High-res multi-view results

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

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

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

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

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

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

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

MM		80.20 7	80.28 8	79.99 2	82.19 85	60.01 49	86.19 17	83.93 55	73.19 1	77.08 38	91.21 18	57.23 33	90.73 10	83.35 1	88.12 34	89.22 6

MVS_0304		78.45 18	78.28 19	78.98 26	80.73 110	57.91 85	84.68 36	81.64 111	68.35 2	75.77 44	90.38 30	53.98 62	90.26 13	81.30 3	87.68 42	88.77 11

CANet		76.46 41	75.93 45	78.06 39	81.29 100	57.53 91	82.35 75	83.31 81	67.78 3	70.09 130	86.34 122	54.92 53	88.90 25	72.68 68	84.55 69	87.76 39

UA-Net		73.13 82	72.93 82	73.76 131	83.58 67	51.66 208	78.75 125	77.66 205	67.75 4	72.61 105	89.42 52	49.82 123	83.29 157	53.61 234	83.14 83	86.32 97

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 40	85.03 37	66.96 5	77.58 33	90.06 41	59.47 21	89.13 22	78.67 17	89.73 16	87.03 67

TranMVSNet+NR-MVSNet		70.36 138	70.10 133	71.17 213	78.64 161	42.97 324	76.53 190	81.16 132	66.95 6	68.53 161	85.42 150	51.61 102	83.07 161	52.32 242	69.70 294	87.46 49

3Dnovator+	66.72 4	75.84 51	74.57 63	79.66 9	82.40 82	59.92 51	85.83 23	86.32 16	66.92 7	67.80 184	89.24 56	42.03 224	89.38 19	64.07 135	86.50 59	89.69 3

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 31	84.42 46	66.73 8	74.67 67	89.38 54	55.30 48	89.18 21	74.19 56	87.34 46	86.38 89

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 76	62.18 16	87.60 9	85.83 20	66.69 9	78.03 30	90.98 19	54.26 59	90.06 14	78.42 23	89.02 23	87.69 40
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 83	72.16 92	75.90 74	75.95 243	56.28 110	83.05 62	72.39 289	66.53 10	65.27 235	87.00 98	50.40 118	85.47 112	62.48 157	86.32 60	85.94 109
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 120	71.00 114	71.44 202	79.20 143	44.13 310	76.02 205	82.60 98	66.48 11	68.20 166	84.60 164	56.82 37	82.82 173	54.62 224	70.43 274	87.36 58

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 26	63.71 12	89.23 20	81.51 2	88.44 27	88.09 28
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 66	65.37 13	78.78 24	90.64 22	58.63 25	87.24 55	79.00 14	90.37 14	85.26 146

NR-MVSNet		69.54 163	68.85 155	71.59 197	78.05 184	43.81 315	74.20 245	80.86 139	65.18 14	62.76 279	84.52 165	52.35 89	83.59 151	50.96 257	70.78 269	87.37 56

MTAPA		76.90 35	76.42 39	78.35 35	86.08 37	63.57 2	74.92 230	80.97 137	65.13 15	75.77 44	90.88 20	48.63 140	86.66 74	77.23 28	88.17 33	84.81 162

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 65	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 17

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 7	90.63 10	88.09 28

EI-MVSNet-Vis-set		72.42 98	71.59 98	74.91 95	78.47 165	54.02 153	77.05 177	79.33 164	65.03 18	71.68 116	79.35 285	52.75 81	84.89 125	66.46 115	74.23 214	85.83 115

casdiffmvs_mvg		76.14 47	76.30 40	75.66 82	76.46 237	51.83 207	79.67 114	85.08 34	65.02 19	75.84 43	88.58 68	59.42 22	85.08 118	72.75 67	83.93 78	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 11

ETV-MVS		74.46 68	73.84 73	76.33 70	79.27 141	55.24 136	79.22 120	85.00 39	64.97 21	72.65 104	79.46 281	53.65 73	87.87 44	67.45 108	82.91 89	85.89 112

NormalMVS		76.26 45	75.74 48	77.83 45	82.75 80	59.89 52	84.36 41	83.21 85	64.69 22	74.21 74	87.40 88	49.48 127	86.17 90	68.04 100	87.55 43	87.42 51

SymmetryMVS		75.28 56	74.60 62	77.30 54	83.85 65	59.89 52	84.36 41	75.51 241	64.69 22	74.21 74	87.40 88	49.48 127	86.17 90	68.04 100	83.88 79	85.85 113

WR-MVS		68.47 190	68.47 166	68.44 264	80.20 121	39.84 352	73.75 257	76.07 230	64.68 24	68.11 172	83.63 184	50.39 119	79.14 251	49.78 262	69.66 295	86.34 93

XVS		77.17 32	76.56 37	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 110	90.01 45	47.95 147	88.01 40	71.55 81	86.74 55	86.37 91

X-MVStestdata		70.21 141	67.28 197	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 110	6.49 453	47.95 147	88.01 40	71.55 81	86.74 55	86.37 91

HQP_MVS		74.31 69	73.73 74	76.06 72	81.41 97	56.31 108	84.22 46	84.01 53	64.52 27	69.27 149	86.10 129	45.26 190	87.21 59	68.16 98	80.58 115	84.65 166

plane_prior2							84.22 46		64.52 27

EI-MVSNet-UG-set		71.92 107	71.06 113	74.52 111	77.98 187	53.56 164	76.62 187	79.16 165	64.40 29	71.18 121	78.95 290	52.19 91	84.66 132	65.47 126	73.57 227	85.32 142

DU-MVS		70.01 146	69.53 140	71.44 202	78.05 184	44.13 310	75.01 226	81.51 114	64.37 30	68.20 166	84.52 165	49.12 137	82.82 173	54.62 224	70.43 274	87.37 56

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 69	87.85 5	85.03 37	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 134
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 69	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 74		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 74	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 23

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 42

LFMVS		71.78 109	71.59 98	72.32 178	83.40 71	46.38 286	79.75 112	71.08 298	64.18 34	72.80 101	88.64 67	42.58 219	83.72 147	57.41 202	84.49 72	86.86 72

IS-MVSNet		71.57 113	71.00 114	73.27 156	78.86 153	45.63 297	80.22 103	78.69 177	64.14 37	66.46 211	87.36 91	49.30 131	85.60 105	50.26 261	83.71 82	88.59 13

plane_prior3						56.09 114			63.92 38	69.27 149

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 70	63.89 39	73.60 83	90.60 23	54.85 54	86.72 72	77.20 29	88.06 36	85.74 122
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DELS-MVS		74.76 61	74.46 64	75.65 83	77.84 191	52.25 197	75.59 213	84.17 50	63.76 40	73.15 91	82.79 199	59.58 20	86.80 70	67.24 109	86.04 61	87.89 31
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 62	74.25 68	76.19 71	80.81 109	59.01 72	82.60 72	83.64 67	63.74 41	72.52 106	87.49 85	47.18 163	85.88 100	69.47 91	80.78 110	83.66 205
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 131	70.20 128	71.89 184	78.55 162	45.29 300	75.94 206	82.92 92	63.68 42	68.16 169	83.59 185	53.89 65	83.49 154	53.97 230	71.12 267	86.89 71

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 22	85.21 31	63.56 43	74.29 73	90.03 43	52.56 83	88.53 29	74.79 52	88.34 29	86.63 83

testing3-2		62.06 293	62.36 276	61.17 345	79.29 138	30.31 425	64.09 369	63.49 365	63.50 44	62.84 276	82.22 220	32.35 348	69.02 352	40.01 350	73.43 232	84.17 181

EC-MVSNet		75.84 51	75.87 47	75.74 80	78.86 153	52.65 187	83.73 56	86.08 18	63.47 45	72.77 102	87.25 95	53.13 77	87.93 42	71.97 76	85.57 64	86.66 81

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 19	63.32 46	75.08 54	90.47 29	53.96 64	88.68 27	76.48 34	89.63 20	87.16 64

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 27	86.42 14	63.28 47	83.27 13	91.83 10	64.96 7	90.47 11	76.41 35	89.67 18	86.84 73
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS		76.25 46	75.98 44	77.06 56	80.15 124	55.63 126	84.51 39	83.90 58	63.24 48	73.30 86	87.27 94	55.06 50	86.30 88	71.78 78	84.58 68	89.25 5

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 66	61.62 23	84.17 48	86.85 6	63.23 49	73.84 81	90.25 36	57.68 29	89.96 15	74.62 53	89.03 22	87.89 31
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 94	72.09 93	73.75 133	81.58 93	49.69 244	77.76 156	77.63 206	63.21 50	73.21 89	89.02 58	42.14 223	83.32 156	61.72 164	82.50 95	88.25 22

plane_prior						56.31 108	83.58 59		63.19 51						80.48 118

ACMMP		76.02 49	75.33 53	78.07 38	85.20 49	61.91 20	85.49 30	84.44 45	63.04 52	69.80 140	89.74 51	45.43 186	87.16 61	72.01 74	82.87 91	85.14 148
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		66.60 234	66.45 214	67.04 278	77.11 219	36.56 385	77.03 178	80.42 146	62.95 53	62.51 287	84.03 175	46.69 171	79.07 253	44.22 312	63.08 358	85.51 129

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 54	82.40 14	92.12 2	59.64 19	89.76 16	78.70 15	88.32 31	86.79 75
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mPP-MVS		76.54 40	75.93 45	78.34 36	86.47 26	63.50 3	85.74 26	82.28 101	62.90 55	71.77 114	90.26 35	46.61 172	86.55 79	71.71 79	85.66 63	84.97 157

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 55	85.16 32	62.88 56	78.10 28	91.26 17	52.51 84	88.39 30	79.34 9	90.52 13	86.78 76

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 63	85.33 29	62.86 57	80.17 17	90.03 43	61.76 14	88.95 24	74.21 55	88.67 26	88.12 27

HFP-MVS		78.01 24	77.65 26	79.10 21	86.71 19	62.81 8	86.29 14	84.32 48	62.82 58	73.96 78	90.50 27	53.20 76	88.35 31	74.02 58	87.05 47	86.13 104

ACMMPR		77.71 26	77.23 29	79.16 17	86.75 18	62.93 7	86.29 14	84.24 49	62.82 58	73.55 84	90.56 25	49.80 124	88.24 33	74.02 58	87.03 48	86.32 97

region2R		77.67 28	77.18 30	79.15 18	86.76 17	62.95 6	86.29 14	84.16 51	62.81 60	73.30 86	90.58 24	49.90 121	88.21 34	73.78 60	87.03 48	86.29 101

casdiffmvs		74.80 60	74.89 60	74.53 110	75.59 250	50.37 228	78.17 142	85.06 36	62.80 61	74.40 70	87.86 79	57.88 27	83.61 150	69.46 92	82.79 93	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 65	74.70 61	74.34 114	75.70 246	49.99 236	77.54 161	84.63 43	62.73 62	73.98 77	87.79 82	57.67 30	83.82 146	69.49 90	82.74 94	89.20 7

HPM-MVS		77.28 30	76.85 31	78.54 32	85.00 51	60.81 38	82.91 66	85.08 34	62.57 63	73.09 95	89.97 46	50.90 114	87.48 53	75.30 46	86.85 53	87.33 59
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 247	65.34 238	66.31 289	76.06 242	34.79 398	76.43 192	79.38 163	62.55 64	61.66 298	83.83 180	45.60 180	79.15 250	41.64 342	60.88 373	85.00 154

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 22	62.49 65	82.20 15	92.28 1	56.53 38	89.70 17	79.85 6	91.48 1	88.19 25
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		66.49 237	66.41 218	66.72 280	77.67 198	36.33 388	76.83 185	79.52 160	62.45 66	62.54 285	83.47 191	46.32 174	78.37 264	45.47 307	63.43 355	85.45 134

CP-MVS		77.12 33	76.68 33	78.43 33	86.05 38	63.18 5	87.55 10	83.45 73	62.44 67	72.68 103	90.50 27	48.18 145	87.34 54	73.59 62	85.71 62	84.76 165

PS-CasMVS		66.42 238	66.32 222	66.70 282	77.60 206	36.30 390	76.94 180	79.61 158	62.36 68	62.43 290	83.66 183	45.69 178	78.37 264	45.35 309	63.26 356	85.42 137

3Dnovator	64.47 5	72.49 95	71.39 104	75.79 77	77.70 196	58.99 73	80.66 99	83.15 89	62.24 69	65.46 231	86.59 113	42.38 222	85.52 108	59.59 183	84.72 67	82.85 228

MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 58	82.93 65	85.39 28	62.15 70	76.41 42	91.51 11	52.47 86	86.78 71	80.66 4	89.64 19	87.80 37
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 111		82.31 77		62.10 71	67.85 178

ACMP_Plane					80.66 111		82.31 77		62.10 71	67.85 178

HQP-MVS		73.45 77	72.80 84	75.40 87	80.66 111	54.94 139	82.31 77	83.90 58	62.10 71	67.85 178	85.54 148	45.46 184	86.93 67	67.04 111	80.35 119	84.32 174

SPE-MVS-test		75.62 54	75.31 54	76.56 67	80.63 114	55.13 137	83.88 54	85.22 30	62.05 74	71.49 119	86.03 132	53.83 66	86.36 86	67.74 103	86.91 52	88.19 25

VPNet		67.52 213	68.11 176	65.74 303	79.18 145	36.80 383	72.17 283	72.83 285	62.04 75	67.79 185	85.83 139	48.88 139	76.60 304	51.30 253	72.97 241	83.81 195

WR-MVS_H		67.02 225	66.92 207	67.33 277	77.95 188	37.75 372	77.57 159	82.11 104	62.03 76	62.65 282	82.48 213	50.57 117	79.46 241	42.91 330	64.01 348	84.79 163

DeepC-MVS_fast	68.24 3	77.25 31	76.63 34	79.12 20	86.15 34	60.86 36	84.71 35	84.85 41	61.98 77	73.06 96	88.88 62	53.72 69	89.06 23	68.27 95	88.04 37	87.42 51
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		78.82 13	79.22 12	77.60 47	82.88 78	57.83 86	84.99 32	88.13 2	61.86 78	79.16 21	90.75 21	57.96 26	87.09 64	77.08 31	90.18 15	87.87 33

PGM-MVS		76.77 38	76.06 43	78.88 28	86.14 35	62.73 9	82.55 73	83.74 65	61.71 79	72.45 109	90.34 33	48.48 143	88.13 37	72.32 71	86.85 53	85.78 116

fmvsm_s_conf0.5_n_8		74.30 70	74.39 65	74.01 123	75.33 256	52.89 182	78.24 138	77.32 214	61.65 80	78.13 27	88.90 61	52.82 80	81.54 199	78.46 22	78.67 152	87.60 45

Effi-MVS+		73.31 80	72.54 88	75.62 84	77.87 189	53.64 161	79.62 116	79.61 158	61.63 81	72.02 112	82.61 204	56.44 40	85.97 98	63.99 138	79.07 144	87.25 61

MG-MVS		73.96 73	73.89 72	74.16 120	85.65 42	49.69 244	81.59 88	81.29 125	61.45 82	71.05 122	88.11 71	51.77 99	87.73 48	61.05 169	83.09 84	85.05 153

fmvsm_s_conf0.5_n_9		75.16 57	75.22 56	75.01 94	78.34 172	55.37 134	77.30 169	73.95 272	61.40 83	79.46 19	90.14 37	57.07 34	81.15 209	80.00 5	79.31 136	88.51 16

LPG-MVS_test		72.74 88	71.74 97	75.76 78	80.22 119	57.51 92	82.55 73	83.40 75	61.32 84	66.67 208	87.33 92	39.15 262	86.59 75	67.70 104	77.30 177	83.19 218

LGP-MVS_train				75.76 78	80.22 119	57.51 92		83.40 75	61.32 84	66.67 208	87.33 92	39.15 262	86.59 75	67.70 104	77.30 177	83.19 218

CLD-MVS		73.33 79	72.68 86	75.29 91	78.82 155	53.33 171	78.23 139	84.79 42	61.30 86	70.41 127	81.04 247	52.41 87	87.12 62	64.61 134	82.49 96	85.41 138
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT-MVS		71.46 116	70.70 119	73.74 134	77.76 194	49.30 251	76.60 188	80.45 145	61.25 87	68.17 168	84.78 156	44.64 197	84.90 124	64.79 130	77.88 166	87.03 67

fmvsm_s_conf0.5_n_3		73.55 76	74.39 65	71.03 217	74.09 290	51.86 206	77.77 155	75.60 237	61.18 88	78.67 25	88.98 59	55.88 45	77.73 278	78.69 16	78.68 151	83.50 210

MVS_111021_HR		74.02 72	73.46 77	75.69 81	83.01 76	60.63 40	77.29 170	78.40 194	61.18 88	70.58 125	85.97 134	54.18 61	84.00 143	67.52 107	82.98 88	82.45 239

balanced_conf03		76.58 39	76.55 38	76.68 62	81.73 91	52.90 180	80.94 94	85.70 24	61.12 90	74.90 60	87.17 96	56.46 39	88.14 36	72.87 66	88.03 38	89.00 8

FIs		70.82 128	71.43 102	68.98 257	78.33 173	38.14 368	76.96 179	83.59 69	61.02 91	67.33 192	86.73 106	55.07 49	81.64 195	54.61 226	79.22 139	87.14 65

FOURS1					86.12 36	60.82 37	88.18 1	83.61 68	60.87 92	81.50 16

FC-MVSNet-test		69.80 153	70.58 122	67.46 273	77.61 205	34.73 401	76.05 203	83.19 88	60.84 93	65.88 225	86.46 119	54.52 58	80.76 222	52.52 241	78.12 162	86.91 70

v8		70.33 139	69.28 146	73.49 148	73.15 302	50.22 230	78.62 129	80.78 140	60.79 94	66.45 212	82.11 227	49.35 130	84.98 121	63.58 147	68.71 310	85.28 144

CSCG		76.92 34	76.75 32	77.41 51	83.96 64	59.60 56	82.95 64	86.50 13	60.78 95	75.27 49	84.83 154	60.76 15	86.56 77	67.86 102	87.87 41	86.06 106

Vis-MVSNet		72.18 101	71.37 105	74.61 105	81.29 100	55.41 132	80.90 95	78.28 196	60.73 96	69.23 152	88.09 72	44.36 201	82.65 177	57.68 199	81.75 106	85.77 119
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		71.26 119	70.16 130	74.57 108	74.59 273	52.77 186	75.91 207	81.20 129	60.72 97	69.10 155	85.71 143	41.67 231	83.53 152	63.91 141	78.62 154	87.42 51

BP-MVS1		73.41 78	72.25 91	76.88 57	76.68 230	53.70 159	79.15 121	81.07 133	60.66 98	71.81 113	87.39 90	40.93 244	87.24 55	71.23 83	81.29 109	89.71 2

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 28	84.36 47	60.61 99	79.05 22	90.30 34	55.54 47	88.32 32	73.48 63	87.03 48	84.83 161
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 99	71.20 110	75.59 86	80.28 117	57.54 90	82.74 69	82.84 96	60.58 100	65.24 239	86.18 126	39.25 260	86.03 96	66.95 114	76.79 185	83.22 216
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
lecture		77.75 25	77.84 25	77.50 49	82.75 80	57.62 89	85.92 21	86.20 17	60.53 101	78.99 23	91.45 12	51.51 104	87.78 47	75.65 42	87.55 43	87.10 66

testdata1							72.65 273		60.50 102

UGNet		68.81 180	67.39 192	73.06 159	78.33 173	54.47 145	79.77 111	75.40 244	60.45 103	63.22 268	84.40 168	32.71 337	80.91 218	51.71 251	80.56 117	83.81 195
Wanjuan Su, Qingshan Xu, Wenbing Tao: Uncertainty-guided Multi-view Stereo Network for Depth Estimation. IEEE Transactions on Circuits and Systems for Video Technology, 2022
h-mvs33		72.71 89	71.49 101	76.40 68	81.99 88	59.58 57	76.92 181	76.74 223	60.40 104	74.81 62	85.95 135	45.54 182	85.76 103	70.41 87	70.61 272	83.86 194

hse-mvs2		71.04 121	69.86 134	74.60 106	79.58 133	57.12 102	73.96 249	75.25 247	60.40 104	74.81 62	81.95 229	45.54 182	82.90 166	70.41 87	66.83 327	83.77 199

EPP-MVSNet		72.16 104	71.31 107	74.71 99	78.68 159	49.70 242	82.10 81	81.65 110	60.40 104	65.94 221	85.84 138	51.74 100	86.37 85	55.93 210	79.55 131	88.07 30

UniMVSNet_ETH3D		67.60 212	67.07 206	69.18 254	77.39 211	42.29 329	74.18 246	75.59 238	60.37 107	66.77 204	86.06 131	37.64 278	78.93 260	52.16 244	73.49 229	86.32 97

test_prior2							81.75 84		60.37 107	75.01 55	89.06 57	56.22 42		72.19 72	88.96 24

SD-MVS		77.70 27	77.62 27	77.93 42	84.47 59	61.88 21	84.55 38	83.87 61	60.37 107	79.89 18	89.38 54	54.97 52	85.58 107	76.12 38	84.94 66	86.33 95
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		69.68 157	70.19 129	68.16 267	79.73 130	41.63 338	70.53 307	77.38 211	60.37 107	70.69 124	86.63 111	51.08 110	77.09 290	53.61 234	81.69 108	85.75 121

sasdasda		74.67 63	74.98 58	73.71 136	78.94 151	50.56 225	80.23 101	83.87 61	60.30 111	77.15 36	86.56 115	59.65 17	82.00 189	66.01 120	82.12 97	88.58 14

canonicalmvs		74.67 63	74.98 58	73.71 136	78.94 151	50.56 225	80.23 101	83.87 61	60.30 111	77.15 36	86.56 115	59.65 17	82.00 189	66.01 120	82.12 97	88.58 14

v7n		69.01 177	67.36 194	73.98 124	72.51 316	52.65 187	78.54 133	81.30 124	60.26 113	62.67 281	81.62 236	43.61 207	84.49 133	57.01 203	68.70 311	84.79 163

reproduce-ours		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 114	77.85 31	91.42 14	50.67 115	87.69 49	72.46 69	84.53 70	85.46 132

our_new_method		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 114	77.85 31	91.42 14	50.67 115	87.69 49	72.46 69	84.53 70	85.46 132

HPM-MVS_fast		74.30 70	73.46 77	76.80 59	84.45 60	59.04 71	83.65 58	81.05 134	60.15 116	70.43 126	89.84 48	41.09 243	85.59 106	67.61 106	82.90 90	85.77 119

VPA-MVSNet		69.02 176	69.47 142	67.69 271	77.42 210	41.00 345	74.04 247	79.68 156	60.06 117	69.26 151	84.81 155	51.06 111	77.58 280	54.44 227	74.43 212	84.48 171

v10		70.21 141	69.02 151	73.81 128	73.51 296	50.92 217	78.74 126	81.39 117	60.05 118	66.39 213	81.83 232	47.58 154	85.41 115	62.80 154	68.86 309	85.09 152

SR-MVS		76.13 48	75.70 49	77.40 53	85.87 40	61.20 29	85.52 28	82.19 102	59.99 119	75.10 53	90.35 32	47.66 152	86.52 80	71.64 80	82.99 86	84.47 172

SSC-MVS3.2		60.57 306	61.39 288	58.12 367	74.29 283	32.63 415	59.52 393	65.53 346	59.90 120	62.45 288	79.75 274	41.96 225	63.90 383	39.47 354	69.65 297	77.84 319

9.14			78.75 15		83.10 73		84.15 49	88.26 1	59.90 120	78.57 26	90.36 31	57.51 32	86.86 69	77.39 27	89.52 21

v2v482		70.50 134	69.45 143	73.66 139	72.62 312	50.03 235	77.58 158	80.51 144	59.90 120	69.52 142	82.14 225	47.53 156	84.88 127	65.07 129	70.17 282	86.09 105

Baseline_NR-MVSNet		67.05 224	67.56 184	65.50 307	75.65 247	37.70 374	75.42 216	74.65 260	59.90 120	68.14 170	83.15 197	49.12 137	77.20 288	52.23 243	69.78 291	81.60 252

API-MVS		72.17 102	71.41 103	74.45 112	81.95 89	57.22 95	84.03 51	80.38 147	59.89 124	68.40 163	82.33 216	49.64 125	87.83 46	51.87 248	84.16 77	78.30 310

Effi-MVS+-dtu		69.64 159	67.53 187	75.95 73	76.10 241	62.29 15	80.20 104	76.06 231	59.83 125	65.26 238	77.09 323	41.56 234	84.02 142	60.60 174	71.09 268	81.53 253

reproduce_model		76.43 42	76.08 42	77.49 50	83.47 70	60.09 47	84.60 37	82.90 93	59.65 126	77.31 34	91.43 13	49.62 126	87.24 55	71.99 75	83.75 81	85.14 148

MVSMamba_PlusPlus		75.75 53	75.44 51	76.67 63	80.84 108	53.06 177	78.62 129	85.13 33	59.65 126	71.53 118	87.47 86	56.92 35	88.17 35	72.18 73	86.63 58	88.80 10

CANet_DTU		68.18 198	67.71 183	69.59 245	74.83 266	46.24 288	78.66 128	76.85 220	59.60 128	63.45 266	82.09 228	35.25 302	77.41 283	59.88 180	78.76 149	85.14 148

EI-MVSNet		69.27 172	68.44 168	71.73 191	74.47 276	49.39 249	75.20 221	78.45 190	59.60 128	69.16 153	76.51 335	51.29 106	82.50 181	59.86 182	71.45 264	83.30 213

IterMVS-LS		69.22 174	68.48 164	71.43 204	74.44 278	49.40 248	76.23 197	77.55 207	59.60 128	65.85 226	81.59 239	51.28 107	81.58 198	59.87 181	69.90 289	83.30 213
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 96	73.34 79	69.81 242	77.77 193	43.21 321	75.84 210	81.18 130	59.59 131	75.45 47	86.64 109	57.74 28	77.94 271	63.92 139	81.90 102	88.30 20

VDDNet		71.81 108	71.33 106	73.26 157	82.80 79	47.60 277	78.74 126	75.27 246	59.59 131	72.94 98	89.40 53	41.51 236	83.91 144	58.75 194	82.99 86	88.26 21

alignmvs		73.86 74	73.99 70	73.45 150	78.20 176	50.50 227	78.57 131	82.43 99	59.40 133	76.57 40	86.71 108	56.42 41	81.23 208	65.84 123	81.79 103	88.62 12

MVS_Test		72.45 96	72.46 89	72.42 176	74.88 263	48.50 264	76.28 195	83.14 90	59.40 133	72.46 107	84.68 157	55.66 46	81.12 210	65.98 122	79.66 128	87.63 43

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 51	83.82 64	59.34 135	79.37 20	89.76 50	59.84 16	87.62 52	76.69 32	86.74 55	87.68 41
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 75	73.47 76	74.66 102	83.02 75	59.29 63	82.30 80	81.88 106	59.34 135	71.59 117	86.83 102	45.94 177	83.65 149	65.09 128	85.22 65	81.06 268

PAPM_NR		72.63 92	71.80 96	75.13 92	81.72 92	53.42 169	79.91 109	83.28 83	59.14 137	66.31 215	85.90 136	51.86 97	86.06 94	57.45 201	80.62 113	85.91 111

testing91		64.46 263	63.80 254	66.47 286	78.43 167	40.06 350	67.63 335	69.59 312	59.06 138	63.18 270	78.05 303	34.05 315	76.99 294	48.30 278	75.87 196	82.37 241

myMVS_eth3d28		60.66 305	61.04 296	59.51 352	77.32 213	31.58 420	63.11 373	63.87 361	59.00 139	60.90 307	78.26 300	32.69 339	66.15 373	36.10 380	78.13 161	80.81 273

save fliter					86.17 33	61.30 28	83.98 53	79.66 157	59.00 139

v148		68.24 196	67.19 204	71.40 205	70.43 353	47.77 274	75.76 211	77.03 218	58.91 141	67.36 191	80.10 267	48.60 142	81.89 191	60.01 178	66.52 330	84.53 169

TransMVSNet (Re)		64.72 257	64.33 247	65.87 302	75.22 257	38.56 364	74.66 236	75.08 255	58.90 142	61.79 296	82.63 203	51.18 108	78.07 269	43.63 323	55.87 396	80.99 270

Anonymous202405211		66.84 229	65.99 228	69.40 249	80.19 122	42.21 331	71.11 300	71.31 297	58.80 143	67.90 176	86.39 121	29.83 365	79.65 238	49.60 268	78.78 148	86.33 95

test2506		65.33 252	64.61 245	67.50 272	79.46 136	34.19 406	74.43 242	51.92 416	58.72 144	66.75 205	88.05 74	25.99 398	80.92 217	51.94 247	84.25 74	87.39 54

ECVR-MVS		67.72 210	67.51 188	68.35 265	79.46 136	36.29 391	74.79 233	66.93 335	58.72 144	67.19 196	88.05 74	36.10 295	81.38 203	52.07 245	84.25 74	87.39 54

test1111		67.21 217	67.14 205	67.42 274	79.24 142	34.76 400	73.89 254	65.65 344	58.71 146	66.96 201	87.95 78	36.09 296	80.53 224	52.03 246	83.79 80	86.97 69

LCM-MVSNet-Re		61.88 296	61.35 289	63.46 325	74.58 274	31.48 421	61.42 383	58.14 394	58.71 146	53.02 390	79.55 279	43.07 213	76.80 298	45.69 300	77.96 164	82.11 247

testing99		64.05 267	63.29 265	66.34 288	78.17 180	39.76 354	67.33 340	68.00 326	58.60 148	63.03 273	78.10 302	32.57 344	76.94 296	48.22 279	75.58 200	82.34 242

v1144		70.42 136	69.31 145	73.76 131	73.22 300	50.64 222	77.83 153	81.43 116	58.58 149	69.40 146	81.16 244	47.53 156	85.29 117	64.01 137	70.64 270	85.34 141

TSAR-MVS + GP.		74.90 59	74.15 69	77.17 55	82.00 87	58.77 77	81.80 83	78.57 183	58.58 149	74.32 72	84.51 167	55.94 44	87.22 58	67.11 110	84.48 73	85.52 128

BH-RMVSNet		68.81 180	67.42 191	72.97 160	80.11 125	52.53 191	74.26 244	76.29 226	58.48 151	68.38 164	84.20 170	42.59 218	83.83 145	46.53 292	75.91 195	82.56 233

APD-MVS_3200maxsize		74.96 58	74.39 65	76.67 63	82.20 84	58.24 82	83.67 57	83.29 82	58.41 152	73.71 82	90.14 37	45.62 179	85.99 97	69.64 89	82.85 92	85.78 116

OMC-MVS		71.40 118	70.60 120	73.78 129	76.60 233	53.15 174	79.74 113	79.78 154	58.37 153	68.75 157	86.45 120	45.43 186	80.60 223	62.58 155	77.73 167	87.58 47

nrg030		72.96 85	73.01 81	72.84 163	75.41 254	50.24 229	80.02 105	82.89 95	58.36 154	74.44 69	86.73 106	58.90 24	80.83 219	65.84 123	74.46 210	87.44 50

K. test v3		60.47 309	57.11 328	70.56 227	73.74 293	48.22 267	75.10 225	62.55 373	58.27 155	53.62 385	76.31 339	27.81 382	81.59 197	47.42 283	39.18 432	81.88 250

FA-MVS(test-final)		69.82 151	68.48 164	73.84 127	78.44 166	50.04 234	75.58 215	78.99 169	58.16 156	67.59 188	82.14 225	42.66 217	85.63 104	56.60 205	76.19 191	85.84 114

MVS_111021_LR		69.50 166	68.78 158	71.65 195	78.38 168	59.33 61	74.82 232	70.11 306	58.08 157	67.83 183	84.68 157	41.96 225	76.34 309	65.62 125	77.54 170	79.30 301

SR-MVS-dyc-post		74.57 66	73.90 71	76.58 66	83.49 68	59.87 54	84.29 43	81.36 119	58.07 158	73.14 92	90.07 39	44.74 195	85.84 101	68.20 96	81.76 104	84.03 184

RE-MVS-def			73.71 75		83.49 68	59.87 54	84.29 43	81.36 119	58.07 158	73.14 92	90.07 39	43.06 214		68.20 96	81.76 104	84.03 184

SDMVSNet		68.03 201	68.10 177	67.84 269	77.13 217	48.72 262	65.32 356	79.10 166	58.02 160	65.08 242	82.55 209	47.83 149	73.40 323	63.92 139	73.92 218	81.41 255

sd_testset		64.46 263	64.45 246	64.51 317	77.13 217	42.25 330	62.67 376	72.11 292	58.02 160	65.08 242	82.55 209	41.22 242	69.88 348	47.32 285	73.92 218	81.41 255

GeoE		71.01 122	70.15 131	73.60 144	79.57 134	52.17 198	78.93 124	78.12 198	58.02 160	67.76 187	83.87 179	52.36 88	82.72 175	56.90 204	75.79 197	85.92 110

ZD-MVS					86.64 21	60.38 45		82.70 97	57.95 163	78.10 28	90.06 41	56.12 43	88.84 26	74.05 57	87.00 51

EIA-MVS		71.78 109	70.60 120	75.30 90	79.85 128	53.54 165	77.27 172	83.26 84	57.92 164	66.49 210	79.39 283	52.07 94	86.69 73	60.05 177	79.14 143	85.66 124

test_yl		69.69 155	69.13 148	71.36 206	78.37 170	45.74 293	74.71 234	80.20 149	57.91 165	70.01 135	83.83 180	42.44 220	82.87 169	54.97 220	79.72 126	85.48 130

DCV-MVSNet		69.69 155	69.13 148	71.36 206	78.37 170	45.74 293	74.71 234	80.20 149	57.91 165	70.01 135	83.83 180	42.44 220	82.87 169	54.97 220	79.72 126	85.48 130

MonoMVSNet		64.15 266	63.31 264	66.69 283	70.51 351	44.12 312	74.47 240	74.21 267	57.81 167	63.03 273	76.62 331	38.33 271	77.31 286	54.22 228	60.59 378	78.64 308

dcpmvs_2		74.55 67	75.23 55	72.48 172	82.34 83	53.34 170	77.87 150	81.46 115	57.80 168	75.49 46	86.81 103	62.22 13	77.75 277	71.09 84	82.02 100	86.34 93

fmvsm_s_conf0.5_n_6		72.59 93	72.87 83	71.73 191	75.14 261	51.96 204	76.28 195	77.12 217	57.63 169	73.85 80	86.91 100	51.54 103	77.87 274	77.18 30	80.18 123	85.37 140

Fast-Effi-MVS+-dtu		67.37 215	65.33 239	73.48 149	72.94 307	57.78 88	77.47 163	76.88 219	57.60 170	61.97 293	76.85 327	39.31 258	80.49 227	54.72 223	70.28 280	82.17 246

v1192		69.97 148	68.68 160	73.85 126	73.19 301	50.94 215	77.68 157	81.36 119	57.51 171	68.95 156	80.85 254	45.28 189	85.33 116	62.97 153	70.37 276	85.27 145

ACMH+	57.40 11	66.12 241	64.06 249	72.30 179	77.79 192	52.83 184	80.39 100	78.03 199	57.30 172	57.47 345	82.55 209	27.68 384	84.17 137	45.54 303	69.78 291	79.90 290

diffmvs		70.69 130	70.43 123	71.46 200	69.45 369	48.95 258	72.93 270	78.46 189	57.27 173	71.69 115	83.97 178	51.48 105	77.92 273	70.70 86	77.95 165	87.53 48
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		68.27 194	67.29 196	71.21 210	79.74 129	53.22 172	76.06 202	77.46 210	57.19 174	66.10 218	81.61 237	45.37 188	83.50 153	45.42 308	76.68 187	76.91 335

thres100view900		63.28 276	62.41 275	65.89 300	77.31 214	38.66 363	72.65 273	69.11 319	57.07 175	62.45 288	81.03 248	37.01 290	79.17 247	31.84 401	73.25 236	79.83 293

fmvsm_s_conf0.5_n_7		69.54 163	69.67 138	69.15 256	73.47 298	51.41 210	70.35 311	73.34 278	57.05 176	68.41 162	85.83 139	49.86 122	72.84 326	71.86 77	76.83 184	83.19 218

DP-MVS Recon		72.15 105	70.73 118	76.40 68	86.57 24	57.99 84	81.15 93	82.96 91	57.03 177	66.78 203	85.56 145	44.50 199	88.11 38	51.77 250	80.23 122	83.10 223

thres600view7		63.30 275	62.27 277	66.41 287	77.18 216	38.87 361	72.35 280	69.11 319	56.98 178	62.37 291	80.96 250	37.01 290	79.00 258	31.43 408	73.05 240	81.36 258

V42		68.65 184	67.35 195	72.56 169	68.93 375	50.18 231	72.90 271	79.47 161	56.92 179	69.45 145	80.26 263	46.29 175	82.99 163	64.07 135	67.82 318	84.53 169

MCST-MVS		77.48 29	77.45 28	77.54 48	86.67 20	58.36 81	83.22 61	86.93 5	56.91 180	74.91 59	88.19 70	59.15 23	87.68 51	73.67 61	87.45 45	86.57 84

GA-MVS		65.53 248	63.70 256	71.02 218	70.87 346	48.10 269	70.48 308	74.40 262	56.69 181	64.70 251	76.77 328	33.66 323	81.10 211	55.42 219	70.32 279	83.87 193

v144192		69.71 154	68.51 163	73.33 155	73.10 303	50.13 232	77.54 161	80.64 141	56.65 182	68.57 160	80.55 257	46.87 170	84.96 123	62.98 152	69.66 295	84.89 160

fmvsm_l_conf0.5_n_3		73.23 81	73.13 80	73.55 146	74.40 279	55.13 137	78.97 123	74.96 256	56.64 183	74.76 65	88.75 66	55.02 51	78.77 262	76.33 36	78.31 160	86.74 77

tfpn200view9		63.18 278	62.18 279	66.21 292	76.85 227	39.62 355	71.96 287	69.44 315	56.63 184	62.61 283	79.83 270	37.18 284	79.17 247	31.84 401	73.25 236	79.83 293

thres400		63.31 274	62.18 279	66.72 280	76.85 227	39.62 355	71.96 287	69.44 315	56.63 184	62.61 283	79.83 270	37.18 284	79.17 247	31.84 401	73.25 236	81.36 258

GBi-Net		67.21 217	66.55 212	69.19 251	77.63 200	43.33 318	77.31 166	77.83 202	56.62 186	65.04 244	82.70 200	41.85 228	80.33 229	47.18 287	72.76 244	83.92 190

test1		67.21 217	66.55 212	69.19 251	77.63 200	43.33 318	77.31 166	77.83 202	56.62 186	65.04 244	82.70 200	41.85 228	80.33 229	47.18 287	72.76 244	83.92 190

FMVSNet2		66.93 227	66.31 223	68.79 260	77.63 200	42.98 323	76.11 200	77.47 208	56.62 186	65.22 241	82.17 223	41.85 228	80.18 235	47.05 290	72.72 247	83.20 217

DPM-MVS		75.47 55	75.00 57	76.88 57	81.38 99	59.16 64	79.94 107	85.71 23	56.59 189	72.46 107	86.76 104	56.89 36	87.86 45	66.36 116	88.91 25	83.64 207

v1921920		69.47 167	68.17 174	73.36 154	73.06 304	50.10 233	77.39 164	80.56 142	56.58 190	68.59 158	80.37 259	44.72 196	84.98 121	62.47 158	69.82 290	85.00 154

FMVSNet1		66.70 232	65.87 229	69.19 251	77.49 208	43.33 318	77.31 166	77.83 202	56.45 191	64.60 253	82.70 200	38.08 276	80.33 229	46.08 296	72.31 253	83.92 190

v1240		69.24 173	67.91 179	73.25 158	73.02 306	49.82 237	77.21 173	80.54 143	56.43 192	68.34 165	80.51 258	43.33 210	84.99 119	62.03 162	69.77 293	84.95 158

fmvsm_s_conf0.5_n_4		72.04 106	71.85 95	72.58 168	73.74 293	52.49 193	76.69 186	72.42 288	56.42 193	75.32 48	87.04 97	52.13 93	78.01 270	79.29 12	73.65 224	87.26 60

testing222		62.29 290	61.31 290	65.25 312	77.87 189	38.53 365	68.34 329	66.31 341	56.37 194	63.15 272	77.58 317	28.47 376	76.18 312	37.04 369	76.65 188	81.05 269

CDPH-MVS		76.31 43	75.67 50	78.22 37	85.35 48	59.14 67	81.31 91	84.02 52	56.32 195	74.05 76	88.98 59	53.34 75	87.92 43	69.23 93	88.42 28	87.59 46

Vis-MVSNet (Re-imp)		63.69 271	63.88 252	63.14 329	74.75 268	31.04 423	71.16 298	63.64 364	56.32 195	59.80 319	84.99 152	44.51 198	75.46 314	39.12 356	80.62 113	82.92 225

AdaColmap		69.99 147	68.66 161	73.97 125	84.94 54	57.83 86	82.63 71	78.71 176	56.28 197	64.34 254	84.14 172	41.57 233	87.06 65	46.45 293	78.88 145	77.02 331

PS-MVSNAJss		72.24 100	71.21 109	75.31 89	78.50 163	55.93 118	81.63 85	82.12 103	56.24 198	70.02 134	85.68 144	47.05 165	84.34 136	65.27 127	74.41 213	85.67 123

c3_l		68.33 193	67.56 184	70.62 226	70.87 346	46.21 289	74.47 240	78.80 174	56.22 199	66.19 216	78.53 298	51.88 96	81.40 202	62.08 159	69.04 305	84.25 177

Fast-Effi-MVS+		70.28 140	69.12 150	73.73 135	78.50 163	51.50 209	75.01 226	79.46 162	56.16 200	68.59 158	79.55 279	53.97 63	84.05 139	53.34 236	77.53 171	85.65 125

PHI-MVS		75.87 50	75.36 52	77.41 51	80.62 115	55.91 119	84.28 45	85.78 21	56.08 201	73.41 85	86.58 114	50.94 113	88.54 28	70.79 85	89.71 17	87.79 38

baseline1		63.81 270	63.87 253	63.62 324	76.29 238	36.36 386	71.78 290	67.29 331	56.05 202	64.23 259	82.95 198	47.11 164	74.41 319	47.30 286	61.85 367	80.10 287

train_agg		76.27 44	76.15 41	76.64 65	85.58 43	61.59 24	81.62 86	81.26 126	55.86 203	74.93 57	88.81 63	53.70 70	84.68 130	75.24 48	88.33 30	83.65 206

test_8					85.40 46	60.96 34	81.54 89	81.18 130	55.86 203	74.81 62	88.80 65	53.70 70	84.45 134

FMVSNet3		66.32 240	65.61 234	68.46 263	76.48 236	42.34 328	74.98 228	77.15 216	55.83 205	65.04 244	81.16 244	39.91 251	80.14 236	47.18 287	72.76 244	82.90 227

PAPR		71.72 112	70.82 116	74.41 113	81.20 104	51.17 211	79.55 118	83.33 80	55.81 206	66.93 202	84.61 161	50.95 112	86.06 94	55.79 213	79.20 140	86.00 107

eth_miper_zixun_eth		67.63 211	66.28 224	71.67 194	71.60 332	48.33 266	73.68 258	77.88 200	55.80 207	65.91 222	78.62 296	47.35 162	82.88 168	59.45 184	66.25 331	83.81 195

ACMH	55.70 15	65.20 254	63.57 258	70.07 235	78.07 183	52.01 203	79.48 119	79.69 155	55.75 208	56.59 352	80.98 249	27.12 389	80.94 215	42.90 331	71.58 262	77.25 329
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 251	62.73 272	73.40 153	74.89 262	52.78 185	73.09 269	75.13 251	55.69 209	58.48 337	73.73 368	32.86 332	86.32 87	50.63 258	70.11 283	81.10 267
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		61.53 299	60.94 298	63.30 327	68.95 374	36.93 382	67.60 336	72.80 286	55.67 210	59.95 316	76.63 330	45.01 194	72.22 332	39.74 353	62.09 366	80.74 275

TEST9					85.58 43	61.59 24	81.62 86	81.26 126	55.65 211	74.93 57	88.81 63	53.70 70	84.68 130

thres200		62.20 291	61.16 295	65.34 310	75.38 255	39.99 351	69.60 320	69.29 317	55.64 212	61.87 295	76.99 324	37.07 289	78.96 259	31.28 409	73.28 235	77.06 330

guyue		68.10 200	67.23 203	70.71 225	73.67 295	49.27 252	73.65 259	76.04 232	55.62 213	67.84 182	82.26 219	41.24 241	78.91 261	61.01 170	73.72 222	83.94 188

pm-mvs1		65.24 253	64.97 243	66.04 297	72.38 319	39.40 358	72.62 275	75.63 236	55.53 214	62.35 292	83.18 196	47.45 158	76.47 307	49.06 272	66.54 329	82.24 243

testing11		62.81 282	61.90 282	65.54 305	78.38 168	40.76 347	67.59 337	66.78 337	55.48 215	60.13 311	77.11 322	31.67 351	76.79 299	45.53 304	74.45 211	79.06 303

ACMM	61.98 7	70.80 129	69.73 136	74.02 122	80.59 116	58.59 79	82.68 70	82.02 105	55.46 216	67.18 197	84.39 169	38.51 268	83.17 160	60.65 173	76.10 193	80.30 282
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AstraMVS		67.86 207	66.83 208	70.93 219	73.50 297	49.34 250	73.28 266	74.01 270	55.45 217	68.10 173	83.28 192	38.93 265	79.14 251	63.22 150	71.74 259	84.30 176

Anonymous20240529		69.91 149	69.02 151	72.56 169	80.19 122	47.65 275	77.56 160	80.99 136	55.45 217	69.88 138	86.76 104	39.24 261	82.18 187	54.04 229	77.10 181	87.85 34

tt0805		67.77 209	67.24 201	69.34 250	74.87 264	40.08 349	77.36 165	81.37 118	55.31 219	66.33 214	84.65 159	37.35 282	82.55 180	55.65 216	72.28 254	85.39 139

GDP-MVS		72.64 91	71.28 108	76.70 60	77.72 195	54.22 151	79.57 117	84.45 44	55.30 220	71.38 120	86.97 99	39.94 250	87.00 66	67.02 113	79.20 140	88.89 9

CPTT-MVS		72.78 87	72.08 94	74.87 97	84.88 57	61.41 26	84.15 49	77.86 201	55.27 221	67.51 190	88.08 73	41.93 227	81.85 192	69.04 94	80.01 124	81.35 260

XVG-OURS		68.76 183	67.37 193	72.90 162	74.32 282	57.22 95	70.09 315	78.81 173	55.24 222	67.79 185	85.81 142	36.54 293	78.28 266	62.04 161	75.74 198	83.19 218

tfpnnormal		62.47 286	61.63 285	64.99 314	74.81 267	39.01 360	71.22 296	73.72 274	55.22 223	60.21 310	80.09 268	41.26 240	76.98 295	30.02 414	68.09 316	78.97 306

cl____		67.18 220	66.26 225	69.94 237	70.20 356	45.74 293	73.30 263	76.83 221	55.10 224	65.27 235	79.57 278	47.39 160	80.53 224	59.41 186	69.22 303	83.53 209

DIV-MVS_self_test		67.18 220	66.26 225	69.94 237	70.20 356	45.74 293	73.29 265	76.83 221	55.10 224	65.27 235	79.58 277	47.38 161	80.53 224	59.43 185	69.22 303	83.54 208

PC_three_1452									55.09 226	84.46 4	89.84 48	66.68 5	89.41 18	74.24 54	91.38 2	88.42 17

EPNet_dtu		61.90 295	61.97 281	61.68 338	72.89 308	39.78 353	75.85 209	65.62 345	55.09 226	54.56 375	79.36 284	37.59 279	67.02 367	39.80 352	76.95 182	78.25 311
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 117	70.39 124	74.65 103	82.01 86	58.82 76	79.93 108	80.35 148	55.09 226	65.82 227	82.16 224	49.17 134	82.64 178	60.34 175	78.62 154	82.50 238

cl22		67.47 214	66.45 214	70.54 228	69.85 364	46.49 285	73.85 255	77.35 212	55.07 229	65.51 230	77.92 307	47.64 153	81.10 211	61.58 167	69.32 299	84.01 186

miper_ehance_all_eth		68.03 201	67.24 201	70.40 230	70.54 350	46.21 289	73.98 248	78.68 178	55.07 229	66.05 219	77.80 311	52.16 92	81.31 205	61.53 168	69.32 299	83.67 203

fmvsm_s_conf0.5_n_2		69.82 151	69.27 147	71.46 200	72.00 326	51.08 212	73.30 263	67.79 327	55.06 231	75.24 50	87.51 84	44.02 204	77.00 293	75.67 41	72.86 242	86.31 100

Elysia		70.19 143	68.29 171	75.88 75	74.15 286	54.33 149	78.26 135	83.21 85	55.04 232	67.28 193	83.59 185	30.16 360	86.11 92	63.67 145	79.26 137	87.20 62

StellarMVS		70.19 143	68.29 171	75.88 75	74.15 286	54.33 149	78.26 135	83.21 85	55.04 232	67.28 193	83.59 185	30.16 360	86.11 92	63.67 145	79.26 137	87.20 62

PS-MVSNAJ		70.51 133	69.70 137	72.93 161	81.52 94	55.79 122	74.92 230	79.00 168	55.04 232	69.88 138	78.66 293	47.05 165	82.19 186	61.61 165	79.58 129	80.83 272

fmvsm_s_conf0.1_n_2		69.64 159	69.01 153	71.52 198	71.66 331	51.04 213	73.39 262	67.14 333	55.02 235	75.11 52	87.64 83	42.94 216	77.01 292	75.55 43	72.63 248	86.52 87

mmtdpeth		60.40 310	59.12 311	64.27 320	69.59 366	48.99 256	70.67 305	70.06 307	54.96 236	62.78 277	73.26 372	27.00 391	67.66 360	58.44 197	45.29 424	76.16 340

xiu_mvs_v2_base		70.52 132	69.75 135	72.84 163	81.21 103	55.63 126	75.11 223	78.92 170	54.92 237	69.96 137	79.68 276	47.00 169	82.09 188	61.60 166	79.37 132	80.81 273

MAR-MVS		71.51 114	70.15 131	75.60 85	81.84 90	59.39 60	81.38 90	82.90 93	54.90 238	68.08 174	78.70 291	47.73 150	85.51 109	51.68 252	84.17 76	81.88 250
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		62.56 284	61.20 294	66.62 284	70.62 349	44.30 309	70.13 314	73.13 283	54.78 239	61.13 304	76.37 338	25.63 401	75.63 313	58.75 194	60.29 379	79.93 289

XVG-OURS-SEG-HR		68.81 180	67.47 190	72.82 165	74.40 279	56.87 105	70.59 306	79.04 167	54.77 240	66.99 200	86.01 133	39.57 256	78.21 267	62.54 156	73.33 234	83.37 212

testing3		56.54 341	55.92 343	58.41 362	77.52 207	27.93 433	69.72 318	56.36 403	54.75 241	58.63 335	77.80 311	20.88 417	71.75 335	25.31 430	62.25 364	75.53 347

Anonymous20231211		69.28 171	68.47 166	71.73 191	80.28 117	47.18 281	79.98 106	82.37 100	54.61 242	67.24 195	84.01 176	39.43 257	82.41 184	55.45 218	72.83 243	85.62 126

SixPastTwentyTwo		61.65 298	58.80 315	70.20 233	75.80 244	47.22 280	75.59 213	69.68 310	54.61 242	54.11 379	79.26 286	27.07 390	82.96 164	43.27 325	49.79 417	80.41 280

test_0402		63.25 277	61.01 297	69.96 236	80.00 126	54.37 148	76.86 184	72.02 293	54.58 244	58.71 331	80.79 256	35.00 305	84.36 135	26.41 428	64.71 342	71.15 398

tttt0517		67.83 208	65.66 233	74.33 115	76.69 229	50.82 219	77.86 151	73.99 271	54.54 245	64.64 252	82.53 212	35.06 304	85.50 110	55.71 214	69.91 288	86.67 80

BH-w/o		66.85 228	65.83 230	69.90 240	79.29 138	52.46 194	74.66 236	76.65 224	54.51 246	64.85 249	78.12 301	45.59 181	82.95 165	43.26 326	75.54 201	74.27 365

AUN-MVS		68.45 192	66.41 218	74.57 108	79.53 135	57.08 103	73.93 252	75.23 248	54.44 247	66.69 206	81.85 231	37.10 288	82.89 167	62.07 160	66.84 326	83.75 200

LTVRE_ROB	55.42 16	63.15 279	61.23 293	68.92 258	76.57 234	47.80 272	59.92 392	76.39 225	54.35 248	58.67 333	82.46 214	29.44 369	81.49 200	42.12 335	71.14 266	77.46 323
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 84	72.59 87	74.27 117	71.28 341	55.88 120	78.21 141	75.56 239	54.31 249	74.86 61	87.80 81	54.72 55	80.23 233	78.07 25	78.48 156	86.70 78

test_fmvsmconf0.01_n		72.17 102	71.50 100	74.16 120	67.96 381	55.58 129	78.06 146	74.67 259	54.19 250	74.54 68	88.23 69	50.35 120	80.24 232	78.07 25	77.46 173	86.65 82

test_fmvsmconf0.1_n		72.81 86	72.33 90	74.24 118	69.89 363	55.81 121	78.22 140	75.40 244	54.17 251	75.00 56	88.03 77	53.82 67	80.23 233	78.08 24	78.34 159	86.69 79

ETVMVS		59.51 320	58.81 313	61.58 340	77.46 209	34.87 397	64.94 361	59.35 389	54.06 252	61.08 305	76.67 329	29.54 366	71.87 334	32.16 397	74.07 216	78.01 318

ab-mvs		66.65 233	66.42 217	67.37 275	76.17 240	41.73 335	70.41 310	76.14 229	53.99 253	65.98 220	83.51 189	49.48 127	76.24 310	48.60 275	73.46 231	84.14 182

fmvsm_s_conf0.5_n_5		72.69 90	72.80 84	72.37 177	74.11 289	53.21 173	78.12 143	73.31 279	53.98 254	76.81 39	88.05 74	53.38 74	77.37 285	76.64 33	80.78 110	86.53 86

IU-MVS					87.77 4	59.15 65		85.53 27	53.93 255	84.64 3				79.07 13	90.87 5	88.37 19

mamba_test_0407		70.41 137	68.96 154	74.75 98	78.65 160	53.46 167	77.28 171	80.00 152	53.88 256	68.14 170	84.61 161	43.21 211	86.26 89	58.80 192	76.11 192	84.54 168

mamba_0404		70.84 125	69.41 144	75.12 93	79.20 143	53.86 155	77.89 149	80.00 152	53.88 256	69.40 146	84.61 161	43.21 211	86.56 77	58.80 192	77.68 169	84.95 158

XVG-ACMP-BASELINE		64.36 265	62.23 278	70.74 223	72.35 320	52.45 195	70.80 304	78.45 190	53.84 258	59.87 317	81.10 246	16.24 425	79.32 244	55.64 217	71.76 258	80.47 277

VortexMVS		66.41 239	65.50 236	69.16 255	73.75 291	48.14 268	73.41 261	78.28 196	53.73 259	64.98 248	78.33 299	40.62 246	79.07 253	58.88 191	67.50 321	80.26 283

FE-MVS		65.91 243	63.33 263	73.63 142	77.36 212	51.95 205	72.62 275	75.81 233	53.70 260	65.31 233	78.96 289	28.81 374	86.39 84	43.93 317	73.48 230	82.55 234

thisisatest0530		67.92 205	65.78 231	74.33 115	76.29 238	51.03 214	76.89 182	74.25 266	53.67 261	65.59 229	81.76 234	35.15 303	85.50 110	55.94 209	72.47 249	86.47 88

PVSNet_BlendedMVS		68.56 189	67.72 181	71.07 216	77.03 224	50.57 223	74.50 239	81.52 112	53.66 262	64.22 260	79.72 275	49.13 135	82.87 169	55.82 211	73.92 218	79.77 296

patch_mono-2		69.85 150	71.09 112	66.16 293	79.11 148	54.80 143	71.97 286	74.31 264	53.50 263	70.90 123	84.17 171	57.63 31	63.31 384	66.17 117	82.02 100	80.38 281

EG-PatchMatch MVS		64.71 258	62.87 269	70.22 231	77.68 197	53.48 166	77.99 147	78.82 172	53.37 264	56.03 359	77.41 319	24.75 406	84.04 140	46.37 294	73.42 233	73.14 371

SD_0403		63.07 280	63.49 260	61.82 337	75.16 260	31.14 422	71.89 289	73.47 276	53.34 265	58.22 339	81.81 233	45.17 192	73.86 322	37.43 365	74.87 208	80.45 278

DP-MVS		65.68 245	63.66 257	71.75 190	84.93 55	56.87 105	80.74 98	73.16 282	53.06 266	59.09 328	82.35 215	36.79 292	85.94 99	32.82 395	69.96 287	72.45 379

TR-MVS		66.59 236	65.07 242	71.17 213	79.18 145	49.63 246	73.48 260	75.20 250	52.95 267	67.90 176	80.33 262	39.81 254	83.68 148	43.20 327	73.56 228	80.20 284

ET-MVSNet_ETH3D		67.96 204	65.72 232	74.68 101	76.67 231	55.62 128	75.11 223	74.74 257	52.91 268	60.03 314	80.12 266	33.68 322	82.64 178	61.86 163	76.34 189	85.78 116

QAPM		70.05 145	68.81 157	73.78 129	76.54 235	53.43 168	83.23 60	83.48 71	52.89 269	65.90 223	86.29 123	41.55 235	86.49 82	51.01 255	78.40 158	81.42 254

LuminaMVS		68.24 196	66.82 209	72.51 171	73.46 299	53.60 163	76.23 197	78.88 171	52.78 270	68.08 174	80.13 265	32.70 338	81.41 201	63.16 151	75.97 194	82.53 235

icg_test_0407		68.90 178	67.93 178	71.82 187	77.06 220	49.73 239	74.40 243	78.60 180	52.70 271	66.19 216	82.58 205	45.17 192	83.00 162	59.20 188	75.46 203	82.74 230

ICG_test_0404		64.63 260	64.22 248	65.88 301	77.06 220	49.73 239	64.40 364	78.60 180	52.70 271	53.16 389	82.58 205	34.82 307	65.16 378	59.20 188	75.46 203	82.74 230

icg_test_0403		69.09 175	68.14 175	71.95 182	77.06 220	49.73 239	74.51 238	78.60 180	52.70 271	66.69 206	82.58 205	46.43 173	83.38 155	59.20 188	75.46 203	82.74 230

OpenMVS	61.03 9	68.85 179	67.56 184	72.70 167	74.26 284	53.99 154	81.21 92	81.34 123	52.70 271	62.75 280	85.55 147	38.86 266	84.14 138	48.41 277	83.01 85	79.97 288

pmmvs6		63.69 271	62.82 271	66.27 291	70.63 348	39.27 359	73.13 268	75.47 243	52.69 275	59.75 321	82.30 217	39.71 255	77.03 291	47.40 284	64.35 347	82.53 235

IterMVS		62.79 283	61.27 291	67.35 276	69.37 370	52.04 202	71.17 297	68.24 325	52.63 276	59.82 318	76.91 326	37.32 283	72.36 328	52.80 240	63.19 357	77.66 321
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 198	66.36 220	73.63 142	75.61 249	55.35 135	80.77 97	78.56 184	52.48 277	64.27 257	84.10 174	27.45 386	81.84 193	63.45 149	70.56 273	83.69 202

jajsoiax		68.25 195	66.45 214	73.66 139	75.62 248	55.49 131	80.82 96	78.51 186	52.33 278	64.33 255	84.11 173	28.28 378	81.81 194	63.48 148	70.62 271	83.67 203

TAMVS		66.78 231	65.27 240	71.33 209	79.16 147	53.67 160	73.84 256	69.59 312	52.32 279	65.28 234	81.72 235	44.49 200	77.40 284	42.32 334	78.66 153	82.92 225

CDS-MVSNet		66.80 230	65.37 237	71.10 215	78.98 150	53.13 176	73.27 267	71.07 299	52.15 280	64.72 250	80.23 264	43.56 208	77.10 289	45.48 306	78.88 145	83.05 224
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		68.47 190	66.56 211	74.21 119	79.60 132	52.95 178	74.94 229	75.48 242	52.09 281	60.10 312	83.27 193	36.54 293	84.70 129	59.32 187	77.69 168	84.99 156

PVSNet_Blended		68.59 185	67.72 181	71.19 211	77.03 224	50.57 223	72.51 278	81.52 112	51.91 282	64.22 260	77.77 314	49.13 135	82.87 169	55.82 211	79.58 129	80.14 286

mvs_anonymous		68.03 201	67.51 188	69.59 245	72.08 324	44.57 307	71.99 285	75.23 248	51.67 283	67.06 199	82.57 208	54.68 56	77.94 271	56.56 206	75.71 199	86.26 102

xiu_mvs_v1_base_debu		68.58 186	67.28 197	72.48 172	78.19 177	57.19 97	75.28 218	75.09 252	51.61 284	70.04 131	81.41 241	32.79 333	79.02 255	63.81 142	77.31 174	81.22 263

xiu_mvs_v1_base		68.58 186	67.28 197	72.48 172	78.19 177	57.19 97	75.28 218	75.09 252	51.61 284	70.04 131	81.41 241	32.79 333	79.02 255	63.81 142	77.31 174	81.22 263

xiu_mvs_v1_base_debi		68.58 186	67.28 197	72.48 172	78.19 177	57.19 97	75.28 218	75.09 252	51.61 284	70.04 131	81.41 241	32.79 333	79.02 255	63.81 142	77.31 174	81.22 263

MVSTER		67.16 222	65.58 235	71.88 185	70.37 355	49.70 242	70.25 313	78.45 190	51.52 287	69.16 153	80.37 259	38.45 269	82.50 181	60.19 176	71.46 263	83.44 211

CNLPA		65.43 249	64.02 250	69.68 243	78.73 158	58.07 83	77.82 154	70.71 302	51.49 288	61.57 300	83.58 188	38.23 274	70.82 340	43.90 318	70.10 284	80.16 285

原ACMM1				74.69 100	85.39 47	59.40 59		83.42 74	51.47 289	70.27 129	86.61 112	48.61 141	86.51 81	53.85 232	87.96 39	78.16 312

miper_enhance_ethall		67.11 223	66.09 227	70.17 234	69.21 372	45.98 291	72.85 272	78.41 193	51.38 290	65.65 228	75.98 345	51.17 109	81.25 206	60.82 172	69.32 299	83.29 215

MSDG		61.81 297	59.23 309	69.55 248	72.64 311	52.63 189	70.45 309	75.81 233	51.38 290	53.70 382	76.11 340	29.52 367	81.08 213	37.70 363	65.79 335	74.93 356

test20.03		53.87 362	54.02 360	53.41 394	61.47 416	28.11 432	61.30 384	59.21 390	51.34 292	52.09 393	77.43 318	33.29 327	58.55 405	29.76 415	60.27 380	73.58 370

MVSFormer		71.50 115	70.38 125	74.88 96	78.76 156	57.15 100	82.79 67	78.48 187	51.26 293	69.49 143	83.22 194	43.99 205	83.24 158	66.06 118	79.37 132	84.23 178

test_djsdf		69.45 168	67.74 180	74.58 107	74.57 275	54.92 141	82.79 67	78.48 187	51.26 293	65.41 232	83.49 190	38.37 270	83.24 158	66.06 118	69.25 302	85.56 127

dmvs_testset		50.16 380	51.90 370	44.94 415	66.49 392	11.78 455	61.01 389	51.50 417	51.17 295	50.30 405	67.44 409	39.28 259	60.29 395	22.38 434	57.49 389	62.76 420

PAPM		67.92 205	66.69 210	71.63 196	78.09 182	49.02 255	77.09 176	81.24 128	51.04 296	60.91 306	83.98 177	47.71 151	84.99 119	40.81 344	79.32 135	80.90 271

Syy-MVS		56.00 348	56.23 341	55.32 380	74.69 270	26.44 439	65.52 351	57.49 398	50.97 297	56.52 353	72.18 376	39.89 252	68.09 356	24.20 431	64.59 345	71.44 394

myMVS_eth3d		54.86 358	54.61 352	55.61 379	74.69 270	27.31 436	65.52 351	57.49 398	50.97 297	56.52 353	72.18 376	21.87 415	68.09 356	27.70 422	64.59 345	71.44 394

miper_lstm_enhance		62.03 294	60.88 299	65.49 308	66.71 390	46.25 287	56.29 411	75.70 235	50.68 299	61.27 302	75.48 352	40.21 249	68.03 358	56.31 208	65.25 338	82.18 244

gg-mvs-nofinetune		57.86 332	56.43 338	62.18 335	72.62 312	35.35 396	66.57 341	56.33 404	50.65 300	57.64 344	57.10 431	30.65 354	76.36 308	37.38 366	78.88 145	74.82 358

TAPA-MVS	59.36 10	66.60 234	65.20 241	70.81 221	76.63 232	48.75 260	76.52 191	80.04 151	50.64 301	65.24 239	84.93 153	39.15 262	78.54 263	36.77 371	76.88 183	85.14 148
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 340	56.83 333	56.61 374	69.23 371	41.02 342	58.37 398	64.18 357	50.59 302	57.45 346	71.42 384	35.54 300	58.94 403	37.23 367	67.45 322	69.87 407

MVP-Stereo		65.41 250	63.80 254	70.22 231	77.62 204	55.53 130	76.30 194	78.53 185	50.59 302	56.47 355	78.65 294	39.84 253	82.68 176	44.10 316	72.12 256	72.44 380
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 124	69.49 141	75.35 88	77.63 200	55.71 123	76.04 204	81.81 108	50.30 304	69.66 141	85.40 151	52.51 84	84.89 125	51.82 249	80.24 121	85.45 134
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs5depth		55.64 351	53.81 362	61.11 346	59.39 426	40.98 346	65.89 346	68.28 324	50.21 305	58.11 341	75.42 353	17.03 421	67.63 362	43.79 320	46.21 421	74.73 360

baseline2		63.42 273	61.26 292	69.89 241	72.55 314	47.62 276	71.54 291	68.38 323	50.11 306	54.82 371	75.55 350	43.06 214	80.96 214	48.13 280	67.16 325	81.11 266

test-LLR		58.15 330	58.13 323	58.22 364	68.57 376	44.80 303	65.46 353	57.92 395	50.08 307	55.44 363	69.82 397	32.62 341	57.44 410	49.66 266	73.62 225	72.41 381

test0.0.03 1		53.32 367	53.59 364	52.50 400	62.81 411	29.45 427	59.51 394	54.11 412	50.08 307	54.40 377	74.31 362	32.62 341	55.92 419	30.50 412	63.95 350	72.15 386

fmvsm_s_conf0.5_n		69.58 161	68.84 156	71.79 189	72.31 322	52.90 180	77.90 148	62.43 376	49.97 309	72.85 100	85.90 136	52.21 90	76.49 305	75.75 40	70.26 281	85.97 108

COLMAP_ROB	52.97 17	61.27 303	58.81 313	68.64 261	74.63 272	52.51 192	78.42 134	73.30 280	49.92 310	50.96 397	81.51 240	23.06 409	79.40 242	31.63 405	65.85 333	74.01 368
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		69.54 163	68.74 159	71.93 183	72.47 317	53.82 157	78.25 137	62.26 378	49.78 311	73.12 94	86.21 125	52.66 82	76.79 299	75.02 49	68.88 307	85.18 147

WBMVS		60.54 307	60.61 301	60.34 349	78.00 186	35.95 393	64.55 363	64.89 350	49.63 312	63.39 267	78.70 291	33.85 320	67.65 361	42.10 336	70.35 278	77.43 324

tpmvs		58.47 325	56.95 331	63.03 331	70.20 356	41.21 341	67.90 334	67.23 332	49.62 313	54.73 373	70.84 388	34.14 314	76.24 310	36.64 375	61.29 371	71.64 390

fmvsm_s_conf0.1_n		69.41 169	68.60 162	71.83 186	71.07 343	52.88 183	77.85 152	62.44 375	49.58 314	72.97 97	86.22 124	51.68 101	76.48 306	75.53 44	70.10 284	86.14 103

UBG		59.62 319	59.53 307	59.89 350	78.12 181	35.92 394	64.11 368	60.81 386	49.45 315	61.34 301	75.55 350	33.05 328	67.39 365	38.68 358	74.62 209	76.35 339

thisisatest0515		65.83 244	63.50 259	72.82 165	73.75 291	49.50 247	71.32 294	73.12 284	49.39 316	63.82 262	76.50 337	34.95 306	84.84 128	53.20 238	75.49 202	84.13 183

fmvsm_s_conf0.1_n_a		69.32 170	68.44 168	71.96 181	70.91 345	53.78 158	78.12 143	62.30 377	49.35 317	73.20 90	86.55 117	51.99 95	76.79 299	74.83 51	68.68 312	85.32 142

HY-MVS	56.14 13	64.55 262	63.89 251	66.55 285	74.73 269	41.02 342	69.96 316	74.43 261	49.29 318	61.66 298	80.92 251	47.43 159	76.68 303	44.91 311	71.69 260	81.94 248

MIMVSNet1		55.17 356	54.31 357	57.77 370	70.03 360	32.01 418	65.68 349	64.81 351	49.19 319	46.75 415	76.00 342	25.53 402	64.04 381	28.65 419	62.13 365	77.26 328

SCA		60.49 308	58.38 319	66.80 279	74.14 288	48.06 270	63.35 372	63.23 368	49.13 320	59.33 327	72.10 378	37.45 280	74.27 320	44.17 313	62.57 361	78.05 314

test_fmvsmvis_n_1920		70.84 125	70.38 125	72.22 180	71.16 342	55.39 133	75.86 208	72.21 291	49.03 321	73.28 88	86.17 127	51.83 98	77.29 287	75.80 39	78.05 163	83.98 187

testgi		51.90 372	52.37 368	50.51 407	60.39 424	23.55 446	58.42 397	58.15 393	49.03 321	51.83 394	79.21 287	22.39 410	55.59 420	29.24 418	62.64 360	72.40 383

sc_t1		59.76 315	57.84 326	65.54 305	74.87 264	42.95 325	69.61 319	64.16 359	48.90 323	58.68 332	77.12 321	28.19 379	72.35 329	43.75 322	55.28 398	81.31 261

MIMVSNet		57.35 334	57.07 329	58.22 364	74.21 285	37.18 377	62.46 377	60.88 385	48.88 324	55.29 366	75.99 344	31.68 350	62.04 389	31.87 400	72.35 251	75.43 349

gm-plane-assit					71.40 338	41.72 337			48.85 325		73.31 370		82.48 183	48.90 273

fmvsm_l_conf0.5_n		70.99 123	70.82 116	71.48 199	71.45 334	54.40 147	77.18 174	70.46 304	48.67 326	75.17 51	86.86 101	53.77 68	76.86 297	76.33 36	77.51 172	83.17 222

UWE-MVS		60.18 311	59.78 305	61.39 343	77.67 198	33.92 409	69.04 326	63.82 362	48.56 327	64.27 257	77.64 316	27.20 388	70.40 345	33.56 392	76.24 190	79.83 293

cascas		65.98 242	63.42 261	73.64 141	77.26 215	52.58 190	72.26 282	77.21 215	48.56 327	61.21 303	74.60 360	32.57 344	85.82 102	50.38 260	76.75 186	82.52 237

PLC	56.13 14	65.09 255	63.21 266	70.72 224	81.04 106	54.87 142	78.57 131	77.47 208	48.51 329	55.71 360	81.89 230	33.71 321	79.71 237	41.66 340	70.37 276	77.58 322
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 258	62.50 274	71.34 208	79.72 131	55.71 123	79.82 110	74.72 258	48.50 330	56.62 351	84.62 160	33.59 324	82.34 185	29.65 416	75.23 206	75.97 341

anonymousdsp		67.00 226	64.82 244	73.57 145	70.09 359	56.13 113	76.35 193	77.35 212	48.43 331	64.99 247	80.84 255	33.01 330	80.34 228	64.66 132	67.64 320	84.23 178

无先验							79.66 115	74.30 265	48.40 332				80.78 221	53.62 233		79.03 305

114514_t		70.83 127	69.56 139	74.64 104	86.21 31	54.63 144	82.34 76	81.81 108	48.22 333	63.01 275	85.83 139	40.92 245	87.10 63	57.91 198	79.79 125	82.18 244

tpm		57.34 335	58.16 321	54.86 383	71.80 330	34.77 399	67.47 339	56.04 407	48.20 334	60.10 312	76.92 325	37.17 286	53.41 427	40.76 345	65.01 339	76.40 338

test_fmvsm_n_1920		71.73 111	71.14 111	73.50 147	72.52 315	56.53 107	75.60 212	76.16 227	48.11 335	77.22 35	85.56 145	53.10 78	77.43 282	74.86 50	77.14 179	86.55 85

MDA-MVSNet-bldmvs		53.87 362	50.81 375	63.05 330	66.25 394	48.58 263	56.93 409	63.82 362	48.09 336	41.22 427	70.48 393	30.34 357	68.00 359	34.24 387	45.92 423	72.57 377

XXY-MVS		60.68 304	61.67 284	57.70 371	70.43 353	38.45 366	64.19 366	66.47 338	48.05 337	63.22 268	80.86 253	49.28 132	60.47 393	45.25 310	67.28 324	74.19 366

F-COLMAP		63.05 281	60.87 300	69.58 247	76.99 226	53.63 162	78.12 143	76.16 227	47.97 338	52.41 392	81.61 237	27.87 381	78.11 268	40.07 347	66.66 328	77.00 332

tt0320-xc		58.33 327	56.41 339	64.08 321	75.79 245	41.34 339	68.30 330	62.72 372	47.90 339	56.29 356	74.16 365	28.53 375	71.04 339	41.50 343	52.50 409	79.88 291

fmvsm_l_conf0.5_n_a		70.50 134	70.27 127	71.18 212	71.30 340	54.09 152	76.89 182	69.87 308	47.90 339	74.37 71	86.49 118	53.07 79	76.69 302	75.41 45	77.11 180	82.76 229

Patchmatch-RL test		58.16 329	55.49 346	66.15 294	67.92 382	48.89 259	60.66 390	51.07 420	47.86 341	59.36 324	62.71 425	34.02 317	72.27 331	56.41 207	59.40 382	77.30 326

D2MVS		62.30 289	60.29 303	68.34 266	66.46 393	48.42 265	65.70 348	73.42 277	47.71 342	58.16 340	75.02 356	30.51 355	77.71 279	53.96 231	71.68 261	78.90 307

ANet_high		41.38 399	37.47 406	53.11 396	39.73 452	24.45 444	56.94 408	69.69 309	47.65 343	26.04 444	52.32 434	12.44 433	62.38 388	21.80 435	10.61 453	72.49 378

CostFormer		64.04 268	62.51 273	68.61 262	71.88 328	45.77 292	71.30 295	70.60 303	47.55 344	64.31 256	76.61 333	41.63 232	79.62 240	49.74 264	69.00 306	80.42 279

PatchmatchNet		59.84 314	58.24 320	64.65 316	73.05 305	46.70 284	69.42 322	62.18 379	47.55 344	58.88 330	71.96 380	34.49 311	69.16 350	42.99 329	63.60 352	78.07 313
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 355	53.89 361	59.21 356	57.80 430	27.47 435	57.75 404	74.32 263	47.38 346	50.90 398	70.00 396	28.45 377	70.30 346	40.44 346	57.92 387	79.87 292

ITE_SJBPF				62.09 336	66.16 395	44.55 308		64.32 355	47.36 347	55.31 365	80.34 261	19.27 418	62.68 387	36.29 379	62.39 363	79.04 304

KD-MVS_2432*1600		53.45 364	51.50 373	59.30 353	62.82 409	37.14 378	55.33 412	71.79 295	47.34 348	55.09 368	70.52 391	21.91 413	70.45 343	35.72 382	42.97 427	70.31 403

miper_refine_blended		53.45 364	51.50 373	59.30 353	62.82 409	37.14 378	55.33 412	71.79 295	47.34 348	55.09 368	70.52 391	21.91 413	70.45 343	35.72 382	42.97 427	70.31 403

OurMVSNet-221017-0		61.37 302	58.63 317	69.61 244	72.05 325	48.06 270	73.93 252	72.51 287	47.23 350	54.74 372	80.92 251	21.49 416	81.24 207	48.57 276	56.22 395	79.53 298

tpmrst		58.24 328	58.70 316	56.84 373	66.97 387	34.32 404	69.57 321	61.14 384	47.17 351	58.58 336	71.60 383	41.28 239	60.41 394	49.20 270	62.84 359	75.78 344

tt0320		58.59 324	56.81 334	63.92 323	75.46 252	41.32 340	68.63 328	64.06 360	47.05 352	56.19 357	74.19 363	30.34 357	71.36 336	39.92 351	55.45 397	79.09 302

PVSNet	50.76 19	58.40 326	57.39 327	61.42 341	75.53 251	44.04 313	61.43 382	63.45 366	47.04 353	56.91 349	73.61 369	27.00 391	64.76 379	39.12 356	72.40 250	75.47 348

WB-MVSnew		59.66 317	59.69 306	59.56 351	75.19 259	35.78 395	69.34 323	64.28 356	46.88 354	61.76 297	75.79 346	40.61 247	65.20 377	32.16 397	71.21 265	77.70 320

UWE-MVS-28		52.25 371	52.35 369	51.93 404	66.99 386	22.79 447	63.48 371	48.31 428	46.78 355	52.73 391	76.11 340	27.78 383	57.82 409	20.58 437	68.41 314	75.17 350

FMVSNet5		55.86 349	54.93 349	58.66 361	71.05 344	36.35 387	64.18 367	62.48 374	46.76 356	50.66 402	74.73 359	25.80 399	64.04 381	33.11 393	65.57 336	75.59 346

jason		69.65 158	68.39 170	73.43 152	78.27 175	56.88 104	77.12 175	73.71 275	46.53 357	69.34 148	83.22 194	43.37 209	79.18 246	64.77 131	79.20 140	84.23 178
jason: jason.
MS-PatchMatch		62.42 287	61.46 287	65.31 311	75.21 258	52.10 199	72.05 284	74.05 269	46.41 358	57.42 347	74.36 361	34.35 313	77.57 281	45.62 302	73.67 223	66.26 417

1112_ss		64.00 269	63.36 262	65.93 299	79.28 140	42.58 327	71.35 293	72.36 290	46.41 358	60.55 309	77.89 309	46.27 176	73.28 324	46.18 295	69.97 286	81.92 249

lupinMVS		69.57 162	68.28 173	73.44 151	78.76 156	57.15 100	76.57 189	73.29 281	46.19 360	69.49 143	82.18 221	43.99 205	79.23 245	64.66 132	79.37 132	83.93 189

testdata				64.66 315	81.52 94	52.93 179		65.29 348	46.09 361	73.88 79	87.46 87	38.08 276	66.26 372	53.31 237	78.48 156	74.78 359

UnsupCasMVSNet_eth		53.16 369	52.47 367	55.23 381	59.45 425	33.39 412	59.43 395	69.13 318	45.98 362	50.35 404	72.32 375	29.30 370	58.26 407	42.02 338	44.30 425	74.05 367

AllTest		57.08 337	54.65 351	64.39 318	71.44 335	49.03 253	69.92 317	67.30 329	45.97 363	47.16 412	79.77 272	17.47 419	67.56 363	33.65 389	59.16 383	76.57 336

TestCases				64.39 318	71.44 335	49.03 253		67.30 329	45.97 363	47.16 412	79.77 272	17.47 419	67.56 363	33.65 389	59.16 383	76.57 336

WTY-MVS		59.75 316	60.39 302	57.85 369	72.32 321	37.83 371	61.05 388	64.18 357	45.95 365	61.91 294	79.11 288	47.01 168	60.88 392	42.50 333	69.49 298	74.83 357

IterMVS-SCA-FT		62.49 285	61.52 286	65.40 309	71.99 327	50.80 220	71.15 299	69.63 311	45.71 366	60.61 308	77.93 306	37.45 280	65.99 374	55.67 215	63.50 354	79.42 299

WB-MVS		43.26 393	43.41 393	42.83 419	63.32 408	10.32 457	58.17 400	45.20 435	45.42 367	40.44 430	67.26 412	34.01 318	58.98 402	11.96 448	24.88 442	59.20 423

旧先验2							76.08 201		45.32 368	76.55 41			65.56 376	58.75 194

OpenMVS_ROB	52.78 18	60.03 312	58.14 322	65.69 304	70.47 352	44.82 302	75.33 217	70.86 301	45.04 369	56.06 358	76.00 342	26.89 393	79.65 238	35.36 384	67.29 323	72.60 376

TinyColmap		54.14 359	51.72 371	61.40 342	66.84 389	41.97 332	66.52 342	68.51 322	44.81 370	42.69 426	75.77 347	11.66 435	72.94 325	31.96 399	56.77 393	69.27 411

MDTV_nov1_ep13			57.00 330		72.73 310	38.26 367	65.02 360	64.73 353	44.74 371	55.46 362	72.48 374	32.61 343	70.47 342	37.47 364	67.75 319

新几何1				70.76 222	85.66 41	61.13 30		66.43 339	44.68 372	70.29 128	86.64 109	41.29 238	75.23 315	49.72 265	81.75 106	75.93 342

Patchmtry		57.16 336	56.47 337	59.23 355	69.17 373	34.58 402	62.98 374	63.15 369	44.53 373	56.83 350	74.84 357	35.83 298	68.71 353	40.03 348	60.91 372	74.39 364

ppachtmachnet_test		58.06 331	55.38 347	66.10 296	69.51 367	48.99 256	68.01 333	66.13 342	44.50 374	54.05 380	70.74 389	32.09 349	72.34 330	36.68 374	56.71 394	76.99 334

PatchT		53.17 368	53.44 365	52.33 401	68.29 380	25.34 443	58.21 399	54.41 411	44.46 375	54.56 375	69.05 403	33.32 326	60.94 391	36.93 370	61.76 369	70.73 401

EPMVS		53.96 360	53.69 363	54.79 384	66.12 396	31.96 419	62.34 379	49.05 424	44.42 376	55.54 361	71.33 386	30.22 359	56.70 413	41.65 341	62.54 362	75.71 345

pmmvs4		61.48 301	59.39 308	67.76 270	71.57 333	53.86 155	71.42 292	65.34 347	44.20 377	59.46 323	77.92 307	35.90 297	74.71 317	43.87 319	64.87 341	74.71 361

dp		51.89 373	51.60 372	52.77 398	68.44 379	32.45 417	62.36 378	54.57 410	44.16 378	49.31 407	67.91 405	28.87 373	56.61 415	33.89 388	54.89 400	69.24 412

PatchMatch-RL		56.25 346	54.55 353	61.32 344	77.06 220	56.07 115	65.57 350	54.10 413	44.13 379	53.49 388	71.27 387	25.20 403	66.78 368	36.52 377	63.66 351	61.12 421

our_test_3		56.49 342	54.42 354	62.68 333	69.51 367	45.48 298	66.08 345	61.49 382	44.11 380	50.73 401	69.60 400	33.05 328	68.15 355	38.38 360	56.86 391	74.40 363

USDC		56.35 345	54.24 358	62.69 332	64.74 401	40.31 348	65.05 359	73.83 273	43.93 381	47.58 410	77.71 315	15.36 428	75.05 316	38.19 362	61.81 368	72.70 375

PM-MVS		52.33 370	50.19 379	58.75 360	62.10 414	45.14 301	65.75 347	40.38 442	43.60 382	53.52 386	72.65 373	9.16 443	65.87 375	50.41 259	54.18 403	65.24 419

pmmvs-eth3d		58.81 323	56.31 340	66.30 290	67.61 383	52.42 196	72.30 281	64.76 352	43.55 383	54.94 370	74.19 363	28.95 371	72.60 327	43.31 324	57.21 390	73.88 369

SSC-MVS		41.96 398	41.99 397	41.90 420	62.46 413	9.28 459	57.41 407	44.32 438	43.38 384	38.30 436	66.45 415	32.67 340	58.42 406	10.98 449	21.91 445	57.99 427

new-patchmatchnet		47.56 387	47.73 387	47.06 410	58.81 428	9.37 458	48.78 429	59.21 390	43.28 385	44.22 422	68.66 404	25.67 400	57.20 412	31.57 407	49.35 418	74.62 362

Test_1112_low_res		62.32 288	61.77 283	64.00 322	79.08 149	39.53 357	68.17 331	70.17 305	43.25 386	59.03 329	79.90 269	44.08 202	71.24 338	43.79 320	68.42 313	81.25 262

RPMNet		61.53 299	58.42 318	70.86 220	69.96 361	52.07 200	65.31 357	81.36 119	43.20 387	59.36 324	70.15 395	35.37 301	85.47 112	36.42 378	64.65 343	75.06 352

tpm2		62.07 292	60.10 304	67.99 268	72.79 309	43.86 314	71.05 302	66.85 336	43.14 388	62.77 278	75.39 354	38.32 272	80.80 220	41.69 339	68.88 307	79.32 300

JIA-IIPM		51.56 374	47.68 388	63.21 328	64.61 402	50.73 221	47.71 431	58.77 392	42.90 389	48.46 409	51.72 435	24.97 404	70.24 347	36.06 381	53.89 404	68.64 413

1314		64.61 261	63.21 266	68.80 259	71.87 329	47.46 278	73.95 250	78.39 195	42.88 390	59.97 315	76.60 334	38.11 275	79.39 243	54.84 222	72.32 252	79.55 297

HyFIR lowres test		65.67 246	63.01 268	73.67 138	79.97 127	55.65 125	69.07 325	75.52 240	42.68 391	63.53 265	77.95 305	40.43 248	81.64 195	46.01 297	71.91 257	83.73 201

CR-MVSNet		59.91 313	57.90 325	65.96 298	69.96 361	52.07 200	65.31 357	63.15 369	42.48 392	59.36 324	74.84 357	35.83 298	70.75 341	45.50 305	64.65 343	75.06 352

test222					83.14 72	58.68 78	72.57 277	63.45 366	41.78 393	67.56 189	86.12 128	37.13 287			78.73 150	74.98 355

TDRefinement		53.44 366	50.72 376	61.60 339	64.31 404	46.96 282	70.89 303	65.27 349	41.78 393	44.61 421	77.98 304	11.52 437	66.36 371	28.57 420	51.59 411	71.49 393

sss		56.17 347	56.57 336	54.96 382	66.93 388	36.32 389	57.94 401	61.69 381	41.67 395	58.64 334	75.32 355	38.72 267	56.25 417	42.04 337	66.19 332	72.31 384

PVSNet_0	43.31 20	47.46 388	45.64 391	52.92 397	67.60 384	44.65 305	54.06 417	54.64 409	41.59 396	46.15 417	58.75 428	30.99 353	58.66 404	32.18 396	24.81 443	55.46 431

MVS		67.37 215	66.33 221	70.51 229	75.46 252	50.94 215	73.95 250	81.85 107	41.57 397	62.54 285	78.57 297	47.98 146	85.47 112	52.97 239	82.05 99	75.14 351

Anonymous20240521		55.30 353	54.41 355	57.96 368	60.92 423	41.73 335	71.09 301	71.06 300	41.18 398	48.65 408	73.31 370	16.93 422	59.25 400	42.54 332	64.01 348	72.90 373

Anonymous20231206		55.10 357	55.30 348	54.48 385	69.81 365	33.94 408	62.91 375	62.13 380	41.08 399	55.18 367	75.65 348	32.75 336	56.59 416	30.32 413	67.86 317	72.91 372

MDA-MVSNet_test_wron		50.71 379	48.95 381	56.00 378	61.17 418	41.84 333	51.90 423	56.45 401	40.96 400	44.79 420	67.84 406	30.04 363	55.07 424	36.71 373	50.69 414	71.11 399

YYNet1		50.73 378	48.96 380	56.03 377	61.10 419	41.78 334	51.94 422	56.44 402	40.94 401	44.84 419	67.80 407	30.08 362	55.08 423	36.77 371	50.71 413	71.22 396

dongtai		34.52 408	34.94 408	33.26 429	61.06 420	16.00 454	52.79 421	23.78 455	40.71 402	39.33 434	48.65 443	16.91 423	48.34 435	12.18 447	19.05 447	35.44 446

CHOSEN 1792x2688		65.08 256	62.84 270	71.82 187	81.49 96	56.26 111	66.32 344	74.20 268	40.53 403	63.16 271	78.65 294	41.30 237	77.80 276	45.80 299	74.09 215	81.40 257

pmmvs5		56.47 343	55.68 345	58.86 359	61.41 417	36.71 384	66.37 343	62.75 371	40.38 404	53.70 382	76.62 331	34.56 309	67.05 366	40.02 349	65.27 337	72.83 374

test_vis1_n_1920		58.86 322	59.06 312	58.25 363	63.76 405	43.14 322	67.49 338	66.36 340	40.22 405	65.89 224	71.95 381	31.04 352	59.75 398	59.94 179	64.90 340	71.85 388

MDTV_nov1_ep13_2view						25.89 441	61.22 385		40.10 406	51.10 396		32.97 331		38.49 359		78.61 309

tpm cat1		59.25 321	56.95 331	66.15 294	72.19 323	46.96 282	68.09 332	65.76 343	40.03 407	57.81 343	70.56 390	38.32 272	74.51 318	38.26 361	61.50 370	77.00 332

test-mter		56.42 344	55.82 344	58.22 364	68.57 376	44.80 303	65.46 353	57.92 395	39.94 408	55.44 363	69.82 397	21.92 412	57.44 410	49.66 266	73.62 225	72.41 381

UnsupCasMVSNet_bld		50.07 381	48.87 382	53.66 390	60.97 422	33.67 410	57.62 405	64.56 354	39.47 409	47.38 411	64.02 423	27.47 385	59.32 399	34.69 386	43.68 426	67.98 415

TESTMET0.1,1		55.28 354	54.90 350	56.42 375	66.56 391	43.67 316	65.46 353	56.27 405	39.18 410	53.83 381	67.44 409	24.21 407	55.46 421	48.04 281	73.11 239	70.13 405

mamv4		56.85 339	58.00 324	53.43 393	72.46 318	54.47 145	57.56 406	54.74 408	38.81 411	57.42 347	79.45 282	47.57 155	38.70 446	60.88 171	53.07 406	67.11 416

ADS-MVSNet2		51.33 376	48.76 383	59.07 358	66.02 397	44.60 306	50.90 425	59.76 388	36.90 412	50.74 399	66.18 417	26.38 394	63.11 385	27.17 424	54.76 401	69.50 409

ADS-MVSNet		48.48 385	47.77 386	50.63 406	66.02 397	29.92 426	50.90 425	50.87 422	36.90 412	50.74 399	66.18 417	26.38 394	52.47 429	27.17 424	54.76 401	69.50 409

RPSCF		55.80 350	54.22 359	60.53 348	65.13 400	42.91 326	64.30 365	57.62 397	36.84 414	58.05 342	82.28 218	28.01 380	56.24 418	37.14 368	58.61 385	82.44 240

test_cas_vis1_n_1920		56.91 338	56.71 335	57.51 372	59.13 427	45.40 299	63.58 370	61.29 383	36.24 415	67.14 198	71.85 382	29.89 364	56.69 414	57.65 200	63.58 353	70.46 402

Patchmatch-test		49.08 383	48.28 385	51.50 405	64.40 403	30.85 424	45.68 435	48.46 427	35.60 416	46.10 418	72.10 378	34.47 312	46.37 438	27.08 426	60.65 376	77.27 327

CHOSEN 280x420		47.83 386	46.36 390	52.24 403	67.37 385	49.78 238	38.91 443	43.11 440	35.00 417	43.27 425	63.30 424	28.95 371	49.19 434	36.53 376	60.80 374	57.76 428

N_pmnet		39.35 403	40.28 400	36.54 426	63.76 405	1.62 463	49.37 428	0.76 462	34.62 418	43.61 424	66.38 416	26.25 396	42.57 442	26.02 429	51.77 410	65.44 418

kuosan		29.62 415	30.82 414	26.02 434	52.99 433	16.22 453	51.09 424	22.71 456	33.91 419	33.99 438	40.85 444	15.89 426	33.11 451	7.59 455	18.37 448	28.72 448

PMMVS		53.96 360	53.26 366	56.04 376	62.60 412	50.92 217	61.17 386	56.09 406	32.81 420	53.51 387	66.84 414	34.04 316	59.93 397	44.14 315	68.18 315	57.27 429

CMPMVS	42.80 21	57.81 333	55.97 342	63.32 326	60.98 421	47.38 279	64.66 362	69.50 314	32.06 421	46.83 414	77.80 311	29.50 368	71.36 336	48.68 274	73.75 221	71.21 397
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 389	42.95 394	53.39 395	52.33 437	29.15 428	57.77 402	48.20 429	31.81 422	49.86 406	77.21 320	8.69 444	59.16 401	27.31 423	33.40 439	71.84 389

CVMVSNet		59.63 318	59.14 310	61.08 347	74.47 276	38.84 362	75.20 221	68.74 321	31.15 423	58.24 338	76.51 335	32.39 346	68.58 354	49.77 263	65.84 334	75.81 343

FPMVS		42.18 397	41.11 399	45.39 412	58.03 429	41.01 344	49.50 427	53.81 414	30.07 424	33.71 439	64.03 421	11.69 434	52.08 432	14.01 443	55.11 399	43.09 440

EU-MVSNet		55.61 352	54.41 355	59.19 357	65.41 399	33.42 411	72.44 279	71.91 294	28.81 425	51.27 395	73.87 367	24.76 405	69.08 351	43.04 328	58.20 386	75.06 352

test_vis1_n		49.89 382	48.69 384	53.50 392	53.97 431	37.38 376	61.53 381	47.33 432	28.54 426	59.62 322	67.10 413	13.52 430	52.27 430	49.07 271	57.52 388	70.84 400

test_fmvs1_n		51.37 375	50.35 378	54.42 387	52.85 434	37.71 373	61.16 387	51.93 415	28.15 427	63.81 263	69.73 399	13.72 429	53.95 425	51.16 254	60.65 376	71.59 391

LF4IMVS		42.95 394	42.26 396	45.04 413	48.30 442	32.50 416	54.80 414	48.49 426	28.03 428	40.51 429	70.16 394	9.24 442	43.89 441	31.63 405	49.18 419	58.72 425

test_fmvs1		51.32 377	50.48 377	53.81 389	53.57 432	37.51 375	60.63 391	51.16 418	28.02 429	63.62 264	69.23 402	16.41 424	53.93 426	51.01 255	60.70 375	69.99 406

MVS-HIRNet		45.52 390	44.48 392	48.65 409	68.49 378	34.05 407	59.41 396	44.50 437	27.03 430	37.96 437	50.47 439	26.16 397	64.10 380	26.74 427	59.52 381	47.82 438

PMVS	28.69 22	36.22 406	33.29 411	45.02 414	36.82 454	35.98 392	54.68 415	48.74 425	26.31 431	21.02 447	51.61 436	2.88 456	60.10 396	9.99 452	47.58 420	38.99 445
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 391	41.95 398	53.86 388	52.58 436	43.55 317	62.11 380	46.90 434	26.05 432	40.63 428	60.19 427	11.08 440	57.91 408	31.83 404	46.15 422	60.11 422

test_fmvs2		48.69 384	47.49 389	52.29 402	48.63 441	33.06 414	57.76 403	48.05 430	25.71 433	59.76 320	69.60 400	11.57 436	52.23 431	49.45 269	56.86 391	71.58 392

PMMVS2		27.40 416	25.91 419	31.87 431	39.46 453	6.57 460	31.17 446	28.52 451	23.96 434	20.45 448	48.94 442	4.20 452	37.94 447	16.51 440	19.97 446	51.09 433

MVStest1		42.65 395	39.29 402	52.71 399	47.26 444	34.58 402	54.41 416	50.84 423	23.35 435	39.31 435	74.08 366	12.57 432	55.09 422	23.32 432	28.47 441	68.47 414

Gipuma		34.77 407	31.91 412	43.33 417	62.05 415	37.87 369	20.39 448	67.03 334	23.23 436	18.41 449	25.84 449	4.24 450	62.73 386	14.71 442	51.32 412	29.38 447
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 400	39.45 401	47.03 411	46.65 445	37.86 370	47.76 430	38.65 443	23.10 437	44.21 423	51.22 437	11.20 439	44.08 440	39.27 355	53.02 407	59.14 424

new_pmnet		34.13 409	34.29 410	33.64 428	52.63 435	18.23 452	44.43 438	33.90 448	22.81 438	30.89 441	53.18 433	10.48 441	35.72 450	20.77 436	39.51 431	46.98 439

mvsany_test1		39.38 402	38.16 405	43.02 418	49.05 439	34.28 405	44.16 439	25.94 453	22.74 439	46.57 416	62.21 426	23.85 408	41.16 445	33.01 394	35.91 435	53.63 432

LCM-MVSNet		40.30 401	35.88 407	53.57 391	42.24 447	29.15 428	45.21 437	60.53 387	22.23 440	28.02 442	50.98 438	3.72 453	61.78 390	31.22 410	38.76 433	69.78 408

test_fmvs3		44.30 392	42.55 395	49.55 408	42.83 446	27.15 438	53.03 419	44.93 436	22.03 441	53.69 384	64.94 420	4.21 451	49.63 433	47.47 282	49.82 416	71.88 387

APD_test1		37.39 405	34.94 408	44.72 416	48.88 440	33.19 413	52.95 420	44.00 439	19.49 442	27.28 443	58.59 429	3.18 455	52.84 428	18.92 438	41.17 430	48.14 437

mvsany_test3		32.62 410	30.57 415	38.77 424	36.16 455	24.20 445	38.10 444	20.63 457	19.14 443	40.36 431	57.43 430	5.06 448	36.63 449	29.59 417	28.66 440	55.49 430

E-PMN		23.77 417	22.73 421	26.90 432	42.02 448	20.67 449	42.66 440	35.70 446	17.43 444	10.28 454	25.05 450	6.42 446	42.39 443	10.28 451	14.71 450	17.63 449

EMVS		22.97 418	21.84 422	26.36 433	40.20 451	19.53 451	41.95 441	34.64 447	17.09 445	9.73 455	22.83 451	7.29 445	42.22 444	9.18 453	13.66 451	17.32 450

test_vis3_rt		32.09 411	30.20 416	37.76 425	35.36 456	27.48 434	40.60 442	28.29 452	16.69 446	32.52 440	40.53 445	1.96 457	37.40 448	33.64 391	42.21 429	48.39 435

test_f		31.86 412	31.05 413	34.28 427	32.33 458	21.86 448	32.34 445	30.46 450	16.02 447	39.78 433	55.45 432	4.80 449	32.36 452	30.61 411	37.66 434	48.64 434

DSMNet-mixed		39.30 404	38.72 403	41.03 421	51.22 438	19.66 450	45.53 436	31.35 449	15.83 448	39.80 432	67.42 411	22.19 411	45.13 439	22.43 433	52.69 408	58.31 426

testf1		31.46 413	28.89 417	39.16 422	41.99 449	28.78 430	46.45 433	37.56 444	14.28 449	21.10 445	48.96 440	1.48 459	47.11 436	13.63 444	34.56 436	41.60 441

APD_test2		31.46 413	28.89 417	39.16 422	41.99 449	28.78 430	46.45 433	37.56 444	14.28 449	21.10 445	48.96 440	1.48 459	47.11 436	13.63 444	34.56 436	41.60 441

MVE	17.77 23	21.41 419	17.77 424	32.34 430	34.34 457	25.44 442	16.11 449	24.11 454	11.19 451	13.22 451	31.92 447	1.58 458	30.95 453	10.47 450	17.03 449	40.62 444
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 437	17.97 459	10.91 456		10.60 460	7.46 452	11.07 453	28.36 448	3.28 454	11.29 456	8.01 454	9.74 455	13.89 451

wuyk23d		13.32 422	12.52 425	15.71 436	47.54 443	26.27 440	31.06 447	1.98 461	4.93 453	5.18 456	1.94 456	0.45 461	18.54 455	6.81 456	12.83 452	2.33 453

test_method		19.68 420	18.10 423	24.41 435	13.68 460	3.11 462	12.06 451	42.37 441	2.00 454	11.97 452	36.38 446	5.77 447	29.35 454	15.06 441	23.65 444	40.76 443

tmp_tt		9.43 423	11.14 426	4.30 438	2.38 461	4.40 461	13.62 450	16.08 459	0.39 455	15.89 450	13.06 452	15.80 427	5.54 457	12.63 446	10.46 454	2.95 452

EGC-MVSNET		42.47 396	38.48 404	54.46 386	74.33 281	48.73 261	70.33 312	51.10 419	0.03 456	0.18 457	67.78 408	13.28 431	66.49 370	18.91 439	50.36 415	48.15 436

testmvs		4.52 426	6.03 429	0.01 440	0.01 462	0.00 465	53.86 418	0.00 463	0.01 457	0.04 458	0.27 457	0.00 463	0.00 458	0.04 457	0.00 456	0.03 455

test123		4.73 425	6.30 428	0.02 439	0.01 462	0.01 464	56.36 410	0.00 463	0.01 457	0.04 458	0.21 458	0.01 462	0.00 458	0.03 458	0.00 456	0.04 454

mmdepth		0.00 428	0.00 431	0.00 441	0.00 464	0.00 465	0.00 452	0.00 463	0.00 459	0.00 460	0.00 459	0.00 463	0.00 458	0.00 459	0.00 456	0.00 456

monomultidepth		0.00 428	0.00 431	0.00 441	0.00 464	0.00 465	0.00 452	0.00 463	0.00 459	0.00 460	0.00 459	0.00 463	0.00 458	0.00 459	0.00 456	0.00 456

test_blank		0.00 428	0.00 431	0.00 441	0.00 464	0.00 465	0.00 452	0.00 463	0.00 459	0.00 460	0.00 459	0.00 463	0.00 458	0.00 459	0.00 456	0.00 456

uanet_test		0.00 428	0.00 431	0.00 441	0.00 464	0.00 465	0.00 452	0.00 463	0.00 459	0.00 460	0.00 459	0.00 463	0.00 458	0.00 459	0.00 456	0.00 456

DCPMVS		0.00 428	0.00 431	0.00 441	0.00 464	0.00 465	0.00 452	0.00 463	0.00 459	0.00 460	0.00 459	0.00 463	0.00 458	0.00 459	0.00 456	0.00 456

cdsmvs_eth3d_5k		17.50 421	23.34 420	0.00 441	0.00 464	0.00 465	0.00 452	78.63 179	0.00 459	0.00 460	82.18 221	49.25 133	0.00 458	0.00 459	0.00 456	0.00 456

pcd_1.5k_mvsjas		3.92 427	5.23 430	0.00 441	0.00 464	0.00 465	0.00 452	0.00 463	0.00 459	0.00 460	0.00 459	47.05 165	0.00 458	0.00 459	0.00 456	0.00 456

sosnet-low-res		0.00 428	0.00 431	0.00 441	0.00 464	0.00 465	0.00 452	0.00 463	0.00 459	0.00 460	0.00 459	0.00 463	0.00 458	0.00 459	0.00 456	0.00 456

sosnet		0.00 428	0.00 431	0.00 441	0.00 464	0.00 465	0.00 452	0.00 463	0.00 459	0.00 460	0.00 459	0.00 463	0.00 458	0.00 459	0.00 456	0.00 456

uncertanet		0.00 428	0.00 431	0.00 441	0.00 464	0.00 465	0.00 452	0.00 463	0.00 459	0.00 460	0.00 459	0.00 463	0.00 458	0.00 459	0.00 456	0.00 456

Regformer		0.00 428	0.00 431	0.00 441	0.00 464	0.00 465	0.00 452	0.00 463	0.00 459	0.00 460	0.00 459	0.00 463	0.00 458	0.00 459	0.00 456	0.00 456

ab-mvs-re		6.49 424	8.65 427	0.00 441	0.00 464	0.00 465	0.00 452	0.00 463	0.00 459	0.00 460	77.89 309	0.00 463	0.00 458	0.00 459	0.00 456	0.00 456

uanet		0.00 428	0.00 431	0.00 441	0.00 464	0.00 465	0.00 452	0.00 463	0.00 459	0.00 460	0.00 459	0.00 463	0.00 458	0.00 459	0.00 456	0.00 456

WAC-MVS						27.31 436								27.77 421

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 34

No_MVS				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 34

eth-test2					0.00 464

eth-test					0.00 464

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 47	67.01 1	90.33 12	73.16 64	91.15 4	88.23 23

test_0728_SECOND				79.19 16	87.82 3	59.11 68	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 44

GSMVS																78.05 314

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 308				78.05 314

sam_mvs												33.43 325

ambc				65.13 313	63.72 407	37.07 380	47.66 432	78.78 175		54.37 378	71.42 384	11.24 438	80.94 215	45.64 301	53.85 405	77.38 325

MTGPA								80.97 137

test_post1							68.67 327				3.64 454	32.39 346	69.49 349	44.17 313

test_post											3.55 455	33.90 319	66.52 369

patchmatchnet-post											64.03 421	34.50 310	74.27 320

GG-mvs-BLEND				62.34 334	71.36 339	37.04 381	69.20 324	57.33 400		54.73 373	65.48 419	30.37 356	77.82 275	34.82 385	74.93 207	72.17 385

MTMP							86.03 19	17.08 458

test9_res														75.28 47	88.31 32	83.81 195

agg_prior2														73.09 65	87.93 40	84.33 173

agg_prior					85.04 50	59.96 50		81.04 135		74.68 66			84.04 140

test_prior4						62.51 14	82.08 82

test_prior				76.69 61	84.20 61	57.27 94		84.88 40					86.43 83			86.38 89

新几何2							76.12 199

旧先验1					83.04 74	53.15 174		67.52 328			87.85 80	44.08 202			80.76 112	78.03 317

原ACMM2							79.02 122

testdata2													72.18 333	46.95 291

segment_acmp												54.23 60

test12				77.76 46	84.52 58	58.41 80		83.36 77		72.93 99		54.61 57	88.05 39		88.12 34	86.81 74

plane_prior7					81.41 97	55.96 117

plane_prior6					81.20 104	56.24 112						45.26 190

plane_prior5								84.01 53					87.21 59	68.16 98	80.58 115	84.65 166

plane_prior4											86.10 129

plane_prior1					81.27 102

n2								0.00 463

nn								0.00 463

door-mid								47.19 433

lessismore_v0				69.91 239	71.42 337	47.80 272		50.90 421		50.39 403	75.56 349	27.43 387	81.33 204	45.91 298	34.10 438	80.59 276

test11								83.47 72

door								47.60 431

HQP5-MVS						54.94 139

BP-MVS														67.04 111

HQP4-MVS										67.85 178			86.93 67			84.32 174

HQP3-MVS								83.90 58							80.35 119

HQP2-MVS												45.46 184

NP-MVS					80.98 107	56.05 116					85.54 148

ACMMP++_ref															74.07 216

ACMMP++															72.16 255

Test By Simon												48.33 144

High-resolution multi-view benchmark