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 8	80.28 11	79.99 2	82.19 89	60.01 49	86.19 21	83.93 59	73.19 1	77.08 44	91.21 18	57.23 36	90.73 10	83.35 1	88.12 38	89.22 7

MGCNet		78.45 21	78.28 22	78.98 29	80.73 114	57.91 89	84.68 41	81.64 122	68.35 2	75.77 50	90.38 34	53.98 72	90.26 13	81.30 3	87.68 46	88.77 15

CANet		76.46 44	75.93 48	78.06 43	81.29 104	57.53 95	82.35 80	83.31 88	67.78 3	70.09 149	86.34 134	54.92 61	88.90 29	72.68 75	84.55 73	87.76 51

UA-Net		73.13 93	72.93 92	73.76 142	83.58 71	51.66 219	78.75 132	77.66 221	67.75 4	72.61 117	89.42 56	49.82 141	83.29 163	53.61 256	83.14 87	86.32 113

CNVR-MVS		79.84 13	79.97 13	79.45 11	87.90 2	62.17 17	84.37 45	85.03 41	66.96 5	77.58 38	90.06 45	59.47 24	89.13 26	78.67 17	89.73 16	87.03 81

TranMVSNet+NR-MVSNet		70.36 153	70.10 148	71.17 232	78.64 167	42.97 346	76.53 205	81.16 143	66.95 6	68.53 180	85.42 164	51.61 117	83.07 167	52.32 264	69.70 316	87.46 63

3Dnovator+	66.72 4	75.84 54	74.57 66	79.66 9	82.40 86	59.92 51	85.83 27	86.32 17	66.92 7	67.80 205	89.24 60	42.03 243	89.38 23	64.07 149	86.50 63	89.69 3

NCCC		78.58 19	78.31 21	79.39 12	87.51 12	62.61 13	85.20 36	84.42 50	66.73 8	74.67 73	89.38 58	55.30 56	89.18 25	74.19 63	87.34 50	86.38 105

SteuartSystems-ACMMP		79.48 14	79.31 14	79.98 3	83.01 80	62.18 16	87.60 9	85.83 24	66.69 9	78.03 35	90.98 19	54.26 67	90.06 14	78.42 23	89.02 27	87.69 53
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 94	72.16 104	75.90 79	75.95 254	56.28 114	83.05 67	72.39 311	66.53 10	65.27 257	87.00 106	50.40 134	85.47 118	62.48 175	86.32 64	85.94 125
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 134	71.00 128	71.44 219	79.20 147	44.13 332	76.02 220	82.60 108	66.48 11	68.20 185	84.60 182	56.82 40	82.82 182	54.62 246	70.43 296	87.36 72

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 32	62.73 9	86.09 22	86.83 8	65.51 12	83.81 10	90.51 30	63.71 14	89.23 24	81.51 2	88.44 31	88.09 39
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 12	80.14 12	79.10 21	88.17 1	64.80 1	86.59 16	83.70 72	65.37 13	78.78 28	90.64 22	58.63 28	87.24 59	79.00 14	90.37 14	85.26 165

NR-MVSNet		69.54 178	68.85 170	71.59 213	78.05 190	43.81 337	74.20 261	80.86 150	65.18 14	62.76 301	84.52 183	52.35 103	83.59 157	50.96 279	70.78 291	87.37 70

MTAPA		76.90 38	76.42 42	78.35 39	86.08 38	63.57 2	74.92 246	80.97 148	65.13 15	75.77 50	90.88 20	48.63 158	86.66 78	77.23 30	88.17 37	84.81 181

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 68	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 23

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 12	90.75 8	79.48 7	90.63 10	88.09 39

EI-MVSNet-Vis-set		72.42 110	71.59 112	74.91 100	78.47 171	54.02 157	77.05 189	79.33 176	65.03 18	71.68 130	79.35 307	52.75 95	84.89 131	66.46 128	74.23 234	85.83 132

casdiffmvs_mvg		76.14 50	76.30 43	75.66 87	76.46 248	51.83 217	79.67 120	85.08 38	65.02 19	75.84 49	88.58 73	59.42 25	85.08 124	72.75 74	83.93 82	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 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 13

ETV-MVS		74.46 71	73.84 77	76.33 74	79.27 145	55.24 140	79.22 126	85.00 43	64.97 21	72.65 116	79.46 303	53.65 84	87.87 48	67.45 118	82.91 93	85.89 128

NormalMVS		76.26 48	75.74 51	77.83 49	82.75 84	59.89 52	84.36 46	83.21 92	64.69 22	74.21 80	87.40 94	49.48 145	86.17 96	68.04 108	87.55 47	87.42 65

SymmetryMVS		75.28 59	74.60 65	77.30 58	83.85 69	59.89 52	84.36 46	75.51 260	64.69 22	74.21 80	87.40 94	49.48 145	86.17 96	68.04 108	83.88 83	85.85 130

WR-MVS		68.47 208	68.47 181	68.44 284	80.20 125	39.84 374	73.75 273	76.07 248	64.68 24	68.11 193	83.63 205	50.39 135	79.14 268	49.78 284	69.66 317	86.34 109

XVS		77.17 35	76.56 40	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 123	90.01 49	47.95 165	88.01 44	71.55 88	86.74 59	86.37 107

X-MVStestdata		70.21 156	67.28 215	79.00 26	86.32 30	62.62 11	85.83 27	83.92 60	64.55 25	72.17 123	6.49 476	47.95 165	88.01 44	71.55 88	86.74 59	86.37 107

HQP_MVS		74.31 72	73.73 78	76.06 77	81.41 101	56.31 112	84.22 51	84.01 57	64.52 27	69.27 168	86.10 142	45.26 208	87.21 63	68.16 106	80.58 123	84.65 185

plane_prior2							84.22 51		64.52 27

EI-MVSNet-UG-set		71.92 120	71.06 127	74.52 117	77.98 193	53.56 168	76.62 202	79.16 177	64.40 29	71.18 137	78.95 312	52.19 105	84.66 138	65.47 139	73.57 247	85.32 161

DU-MVS		70.01 161	69.53 155	71.44 219	78.05 190	44.13 332	75.01 242	81.51 125	64.37 30	68.20 185	84.52 183	49.12 155	82.82 182	54.62 246	70.43 296	87.37 70

DVP-MVS		80.84 4	81.64 3	78.42 38	87.75 7	59.07 72	87.85 5	85.03 41	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 153
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 72	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

test_241102_ONE					87.77 4	58.90 77		86.78 10	64.20 33	85.97 1	91.34 16	66.87 3	90.78 7

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 77	87.82 7	86.78 10	64.18 34	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 31

test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 55

LFMVS		71.78 123	71.59 112	72.32 192	83.40 75	46.38 308	79.75 118	71.08 320	64.18 34	72.80 113	88.64 72	42.58 238	83.72 153	57.41 222	84.49 76	86.86 86

IS-MVSNet		71.57 127	71.00 128	73.27 167	78.86 157	45.63 319	80.22 109	78.69 191	64.14 37	66.46 232	87.36 97	49.30 149	85.60 111	50.26 283	83.71 86	88.59 19

plane_prior3						56.09 118			63.92 38	69.27 168

MP-MVS		78.35 23	78.26 24	78.64 35	86.54 26	63.47 4	86.02 24	83.55 77	63.89 39	73.60 89	90.60 23	54.85 62	86.72 76	77.20 31	88.06 40	85.74 139
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DELS-MVS		74.76 64	74.46 67	75.65 88	77.84 197	52.25 207	75.59 228	84.17 54	63.76 40	73.15 101	82.79 220	59.58 23	86.80 74	67.24 119	86.04 65	87.89 43
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 65	74.25 71	76.19 76	80.81 113	59.01 75	82.60 77	83.64 74	63.74 41	72.52 118	87.49 91	47.18 181	85.88 106	69.47 99	80.78 117	83.66 226
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 146	70.20 143	71.89 199	78.55 168	45.29 322	75.94 221	82.92 102	63.68 42	68.16 188	83.59 206	53.89 75	83.49 160	53.97 252	71.12 287	86.89 85

GST-MVS		78.14 25	77.85 27	78.99 28	86.05 39	61.82 22	85.84 26	85.21 35	63.56 43	74.29 79	90.03 47	52.56 97	88.53 33	74.79 59	88.34 33	86.63 98

testing3-2		62.06 314	62.36 297	61.17 367	79.29 142	30.31 448	64.09 390	63.49 388	63.50 44	62.84 298	82.22 242	32.35 369	69.02 373	40.01 372	73.43 252	84.17 202

EC-MVSNet		75.84 54	75.87 50	75.74 85	78.86 157	52.65 196	83.73 61	86.08 19	63.47 45	72.77 114	87.25 102	53.13 89	87.93 46	71.97 83	85.57 68	86.66 96

ZNCC-MVS		78.82 16	78.67 19	79.30 14	86.43 29	62.05 18	86.62 15	86.01 20	63.32 46	75.08 60	90.47 33	53.96 74	88.68 31	76.48 39	89.63 20	87.16 78

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 31	86.42 15	63.28 47	83.27 16	91.83 10	64.96 7	90.47 11	76.41 40	89.67 18	86.84 87
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS		76.25 49	75.98 47	77.06 60	80.15 128	55.63 130	84.51 44	83.90 62	63.24 48	73.30 95	87.27 101	55.06 58	86.30 93	71.78 85	84.58 72	89.25 6

DeepC-MVS	69.38 2	78.56 20	78.14 25	79.83 7	83.60 70	61.62 23	84.17 53	86.85 6	63.23 49	73.84 87	90.25 40	57.68 32	89.96 15	74.62 60	89.03 26	87.89 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		72.50 106	72.09 105	73.75 144	81.58 97	49.69 259	77.76 165	77.63 222	63.21 50	73.21 98	89.02 62	42.14 242	83.32 162	61.72 182	82.50 99	88.25 29

plane_prior						56.31 112	83.58 64		63.19 51						80.48 126

MED-MVS		80.31 6	80.72 6	79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	63.10 52	83.65 12	90.57 25	64.70 10	89.91 16	77.02 34	89.43 22	88.10 36

ME-MVS		80.04 10	80.36 10	79.08 25	86.63 23	59.25 64	85.62 32	86.73 12	63.10 52	82.27 18	90.57 25	61.90 16	89.88 19	77.02 34	89.43 22	88.10 36

ACMMP		76.02 52	75.33 56	78.07 42	85.20 53	61.91 20	85.49 35	84.44 49	63.04 54	69.80 159	89.74 55	45.43 204	87.16 65	72.01 81	82.87 95	85.14 167
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		66.60 253	66.45 233	67.04 298	77.11 229	36.56 407	77.03 190	80.42 158	62.95 55	62.51 309	84.03 194	46.69 189	79.07 270	44.22 334	63.08 380	85.51 148

APDe-MVS		80.16 9	80.59 7	78.86 32	86.64 21	60.02 48	88.12 3	86.42 15	62.94 56	82.40 17	92.12 2	59.64 22	89.76 20	78.70 15	88.32 35	86.79 89
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mPP-MVS		76.54 43	75.93 48	78.34 40	86.47 27	63.50 3	85.74 30	82.28 112	62.90 57	71.77 128	90.26 39	46.61 190	86.55 84	71.71 86	85.66 67	84.97 176

ACMMP_NAP		78.77 18	78.78 17	78.74 33	85.44 46	61.04 31	83.84 60	85.16 36	62.88 58	78.10 33	91.26 17	52.51 98	88.39 34	79.34 9	90.52 13	86.78 90

DeepPCF-MVS	69.58 1	79.03 15	79.00 16	79.13 19	84.92 60	60.32 46	83.03 68	85.33 33	62.86 59	80.17 21	90.03 47	61.76 17	88.95 28	74.21 62	88.67 30	88.12 35

HFP-MVS		78.01 27	77.65 29	79.10 21	86.71 19	62.81 8	86.29 18	84.32 52	62.82 60	73.96 84	90.50 31	53.20 88	88.35 35	74.02 65	87.05 51	86.13 120

ACMMPR		77.71 29	77.23 32	79.16 17	86.75 18	62.93 7	86.29 18	84.24 53	62.82 60	73.55 91	90.56 29	49.80 142	88.24 37	74.02 65	87.03 52	86.32 113

region2R		77.67 31	77.18 33	79.15 18	86.76 17	62.95 6	86.29 18	84.16 55	62.81 62	73.30 95	90.58 24	49.90 139	88.21 38	73.78 67	87.03 52	86.29 117

casdiffmvs		74.80 63	74.89 63	74.53 116	75.59 262	50.37 241	78.17 150	85.06 40	62.80 63	74.40 76	87.86 85	57.88 30	83.61 156	69.46 100	82.79 97	89.59 4
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 68	74.70 64	74.34 121	75.70 257	49.99 250	77.54 170	84.63 47	62.73 64	73.98 83	87.79 88	57.67 33	83.82 152	69.49 98	82.74 98	89.20 8

HPM-MVS		77.28 33	76.85 34	78.54 36	85.00 55	60.81 38	82.91 71	85.08 38	62.57 65	73.09 106	89.97 50	50.90 130	87.48 57	75.30 53	86.85 57	87.33 73
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 267	65.34 259	66.31 309	76.06 253	34.79 420	76.43 207	79.38 175	62.55 66	61.66 320	83.83 199	45.60 198	79.15 267	41.64 364	60.88 395	85.00 173

SMA-MVS		80.28 7	80.39 9	79.95 4	86.60 24	61.95 19	86.33 17	85.75 26	62.49 67	82.20 19	92.28 1	56.53 41	89.70 21	79.85 6	91.48 1	88.19 33
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
CP-MVSNet		66.49 256	66.41 237	66.72 300	77.67 204	36.33 410	76.83 200	79.52 172	62.45 68	62.54 307	83.47 212	46.32 192	78.37 282	45.47 329	63.43 377	85.45 153

CP-MVS		77.12 36	76.68 36	78.43 37	86.05 39	63.18 5	87.55 10	83.45 80	62.44 69	72.68 115	90.50 31	48.18 163	87.34 58	73.59 69	85.71 66	84.76 184

PS-CasMVS		66.42 257	66.32 241	66.70 302	77.60 212	36.30 412	76.94 194	79.61 170	62.36 70	62.43 312	83.66 204	45.69 196	78.37 282	45.35 331	63.26 378	85.42 156

3Dnovator	64.47 5	72.49 107	71.39 118	75.79 82	77.70 202	58.99 76	80.66 104	83.15 97	62.24 71	65.46 253	86.59 124	42.38 241	85.52 114	59.59 202	84.72 71	82.85 249

MP-MVS-pluss		78.35 23	78.46 20	78.03 44	84.96 56	59.52 58	82.93 70	85.39 32	62.15 72	76.41 48	91.51 11	52.47 100	86.78 75	80.66 4	89.64 19	87.80 49
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 115		82.31 82		62.10 73	67.85 199

ACMP_Plane					80.66 115		82.31 82		62.10 73	67.85 199

HQP-MVS		73.45 84	72.80 95	75.40 92	80.66 115	54.94 143	82.31 82	83.90 62	62.10 73	67.85 199	85.54 162	45.46 202	86.93 71	67.04 122	80.35 127	84.32 195

SPE-MVS-test		75.62 57	75.31 57	76.56 71	80.63 118	55.13 141	83.88 59	85.22 34	62.05 76	71.49 135	86.03 145	53.83 76	86.36 91	67.74 111	86.91 56	88.19 33

VPNet		67.52 232	68.11 194	65.74 323	79.18 149	36.80 405	72.17 303	72.83 307	62.04 77	67.79 206	85.83 152	48.88 157	76.60 324	51.30 275	72.97 261	83.81 216

WR-MVS_H		67.02 244	66.92 225	67.33 297	77.95 194	37.75 394	77.57 168	82.11 115	62.03 78	62.65 304	82.48 235	50.57 133	79.46 257	42.91 352	64.01 370	84.79 182

DeepC-MVS_fast	68.24 3	77.25 34	76.63 37	79.12 20	86.15 35	60.86 36	84.71 40	84.85 45	61.98 79	73.06 107	88.88 66	53.72 80	89.06 27	68.27 103	88.04 41	87.42 65
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 16	79.22 15	77.60 51	82.88 82	57.83 90	84.99 37	88.13 2	61.86 80	79.16 25	90.75 21	57.96 29	87.09 68	77.08 33	90.18 15	87.87 45

PGM-MVS		76.77 41	76.06 46	78.88 31	86.14 36	62.73 9	82.55 78	83.74 69	61.71 81	72.45 121	90.34 37	48.48 161	88.13 41	72.32 78	86.85 57	85.78 133

fmvsm_s_conf0.5_n_8		74.30 73	74.39 68	74.01 133	75.33 269	52.89 189	78.24 146	77.32 230	61.65 82	78.13 32	88.90 65	52.82 94	81.54 210	78.46 22	78.67 165	87.60 58

E2		73.72 80	73.60 81	74.06 130	77.16 224	50.40 239	76.97 191	83.74 69	61.64 83	73.36 93	86.75 115	56.14 48	82.99 169	67.50 116	79.18 152	88.80 12

E3		73.72 80	73.60 81	74.06 130	77.16 224	50.40 239	76.97 191	83.74 69	61.64 83	73.36 93	86.76 112	56.13 49	82.99 169	67.50 116	79.18 152	88.80 12

Effi-MVS+		73.31 88	72.54 99	75.62 89	77.87 195	53.64 165	79.62 122	79.61 170	61.63 85	72.02 126	82.61 225	56.44 43	85.97 104	63.99 152	79.07 155	87.25 75

MG-MVS		73.96 77	73.89 76	74.16 128	85.65 43	49.69 259	81.59 93	81.29 136	61.45 86	71.05 138	88.11 77	51.77 114	87.73 52	61.05 188	83.09 88	85.05 172

fmvsm_s_conf0.5_n_9		75.16 60	75.22 59	75.01 99	78.34 178	55.37 138	77.30 180	73.95 292	61.40 87	79.46 23	90.14 41	57.07 37	81.15 220	80.00 5	79.31 144	88.51 22

LPG-MVS_test		72.74 100	71.74 111	75.76 83	80.22 123	57.51 96	82.55 78	83.40 82	61.32 88	66.67 229	87.33 99	39.15 283	86.59 79	67.70 112	77.30 193	83.19 239

LGP-MVS_train				75.76 83	80.22 123	57.51 96		83.40 82	61.32 88	66.67 229	87.33 99	39.15 283	86.59 79	67.70 112	77.30 193	83.19 239

CLD-MVS		73.33 87	72.68 97	75.29 96	78.82 159	53.33 177	78.23 147	84.79 46	61.30 90	70.41 146	81.04 269	52.41 101	87.12 66	64.61 148	82.49 100	85.41 157
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT-MVS		71.46 130	70.70 134	73.74 145	77.76 200	49.30 267	76.60 203	80.45 157	61.25 91	68.17 187	84.78 172	44.64 216	84.90 130	64.79 144	77.88 181	87.03 81

viewcassd2359sk11		73.56 82	73.41 86	74.00 134	77.13 226	50.35 242	76.86 198	83.69 73	61.23 92	73.14 102	86.38 133	56.09 51	82.96 172	67.15 120	79.01 157	88.70 17

fmvsm_s_conf0.5_n_3		73.55 83	74.39 68	71.03 237	74.09 307	51.86 216	77.77 164	75.60 256	61.18 93	78.67 29	88.98 63	55.88 53	77.73 297	78.69 16	78.68 164	83.50 231

MVS_111021_HR		74.02 76	73.46 84	75.69 86	83.01 80	60.63 40	77.29 181	78.40 209	61.18 93	70.58 144	85.97 147	54.18 69	84.00 149	67.52 115	82.98 92	82.45 261

balanced_conf03		76.58 42	76.55 41	76.68 66	81.73 95	52.90 187	80.94 99	85.70 28	61.12 95	74.90 66	87.17 104	56.46 42	88.14 40	72.87 73	88.03 42	89.00 9

FIs		70.82 143	71.43 116	68.98 277	78.33 179	38.14 390	76.96 193	83.59 76	61.02 96	67.33 213	86.73 116	55.07 57	81.64 206	54.61 248	79.22 148	87.14 79

MED-MVS test				79.09 23	85.30 50	59.25 64	86.84 11	85.86 21	60.95 97	83.65 12	90.57 25		89.91 16	77.02 34	89.43 22	88.10 36

TestfortrainingZip a		79.97 11	80.40 8	78.69 34	85.30 50	58.20 86	86.84 11	85.86 21	60.95 97	83.65 12	90.57 25	64.70 10	89.91 16	76.25 43	89.43 22	87.96 42

FOURS1					86.12 37	60.82 37	88.18 1	83.61 75	60.87 99	81.50 20

FC-MVSNet-test		69.80 168	70.58 137	67.46 293	77.61 211	34.73 423	76.05 218	83.19 96	60.84 100	65.88 247	86.46 130	54.52 66	80.76 235	52.52 263	78.12 177	86.91 84

v8		70.33 154	69.28 161	73.49 159	73.15 320	50.22 244	78.62 137	80.78 151	60.79 101	66.45 233	82.11 249	49.35 148	84.98 127	63.58 162	68.71 332	85.28 163

CSCG		76.92 37	76.75 35	77.41 55	83.96 68	59.60 56	82.95 69	86.50 14	60.78 102	75.27 55	84.83 170	60.76 18	86.56 81	67.86 110	87.87 45	86.06 122

Vis-MVSNet		72.18 114	71.37 119	74.61 111	81.29 104	55.41 136	80.90 100	78.28 212	60.73 103	69.23 171	88.09 78	44.36 220	82.65 186	57.68 219	81.75 110	85.77 136
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		71.26 133	70.16 145	74.57 114	74.59 290	52.77 194	75.91 222	81.20 140	60.72 104	69.10 174	85.71 157	41.67 252	83.53 158	63.91 155	78.62 167	87.42 65

BP-MVS1		73.41 86	72.25 103	76.88 61	76.68 241	53.70 163	79.15 127	81.07 144	60.66 105	71.81 127	87.39 96	40.93 265	87.24 59	71.23 90	81.29 114	89.71 2

APD-MVS		78.02 26	78.04 26	77.98 45	86.44 28	60.81 38	85.52 33	84.36 51	60.61 106	79.05 26	90.30 38	55.54 55	88.32 36	73.48 70	87.03 52	84.83 180
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 112	71.20 124	75.59 91	80.28 121	57.54 94	82.74 74	82.84 106	60.58 107	65.24 261	86.18 139	39.25 281	86.03 102	66.95 126	76.79 201	83.22 237
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
lecture		77.75 28	77.84 28	77.50 53	82.75 84	57.62 93	85.92 25	86.20 18	60.53 108	78.99 27	91.45 12	51.51 119	87.78 51	75.65 49	87.55 47	87.10 80

testdata1							72.65 292		60.50 109

UGNet		68.81 198	67.39 210	73.06 171	78.33 179	54.47 149	79.77 117	75.40 263	60.45 110	63.22 290	84.40 187	32.71 358	80.91 231	51.71 273	80.56 125	83.81 216
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
viewmacassd2359aftdt		73.15 92	73.16 89	73.11 170	75.15 275	49.31 266	77.53 172	83.21 92	60.42 111	73.20 99	87.34 98	53.82 77	81.05 225	67.02 124	80.79 116	88.96 10

h-mvs33		72.71 101	71.49 115	76.40 72	81.99 92	59.58 57	76.92 195	76.74 241	60.40 112	74.81 68	85.95 148	45.54 200	85.76 109	70.41 95	70.61 294	83.86 215

hse-mvs2		71.04 135	69.86 149	74.60 112	79.58 137	57.12 106	73.96 265	75.25 266	60.40 112	74.81 68	81.95 251	45.54 200	82.90 175	70.41 95	66.83 349	83.77 220

EPP-MVSNet		72.16 117	71.31 121	74.71 105	78.68 163	49.70 257	82.10 86	81.65 121	60.40 112	65.94 243	85.84 151	51.74 115	86.37 90	55.93 232	79.55 139	88.07 41

UniMVSNet_ETH3D		67.60 231	67.07 224	69.18 274	77.39 217	42.29 351	74.18 262	75.59 257	60.37 115	66.77 225	86.06 144	37.64 299	78.93 277	52.16 266	73.49 249	86.32 113

test_prior2							81.75 89		60.37 115	75.01 61	89.06 61	56.22 46		72.19 79	88.96 28

SD-MVS		77.70 30	77.62 30	77.93 46	84.47 63	61.88 21	84.55 43	83.87 65	60.37 115	79.89 22	89.38 58	54.97 60	85.58 113	76.12 45	84.94 70	86.33 111
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		69.68 172	70.19 144	68.16 287	79.73 134	41.63 360	70.53 327	77.38 227	60.37 115	70.69 141	86.63 121	51.08 126	77.09 309	53.61 256	81.69 112	85.75 138

sasdasda		74.67 66	74.98 61	73.71 147	78.94 155	50.56 236	80.23 107	83.87 65	60.30 119	77.15 41	86.56 126	59.65 20	82.00 200	66.01 133	82.12 101	88.58 20

canonicalmvs		74.67 66	74.98 61	73.71 147	78.94 155	50.56 236	80.23 107	83.87 65	60.30 119	77.15 41	86.56 126	59.65 20	82.00 200	66.01 133	82.12 101	88.58 20

v7n		69.01 194	67.36 212	73.98 135	72.51 334	52.65 196	78.54 141	81.30 135	60.26 121	62.67 303	81.62 258	43.61 226	84.49 139	57.01 223	68.70 333	84.79 182

reproduce-ours		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 85	60.22 122	77.85 36	91.42 14	50.67 131	87.69 53	72.46 76	84.53 74	85.46 151

our_new_method		76.90 38	76.58 38	77.87 47	83.99 66	60.46 43	84.75 38	83.34 85	60.22 122	77.85 36	91.42 14	50.67 131	87.69 53	72.46 76	84.53 74	85.46 151

HPM-MVS_fast		74.30 73	73.46 84	76.80 63	84.45 64	59.04 74	83.65 63	81.05 145	60.15 124	70.43 145	89.84 52	41.09 264	85.59 112	67.61 114	82.90 94	85.77 136

VPA-MVSNet		69.02 193	69.47 157	67.69 291	77.42 216	41.00 367	74.04 263	79.68 168	60.06 125	69.26 170	84.81 171	51.06 127	77.58 299	54.44 249	74.43 232	84.48 192

v10		70.21 156	69.02 166	73.81 139	73.51 314	50.92 228	78.74 133	81.39 128	60.05 126	66.39 234	81.83 254	47.58 172	85.41 121	62.80 172	68.86 331	85.09 171

viewdifsd2359ckpt07		71.90 121	71.97 107	71.69 209	74.81 282	48.08 290	75.30 233	80.49 156	60.00 127	71.63 131	86.33 135	56.34 45	79.25 261	65.40 140	77.41 189	87.76 51

SR-MVS		76.13 51	75.70 52	77.40 57	85.87 41	61.20 29	85.52 33	82.19 113	59.99 128	75.10 59	90.35 36	47.66 170	86.52 85	71.64 87	82.99 90	84.47 193

SSC-MVS3.2		60.57 327	61.39 309	58.12 390	74.29 300	32.63 438	59.52 415	65.53 369	59.90 129	62.45 310	79.75 296	41.96 244	63.90 404	39.47 376	69.65 319	77.84 341

9.14			78.75 18		83.10 77		84.15 54	88.26 1	59.90 129	78.57 30	90.36 35	57.51 35	86.86 73	77.39 29	89.52 21

v2v482		70.50 149	69.45 158	73.66 150	72.62 330	50.03 249	77.58 167	80.51 155	59.90 129	69.52 161	82.14 247	47.53 174	84.88 133	65.07 143	70.17 304	86.09 121

Baseline_NR-MVSNet		67.05 243	67.56 202	65.50 327	75.65 258	37.70 396	75.42 231	74.65 279	59.90 129	68.14 189	83.15 218	49.12 155	77.20 307	52.23 265	69.78 313	81.60 274

API-MVS		72.17 115	71.41 117	74.45 119	81.95 93	57.22 99	84.03 56	80.38 159	59.89 133	68.40 182	82.33 238	49.64 143	87.83 50	51.87 270	84.16 81	78.30 332

Effi-MVS+-dtu		69.64 174	67.53 205	75.95 78	76.10 252	62.29 15	80.20 110	76.06 249	59.83 134	65.26 260	77.09 345	41.56 255	84.02 148	60.60 193	71.09 290	81.53 275

reproduce_model		76.43 45	76.08 45	77.49 54	83.47 74	60.09 47	84.60 42	82.90 103	59.65 135	77.31 39	91.43 13	49.62 144	87.24 59	71.99 82	83.75 85	85.14 167

MVSMamba_PlusPlus		75.75 56	75.44 54	76.67 67	80.84 112	53.06 184	78.62 137	85.13 37	59.65 135	71.53 134	87.47 92	56.92 38	88.17 39	72.18 80	86.63 62	88.80 12

CANet_DTU		68.18 216	67.71 201	69.59 265	74.83 281	46.24 310	78.66 136	76.85 237	59.60 137	63.45 288	82.09 250	35.25 323	77.41 302	59.88 199	78.76 162	85.14 167

EI-MVSNet		69.27 187	68.44 183	71.73 206	74.47 293	49.39 264	75.20 237	78.45 205	59.60 137	69.16 172	76.51 357	51.29 122	82.50 191	59.86 201	71.45 284	83.30 234

IterMVS-LS		69.22 189	68.48 179	71.43 221	74.44 295	49.40 263	76.23 212	77.55 223	59.60 137	65.85 248	81.59 261	51.28 123	81.58 209	59.87 200	69.90 311	83.30 234
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 108	73.34 88	69.81 262	77.77 199	43.21 343	75.84 225	81.18 141	59.59 140	75.45 53	86.64 119	57.74 31	77.94 289	63.92 153	81.90 106	88.30 27

VDDNet		71.81 122	71.33 120	73.26 168	82.80 83	47.60 299	78.74 133	75.27 265	59.59 140	72.94 109	89.40 57	41.51 257	83.91 150	58.75 214	82.99 90	88.26 28

viewmanbaseed2359cas		72.92 97	72.89 93	73.00 172	75.16 273	49.25 269	77.25 184	83.11 100	59.52 142	72.93 110	86.63 121	54.11 70	80.98 226	66.63 127	80.67 120	88.76 16

alignmvs		73.86 78	73.99 73	73.45 161	78.20 182	50.50 238	78.57 139	82.43 110	59.40 143	76.57 46	86.71 118	56.42 44	81.23 219	65.84 136	81.79 107	88.62 18

MVS_Test		72.45 108	72.46 100	72.42 190	74.88 278	48.50 284	76.28 210	83.14 98	59.40 143	72.46 119	84.68 175	55.66 54	81.12 221	65.98 135	79.66 136	87.63 56

TSAR-MVS + MP.		78.44 22	78.28 22	78.90 30	84.96 56	61.41 26	84.03 56	83.82 68	59.34 145	79.37 24	89.76 54	59.84 19	87.62 56	76.69 37	86.74 59	87.68 54
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 79	73.47 83	74.66 108	83.02 79	59.29 63	82.30 85	81.88 117	59.34 145	71.59 132	86.83 110	45.94 195	83.65 155	65.09 142	85.22 69	81.06 290

PAPM_NR		72.63 104	71.80 109	75.13 97	81.72 96	53.42 175	79.91 115	83.28 90	59.14 147	66.31 236	85.90 149	51.86 111	86.06 100	57.45 221	80.62 121	85.91 127

testing91		64.46 284	63.80 275	66.47 306	78.43 173	40.06 372	67.63 355	69.59 334	59.06 148	63.18 292	78.05 325	34.05 336	76.99 314	48.30 300	75.87 214	82.37 263

myMVS_eth3d28		60.66 326	61.04 317	59.51 374	77.32 219	31.58 443	63.11 395	63.87 384	59.00 149	60.90 329	78.26 322	32.69 360	66.15 394	36.10 402	78.13 176	80.81 295

save fliter					86.17 34	61.30 28	83.98 58	79.66 169	59.00 149

v148		68.24 214	67.19 222	71.40 222	70.43 373	47.77 296	75.76 226	77.03 235	58.91 151	67.36 212	80.10 289	48.60 160	81.89 202	60.01 197	66.52 352	84.53 190

TransMVSNet (Re)		64.72 278	64.33 268	65.87 322	75.22 270	38.56 386	74.66 252	75.08 274	58.90 152	61.79 318	82.63 224	51.18 124	78.07 287	43.63 345	55.87 418	80.99 292

Anonymous202405211		66.84 248	65.99 247	69.40 269	80.19 126	42.21 353	71.11 320	71.31 319	58.80 153	67.90 197	86.39 132	29.83 386	79.65 253	49.60 290	78.78 161	86.33 111

test2506		65.33 272	64.61 266	67.50 292	79.46 140	34.19 428	74.43 258	51.92 439	58.72 154	66.75 226	88.05 80	25.99 420	80.92 230	51.94 269	84.25 78	87.39 68

ECVR-MVS		67.72 229	67.51 206	68.35 285	79.46 140	36.29 413	74.79 249	66.93 357	58.72 154	67.19 217	88.05 80	36.10 316	81.38 214	52.07 267	84.25 78	87.39 68

test1111		67.21 236	67.14 223	67.42 294	79.24 146	34.76 422	73.89 270	65.65 367	58.71 156	66.96 222	87.95 84	36.09 317	80.53 237	52.03 268	83.79 84	86.97 83

LCM-MVSNet-Re		61.88 317	61.35 310	63.46 347	74.58 291	31.48 444	61.42 405	58.14 417	58.71 156	53.02 412	79.55 301	43.07 232	76.80 318	45.69 322	77.96 179	82.11 269

fmvsm_s_conf0.5_n_11		73.16 91	73.35 87	72.58 181	75.48 264	52.41 206	78.84 131	76.85 237	58.64 158	73.58 90	87.25 102	54.09 71	79.47 256	76.19 44	79.27 145	85.86 129

testing99		64.05 288	63.29 286	66.34 308	78.17 186	39.76 376	67.33 360	68.00 348	58.60 159	63.03 295	78.10 324	32.57 365	76.94 316	48.22 301	75.58 218	82.34 264

v1144		70.42 151	69.31 160	73.76 142	73.22 318	50.64 233	77.83 162	81.43 127	58.58 160	69.40 165	81.16 266	47.53 174	85.29 123	64.01 151	70.64 292	85.34 160

TSAR-MVS + GP.		74.90 62	74.15 72	77.17 59	82.00 91	58.77 80	81.80 88	78.57 198	58.58 160	74.32 78	84.51 185	55.94 52	87.22 62	67.11 121	84.48 77	85.52 147

BH-RMVSNet		68.81 198	67.42 209	72.97 173	80.11 129	52.53 200	74.26 260	76.29 244	58.48 162	68.38 183	84.20 189	42.59 237	83.83 151	46.53 314	75.91 213	82.56 255

APD-MVS_3200maxsize		74.96 61	74.39 68	76.67 67	82.20 88	58.24 85	83.67 62	83.29 89	58.41 163	73.71 88	90.14 41	45.62 197	85.99 103	69.64 97	82.85 96	85.78 133

OMC-MVS		71.40 132	70.60 135	73.78 140	76.60 244	53.15 181	79.74 119	79.78 166	58.37 164	68.75 176	86.45 131	45.43 204	80.60 236	62.58 173	77.73 182	87.58 60

nrg030		72.96 96	73.01 91	72.84 176	75.41 267	50.24 243	80.02 111	82.89 105	58.36 165	74.44 75	86.73 116	58.90 27	80.83 232	65.84 136	74.46 230	87.44 64

K. test v3		60.47 330	57.11 349	70.56 247	73.74 311	48.22 287	75.10 241	62.55 396	58.27 166	53.62 407	76.31 361	27.81 404	81.59 208	47.42 305	39.18 455	81.88 272

FA-MVS(test-final)		69.82 166	68.48 179	73.84 138	78.44 172	50.04 248	75.58 230	78.99 183	58.16 167	67.59 209	82.14 247	42.66 236	85.63 110	56.60 225	76.19 207	85.84 131

MVS_111021_LR		69.50 181	68.78 173	71.65 211	78.38 174	59.33 61	74.82 248	70.11 328	58.08 168	67.83 204	84.68 175	41.96 244	76.34 329	65.62 138	77.54 185	79.30 323

SR-MVS-dyc-post		74.57 69	73.90 75	76.58 70	83.49 72	59.87 54	84.29 48	81.36 130	58.07 169	73.14 102	90.07 43	44.74 214	85.84 107	68.20 104	81.76 108	84.03 205

RE-MVS-def			73.71 79		83.49 72	59.87 54	84.29 48	81.36 130	58.07 169	73.14 102	90.07 43	43.06 233		68.20 104	81.76 108	84.03 205

SDMVSNet		68.03 219	68.10 195	67.84 289	77.13 226	48.72 280	65.32 377	79.10 178	58.02 171	65.08 264	82.55 231	47.83 167	73.40 343	63.92 153	73.92 238	81.41 277

sd_testset		64.46 284	64.45 267	64.51 338	77.13 226	42.25 352	62.67 398	72.11 314	58.02 171	65.08 264	82.55 231	41.22 263	69.88 369	47.32 307	73.92 238	81.41 277

GeoE		71.01 137	70.15 146	73.60 155	79.57 138	52.17 208	78.93 130	78.12 214	58.02 171	67.76 208	83.87 198	52.36 102	82.72 184	56.90 224	75.79 215	85.92 126

viewdifsd2359ckpt09		73.42 85	72.45 101	76.30 75	77.25 222	53.27 178	80.36 106	82.48 109	57.96 174	72.24 122	85.73 156	53.22 87	86.27 94	63.79 159	79.06 156	89.36 5

ZD-MVS					86.64 21	60.38 45		82.70 107	57.95 175	78.10 33	90.06 45	56.12 50	88.84 30	74.05 64	87.00 55

EIA-MVS		71.78 123	70.60 135	75.30 95	79.85 132	53.54 169	77.27 183	83.26 91	57.92 176	66.49 231	79.39 305	52.07 108	86.69 77	60.05 196	79.14 154	85.66 143

test_yl		69.69 170	69.13 163	71.36 225	78.37 176	45.74 315	74.71 250	80.20 161	57.91 177	70.01 154	83.83 199	42.44 239	82.87 178	54.97 242	79.72 134	85.48 149

DCV-MVSNet		69.69 170	69.13 163	71.36 225	78.37 176	45.74 315	74.71 250	80.20 161	57.91 177	70.01 154	83.83 199	42.44 239	82.87 178	54.97 242	79.72 134	85.48 149

MonoMVSNet		64.15 287	63.31 285	66.69 303	70.51 371	44.12 334	74.47 256	74.21 287	57.81 179	63.03 295	76.62 353	38.33 292	77.31 305	54.22 250	60.59 400	78.64 330

dcpmvs_2		74.55 70	75.23 58	72.48 186	82.34 87	53.34 176	77.87 159	81.46 126	57.80 180	75.49 52	86.81 111	62.22 15	77.75 296	71.09 91	82.02 104	86.34 109

diffmvs_AUTHOR		71.02 136	70.87 130	71.45 218	69.89 384	48.97 275	73.16 286	78.33 211	57.79 181	72.11 125	85.26 167	51.84 112	77.89 292	71.00 92	78.47 172	87.49 62

viewdifsd2359ckpt11		69.13 190	68.38 186	71.38 223	71.57 351	48.61 281	73.22 284	73.18 302	57.65 182	70.67 142	84.73 173	50.03 137	79.80 250	63.25 165	71.10 288	85.74 139

viewmsd2359difaftdt		69.13 190	68.38 186	71.38 223	71.57 351	48.61 281	73.22 284	73.18 302	57.65 182	70.67 142	84.73 173	50.03 137	79.80 250	63.25 165	71.10 288	85.74 139

fmvsm_s_conf0.5_n_6		72.59 105	72.87 94	71.73 206	75.14 276	51.96 214	76.28 210	77.12 233	57.63 184	73.85 86	86.91 108	51.54 118	77.87 293	77.18 32	80.18 131	85.37 159

Fast-Effi-MVS+-dtu		67.37 234	65.33 260	73.48 160	72.94 325	57.78 92	77.47 173	76.88 236	57.60 185	61.97 315	76.85 349	39.31 279	80.49 240	54.72 245	70.28 302	82.17 268

v1192		69.97 163	68.68 175	73.85 137	73.19 319	50.94 226	77.68 166	81.36 130	57.51 186	68.95 175	80.85 276	45.28 207	85.33 122	62.97 171	70.37 298	85.27 164

ACMH+	57.40 11	66.12 261	64.06 270	72.30 193	77.79 198	52.83 192	80.39 105	78.03 215	57.30 187	57.47 367	82.55 231	27.68 406	84.17 143	45.54 325	69.78 313	79.90 312

diffmvs		70.69 145	70.43 138	71.46 216	69.45 391	48.95 276	72.93 289	78.46 204	57.27 188	71.69 129	83.97 197	51.48 120	77.92 291	70.70 94	77.95 180	87.53 61
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		68.27 212	67.29 214	71.21 229	79.74 133	53.22 179	76.06 217	77.46 226	57.19 189	66.10 240	81.61 259	45.37 206	83.50 159	45.42 330	76.68 203	76.91 357

fmvsm_s_conf0.5_n_10		74.11 75	73.98 74	74.48 118	74.61 289	52.86 191	78.10 154	77.06 234	57.14 190	78.24 31	88.79 70	52.83 93	82.26 196	77.79 28	81.30 113	88.32 26

viewdifsd2359ckpt13		72.40 111	71.79 110	74.22 126	75.63 259	51.77 218	78.67 135	83.13 99	57.08 191	71.59 132	85.36 166	53.10 90	82.64 187	63.07 169	78.51 169	88.24 30

thres100view900		63.28 297	62.41 296	65.89 320	77.31 220	38.66 385	72.65 292	69.11 341	57.07 192	62.45 310	81.03 270	37.01 311	79.17 264	31.84 423	73.25 256	79.83 315

fmvsm_s_conf0.5_n_7		69.54 178	69.67 153	69.15 276	73.47 316	51.41 221	70.35 331	73.34 298	57.05 193	68.41 181	85.83 152	49.86 140	72.84 346	71.86 84	76.83 200	83.19 239

DP-MVS Recon		72.15 118	70.73 133	76.40 72	86.57 25	57.99 88	81.15 98	82.96 101	57.03 194	66.78 224	85.56 159	44.50 218	88.11 42	51.77 272	80.23 130	83.10 244

thres600view7		63.30 296	62.27 298	66.41 307	77.18 223	38.87 383	72.35 299	69.11 341	56.98 195	62.37 313	80.96 272	37.01 311	79.00 275	31.43 430	73.05 260	81.36 280

V42		68.65 202	67.35 213	72.56 183	68.93 397	50.18 245	72.90 290	79.47 173	56.92 196	69.45 164	80.26 285	46.29 193	82.99 169	64.07 149	67.82 340	84.53 190

MCST-MVS		77.48 32	77.45 31	77.54 52	86.67 20	58.36 84	83.22 66	86.93 5	56.91 197	74.91 65	88.19 75	59.15 26	87.68 55	73.67 68	87.45 49	86.57 99

GA-MVS		65.53 268	63.70 277	71.02 238	70.87 366	48.10 289	70.48 328	74.40 281	56.69 198	64.70 273	76.77 350	33.66 344	81.10 222	55.42 241	70.32 301	83.87 214

v144192		69.71 169	68.51 178	73.33 166	73.10 321	50.13 246	77.54 170	80.64 152	56.65 199	68.57 179	80.55 279	46.87 188	84.96 129	62.98 170	69.66 317	84.89 179

fmvsm_l_conf0.5_n_3		73.23 90	73.13 90	73.55 157	74.40 296	55.13 141	78.97 129	74.96 275	56.64 200	74.76 71	88.75 71	55.02 59	78.77 279	76.33 41	78.31 175	86.74 91

tfpn200view9		63.18 299	62.18 300	66.21 312	76.85 238	39.62 377	71.96 307	69.44 337	56.63 201	62.61 305	79.83 292	37.18 305	79.17 264	31.84 423	73.25 256	79.83 315

thres400		63.31 295	62.18 300	66.72 300	76.85 238	39.62 377	71.96 307	69.44 337	56.63 201	62.61 305	79.83 292	37.18 305	79.17 264	31.84 423	73.25 256	81.36 280

GBi-Net		67.21 236	66.55 231	69.19 271	77.63 206	43.33 340	77.31 177	77.83 218	56.62 203	65.04 266	82.70 221	41.85 247	80.33 242	47.18 309	72.76 264	83.92 211

test1		67.21 236	66.55 231	69.19 271	77.63 206	43.33 340	77.31 177	77.83 218	56.62 203	65.04 266	82.70 221	41.85 247	80.33 242	47.18 309	72.76 264	83.92 211

FMVSNet2		66.93 246	66.31 242	68.79 280	77.63 206	42.98 345	76.11 215	77.47 224	56.62 203	65.22 263	82.17 245	41.85 247	80.18 248	47.05 312	72.72 267	83.20 238

fmvsm_l_conf0.5_n_9		73.27 89	73.66 80	72.09 195	73.82 308	52.72 195	77.45 174	74.28 285	56.61 206	77.10 43	88.16 76	56.17 47	77.09 309	78.27 24	81.13 115	86.48 103

DPM-MVS		75.47 58	75.00 60	76.88 61	81.38 103	59.16 67	79.94 113	85.71 27	56.59 207	72.46 119	86.76 112	56.89 39	87.86 49	66.36 129	88.91 29	83.64 228

v1921920		69.47 182	68.17 192	73.36 165	73.06 322	50.10 247	77.39 175	80.56 153	56.58 208	68.59 177	80.37 281	44.72 215	84.98 127	62.47 176	69.82 312	85.00 173

FMVSNet1		66.70 251	65.87 248	69.19 271	77.49 214	43.33 340	77.31 177	77.83 218	56.45 209	64.60 275	82.70 221	38.08 297	80.33 242	46.08 318	72.31 273	83.92 211

v1240		69.24 188	67.91 197	73.25 169	73.02 324	49.82 251	77.21 185	80.54 154	56.43 210	68.34 184	80.51 280	43.33 229	84.99 125	62.03 180	69.77 315	84.95 177

fmvsm_s_conf0.5_n_4		72.04 119	71.85 108	72.58 181	73.74 311	52.49 202	76.69 201	72.42 310	56.42 211	75.32 54	87.04 105	52.13 107	78.01 288	79.29 12	73.65 244	87.26 74

testing222		62.29 311	61.31 311	65.25 333	77.87 195	38.53 387	68.34 349	66.31 363	56.37 212	63.15 294	77.58 339	28.47 398	76.18 332	37.04 391	76.65 204	81.05 291

CDPH-MVS		76.31 46	75.67 53	78.22 41	85.35 49	59.14 70	81.31 96	84.02 56	56.32 213	74.05 82	88.98 63	53.34 86	87.92 47	69.23 101	88.42 32	87.59 59

Vis-MVSNet (Re-imp)		63.69 292	63.88 273	63.14 351	74.75 284	31.04 446	71.16 318	63.64 387	56.32 213	59.80 341	84.99 168	44.51 217	75.46 334	39.12 378	80.62 121	82.92 246

AdaColmap		69.99 162	68.66 176	73.97 136	84.94 58	57.83 90	82.63 76	78.71 190	56.28 215	64.34 276	84.14 191	41.57 254	87.06 69	46.45 315	78.88 158	77.02 353

PS-MVSNAJss		72.24 113	71.21 123	75.31 94	78.50 169	55.93 122	81.63 90	82.12 114	56.24 216	70.02 153	85.68 158	47.05 183	84.34 142	65.27 141	74.41 233	85.67 142

c3_l		68.33 211	67.56 202	70.62 246	70.87 366	46.21 311	74.47 256	78.80 188	56.22 217	66.19 237	78.53 320	51.88 110	81.40 213	62.08 177	69.04 327	84.25 198

Fast-Effi-MVS+		70.28 155	69.12 165	73.73 146	78.50 169	51.50 220	75.01 242	79.46 174	56.16 218	68.59 177	79.55 301	53.97 73	84.05 145	53.34 258	77.53 186	85.65 144

PHI-MVS		75.87 53	75.36 55	77.41 55	80.62 119	55.91 123	84.28 50	85.78 25	56.08 219	73.41 92	86.58 125	50.94 129	88.54 32	70.79 93	89.71 17	87.79 50

baseline1		63.81 291	63.87 274	63.62 346	76.29 249	36.36 408	71.78 310	67.29 353	56.05 220	64.23 281	82.95 219	47.11 182	74.41 339	47.30 308	61.85 389	80.10 309

train_agg		76.27 47	76.15 44	76.64 69	85.58 44	61.59 24	81.62 91	81.26 137	55.86 221	74.93 63	88.81 67	53.70 81	84.68 136	75.24 55	88.33 34	83.65 227

test_8					85.40 47	60.96 34	81.54 94	81.18 141	55.86 221	74.81 68	88.80 69	53.70 81	84.45 140

FMVSNet3		66.32 260	65.61 253	68.46 283	76.48 247	42.34 350	74.98 244	77.15 232	55.83 223	65.04 266	81.16 266	39.91 272	80.14 249	47.18 309	72.76 264	82.90 248

PAPR		71.72 126	70.82 131	74.41 120	81.20 108	51.17 222	79.55 124	83.33 87	55.81 224	66.93 223	84.61 179	50.95 128	86.06 100	55.79 235	79.20 149	86.00 123

eth_miper_zixun_eth		67.63 230	66.28 243	71.67 210	71.60 350	48.33 286	73.68 274	77.88 216	55.80 225	65.91 244	78.62 318	47.35 180	82.88 177	59.45 203	66.25 353	83.81 216

ACMH	55.70 15	65.20 274	63.57 279	70.07 255	78.07 189	52.01 213	79.48 125	79.69 167	55.75 226	56.59 374	80.98 271	27.12 411	80.94 228	42.90 353	71.58 282	77.25 351
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 271	62.73 293	73.40 164	74.89 277	52.78 193	73.09 288	75.13 270	55.69 227	58.48 359	73.73 390	32.86 353	86.32 92	50.63 280	70.11 305	81.10 289
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		61.53 320	60.94 319	63.30 349	68.95 396	36.93 404	67.60 356	72.80 308	55.67 228	59.95 338	76.63 352	45.01 213	72.22 353	39.74 375	62.09 388	80.74 297

TEST9					85.58 44	61.59 24	81.62 91	81.26 137	55.65 229	74.93 63	88.81 67	53.70 81	84.68 136

thres200		62.20 312	61.16 316	65.34 331	75.38 268	39.99 373	69.60 340	69.29 339	55.64 230	61.87 317	76.99 346	37.07 310	78.96 276	31.28 431	73.28 255	77.06 352

guyue		68.10 218	67.23 221	70.71 245	73.67 313	49.27 268	73.65 275	76.04 250	55.62 231	67.84 203	82.26 241	41.24 262	78.91 278	61.01 189	73.72 242	83.94 209

pm-mvs1		65.24 273	64.97 264	66.04 317	72.38 337	39.40 380	72.62 294	75.63 255	55.53 232	62.35 314	83.18 217	47.45 176	76.47 327	49.06 294	66.54 351	82.24 265

testing11		62.81 303	61.90 303	65.54 325	78.38 174	40.76 369	67.59 357	66.78 359	55.48 233	60.13 333	77.11 344	31.67 372	76.79 319	45.53 326	74.45 231	79.06 325

ACMM	61.98 7	70.80 144	69.73 151	74.02 132	80.59 120	58.59 82	82.68 75	82.02 116	55.46 234	67.18 218	84.39 188	38.51 289	83.17 166	60.65 192	76.10 211	80.30 304
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AstraMVS		67.86 225	66.83 226	70.93 239	73.50 315	49.34 265	73.28 282	74.01 290	55.45 235	68.10 194	83.28 213	38.93 286	79.14 268	63.22 167	71.74 279	84.30 197

Anonymous20240529		69.91 164	69.02 166	72.56 183	80.19 126	47.65 297	77.56 169	80.99 147	55.45 235	69.88 157	86.76 112	39.24 282	82.18 198	54.04 251	77.10 197	87.85 46

tt0805		67.77 228	67.24 219	69.34 270	74.87 279	40.08 371	77.36 176	81.37 129	55.31 237	66.33 235	84.65 177	37.35 303	82.55 190	55.65 238	72.28 274	85.39 158

GDP-MVS		72.64 103	71.28 122	76.70 64	77.72 201	54.22 155	79.57 123	84.45 48	55.30 238	71.38 136	86.97 107	39.94 271	87.00 70	67.02 124	79.20 149	88.89 11

CPTT-MVS		72.78 99	72.08 106	74.87 102	84.88 61	61.41 26	84.15 54	77.86 217	55.27 239	67.51 211	88.08 79	41.93 246	81.85 203	69.04 102	80.01 132	81.35 282

XVG-OURS		68.76 201	67.37 211	72.90 175	74.32 299	57.22 99	70.09 335	78.81 187	55.24 240	67.79 206	85.81 155	36.54 314	78.28 284	62.04 179	75.74 216	83.19 239

tfpnnormal		62.47 307	61.63 306	64.99 335	74.81 282	39.01 382	71.22 316	73.72 294	55.22 241	60.21 332	80.09 290	41.26 261	76.98 315	30.02 437	68.09 338	78.97 328

cl____		67.18 239	66.26 244	69.94 257	70.20 377	45.74 315	73.30 279	76.83 239	55.10 242	65.27 257	79.57 300	47.39 178	80.53 237	59.41 205	69.22 325	83.53 230

DIV-MVS_self_test		67.18 239	66.26 244	69.94 257	70.20 377	45.74 315	73.29 281	76.83 239	55.10 242	65.27 257	79.58 299	47.38 179	80.53 237	59.43 204	69.22 325	83.54 229

PC_three_1452									55.09 244	84.46 4	89.84 52	66.68 5	89.41 22	74.24 61	91.38 2	88.42 23

EPNet_dtu		61.90 316	61.97 302	61.68 360	72.89 326	39.78 375	75.85 224	65.62 368	55.09 244	54.56 397	79.36 306	37.59 300	67.02 388	39.80 374	76.95 198	78.25 333
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 131	70.39 139	74.65 109	82.01 90	58.82 79	79.93 114	80.35 160	55.09 244	65.82 249	82.16 246	49.17 152	82.64 187	60.34 194	78.62 167	82.50 260

cl22		67.47 233	66.45 233	70.54 248	69.85 386	46.49 307	73.85 271	77.35 228	55.07 247	65.51 252	77.92 329	47.64 171	81.10 222	61.58 185	69.32 321	84.01 207

miper_ehance_all_eth		68.03 219	67.24 219	70.40 250	70.54 370	46.21 311	73.98 264	78.68 192	55.07 247	66.05 241	77.80 333	52.16 106	81.31 216	61.53 187	69.32 321	83.67 224

fmvsm_s_conf0.5_n_2		69.82 166	69.27 162	71.46 216	72.00 344	51.08 223	73.30 279	67.79 349	55.06 249	75.24 56	87.51 90	44.02 223	77.00 313	75.67 48	72.86 262	86.31 116

Elysia		70.19 158	68.29 188	75.88 80	74.15 303	54.33 153	78.26 143	83.21 92	55.04 250	67.28 214	83.59 206	30.16 381	86.11 98	63.67 160	79.26 146	87.20 76

StellarMVS		70.19 158	68.29 188	75.88 80	74.15 303	54.33 153	78.26 143	83.21 92	55.04 250	67.28 214	83.59 206	30.16 381	86.11 98	63.67 160	79.26 146	87.20 76

PS-MVSNAJ		70.51 148	69.70 152	72.93 174	81.52 98	55.79 126	74.92 246	79.00 182	55.04 250	69.88 157	78.66 315	47.05 183	82.19 197	61.61 183	79.58 137	80.83 294

fmvsm_s_conf0.1_n_2		69.64 174	69.01 168	71.52 214	71.66 349	51.04 224	73.39 278	67.14 355	55.02 253	75.11 58	87.64 89	42.94 235	77.01 312	75.55 50	72.63 268	86.52 102

mmtdpeth		60.40 331	59.12 332	64.27 341	69.59 388	48.99 273	70.67 325	70.06 329	54.96 254	62.78 299	73.26 395	27.00 413	67.66 381	58.44 217	45.29 447	76.16 362

xiu_mvs_v2_base		70.52 147	69.75 150	72.84 176	81.21 107	55.63 130	75.11 239	78.92 184	54.92 255	69.96 156	79.68 298	47.00 187	82.09 199	61.60 184	79.37 140	80.81 295

MAR-MVS		71.51 128	70.15 146	75.60 90	81.84 94	59.39 60	81.38 95	82.90 103	54.90 256	68.08 195	78.70 313	47.73 168	85.51 115	51.68 274	84.17 80	81.88 272
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
reproduce_monomvs		62.56 305	61.20 315	66.62 304	70.62 369	44.30 331	70.13 334	73.13 305	54.78 257	61.13 326	76.37 360	25.63 423	75.63 333	58.75 214	60.29 401	79.93 311

XVG-OURS-SEG-HR		68.81 198	67.47 208	72.82 178	74.40 296	56.87 109	70.59 326	79.04 181	54.77 258	66.99 221	86.01 146	39.57 277	78.21 285	62.54 174	73.33 254	83.37 233

testing3		56.54 362	55.92 364	58.41 385	77.52 213	27.93 456	69.72 338	56.36 426	54.75 259	58.63 357	77.80 333	20.88 439	71.75 356	25.31 453	62.25 386	75.53 369

Anonymous20231211		69.28 186	68.47 181	71.73 206	80.28 121	47.18 303	79.98 112	82.37 111	54.61 260	67.24 216	84.01 195	39.43 278	82.41 194	55.45 240	72.83 263	85.62 145

SixPastTwentyTwo		61.65 319	58.80 336	70.20 253	75.80 255	47.22 302	75.59 228	69.68 332	54.61 260	54.11 401	79.26 308	27.07 412	82.96 172	43.27 347	49.79 440	80.41 302

test_0402		63.25 298	61.01 318	69.96 256	80.00 130	54.37 152	76.86 198	72.02 315	54.58 262	58.71 353	80.79 278	35.00 326	84.36 141	26.41 451	64.71 364	71.15 421

tttt0517		67.83 226	65.66 252	74.33 122	76.69 240	50.82 230	77.86 160	73.99 291	54.54 263	64.64 274	82.53 234	35.06 325	85.50 116	55.71 236	69.91 310	86.67 95

BH-w/o		66.85 247	65.83 249	69.90 260	79.29 142	52.46 203	74.66 252	76.65 242	54.51 264	64.85 271	78.12 323	45.59 199	82.95 174	43.26 348	75.54 219	74.27 387

AUN-MVS		68.45 210	66.41 237	74.57 114	79.53 139	57.08 107	73.93 268	75.23 267	54.44 265	66.69 227	81.85 253	37.10 309	82.89 176	62.07 178	66.84 348	83.75 221

LTVRE_ROB	55.42 16	63.15 300	61.23 314	68.92 278	76.57 245	47.80 294	59.92 414	76.39 243	54.35 266	58.67 355	82.46 236	29.44 390	81.49 211	42.12 357	71.14 286	77.46 345
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 95	72.59 98	74.27 124	71.28 361	55.88 124	78.21 149	75.56 258	54.31 267	74.86 67	87.80 87	54.72 63	80.23 246	78.07 26	78.48 170	86.70 92

test_fmvsmconf0.01_n		72.17 115	71.50 114	74.16 128	67.96 403	55.58 133	78.06 155	74.67 278	54.19 268	74.54 74	88.23 74	50.35 136	80.24 245	78.07 26	77.46 188	86.65 97

test_fmvsmconf0.1_n		72.81 98	72.33 102	74.24 125	69.89 384	55.81 125	78.22 148	75.40 263	54.17 269	75.00 62	88.03 83	53.82 77	80.23 246	78.08 25	78.34 174	86.69 93

ETVMVS		59.51 341	58.81 334	61.58 362	77.46 215	34.87 419	64.94 382	59.35 412	54.06 270	61.08 327	76.67 351	29.54 387	71.87 355	32.16 419	74.07 236	78.01 340

ab-mvs		66.65 252	66.42 236	67.37 295	76.17 251	41.73 357	70.41 330	76.14 247	53.99 271	65.98 242	83.51 210	49.48 145	76.24 330	48.60 297	73.46 251	84.14 203

fmvsm_s_conf0.5_n_5		72.69 102	72.80 95	72.37 191	74.11 306	53.21 180	78.12 151	73.31 299	53.98 272	76.81 45	88.05 80	53.38 85	77.37 304	76.64 38	80.78 117	86.53 101

IU-MVS					87.77 4	59.15 68		85.53 31	53.93 273	84.64 3				79.07 13	90.87 5	88.37 25

SSM_0407		70.41 152	68.96 169	74.75 104	78.65 164	53.46 171	77.28 182	80.00 164	53.88 274	68.14 189	84.61 179	43.21 230	86.26 95	58.80 212	76.11 208	84.54 187

SSM_0404		70.84 140	69.41 159	75.12 98	79.20 147	53.86 159	77.89 158	80.00 164	53.88 274	69.40 165	84.61 179	43.21 230	86.56 81	58.80 212	77.68 184	84.95 177

XVG-ACMP-BASELINE		64.36 286	62.23 299	70.74 243	72.35 338	52.45 204	70.80 324	78.45 205	53.84 276	59.87 339	81.10 268	16.24 447	79.32 260	55.64 239	71.76 278	80.47 299

mamba_0408		67.78 227	65.42 256	74.85 103	78.65 164	53.46 171	50.83 449	79.09 179	53.75 277	68.14 189	83.83 199	41.79 250	86.56 81	56.58 226	76.11 208	84.54 187

SSM_04072		64.98 277	65.42 256	63.68 345	78.65 164	53.46 171	50.83 449	79.09 179	53.75 277	68.14 189	83.83 199	41.79 250	53.03 450	56.58 226	76.11 208	84.54 187

VortexMVS		66.41 258	65.50 255	69.16 275	73.75 309	48.14 288	73.41 277	78.28 212	53.73 279	64.98 270	78.33 321	40.62 267	79.07 270	58.88 211	67.50 343	80.26 305

FE-MVS		65.91 263	63.33 284	73.63 153	77.36 218	51.95 215	72.62 294	75.81 252	53.70 280	65.31 255	78.96 311	28.81 396	86.39 89	43.93 339	73.48 250	82.55 256

thisisatest0530		67.92 223	65.78 250	74.33 122	76.29 249	51.03 225	76.89 196	74.25 286	53.67 281	65.59 251	81.76 256	35.15 324	85.50 116	55.94 231	72.47 269	86.47 104

PVSNet_BlendedMVS		68.56 207	67.72 199	71.07 236	77.03 235	50.57 234	74.50 255	81.52 123	53.66 282	64.22 282	79.72 297	49.13 153	82.87 178	55.82 233	73.92 238	79.77 318

patch_mono-2		69.85 165	71.09 126	66.16 313	79.11 152	54.80 147	71.97 306	74.31 283	53.50 283	70.90 140	84.17 190	57.63 34	63.31 406	66.17 130	82.02 104	80.38 303

EG-PatchMatch MVS		64.71 279	62.87 290	70.22 251	77.68 203	53.48 170	77.99 156	78.82 186	53.37 284	56.03 381	77.41 341	24.75 428	84.04 146	46.37 316	73.42 253	73.14 393

SD_0403		63.07 301	63.49 281	61.82 359	75.16 273	31.14 445	71.89 309	73.47 296	53.34 285	58.22 361	81.81 255	45.17 210	73.86 342	37.43 387	74.87 228	80.45 300

DP-MVS		65.68 265	63.66 278	71.75 205	84.93 59	56.87 109	80.74 103	73.16 304	53.06 286	59.09 350	82.35 237	36.79 313	85.94 105	32.82 417	69.96 309	72.45 402

TR-MVS		66.59 255	65.07 263	71.17 232	79.18 149	49.63 261	73.48 276	75.20 269	52.95 287	67.90 197	80.33 284	39.81 275	83.68 154	43.20 349	73.56 248	80.20 306

ET-MVSNet_ETH3D		67.96 222	65.72 251	74.68 107	76.67 242	55.62 132	75.11 239	74.74 276	52.91 288	60.03 336	80.12 288	33.68 343	82.64 187	61.86 181	76.34 205	85.78 133

QAPM		70.05 160	68.81 172	73.78 140	76.54 246	53.43 174	83.23 65	83.48 78	52.89 289	65.90 245	86.29 136	41.55 256	86.49 87	51.01 277	78.40 173	81.42 276

LuminaMVS		68.24 214	66.82 227	72.51 185	73.46 317	53.60 167	76.23 212	78.88 185	52.78 290	68.08 195	80.13 287	32.70 359	81.41 212	63.16 168	75.97 212	82.53 257

icg_test_0407_2		66.41 258	66.75 228	65.37 330	77.06 230	49.73 253	63.79 391	78.60 194	52.70 291	66.19 237	82.58 226	45.17 210	63.65 405	59.20 207	75.46 221	82.74 251

IMVS_0407		68.90 196	67.93 196	71.82 202	77.06 230	49.73 253	74.40 259	78.60 194	52.70 291	66.19 237	82.58 226	45.17 210	83.00 168	59.20 207	75.46 221	82.74 251

IMVS_0404		64.63 281	64.22 269	65.88 321	77.06 230	49.73 253	64.40 385	78.60 194	52.70 291	53.16 411	82.58 226	34.82 328	65.16 399	59.20 207	75.46 221	82.74 251

IMVS_0403		69.09 192	68.14 193	71.95 197	77.06 230	49.73 253	74.51 254	78.60 194	52.70 291	66.69 227	82.58 226	46.43 191	83.38 161	59.20 207	75.46 221	82.74 251

OpenMVS	61.03 9	68.85 197	67.56 202	72.70 180	74.26 301	53.99 158	81.21 97	81.34 134	52.70 291	62.75 302	85.55 161	38.86 287	84.14 144	48.41 299	83.01 89	79.97 310

pmmvs6		63.69 292	62.82 292	66.27 311	70.63 368	39.27 381	73.13 287	75.47 262	52.69 296	59.75 343	82.30 239	39.71 276	77.03 311	47.40 306	64.35 369	82.53 257

IterMVS		62.79 304	61.27 312	67.35 296	69.37 392	52.04 212	71.17 317	68.24 347	52.63 297	59.82 340	76.91 348	37.32 304	72.36 349	52.80 262	63.19 379	77.66 343
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 216	66.36 239	73.63 153	75.61 261	55.35 139	80.77 102	78.56 199	52.48 298	64.27 279	84.10 193	27.45 408	81.84 204	63.45 164	70.56 295	83.69 223

jajsoiax		68.25 213	66.45 233	73.66 150	75.62 260	55.49 135	80.82 101	78.51 201	52.33 299	64.33 277	84.11 192	28.28 400	81.81 205	63.48 163	70.62 293	83.67 224

TAMVS		66.78 250	65.27 261	71.33 228	79.16 151	53.67 164	73.84 272	69.59 334	52.32 300	65.28 256	81.72 257	44.49 219	77.40 303	42.32 356	78.66 166	82.92 246

CDS-MVSNet		66.80 249	65.37 258	71.10 235	78.98 154	53.13 183	73.27 283	71.07 321	52.15 301	64.72 272	80.23 286	43.56 227	77.10 308	45.48 328	78.88 158	83.05 245
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		68.47 208	66.56 230	74.21 127	79.60 136	52.95 185	74.94 245	75.48 261	52.09 302	60.10 334	83.27 214	36.54 314	84.70 135	59.32 206	77.69 183	84.99 175

viewmambaseed2359dif		68.91 195	68.18 191	71.11 234	70.21 376	48.05 293	72.28 301	75.90 251	51.96 303	70.93 139	84.47 186	51.37 121	78.59 280	61.55 186	74.97 226	86.68 94

PVSNet_Blended		68.59 203	67.72 199	71.19 230	77.03 235	50.57 234	72.51 297	81.52 123	51.91 304	64.22 282	77.77 336	49.13 153	82.87 178	55.82 233	79.58 137	80.14 308

mvs_anonymous		68.03 219	67.51 206	69.59 265	72.08 342	44.57 329	71.99 305	75.23 267	51.67 305	67.06 220	82.57 230	54.68 64	77.94 289	56.56 228	75.71 217	86.26 118

xiu_mvs_v1_base_debu		68.58 204	67.28 215	72.48 186	78.19 183	57.19 101	75.28 234	75.09 271	51.61 306	70.04 150	81.41 263	32.79 354	79.02 272	63.81 156	77.31 190	81.22 285

xiu_mvs_v1_base		68.58 204	67.28 215	72.48 186	78.19 183	57.19 101	75.28 234	75.09 271	51.61 306	70.04 150	81.41 263	32.79 354	79.02 272	63.81 156	77.31 190	81.22 285

xiu_mvs_v1_base_debi		68.58 204	67.28 215	72.48 186	78.19 183	57.19 101	75.28 234	75.09 271	51.61 306	70.04 150	81.41 263	32.79 354	79.02 272	63.81 156	77.31 190	81.22 285

MVSTER		67.16 241	65.58 254	71.88 200	70.37 375	49.70 257	70.25 333	78.45 205	51.52 309	69.16 172	80.37 281	38.45 290	82.50 191	60.19 195	71.46 283	83.44 232

CNLPA		65.43 269	64.02 271	69.68 263	78.73 162	58.07 87	77.82 163	70.71 324	51.49 310	61.57 322	83.58 209	38.23 295	70.82 361	43.90 340	70.10 306	80.16 307

原ACMM1				74.69 106	85.39 48	59.40 59		83.42 81	51.47 311	70.27 148	86.61 123	48.61 159	86.51 86	53.85 254	87.96 43	78.16 334

miper_enhance_ethall		67.11 242	66.09 246	70.17 254	69.21 394	45.98 313	72.85 291	78.41 208	51.38 312	65.65 250	75.98 367	51.17 125	81.25 217	60.82 191	69.32 321	83.29 236

MSDG		61.81 318	59.23 330	69.55 268	72.64 329	52.63 198	70.45 329	75.81 252	51.38 312	53.70 404	76.11 362	29.52 388	81.08 224	37.70 385	65.79 357	74.93 378

test20.03		53.87 384	54.02 381	53.41 417	61.47 439	28.11 455	61.30 406	59.21 413	51.34 314	52.09 415	77.43 340	33.29 348	58.55 427	29.76 438	60.27 402	73.58 392

MVSFormer		71.50 129	70.38 140	74.88 101	78.76 160	57.15 104	82.79 72	78.48 202	51.26 315	69.49 162	83.22 215	43.99 224	83.24 164	66.06 131	79.37 140	84.23 199

test_djsdf		69.45 183	67.74 198	74.58 113	74.57 292	54.92 145	82.79 72	78.48 202	51.26 315	65.41 254	83.49 211	38.37 291	83.24 164	66.06 131	69.25 324	85.56 146

dmvs_testset		50.16 402	51.90 392	44.94 438	66.49 414	11.78 478	61.01 411	51.50 440	51.17 317	50.30 427	67.44 432	39.28 280	60.29 417	22.38 457	57.49 411	62.76 443

PAPM		67.92 223	66.69 229	71.63 212	78.09 188	49.02 272	77.09 188	81.24 139	51.04 318	60.91 328	83.98 196	47.71 169	84.99 125	40.81 366	79.32 143	80.90 293

Syy-MVS		56.00 369	56.23 362	55.32 403	74.69 286	26.44 462	65.52 372	57.49 421	50.97 319	56.52 375	72.18 399	39.89 273	68.09 377	24.20 454	64.59 367	71.44 417

myMVS_eth3d		54.86 380	54.61 373	55.61 402	74.69 286	27.31 459	65.52 372	57.49 421	50.97 319	56.52 375	72.18 399	21.87 437	68.09 377	27.70 445	64.59 367	71.44 417

miper_lstm_enhance		62.03 315	60.88 320	65.49 328	66.71 412	46.25 309	56.29 433	75.70 254	50.68 321	61.27 324	75.48 374	40.21 270	68.03 379	56.31 230	65.25 360	82.18 266

gg-mvs-nofinetune		57.86 353	56.43 359	62.18 357	72.62 330	35.35 418	66.57 362	56.33 427	50.65 322	57.64 366	57.10 454	30.65 375	76.36 328	37.38 388	78.88 158	74.82 380

TAPA-MVS	59.36 10	66.60 253	65.20 262	70.81 241	76.63 243	48.75 278	76.52 206	80.04 163	50.64 323	65.24 261	84.93 169	39.15 283	78.54 281	36.77 393	76.88 199	85.14 167
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 361	56.83 354	56.61 397	69.23 393	41.02 364	58.37 420	64.18 380	50.59 324	57.45 368	71.42 407	35.54 321	58.94 425	37.23 389	67.45 344	69.87 430

MVP-Stereo		65.41 270	63.80 275	70.22 251	77.62 210	55.53 134	76.30 209	78.53 200	50.59 324	56.47 377	78.65 316	39.84 274	82.68 185	44.10 338	72.12 276	72.44 403
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 139	69.49 156	75.35 93	77.63 206	55.71 127	76.04 219	81.81 119	50.30 326	69.66 160	85.40 165	52.51 98	84.89 131	51.82 271	80.24 129	85.45 153
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs5depth		55.64 372	53.81 383	61.11 368	59.39 449	40.98 368	65.89 367	68.28 346	50.21 327	58.11 363	75.42 375	17.03 443	67.63 383	43.79 342	46.21 444	74.73 382

baseline2		63.42 294	61.26 313	69.89 261	72.55 332	47.62 298	71.54 311	68.38 345	50.11 328	54.82 393	75.55 372	43.06 233	80.96 227	48.13 302	67.16 347	81.11 288

test-LLR		58.15 351	58.13 344	58.22 387	68.57 398	44.80 325	65.46 374	57.92 418	50.08 329	55.44 385	69.82 420	32.62 362	57.44 432	49.66 288	73.62 245	72.41 404

test0.0.03 1		53.32 389	53.59 386	52.50 423	62.81 434	29.45 450	59.51 416	54.11 435	50.08 329	54.40 399	74.31 384	32.62 362	55.92 441	30.50 434	63.95 372	72.15 409

fmvsm_s_conf0.5_n		69.58 176	68.84 171	71.79 204	72.31 340	52.90 187	77.90 157	62.43 399	49.97 331	72.85 112	85.90 149	52.21 104	76.49 325	75.75 47	70.26 303	85.97 124

COLMAP_ROB	52.97 17	61.27 324	58.81 334	68.64 281	74.63 288	52.51 201	78.42 142	73.30 300	49.92 332	50.96 419	81.51 262	23.06 431	79.40 258	31.63 427	65.85 355	74.01 390
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		69.54 178	68.74 174	71.93 198	72.47 335	53.82 161	78.25 145	62.26 401	49.78 333	73.12 105	86.21 138	52.66 96	76.79 319	75.02 56	68.88 329	85.18 166

WBMVS		60.54 328	60.61 322	60.34 371	78.00 192	35.95 415	64.55 384	64.89 373	49.63 334	63.39 289	78.70 313	33.85 341	67.65 382	42.10 358	70.35 300	77.43 346

tpmvs		58.47 346	56.95 352	63.03 353	70.20 377	41.21 363	67.90 354	67.23 354	49.62 335	54.73 395	70.84 411	34.14 335	76.24 330	36.64 397	61.29 393	71.64 413

fmvsm_s_conf0.1_n		69.41 184	68.60 177	71.83 201	71.07 363	52.88 190	77.85 161	62.44 398	49.58 336	72.97 108	86.22 137	51.68 116	76.48 326	75.53 51	70.10 306	86.14 119

UBG		59.62 340	59.53 328	59.89 372	78.12 187	35.92 416	64.11 389	60.81 409	49.45 337	61.34 323	75.55 372	33.05 349	67.39 386	38.68 380	74.62 229	76.35 361

thisisatest0515		65.83 264	63.50 280	72.82 178	73.75 309	49.50 262	71.32 314	73.12 306	49.39 338	63.82 284	76.50 359	34.95 327	84.84 134	53.20 260	75.49 220	84.13 204

fmvsm_s_conf0.1_n_a		69.32 185	68.44 183	71.96 196	70.91 365	53.78 162	78.12 151	62.30 400	49.35 339	73.20 99	86.55 128	51.99 109	76.79 319	74.83 58	68.68 334	85.32 161

HY-MVS	56.14 13	64.55 283	63.89 272	66.55 305	74.73 285	41.02 364	69.96 336	74.43 280	49.29 340	61.66 320	80.92 273	47.43 177	76.68 323	44.91 333	71.69 280	81.94 270

MIMVSNet1		55.17 377	54.31 378	57.77 393	70.03 381	32.01 441	65.68 370	64.81 374	49.19 341	46.75 438	76.00 364	25.53 424	64.04 402	28.65 442	62.13 387	77.26 350

SCA		60.49 329	58.38 340	66.80 299	74.14 305	48.06 291	63.35 394	63.23 391	49.13 342	59.33 349	72.10 401	37.45 301	74.27 340	44.17 335	62.57 383	78.05 336

test_fmvsmvis_n_1920		70.84 140	70.38 140	72.22 194	71.16 362	55.39 137	75.86 223	72.21 313	49.03 343	73.28 97	86.17 140	51.83 113	77.29 306	75.80 46	78.05 178	83.98 208

testgi		51.90 394	52.37 390	50.51 430	60.39 447	23.55 469	58.42 419	58.15 416	49.03 343	51.83 416	79.21 309	22.39 432	55.59 442	29.24 441	62.64 382	72.40 406

sc_t1		59.76 336	57.84 347	65.54 325	74.87 279	42.95 347	69.61 339	64.16 382	48.90 345	58.68 354	77.12 343	28.19 401	72.35 350	43.75 344	55.28 420	81.31 283

MIMVSNet		57.35 355	57.07 350	58.22 387	74.21 302	37.18 399	62.46 399	60.88 408	48.88 346	55.29 388	75.99 366	31.68 371	62.04 411	31.87 422	72.35 271	75.43 371

gm-plane-assit					71.40 358	41.72 359			48.85 347		73.31 393		82.48 193	48.90 295

fmvsm_l_conf0.5_n		70.99 138	70.82 131	71.48 215	71.45 354	54.40 151	77.18 186	70.46 326	48.67 348	75.17 57	86.86 109	53.77 79	76.86 317	76.33 41	77.51 187	83.17 243

UWE-MVS		60.18 332	59.78 326	61.39 365	77.67 204	33.92 431	69.04 346	63.82 385	48.56 349	64.27 279	77.64 338	27.20 410	70.40 366	33.56 414	76.24 206	79.83 315

cascas		65.98 262	63.42 282	73.64 152	77.26 221	52.58 199	72.26 302	77.21 231	48.56 349	61.21 325	74.60 382	32.57 365	85.82 108	50.38 282	76.75 202	82.52 259

PLC	56.13 14	65.09 275	63.21 287	70.72 244	81.04 110	54.87 146	78.57 139	77.47 224	48.51 351	55.71 382	81.89 252	33.71 342	79.71 252	41.66 362	70.37 298	77.58 344
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 279	62.50 295	71.34 227	79.72 135	55.71 127	79.82 116	74.72 277	48.50 352	56.62 373	84.62 178	33.59 345	82.34 195	29.65 439	75.23 225	75.97 363

anonymousdsp		67.00 245	64.82 265	73.57 156	70.09 380	56.13 117	76.35 208	77.35 228	48.43 353	64.99 269	80.84 277	33.01 351	80.34 241	64.66 146	67.64 342	84.23 199

无先验							79.66 121	74.30 284	48.40 354				80.78 234	53.62 255		79.03 327

FE-MVSNET		55.16 378	53.75 384	59.41 375	65.29 422	33.20 435	67.21 361	66.21 364	48.39 355	49.56 429	73.53 392	29.03 392	72.51 348	30.38 435	54.10 426	72.52 400

114514_t		70.83 142	69.56 154	74.64 110	86.21 32	54.63 148	82.34 81	81.81 119	48.22 356	63.01 297	85.83 152	40.92 266	87.10 67	57.91 218	79.79 133	82.18 266

tpm		57.34 356	58.16 342	54.86 406	71.80 348	34.77 421	67.47 359	56.04 430	48.20 357	60.10 334	76.92 347	37.17 307	53.41 449	40.76 367	65.01 361	76.40 360

test_fmvsm_n_1920		71.73 125	71.14 125	73.50 158	72.52 333	56.53 111	75.60 227	76.16 245	48.11 358	77.22 40	85.56 159	53.10 90	77.43 301	74.86 57	77.14 195	86.55 100

MDA-MVSNet-bldmvs		53.87 384	50.81 397	63.05 352	66.25 416	48.58 283	56.93 431	63.82 385	48.09 359	41.22 450	70.48 416	30.34 378	68.00 380	34.24 409	45.92 446	72.57 399

XXY-MVS		60.68 325	61.67 305	57.70 394	70.43 373	38.45 388	64.19 387	66.47 360	48.05 360	63.22 290	80.86 275	49.28 150	60.47 415	45.25 332	67.28 346	74.19 388

F-COLMAP		63.05 302	60.87 321	69.58 267	76.99 237	53.63 166	78.12 151	76.16 245	47.97 361	52.41 414	81.61 259	27.87 403	78.11 286	40.07 369	66.66 350	77.00 354

tt0320-xc		58.33 348	56.41 360	64.08 342	75.79 256	41.34 361	68.30 350	62.72 395	47.90 362	56.29 378	74.16 387	28.53 397	71.04 360	41.50 365	52.50 432	79.88 313

fmvsm_l_conf0.5_n_a		70.50 149	70.27 142	71.18 231	71.30 360	54.09 156	76.89 196	69.87 330	47.90 362	74.37 77	86.49 129	53.07 92	76.69 322	75.41 52	77.11 196	82.76 250

Patchmatch-RL test		58.16 350	55.49 367	66.15 314	67.92 404	48.89 277	60.66 412	51.07 443	47.86 364	59.36 346	62.71 448	34.02 338	72.27 352	56.41 229	59.40 404	77.30 348

D2MVS		62.30 310	60.29 324	68.34 286	66.46 415	48.42 285	65.70 369	73.42 297	47.71 365	58.16 362	75.02 378	30.51 376	77.71 298	53.96 253	71.68 281	78.90 329

ANet_high		41.38 421	37.47 428	53.11 419	39.73 475	24.45 467	56.94 430	69.69 331	47.65 366	26.04 467	52.32 457	12.44 455	62.38 410	21.80 458	10.61 476	72.49 401

CostFormer		64.04 289	62.51 294	68.61 282	71.88 346	45.77 314	71.30 315	70.60 325	47.55 367	64.31 278	76.61 355	41.63 253	79.62 255	49.74 286	69.00 328	80.42 301

PatchmatchNet		59.84 335	58.24 341	64.65 337	73.05 323	46.70 306	69.42 342	62.18 402	47.55 367	58.88 352	71.96 403	34.49 332	69.16 371	42.99 351	63.60 374	78.07 335
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 376	53.89 382	59.21 379	57.80 453	27.47 458	57.75 426	74.32 282	47.38 369	50.90 420	70.00 419	28.45 399	70.30 367	40.44 368	57.92 409	79.87 314

ITE_SJBPF				62.09 358	66.16 417	44.55 330		64.32 378	47.36 370	55.31 387	80.34 283	19.27 440	62.68 409	36.29 401	62.39 385	79.04 326

KD-MVS_2432*1600		53.45 386	51.50 395	59.30 376	62.82 432	37.14 400	55.33 434	71.79 317	47.34 371	55.09 390	70.52 414	21.91 435	70.45 364	35.72 404	42.97 450	70.31 426

miper_refine_blended		53.45 386	51.50 395	59.30 376	62.82 432	37.14 400	55.33 434	71.79 317	47.34 371	55.09 390	70.52 414	21.91 435	70.45 364	35.72 404	42.97 450	70.31 426

OurMVSNet-221017-0		61.37 323	58.63 338	69.61 264	72.05 343	48.06 291	73.93 268	72.51 309	47.23 373	54.74 394	80.92 273	21.49 438	81.24 218	48.57 298	56.22 417	79.53 320

tpmrst		58.24 349	58.70 337	56.84 396	66.97 409	34.32 426	69.57 341	61.14 407	47.17 374	58.58 358	71.60 406	41.28 260	60.41 416	49.20 292	62.84 381	75.78 366

tt0320		58.59 345	56.81 355	63.92 344	75.46 265	41.32 362	68.63 348	64.06 383	47.05 375	56.19 379	74.19 385	30.34 378	71.36 357	39.92 373	55.45 419	79.09 324

PVSNet	50.76 19	58.40 347	57.39 348	61.42 363	75.53 263	44.04 335	61.43 404	63.45 389	47.04 376	56.91 371	73.61 391	27.00 413	64.76 400	39.12 378	72.40 270	75.47 370

WB-MVSnew		59.66 338	59.69 327	59.56 373	75.19 272	35.78 417	69.34 343	64.28 379	46.88 377	61.76 319	75.79 368	40.61 268	65.20 398	32.16 419	71.21 285	77.70 342

UWE-MVS-28		52.25 393	52.35 391	51.93 427	66.99 408	22.79 470	63.48 393	48.31 451	46.78 378	52.73 413	76.11 362	27.78 405	57.82 431	20.58 460	68.41 336	75.17 372

FMVSNet5		55.86 370	54.93 370	58.66 384	71.05 364	36.35 409	64.18 388	62.48 397	46.76 379	50.66 424	74.73 381	25.80 421	64.04 402	33.11 415	65.57 358	75.59 368

jason		69.65 173	68.39 185	73.43 163	78.27 181	56.88 108	77.12 187	73.71 295	46.53 380	69.34 167	83.22 215	43.37 228	79.18 263	64.77 145	79.20 149	84.23 199
jason: jason.
MS-PatchMatch		62.42 308	61.46 308	65.31 332	75.21 271	52.10 209	72.05 304	74.05 289	46.41 381	57.42 369	74.36 383	34.35 334	77.57 300	45.62 324	73.67 243	66.26 440

1112_ss		64.00 290	63.36 283	65.93 319	79.28 144	42.58 349	71.35 313	72.36 312	46.41 381	60.55 331	77.89 331	46.27 194	73.28 344	46.18 317	69.97 308	81.92 271

lupinMVS		69.57 177	68.28 190	73.44 162	78.76 160	57.15 104	76.57 204	73.29 301	46.19 383	69.49 162	82.18 243	43.99 224	79.23 262	64.66 146	79.37 140	83.93 210

testdata				64.66 336	81.52 98	52.93 186		65.29 371	46.09 384	73.88 85	87.46 93	38.08 297	66.26 393	53.31 259	78.48 170	74.78 381

UnsupCasMVSNet_eth		53.16 391	52.47 389	55.23 404	59.45 448	33.39 434	59.43 417	69.13 340	45.98 385	50.35 426	72.32 398	29.30 391	58.26 429	42.02 360	44.30 448	74.05 389

AllTest		57.08 358	54.65 372	64.39 339	71.44 355	49.03 270	69.92 337	67.30 351	45.97 386	47.16 435	79.77 294	17.47 441	67.56 384	33.65 411	59.16 405	76.57 358

TestCases				64.39 339	71.44 355	49.03 270		67.30 351	45.97 386	47.16 435	79.77 294	17.47 441	67.56 384	33.65 411	59.16 405	76.57 358

WTY-MVS		59.75 337	60.39 323	57.85 392	72.32 339	37.83 393	61.05 410	64.18 380	45.95 388	61.91 316	79.11 310	47.01 186	60.88 414	42.50 355	69.49 320	74.83 379

IterMVS-SCA-FT		62.49 306	61.52 307	65.40 329	71.99 345	50.80 231	71.15 319	69.63 333	45.71 389	60.61 330	77.93 328	37.45 301	65.99 395	55.67 237	63.50 376	79.42 321

WB-MVS		43.26 415	43.41 415	42.83 442	63.32 431	10.32 480	58.17 422	45.20 458	45.42 390	40.44 453	67.26 435	34.01 339	58.98 424	11.96 471	24.88 465	59.20 446

旧先验2							76.08 216		45.32 391	76.55 47			65.56 397	58.75 214

OpenMVS_ROB	52.78 18	60.03 333	58.14 343	65.69 324	70.47 372	44.82 324	75.33 232	70.86 323	45.04 392	56.06 380	76.00 364	26.89 415	79.65 253	35.36 406	67.29 345	72.60 398

TinyColmap		54.14 381	51.72 393	61.40 364	66.84 411	41.97 354	66.52 363	68.51 344	44.81 393	42.69 449	75.77 369	11.66 457	72.94 345	31.96 421	56.77 415	69.27 434

MDTV_nov1_ep13			57.00 351		72.73 328	38.26 389	65.02 381	64.73 376	44.74 394	55.46 384	72.48 397	32.61 364	70.47 363	37.47 386	67.75 341

新几何1				70.76 242	85.66 42	61.13 30		66.43 361	44.68 395	70.29 147	86.64 119	41.29 259	75.23 335	49.72 287	81.75 110	75.93 364

Patchmtry		57.16 357	56.47 358	59.23 378	69.17 395	34.58 424	62.98 396	63.15 392	44.53 396	56.83 372	74.84 379	35.83 319	68.71 374	40.03 370	60.91 394	74.39 386

ppachtmachnet_test		58.06 352	55.38 368	66.10 316	69.51 389	48.99 273	68.01 353	66.13 365	44.50 397	54.05 402	70.74 412	32.09 370	72.34 351	36.68 396	56.71 416	76.99 356

PatchT		53.17 390	53.44 387	52.33 424	68.29 402	25.34 466	58.21 421	54.41 434	44.46 398	54.56 397	69.05 426	33.32 347	60.94 413	36.93 392	61.76 391	70.73 424

EPMVS		53.96 382	53.69 385	54.79 407	66.12 418	31.96 442	62.34 401	49.05 447	44.42 399	55.54 383	71.33 409	30.22 380	56.70 435	41.65 363	62.54 384	75.71 367

pmmvs4		61.48 322	59.39 329	67.76 290	71.57 351	53.86 159	71.42 312	65.34 370	44.20 400	59.46 345	77.92 329	35.90 318	74.71 337	43.87 341	64.87 363	74.71 383

dp		51.89 395	51.60 394	52.77 421	68.44 401	32.45 440	62.36 400	54.57 433	44.16 401	49.31 430	67.91 428	28.87 395	56.61 437	33.89 410	54.89 422	69.24 435

PatchMatch-RL		56.25 367	54.55 374	61.32 366	77.06 230	56.07 119	65.57 371	54.10 436	44.13 402	53.49 410	71.27 410	25.20 425	66.78 389	36.52 399	63.66 373	61.12 444

our_test_3		56.49 363	54.42 375	62.68 355	69.51 389	45.48 320	66.08 366	61.49 405	44.11 403	50.73 423	69.60 423	33.05 349	68.15 376	38.38 382	56.86 413	74.40 385

USDC		56.35 366	54.24 379	62.69 354	64.74 424	40.31 370	65.05 380	73.83 293	43.93 404	47.58 433	77.71 337	15.36 450	75.05 336	38.19 384	61.81 390	72.70 397

PM-MVS		52.33 392	50.19 401	58.75 383	62.10 437	45.14 323	65.75 368	40.38 465	43.60 405	53.52 408	72.65 396	9.16 465	65.87 396	50.41 281	54.18 425	65.24 442

pmmvs-eth3d		58.81 344	56.31 361	66.30 310	67.61 405	52.42 205	72.30 300	64.76 375	43.55 406	54.94 392	74.19 385	28.95 393	72.60 347	43.31 346	57.21 412	73.88 391

SSC-MVS		41.96 420	41.99 419	41.90 443	62.46 436	9.28 482	57.41 429	44.32 461	43.38 407	38.30 459	66.45 438	32.67 361	58.42 428	10.98 472	21.91 468	57.99 450

new-patchmatchnet		47.56 409	47.73 409	47.06 433	58.81 451	9.37 481	48.78 453	59.21 413	43.28 408	44.22 445	68.66 427	25.67 422	57.20 434	31.57 429	49.35 441	74.62 384

Test_1112_low_res		62.32 309	61.77 304	64.00 343	79.08 153	39.53 379	68.17 351	70.17 327	43.25 409	59.03 351	79.90 291	44.08 221	71.24 359	43.79 342	68.42 335	81.25 284

RPMNet		61.53 320	58.42 339	70.86 240	69.96 382	52.07 210	65.31 378	81.36 130	43.20 410	59.36 346	70.15 418	35.37 322	85.47 118	36.42 400	64.65 365	75.06 374

tpm2		62.07 313	60.10 325	67.99 288	72.79 327	43.86 336	71.05 322	66.85 358	43.14 411	62.77 300	75.39 376	38.32 293	80.80 233	41.69 361	68.88 329	79.32 322

JIA-IIPM		51.56 396	47.68 410	63.21 350	64.61 425	50.73 232	47.71 455	58.77 415	42.90 412	48.46 432	51.72 458	24.97 426	70.24 368	36.06 403	53.89 427	68.64 436

1314		64.61 282	63.21 287	68.80 279	71.87 347	47.46 300	73.95 266	78.39 210	42.88 413	59.97 337	76.60 356	38.11 296	79.39 259	54.84 244	72.32 272	79.55 319

HyFIR lowres test		65.67 266	63.01 289	73.67 149	79.97 131	55.65 129	69.07 345	75.52 259	42.68 414	63.53 287	77.95 327	40.43 269	81.64 206	46.01 319	71.91 277	83.73 222

CR-MVSNet		59.91 334	57.90 346	65.96 318	69.96 382	52.07 210	65.31 378	63.15 392	42.48 415	59.36 346	74.84 379	35.83 319	70.75 362	45.50 327	64.65 365	75.06 374

test222					83.14 76	58.68 81	72.57 296	63.45 389	41.78 416	67.56 210	86.12 141	37.13 308			78.73 163	74.98 377

TDRefinement		53.44 388	50.72 398	61.60 361	64.31 427	46.96 304	70.89 323	65.27 372	41.78 416	44.61 444	77.98 326	11.52 459	66.36 392	28.57 443	51.59 434	71.49 416

sss		56.17 368	56.57 357	54.96 405	66.93 410	36.32 411	57.94 423	61.69 404	41.67 418	58.64 356	75.32 377	38.72 288	56.25 439	42.04 359	66.19 354	72.31 407

PVSNet_0	43.31 20	47.46 410	45.64 413	52.92 420	67.60 406	44.65 327	54.06 439	54.64 432	41.59 419	46.15 440	58.75 451	30.99 374	58.66 426	32.18 418	24.81 466	55.46 454

MVS		67.37 234	66.33 240	70.51 249	75.46 265	50.94 226	73.95 266	81.85 118	41.57 420	62.54 307	78.57 319	47.98 164	85.47 118	52.97 261	82.05 103	75.14 373

Anonymous20240521		55.30 374	54.41 376	57.96 391	60.92 446	41.73 357	71.09 321	71.06 322	41.18 421	48.65 431	73.31 393	16.93 444	59.25 422	42.54 354	64.01 370	72.90 395

Anonymous20231206		55.10 379	55.30 369	54.48 408	69.81 387	33.94 430	62.91 397	62.13 403	41.08 422	55.18 389	75.65 370	32.75 357	56.59 438	30.32 436	67.86 339	72.91 394

MDA-MVSNet_test_wron		50.71 401	48.95 403	56.00 401	61.17 441	41.84 355	51.90 445	56.45 424	40.96 423	44.79 443	67.84 429	30.04 384	55.07 446	36.71 395	50.69 437	71.11 422

YYNet1		50.73 400	48.96 402	56.03 400	61.10 442	41.78 356	51.94 444	56.44 425	40.94 424	44.84 442	67.80 430	30.08 383	55.08 445	36.77 393	50.71 436	71.22 419

dongtai		34.52 430	34.94 430	33.26 452	61.06 443	16.00 477	52.79 443	23.78 478	40.71 425	39.33 457	48.65 466	16.91 445	48.34 458	12.18 470	19.05 470	35.44 469

CHOSEN 1792x2688		65.08 276	62.84 291	71.82 202	81.49 100	56.26 115	66.32 365	74.20 288	40.53 426	63.16 293	78.65 316	41.30 258	77.80 295	45.80 321	74.09 235	81.40 279

pmmvs5		56.47 364	55.68 366	58.86 382	61.41 440	36.71 406	66.37 364	62.75 394	40.38 427	53.70 404	76.62 353	34.56 330	67.05 387	40.02 371	65.27 359	72.83 396

test_vis1_n_1920		58.86 343	59.06 333	58.25 386	63.76 428	43.14 344	67.49 358	66.36 362	40.22 428	65.89 246	71.95 404	31.04 373	59.75 420	59.94 198	64.90 362	71.85 411

MDTV_nov1_ep13_2view						25.89 464	61.22 407		40.10 429	51.10 418		32.97 352		38.49 381		78.61 331

tpm cat1		59.25 342	56.95 352	66.15 314	72.19 341	46.96 304	68.09 352	65.76 366	40.03 430	57.81 365	70.56 413	38.32 293	74.51 338	38.26 383	61.50 392	77.00 354

test-mter		56.42 365	55.82 365	58.22 387	68.57 398	44.80 325	65.46 374	57.92 418	39.94 431	55.44 385	69.82 420	21.92 434	57.44 432	49.66 288	73.62 245	72.41 404

UnsupCasMVSNet_bld		50.07 403	48.87 404	53.66 413	60.97 445	33.67 432	57.62 427	64.56 377	39.47 432	47.38 434	64.02 446	27.47 407	59.32 421	34.69 408	43.68 449	67.98 438

TESTMET0.1,1		55.28 375	54.90 371	56.42 398	66.56 413	43.67 338	65.46 374	56.27 428	39.18 433	53.83 403	67.44 432	24.21 429	55.46 443	48.04 303	73.11 259	70.13 428

mamv4		56.85 360	58.00 345	53.43 416	72.46 336	54.47 149	57.56 428	54.74 431	38.81 434	57.42 369	79.45 304	47.57 173	38.70 469	60.88 190	53.07 429	67.11 439

ADS-MVSNet2		51.33 398	48.76 405	59.07 381	66.02 419	44.60 328	50.90 447	59.76 411	36.90 435	50.74 421	66.18 440	26.38 416	63.11 407	27.17 447	54.76 423	69.50 432

ADS-MVSNet		48.48 407	47.77 408	50.63 429	66.02 419	29.92 449	50.90 447	50.87 445	36.90 435	50.74 421	66.18 440	26.38 416	52.47 452	27.17 447	54.76 423	69.50 432

RPSCF		55.80 371	54.22 380	60.53 370	65.13 423	42.91 348	64.30 386	57.62 420	36.84 437	58.05 364	82.28 240	28.01 402	56.24 440	37.14 390	58.61 407	82.44 262

test_cas_vis1_n_1920		56.91 359	56.71 356	57.51 395	59.13 450	45.40 321	63.58 392	61.29 406	36.24 438	67.14 219	71.85 405	29.89 385	56.69 436	57.65 220	63.58 375	70.46 425

Patchmatch-test		49.08 405	48.28 407	51.50 428	64.40 426	30.85 447	45.68 459	48.46 450	35.60 439	46.10 441	72.10 401	34.47 333	46.37 461	27.08 449	60.65 398	77.27 349

CHOSEN 280x420		47.83 408	46.36 412	52.24 426	67.37 407	49.78 252	38.91 467	43.11 463	35.00 440	43.27 448	63.30 447	28.95 393	49.19 457	36.53 398	60.80 396	57.76 451

N_pmnet		39.35 425	40.28 422	36.54 449	63.76 428	1.62 486	49.37 452	0.76 485	34.62 441	43.61 447	66.38 439	26.25 418	42.57 465	26.02 452	51.77 433	65.44 441

kuosan		29.62 437	30.82 436	26.02 457	52.99 456	16.22 476	51.09 446	22.71 479	33.91 442	33.99 461	40.85 467	15.89 448	33.11 474	7.59 478	18.37 471	28.72 471

PMMVS		53.96 382	53.26 388	56.04 399	62.60 435	50.92 228	61.17 408	56.09 429	32.81 443	53.51 409	66.84 437	34.04 337	59.93 419	44.14 337	68.18 337	57.27 452

CMPMVS	42.80 21	57.81 354	55.97 363	63.32 348	60.98 444	47.38 301	64.66 383	69.50 336	32.06 444	46.83 437	77.80 333	29.50 389	71.36 357	48.68 296	73.75 241	71.21 420
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 411	42.95 416	53.39 418	52.33 460	29.15 451	57.77 424	48.20 452	31.81 445	49.86 428	77.21 342	8.69 466	59.16 423	27.31 446	33.40 462	71.84 412

CVMVSNet		59.63 339	59.14 331	61.08 369	74.47 293	38.84 384	75.20 237	68.74 343	31.15 446	58.24 360	76.51 357	32.39 367	68.58 375	49.77 285	65.84 356	75.81 365

FPMVS		42.18 419	41.11 421	45.39 435	58.03 452	41.01 366	49.50 451	53.81 437	30.07 447	33.71 462	64.03 444	11.69 456	52.08 455	14.01 466	55.11 421	43.09 463

EU-MVSNet		55.61 373	54.41 376	59.19 380	65.41 421	33.42 433	72.44 298	71.91 316	28.81 448	51.27 417	73.87 389	24.76 427	69.08 372	43.04 350	58.20 408	75.06 374

test_vis1_n		49.89 404	48.69 406	53.50 415	53.97 454	37.38 398	61.53 403	47.33 455	28.54 449	59.62 344	67.10 436	13.52 452	52.27 453	49.07 293	57.52 410	70.84 423

test_fmvs1_n		51.37 397	50.35 400	54.42 410	52.85 457	37.71 395	61.16 409	51.93 438	28.15 450	63.81 285	69.73 422	13.72 451	53.95 447	51.16 276	60.65 398	71.59 414

LF4IMVS		42.95 416	42.26 418	45.04 436	48.30 465	32.50 439	54.80 436	48.49 449	28.03 451	40.51 452	70.16 417	9.24 464	43.89 464	31.63 427	49.18 442	58.72 448

test_fmvs1		51.32 399	50.48 399	53.81 412	53.57 455	37.51 397	60.63 413	51.16 441	28.02 452	63.62 286	69.23 425	16.41 446	53.93 448	51.01 277	60.70 397	69.99 429

MVS-HIRNet		45.52 412	44.48 414	48.65 432	68.49 400	34.05 429	59.41 418	44.50 460	27.03 453	37.96 460	50.47 462	26.16 419	64.10 401	26.74 450	59.52 403	47.82 461

PMVS	28.69 22	36.22 428	33.29 433	45.02 437	36.82 477	35.98 414	54.68 437	48.74 448	26.31 454	21.02 470	51.61 459	2.88 478	60.10 418	9.99 475	47.58 443	38.99 468
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 413	41.95 420	53.86 411	52.58 459	43.55 339	62.11 402	46.90 457	26.05 455	40.63 451	60.19 450	11.08 462	57.91 430	31.83 426	46.15 445	60.11 445

test_fmvs2		48.69 406	47.49 411	52.29 425	48.63 464	33.06 437	57.76 425	48.05 453	25.71 456	59.76 342	69.60 423	11.57 458	52.23 454	49.45 291	56.86 413	71.58 415

PMMVS2		27.40 438	25.91 441	31.87 454	39.46 476	6.57 483	31.17 470	28.52 474	23.96 457	20.45 471	48.94 465	4.20 474	37.94 470	16.51 463	19.97 469	51.09 456

MVStest1		42.65 417	39.29 424	52.71 422	47.26 467	34.58 424	54.41 438	50.84 446	23.35 458	39.31 458	74.08 388	12.57 454	55.09 444	23.32 455	28.47 464	68.47 437

Gipuma		34.77 429	31.91 434	43.33 440	62.05 438	37.87 391	20.39 472	67.03 356	23.23 459	18.41 472	25.84 472	4.24 472	62.73 408	14.71 465	51.32 435	29.38 470
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 422	39.45 423	47.03 434	46.65 468	37.86 392	47.76 454	38.65 466	23.10 460	44.21 446	51.22 460	11.20 461	44.08 463	39.27 377	53.02 430	59.14 447

new_pmnet		34.13 431	34.29 432	33.64 451	52.63 458	18.23 475	44.43 462	33.90 471	22.81 461	30.89 464	53.18 456	10.48 463	35.72 473	20.77 459	39.51 454	46.98 462

mvsany_test1		39.38 424	38.16 427	43.02 441	49.05 462	34.28 427	44.16 463	25.94 476	22.74 462	46.57 439	62.21 449	23.85 430	41.16 468	33.01 416	35.91 458	53.63 455

LCM-MVSNet		40.30 423	35.88 429	53.57 414	42.24 470	29.15 451	45.21 461	60.53 410	22.23 463	28.02 465	50.98 461	3.72 475	61.78 412	31.22 432	38.76 456	69.78 431

test_fmvs3		44.30 414	42.55 417	49.55 431	42.83 469	27.15 461	53.03 441	44.93 459	22.03 464	53.69 406	64.94 443	4.21 473	49.63 456	47.47 304	49.82 439	71.88 410

APD_test1		37.39 427	34.94 430	44.72 439	48.88 463	33.19 436	52.95 442	44.00 462	19.49 465	27.28 466	58.59 452	3.18 477	52.84 451	18.92 461	41.17 453	48.14 460

mvsany_test3		32.62 432	30.57 437	38.77 447	36.16 478	24.20 468	38.10 468	20.63 480	19.14 466	40.36 454	57.43 453	5.06 470	36.63 472	29.59 440	28.66 463	55.49 453

E-PMN		23.77 439	22.73 443	26.90 455	42.02 471	20.67 472	42.66 464	35.70 469	17.43 467	10.28 477	25.05 473	6.42 468	42.39 466	10.28 474	14.71 473	17.63 472

EMVS		22.97 440	21.84 444	26.36 456	40.20 474	19.53 474	41.95 465	34.64 470	17.09 468	9.73 478	22.83 474	7.29 467	42.22 467	9.18 476	13.66 474	17.32 473

test_vis3_rt		32.09 433	30.20 438	37.76 448	35.36 479	27.48 457	40.60 466	28.29 475	16.69 469	32.52 463	40.53 468	1.96 479	37.40 471	33.64 413	42.21 452	48.39 458

test_f		31.86 434	31.05 435	34.28 450	32.33 481	21.86 471	32.34 469	30.46 473	16.02 470	39.78 456	55.45 455	4.80 471	32.36 475	30.61 433	37.66 457	48.64 457

DSMNet-mixed		39.30 426	38.72 425	41.03 444	51.22 461	19.66 473	45.53 460	31.35 472	15.83 471	39.80 455	67.42 434	22.19 433	45.13 462	22.43 456	52.69 431	58.31 449

testf1		31.46 435	28.89 439	39.16 445	41.99 472	28.78 453	46.45 457	37.56 467	14.28 472	21.10 468	48.96 463	1.48 481	47.11 459	13.63 467	34.56 459	41.60 464

APD_test2		31.46 435	28.89 439	39.16 445	41.99 472	28.78 453	46.45 457	37.56 467	14.28 472	21.10 468	48.96 463	1.48 481	47.11 459	13.63 467	34.56 459	41.60 464

MVE	17.77 23	21.41 441	17.77 446	32.34 453	34.34 480	25.44 465	16.11 473	24.11 477	11.19 474	13.22 474	31.92 470	1.58 480	30.95 476	10.47 473	17.03 472	40.62 467
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 460	17.97 482	10.91 479		10.60 483	7.46 475	11.07 476	28.36 471	3.28 476	11.29 479	8.01 477	9.74 478	13.89 474

wuyk23d		13.32 444	12.52 447	15.71 459	47.54 466	26.27 463	31.06 471	1.98 484	4.93 476	5.18 479	1.94 479	0.45 483	18.54 478	6.81 479	12.83 475	2.33 476

test_method		19.68 442	18.10 445	24.41 458	13.68 483	3.11 485	12.06 475	42.37 464	2.00 477	11.97 475	36.38 469	5.77 469	29.35 477	15.06 464	23.65 467	40.76 466

tmp_tt		9.43 445	11.14 448	4.30 461	2.38 484	4.40 484	13.62 474	16.08 482	0.39 478	15.89 473	13.06 475	15.80 449	5.54 480	12.63 469	10.46 477	2.95 475

EGC-MVSNET		42.47 418	38.48 426	54.46 409	74.33 298	48.73 279	70.33 332	51.10 442	0.03 479	0.18 480	67.78 431	13.28 453	66.49 391	18.91 462	50.36 438	48.15 459

testmvs		4.52 448	6.03 451	0.01 463	0.01 485	0.00 488	53.86 440	0.00 486	0.01 480	0.04 481	0.27 480	0.00 485	0.00 481	0.04 480	0.00 479	0.03 478

test123		4.73 447	6.30 450	0.02 462	0.01 485	0.01 487	56.36 432	0.00 486	0.01 480	0.04 481	0.21 481	0.01 484	0.00 481	0.03 481	0.00 479	0.04 477

mmdepth		0.00 450	0.00 453	0.00 464	0.00 487	0.00 488	0.00 476	0.00 486	0.00 482	0.00 483	0.00 482	0.00 485	0.00 481	0.00 482	0.00 479	0.00 479

monomultidepth		0.00 450	0.00 453	0.00 464	0.00 487	0.00 488	0.00 476	0.00 486	0.00 482	0.00 483	0.00 482	0.00 485	0.00 481	0.00 482	0.00 479	0.00 479

test_blank		0.00 450	0.00 453	0.00 464	0.00 487	0.00 488	0.00 476	0.00 486	0.00 482	0.00 483	0.00 482	0.00 485	0.00 481	0.00 482	0.00 479	0.00 479

uanet_test		0.00 450	0.00 453	0.00 464	0.00 487	0.00 488	0.00 476	0.00 486	0.00 482	0.00 483	0.00 482	0.00 485	0.00 481	0.00 482	0.00 479	0.00 479

DCPMVS		0.00 450	0.00 453	0.00 464	0.00 487	0.00 488	0.00 476	0.00 486	0.00 482	0.00 483	0.00 482	0.00 485	0.00 481	0.00 482	0.00 479	0.00 479

cdsmvs_eth3d_5k		17.50 443	23.34 442	0.00 464	0.00 487	0.00 488	0.00 476	78.63 193	0.00 482	0.00 483	82.18 243	49.25 151	0.00 481	0.00 482	0.00 479	0.00 479

pcd_1.5k_mvsjas		3.92 449	5.23 452	0.00 464	0.00 487	0.00 488	0.00 476	0.00 486	0.00 482	0.00 483	0.00 482	47.05 183	0.00 481	0.00 482	0.00 479	0.00 479

sosnet-low-res		0.00 450	0.00 453	0.00 464	0.00 487	0.00 488	0.00 476	0.00 486	0.00 482	0.00 483	0.00 482	0.00 485	0.00 481	0.00 482	0.00 479	0.00 479

sosnet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 488	0.00 476	0.00 486	0.00 482	0.00 483	0.00 482	0.00 485	0.00 481	0.00 482	0.00 479	0.00 479

uncertanet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 488	0.00 476	0.00 486	0.00 482	0.00 483	0.00 482	0.00 485	0.00 481	0.00 482	0.00 479	0.00 479

Regformer		0.00 450	0.00 453	0.00 464	0.00 487	0.00 488	0.00 476	0.00 486	0.00 482	0.00 483	0.00 482	0.00 485	0.00 481	0.00 482	0.00 479	0.00 479

ab-mvs-re		6.49 446	8.65 449	0.00 464	0.00 487	0.00 488	0.00 476	0.00 486	0.00 482	0.00 483	77.89 331	0.00 485	0.00 481	0.00 482	0.00 479	0.00 479

uanet		0.00 450	0.00 453	0.00 464	0.00 487	0.00 488	0.00 476	0.00 486	0.00 482	0.00 483	0.00 482	0.00 485	0.00 481	0.00 482	0.00 479	0.00 479

TestfortrainingZip							86.84 11

WAC-MVS						27.31 459								27.77 444

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 29					90.96 1	79.31 10	90.65 8	87.85 46

No_MVS				79.95 4	87.24 14	61.04 31		85.62 29					90.96 1	79.31 10	90.65 8	87.85 46

eth-test2					0.00 487

eth-test					0.00 487

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 51	67.01 1	90.33 12	73.16 71	91.15 4	88.23 31

test_0728_SECOND				79.19 16	87.82 3	59.11 71	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 57

GSMVS																78.05 336

test_part2					87.58 9	60.47 42				83.42 15

sam_mvs1												34.74 329				78.05 336

sam_mvs												33.43 346

ambc				65.13 334	63.72 430	37.07 402	47.66 456	78.78 189		54.37 400	71.42 407	11.24 460	80.94 228	45.64 323	53.85 428	77.38 347

MTGPA								80.97 148

test_post1							68.67 347				3.64 477	32.39 367	69.49 370	44.17 335

test_post											3.55 478	33.90 340	66.52 390

patchmatchnet-post											64.03 444	34.50 331	74.27 340

GG-mvs-BLEND				62.34 356	71.36 359	37.04 403	69.20 344	57.33 423		54.73 395	65.48 442	30.37 377	77.82 294	34.82 407	74.93 227	72.17 408

MTMP							86.03 23	17.08 481

test9_res														75.28 54	88.31 36	83.81 216

agg_prior2														73.09 72	87.93 44	84.33 194

agg_prior					85.04 54	59.96 50		81.04 146		74.68 72			84.04 146

test_prior4						62.51 14	82.08 87

test_prior				76.69 65	84.20 65	57.27 98		84.88 44					86.43 88			86.38 105

新几何2							76.12 214

旧先验1					83.04 78	53.15 181		67.52 350			87.85 86	44.08 221			80.76 119	78.03 339

原ACMM2							79.02 128

testdata2													72.18 354	46.95 313

segment_acmp												54.23 68

test12				77.76 50	84.52 62	58.41 83		83.36 84		72.93 110		54.61 65	88.05 43		88.12 38	86.81 88

plane_prior7					81.41 101	55.96 121

plane_prior6					81.20 108	56.24 116						45.26 208

plane_prior5								84.01 57					87.21 63	68.16 106	80.58 123	84.65 185

plane_prior4											86.10 142

plane_prior1					81.27 106

n2								0.00 486

nn								0.00 486

door-mid								47.19 456

lessismore_v0				69.91 259	71.42 357	47.80 294		50.90 444		50.39 425	75.56 371	27.43 409	81.33 215	45.91 320	34.10 461	80.59 298

test11								83.47 79

door								47.60 454

HQP5-MVS						54.94 143

BP-MVS														67.04 122

HQP4-MVS										67.85 199			86.93 71			84.32 195

HQP3-MVS								83.90 62							80.35 127

HQP2-MVS												45.46 202

NP-MVS					80.98 111	56.05 120					85.54 162

ACMMP++_ref															74.07 236

ACMMP++															72.16 275

Test By Simon												48.33 162

High-resolution multi-view benchmark