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 55	73.19 1	77.08 31	91.21 14	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

MVS_0304		78.45 18	78.28 19	78.98 26	80.73 106	57.91 80	84.68 33	81.64 105	68.35 2	75.77 36	90.38 26	53.98 60	90.26 13	81.30 3	87.68 43	88.77 11

CANet		76.46 38	75.93 41	78.06 39	81.29 94	57.53 85	82.35 69	83.31 79	67.78 3	70.09 115	86.34 106	54.92 51	88.90 25	72.68 58	84.55 69	87.76 38

UA-Net		73.13 73	72.93 74	73.76 118	83.58 64	51.66 189	78.75 116	77.66 189	67.75 4	72.61 91	89.42 47	49.82 114	83.29 144	53.61 202	83.14 79	86.32 85

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 36	85.03 37	66.96 5	77.58 27	90.06 36	59.47 21	89.13 22	78.67 14	89.73 16	87.03 59

TranMVSNet+NR-MVSNet		70.36 121	70.10 118	71.17 190	78.64 154	42.97 292	76.53 173	81.16 125	66.95 6	68.53 144	85.42 133	51.61 98	83.07 148	52.32 210	69.70 263	87.46 47

3Dnovator+	66.72 4	75.84 46	74.57 57	79.66 9	82.40 76	59.92 48	85.83 22	86.32 16	66.92 7	67.80 161	89.24 51	42.03 204	89.38 19	64.07 118	86.50 59	89.69 2

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 30	84.42 46	66.73 8	74.67 55	89.38 49	55.30 47	89.18 21	74.19 46	87.34 45	86.38 77

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 15	54.26 57	90.06 14	78.42 19	89.02 23	87.69 39
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 74	72.16 81	75.90 68	75.95 228	56.28 104	83.05 56	72.39 259	66.53 10	65.27 208	87.00 84	50.40 110	85.47 102	62.48 135	86.32 60	85.94 96
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 105	71.00 101	71.44 179	79.20 138	44.13 279	76.02 186	82.60 92	66.48 11	68.20 148	84.60 144	56.82 37	82.82 159	54.62 193	70.43 243	87.36 54

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 22	63.71 12	89.23 20	81.51 2	88.44 27	88.09 27
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 66	65.37 13	78.78 22	90.64 18	58.63 25	87.24 54	79.00 12	90.37 14	85.26 129

NR-MVSNet		69.54 142	68.85 135	71.59 176	78.05 176	43.81 283	74.20 222	80.86 131	65.18 14	62.76 248	84.52 145	52.35 86	83.59 140	50.96 225	70.78 238	87.37 52

MTAPA		76.90 34	76.42 36	78.35 35	86.08 37	63.57 2	74.92 210	80.97 129	65.13 15	75.77 36	90.88 16	48.63 128	86.66 70	77.23 24	88.17 33	84.81 143

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 16

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 27

EI-MVSNet-Vis-set		72.42 86	71.59 86	74.91 85	78.47 158	54.02 142	77.05 162	79.33 153	65.03 18	71.68 101	79.35 254	52.75 78	84.89 114	66.46 99	74.23 189	85.83 101

casdiffmvs_mvg		76.14 42	76.30 37	75.66 74	76.46 222	51.83 188	79.67 108	85.08 34	65.02 19	75.84 35	88.58 60	59.42 22	85.08 108	72.75 57	83.93 76	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 63	73.84 66	76.33 64	79.27 136	55.24 129	79.22 113	85.00 39	64.97 21	72.65 90	79.46 250	53.65 72	87.87 46	67.45 93	82.91 85	85.89 99

WR-MVS		68.47 166	68.47 146	68.44 236	80.20 117	39.84 316	73.75 234	76.07 212	64.68 22	68.11 152	83.63 164	50.39 111	79.14 234	49.78 230	69.66 264	86.34 81

XVS		77.17 31	76.56 34	79.00 23	86.32 29	62.62 11	85.83 22	83.92 56	64.55 23	72.17 96	90.01 40	47.95 135	88.01 42	71.55 68	86.74 54	86.37 79

X-MVStestdata		70.21 124	67.28 173	79.00 23	86.32 29	62.62 11	85.83 22	83.92 56	64.55 23	72.17 96	6.49 412	47.95 135	88.01 42	71.55 68	86.74 54	86.37 79

HQP_MVS		74.31 64	73.73 67	76.06 66	81.41 91	56.31 102	84.22 40	84.01 53	64.52 25	69.27 133	86.10 113	45.26 177	87.21 56	68.16 84	80.58 109	84.65 147

plane_prior2							84.22 40		64.52 25

EI-MVSNet-UG-set		71.92 94	71.06 100	74.52 99	77.98 179	53.56 150	76.62 171	79.16 154	64.40 27	71.18 106	78.95 259	52.19 88	84.66 121	65.47 110	73.57 200	85.32 125

DU-MVS		70.01 127	69.53 124	71.44 179	78.05 176	44.13 279	75.01 206	81.51 108	64.37 28	68.20 148	84.52 145	49.12 125	82.82 159	54.62 193	70.43 243	87.37 52

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 64	87.85 5	85.03 37	64.26 29	83.82 8	92.00 3	64.82 8	90.75 8	78.66 15	90.61 11	85.45 118
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 22

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 41

LFMVS		71.78 96	71.59 86	72.32 160	83.40 67	46.38 256	79.75 106	71.08 268	64.18 32	72.80 87	88.64 59	42.58 199	83.72 136	57.41 171	84.49 70	86.86 63

IS-MVSNet		71.57 100	71.00 101	73.27 141	78.86 147	45.63 267	80.22 97	78.69 165	64.14 35	66.46 185	87.36 78	49.30 119	85.60 95	50.26 229	83.71 78	88.59 13

plane_prior3						56.09 108			63.92 36	69.27 133

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 70	63.89 37	73.60 68	90.60 19	54.85 52	86.72 68	77.20 25	88.06 36	85.74 108
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DELS-MVS		74.76 55	74.46 58	75.65 75	77.84 183	52.25 179	75.59 193	84.17 50	63.76 38	73.15 76	82.79 176	59.58 20	86.80 66	67.24 94	86.04 61	87.89 30
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 57	74.25 60	76.19 65	80.81 105	59.01 67	82.60 66	83.64 67	63.74 39	72.52 92	87.49 74	47.18 151	85.88 90	69.47 77	80.78 105	83.66 182
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 115	70.20 114	71.89 165	78.55 155	45.29 270	75.94 187	82.92 87	63.68 40	68.16 150	83.59 165	53.89 64	83.49 142	53.97 198	71.12 236	86.89 62

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 21	85.21 30	63.56 41	74.29 61	90.03 38	52.56 80	88.53 30	74.79 42	88.34 29	86.63 73

EC-MVSNet		75.84 46	75.87 43	75.74 72	78.86 147	52.65 170	83.73 50	86.08 17	63.47 42	72.77 88	87.25 82	53.13 75	87.93 44	71.97 64	85.57 64	86.66 71

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 18	63.32 43	75.08 43	90.47 25	53.96 63	88.68 28	76.48 28	89.63 20	87.16 57

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 64
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS		76.25 41	75.98 40	77.06 50	80.15 120	55.63 120	84.51 35	83.90 58	63.24 45	73.30 71	87.27 81	55.06 49	86.30 83	71.78 65	84.58 68	89.25 4

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 63	61.62 23	84.17 42	86.85 6	63.23 46	73.84 66	90.25 32	57.68 29	89.96 15	74.62 43	89.03 22	87.89 30
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 82	72.09 82	73.75 120	81.58 87	49.69 219	77.76 143	77.63 190	63.21 47	73.21 74	89.02 53	42.14 203	83.32 143	61.72 142	82.50 91	88.25 21

plane_prior						56.31 102	83.58 53		63.19 48						80.48 112

ACMMP		76.02 44	75.33 49	78.07 38	85.20 49	61.91 20	85.49 29	84.44 45	63.04 49	69.80 125	89.74 46	45.43 173	87.16 58	72.01 63	82.87 87	85.14 131
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		66.60 207	66.45 187	67.04 250	77.11 208	36.56 349	77.03 163	80.42 137	62.95 50	62.51 256	84.03 155	46.69 159	79.07 235	44.22 280	63.08 324	85.51 115

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 45	88.12 3	86.42 14	62.94 51	82.40 14	92.12 2	59.64 19	89.76 16	78.70 13	88.32 31	86.79 66
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mPP-MVS		76.54 37	75.93 41	78.34 36	86.47 26	63.50 3	85.74 25	82.28 95	62.90 52	71.77 99	90.26 31	46.61 160	86.55 74	71.71 66	85.66 63	84.97 139

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 49	85.16 31	62.88 53	78.10 24	91.26 13	52.51 81	88.39 31	79.34 8	90.52 13	86.78 67

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 28	62.86 54	80.17 17	90.03 38	61.76 14	88.95 24	74.21 45	88.67 26	88.12 26

HFP-MVS		78.01 24	77.65 25	79.10 21	86.71 19	62.81 8	86.29 14	84.32 48	62.82 55	73.96 64	90.50 23	53.20 74	88.35 32	74.02 48	87.05 46	86.13 91

ACMMPR		77.71 25	77.23 28	79.16 17	86.75 18	62.93 7	86.29 14	84.24 49	62.82 55	73.55 69	90.56 21	49.80 115	88.24 34	74.02 48	87.03 47	86.32 85

region2R		77.67 27	77.18 29	79.15 18	86.76 17	62.95 6	86.29 14	84.16 51	62.81 57	73.30 71	90.58 20	49.90 113	88.21 35	73.78 50	87.03 47	86.29 88

casdiffmvs		74.80 54	74.89 55	74.53 98	75.59 234	50.37 206	78.17 131	85.06 36	62.80 58	74.40 58	87.86 70	57.88 27	83.61 139	69.46 78	82.79 89	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 60	74.70 56	74.34 102	75.70 230	49.99 214	77.54 148	84.63 44	62.73 59	73.98 63	87.79 73	57.67 30	83.82 135	69.49 76	82.74 90	89.20 7

HPM-MVS		77.28 29	76.85 30	78.54 32	85.00 51	60.81 38	82.91 60	85.08 34	62.57 60	73.09 80	89.97 41	50.90 108	87.48 52	75.30 36	86.85 52	87.33 55
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 219	65.34 210	66.31 259	76.06 227	34.79 362	76.43 175	79.38 152	62.55 61	61.66 266	83.83 160	45.60 167	79.15 233	41.64 308	60.88 339	85.00 136

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 38	89.70 17	79.85 5	91.48 1	88.19 24
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		66.49 210	66.41 191	66.72 252	77.67 188	36.33 352	76.83 170	79.52 149	62.45 63	62.54 254	83.47 169	46.32 161	78.37 243	45.47 275	63.43 321	85.45 118

CP-MVS		77.12 32	76.68 32	78.43 33	86.05 38	63.18 5	87.55 10	83.45 73	62.44 64	72.68 89	90.50 23	48.18 133	87.34 53	73.59 52	85.71 62	84.76 146

PS-CasMVS		66.42 211	66.32 195	66.70 254	77.60 196	36.30 354	76.94 165	79.61 147	62.36 65	62.43 258	83.66 163	45.69 165	78.37 243	45.35 277	63.26 322	85.42 121

3Dnovator	64.47 5	72.49 83	71.39 92	75.79 69	77.70 186	58.99 68	80.66 93	83.15 84	62.24 66	65.46 204	86.59 97	42.38 202	85.52 98	59.59 160	84.72 67	82.85 203

MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 53	82.93 59	85.39 27	62.15 67	76.41 34	91.51 11	52.47 83	86.78 67	80.66 4	89.64 19	87.80 36
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 107		82.31 71		62.10 68	67.85 156

ACMP_Plane					80.66 107		82.31 71		62.10 68	67.85 156

HQP-MVS		73.45 70	72.80 75	75.40 79	80.66 107	54.94 131	82.31 71	83.90 58	62.10 68	67.85 156	85.54 131	45.46 171	86.93 63	67.04 96	80.35 113	84.32 154

CS-MVS-test		75.62 50	75.31 50	76.56 60	80.63 110	55.13 130	83.88 48	85.22 29	62.05 71	71.49 105	86.03 116	53.83 65	86.36 81	67.74 88	86.91 51	88.19 24

VPNet		67.52 186	68.11 153	65.74 272	79.18 139	36.80 347	72.17 255	72.83 256	62.04 72	67.79 162	85.83 124	48.88 127	76.60 276	51.30 221	72.97 213	83.81 172

WR-MVS_H		67.02 198	66.92 182	67.33 249	77.95 180	37.75 336	77.57 146	82.11 98	62.03 73	62.65 251	82.48 187	50.57 109	79.46 224	42.91 296	64.01 314	84.79 144

DeepC-MVS_fast	68.24 3	77.25 30	76.63 33	79.12 20	86.15 34	60.86 36	84.71 32	84.85 41	61.98 74	73.06 81	88.88 55	53.72 68	89.06 23	68.27 81	88.04 37	87.42 49
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 81	84.99 31	88.13 2	61.86 75	79.16 20	90.75 17	57.96 26	87.09 61	77.08 26	90.18 15	87.87 32

PGM-MVS		76.77 35	76.06 39	78.88 28	86.14 35	62.73 9	82.55 67	83.74 65	61.71 76	72.45 95	90.34 29	48.48 131	88.13 39	72.32 59	86.85 52	85.78 102

Effi-MVS+		73.31 72	72.54 78	75.62 76	77.87 181	53.64 148	79.62 110	79.61 147	61.63 77	72.02 98	82.61 181	56.44 40	85.97 88	63.99 121	79.07 133	87.25 56

MG-MVS		73.96 67	73.89 65	74.16 108	85.65 42	49.69 219	81.59 82	81.29 119	61.45 78	71.05 107	88.11 63	51.77 95	87.73 49	61.05 147	83.09 80	85.05 135

LPG-MVS_test		72.74 79	71.74 85	75.76 70	80.22 115	57.51 86	82.55 67	83.40 75	61.32 79	66.67 182	87.33 79	39.15 236	86.59 71	67.70 89	77.30 159	83.19 194

LGP-MVS_train				75.76 70	80.22 115	57.51 86		83.40 75	61.32 79	66.67 182	87.33 79	39.15 236	86.59 71	67.70 89	77.30 159	83.19 194

CLD-MVS		73.33 71	72.68 76	75.29 83	78.82 149	53.33 156	78.23 128	84.79 43	61.30 81	70.41 112	81.04 218	52.41 84	87.12 59	64.61 117	82.49 92	85.41 122
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 66	73.46 70	75.69 73	83.01 72	60.63 40	77.29 156	78.40 179	61.18 82	70.58 110	85.97 118	54.18 59	84.00 132	67.52 92	82.98 84	82.45 210

balanced_conf03		76.58 36	76.55 35	76.68 55	81.73 85	52.90 165	80.94 88	85.70 23	61.12 83	74.90 49	87.17 83	56.46 39	88.14 38	72.87 56	88.03 38	89.00 8

FIs		70.82 112	71.43 90	68.98 229	78.33 165	38.14 332	76.96 164	83.59 69	61.02 84	67.33 169	86.73 90	55.07 48	81.64 181	54.61 195	79.22 129	87.14 58

FOURS1					86.12 36	60.82 37	88.18 1	83.61 68	60.87 85	81.50 16

FC-MVSNet-test		69.80 133	70.58 108	67.46 245	77.61 195	34.73 365	76.05 184	83.19 83	60.84 86	65.88 198	86.46 103	54.52 56	80.76 205	52.52 209	78.12 146	86.91 61

v8		70.33 122	69.28 129	73.49 133	73.15 268	50.22 208	78.62 120	80.78 132	60.79 87	66.45 186	82.11 199	49.35 118	84.98 111	63.58 127	68.71 278	85.28 127

CSCG		76.92 33	76.75 31	77.41 46	83.96 62	59.60 51	82.95 58	86.50 13	60.78 88	75.27 40	84.83 137	60.76 15	86.56 73	67.86 87	87.87 41	86.06 93

Vis-MVSNet		72.18 89	71.37 93	74.61 94	81.29 94	55.41 126	80.90 89	78.28 181	60.73 89	69.23 136	88.09 64	44.36 185	82.65 163	57.68 168	81.75 102	85.77 105
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 47	60.61 90	79.05 21	90.30 30	55.54 46	88.32 33	73.48 53	87.03 47	84.83 142
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 87	71.20 97	75.59 78	80.28 113	57.54 84	82.74 63	82.84 90	60.58 91	65.24 212	86.18 110	39.25 234	86.03 86	66.95 98	76.79 166	83.22 192
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
testdata1							72.65 245		60.50 92

UGNet		68.81 156	67.39 168	73.06 144	78.33 165	54.47 137	79.77 105	75.40 223	60.45 93	63.22 240	84.40 148	32.71 308	80.91 201	51.71 219	80.56 111	83.81 172
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 80	71.49 89	76.40 62	81.99 82	59.58 52	76.92 166	76.74 205	60.40 94	74.81 51	85.95 119	45.54 169	85.76 93	70.41 73	70.61 241	83.86 171

hse-mvs2		71.04 106	69.86 119	74.60 95	79.58 129	57.12 96	73.96 226	75.25 226	60.40 94	74.81 51	81.95 201	45.54 169	82.90 152	70.41 73	66.83 293	83.77 176

EPP-MVSNet		72.16 92	71.31 95	74.71 88	78.68 153	49.70 217	82.10 75	81.65 104	60.40 94	65.94 194	85.84 123	51.74 96	86.37 80	55.93 179	79.55 124	88.07 29

UniMVSNet_ETH3D		67.60 185	67.07 181	69.18 228	77.39 201	42.29 296	74.18 223	75.59 218	60.37 97	66.77 179	86.06 115	37.64 250	78.93 241	52.16 212	73.49 202	86.32 85

test_prior2							81.75 78		60.37 97	75.01 44	89.06 52	56.22 42		72.19 60	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 61	60.37 97	79.89 18	89.38 49	54.97 50	85.58 97	76.12 31	84.94 66	86.33 83
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		69.68 137	70.19 115	68.16 239	79.73 126	41.63 305	70.53 277	77.38 195	60.37 97	70.69 109	86.63 95	51.08 104	77.09 264	53.61 202	81.69 104	85.75 107

sasdasda		74.67 58	74.98 53	73.71 122	78.94 145	50.56 203	80.23 95	83.87 61	60.30 101	77.15 29	86.56 99	59.65 17	82.00 175	66.01 104	82.12 93	88.58 14

canonicalmvs		74.67 58	74.98 53	73.71 122	78.94 145	50.56 203	80.23 95	83.87 61	60.30 101	77.15 29	86.56 99	59.65 17	82.00 175	66.01 104	82.12 93	88.58 14

v7n		69.01 154	67.36 170	73.98 111	72.51 282	52.65 170	78.54 125	81.30 118	60.26 103	62.67 250	81.62 207	43.61 190	84.49 122	57.01 172	68.70 279	84.79 144

HPM-MVS_fast		74.30 65	73.46 70	76.80 53	84.45 60	59.04 66	83.65 52	81.05 126	60.15 104	70.43 111	89.84 43	41.09 220	85.59 96	67.61 91	82.90 86	85.77 105

VPA-MVSNet		69.02 153	69.47 126	67.69 243	77.42 200	41.00 310	74.04 224	79.68 145	60.06 105	69.26 135	84.81 138	51.06 105	77.58 256	54.44 196	74.43 187	84.48 151

v10		70.21 124	69.02 133	73.81 115	73.51 265	50.92 195	78.74 117	81.39 111	60.05 106	66.39 187	81.83 204	47.58 142	85.41 105	62.80 132	68.86 277	85.09 134

SR-MVS		76.13 43	75.70 44	77.40 48	85.87 40	61.20 29	85.52 27	82.19 96	59.99 107	75.10 42	90.35 28	47.66 140	86.52 75	71.64 67	82.99 82	84.47 152

9.14			78.75 15		83.10 69		84.15 43	88.26 1	59.90 108	78.57 23	90.36 27	57.51 32	86.86 65	77.39 23	89.52 21

v2v482		70.50 118	69.45 127	73.66 125	72.62 278	50.03 213	77.58 145	80.51 136	59.90 108	69.52 127	82.14 197	47.53 144	84.88 116	65.07 113	70.17 251	86.09 92

Baseline_NR-MVSNet		67.05 197	67.56 160	65.50 275	75.65 231	37.70 338	75.42 196	74.65 238	59.90 108	68.14 151	83.15 174	49.12 125	77.20 262	52.23 211	69.78 260	81.60 223

API-MVS		72.17 90	71.41 91	74.45 100	81.95 83	57.22 89	84.03 45	80.38 138	59.89 111	68.40 145	82.33 190	49.64 116	87.83 48	51.87 216	84.16 75	78.30 273

Effi-MVS+-dtu		69.64 139	67.53 163	75.95 67	76.10 226	62.29 15	80.20 98	76.06 213	59.83 112	65.26 211	77.09 289	41.56 212	84.02 131	60.60 151	71.09 237	81.53 224

MVSMamba_PlusPlus		75.75 49	75.44 47	76.67 56	80.84 103	53.06 161	78.62 120	85.13 32	59.65 113	71.53 103	87.47 75	56.92 34	88.17 36	72.18 61	86.63 57	88.80 9

iter_conf05		75.83 48	75.63 46	76.43 61	80.84 103	51.87 187	78.13 132	84.81 42	59.65 113	72.86 85	87.47 75	56.92 34	88.17 36	72.18 61	87.79 42	89.24 5

CANet_DTU		68.18 173	67.71 159	69.59 219	74.83 245	46.24 258	78.66 119	76.85 202	59.60 115	63.45 238	82.09 200	35.25 274	77.41 259	59.88 157	78.76 138	85.14 131

EI-MVSNet		69.27 150	68.44 148	71.73 171	74.47 254	49.39 224	75.20 201	78.45 175	59.60 115	69.16 137	76.51 300	51.29 100	82.50 167	59.86 159	71.45 233	83.30 189

IterMVS-LS		69.22 152	68.48 144	71.43 181	74.44 256	49.40 223	76.23 179	77.55 191	59.60 115	65.85 199	81.59 210	51.28 101	81.58 184	59.87 158	69.90 258	83.30 189
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 84	73.34 72	69.81 216	77.77 185	43.21 289	75.84 190	81.18 123	59.59 118	75.45 39	86.64 93	57.74 28	77.94 249	63.92 122	81.90 98	88.30 19

VDDNet		71.81 95	71.33 94	73.26 142	82.80 75	47.60 247	78.74 117	75.27 225	59.59 118	72.94 83	89.40 48	41.51 214	83.91 133	58.75 165	82.99 82	88.26 20

alignmvs		73.86 68	73.99 63	73.45 135	78.20 168	50.50 205	78.57 123	82.43 93	59.40 120	76.57 32	86.71 92	56.42 41	81.23 192	65.84 107	81.79 99	88.62 12

MVS_Test		72.45 84	72.46 79	72.42 159	74.88 243	48.50 235	76.28 178	83.14 85	59.40 120	72.46 93	84.68 139	55.66 45	81.12 193	65.98 106	79.66 121	87.63 42

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 45	83.82 64	59.34 122	79.37 19	89.76 45	59.84 16	87.62 51	76.69 27	86.74 54	87.68 40
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 69	73.47 69	74.66 91	83.02 71	59.29 58	82.30 74	81.88 100	59.34 122	71.59 102	86.83 86	45.94 164	83.65 138	65.09 112	85.22 65	81.06 238

PAPM_NR		72.63 81	71.80 84	75.13 84	81.72 86	53.42 154	79.91 103	83.28 81	59.14 124	66.31 189	85.90 121	51.86 93	86.06 84	57.45 170	80.62 107	85.91 98

testing91		64.46 234	63.80 225	66.47 256	78.43 160	40.06 314	67.63 300	69.59 281	59.06 125	63.18 242	78.05 270	34.05 286	76.99 266	48.30 246	75.87 175	82.37 212

save fliter					86.17 33	61.30 28	83.98 47	79.66 146	59.00 126

v148		68.24 172	67.19 179	71.40 182	70.43 315	47.77 244	75.76 191	77.03 200	58.91 127	67.36 168	80.10 237	48.60 130	81.89 177	60.01 155	66.52 296	84.53 149

TransMVSNet (Re)		64.72 229	64.33 219	65.87 271	75.22 239	38.56 328	74.66 216	75.08 234	58.90 128	61.79 264	82.63 180	51.18 102	78.07 248	43.63 289	55.87 360	80.99 240

Anonymous202405211		66.84 202	65.99 201	69.40 223	80.19 118	42.21 298	71.11 271	71.31 267	58.80 129	67.90 154	86.39 105	29.83 330	79.65 221	49.60 236	78.78 137	86.33 83

test2506		65.33 224	64.61 217	67.50 244	79.46 132	34.19 370	74.43 220	51.92 376	58.72 130	66.75 180	88.05 66	25.99 359	80.92 200	51.94 215	84.25 72	87.39 50

ECVR-MVS		67.72 183	67.51 164	68.35 237	79.46 132	36.29 355	74.79 213	66.93 301	58.72 130	67.19 171	88.05 66	36.10 267	81.38 187	52.07 213	84.25 72	87.39 50

test1111		67.21 190	67.14 180	67.42 246	79.24 137	34.76 364	73.89 231	65.65 310	58.71 132	66.96 176	87.95 69	36.09 268	80.53 207	52.03 214	83.79 77	86.97 60

LCM-MVSNet-Re		61.88 263	61.35 256	63.46 290	74.58 252	31.48 383	61.42 343	58.14 354	58.71 132	53.02 350	79.55 248	43.07 194	76.80 270	45.69 268	77.96 148	82.11 218

testing99		64.05 237	63.29 234	66.34 258	78.17 172	39.76 318	67.33 305	68.00 294	58.60 134	63.03 245	78.10 269	32.57 313	76.94 268	48.22 247	75.58 179	82.34 213

v1144		70.42 120	69.31 128	73.76 118	73.22 266	50.64 200	77.83 141	81.43 110	58.58 135	69.40 131	81.16 215	47.53 144	85.29 107	64.01 120	70.64 239	85.34 124

TSAR-MVS + GP.		74.90 53	74.15 61	77.17 49	82.00 81	58.77 72	81.80 77	78.57 168	58.58 135	74.32 60	84.51 147	55.94 44	87.22 55	67.11 95	84.48 71	85.52 114

BH-RMVSNet		68.81 156	67.42 167	72.97 145	80.11 121	52.53 174	74.26 221	76.29 208	58.48 137	68.38 146	84.20 150	42.59 198	83.83 134	46.53 260	75.91 174	82.56 205

APD-MVS_3200maxsize		74.96 52	74.39 59	76.67 56	82.20 78	58.24 77	83.67 51	83.29 80	58.41 138	73.71 67	90.14 33	45.62 166	85.99 87	69.64 75	82.85 88	85.78 102

OMC-MVS		71.40 104	70.60 106	73.78 116	76.60 218	53.15 158	79.74 107	79.78 143	58.37 139	68.75 140	86.45 104	45.43 173	80.60 206	62.58 133	77.73 150	87.58 45

nrg030		72.96 76	73.01 73	72.84 148	75.41 237	50.24 207	80.02 99	82.89 89	58.36 140	74.44 57	86.73 90	58.90 24	80.83 202	65.84 107	74.46 185	87.44 48

K. test v3		60.47 274	57.11 291	70.56 201	73.74 264	48.22 238	75.10 205	62.55 333	58.27 141	53.62 346	76.31 303	27.81 345	81.59 183	47.42 251	39.18 391	81.88 221

FA-MVS(test-final)		69.82 132	68.48 144	73.84 114	78.44 159	50.04 212	75.58 195	78.99 158	58.16 142	67.59 165	82.14 197	42.66 197	85.63 94	56.60 174	76.19 172	85.84 100

MVS_111021_LR		69.50 144	68.78 138	71.65 174	78.38 161	59.33 56	74.82 212	70.11 276	58.08 143	67.83 160	84.68 139	41.96 205	76.34 281	65.62 109	77.54 152	79.30 266

SR-MVS-dyc-post		74.57 61	73.90 64	76.58 59	83.49 65	59.87 49	84.29 37	81.36 113	58.07 144	73.14 77	90.07 34	44.74 180	85.84 91	68.20 82	81.76 100	84.03 162

RE-MVS-def			73.71 68		83.49 65	59.87 49	84.29 37	81.36 113	58.07 144	73.14 77	90.07 34	43.06 195		68.20 82	81.76 100	84.03 162

SDMVSNet		68.03 175	68.10 154	67.84 241	77.13 206	48.72 233	65.32 320	79.10 155	58.02 146	65.08 215	82.55 183	47.83 137	73.40 293	63.92 122	73.92 193	81.41 226

sd_testset		64.46 234	64.45 218	64.51 285	77.13 206	42.25 297	62.67 336	72.11 262	58.02 146	65.08 215	82.55 183	41.22 219	69.88 314	47.32 253	73.92 193	81.41 226

GeoE		71.01 107	70.15 116	73.60 130	79.57 130	52.17 180	78.93 115	78.12 182	58.02 146	67.76 164	83.87 159	52.36 85	82.72 161	56.90 173	75.79 176	85.92 97

ZD-MVS					86.64 21	60.38 43		82.70 91	57.95 149	78.10 24	90.06 36	56.12 43	88.84 26	74.05 47	87.00 50

EIA-MVS		71.78 96	70.60 106	75.30 82	79.85 124	53.54 151	77.27 157	83.26 82	57.92 150	66.49 184	79.39 252	52.07 90	86.69 69	60.05 154	79.14 132	85.66 110

test_yl		69.69 135	69.13 130	71.36 183	78.37 163	45.74 263	74.71 214	80.20 140	57.91 151	70.01 120	83.83 160	42.44 200	82.87 155	54.97 189	79.72 119	85.48 116

DCV-MVSNet		69.69 135	69.13 130	71.36 183	78.37 163	45.74 263	74.71 214	80.20 140	57.91 151	70.01 120	83.83 160	42.44 200	82.87 155	54.97 189	79.72 119	85.48 116

dcpmvs_2		74.55 62	75.23 51	72.48 155	82.34 77	53.34 155	77.87 138	81.46 109	57.80 153	75.49 38	86.81 87	62.22 13	77.75 254	71.09 70	82.02 96	86.34 81

Fast-Effi-MVS+-dtu		67.37 188	65.33 211	73.48 134	72.94 273	57.78 83	77.47 150	76.88 201	57.60 154	61.97 261	76.85 293	39.31 232	80.49 210	54.72 192	70.28 249	82.17 217

v1192		69.97 129	68.68 140	73.85 113	73.19 267	50.94 193	77.68 144	81.36 113	57.51 155	68.95 139	80.85 225	45.28 176	85.33 106	62.97 131	70.37 245	85.27 128

ACMH+	57.40 11	66.12 213	64.06 220	72.30 161	77.79 184	52.83 168	80.39 94	78.03 183	57.30 156	57.47 308	82.55 183	27.68 346	84.17 126	45.54 271	69.78 260	79.90 256

diffmvs		70.69 114	70.43 109	71.46 178	69.45 330	48.95 229	72.93 242	78.46 174	57.27 157	71.69 100	83.97 158	51.48 99	77.92 251	70.70 72	77.95 149	87.53 46
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		68.27 170	67.29 172	71.21 187	79.74 125	53.22 157	76.06 183	77.46 194	57.19 158	66.10 191	81.61 208	45.37 175	83.50 141	45.42 276	76.68 168	76.91 297

thres100view900		63.28 246	62.41 244	65.89 270	77.31 203	38.66 327	72.65 245	69.11 288	57.07 159	62.45 257	81.03 219	37.01 262	79.17 230	31.84 361	73.25 208	79.83 258

DP-MVS Recon		72.15 93	70.73 105	76.40 62	86.57 24	57.99 79	81.15 87	82.96 86	57.03 160	66.78 178	85.56 128	44.50 183	88.11 40	51.77 218	80.23 116	83.10 198

thres600view7		63.30 245	62.27 245	66.41 257	77.18 205	38.87 325	72.35 252	69.11 288	56.98 161	62.37 259	80.96 221	37.01 262	79.00 239	31.43 368	73.05 212	81.36 229

V42		68.65 160	67.35 171	72.56 153	68.93 336	50.18 209	72.90 243	79.47 150	56.92 162	69.45 130	80.26 234	46.29 162	82.99 149	64.07 118	67.82 285	84.53 149

MCST-MVS		77.48 28	77.45 27	77.54 45	86.67 20	58.36 76	83.22 55	86.93 5	56.91 163	74.91 48	88.19 62	59.15 23	87.68 50	73.67 51	87.45 44	86.57 74

GA-MVS		65.53 220	63.70 227	71.02 194	70.87 310	48.10 239	70.48 278	74.40 240	56.69 164	64.70 223	76.77 294	33.66 294	81.10 194	55.42 188	70.32 248	83.87 170

v144192		69.71 134	68.51 143	73.33 140	73.10 269	50.13 210	77.54 148	80.64 133	56.65 165	68.57 143	80.55 228	46.87 158	84.96 113	62.98 130	69.66 264	84.89 141

tfpn200view9		63.18 248	62.18 247	66.21 262	76.85 213	39.62 319	71.96 259	69.44 284	56.63 166	62.61 252	79.83 240	37.18 256	79.17 230	31.84 361	73.25 208	79.83 258

thres400		63.31 244	62.18 247	66.72 252	76.85 213	39.62 319	71.96 259	69.44 284	56.63 166	62.61 252	79.83 240	37.18 256	79.17 230	31.84 361	73.25 208	81.36 229

GBi-Net		67.21 190	66.55 185	69.19 225	77.63 190	43.33 286	77.31 153	77.83 186	56.62 168	65.04 217	82.70 177	41.85 207	80.33 212	47.18 255	72.76 215	83.92 167

test1		67.21 190	66.55 185	69.19 225	77.63 190	43.33 286	77.31 153	77.83 186	56.62 168	65.04 217	82.70 177	41.85 207	80.33 212	47.18 255	72.76 215	83.92 167

FMVSNet2		66.93 200	66.31 196	68.79 232	77.63 190	42.98 291	76.11 181	77.47 192	56.62 168	65.22 214	82.17 195	41.85 207	80.18 218	47.05 258	72.72 218	83.20 193

DPM-MVS		75.47 51	75.00 52	76.88 51	81.38 93	59.16 59	79.94 101	85.71 22	56.59 171	72.46 93	86.76 88	56.89 36	87.86 47	66.36 100	88.91 25	83.64 184

v1921920		69.47 145	68.17 152	73.36 139	73.06 270	50.10 211	77.39 151	80.56 134	56.58 172	68.59 141	80.37 230	44.72 181	84.98 111	62.47 136	69.82 259	85.00 136

FMVSNet1		66.70 205	65.87 202	69.19 225	77.49 198	43.33 286	77.31 153	77.83 186	56.45 173	64.60 225	82.70 177	38.08 248	80.33 212	46.08 264	72.31 223	83.92 167

v1240		69.24 151	67.91 155	73.25 143	73.02 272	49.82 215	77.21 158	80.54 135	56.43 174	68.34 147	80.51 229	43.33 193	84.99 109	62.03 140	69.77 262	84.95 140

testing222		62.29 258	61.31 257	65.25 280	77.87 181	38.53 329	68.34 295	66.31 307	56.37 175	63.15 244	77.58 284	28.47 340	76.18 284	37.04 330	76.65 169	81.05 239

CDPH-MVS		76.31 39	75.67 45	78.22 37	85.35 48	59.14 62	81.31 85	84.02 52	56.32 176	74.05 62	88.98 54	53.34 73	87.92 45	69.23 79	88.42 28	87.59 44

Vis-MVSNet (Re-imp)		63.69 241	63.88 223	63.14 294	74.75 247	31.04 384	71.16 269	63.64 326	56.32 176	59.80 285	84.99 135	44.51 182	75.46 285	39.12 318	80.62 107	82.92 200

AdaColmap		69.99 128	68.66 141	73.97 112	84.94 54	57.83 81	82.63 65	78.71 164	56.28 178	64.34 226	84.14 152	41.57 211	87.06 62	46.45 261	78.88 134	77.02 293

PS-MVSNAJss		72.24 88	71.21 96	75.31 81	78.50 156	55.93 112	81.63 79	82.12 97	56.24 179	70.02 119	85.68 127	47.05 153	84.34 125	65.27 111	74.41 188	85.67 109

c3_l		68.33 169	67.56 160	70.62 200	70.87 310	46.21 259	74.47 219	78.80 162	56.22 180	66.19 190	78.53 267	51.88 92	81.40 186	62.08 137	69.04 273	84.25 156

Fast-Effi-MVS+		70.28 123	69.12 132	73.73 121	78.50 156	51.50 190	75.01 206	79.46 151	56.16 181	68.59 141	79.55 248	53.97 62	84.05 128	53.34 204	77.53 153	85.65 111

PHI-MVS		75.87 45	75.36 48	77.41 46	80.62 111	55.91 113	84.28 39	85.78 20	56.08 182	73.41 70	86.58 98	50.94 107	88.54 29	70.79 71	89.71 17	87.79 37

baseline1		63.81 240	63.87 224	63.62 289	76.29 223	36.36 350	71.78 261	67.29 298	56.05 183	64.23 231	82.95 175	47.11 152	74.41 290	47.30 254	61.85 333	80.10 254

train_agg		76.27 40	76.15 38	76.64 58	85.58 43	61.59 24	81.62 80	81.26 120	55.86 184	74.93 46	88.81 56	53.70 69	84.68 119	75.24 38	88.33 30	83.65 183

test_8					85.40 46	60.96 34	81.54 83	81.18 123	55.86 184	74.81 51	88.80 58	53.70 69	84.45 123

FMVSNet3		66.32 212	65.61 207	68.46 235	76.48 221	42.34 295	74.98 208	77.15 199	55.83 186	65.04 217	81.16 215	39.91 225	80.14 219	47.18 255	72.76 215	82.90 202

PAPR		71.72 99	70.82 103	74.41 101	81.20 98	51.17 191	79.55 111	83.33 78	55.81 187	66.93 177	84.61 143	50.95 106	86.06 84	55.79 182	79.20 130	86.00 94

eth_miper_zixun_eth		67.63 184	66.28 197	71.67 173	71.60 296	48.33 237	73.68 235	77.88 184	55.80 188	65.91 195	78.62 265	47.35 150	82.88 154	59.45 161	66.25 297	83.81 172

ACMH	55.70 15	65.20 226	63.57 229	70.07 209	78.07 175	52.01 185	79.48 112	79.69 144	55.75 189	56.59 315	80.98 220	27.12 351	80.94 198	42.90 297	71.58 231	77.25 291
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 223	62.73 241	73.40 138	74.89 242	52.78 169	73.09 241	75.13 230	55.69 190	58.48 302	73.73 328	32.86 303	86.32 82	50.63 226	70.11 252	81.10 237
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		61.53 266	60.94 263	63.30 292	68.95 335	36.93 346	67.60 301	72.80 257	55.67 191	59.95 282	76.63 296	45.01 179	72.22 300	39.74 316	62.09 332	80.74 244

bld_raw_conf03		74.74 56	74.04 62	76.85 52	80.93 102	53.06 161	78.62 120	85.13 32	55.66 192	71.53 103	85.93 120	53.98 60	88.69 27	67.93 86	86.63 57	88.80 9

TEST9					85.58 43	61.59 24	81.62 80	81.26 120	55.65 193	74.93 46	88.81 56	53.70 69	84.68 119

thres200		62.20 259	61.16 261	65.34 278	75.38 238	39.99 315	69.60 287	69.29 286	55.64 194	61.87 263	76.99 290	37.07 261	78.96 240	31.28 369	73.28 207	77.06 292

pm-mvs1		65.24 225	64.97 215	66.04 267	72.38 285	39.40 322	72.62 247	75.63 217	55.53 195	62.35 260	83.18 173	47.45 146	76.47 279	49.06 240	66.54 295	82.24 214

testing11		62.81 251	61.90 250	65.54 274	78.38 161	40.76 311	67.59 302	66.78 303	55.48 196	60.13 277	77.11 288	31.67 319	76.79 271	45.53 272	74.45 186	79.06 267

ACMM	61.98 7	70.80 113	69.73 121	74.02 110	80.59 112	58.59 74	82.68 64	82.02 99	55.46 197	67.18 172	84.39 149	38.51 241	83.17 147	60.65 150	76.10 173	80.30 250
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Anonymous20240529		69.91 130	69.02 133	72.56 153	80.19 118	47.65 245	77.56 147	80.99 128	55.45 198	69.88 123	86.76 88	39.24 235	82.18 173	54.04 197	77.10 163	87.85 33

tt0805		67.77 182	67.24 177	69.34 224	74.87 244	40.08 313	77.36 152	81.37 112	55.31 199	66.33 188	84.65 141	37.35 254	82.55 166	55.65 185	72.28 224	85.39 123

CPTT-MVS		72.78 78	72.08 83	74.87 87	84.88 57	61.41 26	84.15 43	77.86 185	55.27 200	67.51 167	88.08 65	41.93 206	81.85 178	69.04 80	80.01 117	81.35 231

XVG-OURS		68.76 159	67.37 169	72.90 147	74.32 259	57.22 89	70.09 283	78.81 161	55.24 201	67.79 162	85.81 126	36.54 265	78.28 245	62.04 139	75.74 177	83.19 194

tfpnnormal		62.47 254	61.63 253	64.99 282	74.81 246	39.01 324	71.22 267	73.72 249	55.22 202	60.21 276	80.09 238	41.26 218	76.98 267	30.02 374	68.09 283	78.97 270

cl____		67.18 193	66.26 198	69.94 211	70.20 318	45.74 263	73.30 237	76.83 203	55.10 203	65.27 208	79.57 247	47.39 148	80.53 207	59.41 163	69.22 271	83.53 186

DIV-MVS_self_test		67.18 193	66.26 198	69.94 211	70.20 318	45.74 263	73.29 238	76.83 203	55.10 203	65.27 208	79.58 246	47.38 149	80.53 207	59.43 162	69.22 271	83.54 185

PC_three_1452									55.09 205	84.46 4	89.84 43	66.68 5	89.41 18	74.24 44	91.38 2	88.42 16

EPNet_dtu		61.90 262	61.97 249	61.68 302	72.89 274	39.78 317	75.85 189	65.62 311	55.09 205	54.56 336	79.36 253	37.59 251	67.02 330	39.80 315	76.95 164	78.25 274
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 103	70.39 110	74.65 92	82.01 80	58.82 71	79.93 102	80.35 139	55.09 205	65.82 200	82.16 196	49.17 122	82.64 164	60.34 152	78.62 141	82.50 209

cl22		67.47 187	66.45 187	70.54 202	69.85 326	46.49 255	73.85 232	77.35 196	55.07 208	65.51 203	77.92 274	47.64 141	81.10 194	61.58 145	69.32 267	84.01 164

miper_ehance_all_eth		68.03 175	67.24 177	70.40 204	70.54 313	46.21 259	73.98 225	78.68 166	55.07 208	66.05 192	77.80 278	52.16 89	81.31 189	61.53 146	69.32 267	83.67 180

PS-MVSNAJ		70.51 117	69.70 122	72.93 146	81.52 88	55.79 116	74.92 210	79.00 157	55.04 210	69.88 123	78.66 262	47.05 153	82.19 172	61.61 143	79.58 122	80.83 242

xiu_mvs_v2_base		70.52 116	69.75 120	72.84 148	81.21 97	55.63 120	75.11 203	78.92 159	54.92 211	69.96 122	79.68 245	47.00 157	82.09 174	61.60 144	79.37 125	80.81 243

MAR-MVS		71.51 101	70.15 116	75.60 77	81.84 84	59.39 55	81.38 84	82.90 88	54.90 212	68.08 153	78.70 260	47.73 138	85.51 99	51.68 220	84.17 74	81.88 221
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		68.81 156	67.47 166	72.82 150	74.40 257	56.87 99	70.59 276	79.04 156	54.77 213	66.99 175	86.01 117	39.57 230	78.21 246	62.54 134	73.33 206	83.37 188

testing3		56.54 302	55.92 304	58.41 323	77.52 197	27.93 393	69.72 286	56.36 363	54.75 214	58.63 300	77.80 278	20.88 377	71.75 303	25.31 390	62.25 330	75.53 308

Anonymous20231211		69.28 149	68.47 146	71.73 171	80.28 113	47.18 251	79.98 100	82.37 94	54.61 215	67.24 170	84.01 156	39.43 231	82.41 170	55.45 187	72.83 214	85.62 112

SixPastTwentyTwo		61.65 265	58.80 279	70.20 207	75.80 229	47.22 250	75.59 193	69.68 279	54.61 215	54.11 340	79.26 255	27.07 352	82.96 150	43.27 291	49.79 378	80.41 248

test_0402		63.25 247	61.01 262	69.96 210	80.00 122	54.37 140	76.86 169	72.02 263	54.58 217	58.71 297	80.79 227	35.00 277	84.36 124	26.41 388	64.71 308	71.15 357

tttt0517		67.83 181	65.66 206	74.33 103	76.69 215	50.82 197	77.86 139	73.99 247	54.54 218	64.64 224	82.53 186	35.06 276	85.50 100	55.71 183	69.91 257	86.67 70

BH-w/o		66.85 201	65.83 203	69.90 214	79.29 134	52.46 176	74.66 216	76.65 206	54.51 219	64.85 221	78.12 268	45.59 168	82.95 151	43.26 292	75.54 180	74.27 324

AUN-MVS		68.45 168	66.41 191	74.57 97	79.53 131	57.08 97	73.93 229	75.23 227	54.44 220	66.69 181	81.85 203	37.10 260	82.89 153	62.07 138	66.84 292	83.75 177

LTVRE_ROB	55.42 16	63.15 249	61.23 260	68.92 230	76.57 219	47.80 242	59.92 352	76.39 207	54.35 221	58.67 298	82.46 188	29.44 334	81.49 185	42.12 301	71.14 235	77.46 285
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 75	72.59 77	74.27 105	71.28 305	55.88 114	78.21 130	75.56 219	54.31 222	74.86 50	87.80 72	54.72 53	80.23 216	78.07 21	78.48 142	86.70 68

test_fmvsmconf0.01_n		72.17 90	71.50 88	74.16 108	67.96 342	55.58 123	78.06 135	74.67 237	54.19 223	74.54 56	88.23 61	50.35 112	80.24 215	78.07 21	77.46 155	86.65 72

test_fmvsmconf0.1_n		72.81 77	72.33 80	74.24 106	69.89 325	55.81 115	78.22 129	75.40 223	54.17 224	75.00 45	88.03 68	53.82 66	80.23 216	78.08 20	78.34 145	86.69 69

ETVMVS		59.51 283	58.81 277	61.58 304	77.46 199	34.87 361	64.94 325	59.35 349	54.06 225	61.08 272	76.67 295	29.54 331	71.87 302	32.16 357	74.07 191	78.01 281

ab-mvs		66.65 206	66.42 190	67.37 247	76.17 225	41.73 302	70.41 280	76.14 211	53.99 226	65.98 193	83.51 167	49.48 117	76.24 282	48.60 243	73.46 204	84.14 160

IU-MVS					87.77 4	59.15 60		85.53 26	53.93 227	84.64 3				79.07 11	90.87 5	88.37 18

XVG-ACMP-BASELINE		64.36 236	62.23 246	70.74 198	72.35 286	52.45 177	70.80 275	78.45 175	53.84 228	59.87 283	81.10 217	16.24 384	79.32 227	55.64 186	71.76 228	80.47 246

FE-MVS		65.91 215	63.33 233	73.63 128	77.36 202	51.95 186	72.62 247	75.81 214	53.70 229	65.31 206	78.96 258	28.81 339	86.39 79	43.93 285	73.48 203	82.55 206

thisisatest0530		67.92 179	65.78 204	74.33 103	76.29 223	51.03 192	76.89 167	74.25 244	53.67 230	65.59 202	81.76 205	35.15 275	85.50 100	55.94 178	72.47 219	86.47 76

PVSNet_BlendedMVS		68.56 165	67.72 157	71.07 193	77.03 210	50.57 201	74.50 218	81.52 106	53.66 231	64.22 232	79.72 244	49.13 123	82.87 155	55.82 180	73.92 193	79.77 261

patch_mono-2		69.85 131	71.09 99	66.16 263	79.11 142	54.80 135	71.97 258	74.31 242	53.50 232	70.90 108	84.17 151	57.63 31	63.31 344	66.17 101	82.02 96	80.38 249

EG-PatchMatch MVS		64.71 230	62.87 238	70.22 205	77.68 187	53.48 152	77.99 136	78.82 160	53.37 233	56.03 320	77.41 286	24.75 366	84.04 129	46.37 262	73.42 205	73.14 330

DP-MVS		65.68 217	63.66 228	71.75 170	84.93 55	56.87 99	80.74 92	73.16 254	53.06 234	59.09 294	82.35 189	36.79 264	85.94 89	32.82 355	69.96 256	72.45 338

TR-MVS		66.59 209	65.07 214	71.17 190	79.18 139	49.63 221	73.48 236	75.20 229	52.95 235	67.90 154	80.33 233	39.81 228	83.68 137	43.20 293	73.56 201	80.20 251

ET-MVSNet_ETH3D		67.96 178	65.72 205	74.68 90	76.67 216	55.62 122	75.11 203	74.74 235	52.91 236	60.03 280	80.12 236	33.68 293	82.64 164	61.86 141	76.34 170	85.78 102

QAPM		70.05 126	68.81 137	73.78 116	76.54 220	53.43 153	83.23 54	83.48 71	52.89 237	65.90 196	86.29 107	41.55 213	86.49 77	51.01 223	78.40 144	81.42 225

OpenMVS	61.03 9	68.85 155	67.56 160	72.70 152	74.26 260	53.99 143	81.21 86	81.34 117	52.70 238	62.75 249	85.55 130	38.86 239	84.14 127	48.41 245	83.01 81	79.97 255

pmmvs6		63.69 241	62.82 240	66.27 261	70.63 312	39.27 323	73.13 240	75.47 222	52.69 239	59.75 287	82.30 191	39.71 229	77.03 265	47.40 252	64.35 313	82.53 207

IterMVS		62.79 252	61.27 258	67.35 248	69.37 331	52.04 184	71.17 268	68.24 293	52.63 240	59.82 284	76.91 292	37.32 255	72.36 297	52.80 208	63.19 323	77.66 283
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 173	66.36 193	73.63 128	75.61 233	55.35 128	80.77 91	78.56 169	52.48 241	64.27 229	84.10 154	27.45 348	81.84 179	63.45 129	70.56 242	83.69 179

jajsoiax		68.25 171	66.45 187	73.66 125	75.62 232	55.49 125	80.82 90	78.51 171	52.33 242	64.33 227	84.11 153	28.28 342	81.81 180	63.48 128	70.62 240	83.67 180

TAMVS		66.78 204	65.27 212	71.33 186	79.16 141	53.67 147	73.84 233	69.59 281	52.32 243	65.28 207	81.72 206	44.49 184	77.40 260	42.32 300	78.66 140	82.92 200

CDS-MVSNet		66.80 203	65.37 209	71.10 192	78.98 144	53.13 160	73.27 239	71.07 269	52.15 244	64.72 222	80.23 235	43.56 191	77.10 263	45.48 274	78.88 134	83.05 199
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		68.47 166	66.56 184	74.21 107	79.60 128	52.95 163	74.94 209	75.48 221	52.09 245	60.10 278	83.27 170	36.54 265	84.70 118	59.32 164	77.69 151	84.99 138

PVSNet_Blended		68.59 161	67.72 157	71.19 188	77.03 210	50.57 201	72.51 250	81.52 106	51.91 246	64.22 232	77.77 281	49.13 123	82.87 155	55.82 180	79.58 122	80.14 253

mvs_anonymous		68.03 175	67.51 164	69.59 219	72.08 290	44.57 277	71.99 257	75.23 227	51.67 247	67.06 174	82.57 182	54.68 54	77.94 249	56.56 175	75.71 178	86.26 89

xiu_mvs_v1_base_debu		68.58 162	67.28 173	72.48 155	78.19 169	57.19 91	75.28 198	75.09 231	51.61 248	70.04 116	81.41 212	32.79 304	79.02 236	63.81 124	77.31 156	81.22 233

xiu_mvs_v1_base		68.58 162	67.28 173	72.48 155	78.19 169	57.19 91	75.28 198	75.09 231	51.61 248	70.04 116	81.41 212	32.79 304	79.02 236	63.81 124	77.31 156	81.22 233

xiu_mvs_v1_base_debi		68.58 162	67.28 173	72.48 155	78.19 169	57.19 91	75.28 198	75.09 231	51.61 248	70.04 116	81.41 212	32.79 304	79.02 236	63.81 124	77.31 156	81.22 233

MVSTER		67.16 195	65.58 208	71.88 166	70.37 317	49.70 217	70.25 282	78.45 175	51.52 251	69.16 137	80.37 230	38.45 242	82.50 167	60.19 153	71.46 232	83.44 187

CNLPA		65.43 221	64.02 221	69.68 217	78.73 152	58.07 78	77.82 142	70.71 272	51.49 252	61.57 268	83.58 166	38.23 246	70.82 306	43.90 286	70.10 253	80.16 252

原ACMM1				74.69 89	85.39 47	59.40 54		83.42 74	51.47 253	70.27 114	86.61 96	48.61 129	86.51 76	53.85 200	87.96 39	78.16 275

miper_enhance_ethall		67.11 196	66.09 200	70.17 208	69.21 333	45.98 261	72.85 244	78.41 178	51.38 254	65.65 201	75.98 308	51.17 103	81.25 190	60.82 149	69.32 267	83.29 191

MSDG		61.81 264	59.23 274	69.55 222	72.64 277	52.63 172	70.45 279	75.81 214	51.38 254	53.70 343	76.11 304	29.52 332	81.08 196	37.70 325	65.79 301	74.93 316

test20.03		53.87 322	54.02 321	53.41 354	61.47 376	28.11 392	61.30 344	59.21 350	51.34 256	52.09 352	77.43 285	33.29 298	58.55 365	29.76 375	60.27 344	73.58 329

MVSFormer		71.50 102	70.38 111	74.88 86	78.76 150	57.15 94	82.79 61	78.48 172	51.26 257	69.49 128	83.22 171	43.99 188	83.24 145	66.06 102	79.37 125	84.23 157

test_djsdf		69.45 146	67.74 156	74.58 96	74.57 253	54.92 133	82.79 61	78.48 172	51.26 257	65.41 205	83.49 168	38.37 243	83.24 145	66.06 102	69.25 270	85.56 113

dmvs_testset		50.16 339	51.90 329	44.94 374	66.49 352	11.78 414	61.01 349	51.50 377	51.17 259	50.30 364	67.44 368	39.28 233	60.29 355	22.38 394	57.49 353	62.76 379

PAPM		67.92 179	66.69 183	71.63 175	78.09 174	49.02 227	77.09 161	81.24 122	51.04 260	60.91 273	83.98 157	47.71 139	84.99 109	40.81 309	79.32 128	80.90 241

Syy-MVS		56.00 309	56.23 302	55.32 340	74.69 249	26.44 399	65.52 315	57.49 358	50.97 261	56.52 316	72.18 335	39.89 226	68.09 321	24.20 391	64.59 311	71.44 353

myMVS_eth3d		54.86 318	54.61 313	55.61 339	74.69 249	27.31 396	65.52 315	57.49 358	50.97 261	56.52 316	72.18 335	21.87 375	68.09 321	27.70 382	64.59 311	71.44 353

miper_lstm_enhance		62.03 261	60.88 264	65.49 276	66.71 350	46.25 257	56.29 370	75.70 216	50.68 263	61.27 270	75.48 315	40.21 224	68.03 323	56.31 177	65.25 304	82.18 215

gg-mvs-nofinetune		57.86 293	56.43 300	62.18 300	72.62 278	35.35 360	66.57 306	56.33 364	50.65 264	57.64 307	57.10 390	30.65 322	76.36 280	37.38 327	78.88 134	74.82 318

TAPA-MVS	59.36 10	66.60 207	65.20 213	70.81 196	76.63 217	48.75 231	76.52 174	80.04 142	50.64 265	65.24 212	84.93 136	39.15 236	78.54 242	36.77 332	76.88 165	85.14 131
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 301	56.83 296	56.61 334	69.23 332	41.02 307	58.37 357	64.18 322	50.59 266	57.45 309	71.42 343	35.54 272	58.94 363	37.23 328	67.45 288	69.87 366

MVP-Stereo		65.41 222	63.80 225	70.22 205	77.62 194	55.53 124	76.30 177	78.53 170	50.59 266	56.47 318	78.65 263	39.84 227	82.68 162	44.10 284	72.12 226	72.44 339
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 109	69.49 125	75.35 80	77.63 190	55.71 117	76.04 185	81.81 102	50.30 268	69.66 126	85.40 134	52.51 81	84.89 114	51.82 217	80.24 115	85.45 118
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
baseline2		63.42 243	61.26 259	69.89 215	72.55 280	47.62 246	71.54 262	68.38 292	50.11 269	54.82 332	75.55 313	43.06 195	80.96 197	48.13 248	67.16 291	81.11 236

test-LLR		58.15 291	58.13 287	58.22 325	68.57 337	44.80 273	65.46 317	57.92 355	50.08 270	55.44 324	69.82 356	32.62 310	57.44 369	49.66 234	73.62 198	72.41 340

test0.0.03 1		53.32 327	53.59 324	52.50 360	62.81 371	29.45 387	59.51 353	54.11 372	50.08 270	54.40 338	74.31 324	32.62 310	55.92 378	30.50 372	63.95 316	72.15 345

fmvsm_s_conf0.5_n		69.58 140	68.84 136	71.79 169	72.31 288	52.90 165	77.90 137	62.43 336	49.97 272	72.85 86	85.90 121	52.21 87	76.49 277	75.75 33	70.26 250	85.97 95

COLMAP_ROB	52.97 17	61.27 270	58.81 277	68.64 233	74.63 251	52.51 175	78.42 126	73.30 252	49.92 273	50.96 356	81.51 211	23.06 369	79.40 225	31.63 365	65.85 299	74.01 327
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		69.54 142	68.74 139	71.93 164	72.47 283	53.82 145	78.25 127	62.26 338	49.78 274	73.12 79	86.21 109	52.66 79	76.79 271	75.02 39	68.88 275	85.18 130

WBMVS		60.54 272	60.61 266	60.34 311	78.00 178	35.95 357	64.55 327	64.89 315	49.63 275	63.39 239	78.70 260	33.85 291	67.65 325	42.10 302	70.35 247	77.43 286

tpmvs		58.47 287	56.95 294	63.03 296	70.20 318	41.21 306	67.90 299	67.23 299	49.62 276	54.73 334	70.84 347	34.14 285	76.24 282	36.64 336	61.29 337	71.64 349

fmvsm_s_conf0.1_n		69.41 147	68.60 142	71.83 167	71.07 307	52.88 167	77.85 140	62.44 335	49.58 277	72.97 82	86.22 108	51.68 97	76.48 278	75.53 34	70.10 253	86.14 90

UBG		59.62 282	59.53 272	59.89 312	78.12 173	35.92 358	64.11 331	60.81 346	49.45 278	61.34 269	75.55 313	33.05 299	67.39 328	38.68 320	74.62 184	76.35 301

thisisatest0515		65.83 216	63.50 230	72.82 150	73.75 263	49.50 222	71.32 265	73.12 255	49.39 279	63.82 234	76.50 302	34.95 278	84.84 117	53.20 206	75.49 181	84.13 161

fmvsm_s_conf0.1_n_a		69.32 148	68.44 148	71.96 163	70.91 309	53.78 146	78.12 133	62.30 337	49.35 280	73.20 75	86.55 101	51.99 91	76.79 271	74.83 41	68.68 280	85.32 125

HY-MVS	56.14 13	64.55 233	63.89 222	66.55 255	74.73 248	41.02 307	69.96 284	74.43 239	49.29 281	61.66 266	80.92 222	47.43 147	76.68 275	44.91 279	71.69 229	81.94 219

MIMVSNet1		55.17 316	54.31 318	57.77 330	70.03 322	32.01 381	65.68 313	64.81 316	49.19 282	46.75 374	76.00 305	25.53 362	64.04 342	28.65 379	62.13 331	77.26 290

SCA		60.49 273	58.38 283	66.80 251	74.14 262	48.06 240	63.35 333	63.23 329	49.13 283	59.33 293	72.10 337	37.45 252	74.27 291	44.17 281	62.57 327	78.05 277

test_fmvsmvis_n_1920		70.84 110	70.38 111	72.22 162	71.16 306	55.39 127	75.86 188	72.21 261	49.03 284	73.28 73	86.17 111	51.83 94	77.29 261	75.80 32	78.05 147	83.98 165

testgi		51.90 331	52.37 328	50.51 366	60.39 384	23.55 406	58.42 356	58.15 353	49.03 284	51.83 353	79.21 256	22.39 370	55.59 379	29.24 378	62.64 326	72.40 342

MIMVSNet		57.35 295	57.07 292	58.22 325	74.21 261	37.18 341	62.46 337	60.88 345	48.88 286	55.29 327	75.99 307	31.68 318	62.04 349	31.87 360	72.35 221	75.43 310

gm-plane-assit					71.40 302	41.72 304			48.85 287		73.31 330		82.48 169	48.90 241

fmvsm_l_conf0.5_n		70.99 108	70.82 103	71.48 177	71.45 298	54.40 139	77.18 159	70.46 274	48.67 288	75.17 41	86.86 85	53.77 67	76.86 269	76.33 30	77.51 154	83.17 197

UWE-MVS		60.18 275	59.78 270	61.39 307	77.67 188	33.92 373	69.04 293	63.82 324	48.56 289	64.27 229	77.64 283	27.20 350	70.40 311	33.56 352	76.24 171	79.83 258

cascas		65.98 214	63.42 231	73.64 127	77.26 204	52.58 173	72.26 254	77.21 198	48.56 289	61.21 271	74.60 322	32.57 313	85.82 92	50.38 228	76.75 167	82.52 208

PLC	56.13 14	65.09 227	63.21 235	70.72 199	81.04 100	54.87 134	78.57 123	77.47 192	48.51 291	55.71 321	81.89 202	33.71 292	79.71 220	41.66 306	70.37 245	77.58 284
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 230	62.50 243	71.34 185	79.72 127	55.71 117	79.82 104	74.72 236	48.50 292	56.62 314	84.62 142	33.59 295	82.34 171	29.65 376	75.23 182	75.97 302

anonymousdsp		67.00 199	64.82 216	73.57 131	70.09 321	56.13 107	76.35 176	77.35 196	48.43 293	64.99 220	80.84 226	33.01 301	80.34 211	64.66 115	67.64 287	84.23 157

无先验							79.66 109	74.30 243	48.40 294				80.78 204	53.62 201		79.03 269

114514_t		70.83 111	69.56 123	74.64 93	86.21 31	54.63 136	82.34 70	81.81 102	48.22 295	63.01 246	85.83 124	40.92 221	87.10 60	57.91 167	79.79 118	82.18 215

tpm		57.34 296	58.16 285	54.86 343	71.80 295	34.77 363	67.47 304	56.04 367	48.20 296	60.10 278	76.92 291	37.17 258	53.41 386	40.76 310	65.01 305	76.40 300

test_fmvsm_n_1920		71.73 98	71.14 98	73.50 132	72.52 281	56.53 101	75.60 192	76.16 209	48.11 297	77.22 28	85.56 128	53.10 76	77.43 258	74.86 40	77.14 161	86.55 75

MDA-MVSNet-bldmvs		53.87 322	50.81 334	63.05 295	66.25 354	48.58 234	56.93 368	63.82 324	48.09 298	41.22 386	70.48 352	30.34 325	68.00 324	34.24 347	45.92 383	72.57 336

XXY-MVS		60.68 271	61.67 252	57.70 331	70.43 315	38.45 330	64.19 329	66.47 304	48.05 299	63.22 240	80.86 224	49.28 120	60.47 353	45.25 278	67.28 290	74.19 325

F-COLMAP		63.05 250	60.87 265	69.58 221	76.99 212	53.63 149	78.12 133	76.16 209	47.97 300	52.41 351	81.61 208	27.87 344	78.11 247	40.07 312	66.66 294	77.00 294

fmvsm_l_conf0.5_n_a		70.50 118	70.27 113	71.18 189	71.30 304	54.09 141	76.89 167	69.87 277	47.90 301	74.37 59	86.49 102	53.07 77	76.69 274	75.41 35	77.11 162	82.76 204

Patchmatch-RL test		58.16 290	55.49 307	66.15 264	67.92 343	48.89 230	60.66 350	51.07 380	47.86 302	59.36 290	62.71 384	34.02 288	72.27 299	56.41 176	59.40 346	77.30 288

D2MVS		62.30 257	60.29 268	68.34 238	66.46 353	48.42 236	65.70 312	73.42 251	47.71 303	58.16 304	75.02 318	30.51 323	77.71 255	53.96 199	71.68 230	78.90 271

ANet_high		41.38 358	37.47 365	53.11 356	39.73 411	24.45 404	56.94 367	69.69 278	47.65 304	26.04 403	52.32 393	12.44 392	62.38 348	21.80 395	10.61 412	72.49 337

CostFormer		64.04 238	62.51 242	68.61 234	71.88 293	45.77 262	71.30 266	70.60 273	47.55 305	64.31 228	76.61 298	41.63 210	79.62 223	49.74 232	69.00 274	80.42 247

PatchmatchNet		59.84 278	58.24 284	64.65 284	73.05 271	46.70 254	69.42 289	62.18 339	47.55 305	58.88 296	71.96 339	34.49 282	69.16 316	42.99 295	63.60 318	78.07 276
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 315	53.89 322	59.21 317	57.80 389	27.47 395	57.75 363	74.32 241	47.38 307	50.90 357	70.00 355	28.45 341	70.30 312	40.44 311	57.92 351	79.87 257

ITE_SJBPF				62.09 301	66.16 355	44.55 278		64.32 320	47.36 308	55.31 326	80.34 232	19.27 378	62.68 347	36.29 340	62.39 329	79.04 268

KD-MVS_2432*1600		53.45 324	51.50 332	59.30 314	62.82 369	37.14 342	55.33 371	71.79 265	47.34 309	55.09 329	70.52 350	21.91 373	70.45 309	35.72 342	42.97 386	70.31 362

miper_refine_blended		53.45 324	51.50 332	59.30 314	62.82 369	37.14 342	55.33 371	71.79 265	47.34 309	55.09 329	70.52 350	21.91 373	70.45 309	35.72 342	42.97 386	70.31 362

OurMVSNet-221017-0		61.37 269	58.63 281	69.61 218	72.05 291	48.06 240	73.93 229	72.51 258	47.23 311	54.74 333	80.92 222	21.49 376	81.24 191	48.57 244	56.22 359	79.53 263

tpmrst		58.24 289	58.70 280	56.84 333	66.97 347	34.32 368	69.57 288	61.14 344	47.17 312	58.58 301	71.60 342	41.28 217	60.41 354	49.20 238	62.84 325	75.78 305

PVSNet	50.76 19	58.40 288	57.39 290	61.42 305	75.53 235	44.04 281	61.43 342	63.45 327	47.04 313	56.91 312	73.61 329	27.00 353	64.76 340	39.12 318	72.40 220	75.47 309

WB-MVSnew		59.66 280	59.69 271	59.56 313	75.19 241	35.78 359	69.34 290	64.28 321	46.88 314	61.76 265	75.79 309	40.61 222	65.20 339	32.16 357	71.21 234	77.70 282

FMVSNet5		55.86 310	54.93 310	58.66 322	71.05 308	36.35 351	64.18 330	62.48 334	46.76 315	50.66 361	74.73 321	25.80 360	64.04 342	33.11 353	65.57 302	75.59 307

jason		69.65 138	68.39 150	73.43 137	78.27 167	56.88 98	77.12 160	73.71 250	46.53 316	69.34 132	83.22 171	43.37 192	79.18 229	64.77 114	79.20 130	84.23 157
jason: jason.
MS-PatchMatch		62.42 255	61.46 255	65.31 279	75.21 240	52.10 181	72.05 256	74.05 246	46.41 317	57.42 310	74.36 323	34.35 284	77.57 257	45.62 270	73.67 197	66.26 376

1112_ss		64.00 239	63.36 232	65.93 269	79.28 135	42.58 294	71.35 264	72.36 260	46.41 317	60.55 275	77.89 276	46.27 163	73.28 294	46.18 263	69.97 255	81.92 220

lupinMVS		69.57 141	68.28 151	73.44 136	78.76 150	57.15 94	76.57 172	73.29 253	46.19 319	69.49 128	82.18 193	43.99 188	79.23 228	64.66 115	79.37 125	83.93 166

testdata				64.66 283	81.52 88	52.93 164		65.29 313	46.09 320	73.88 65	87.46 77	38.08 248	66.26 335	53.31 205	78.48 142	74.78 319

UnsupCasMVSNet_eth		53.16 329	52.47 327	55.23 341	59.45 385	33.39 376	59.43 354	69.13 287	45.98 321	50.35 363	72.32 334	29.30 335	58.26 367	42.02 304	44.30 384	74.05 326

AllTest		57.08 298	54.65 312	64.39 286	71.44 299	49.03 225	69.92 285	67.30 296	45.97 322	47.16 371	79.77 242	17.47 379	67.56 326	33.65 349	59.16 347	76.57 298

TestCases				64.39 286	71.44 299	49.03 225		67.30 296	45.97 322	47.16 371	79.77 242	17.47 379	67.56 326	33.65 349	59.16 347	76.57 298

WTY-MVS		59.75 279	60.39 267	57.85 329	72.32 287	37.83 335	61.05 348	64.18 322	45.95 324	61.91 262	79.11 257	47.01 156	60.88 352	42.50 299	69.49 266	74.83 317

IterMVS-SCA-FT		62.49 253	61.52 254	65.40 277	71.99 292	50.80 198	71.15 270	69.63 280	45.71 325	60.61 274	77.93 273	37.45 252	65.99 336	55.67 184	63.50 320	79.42 264

WB-MVS		43.26 352	43.41 352	42.83 378	63.32 368	10.32 416	58.17 359	45.20 394	45.42 326	40.44 389	67.26 371	34.01 289	58.98 362	11.96 407	24.88 401	59.20 382

旧先验2							76.08 182		45.32 327	76.55 33			65.56 338	58.75 165

OpenMVS_ROB	52.78 18	60.03 276	58.14 286	65.69 273	70.47 314	44.82 272	75.33 197	70.86 271	45.04 328	56.06 319	76.00 305	26.89 354	79.65 221	35.36 344	67.29 289	72.60 335

TinyColmap		54.14 319	51.72 330	61.40 306	66.84 349	41.97 299	66.52 307	68.51 291	44.81 329	42.69 385	75.77 310	11.66 394	72.94 295	31.96 359	56.77 357	69.27 370

MDTV_nov1_ep13			57.00 293		72.73 276	38.26 331	65.02 324	64.73 318	44.74 330	55.46 323	72.48 333	32.61 312	70.47 308	37.47 326	67.75 286

新几何1				70.76 197	85.66 41	61.13 30		66.43 305	44.68 331	70.29 113	86.64 93	41.29 216	75.23 286	49.72 233	81.75 102	75.93 303

Patchmtry		57.16 297	56.47 299	59.23 316	69.17 334	34.58 366	62.98 334	63.15 330	44.53 332	56.83 313	74.84 319	35.83 270	68.71 318	40.03 313	60.91 338	74.39 323

ppachtmachnet_test		58.06 292	55.38 308	66.10 266	69.51 328	48.99 228	68.01 298	66.13 308	44.50 333	54.05 341	70.74 348	32.09 317	72.34 298	36.68 335	56.71 358	76.99 296

PatchT		53.17 328	53.44 325	52.33 361	68.29 341	25.34 403	58.21 358	54.41 371	44.46 334	54.56 336	69.05 362	33.32 297	60.94 351	36.93 331	61.76 335	70.73 360

EPMVS		53.96 320	53.69 323	54.79 344	66.12 356	31.96 382	62.34 339	49.05 384	44.42 335	55.54 322	71.33 345	30.22 326	56.70 372	41.65 307	62.54 328	75.71 306

pmmvs4		61.48 268	59.39 273	67.76 242	71.57 297	53.86 144	71.42 263	65.34 312	44.20 336	59.46 289	77.92 274	35.90 269	74.71 288	43.87 287	64.87 307	74.71 320

dp		51.89 332	51.60 331	52.77 358	68.44 340	32.45 380	62.36 338	54.57 370	44.16 337	49.31 366	67.91 364	28.87 338	56.61 374	33.89 348	54.89 362	69.24 371

PatchMatch-RL		56.25 307	54.55 314	61.32 308	77.06 209	56.07 109	65.57 314	54.10 373	44.13 338	53.49 349	71.27 346	25.20 363	66.78 331	36.52 338	63.66 317	61.12 380

our_test_3		56.49 303	54.42 315	62.68 298	69.51 328	45.48 268	66.08 310	61.49 342	44.11 339	50.73 360	69.60 359	33.05 299	68.15 320	38.38 322	56.86 355	74.40 322

USDC		56.35 306	54.24 319	62.69 297	64.74 361	40.31 312	65.05 323	73.83 248	43.93 340	47.58 369	77.71 282	15.36 387	75.05 287	38.19 324	61.81 334	72.70 334

PM-MVS		52.33 330	50.19 338	58.75 321	62.10 374	45.14 271	65.75 311	40.38 401	43.60 341	53.52 347	72.65 332	9.16 402	65.87 337	50.41 227	54.18 365	65.24 378

pmmvs-eth3d		58.81 286	56.31 301	66.30 260	67.61 344	52.42 178	72.30 253	64.76 317	43.55 342	54.94 331	74.19 325	28.95 336	72.60 296	43.31 290	57.21 354	73.88 328

SSC-MVS		41.96 357	41.99 356	41.90 379	62.46 373	9.28 418	57.41 366	44.32 397	43.38 343	38.30 395	66.45 374	32.67 309	58.42 366	10.98 408	21.91 404	57.99 386

new-patchmatchnet		47.56 346	47.73 346	47.06 369	58.81 387	9.37 417	48.78 388	59.21 350	43.28 344	44.22 381	68.66 363	25.67 361	57.20 371	31.57 367	49.35 379	74.62 321

Test_1112_low_res		62.32 256	61.77 251	64.00 288	79.08 143	39.53 321	68.17 296	70.17 275	43.25 345	59.03 295	79.90 239	44.08 186	71.24 305	43.79 288	68.42 281	81.25 232

RPMNet		61.53 266	58.42 282	70.86 195	69.96 323	52.07 182	65.31 321	81.36 113	43.20 346	59.36 290	70.15 354	35.37 273	85.47 102	36.42 339	64.65 309	75.06 312

tpm2		62.07 260	60.10 269	67.99 240	72.79 275	43.86 282	71.05 273	66.85 302	43.14 347	62.77 247	75.39 316	38.32 244	80.80 203	41.69 305	68.88 275	79.32 265

JIA-IIPM		51.56 333	47.68 347	63.21 293	64.61 362	50.73 199	47.71 390	58.77 352	42.90 348	48.46 368	51.72 394	24.97 364	70.24 313	36.06 341	53.89 366	68.64 372

1314		64.61 232	63.21 235	68.80 231	71.87 294	47.46 248	73.95 227	78.39 180	42.88 349	59.97 281	76.60 299	38.11 247	79.39 226	54.84 191	72.32 222	79.55 262

HyFIR lowres test		65.67 218	63.01 237	73.67 124	79.97 123	55.65 119	69.07 292	75.52 220	42.68 350	63.53 237	77.95 272	40.43 223	81.64 181	46.01 265	71.91 227	83.73 178

CR-MVSNet		59.91 277	57.90 289	65.96 268	69.96 323	52.07 182	65.31 321	63.15 330	42.48 351	59.36 290	74.84 319	35.83 270	70.75 307	45.50 273	64.65 309	75.06 312

test222					83.14 68	58.68 73	72.57 249	63.45 327	41.78 352	67.56 166	86.12 112	37.13 259			78.73 139	74.98 315

TDRefinement		53.44 326	50.72 335	61.60 303	64.31 364	46.96 252	70.89 274	65.27 314	41.78 352	44.61 380	77.98 271	11.52 396	66.36 334	28.57 380	51.59 372	71.49 352

sss		56.17 308	56.57 298	54.96 342	66.93 348	36.32 353	57.94 360	61.69 341	41.67 354	58.64 299	75.32 317	38.72 240	56.25 376	42.04 303	66.19 298	72.31 343

PVSNet_0	43.31 20	47.46 347	45.64 350	52.92 357	67.60 345	44.65 275	54.06 376	54.64 369	41.59 355	46.15 376	58.75 387	30.99 321	58.66 364	32.18 356	24.81 402	55.46 390

MVS		67.37 188	66.33 194	70.51 203	75.46 236	50.94 193	73.95 227	81.85 101	41.57 356	62.54 254	78.57 266	47.98 134	85.47 102	52.97 207	82.05 95	75.14 311

Anonymous20240521		55.30 313	54.41 316	57.96 328	60.92 383	41.73 302	71.09 272	71.06 270	41.18 357	48.65 367	73.31 330	16.93 381	59.25 360	42.54 298	64.01 314	72.90 332

Anonymous20231206		55.10 317	55.30 309	54.48 345	69.81 327	33.94 372	62.91 335	62.13 340	41.08 358	55.18 328	75.65 311	32.75 307	56.59 375	30.32 373	67.86 284	72.91 331

MDA-MVSNet_test_wron		50.71 338	48.95 340	56.00 338	61.17 378	41.84 300	51.90 382	56.45 361	40.96 359	44.79 379	67.84 365	30.04 328	55.07 383	36.71 334	50.69 375	71.11 358

YYNet1		50.73 337	48.96 339	56.03 337	61.10 379	41.78 301	51.94 381	56.44 362	40.94 360	44.84 378	67.80 366	30.08 327	55.08 382	36.77 332	50.71 374	71.22 355

dongtai		34.52 367	34.94 367	33.26 388	61.06 380	16.00 413	52.79 380	23.78 414	40.71 361	39.33 393	48.65 402	16.91 382	48.34 394	12.18 406	19.05 406	35.44 405

CHOSEN 1792x2688		65.08 228	62.84 239	71.82 168	81.49 90	56.26 105	66.32 309	74.20 245	40.53 362	63.16 243	78.65 263	41.30 215	77.80 253	45.80 267	74.09 190	81.40 228

pmmvs5		56.47 304	55.68 306	58.86 320	61.41 377	36.71 348	66.37 308	62.75 332	40.38 363	53.70 343	76.62 297	34.56 280	67.05 329	40.02 314	65.27 303	72.83 333

test_vis1_n_1920		58.86 285	59.06 276	58.25 324	63.76 365	43.14 290	67.49 303	66.36 306	40.22 364	65.89 197	71.95 340	31.04 320	59.75 358	59.94 156	64.90 306	71.85 347

MDTV_nov1_ep13_2view						25.89 401	61.22 345		40.10 365	51.10 355		32.97 302		38.49 321		78.61 272

tpm cat1		59.25 284	56.95 294	66.15 264	72.19 289	46.96 252	68.09 297	65.76 309	40.03 366	57.81 306	70.56 349	38.32 244	74.51 289	38.26 323	61.50 336	77.00 294

test-mter		56.42 305	55.82 305	58.22 325	68.57 337	44.80 273	65.46 317	57.92 355	39.94 367	55.44 324	69.82 356	21.92 372	57.44 369	49.66 234	73.62 198	72.41 340

UnsupCasMVSNet_bld		50.07 340	48.87 341	53.66 350	60.97 382	33.67 374	57.62 364	64.56 319	39.47 368	47.38 370	64.02 382	27.47 347	59.32 359	34.69 346	43.68 385	67.98 374

TESTMET0.1,1		55.28 314	54.90 311	56.42 335	66.56 351	43.67 284	65.46 317	56.27 365	39.18 369	53.83 342	67.44 368	24.21 367	55.46 380	48.04 249	73.11 211	70.13 364

mamv4		56.85 300	58.00 288	53.43 353	72.46 284	54.47 137	57.56 365	54.74 368	38.81 370	57.42 310	79.45 251	47.57 143	38.70 405	60.88 148	53.07 368	67.11 375

ADS-MVSNet2		51.33 335	48.76 342	59.07 319	66.02 357	44.60 276	50.90 384	59.76 348	36.90 371	50.74 358	66.18 376	26.38 355	63.11 345	27.17 384	54.76 363	69.50 368

ADS-MVSNet		48.48 344	47.77 345	50.63 365	66.02 357	29.92 386	50.90 384	50.87 382	36.90 371	50.74 358	66.18 376	26.38 355	52.47 388	27.17 384	54.76 363	69.50 368

RPSCF		55.80 311	54.22 320	60.53 310	65.13 360	42.91 293	64.30 328	57.62 357	36.84 373	58.05 305	82.28 192	28.01 343	56.24 377	37.14 329	58.61 349	82.44 211

test_cas_vis1_n_1920		56.91 299	56.71 297	57.51 332	59.13 386	45.40 269	63.58 332	61.29 343	36.24 374	67.14 173	71.85 341	29.89 329	56.69 373	57.65 169	63.58 319	70.46 361

Patchmatch-test		49.08 342	48.28 344	51.50 364	64.40 363	30.85 385	45.68 394	48.46 387	35.60 375	46.10 377	72.10 337	34.47 283	46.37 397	27.08 386	60.65 342	77.27 289

CHOSEN 280x420		47.83 345	46.36 349	52.24 363	67.37 346	49.78 216	38.91 402	43.11 399	35.00 376	43.27 384	63.30 383	28.95 336	49.19 393	36.53 337	60.80 340	57.76 387

N_pmnet		39.35 362	40.28 359	36.54 385	63.76 365	1.62 422	49.37 387	0.76 421	34.62 377	43.61 383	66.38 375	26.25 357	42.57 401	26.02 389	51.77 371	65.44 377

kuosan		29.62 374	30.82 373	26.02 393	52.99 392	16.22 412	51.09 383	22.71 415	33.91 378	33.99 397	40.85 403	15.89 385	33.11 410	7.59 414	18.37 407	28.72 407

PMMVS		53.96 320	53.26 326	56.04 336	62.60 372	50.92 195	61.17 346	56.09 366	32.81 379	53.51 348	66.84 373	34.04 287	59.93 357	44.14 283	68.18 282	57.27 388

CMPMVS	42.80 21	57.81 294	55.97 303	63.32 291	60.98 381	47.38 249	64.66 326	69.50 283	32.06 380	46.83 373	77.80 278	29.50 333	71.36 304	48.68 242	73.75 196	71.21 356
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
m2depth		45.56 348	42.95 353	53.39 355	52.33 396	29.15 388	57.77 361	48.20 388	31.81 381	49.86 365	77.21 287	8.69 403	59.16 361	27.31 383	33.40 398	71.84 348

CVMVSNet		59.63 281	59.14 275	61.08 309	74.47 254	38.84 326	75.20 201	68.74 290	31.15 382	58.24 303	76.51 300	32.39 315	68.58 319	49.77 231	65.84 300	75.81 304

FPMVS		42.18 356	41.11 358	45.39 371	58.03 388	41.01 309	49.50 386	53.81 374	30.07 383	33.71 398	64.03 380	11.69 393	52.08 391	14.01 402	55.11 361	43.09 399

EU-MVSNet		55.61 312	54.41 316	59.19 318	65.41 359	33.42 375	72.44 251	71.91 264	28.81 384	51.27 354	73.87 327	24.76 365	69.08 317	43.04 294	58.20 350	75.06 312

test_vis1_n		49.89 341	48.69 343	53.50 352	53.97 390	37.38 340	61.53 341	47.33 391	28.54 385	59.62 288	67.10 372	13.52 389	52.27 389	49.07 239	57.52 352	70.84 359

test_fmvs1_n		51.37 334	50.35 337	54.42 347	52.85 393	37.71 337	61.16 347	51.93 375	28.15 386	63.81 235	69.73 358	13.72 388	53.95 384	51.16 222	60.65 342	71.59 350

LF4IMVS		42.95 353	42.26 355	45.04 372	48.30 401	32.50 379	54.80 373	48.49 386	28.03 387	40.51 388	70.16 353	9.24 401	43.89 400	31.63 365	49.18 380	58.72 384

test_fmvs1		51.32 336	50.48 336	53.81 349	53.57 391	37.51 339	60.63 351	51.16 378	28.02 388	63.62 236	69.23 361	16.41 383	53.93 385	51.01 223	60.70 341	69.99 365

MVS-HIRNet		45.52 349	44.48 351	48.65 368	68.49 339	34.05 371	59.41 355	44.50 396	27.03 389	37.96 396	50.47 398	26.16 358	64.10 341	26.74 387	59.52 345	47.82 397

PMVS	28.69 22	36.22 365	33.29 370	45.02 373	36.82 413	35.98 356	54.68 374	48.74 385	26.31 390	21.02 406	51.61 395	2.88 415	60.10 356	9.99 411	47.58 381	38.99 404
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 350	41.95 357	53.86 348	52.58 395	43.55 285	62.11 340	46.90 393	26.05 391	40.63 387	60.19 386	11.08 399	57.91 368	31.83 364	46.15 382	60.11 381

test_fmvs2		48.69 343	47.49 348	52.29 362	48.63 400	33.06 378	57.76 362	48.05 389	25.71 392	59.76 286	69.60 359	11.57 395	52.23 390	49.45 237	56.86 355	71.58 351

PMMVS2		27.40 375	25.91 378	31.87 390	39.46 412	6.57 419	31.17 405	28.52 410	23.96 393	20.45 407	48.94 401	4.20 411	37.94 406	16.51 399	19.97 405	51.09 392

MVStest1		42.65 354	39.29 361	52.71 359	47.26 403	34.58 366	54.41 375	50.84 383	23.35 394	39.31 394	74.08 326	12.57 391	55.09 381	23.32 392	28.47 400	68.47 373

Gipuma		34.77 366	31.91 371	43.33 376	62.05 375	37.87 333	20.39 407	67.03 300	23.23 395	18.41 408	25.84 408	4.24 409	62.73 346	14.71 401	51.32 373	29.38 406
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 359	39.45 360	47.03 370	46.65 404	37.86 334	47.76 389	38.65 402	23.10 396	44.21 382	51.22 396	11.20 398	44.08 399	39.27 317	53.02 369	59.14 383

new_pmnet		34.13 368	34.29 369	33.64 387	52.63 394	18.23 411	44.43 397	33.90 407	22.81 397	30.89 400	53.18 392	10.48 400	35.72 409	20.77 396	39.51 390	46.98 398

mvsany_test1		39.38 361	38.16 364	43.02 377	49.05 398	34.28 369	44.16 398	25.94 412	22.74 398	46.57 375	62.21 385	23.85 368	41.16 404	33.01 354	35.91 394	53.63 391

LCM-MVSNet		40.30 360	35.88 366	53.57 351	42.24 406	29.15 388	45.21 396	60.53 347	22.23 399	28.02 401	50.98 397	3.72 412	61.78 350	31.22 370	38.76 392	69.78 367

test_fmvs3		44.30 351	42.55 354	49.55 367	42.83 405	27.15 398	53.03 378	44.93 395	22.03 400	53.69 345	64.94 379	4.21 410	49.63 392	47.47 250	49.82 377	71.88 346

APD_test1		37.39 364	34.94 367	44.72 375	48.88 399	33.19 377	52.95 379	44.00 398	19.49 401	27.28 402	58.59 388	3.18 414	52.84 387	18.92 397	41.17 389	48.14 396

mvsany_test3		32.62 369	30.57 374	38.77 383	36.16 414	24.20 405	38.10 403	20.63 416	19.14 402	40.36 390	57.43 389	5.06 407	36.63 408	29.59 377	28.66 399	55.49 389

E-PMN		23.77 376	22.73 380	26.90 391	42.02 407	20.67 408	42.66 399	35.70 405	17.43 403	10.28 413	25.05 409	6.42 405	42.39 402	10.28 410	14.71 409	17.63 408

EMVS		22.97 377	21.84 381	26.36 392	40.20 410	19.53 410	41.95 400	34.64 406	17.09 404	9.73 414	22.83 410	7.29 404	42.22 403	9.18 412	13.66 410	17.32 409

test_vis3_rt		32.09 370	30.20 375	37.76 384	35.36 415	27.48 394	40.60 401	28.29 411	16.69 405	32.52 399	40.53 404	1.96 416	37.40 407	33.64 351	42.21 388	48.39 394

test_f		31.86 371	31.05 372	34.28 386	32.33 417	21.86 407	32.34 404	30.46 409	16.02 406	39.78 392	55.45 391	4.80 408	32.36 411	30.61 371	37.66 393	48.64 393

DSMNet-mixed		39.30 363	38.72 362	41.03 380	51.22 397	19.66 409	45.53 395	31.35 408	15.83 407	39.80 391	67.42 370	22.19 371	45.13 398	22.43 393	52.69 370	58.31 385

testf1		31.46 372	28.89 376	39.16 381	41.99 408	28.78 390	46.45 392	37.56 403	14.28 408	21.10 404	48.96 399	1.48 418	47.11 395	13.63 403	34.56 395	41.60 400

APD_test2		31.46 372	28.89 376	39.16 381	41.99 408	28.78 390	46.45 392	37.56 403	14.28 408	21.10 404	48.96 399	1.48 418	47.11 395	13.63 403	34.56 395	41.60 400

MVE	17.77 23	21.41 378	17.77 383	32.34 389	34.34 416	25.44 402	16.11 408	24.11 413	11.19 410	13.22 410	31.92 406	1.58 417	30.95 412	10.47 409	17.03 408	40.62 403
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 396	17.97 418	10.91 415		10.60 419	7.46 411	11.07 412	28.36 407	3.28 413	11.29 415	8.01 413	9.74 414	13.89 410

wuyk23d		13.32 381	12.52 384	15.71 395	47.54 402	26.27 400	31.06 406	1.98 420	4.93 412	5.18 415	1.94 415	0.45 420	18.54 414	6.81 415	12.83 411	2.33 412

test_method		19.68 379	18.10 382	24.41 394	13.68 419	3.11 421	12.06 410	42.37 400	2.00 413	11.97 411	36.38 405	5.77 406	29.35 413	15.06 400	23.65 403	40.76 402

tmp_tt		9.43 382	11.14 385	4.30 397	2.38 420	4.40 420	13.62 409	16.08 418	0.39 414	15.89 409	13.06 411	15.80 386	5.54 416	12.63 405	10.46 413	2.95 411

EGC-MVSNET		42.47 355	38.48 363	54.46 346	74.33 258	48.73 232	70.33 281	51.10 379	0.03 415	0.18 416	67.78 367	13.28 390	66.49 333	18.91 398	50.36 376	48.15 395

testmvs		4.52 385	6.03 388	0.01 399	0.01 421	0.00 424	53.86 377	0.00 422	0.01 416	0.04 417	0.27 416	0.00 422	0.00 417	0.04 416	0.00 415	0.03 414

test123		4.73 384	6.30 387	0.02 398	0.01 421	0.01 423	56.36 369	0.00 422	0.01 416	0.04 417	0.21 417	0.01 421	0.00 417	0.03 417	0.00 415	0.04 413

test_blank		0.00 387	0.00 390	0.00 400	0.00 423	0.00 424	0.00 411	0.00 422	0.00 418	0.00 419	0.00 418	0.00 422	0.00 417	0.00 418	0.00 415	0.00 415

uanet_test		0.00 387	0.00 390	0.00 400	0.00 423	0.00 424	0.00 411	0.00 422	0.00 418	0.00 419	0.00 418	0.00 422	0.00 417	0.00 418	0.00 415	0.00 415

DCPMVS		0.00 387	0.00 390	0.00 400	0.00 423	0.00 424	0.00 411	0.00 422	0.00 418	0.00 419	0.00 418	0.00 422	0.00 417	0.00 418	0.00 415	0.00 415

cdsmvs_eth3d_5k		17.50 380	23.34 379	0.00 400	0.00 423	0.00 424	0.00 411	78.63 167	0.00 418	0.00 419	82.18 193	49.25 121	0.00 417	0.00 418	0.00 415	0.00 415

pcd_1.5k_mvsjas		3.92 386	5.23 389	0.00 400	0.00 423	0.00 424	0.00 411	0.00 422	0.00 418	0.00 419	0.00 418	47.05 153	0.00 417	0.00 418	0.00 415	0.00 415

sosnet-low-res		0.00 387	0.00 390	0.00 400	0.00 423	0.00 424	0.00 411	0.00 422	0.00 418	0.00 419	0.00 418	0.00 422	0.00 417	0.00 418	0.00 415	0.00 415

sosnet		0.00 387	0.00 390	0.00 400	0.00 423	0.00 424	0.00 411	0.00 422	0.00 418	0.00 419	0.00 418	0.00 422	0.00 417	0.00 418	0.00 415	0.00 415

uncertanet		0.00 387	0.00 390	0.00 400	0.00 423	0.00 424	0.00 411	0.00 422	0.00 418	0.00 419	0.00 418	0.00 422	0.00 417	0.00 418	0.00 415	0.00 415

Regformer		0.00 387	0.00 390	0.00 400	0.00 423	0.00 424	0.00 411	0.00 422	0.00 418	0.00 419	0.00 418	0.00 422	0.00 417	0.00 418	0.00 415	0.00 415

ab-mvs-re		6.49 383	8.65 386	0.00 400	0.00 423	0.00 424	0.00 411	0.00 422	0.00 418	0.00 419	77.89 276	0.00 422	0.00 417	0.00 418	0.00 415	0.00 415

uanet		0.00 387	0.00 390	0.00 400	0.00 423	0.00 424	0.00 411	0.00 422	0.00 418	0.00 419	0.00 418	0.00 422	0.00 417	0.00 418	0.00 415	0.00 415

WAC-MVS						27.31 396								27.77 381

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

No_MVS				79.95 4	87.24 14	61.04 31		85.62 24					90.96 1	79.31 9	90.65 8	87.85 33

eth-test2					0.00 423

eth-test					0.00 423

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 22

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 43

GSMVS																78.05 277

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 279				78.05 277

sam_mvs												33.43 296

ambc				65.13 281	63.72 367	37.07 344	47.66 391	78.78 163		54.37 339	71.42 343	11.24 397	80.94 198	45.64 269	53.85 367	77.38 287

MTGPA								80.97 129

test_post1							68.67 294				3.64 413	32.39 315	69.49 315	44.17 281

test_post											3.55 414	33.90 290	66.52 332

patchmatchnet-post											64.03 380	34.50 281	74.27 291

GG-mvs-BLEND				62.34 299	71.36 303	37.04 345	69.20 291	57.33 360		54.73 334	65.48 378	30.37 324	77.82 252	34.82 345	74.93 183	72.17 344

MTMP							86.03 19	17.08 417

test9_res														75.28 37	88.31 32	83.81 172

agg_prior2														73.09 55	87.93 40	84.33 153

agg_prior					85.04 50	59.96 47		81.04 127		74.68 54			84.04 129

test_prior4						62.51 14	82.08 76

test_prior				76.69 54	84.20 61	57.27 88		84.88 40					86.43 78			86.38 77

新几何2							76.12 180

旧先验1					83.04 70	53.15 158		67.52 295			87.85 71	44.08 186			80.76 106	78.03 280

原ACMM2							79.02 114

testdata2													72.18 301	46.95 259

segment_acmp												54.23 58

test12				77.76 43	84.52 58	58.41 75		83.36 77		72.93 84		54.61 55	88.05 41		88.12 34	86.81 65

plane_prior7					81.41 91	55.96 111

plane_prior6					81.20 98	56.24 106						45.26 177

plane_prior5								84.01 53					87.21 56	68.16 84	80.58 109	84.65 147

plane_prior4											86.10 113

plane_prior1					81.27 96

n2								0.00 422

nn								0.00 422

door-mid								47.19 392

lessismore_v0				69.91 213	71.42 301	47.80 242		50.90 381		50.39 362	75.56 312	27.43 349	81.33 188	45.91 266	34.10 397	80.59 245

test11								83.47 72

door								47.60 390

HQP5-MVS						54.94 131

BP-MVS														67.04 96

HQP4-MVS										67.85 156			86.93 63			84.32 154

HQP3-MVS								83.90 58							80.35 113

HQP2-MVS												45.46 171

NP-MVS					80.98 101	56.05 110					85.54 131

ACMMP++_ref															74.07 191

ACMMP++															72.16 225

Test By Simon												48.33 132

High-resolution multi-view benchmark