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	72.39 312	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	74.20 262	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	75.51 261	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	73.75 274	76.07 249	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	6.49 477	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	71.08 321	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	61.17 368	79.29 142	30.31 449	64.09 391	63.49 389	63.50 44	62.84 298	82.22 242	32.35 369	69.02 374	40.01 373	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	36.56 408	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	34.79 421	76.43 207	79.38 175	62.55 66	61.66 320	83.83 199	45.60 198	79.15 267	41.64 365	60.88 396	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	36.33 411	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	36.30 413	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	65.74 324	79.18 149	36.80 406	72.17 304	72.83 308	62.04 77	67.79 206	85.83 152	48.88 157	76.60 325	51.30 275	72.97 261	83.81 216

WR-MVS_H		67.02 244	66.92 225	67.33 297	77.95 194	37.75 395	77.57 168	82.11 115	62.03 78	62.65 304	82.48 235	50.57 133	79.46 257	42.91 353	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	73.95 293	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	75.60 257	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	38.14 391	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	34.73 424	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							72.65 293		60.50 109

UGNet		68.81 198	67.39 210	73.06 171	78.33 179	54.47 149	79.77 117	75.40 264	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	76.74 242	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	73.96 266	75.25 267	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	74.18 263	75.59 258	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	70.53 328	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	74.04 264	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		60.57 328	61.39 309	58.12 391	74.29 300	32.63 439	59.52 416	65.53 370	59.90 129	62.45 310	79.75 296	41.96 244	63.90 405	39.47 377	69.65 319	77.84 342

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	65.50 328	75.65 258	37.70 397	75.42 231	74.65 280	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	78.30 333

Effi-MVS+-dtu		69.64 174	67.53 205	75.95 78	76.10 252	62.29 15	80.20 110	76.06 250	59.83 134	65.26 260	77.09 345	41.56 255	84.02 148	60.60 193	71.09 290	81.53 276

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	76.85 238	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	76.51 358	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	75.27 266	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	81.06 291

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	67.63 356	69.59 335	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		60.66 327	61.04 317	59.51 375	77.32 219	31.58 444	63.11 396	63.87 385	59.00 149	60.90 329	78.26 322	32.69 360	66.15 395	36.10 403	78.13 176	80.81 296

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	77.03 236	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	38.56 387	74.66 252	75.08 275	58.90 152	61.79 318	82.63 224	51.18 124	78.07 287	43.63 345	55.87 419	80.99 293

Anonymous202405211		66.84 248	65.99 247	69.40 269	80.19 126	42.21 353	71.11 321	71.31 320	58.80 153	67.90 197	86.39 132	29.83 387	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	34.19 429	74.43 258	51.92 440	58.72 154	66.75 226	88.05 80	25.99 421	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	36.29 414	74.79 249	66.93 358	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	34.76 423	73.89 271	65.65 368	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	63.46 348	74.58 291	31.48 445	61.42 406	58.14 418	58.71 156	53.02 413	79.55 301	43.07 232	76.80 319	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	76.85 238	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	67.33 361	68.00 349	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	74.26 261	76.29 245	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		60.47 331	57.11 350	70.56 247	73.74 311	48.22 287	75.10 241	62.55 397	58.27 166	53.62 408	76.31 362	27.81 405	81.59 208	47.42 305	39.18 456	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	70.11 329	58.08 168	67.83 204	84.68 175	41.96 244	76.34 330	65.62 138	77.54 185	79.30 324

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	65.32 378	79.10 178	58.02 171	65.08 264	82.55 231	47.83 167	73.40 344	63.92 153	73.92 238	81.41 278

sd_testset		64.46 284	64.45 267	64.51 339	77.13 226	42.25 352	62.67 399	72.11 315	58.02 171	65.08 264	82.55 231	41.22 263	69.88 370	47.32 307	73.92 238	81.41 278

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	74.21 288	57.81 179	63.03 295	76.62 354	38.33 292	77.31 305	54.22 250	60.59 401	78.64 331

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	73.16 287	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	73.22 285	73.18 303	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	73.22 285	73.18 303	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	77.12 234	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	76.88 237	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	27.68 407	84.17 143	45.54 325	69.78 313	79.90 313

diffmvs		70.69 145	70.43 138	71.46 216	69.45 391	48.95 276	72.93 290	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	76.91 358

fmvsm_s_conf0.5_n_10		74.11 75	73.98 74	74.48 118	74.61 289	52.86 191	78.10 154	77.06 235	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	38.66 386	72.65 293	69.11 342	57.07 192	62.45 310	81.03 270	37.01 311	79.17 264	31.84 424	73.25 256	79.83 316

fmvsm_s_conf0.5_n_7		69.54 178	69.67 153	69.15 276	73.47 316	51.41 221	70.35 332	73.34 299	57.05 193	68.41 181	85.83 152	49.86 140	72.84 347	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	38.87 384	72.35 300	69.11 342	56.98 195	62.37 313	80.96 272	37.01 311	79.00 275	31.43 431	73.05 260	81.36 281

V42		68.65 202	67.35 213	72.56 183	68.93 397	50.18 245	72.90 291	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	70.48 329	74.40 282	56.69 198	64.70 273	76.77 351	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	74.96 276	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	71.96 308	69.44 338	56.63 201	62.61 305	79.83 292	37.18 305	79.17 264	31.84 424	73.25 256	79.83 316

thres400		63.31 295	62.18 300	66.72 300	76.85 238	39.62 377	71.96 308	69.44 338	56.63 201	62.61 305	79.83 292	37.18 305	79.17 264	31.84 424	73.25 256	81.36 281

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	74.28 286	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	72.42 311	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	65.25 334	77.87 195	38.53 388	68.34 350	66.31 364	56.37 212	63.15 294	77.58 339	28.47 399	76.18 333	37.04 392	76.65 204	81.05 292

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	63.14 352	74.75 284	31.04 447	71.16 319	63.64 388	56.32 213	59.80 341	84.99 168	44.51 217	75.46 335	39.12 379	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	77.02 354

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	63.62 347	76.29 249	36.36 409	71.78 311	67.29 354	56.05 220	64.23 281	82.95 219	47.11 182	74.41 340	47.30 308	61.85 390	80.10 310

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	73.68 275	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	27.12 412	80.94 228	42.90 354	71.58 282	77.25 352
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	73.09 289	75.13 271	55.69 227	58.48 359	73.73 391	32.86 353	86.32 92	50.63 280	70.11 305	81.10 290
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		61.53 321	60.94 319	63.30 350	68.95 396	36.93 405	67.60 357	72.80 309	55.67 228	59.95 338	76.63 353	45.01 213	72.22 354	39.74 376	62.09 388	80.74 298

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	65.34 332	75.38 268	39.99 373	69.60 341	69.29 340	55.64 230	61.87 317	76.99 346	37.07 310	78.96 276	31.28 432	73.28 255	77.06 353

guyue		68.10 218	67.23 221	70.71 245	73.67 313	49.27 268	73.65 276	76.04 251	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	72.62 295	75.63 256	55.53 232	62.35 314	83.18 217	47.45 176	76.47 328	49.06 294	66.54 351	82.24 265

testing11		62.81 303	61.90 303	65.54 326	78.38 174	40.76 369	67.59 358	66.78 360	55.48 233	60.13 333	77.11 344	31.67 372	76.79 320	45.53 326	74.45 231	79.06 326

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	80.30 305
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	73.28 283	74.01 291	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	81.35 283

XVG-OURS		68.76 201	67.37 211	72.90 175	74.32 299	57.22 99	70.09 336	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	64.99 336	74.81 282	39.01 383	71.22 317	73.72 295	55.22 241	60.21 332	80.09 290	41.26 261	76.98 315	30.02 438	68.09 338	78.97 329

cl____		67.18 239	66.26 244	69.94 257	70.20 377	45.74 315	73.30 280	76.83 240	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	73.29 282	76.83 240	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	61.68 361	72.89 326	39.78 375	75.85 224	65.62 369	55.09 244	54.56 397	79.36 306	37.59 300	67.02 389	39.80 375	76.95 198	78.25 334
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	73.85 272	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	73.98 265	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	73.30 280	67.79 350	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	30.16 382	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	30.16 382	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	80.83 295

fmvsm_s_conf0.1_n_2		69.64 174	69.01 168	71.52 214	71.66 349	51.04 224	73.39 279	67.14 356	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		60.40 332	59.12 333	64.27 342	69.59 388	48.99 273	70.67 326	70.06 330	54.96 254	62.78 299	73.26 396	27.00 414	67.66 382	58.44 217	45.29 448	76.16 363

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	80.81 296

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
FE-MVSNET1		61.60 320	60.18 325	65.86 323	68.07 403	39.14 382	74.38 260	77.13 233	54.83 257	54.43 399	76.83 350	30.70 375	76.87 317	43.24 349	61.97 389	81.57 275

reproduce_monomvs		62.56 305	61.20 315	66.62 304	70.62 369	44.30 331	70.13 335	73.13 306	54.78 258	61.13 326	76.37 361	25.63 424	75.63 334	58.75 214	60.29 402	79.93 312

XVG-OURS-SEG-HR		68.81 198	67.47 208	72.82 178	74.40 296	56.87 109	70.59 327	79.04 181	54.77 259	66.99 221	86.01 146	39.57 277	78.21 285	62.54 174	73.33 254	83.37 233

testing3		56.54 363	55.92 365	58.41 386	77.52 213	27.93 457	69.72 339	56.36 427	54.75 260	58.63 357	77.80 333	20.88 440	71.75 357	25.31 454	62.25 386	75.53 370

Anonymous20231211		69.28 186	68.47 181	71.73 206	80.28 121	47.18 303	79.98 112	82.37 111	54.61 261	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	58.80 337	70.20 253	75.80 255	47.22 302	75.59 228	69.68 333	54.61 261	54.11 402	79.26 308	27.07 413	82.96 172	43.27 347	49.79 441	80.41 303

test_0402		63.25 298	61.01 318	69.96 256	80.00 130	54.37 152	76.86 198	72.02 316	54.58 263	58.71 353	80.79 278	35.00 326	84.36 141	26.41 452	64.71 364	71.15 422

tttt0517		67.83 226	65.66 252	74.33 122	76.69 240	50.82 230	77.86 160	73.99 292	54.54 264	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	76.65 243	54.51 265	64.85 271	78.12 323	45.59 199	82.95 174	43.26 348	75.54 219	74.27 388

AUN-MVS		68.45 210	66.41 237	74.57 114	79.53 139	57.08 107	73.93 269	75.23 268	54.44 266	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	59.92 415	76.39 244	54.35 267	58.67 355	82.46 236	29.44 391	81.49 211	42.12 358	71.14 286	77.46 346
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	75.56 259	54.31 268	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	67.96 404	55.58 133	78.06 155	74.67 279	54.19 269	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	75.40 264	54.17 270	75.00 62	88.03 83	53.82 77	80.23 246	78.08 25	78.34 174	86.69 93

ETVMVS		59.51 342	58.81 335	61.58 363	77.46 215	34.87 420	64.94 383	59.35 413	54.06 271	61.08 327	76.67 352	29.54 388	71.87 356	32.16 420	74.07 236	78.01 341

ab-mvs		66.65 252	66.42 236	67.37 295	76.17 251	41.73 357	70.41 331	76.14 248	53.99 272	65.98 242	83.51 210	49.48 145	76.24 331	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	73.31 300	53.98 273	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	53.93 274	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	53.88 275	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	53.88 275	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	70.80 325	78.45 205	53.84 277	59.87 339	81.10 268	16.24 448	79.32 260	55.64 239	71.76 278	80.47 300

mamba_0408		67.78 227	65.42 256	74.85 103	78.65 164	53.46 171	50.83 450	79.09 179	53.75 278	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	63.68 346	78.65 164	53.46 171	50.83 450	79.09 179	53.75 278	68.14 189	83.83 199	41.79 250	53.03 451	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	73.41 278	78.28 212	53.73 280	64.98 270	78.33 321	40.62 267	79.07 270	58.88 211	67.50 343	80.26 306

FE-MVS		65.91 263	63.33 284	73.63 153	77.36 218	51.95 215	72.62 295	75.81 253	53.70 281	65.31 255	78.96 311	28.81 397	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	74.25 287	53.67 282	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	53.66 283	64.22 282	79.72 297	49.13 153	82.87 178	55.82 233	73.92 238	79.77 319

patch_mono-2		69.85 165	71.09 126	66.16 313	79.11 152	54.80 147	71.97 307	74.31 284	53.50 284	70.90 140	84.17 190	57.63 34	63.31 407	66.17 130	82.02 104	80.38 304

EG-PatchMatch MVS		64.71 279	62.87 290	70.22 251	77.68 203	53.48 170	77.99 156	78.82 186	53.37 285	56.03 381	77.41 341	24.75 429	84.04 146	46.37 316	73.42 253	73.14 394

SD_0403		63.07 301	63.49 281	61.82 360	75.16 273	31.14 446	71.89 310	73.47 297	53.34 286	58.22 361	81.81 255	45.17 210	73.86 343	37.43 388	74.87 228	80.45 301

DP-MVS		65.68 265	63.66 278	71.75 205	84.93 59	56.87 109	80.74 103	73.16 305	53.06 287	59.09 350	82.35 237	36.79 313	85.94 105	32.82 418	69.96 309	72.45 403

TR-MVS		66.59 255	65.07 263	71.17 232	79.18 149	49.63 261	73.48 277	75.20 270	52.95 288	67.90 197	80.33 284	39.81 275	83.68 154	43.20 350	73.56 248	80.20 307

ET-MVSNet_ETH3D		67.96 222	65.72 251	74.68 107	76.67 242	55.62 132	75.11 239	74.74 277	52.91 289	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	52.89 290	65.90 245	86.29 136	41.55 256	86.49 87	51.01 277	78.40 173	81.42 277

LuminaMVS		68.24 214	66.82 227	72.51 185	73.46 317	53.60 167	76.23 212	78.88 185	52.78 291	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	65.37 331	77.06 230	49.73 253	63.79 392	78.60 194	52.70 292	66.19 237	82.58 226	45.17 210	63.65 406	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	52.70 292	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	64.40 386	78.60 194	52.70 292	53.16 412	82.58 226	34.82 328	65.16 400	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	52.70 292	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	52.70 292	62.75 302	85.55 161	38.86 287	84.14 144	48.41 299	83.01 89	79.97 311

pmmvs6		63.69 292	62.82 292	66.27 311	70.63 368	39.27 381	73.13 288	75.47 263	52.69 297	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	71.17 318	68.24 348	52.63 298	59.82 340	76.91 348	37.32 304	72.36 350	52.80 262	63.19 379	77.66 344
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	52.48 299	64.27 279	84.10 193	27.45 409	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	52.33 300	64.33 277	84.11 192	28.28 401	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	73.84 273	69.59 335	52.32 301	65.28 256	81.72 257	44.49 219	77.40 303	42.32 357	78.66 166	82.92 246

CDS-MVSNet		66.80 249	65.37 258	71.10 235	78.98 154	53.13 183	73.27 284	71.07 322	52.15 302	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	75.48 262	52.09 303	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	72.28 302	75.90 252	51.96 304	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	72.51 298	81.52 123	51.91 305	64.22 282	77.77 336	49.13 153	82.87 178	55.82 233	79.58 137	80.14 309

mvs_anonymous		68.03 219	67.51 206	69.59 265	72.08 342	44.57 329	71.99 306	75.23 268	51.67 306	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	75.09 272	51.61 307	70.04 150	81.41 263	32.79 354	79.02 272	63.81 156	77.31 190	81.22 286

xiu_mvs_v1_base		68.58 204	67.28 215	72.48 186	78.19 183	57.19 101	75.28 234	75.09 272	51.61 307	70.04 150	81.41 263	32.79 354	79.02 272	63.81 156	77.31 190	81.22 286

xiu_mvs_v1_base_debi		68.58 204	67.28 215	72.48 186	78.19 183	57.19 101	75.28 234	75.09 272	51.61 307	70.04 150	81.41 263	32.79 354	79.02 272	63.81 156	77.31 190	81.22 286

MVSTER		67.16 241	65.58 254	71.88 200	70.37 375	49.70 257	70.25 334	78.45 205	51.52 310	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	70.71 325	51.49 311	61.57 322	83.58 209	38.23 295	70.82 362	43.90 340	70.10 306	80.16 308

原ACMM1				74.69 106	85.39 48	59.40 59		83.42 81	51.47 312	70.27 148	86.61 123	48.61 159	86.51 86	53.85 254	87.96 43	78.16 335

miper_enhance_ethall		67.11 242	66.09 246	70.17 254	69.21 394	45.98 313	72.85 292	78.41 208	51.38 313	65.65 250	75.98 368	51.17 125	81.25 217	60.82 191	69.32 321	83.29 236

MSDG		61.81 318	59.23 331	69.55 268	72.64 329	52.63 198	70.45 330	75.81 253	51.38 313	53.70 405	76.11 363	29.52 389	81.08 224	37.70 386	65.79 357	74.93 379

test20.03		53.87 385	54.02 382	53.41 418	61.47 440	28.11 456	61.30 407	59.21 414	51.34 315	52.09 416	77.43 340	33.29 348	58.55 428	29.76 439	60.27 403	73.58 393

MVSFormer		71.50 129	70.38 140	74.88 101	78.76 160	57.15 104	82.79 72	78.48 202	51.26 316	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	51.26 316	65.41 254	83.49 211	38.37 291	83.24 164	66.06 131	69.25 324	85.56 146

dmvs_testset		50.16 403	51.90 393	44.94 439	66.49 415	11.78 479	61.01 412	51.50 441	51.17 318	50.30 428	67.44 433	39.28 280	60.29 418	22.38 458	57.49 412	62.76 444

PAPM		67.92 223	66.69 229	71.63 212	78.09 188	49.02 272	77.09 188	81.24 139	51.04 319	60.91 328	83.98 196	47.71 169	84.99 125	40.81 367	79.32 143	80.90 294

Syy-MVS		56.00 370	56.23 363	55.32 404	74.69 286	26.44 463	65.52 373	57.49 422	50.97 320	56.52 375	72.18 400	39.89 273	68.09 378	24.20 455	64.59 367	71.44 418

myMVS_eth3d		54.86 381	54.61 374	55.61 403	74.69 286	27.31 460	65.52 373	57.49 422	50.97 320	56.52 375	72.18 400	21.87 438	68.09 378	27.70 446	64.59 367	71.44 418

miper_lstm_enhance		62.03 315	60.88 320	65.49 329	66.71 413	46.25 309	56.29 434	75.70 255	50.68 322	61.27 324	75.48 375	40.21 270	68.03 380	56.31 230	65.25 360	82.18 266

gg-mvs-nofinetune		57.86 354	56.43 360	62.18 358	72.62 330	35.35 419	66.57 363	56.33 428	50.65 323	57.64 366	57.10 455	30.65 376	76.36 329	37.38 389	78.88 158	74.82 381

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	50.64 324	65.24 261	84.93 169	39.15 283	78.54 281	36.77 394	76.88 199	85.14 167
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 362	56.83 355	56.61 398	69.23 393	41.02 364	58.37 421	64.18 381	50.59 325	57.45 368	71.42 408	35.54 321	58.94 426	37.23 390	67.45 344	69.87 431

MVP-Stereo		65.41 270	63.80 275	70.22 251	77.62 210	55.53 134	76.30 209	78.53 200	50.59 325	56.47 377	78.65 316	39.84 274	82.68 185	44.10 338	72.12 276	72.44 404
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	50.30 327	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		55.64 373	53.81 384	61.11 369	59.39 450	40.98 368	65.89 368	68.28 347	50.21 328	58.11 363	75.42 376	17.03 444	67.63 384	43.79 342	46.21 445	74.73 383

baseline2		63.42 294	61.26 313	69.89 261	72.55 332	47.62 298	71.54 312	68.38 346	50.11 329	54.82 393	75.55 373	43.06 233	80.96 227	48.13 302	67.16 347	81.11 289

test-LLR		58.15 352	58.13 345	58.22 388	68.57 398	44.80 325	65.46 375	57.92 419	50.08 330	55.44 385	69.82 421	32.62 362	57.44 433	49.66 288	73.62 245	72.41 405

test0.0.03 1		53.32 390	53.59 387	52.50 424	62.81 435	29.45 451	59.51 417	54.11 436	50.08 330	54.40 400	74.31 385	32.62 362	55.92 442	30.50 435	63.95 372	72.15 410

fmvsm_s_conf0.5_n		69.58 176	68.84 171	71.79 204	72.31 340	52.90 187	77.90 157	62.43 400	49.97 332	72.85 112	85.90 149	52.21 104	76.49 326	75.75 47	70.26 303	85.97 124

COLMAP_ROB	52.97 17	61.27 325	58.81 335	68.64 281	74.63 288	52.51 201	78.42 142	73.30 301	49.92 333	50.96 420	81.51 262	23.06 432	79.40 258	31.63 428	65.85 355	74.01 391
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	62.26 402	49.78 334	73.12 105	86.21 138	52.66 96	76.79 320	75.02 56	68.88 329	85.18 166

WBMVS		60.54 329	60.61 322	60.34 372	78.00 192	35.95 416	64.55 385	64.89 374	49.63 335	63.39 289	78.70 313	33.85 341	67.65 383	42.10 359	70.35 300	77.43 347

tpmvs		58.47 347	56.95 353	63.03 354	70.20 377	41.21 363	67.90 355	67.23 355	49.62 336	54.73 395	70.84 412	34.14 335	76.24 331	36.64 398	61.29 394	71.64 414

fmvsm_s_conf0.1_n		69.41 184	68.60 177	71.83 201	71.07 363	52.88 190	77.85 161	62.44 399	49.58 337	72.97 108	86.22 137	51.68 116	76.48 327	75.53 51	70.10 306	86.14 119

UBG		59.62 341	59.53 329	59.89 373	78.12 187	35.92 417	64.11 390	60.81 410	49.45 338	61.34 323	75.55 373	33.05 349	67.39 387	38.68 381	74.62 229	76.35 362

thisisatest0515		65.83 264	63.50 280	72.82 178	73.75 309	49.50 262	71.32 315	73.12 307	49.39 339	63.82 284	76.50 360	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	62.30 401	49.35 340	73.20 99	86.55 128	51.99 109	76.79 320	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	69.96 337	74.43 281	49.29 341	61.66 320	80.92 273	47.43 177	76.68 324	44.91 333	71.69 280	81.94 270

MIMVSNet1		55.17 378	54.31 379	57.77 394	70.03 381	32.01 442	65.68 371	64.81 375	49.19 342	46.75 439	76.00 365	25.53 425	64.04 403	28.65 443	62.13 387	77.26 351

SCA		60.49 330	58.38 341	66.80 299	74.14 305	48.06 291	63.35 395	63.23 392	49.13 343	59.33 349	72.10 402	37.45 301	74.27 341	44.17 335	62.57 383	78.05 337

test_fmvsmvis_n_1920		70.84 140	70.38 140	72.22 194	71.16 362	55.39 137	75.86 223	72.21 314	49.03 344	73.28 97	86.17 140	51.83 113	77.29 306	75.80 46	78.05 178	83.98 208

testgi		51.90 395	52.37 391	50.51 431	60.39 448	23.55 470	58.42 420	58.15 417	49.03 344	51.83 417	79.21 309	22.39 433	55.59 443	29.24 442	62.64 382	72.40 407

sc_t1		59.76 337	57.84 348	65.54 326	74.87 279	42.95 347	69.61 340	64.16 383	48.90 346	58.68 354	77.12 343	28.19 402	72.35 351	43.75 344	55.28 421	81.31 284

MIMVSNet		57.35 356	57.07 351	58.22 388	74.21 302	37.18 400	62.46 400	60.88 409	48.88 347	55.29 388	75.99 367	31.68 371	62.04 412	31.87 423	72.35 271	75.43 372

gm-plane-assit					71.40 358	41.72 359			48.85 348		73.31 394		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	70.46 327	48.67 349	75.17 57	86.86 109	53.77 79	76.86 318	76.33 41	77.51 187	83.17 243

UWE-MVS		60.18 333	59.78 327	61.39 366	77.67 204	33.92 432	69.04 347	63.82 386	48.56 350	64.27 279	77.64 338	27.20 411	70.40 367	33.56 415	76.24 206	79.83 316

cascas		65.98 262	63.42 282	73.64 152	77.26 221	52.58 199	72.26 303	77.21 231	48.56 350	61.21 325	74.60 383	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	48.51 352	55.71 382	81.89 252	33.71 342	79.71 252	41.66 363	70.37 298	77.58 345
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	74.72 278	48.50 353	56.62 373	84.62 178	33.59 345	82.34 195	29.65 440	75.23 225	75.97 364

anonymousdsp		67.00 245	64.82 265	73.57 156	70.09 380	56.13 117	76.35 208	77.35 228	48.43 354	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	74.30 285	48.40 355				80.78 234	53.62 255		79.03 328

FE-MVSNET		55.16 379	53.75 385	59.41 376	65.29 423	33.20 436	67.21 362	66.21 365	48.39 356	49.56 430	73.53 393	29.03 393	72.51 349	30.38 436	54.10 427	72.52 401

114514_t		70.83 142	69.56 154	74.64 110	86.21 32	54.63 148	82.34 81	81.81 119	48.22 357	63.01 297	85.83 152	40.92 266	87.10 67	57.91 218	79.79 133	82.18 266

tpm		57.34 357	58.16 343	54.86 407	71.80 348	34.77 422	67.47 360	56.04 431	48.20 358	60.10 334	76.92 347	37.17 307	53.41 450	40.76 368	65.01 361	76.40 361

test_fmvsm_n_1920		71.73 125	71.14 125	73.50 158	72.52 333	56.53 111	75.60 227	76.16 246	48.11 359	77.22 40	85.56 159	53.10 90	77.43 301	74.86 57	77.14 195	86.55 100

MDA-MVSNet-bldmvs		53.87 385	50.81 398	63.05 353	66.25 417	48.58 283	56.93 432	63.82 386	48.09 360	41.22 451	70.48 417	30.34 379	68.00 381	34.24 410	45.92 447	72.57 400

XXY-MVS		60.68 326	61.67 305	57.70 395	70.43 373	38.45 389	64.19 388	66.47 361	48.05 361	63.22 290	80.86 275	49.28 150	60.47 416	45.25 332	67.28 346	74.19 389

F-COLMAP		63.05 302	60.87 321	69.58 267	76.99 237	53.63 166	78.12 151	76.16 246	47.97 362	52.41 415	81.61 259	27.87 404	78.11 286	40.07 370	66.66 350	77.00 355

tt0320-xc		58.33 349	56.41 361	64.08 343	75.79 256	41.34 361	68.30 351	62.72 396	47.90 363	56.29 378	74.16 388	28.53 398	71.04 361	41.50 366	52.50 433	79.88 314

fmvsm_l_conf0.5_n_a		70.50 149	70.27 142	71.18 231	71.30 360	54.09 156	76.89 196	69.87 331	47.90 363	74.37 77	86.49 129	53.07 92	76.69 323	75.41 52	77.11 196	82.76 250

Patchmatch-RL test		58.16 351	55.49 368	66.15 314	67.92 405	48.89 277	60.66 413	51.07 444	47.86 365	59.36 346	62.71 449	34.02 338	72.27 353	56.41 229	59.40 405	77.30 349

D2MVS		62.30 310	60.29 324	68.34 286	66.46 416	48.42 285	65.70 370	73.42 298	47.71 366	58.16 362	75.02 379	30.51 377	77.71 298	53.96 253	71.68 281	78.90 330

ANet_high		41.38 422	37.47 429	53.11 420	39.73 476	24.45 468	56.94 431	69.69 332	47.65 367	26.04 468	52.32 458	12.44 456	62.38 411	21.80 459	10.61 477	72.49 402

CostFormer		64.04 289	62.51 294	68.61 282	71.88 346	45.77 314	71.30 316	70.60 326	47.55 368	64.31 278	76.61 356	41.63 253	79.62 255	49.74 286	69.00 328	80.42 302

PatchmatchNet		59.84 336	58.24 342	64.65 338	73.05 323	46.70 306	69.42 343	62.18 403	47.55 368	58.88 352	71.96 404	34.49 332	69.16 372	42.99 352	63.60 374	78.07 336
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 377	53.89 383	59.21 380	57.80 454	27.47 459	57.75 427	74.32 283	47.38 370	50.90 421	70.00 420	28.45 400	70.30 368	40.44 369	57.92 410	79.87 315

ITE_SJBPF				62.09 359	66.16 418	44.55 330		64.32 379	47.36 371	55.31 387	80.34 283	19.27 441	62.68 410	36.29 402	62.39 385	79.04 327

KD-MVS_2432*1600		53.45 387	51.50 396	59.30 377	62.82 433	37.14 401	55.33 435	71.79 318	47.34 372	55.09 390	70.52 415	21.91 436	70.45 365	35.72 405	42.97 451	70.31 427

miper_refine_blended		53.45 387	51.50 396	59.30 377	62.82 433	37.14 401	55.33 435	71.79 318	47.34 372	55.09 390	70.52 415	21.91 436	70.45 365	35.72 405	42.97 451	70.31 427

OurMVSNet-221017-0		61.37 324	58.63 339	69.61 264	72.05 343	48.06 291	73.93 269	72.51 310	47.23 374	54.74 394	80.92 273	21.49 439	81.24 218	48.57 298	56.22 418	79.53 321

tpmrst		58.24 350	58.70 338	56.84 397	66.97 410	34.32 427	69.57 342	61.14 408	47.17 375	58.58 358	71.60 407	41.28 260	60.41 417	49.20 292	62.84 381	75.78 367

tt0320		58.59 346	56.81 356	63.92 345	75.46 265	41.32 362	68.63 349	64.06 384	47.05 376	56.19 379	74.19 386	30.34 379	71.36 358	39.92 374	55.45 420	79.09 325

PVSNet	50.76 19	58.40 348	57.39 349	61.42 364	75.53 263	44.04 335	61.43 405	63.45 390	47.04 377	56.91 371	73.61 392	27.00 414	64.76 401	39.12 379	72.40 270	75.47 371

WB-MVSnew		59.66 339	59.69 328	59.56 374	75.19 272	35.78 418	69.34 344	64.28 380	46.88 378	61.76 319	75.79 369	40.61 268	65.20 399	32.16 420	71.21 285	77.70 343

UWE-MVS-28		52.25 394	52.35 392	51.93 428	66.99 409	22.79 471	63.48 394	48.31 452	46.78 379	52.73 414	76.11 363	27.78 406	57.82 432	20.58 461	68.41 336	75.17 373

FMVSNet5		55.86 371	54.93 371	58.66 385	71.05 364	36.35 410	64.18 389	62.48 398	46.76 380	50.66 425	74.73 382	25.80 422	64.04 403	33.11 416	65.57 358	75.59 369

jason		69.65 173	68.39 185	73.43 163	78.27 181	56.88 108	77.12 187	73.71 296	46.53 381	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	65.31 333	75.21 271	52.10 209	72.05 305	74.05 290	46.41 382	57.42 369	74.36 384	34.35 334	77.57 300	45.62 324	73.67 243	66.26 441

1112_ss		64.00 290	63.36 283	65.93 319	79.28 144	42.58 349	71.35 314	72.36 313	46.41 382	60.55 331	77.89 331	46.27 194	73.28 345	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	73.29 302	46.19 384	69.49 162	82.18 243	43.99 224	79.23 262	64.66 146	79.37 140	83.93 210

testdata				64.66 337	81.52 98	52.93 186		65.29 372	46.09 385	73.88 85	87.46 93	38.08 297	66.26 394	53.31 259	78.48 170	74.78 382

UnsupCasMVSNet_eth		53.16 392	52.47 390	55.23 405	59.45 449	33.39 435	59.43 418	69.13 341	45.98 386	50.35 427	72.32 399	29.30 392	58.26 430	42.02 361	44.30 449	74.05 390

AllTest		57.08 359	54.65 373	64.39 340	71.44 355	49.03 270	69.92 338	67.30 352	45.97 387	47.16 436	79.77 294	17.47 442	67.56 385	33.65 412	59.16 406	76.57 359

TestCases				64.39 340	71.44 355	49.03 270		67.30 352	45.97 387	47.16 436	79.77 294	17.47 442	67.56 385	33.65 412	59.16 406	76.57 359

WTY-MVS		59.75 338	60.39 323	57.85 393	72.32 339	37.83 394	61.05 411	64.18 381	45.95 389	61.91 316	79.11 310	47.01 186	60.88 415	42.50 356	69.49 320	74.83 380

IterMVS-SCA-FT		62.49 306	61.52 307	65.40 330	71.99 345	50.80 231	71.15 320	69.63 334	45.71 390	60.61 330	77.93 328	37.45 301	65.99 396	55.67 237	63.50 376	79.42 322

WB-MVS		43.26 416	43.41 416	42.83 443	63.32 432	10.32 481	58.17 423	45.20 459	45.42 391	40.44 454	67.26 436	34.01 339	58.98 425	11.96 472	24.88 466	59.20 447

旧先验2							76.08 216		45.32 392	76.55 47			65.56 398	58.75 214

OpenMVS_ROB	52.78 18	60.03 334	58.14 344	65.69 325	70.47 372	44.82 324	75.33 232	70.86 324	45.04 393	56.06 380	76.00 365	26.89 416	79.65 253	35.36 407	67.29 345	72.60 399

TinyColmap		54.14 382	51.72 394	61.40 365	66.84 412	41.97 354	66.52 364	68.51 345	44.81 394	42.69 450	75.77 370	11.66 458	72.94 346	31.96 422	56.77 416	69.27 435

MDTV_nov1_ep13			57.00 352		72.73 328	38.26 390	65.02 382	64.73 377	44.74 395	55.46 384	72.48 398	32.61 364	70.47 364	37.47 387	67.75 341

新几何1				70.76 242	85.66 42	61.13 30		66.43 362	44.68 396	70.29 147	86.64 119	41.29 259	75.23 336	49.72 287	81.75 110	75.93 365

Patchmtry		57.16 358	56.47 359	59.23 379	69.17 395	34.58 425	62.98 397	63.15 393	44.53 397	56.83 372	74.84 380	35.83 319	68.71 375	40.03 371	60.91 395	74.39 387

ppachtmachnet_test		58.06 353	55.38 369	66.10 316	69.51 389	48.99 273	68.01 354	66.13 366	44.50 398	54.05 403	70.74 413	32.09 370	72.34 352	36.68 397	56.71 417	76.99 357

PatchT		53.17 391	53.44 388	52.33 425	68.29 402	25.34 467	58.21 422	54.41 435	44.46 399	54.56 397	69.05 427	33.32 347	60.94 414	36.93 393	61.76 392	70.73 425

EPMVS		53.96 383	53.69 386	54.79 408	66.12 419	31.96 443	62.34 402	49.05 448	44.42 400	55.54 383	71.33 410	30.22 381	56.70 436	41.65 364	62.54 384	75.71 368

pmmvs4		61.48 323	59.39 330	67.76 290	71.57 351	53.86 159	71.42 313	65.34 371	44.20 401	59.46 345	77.92 329	35.90 318	74.71 338	43.87 341	64.87 363	74.71 384

dp		51.89 396	51.60 395	52.77 422	68.44 401	32.45 441	62.36 401	54.57 434	44.16 402	49.31 431	67.91 429	28.87 396	56.61 438	33.89 411	54.89 423	69.24 436

PatchMatch-RL		56.25 368	54.55 375	61.32 367	77.06 230	56.07 119	65.57 372	54.10 437	44.13 403	53.49 411	71.27 411	25.20 426	66.78 390	36.52 400	63.66 373	61.12 445

our_test_3		56.49 364	54.42 376	62.68 356	69.51 389	45.48 320	66.08 367	61.49 406	44.11 404	50.73 424	69.60 424	33.05 349	68.15 377	38.38 383	56.86 414	74.40 386

USDC		56.35 367	54.24 380	62.69 355	64.74 425	40.31 370	65.05 381	73.83 294	43.93 405	47.58 434	77.71 337	15.36 451	75.05 337	38.19 385	61.81 391	72.70 398

PM-MVS		52.33 393	50.19 402	58.75 384	62.10 438	45.14 323	65.75 369	40.38 466	43.60 406	53.52 409	72.65 397	9.16 466	65.87 397	50.41 281	54.18 426	65.24 443

pmmvs-eth3d		58.81 345	56.31 362	66.30 310	67.61 406	52.42 205	72.30 301	64.76 376	43.55 407	54.94 392	74.19 386	28.95 394	72.60 348	43.31 346	57.21 413	73.88 392

SSC-MVS		41.96 421	41.99 420	41.90 444	62.46 437	9.28 483	57.41 430	44.32 462	43.38 408	38.30 460	66.45 439	32.67 361	58.42 429	10.98 473	21.91 469	57.99 451

new-patchmatchnet		47.56 410	47.73 410	47.06 434	58.81 452	9.37 482	48.78 454	59.21 414	43.28 409	44.22 446	68.66 428	25.67 423	57.20 435	31.57 430	49.35 442	74.62 385

Test_1112_low_res		62.32 309	61.77 304	64.00 344	79.08 153	39.53 379	68.17 352	70.17 328	43.25 410	59.03 351	79.90 291	44.08 221	71.24 360	43.79 342	68.42 335	81.25 285

RPMNet		61.53 321	58.42 340	70.86 240	69.96 382	52.07 210	65.31 379	81.36 130	43.20 411	59.36 346	70.15 419	35.37 322	85.47 118	36.42 401	64.65 365	75.06 375

tpm2		62.07 313	60.10 326	67.99 288	72.79 327	43.86 336	71.05 323	66.85 359	43.14 412	62.77 300	75.39 377	38.32 293	80.80 233	41.69 362	68.88 329	79.32 323

JIA-IIPM		51.56 397	47.68 411	63.21 351	64.61 426	50.73 232	47.71 456	58.77 416	42.90 413	48.46 433	51.72 459	24.97 427	70.24 369	36.06 404	53.89 428	68.64 437

1314		64.61 282	63.21 287	68.80 279	71.87 347	47.46 300	73.95 267	78.39 210	42.88 414	59.97 337	76.60 357	38.11 296	79.39 259	54.84 244	72.32 272	79.55 320

HyFIR lowres test		65.67 266	63.01 289	73.67 149	79.97 131	55.65 129	69.07 346	75.52 260	42.68 415	63.53 287	77.95 327	40.43 269	81.64 206	46.01 319	71.91 277	83.73 222

CR-MVSNet		59.91 335	57.90 347	65.96 318	69.96 382	52.07 210	65.31 379	63.15 393	42.48 416	59.36 346	74.84 380	35.83 319	70.75 363	45.50 327	64.65 365	75.06 375

test222					83.14 76	58.68 81	72.57 297	63.45 390	41.78 417	67.56 210	86.12 141	37.13 308			78.73 163	74.98 378

TDRefinement		53.44 389	50.72 399	61.60 362	64.31 428	46.96 304	70.89 324	65.27 373	41.78 417	44.61 445	77.98 326	11.52 460	66.36 393	28.57 444	51.59 435	71.49 417

sss		56.17 369	56.57 358	54.96 406	66.93 411	36.32 412	57.94 424	61.69 405	41.67 419	58.64 356	75.32 378	38.72 288	56.25 440	42.04 360	66.19 354	72.31 408

PVSNet_0	43.31 20	47.46 411	45.64 414	52.92 421	67.60 407	44.65 327	54.06 440	54.64 433	41.59 420	46.15 441	58.75 452	30.99 374	58.66 427	32.18 419	24.81 467	55.46 455

MVS		67.37 234	66.33 240	70.51 249	75.46 265	50.94 226	73.95 267	81.85 118	41.57 421	62.54 307	78.57 319	47.98 164	85.47 118	52.97 261	82.05 103	75.14 374

Anonymous20240521		55.30 375	54.41 377	57.96 392	60.92 447	41.73 357	71.09 322	71.06 323	41.18 422	48.65 432	73.31 394	16.93 445	59.25 423	42.54 355	64.01 370	72.90 396

Anonymous20231206		55.10 380	55.30 370	54.48 409	69.81 387	33.94 431	62.91 398	62.13 404	41.08 423	55.18 389	75.65 371	32.75 357	56.59 439	30.32 437	67.86 339	72.91 395

MDA-MVSNet_test_wron		50.71 402	48.95 404	56.00 402	61.17 442	41.84 355	51.90 446	56.45 425	40.96 424	44.79 444	67.84 430	30.04 385	55.07 447	36.71 396	50.69 438	71.11 423

YYNet1		50.73 401	48.96 403	56.03 401	61.10 443	41.78 356	51.94 445	56.44 426	40.94 425	44.84 443	67.80 431	30.08 384	55.08 446	36.77 394	50.71 437	71.22 420

dongtai		34.52 431	34.94 431	33.26 453	61.06 444	16.00 478	52.79 444	23.78 479	40.71 426	39.33 458	48.65 467	16.91 446	48.34 459	12.18 471	19.05 471	35.44 470

CHOSEN 1792x2688		65.08 276	62.84 291	71.82 202	81.49 100	56.26 115	66.32 366	74.20 289	40.53 427	63.16 293	78.65 316	41.30 258	77.80 295	45.80 321	74.09 235	81.40 280

pmmvs5		56.47 365	55.68 367	58.86 383	61.41 441	36.71 407	66.37 365	62.75 395	40.38 428	53.70 405	76.62 354	34.56 330	67.05 388	40.02 372	65.27 359	72.83 397

test_vis1_n_1920		58.86 344	59.06 334	58.25 387	63.76 429	43.14 344	67.49 359	66.36 363	40.22 429	65.89 246	71.95 405	31.04 373	59.75 421	59.94 198	64.90 362	71.85 412

MDTV_nov1_ep13_2view						25.89 465	61.22 408		40.10 430	51.10 419		32.97 352		38.49 382		78.61 332

tpm cat1		59.25 343	56.95 353	66.15 314	72.19 341	46.96 304	68.09 353	65.76 367	40.03 431	57.81 365	70.56 414	38.32 293	74.51 339	38.26 384	61.50 393	77.00 355

test-mter		56.42 366	55.82 366	58.22 388	68.57 398	44.80 325	65.46 375	57.92 419	39.94 432	55.44 385	69.82 421	21.92 435	57.44 433	49.66 288	73.62 245	72.41 405

UnsupCasMVSNet_bld		50.07 404	48.87 405	53.66 414	60.97 446	33.67 433	57.62 428	64.56 378	39.47 433	47.38 435	64.02 447	27.47 408	59.32 422	34.69 409	43.68 450	67.98 439

TESTMET0.1,1		55.28 376	54.90 372	56.42 399	66.56 414	43.67 338	65.46 375	56.27 429	39.18 434	53.83 404	67.44 433	24.21 430	55.46 444	48.04 303	73.11 259	70.13 429

mamv4		56.85 361	58.00 346	53.43 417	72.46 336	54.47 149	57.56 429	54.74 432	38.81 435	57.42 369	79.45 304	47.57 173	38.70 470	60.88 190	53.07 430	67.11 440

ADS-MVSNet2		51.33 399	48.76 406	59.07 382	66.02 420	44.60 328	50.90 448	59.76 412	36.90 436	50.74 422	66.18 441	26.38 417	63.11 408	27.17 448	54.76 424	69.50 433

ADS-MVSNet		48.48 408	47.77 409	50.63 430	66.02 420	29.92 450	50.90 448	50.87 446	36.90 436	50.74 422	66.18 441	26.38 417	52.47 453	27.17 448	54.76 424	69.50 433

RPSCF		55.80 372	54.22 381	60.53 371	65.13 424	42.91 348	64.30 387	57.62 421	36.84 438	58.05 364	82.28 240	28.01 403	56.24 441	37.14 391	58.61 408	82.44 262

test_cas_vis1_n_1920		56.91 360	56.71 357	57.51 396	59.13 451	45.40 321	63.58 393	61.29 407	36.24 439	67.14 219	71.85 406	29.89 386	56.69 437	57.65 220	63.58 375	70.46 426

Patchmatch-test		49.08 406	48.28 408	51.50 429	64.40 427	30.85 448	45.68 460	48.46 451	35.60 440	46.10 442	72.10 402	34.47 333	46.37 462	27.08 450	60.65 399	77.27 350

CHOSEN 280x420		47.83 409	46.36 413	52.24 427	67.37 408	49.78 252	38.91 468	43.11 464	35.00 441	43.27 449	63.30 448	28.95 394	49.19 458	36.53 399	60.80 397	57.76 452

N_pmnet		39.35 426	40.28 423	36.54 450	63.76 429	1.62 487	49.37 453	0.76 486	34.62 442	43.61 448	66.38 440	26.25 419	42.57 466	26.02 453	51.77 434	65.44 442

kuosan		29.62 438	30.82 437	26.02 458	52.99 457	16.22 477	51.09 447	22.71 480	33.91 443	33.99 462	40.85 468	15.89 449	33.11 475	7.59 479	18.37 472	28.72 472

PMMVS		53.96 383	53.26 389	56.04 400	62.60 436	50.92 228	61.17 409	56.09 430	32.81 444	53.51 410	66.84 438	34.04 337	59.93 420	44.14 337	68.18 337	57.27 453

CMPMVS	42.80 21	57.81 355	55.97 364	63.32 349	60.98 445	47.38 301	64.66 384	69.50 337	32.06 445	46.83 438	77.80 333	29.50 390	71.36 358	48.68 296	73.75 241	71.21 421
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 412	42.95 417	53.39 419	52.33 461	29.15 452	57.77 425	48.20 453	31.81 446	49.86 429	77.21 342	8.69 467	59.16 424	27.31 447	33.40 463	71.84 413

CVMVSNet		59.63 340	59.14 332	61.08 370	74.47 293	38.84 385	75.20 237	68.74 344	31.15 447	58.24 360	76.51 358	32.39 367	68.58 376	49.77 285	65.84 356	75.81 366

FPMVS		42.18 420	41.11 422	45.39 436	58.03 453	41.01 366	49.50 452	53.81 438	30.07 448	33.71 463	64.03 445	11.69 457	52.08 456	14.01 467	55.11 422	43.09 464

EU-MVSNet		55.61 374	54.41 377	59.19 381	65.41 422	33.42 434	72.44 299	71.91 317	28.81 449	51.27 418	73.87 390	24.76 428	69.08 373	43.04 351	58.20 409	75.06 375

test_vis1_n		49.89 405	48.69 407	53.50 416	53.97 455	37.38 399	61.53 404	47.33 456	28.54 450	59.62 344	67.10 437	13.52 453	52.27 454	49.07 293	57.52 411	70.84 424

test_fmvs1_n		51.37 398	50.35 401	54.42 411	52.85 458	37.71 396	61.16 410	51.93 439	28.15 451	63.81 285	69.73 423	13.72 452	53.95 448	51.16 276	60.65 399	71.59 415

LF4IMVS		42.95 417	42.26 419	45.04 437	48.30 466	32.50 440	54.80 437	48.49 450	28.03 452	40.51 453	70.16 418	9.24 465	43.89 465	31.63 428	49.18 443	58.72 449

test_fmvs1		51.32 400	50.48 400	53.81 413	53.57 456	37.51 398	60.63 414	51.16 442	28.02 453	63.62 286	69.23 426	16.41 447	53.93 449	51.01 277	60.70 398	69.99 430

MVS-HIRNet		45.52 413	44.48 415	48.65 433	68.49 400	34.05 430	59.41 419	44.50 461	27.03 454	37.96 461	50.47 463	26.16 420	64.10 402	26.74 451	59.52 404	47.82 462

PMVS	28.69 22	36.22 429	33.29 434	45.02 438	36.82 478	35.98 415	54.68 438	48.74 449	26.31 455	21.02 471	51.61 460	2.88 479	60.10 419	9.99 476	47.58 444	38.99 469
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 414	41.95 421	53.86 412	52.58 460	43.55 339	62.11 403	46.90 458	26.05 456	40.63 452	60.19 451	11.08 463	57.91 431	31.83 427	46.15 446	60.11 446

test_fmvs2		48.69 407	47.49 412	52.29 426	48.63 465	33.06 438	57.76 426	48.05 454	25.71 457	59.76 342	69.60 424	11.57 459	52.23 455	49.45 291	56.86 414	71.58 416

PMMVS2		27.40 439	25.91 442	31.87 455	39.46 477	6.57 484	31.17 471	28.52 475	23.96 458	20.45 472	48.94 466	4.20 475	37.94 471	16.51 464	19.97 470	51.09 457

MVStest1		42.65 418	39.29 425	52.71 423	47.26 468	34.58 425	54.41 439	50.84 447	23.35 459	39.31 459	74.08 389	12.57 455	55.09 445	23.32 456	28.47 465	68.47 438

Gipuma		34.77 430	31.91 435	43.33 441	62.05 439	37.87 392	20.39 473	67.03 357	23.23 460	18.41 473	25.84 473	4.24 473	62.73 409	14.71 466	51.32 436	29.38 471
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 423	39.45 424	47.03 435	46.65 469	37.86 393	47.76 455	38.65 467	23.10 461	44.21 447	51.22 461	11.20 462	44.08 464	39.27 378	53.02 431	59.14 448

new_pmnet		34.13 432	34.29 433	33.64 452	52.63 459	18.23 476	44.43 463	33.90 472	22.81 462	30.89 465	53.18 457	10.48 464	35.72 474	20.77 460	39.51 455	46.98 463

mvsany_test1		39.38 425	38.16 428	43.02 442	49.05 463	34.28 428	44.16 464	25.94 477	22.74 463	46.57 440	62.21 450	23.85 431	41.16 469	33.01 417	35.91 459	53.63 456

LCM-MVSNet		40.30 424	35.88 430	53.57 415	42.24 471	29.15 452	45.21 462	60.53 411	22.23 464	28.02 466	50.98 462	3.72 476	61.78 413	31.22 433	38.76 457	69.78 432

test_fmvs3		44.30 415	42.55 418	49.55 432	42.83 470	27.15 462	53.03 442	44.93 460	22.03 465	53.69 407	64.94 444	4.21 474	49.63 457	47.47 304	49.82 440	71.88 411

APD_test1		37.39 428	34.94 431	44.72 440	48.88 464	33.19 437	52.95 443	44.00 463	19.49 466	27.28 467	58.59 453	3.18 478	52.84 452	18.92 462	41.17 454	48.14 461

mvsany_test3		32.62 433	30.57 438	38.77 448	36.16 479	24.20 469	38.10 469	20.63 481	19.14 467	40.36 455	57.43 454	5.06 471	36.63 473	29.59 441	28.66 464	55.49 454

E-PMN		23.77 440	22.73 444	26.90 456	42.02 472	20.67 473	42.66 465	35.70 470	17.43 468	10.28 478	25.05 474	6.42 469	42.39 467	10.28 475	14.71 474	17.63 473

EMVS		22.97 441	21.84 445	26.36 457	40.20 475	19.53 475	41.95 466	34.64 471	17.09 469	9.73 479	22.83 475	7.29 468	42.22 468	9.18 477	13.66 475	17.32 474

test_vis3_rt		32.09 434	30.20 439	37.76 449	35.36 480	27.48 458	40.60 467	28.29 476	16.69 470	32.52 464	40.53 469	1.96 480	37.40 472	33.64 414	42.21 453	48.39 459

test_f		31.86 435	31.05 436	34.28 451	32.33 482	21.86 472	32.34 470	30.46 474	16.02 471	39.78 457	55.45 456	4.80 472	32.36 476	30.61 434	37.66 458	48.64 458

DSMNet-mixed		39.30 427	38.72 426	41.03 445	51.22 462	19.66 474	45.53 461	31.35 473	15.83 472	39.80 456	67.42 435	22.19 434	45.13 463	22.43 457	52.69 432	58.31 450

testf1		31.46 436	28.89 440	39.16 446	41.99 473	28.78 454	46.45 458	37.56 468	14.28 473	21.10 469	48.96 464	1.48 482	47.11 460	13.63 468	34.56 460	41.60 465

APD_test2		31.46 436	28.89 440	39.16 446	41.99 473	28.78 454	46.45 458	37.56 468	14.28 473	21.10 469	48.96 464	1.48 482	47.11 460	13.63 468	34.56 460	41.60 465

MVE	17.77 23	21.41 442	17.77 447	32.34 454	34.34 481	25.44 466	16.11 474	24.11 478	11.19 475	13.22 475	31.92 471	1.58 481	30.95 477	10.47 474	17.03 473	40.62 468
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 461	17.97 483	10.91 480		10.60 484	7.46 476	11.07 477	28.36 472	3.28 477	11.29 480	8.01 478	9.74 479	13.89 475

wuyk23d		13.32 445	12.52 448	15.71 460	47.54 467	26.27 464	31.06 472	1.98 485	4.93 477	5.18 480	1.94 480	0.45 484	18.54 479	6.81 480	12.83 476	2.33 477

test_method		19.68 443	18.10 446	24.41 459	13.68 484	3.11 486	12.06 476	42.37 465	2.00 478	11.97 476	36.38 470	5.77 470	29.35 478	15.06 465	23.65 468	40.76 467

tmp_tt		9.43 446	11.14 449	4.30 462	2.38 485	4.40 485	13.62 475	16.08 483	0.39 479	15.89 474	13.06 476	15.80 450	5.54 481	12.63 470	10.46 478	2.95 476

EGC-MVSNET		42.47 419	38.48 427	54.46 410	74.33 298	48.73 279	70.33 333	51.10 443	0.03 480	0.18 481	67.78 432	13.28 454	66.49 392	18.91 463	50.36 439	48.15 460

testmvs		4.52 449	6.03 452	0.01 464	0.01 486	0.00 489	53.86 441	0.00 487	0.01 481	0.04 482	0.27 481	0.00 486	0.00 482	0.04 481	0.00 480	0.03 479

test123		4.73 448	6.30 451	0.02 463	0.01 486	0.01 488	56.36 433	0.00 487	0.01 481	0.04 482	0.21 482	0.01 485	0.00 482	0.03 482	0.00 480	0.04 478

mmdepth		0.00 451	0.00 454	0.00 465	0.00 488	0.00 489	0.00 477	0.00 487	0.00 483	0.00 484	0.00 483	0.00 486	0.00 482	0.00 483	0.00 480	0.00 480

monomultidepth		0.00 451	0.00 454	0.00 465	0.00 488	0.00 489	0.00 477	0.00 487	0.00 483	0.00 484	0.00 483	0.00 486	0.00 482	0.00 483	0.00 480	0.00 480

test_blank		0.00 451	0.00 454	0.00 465	0.00 488	0.00 489	0.00 477	0.00 487	0.00 483	0.00 484	0.00 483	0.00 486	0.00 482	0.00 483	0.00 480	0.00 480

uanet_test		0.00 451	0.00 454	0.00 465	0.00 488	0.00 489	0.00 477	0.00 487	0.00 483	0.00 484	0.00 483	0.00 486	0.00 482	0.00 483	0.00 480	0.00 480

DCPMVS		0.00 451	0.00 454	0.00 465	0.00 488	0.00 489	0.00 477	0.00 487	0.00 483	0.00 484	0.00 483	0.00 486	0.00 482	0.00 483	0.00 480	0.00 480

cdsmvs_eth3d_5k		17.50 444	23.34 443	0.00 465	0.00 488	0.00 489	0.00 477	78.63 193	0.00 483	0.00 484	82.18 243	49.25 151	0.00 482	0.00 483	0.00 480	0.00 480

pcd_1.5k_mvsjas		3.92 450	5.23 453	0.00 465	0.00 488	0.00 489	0.00 477	0.00 487	0.00 483	0.00 484	0.00 483	47.05 183	0.00 482	0.00 483	0.00 480	0.00 480

sosnet-low-res		0.00 451	0.00 454	0.00 465	0.00 488	0.00 489	0.00 477	0.00 487	0.00 483	0.00 484	0.00 483	0.00 486	0.00 482	0.00 483	0.00 480	0.00 480

sosnet		0.00 451	0.00 454	0.00 465	0.00 488	0.00 489	0.00 477	0.00 487	0.00 483	0.00 484	0.00 483	0.00 486	0.00 482	0.00 483	0.00 480	0.00 480

uncertanet		0.00 451	0.00 454	0.00 465	0.00 488	0.00 489	0.00 477	0.00 487	0.00 483	0.00 484	0.00 483	0.00 486	0.00 482	0.00 483	0.00 480	0.00 480

Regformer		0.00 451	0.00 454	0.00 465	0.00 488	0.00 489	0.00 477	0.00 487	0.00 483	0.00 484	0.00 483	0.00 486	0.00 482	0.00 483	0.00 480	0.00 480

ab-mvs-re		6.49 447	8.65 450	0.00 465	0.00 488	0.00 489	0.00 477	0.00 487	0.00 483	0.00 484	77.89 331	0.00 486	0.00 482	0.00 483	0.00 480	0.00 480

uanet		0.00 451	0.00 454	0.00 465	0.00 488	0.00 489	0.00 477	0.00 487	0.00 483	0.00 484	0.00 483	0.00 486	0.00 482	0.00 483	0.00 480	0.00 480

TestfortrainingZip							86.84 11

WAC-MVS						27.31 460								27.77 445

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					0.00 488

eth-test					0.00 488

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																78.05 337

test_part2					87.58 9	60.47 42				83.42 15

sam_mvs1												34.74 329				78.05 337

sam_mvs												33.43 346

ambc				65.13 335	63.72 431	37.07 403	47.66 457	78.78 189		54.37 401	71.42 408	11.24 461	80.94 228	45.64 323	53.85 429	77.38 348

MTGPA								80.97 148

test_post1							68.67 348				3.64 478	32.39 367	69.49 371	44.17 335

test_post											3.55 479	33.90 340	66.52 391

patchmatchnet-post											64.03 445	34.50 331	74.27 341

GG-mvs-BLEND				62.34 357	71.36 359	37.04 404	69.20 345	57.33 424		54.73 395	65.48 443	30.37 378	77.82 294	34.82 408	74.93 227	72.17 409

MTMP							86.03 23	17.08 482

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		67.52 351			87.85 86	44.08 221			80.76 119	78.03 340

原ACMM2							79.02 128

testdata2													72.18 355	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								0.00 487

nn								0.00 487

door-mid								47.19 457

lessismore_v0				69.91 259	71.42 357	47.80 294		50.90 445		50.39 426	75.56 372	27.43 410	81.33 215	45.91 320	34.10 462	80.59 299

test11								83.47 79

door								47.60 455

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