 330	54.57 361	44.16 329	49.31 356	67.91 353	28.87 328	56.61 365	33.89 340	54.89 354	69.24 362

PatchMatch-RL		56.25 298	54.55 305	61.32 302	77.06 200	56.07 109	65.57 308	54.10 364	44.13 330	53.49 340	71.27 335	25.20 353	66.78 323	36.52 330	63.66 309	61.12 369

our_test_3		56.49 294	54.42 306	62.68 293	69.51 319	45.48 266	66.08 304	61.49 335	44.11 331	50.73 351	69.60 348	33.05 290	68.15 314	38.38 314	56.86 347	74.40 314

USDC		56.35 297	54.24 310	62.69 292	64.74 352	40.31 308	65.05 317	73.83 244	43.93 332	47.58 359	77.71 276	15.36 376	75.05 281	38.19 316	61.81 326	72.70 326

PM-MVS		52.33 321	50.19 329	58.75 313	62.10 365	45.14 269	65.75 305	40.38 390	43.60 333	53.52 338	72.65 321	9.16 390	65.87 329	50.41 224	54.18 357	65.24 367

pmmvs-eth3d		58.81 278	56.31 292	66.30 256	67.61 335	52.42 177	72.30 251	64.76 310	43.55 334	54.94 322	74.19 315	28.95 326	72.60 291	43.31 284	57.21 346	73.88 320

SSC-MVS		41.96 346	41.99 346	41.90 368	62.46 364	9.28 405	57.41 356	44.32 386	43.38 335	38.30 383	66.45 363	32.67 299	58.42 357	10.98 396	21.91 393	57.99 375

new-patchmatchnet		47.56 337	47.73 337	47.06 358	58.81 377	9.37 404	48.78 375	59.21 342	43.28 336	44.22 371	68.66 352	25.67 351	57.20 362	31.57 358	49.35 370	74.62 313

Test_1112_low_res		62.32 251	61.77 246	64.00 283	79.08 139	39.53 315	68.17 293	70.17 272	43.25 337	59.03 287	79.90 237	44.08 179	71.24 300	43.79 282	68.42 273	81.25 228

RPMNet		61.53 261	58.42 274	70.86 193	69.96 314	52.07 181	65.31 315	81.36 107	43.20 338	59.36 282	70.15 343	35.37 266	85.47 102	36.42 331	64.65 301	75.06 304

tpm2		62.07 255	60.10 263	67.99 238	72.79 267	43.86 280	71.05 271	66.85 297	43.14 339	62.77 241	75.39 306	38.32 237	80.80 201	41.69 298	68.88 267	79.32 260

JIA-IIPM		51.56 324	47.68 338	63.21 288	64.61 353	50.73 198	47.71 377	58.77 344	42.90 340	48.46 358	51.72 383	24.97 354	70.24 307	36.06 333	53.89 358	68.64 363

1314		64.61 230	63.21 231	68.80 229	71.87 285	47.46 246	73.95 225	78.39 174	42.88 341	59.97 273	76.60 290	38.11 240	79.39 224	54.84 188	72.32 214	79.55 257

HyFIR lowres test		65.67 215	63.01 233	73.67 122	79.97 119	55.65 119	69.07 290	75.52 215	42.68 342	63.53 234	77.95 266	40.43 216	81.64 179	46.01 260	71.91 219	83.73 175

CR-MVSNet		59.91 270	57.90 280	65.96 264	69.96 314	52.07 181	65.31 315	63.15 322	42.48 343	59.36 282	74.84 309	35.83 263	70.75 302	45.50 267	64.65 301	75.06 304

test222					83.14 68	58.68 73	72.57 247	63.45 319	41.78 344	67.56 162	86.12 107	37.13 253			78.73 133	74.98 307

TDRefinement		53.44 317	50.72 326	61.60 298	64.31 355	46.96 250	70.89 272	65.27 308	41.78 344	44.61 370	77.98 265	11.52 384	66.36 326	28.57 371	51.59 363	71.49 343

sss		56.17 299	56.57 289	54.96 334	66.93 339	36.32 347	57.94 352	61.69 334	41.67 346	58.64 291	75.32 307	38.72 233	56.25 367	42.04 296	66.19 290	72.31 335

PVSNet_0	43.31 20	47.46 338	45.64 341	52.92 347	67.60 336	44.65 273	54.06 365	54.64 360	41.59 347	46.15 366	58.75 376	30.99 309	58.66 355	32.18 347	24.81 391	55.46 379

MVS		67.37 185	66.33 191	70.51 201	75.46 227	50.94 192	73.95 225	81.85 96	41.57 348	62.54 248	78.57 262	47.98 128	85.47 102	52.97 204	82.05 90	75.14 303

Anonymous20240521		55.30 304	54.41 307	57.96 320	60.92 373	41.73 299	71.09 270	71.06 267	41.18 349	48.65 357	73.31 319	16.93 372	59.25 352	42.54 292	64.01 306	72.90 324

Anonymous20231206		55.10 308	55.30 300	54.48 337	69.81 318	33.94 363	62.91 327	62.13 332	41.08 350	55.18 319	75.65 302	32.75 297	56.59 366	30.32 364	67.86 276	72.91 323

MDA-MVSNet_test_wron		50.71 329	48.95 331	56.00 330	61.17 369	41.84 297	51.90 370	56.45 353	40.96 351	44.79 369	67.84 354	30.04 317	55.07 373	36.71 326	50.69 366	71.11 349

YYNet1		50.73 328	48.96 330	56.03 329	61.10 370	41.78 298	51.94 369	56.44 354	40.94 352	44.84 368	67.80 355	30.08 316	55.08 372	36.77 324	50.71 365	71.22 346

CHOSEN 1792x2688		65.08 225	62.84 235	71.82 166	81.49 89	56.26 105	66.32 303	74.20 240	40.53 353	63.16 238	78.65 259	41.30 208	77.80 250	45.80 262	74.09 181	81.40 224

pmmvs5		56.47 295	55.68 297	58.86 312	61.41 368	36.71 342	66.37 302	62.75 324	40.38 354	53.70 334	76.62 288	34.56 273	67.05 321	40.02 307	65.27 295	72.83 325

test_vis1_n_1920		58.86 277	59.06 268	58.25 316	63.76 356	43.14 287	67.49 298	66.36 300	40.22 355	65.89 194	71.95 329	31.04 308	59.75 350	59.94 154	64.90 298	71.85 339

MDTV_nov1_ep13_2view						25.89 390	61.22 337		40.10 356	51.10 346		32.97 292		38.49 313		78.61 266

tpm cat1		59.25 276	56.95 285	66.15 260	72.19 280	46.96 250	68.09 294	65.76 303	40.03 357	57.81 298	70.56 338	38.32 237	74.51 283	38.26 315	61.50 328	77.00 287

test-mter		56.42 296	55.82 296	58.22 317	68.57 328	44.80 271	65.46 311	57.92 347	39.94 358	55.44 315	69.82 345	21.92 363	57.44 360	49.66 231	73.62 189	72.41 332

UnsupCasMVSNet_bld		50.07 331	48.87 332	53.66 342	60.97 372	33.67 364	57.62 355	64.56 312	39.47 359	47.38 360	64.02 371	27.47 338	59.32 351	34.69 338	43.68 376	67.98 364

TESTMET0.1,1		55.28 305	54.90 302	56.42 327	66.56 342	43.67 282	65.46 311	56.27 357	39.18 360	53.83 333	67.44 357	24.21 357	55.46 371	48.04 244	73.11 202	70.13 355

ADS-MVSNet2		51.33 326	48.76 333	59.07 311	66.02 348	44.60 274	50.90 371	59.76 340	36.90 361	50.74 349	66.18 365	26.38 345	63.11 337	27.17 374	54.76 355	69.50 359

ADS-MVSNet		48.48 335	47.77 336	50.63 354	66.02 348	29.92 376	50.90 371	50.87 373	36.90 361	50.74 349	66.18 365	26.38 345	52.47 378	27.17 374	54.76 355	69.50 359

RPSCF		55.80 302	54.22 311	60.53 304	65.13 351	42.91 290	64.30 321	57.62 349	36.84 363	58.05 297	82.28 190	28.01 334	56.24 368	37.14 321	58.61 341	82.44 208

test_cas_vis1_n_1920		56.91 291	56.71 288	57.51 324	59.13 376	45.40 267	63.58 324	61.29 336	36.24 364	67.14 169	71.85 330	29.89 318	56.69 364	57.65 166	63.58 311	70.46 352

Patchmatch-test		49.08 333	48.28 335	51.50 353	64.40 354	30.85 375	45.68 381	48.46 377	35.60 365	46.10 367	72.10 326	34.47 276	46.37 386	27.08 376	60.65 334	77.27 282

CHOSEN 280x420		47.83 336	46.36 340	52.24 352	67.37 337	49.78 214	38.91 389	43.11 388	35.00 366	43.27 374	63.30 372	28.95 326	49.19 383	36.53 329	60.80 332	57.76 376

N_pmnet		39.35 351	40.28 349	36.54 374	63.76 356	1.62 409	49.37 374	0.76 408	34.62 367	43.61 373	66.38 364	26.25 347	42.57 390	26.02 379	51.77 362	65.44 366

PMMVS		53.96 311	53.26 317	56.04 328	62.60 363	50.92 194	61.17 338	56.09 358	32.81 368	53.51 339	66.84 362	34.04 279	59.93 349	44.14 277	68.18 274	57.27 377

CMPMVS	42.80 21	57.81 286	55.97 294	63.32 286	60.98 371	47.38 247	64.66 320	69.50 279	32.06 369	46.83 363	77.80 272	29.50 322	71.36 299	48.68 239	73.75 187	71.21 347
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
CVMVSNet		59.63 274	59.14 267	61.08 303	74.47 246	38.84 320	75.20 200	68.74 286	31.15 370	58.24 295	76.51 291	32.39 304	68.58 313	49.77 228	65.84 292	75.81 296

FPMVS		42.18 345	41.11 348	45.39 360	58.03 378	41.01 306	49.50 373	53.81 365	30.07 371	33.71 385	64.03 369	11.69 381	52.08 381	14.01 391	55.11 353	43.09 388

EU-MVSNet		55.61 303	54.41 307	59.19 310	65.41 350	33.42 365	72.44 249	71.91 261	28.81 372	51.27 345	73.87 316	24.76 355	69.08 311	43.04 288	58.20 342	75.06 304

test_vis1_n		49.89 332	48.69 334	53.50 344	53.97 380	37.38 334	61.53 333	47.33 380	28.54 373	59.62 280	67.10 361	13.52 378	52.27 379	49.07 236	57.52 344	70.84 350

test_fmvs1_n		51.37 325	50.35 328	54.42 339	52.85 382	37.71 331	61.16 339	51.93 366	28.15 374	63.81 232	69.73 347	13.72 377	53.95 374	51.16 219	60.65 334	71.59 341

LF4IMVS		42.95 343	42.26 345	45.04 361	48.30 389	32.50 369	54.80 363	48.49 376	28.03 375	40.51 378	70.16 342	9.24 389	43.89 389	31.63 356	49.18 371	58.72 373

test_fmvs1		51.32 327	50.48 327	53.81 341	53.57 381	37.51 333	60.63 343	51.16 369	28.02 376	63.62 233	69.23 350	16.41 373	53.93 375	51.01 220	60.70 333	69.99 356

MVS-HIRNet		45.52 339	44.48 342	48.65 357	68.49 330	34.05 362	59.41 347	44.50 385	27.03 377	37.96 384	50.47 387	26.16 348	64.10 333	26.74 377	59.52 337	47.82 386

PMVS	28.69 22	36.22 354	33.29 358	45.02 362	36.82 400	35.98 350	54.68 364	48.74 375	26.31 378	21.02 393	51.61 384	2.88 402	60.10 348	9.99 399	47.58 372	38.99 393
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 340	41.95 347	53.86 340	52.58 384	43.55 283	62.11 332	46.90 382	26.05 379	40.63 377	60.19 375	11.08 387	57.91 359	31.83 355	46.15 373	60.11 370

test_fmvs2		48.69 334	47.49 339	52.29 351	48.63 388	33.06 368	57.76 353	48.05 378	25.71 380	59.76 278	69.60 348	11.57 383	52.23 380	49.45 234	56.86 347	71.58 342

PMMVS2		27.40 362	25.91 365	31.87 378	39.46 399	6.57 406	31.17 392	28.52 399	23.96 381	20.45 394	48.94 390	4.20 398	37.94 394	16.51 388	19.97 394	51.09 381

Gipuma		34.77 355	31.91 359	43.33 365	62.05 366	37.87 327	20.39 394	67.03 295	23.23 382	18.41 395	25.84 395	4.24 396	62.73 338	14.71 390	51.32 364	29.38 394
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 348	39.45 350	47.03 359	46.65 391	37.86 328	47.76 376	38.65 391	23.10 383	44.21 372	51.22 385	11.20 386	44.08 388	39.27 310	53.02 360	59.14 372

new_pmnet		34.13 356	34.29 357	33.64 376	52.63 383	18.23 400	44.43 384	33.90 396	22.81 384	30.89 387	53.18 381	10.48 388	35.72 397	20.77 385	39.51 381	46.98 387

mvsany_test1		39.38 350	38.16 353	43.02 366	49.05 386	34.28 360	44.16 385	25.94 401	22.74 385	46.57 365	62.21 374	23.85 359	41.16 393	33.01 345	35.91 385	53.63 380

LCM-MVSNet		40.30 349	35.88 355	53.57 343	42.24 393	29.15 378	45.21 383	60.53 339	22.23 386	28.02 388	50.98 386	3.72 399	61.78 342	31.22 361	38.76 383	69.78 358

test_fmvs3		44.30 341	42.55 344	49.55 356	42.83 392	27.15 387	53.03 367	44.93 384	22.03 387	53.69 336	64.94 368	4.21 397	49.63 382	47.47 245	49.82 368	71.88 338

APD_test1		37.39 353	34.94 356	44.72 364	48.88 387	33.19 367	52.95 368	44.00 387	19.49 388	27.28 389	58.59 377	3.18 401	52.84 377	18.92 386	41.17 380	48.14 385

mvsany_test3		32.62 357	30.57 361	38.77 372	36.16 401	24.20 394	38.10 390	20.63 403	19.14 389	40.36 380	57.43 378	5.06 394	36.63 396	29.59 368	28.66 389	55.49 378

E-PMN		23.77 363	22.73 367	26.90 379	42.02 394	20.67 397	42.66 386	35.70 394	17.43 390	10.28 400	25.05 396	6.42 392	42.39 391	10.28 398	14.71 396	17.63 395

EMVS		22.97 364	21.84 368	26.36 380	40.20 397	19.53 399	41.95 387	34.64 395	17.09 391	9.73 401	22.83 397	7.29 391	42.22 392	9.18 400	13.66 397	17.32 396

test_vis3_rt		32.09 358	30.20 362	37.76 373	35.36 402	27.48 383	40.60 388	28.29 400	16.69 392	32.52 386	40.53 391	1.96 403	37.40 395	33.64 343	42.21 379	48.39 383

test_f		31.86 359	31.05 360	34.28 375	32.33 404	21.86 396	32.34 391	30.46 398	16.02 393	39.78 382	55.45 380	4.80 395	32.36 398	30.61 362	37.66 384	48.64 382

DSMNet-mixed		39.30 352	38.72 351	41.03 369	51.22 385	19.66 398	45.53 382	31.35 397	15.83 394	39.80 381	67.42 359	22.19 362	45.13 387	22.43 382	52.69 361	58.31 374

testf1		31.46 360	28.89 363	39.16 370	41.99 395	28.78 379	46.45 379	37.56 392	14.28 395	21.10 391	48.96 388	1.48 405	47.11 384	13.63 392	34.56 386	41.60 389

APD_test2		31.46 360	28.89 363	39.16 370	41.99 395	28.78 379	46.45 379	37.56 392	14.28 395	21.10 391	48.96 388	1.48 405	47.11 384	13.63 392	34.56 386	41.60 389

MVE	17.77 23	21.41 365	17.77 370	32.34 377	34.34 403	25.44 391	16.11 395	24.11 402	11.19 397	13.22 397	31.92 393	1.58 404	30.95 399	10.47 397	17.03 395	40.62 392
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 383	17.97 405	10.91 402		10.60 406	7.46 398	11.07 399	28.36 394	3.28 400	11.29 402	8.01 401	9.74 401	13.89 397

wuyk23d		13.32 368	12.52 371	15.71 382	47.54 390	26.27 389	31.06 393	1.98 407	4.93 399	5.18 402	1.94 402	0.45 407	18.54 401	6.81 402	12.83 398	2.33 399

test_method		19.68 366	18.10 369	24.41 381	13.68 406	3.11 408	12.06 397	42.37 389	2.00 400	11.97 398	36.38 392	5.77 393	29.35 400	15.06 389	23.65 392	40.76 391

tmp_tt		9.43 369	11.14 372	4.30 384	2.38 407	4.40 407	13.62 396	16.08 405	0.39 401	15.89 396	13.06 398	15.80 375	5.54 403	12.63 394	10.46 400	2.95 398

EGC-MVSNET		42.47 344	38.48 352	54.46 338	74.33 250	48.73 230	70.33 279	51.10 370	0.03 402	0.18 403	67.78 356	13.28 379	66.49 325	18.91 387	50.36 367	48.15 384

testmvs		4.52 372	6.03 375	0.01 386	0.01 408	0.00 411	53.86 366	0.00 409	0.01 403	0.04 404	0.27 403	0.00 409	0.00 404	0.04 403	0.00 402	0.03 401

test123		4.73 371	6.30 374	0.02 385	0.01 408	0.01 410	56.36 359	0.00 409	0.01 403	0.04 404	0.21 404	0.01 408	0.00 404	0.03 404	0.00 402	0.04 400

test_blank		0.00 374	0.00 377	0.00 387	0.00 410	0.00 411	0.00 398	0.00 409	0.00 405	0.00 406	0.00 405	0.00 409	0.00 404	0.00 405	0.00 402	0.00 402

uanet_test		0.00 374	0.00 377	0.00 387	0.00 410	0.00 411	0.00 398	0.00 409	0.00 405	0.00 406	0.00 405	0.00 409	0.00 404	0.00 405	0.00 402	0.00 402

DCPMVS		0.00 374	0.00 377	0.00 387	0.00 410	0.00 411	0.00 398	0.00 409	0.00 405	0.00 406	0.00 405	0.00 409	0.00 404	0.00 405	0.00 402	0.00 402

cdsmvs_eth3d_5k		17.50 367	23.34 366	0.00 387	0.00 410	0.00 411	0.00 398	78.63 161	0.00 405	0.00 406	82.18 191	49.25 115	0.00 404	0.00 405	0.00 402	0.00 402

pcd_1.5k_mvsjas		3.92 373	5.23 376	0.00 387	0.00 410	0.00 411	0.00 398	0.00 409	0.00 405	0.00 406	0.00 405	47.05 146	0.00 404	0.00 405	0.00 402	0.00 402

sosnet-low-res		0.00 374	0.00 377	0.00 387	0.00 410	0.00 411	0.00 398	0.00 409	0.00 405	0.00 406	0.00 405	0.00 409	0.00 404	0.00 405	0.00 402	0.00 402

sosnet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 411	0.00 398	0.00 409	0.00 405	0.00 406	0.00 405	0.00 409	0.00 404	0.00 405	0.00 402	0.00 402

uncertanet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 411	0.00 398	0.00 409	0.00 405	0.00 406	0.00 405	0.00 409	0.00 404	0.00 405	0.00 402	0.00 402

Regformer		0.00 374	0.00 377	0.00 387	0.00 410	0.00 411	0.00 398	0.00 409	0.00 405	0.00 406	0.00 405	0.00 409	0.00 404	0.00 405	0.00 402	0.00 402

ab-mvs-re		6.49 370	8.65 373	0.00 387	0.00 410	0.00 411	0.00 398	0.00 409	0.00 405	0.00 406	77.89 270	0.00 409	0.00 404	0.00 405	0.00 402	0.00 402

uanet		0.00 374	0.00 377	0.00 387	0.00 410	0.00 411	0.00 398	0.00 409	0.00 405	0.00 406	0.00 405	0.00 409	0.00 404	0.00 405	0.00 402	0.00 402

WAC-MVS						27.31 385								27.77 372

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 23					90.96 1	79.31 9	90.65 8	87.85 27

No_MVS				79.95 4	87.24 14	61.04 31		85.62 23					90.96 1	79.31 9	90.65 8	87.85 27

eth-test2					0.00 410

eth-test					0.00 410

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 42	67.01 1	90.33 12	73.16 54	91.15 4	88.23 16

test_0728_SECOND				79.19 16	87.82 3	59.11 63	87.85 5	87.15 3					90.84 3	78.66 15	90.61 11	87.62 37

GSMVS																78.05 271

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 272				78.05 271

sam_mvs												33.43 287

ambc				65.13 276	63.72 358	37.07 338	47.66 378	78.78 157		54.37 330	71.42 332	11.24 385	80.94 196	45.64 264	53.85 359	77.38 280

MTGPA								80.97 123

test_post1							68.67 291				3.64 400	32.39 304	69.49 309	44.17 275

test_post											3.55 401	33.90 282	66.52 324

patchmatchnet-post											64.03 369	34.50 274	74.27 285

GG-mvs-BLEND				62.34 294	71.36 294	37.04 339	69.20 289	57.33 352		54.73 325	65.48 367	30.37 313	77.82 249	34.82 337	74.93 176	72.17 336

MTMP							86.03 19	17.08 404

test9_res														75.28 37	88.31 32	83.81 169

agg_prior2														73.09 55	87.93 40	84.33 150

agg_prior					85.04 50	59.96 47		81.04 121		74.68 50			84.04 128

test_prior4						62.51 14	82.08 77

test_prior				76.69 53	84.20 61	57.27 88		84.88 37					86.43 78			86.38 72

新几何2							76.12 180

旧先验1					83.04 70	53.15 159		67.52 290			87.85 71	44.08 179			80.76 100	78.03 274

原ACMM2							79.02 116

testdata2													72.18 296	46.95 254

segment_acmp												54.23 54

test12				77.76 43	84.52 58	58.41 75		83.36 72		72.93 81		54.61 51	88.05 37		88.12 35	86.81 60

plane_prior7					81.41 90	55.96 111

plane_prior6					81.20 97	56.24 106						45.26 170

plane_prior5								84.01 49					87.21 53	68.16 81	80.58 103	84.65 144

plane_prior4											86.10 108

plane_prior1					81.27 95

n2								0.00 409

nn								0.00 409

door-mid								47.19 381

lessismore_v0				69.91 211	71.42 292	47.80 240		50.90 372		50.39 353	75.56 303	27.43 340	81.33 186	45.91 261	34.10 388	80.59 241

test11								83.47 67

door								47.60 379

HQP5-MVS						54.94 131

BP-MVS														67.04 93

HQP4-MVS										67.85 152			86.93 62			84.32 151

HQP3-MVS								83.90 54							80.35 107

HQP2-MVS												45.46 164

NP-MVS					80.98 100	56.05 110					85.54 126

ACMMP++_ref															74.07 182

ACMMP++															72.16 217

Test By Simon												48.33 126

High-resolution multi-view benchmark