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 39	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	81.64 113	68.35 2	75.77 45	90.38 30	53.98 64	90.26 13	81.30 3	87.68 42	88.77 12

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	70.09 137	86.34 125	54.92 54	88.90 25	72.68 69	84.55 69	87.76 41

UA-Net		73.13 84	72.93 84	73.76 132	83.58 67	51.66 211	78.75 125	77.66 211	67.75 4	72.61 108	89.42 52	49.82 130	83.29 158	53.61 244	83.14 83	86.32 102

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 70

TranMVSNet+NR-MVSNet		70.36 142	70.10 137	71.17 220	78.64 163	42.97 334	76.53 193	81.16 134	66.95 6	68.53 168	85.42 153	51.61 106	83.07 162	52.32 252	69.70 304	87.46 52

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	67.80 193	89.24 56	42.03 232	89.38 19	64.07 139	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 68	89.38 54	55.30 49	89.18 21	74.19 57	87.34 46	86.38 94

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 60	90.06 14	78.42 23	89.02 23	87.69 42
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 85	72.16 95	75.90 74	75.95 246	56.28 110	83.05 62	72.39 299	66.53 10	65.27 245	87.00 100	50.40 123	85.47 113	62.48 163	86.32 60	85.94 114
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 123	71.00 117	71.44 207	79.20 143	44.13 320	76.02 208	82.60 100	66.48 11	68.20 173	84.60 170	56.82 37	82.82 174	54.62 234	70.43 284	87.36 61

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 30
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	85.26 153

NR-MVSNet		69.54 167	68.85 159	71.59 201	78.05 186	43.81 325	74.20 248	80.86 141	65.18 14	62.76 289	84.52 171	52.35 92	83.59 152	50.96 267	70.78 279	87.37 59

MTAPA		76.90 35	76.42 39	78.35 35	86.08 37	63.57 2	74.92 233	80.97 139	65.13 15	75.77 45	90.88 20	48.63 147	86.66 74	77.23 29	88.17 33	84.81 169

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 19

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 30

EI-MVSNet-Vis-set		72.42 101	71.59 101	74.91 95	78.47 167	54.02 153	77.05 180	79.33 166	65.03 18	71.68 120	79.35 295	52.75 84	84.89 126	66.46 119	74.23 222	85.83 120

casdiffmvs_mvg		76.14 47	76.30 40	75.66 82	76.46 240	51.83 210	79.67 114	85.08 34	65.02 19	75.84 44	88.58 68	59.42 22	85.08 119	72.75 68	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	72.65 107	79.46 291	53.65 76	87.87 44	67.45 110	82.91 89	85.89 117

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 75	87.40 89	49.48 134	86.17 91	68.04 102	87.55 43	87.42 54

SymmetryMVS		75.28 56	74.60 62	77.30 54	83.85 65	59.89 52	84.36 41	75.51 248	64.69 22	74.21 75	87.40 89	49.48 134	86.17 91	68.04 102	83.88 79	85.85 118

WR-MVS		68.47 197	68.47 170	68.44 272	80.20 121	39.84 362	73.75 260	76.07 236	64.68 24	68.11 181	83.63 193	50.39 124	79.14 256	49.78 272	69.66 305	86.34 98

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	72.17 113	90.01 45	47.95 154	88.01 40	71.55 82	86.74 55	86.37 96

X-MVStestdata		70.21 145	67.28 204	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 113	6.49 463	47.95 154	88.01 40	71.55 82	86.74 55	86.37 96

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	69.27 156	86.10 132	45.26 197	87.21 59	68.16 100	80.58 118	84.65 173

plane_prior2							84.22 46		64.52 27

EI-MVSNet-UG-set		71.92 110	71.06 116	74.52 112	77.98 189	53.56 164	76.62 190	79.16 167	64.40 29	71.18 125	78.95 300	52.19 94	84.66 133	65.47 130	73.57 235	85.32 149

DU-MVS		70.01 150	69.53 144	71.44 207	78.05 186	44.13 320	75.01 229	81.51 116	64.37 30	68.20 173	84.52 171	49.12 144	82.82 174	54.62 234	70.43 284	87.37 59

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	85.45 141
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 25

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 44

LFMVS		71.78 112	71.59 101	72.32 181	83.40 71	46.38 296	79.75 112	71.08 308	64.18 34	72.80 104	88.64 67	42.58 227	83.72 148	57.41 210	84.49 72	86.86 75

IS-MVSNet		71.57 116	71.00 117	73.27 157	78.86 153	45.63 307	80.22 103	78.69 181	64.14 37	66.46 220	87.36 92	49.30 138	85.60 106	50.26 271	83.71 82	88.59 15

plane_prior3						56.09 114			63.92 38	69.27 156

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 84	90.60 23	54.85 55	86.72 72	77.20 30	88.06 36	85.74 127
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	77.84 193	52.25 200	75.59 216	84.17 50	63.76 40	73.15 93	82.79 208	59.58 20	86.80 70	67.24 111	86.04 61	87.89 33
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	72.52 109	87.49 86	47.18 170	85.88 101	69.47 93	80.78 112	83.66 214
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 135	70.20 132	71.89 188	78.55 164	45.29 310	75.94 209	82.92 94	63.68 42	68.16 176	83.59 194	53.89 67	83.49 155	53.97 240	71.12 275	86.89 74

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 74	90.03 43	52.56 86	88.53 29	74.79 53	88.34 29	86.63 87

testing3-2		62.06 303	62.36 286	61.17 355	79.29 138	30.31 435	64.09 376	63.49 375	63.50 44	62.84 286	82.22 230	32.35 358	69.02 360	40.01 360	73.43 240	84.17 190

EC-MVSNet		75.84 51	75.87 47	75.74 80	78.86 153	52.65 190	83.73 56	86.08 18	63.47 45	72.77 105	87.25 97	53.13 80	87.93 42	71.97 77	85.57 64	86.66 85

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 55	90.47 29	53.96 66	88.68 27	76.48 35	89.63 20	87.16 67

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 36	89.67 18	86.84 76
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 87	87.27 96	55.06 51	86.30 89	71.78 79	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 82	90.25 36	57.68 29	89.96 15	74.62 54	89.03 22	87.89 33
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 97	72.09 96	73.75 134	81.58 93	49.69 248	77.76 156	77.63 212	63.21 50	73.21 90	89.02 58	42.14 231	83.32 157	61.72 170	82.50 95	88.25 24

plane_prior						56.31 108	83.58 59		63.19 51						80.48 121

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	69.80 147	89.74 51	45.43 193	87.16 61	72.01 75	82.87 91	85.14 155
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		66.60 242	66.45 222	67.04 286	77.11 221	36.56 395	77.03 181	80.42 148	62.95 53	62.51 297	84.03 182	46.69 178	79.07 258	44.22 322	63.08 368	85.51 136

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 78
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	82.28 103	62.90 55	71.77 118	90.26 35	46.61 179	86.55 80	71.71 80	85.66 63	84.97 164

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 87	88.39 30	79.34 9	90.52 13	86.78 79

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 56	88.67 26	88.12 29

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 79	90.50 27	53.20 79	88.35 31	74.02 59	87.05 47	86.13 109

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 85	90.56 25	49.80 131	88.24 33	74.02 59	87.03 48	86.32 102

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 87	90.58 24	49.90 128	88.21 34	73.78 61	87.03 48	86.29 106

casdiffmvs		74.80 60	74.89 60	74.53 111	75.59 253	50.37 231	78.17 142	85.06 36	62.80 61	74.40 71	87.86 80	57.88 27	83.61 151	69.46 94	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	74.34 115	75.70 249	49.99 239	77.54 161	84.63 43	62.73 62	73.98 78	87.79 83	57.67 30	83.82 147	69.49 92	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 97	89.97 46	50.90 119	87.48 53	75.30 47	86.85 53	87.33 62
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 256	65.34 248	66.31 297	76.06 245	34.79 408	76.43 195	79.38 165	62.55 64	61.66 308	83.83 187	45.60 187	79.15 255	41.64 352	60.88 383	85.00 161

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 27
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		66.49 245	66.41 226	66.72 288	77.67 200	36.33 398	76.83 188	79.52 162	62.45 66	62.54 295	83.47 200	46.32 181	78.37 270	45.47 317	63.43 365	85.45 141

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	72.68 106	90.50 27	48.18 152	87.34 54	73.59 63	85.71 62	84.76 172

PS-CasMVS		66.42 246	66.32 230	66.70 290	77.60 208	36.30 400	76.94 183	79.61 160	62.36 68	62.43 300	83.66 192	45.69 185	78.37 270	45.35 319	63.26 366	85.42 144

3Dnovator	64.47 5	72.49 98	71.39 107	75.79 77	77.70 198	58.99 73	80.66 99	83.15 90	62.24 69	65.46 241	86.59 116	42.38 230	85.52 109	59.59 190	84.72 67	82.85 237

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 43	91.51 11	52.47 89	86.78 71	80.66 4	89.64 19	87.80 39
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 111		82.31 77		62.10 71	67.85 187

ACMP_Plane					80.66 111		82.31 77		62.10 71	67.85 187

HQP-MVS		73.45 77	72.80 87	75.40 87	80.66 111	54.94 139	82.31 77	83.90 58	62.10 71	67.85 187	85.54 151	45.46 191	86.93 67	67.04 113	80.35 122	84.32 183

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	71.49 123	86.03 135	53.83 68	86.36 87	67.74 105	86.91 52	88.19 27

VPNet		67.52 221	68.11 183	65.74 311	79.18 145	36.80 393	72.17 290	72.83 295	62.04 75	67.79 194	85.83 142	48.88 146	76.60 312	51.30 263	72.97 249	83.81 204

WR-MVS_H		67.02 233	66.92 214	67.33 285	77.95 190	37.75 382	77.57 159	82.11 106	62.03 76	62.65 292	82.48 223	50.57 122	79.46 246	42.91 340	64.01 358	84.79 170

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 98	88.88 62	53.72 72	89.06 23	68.27 97	88.04 37	87.42 54
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 32	90.18 15	87.87 35

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	72.45 112	90.34 33	48.48 150	88.13 37	72.32 72	86.85 53	85.78 121

fmvsm_s_conf0.5_n_8		74.30 70	74.39 65	74.01 124	75.33 259	52.89 184	78.24 138	77.32 220	61.65 80	78.13 27	88.90 61	52.82 83	81.54 200	78.46 22	78.67 155	87.60 47

Effi-MVS+		73.31 80	72.54 91	75.62 84	77.87 191	53.64 161	79.62 116	79.61 160	61.63 81	72.02 116	82.61 213	56.44 40	85.97 99	63.99 142	79.07 147	87.25 64

MG-MVS		73.96 73	73.89 72	74.16 121	85.65 42	49.69 248	81.59 88	81.29 127	61.45 82	71.05 126	88.11 72	51.77 103	87.73 48	61.05 176	83.09 84	85.05 160

fmvsm_s_conf0.5_n_9		75.16 57	75.22 56	75.01 94	78.34 174	55.37 134	77.30 171	73.95 280	61.40 83	79.46 19	90.14 37	57.07 34	81.15 210	80.00 5	79.31 139	88.51 18

LPG-MVS_test		72.74 91	71.74 100	75.76 78	80.22 119	57.51 92	82.55 73	83.40 75	61.32 84	66.67 217	87.33 94	39.15 272	86.59 75	67.70 106	77.30 181	83.19 227

LGP-MVS_train				75.76 78	80.22 119	57.51 92		83.40 75	61.32 84	66.67 217	87.33 94	39.15 272	86.59 75	67.70 106	77.30 181	83.19 227

CLD-MVS		73.33 79	72.68 89	75.29 91	78.82 155	53.33 173	78.23 139	84.79 42	61.30 86	70.41 134	81.04 257	52.41 90	87.12 62	64.61 138	82.49 96	85.41 145
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT-MVS		71.46 119	70.70 123	73.74 135	77.76 196	49.30 256	76.60 191	80.45 147	61.25 87	68.17 175	84.78 160	44.64 205	84.90 125	64.79 134	77.88 170	87.03 70

fmvsm_s_conf0.5_n_3		73.55 76	74.39 65	71.03 225	74.09 295	51.86 209	77.77 155	75.60 244	61.18 88	78.67 25	88.98 59	55.88 46	77.73 285	78.69 16	78.68 154	83.50 219

MVS_111021_HR		74.02 72	73.46 78	75.69 81	83.01 76	60.63 40	77.29 172	78.40 199	61.18 88	70.58 132	85.97 137	54.18 62	84.00 144	67.52 109	82.98 88	82.45 249

balanced_conf03		76.58 39	76.55 38	76.68 62	81.73 91	52.90 182	80.94 94	85.70 24	61.12 90	74.90 61	87.17 98	56.46 39	88.14 36	72.87 67	88.03 38	89.00 8

FIs		70.82 132	71.43 105	68.98 265	78.33 175	38.14 378	76.96 182	83.59 69	61.02 91	67.33 201	86.73 108	55.07 50	81.64 196	54.61 236	79.22 142	87.14 68

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

FC-MVSNet-test		69.80 157	70.58 126	67.46 281	77.61 207	34.73 411	76.05 206	83.19 89	60.84 93	65.88 235	86.46 122	54.52 59	80.76 225	52.52 251	78.12 166	86.91 73

v8		70.33 143	69.28 150	73.49 149	73.15 308	50.22 233	78.62 129	80.78 142	60.79 94	66.45 221	82.11 237	49.35 137	84.98 122	63.58 151	68.71 320	85.28 151

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 50	84.83 158	60.76 15	86.56 77	67.86 104	87.87 41	86.06 111

Vis-MVSNet		72.18 104	71.37 108	74.61 106	81.29 100	55.41 132	80.90 95	78.28 202	60.73 96	69.23 159	88.09 73	44.36 209	82.65 178	57.68 207	81.75 106	85.77 124
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		71.26 122	70.16 134	74.57 109	74.59 278	52.77 188	75.91 210	81.20 131	60.72 97	69.10 162	85.71 146	41.67 241	83.53 153	63.91 145	78.62 157	87.42 54

BP-MVS1		73.41 78	72.25 94	76.88 57	76.68 233	53.70 159	79.15 121	81.07 135	60.66 98	71.81 117	87.39 91	40.93 254	87.24 55	71.23 84	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 48	88.32 32	73.48 64	87.03 48	84.83 168
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 102	71.20 113	75.59 86	80.28 117	57.54 90	82.74 69	82.84 98	60.58 100	65.24 249	86.18 129	39.25 270	86.03 97	66.95 117	76.79 189	83.22 225
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	51.51 108	87.78 47	75.65 43	87.55 43	87.10 69

testdata1							72.65 279		60.50 102

UGNet		68.81 187	67.39 199	73.06 161	78.33 175	54.47 145	79.77 111	75.40 251	60.45 103	63.22 278	84.40 175	32.71 347	80.91 221	51.71 261	80.56 120	83.81 204
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 83	73.16 81	73.11 160	75.15 265	49.31 255	77.53 163	83.21 85	60.42 104	73.20 91	87.34 93	53.82 69	81.05 215	67.02 115	80.79 111	88.96 9

h-mvs33		72.71 92	71.49 104	76.40 68	81.99 88	59.58 57	76.92 184	76.74 229	60.40 105	74.81 63	85.95 138	45.54 189	85.76 104	70.41 89	70.61 282	83.86 203

hse-mvs2		71.04 124	69.86 138	74.60 107	79.58 133	57.12 102	73.96 252	75.25 254	60.40 105	74.81 63	81.95 239	45.54 189	82.90 167	70.41 89	66.83 337	83.77 208

EPP-MVSNet		72.16 107	71.31 110	74.71 100	78.68 159	49.70 246	82.10 81	81.65 112	60.40 105	65.94 231	85.84 141	51.74 104	86.37 86	55.93 220	79.55 134	88.07 32

UniMVSNet_ETH3D		67.60 220	67.07 213	69.18 262	77.39 213	42.29 339	74.18 249	75.59 245	60.37 108	66.77 213	86.06 134	37.64 288	78.93 265	52.16 254	73.49 237	86.32 102

test_prior2							81.75 84		60.37 108	75.01 56	89.06 57	56.22 42		72.19 73	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	60.37 108	79.89 18	89.38 54	54.97 53	85.58 108	76.12 39	84.94 66	86.33 100
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		69.68 161	70.19 133	68.16 275	79.73 130	41.63 348	70.53 314	77.38 217	60.37 108	70.69 129	86.63 113	51.08 115	77.09 297	53.61 244	81.69 108	85.75 126

sasdasda		74.67 63	74.98 58	73.71 137	78.94 151	50.56 228	80.23 101	83.87 61	60.30 112	77.15 36	86.56 118	59.65 17	82.00 190	66.01 124	82.12 97	88.58 16

canonicalmvs		74.67 63	74.98 58	73.71 137	78.94 151	50.56 228	80.23 101	83.87 61	60.30 112	77.15 36	86.56 118	59.65 17	82.00 190	66.01 124	82.12 97	88.58 16

v7n		69.01 183	67.36 201	73.98 125	72.51 322	52.65 190	78.54 133	81.30 126	60.26 114	62.67 291	81.62 246	43.61 215	84.49 134	57.01 211	68.70 321	84.79 170

reproduce-ours		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 115	77.85 31	91.42 14	50.67 120	87.69 49	72.46 70	84.53 70	85.46 139

our_new_method		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 78	60.22 115	77.85 31	91.42 14	50.67 120	87.69 49	72.46 70	84.53 70	85.46 139

HPM-MVS_fast		74.30 70	73.46 78	76.80 59	84.45 60	59.04 71	83.65 58	81.05 136	60.15 117	70.43 133	89.84 48	41.09 253	85.59 107	67.61 108	82.90 90	85.77 124

VPA-MVSNet		69.02 182	69.47 146	67.69 279	77.42 212	41.00 355	74.04 250	79.68 158	60.06 118	69.26 158	84.81 159	51.06 116	77.58 287	54.44 237	74.43 220	84.48 180

v10		70.21 145	69.02 155	73.81 129	73.51 302	50.92 220	78.74 126	81.39 119	60.05 119	66.39 222	81.83 242	47.58 161	85.41 116	62.80 160	68.86 319	85.09 159

SR-MVS		76.13 48	75.70 49	77.40 53	85.87 40	61.20 29	85.52 28	82.19 104	59.99 120	75.10 54	90.35 32	47.66 159	86.52 81	71.64 81	82.99 86	84.47 181

SSC-MVS3.2		60.57 316	61.39 298	58.12 377	74.29 288	32.63 425	59.52 401	65.53 356	59.90 121	62.45 298	79.75 284	41.96 233	63.90 391	39.47 364	69.65 307	77.84 329

9.14			78.75 15		83.10 73		84.15 49	88.26 1	59.90 121	78.57 26	90.36 31	57.51 32	86.86 69	77.39 28	89.52 21

v2v482		70.50 138	69.45 147	73.66 140	72.62 318	50.03 238	77.58 158	80.51 146	59.90 121	69.52 149	82.14 235	47.53 163	84.88 128	65.07 133	70.17 292	86.09 110

Baseline_NR-MVSNet		67.05 232	67.56 191	65.50 315	75.65 250	37.70 384	75.42 219	74.65 267	59.90 121	68.14 177	83.15 206	49.12 144	77.20 295	52.23 253	69.78 301	81.60 262

API-MVS		72.17 105	71.41 106	74.45 113	81.95 89	57.22 95	84.03 51	80.38 149	59.89 125	68.40 170	82.33 226	49.64 132	87.83 46	51.87 258	84.16 77	78.30 320

Effi-MVS+-dtu		69.64 163	67.53 194	75.95 73	76.10 244	62.29 15	80.20 104	76.06 237	59.83 126	65.26 248	77.09 333	41.56 244	84.02 143	60.60 181	71.09 278	81.53 263

reproduce_model		76.43 42	76.08 42	77.49 50	83.47 70	60.09 47	84.60 37	82.90 95	59.65 127	77.31 34	91.43 13	49.62 133	87.24 55	71.99 76	83.75 81	85.14 155

MVSMamba_PlusPlus		75.75 53	75.44 51	76.67 63	80.84 108	53.06 179	78.62 129	85.13 33	59.65 127	71.53 122	87.47 87	56.92 35	88.17 35	72.18 74	86.63 58	88.80 11

CANet_DTU		68.18 205	67.71 190	69.59 253	74.83 271	46.24 298	78.66 128	76.85 226	59.60 129	63.45 276	82.09 238	35.25 312	77.41 290	59.88 187	78.76 152	85.14 155

EI-MVSNet		69.27 176	68.44 172	71.73 195	74.47 281	49.39 253	75.20 224	78.45 195	59.60 129	69.16 160	76.51 345	51.29 111	82.50 182	59.86 189	71.45 272	83.30 222

IterMVS-LS		69.22 178	68.48 168	71.43 209	74.44 283	49.40 252	76.23 200	77.55 213	59.60 129	65.85 236	81.59 249	51.28 112	81.58 199	59.87 188	69.90 299	83.30 222
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 99	73.34 80	69.81 250	77.77 195	43.21 331	75.84 213	81.18 132	59.59 132	75.45 48	86.64 111	57.74 28	77.94 277	63.92 143	81.90 102	88.30 22

VDDNet		71.81 111	71.33 109	73.26 158	82.80 79	47.60 287	78.74 126	75.27 253	59.59 132	72.94 100	89.40 53	41.51 246	83.91 145	58.75 202	82.99 86	88.26 23

viewmanbaseed2359cas		72.92 88	72.89 85	73.00 162	75.16 263	49.25 258	77.25 175	83.11 92	59.52 134	72.93 101	86.63 113	54.11 63	80.98 216	66.63 118	80.67 115	88.76 13

alignmvs		73.86 74	73.99 70	73.45 151	78.20 178	50.50 230	78.57 131	82.43 101	59.40 135	76.57 41	86.71 110	56.42 41	81.23 209	65.84 127	81.79 103	88.62 14

MVS_Test		72.45 99	72.46 92	72.42 179	74.88 268	48.50 273	76.28 198	83.14 91	59.40 135	72.46 110	84.68 163	55.66 47	81.12 211	65.98 126	79.66 131	87.63 45

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 51	83.82 64	59.34 137	79.37 20	89.76 50	59.84 16	87.62 52	76.69 33	86.74 55	87.68 43
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 77	74.66 103	83.02 75	59.29 63	82.30 80	81.88 108	59.34 137	71.59 121	86.83 104	45.94 184	83.65 150	65.09 132	85.22 65	81.06 278

PAPM_NR		72.63 95	71.80 99	75.13 92	81.72 92	53.42 171	79.91 109	83.28 83	59.14 139	66.31 224	85.90 139	51.86 100	86.06 95	57.45 209	80.62 116	85.91 116

testing91		64.46 273	63.80 264	66.47 294	78.43 169	40.06 360	67.63 342	69.59 322	59.06 140	63.18 280	78.05 313	34.05 325	76.99 302	48.30 288	75.87 202	82.37 251

myMVS_eth3d28		60.66 315	61.04 306	59.51 362	77.32 215	31.58 430	63.11 381	63.87 371	59.00 141	60.90 317	78.26 310	32.69 349	66.15 381	36.10 390	78.13 165	80.81 283

save fliter					86.17 33	61.30 28	83.98 53	79.66 159	59.00 141

v148		68.24 203	67.19 211	71.40 210	70.43 361	47.77 284	75.76 214	77.03 224	58.91 143	67.36 200	80.10 277	48.60 149	81.89 192	60.01 185	66.52 340	84.53 178

TransMVSNet (Re)		64.72 267	64.33 257	65.87 310	75.22 260	38.56 374	74.66 239	75.08 262	58.90 144	61.79 306	82.63 212	51.18 113	78.07 275	43.63 333	55.87 406	80.99 280

Anonymous202405211		66.84 237	65.99 236	69.40 257	80.19 122	42.21 341	71.11 307	71.31 307	58.80 145	67.90 185	86.39 124	29.83 375	79.65 243	49.60 278	78.78 151	86.33 100

test2506		65.33 261	64.61 255	67.50 280	79.46 136	34.19 416	74.43 245	51.92 426	58.72 146	66.75 214	88.05 75	25.99 408	80.92 220	51.94 257	84.25 74	87.39 57

ECVR-MVS		67.72 218	67.51 195	68.35 273	79.46 136	36.29 401	74.79 236	66.93 345	58.72 146	67.19 205	88.05 75	36.10 305	81.38 204	52.07 255	84.25 74	87.39 57

test1111		67.21 225	67.14 212	67.42 282	79.24 142	34.76 410	73.89 257	65.65 354	58.71 148	66.96 210	87.95 79	36.09 306	80.53 227	52.03 256	83.79 80	86.97 72

LCM-MVSNet-Re		61.88 306	61.35 299	63.46 335	74.58 279	31.48 431	61.42 391	58.14 404	58.71 148	53.02 400	79.55 289	43.07 221	76.80 306	45.69 310	77.96 168	82.11 257

testing99		64.05 277	63.29 275	66.34 296	78.17 182	39.76 364	67.33 347	68.00 336	58.60 150	63.03 283	78.10 312	32.57 354	76.94 304	48.22 289	75.58 206	82.34 252

v1144		70.42 140	69.31 149	73.76 132	73.22 306	50.64 225	77.83 153	81.43 118	58.58 151	69.40 153	81.16 254	47.53 163	85.29 118	64.01 141	70.64 280	85.34 148

TSAR-MVS + GP.		74.90 59	74.15 69	77.17 55	82.00 87	58.77 77	81.80 83	78.57 188	58.58 151	74.32 73	84.51 173	55.94 45	87.22 58	67.11 112	84.48 73	85.52 135

BH-RMVSNet		68.81 187	67.42 198	72.97 163	80.11 125	52.53 194	74.26 247	76.29 232	58.48 153	68.38 171	84.20 177	42.59 226	83.83 146	46.53 302	75.91 201	82.56 243

APD-MVS_3200maxsize		74.96 58	74.39 65	76.67 63	82.20 84	58.24 82	83.67 57	83.29 82	58.41 154	73.71 83	90.14 37	45.62 186	85.99 98	69.64 91	82.85 92	85.78 121

OMC-MVS		71.40 121	70.60 124	73.78 130	76.60 236	53.15 176	79.74 113	79.78 156	58.37 155	68.75 164	86.45 123	45.43 193	80.60 226	62.58 161	77.73 171	87.58 49

nrg030		72.96 87	73.01 83	72.84 166	75.41 257	50.24 232	80.02 105	82.89 97	58.36 156	74.44 70	86.73 108	58.90 24	80.83 222	65.84 127	74.46 218	87.44 53

K. test v3		60.47 319	57.11 338	70.56 235	73.74 299	48.22 276	75.10 228	62.55 383	58.27 157	53.62 395	76.31 349	27.81 392	81.59 198	47.42 293	39.18 442	81.88 260

FA-MVS(test-final)		69.82 155	68.48 168	73.84 128	78.44 168	50.04 237	75.58 218	78.99 173	58.16 158	67.59 197	82.14 235	42.66 225	85.63 105	56.60 213	76.19 195	85.84 119

MVS_111021_LR		69.50 170	68.78 162	71.65 199	78.38 170	59.33 61	74.82 235	70.11 316	58.08 159	67.83 192	84.68 163	41.96 233	76.34 317	65.62 129	77.54 174	79.30 311

SR-MVS-dyc-post		74.57 66	73.90 71	76.58 66	83.49 68	59.87 54	84.29 43	81.36 121	58.07 160	73.14 94	90.07 39	44.74 203	85.84 102	68.20 98	81.76 104	84.03 193

RE-MVS-def			73.71 75		83.49 68	59.87 54	84.29 43	81.36 121	58.07 160	73.14 94	90.07 39	43.06 222		68.20 98	81.76 104	84.03 193

SDMVSNet		68.03 208	68.10 184	67.84 277	77.13 219	48.72 269	65.32 363	79.10 168	58.02 162	65.08 252	82.55 219	47.83 156	73.40 331	63.92 143	73.92 226	81.41 265

sd_testset		64.46 273	64.45 256	64.51 326	77.13 219	42.25 340	62.67 384	72.11 302	58.02 162	65.08 252	82.55 219	41.22 252	69.88 356	47.32 295	73.92 226	81.41 265

GeoE		71.01 126	70.15 135	73.60 145	79.57 134	52.17 201	78.93 124	78.12 204	58.02 162	67.76 196	83.87 186	52.36 91	82.72 176	56.90 212	75.79 203	85.92 115

ZD-MVS					86.64 21	60.38 45		82.70 99	57.95 165	78.10 28	90.06 41	56.12 44	88.84 26	74.05 58	87.00 51

EIA-MVS		71.78 112	70.60 124	75.30 90	79.85 128	53.54 165	77.27 174	83.26 84	57.92 166	66.49 219	79.39 293	52.07 97	86.69 73	60.05 184	79.14 146	85.66 131

test_yl		69.69 159	69.13 152	71.36 213	78.37 172	45.74 303	74.71 237	80.20 151	57.91 167	70.01 142	83.83 187	42.44 228	82.87 170	54.97 230	79.72 129	85.48 137

DCV-MVSNet		69.69 159	69.13 152	71.36 213	78.37 172	45.74 303	74.71 237	80.20 151	57.91 167	70.01 142	83.83 187	42.44 228	82.87 170	54.97 230	79.72 129	85.48 137

MonoMVSNet		64.15 276	63.31 274	66.69 291	70.51 359	44.12 322	74.47 243	74.21 275	57.81 169	63.03 283	76.62 341	38.33 281	77.31 293	54.22 238	60.59 388	78.64 318

dcpmvs_2		74.55 67	75.23 55	72.48 175	82.34 83	53.34 172	77.87 150	81.46 117	57.80 170	75.49 47	86.81 105	62.22 13	77.75 284	71.09 85	82.02 100	86.34 98

diffmvs_AUTHOR		71.02 125	70.87 119	71.45 206	69.89 372	48.97 264	73.16 273	78.33 201	57.79 171	72.11 115	85.26 155	51.84 101	77.89 280	71.00 86	78.47 161	87.49 51

viewdifsd2359ckpt11		69.13 179	68.38 175	71.38 211	71.57 339	48.61 270	73.22 271	73.18 290	57.65 172	70.67 130	84.73 161	50.03 126	79.80 240	63.25 154	71.10 276	85.74 127

viewmsd2359difaftdt		69.13 179	68.38 175	71.38 211	71.57 339	48.61 270	73.22 271	73.18 290	57.65 172	70.67 130	84.73 161	50.03 126	79.80 240	63.25 154	71.10 276	85.74 127

fmvsm_s_conf0.5_n_6		72.59 96	72.87 86	71.73 195	75.14 266	51.96 207	76.28 198	77.12 223	57.63 174	73.85 81	86.91 102	51.54 107	77.87 281	77.18 31	80.18 126	85.37 147

Fast-Effi-MVS+-dtu		67.37 223	65.33 249	73.48 150	72.94 313	57.78 88	77.47 164	76.88 225	57.60 175	61.97 303	76.85 337	39.31 268	80.49 230	54.72 233	70.28 290	82.17 256

v1192		69.97 152	68.68 164	73.85 127	73.19 307	50.94 218	77.68 157	81.36 121	57.51 176	68.95 163	80.85 264	45.28 196	85.33 117	62.97 159	70.37 286	85.27 152

ACMH+	57.40 11	66.12 250	64.06 259	72.30 182	77.79 194	52.83 186	80.39 100	78.03 205	57.30 177	57.47 355	82.55 219	27.68 394	84.17 138	45.54 313	69.78 301	79.90 300

diffmvs		70.69 134	70.43 127	71.46 204	69.45 379	48.95 265	72.93 276	78.46 194	57.27 178	71.69 119	83.97 185	51.48 109	77.92 279	70.70 88	77.95 169	87.53 50
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		68.27 201	67.29 203	71.21 217	79.74 129	53.22 174	76.06 205	77.46 216	57.19 179	66.10 228	81.61 247	45.37 195	83.50 154	45.42 318	76.68 191	76.91 345

thres100view900		63.28 286	62.41 285	65.89 308	77.31 216	38.66 373	72.65 279	69.11 329	57.07 180	62.45 298	81.03 258	37.01 300	79.17 252	31.84 411	73.25 244	79.83 303

fmvsm_s_conf0.5_n_7		69.54 167	69.67 142	69.15 264	73.47 304	51.41 213	70.35 318	73.34 286	57.05 181	68.41 169	85.83 142	49.86 129	72.84 334	71.86 78	76.83 188	83.19 227

DP-MVS Recon		72.15 108	70.73 122	76.40 68	86.57 24	57.99 84	81.15 93	82.96 93	57.03 182	66.78 212	85.56 148	44.50 207	88.11 38	51.77 260	80.23 125	83.10 232

thres600view7		63.30 285	62.27 287	66.41 295	77.18 218	38.87 371	72.35 286	69.11 329	56.98 183	62.37 301	80.96 260	37.01 300	79.00 263	31.43 418	73.05 248	81.36 268

V42		68.65 191	67.35 202	72.56 172	68.93 385	50.18 234	72.90 277	79.47 163	56.92 184	69.45 152	80.26 273	46.29 182	82.99 164	64.07 139	67.82 328	84.53 178

MCST-MVS		77.48 29	77.45 28	77.54 48	86.67 20	58.36 81	83.22 61	86.93 5	56.91 185	74.91 60	88.19 70	59.15 23	87.68 51	73.67 62	87.45 45	86.57 88

GA-MVS		65.53 257	63.70 266	71.02 226	70.87 354	48.10 278	70.48 315	74.40 269	56.69 186	64.70 261	76.77 338	33.66 333	81.10 212	55.42 229	70.32 289	83.87 202

v144192		69.71 158	68.51 167	73.33 156	73.10 309	50.13 235	77.54 161	80.64 143	56.65 187	68.57 167	80.55 267	46.87 177	84.96 124	62.98 158	69.66 305	84.89 167

fmvsm_l_conf0.5_n_3		73.23 82	73.13 82	73.55 147	74.40 284	55.13 137	78.97 123	74.96 263	56.64 188	74.76 66	88.75 66	55.02 52	78.77 267	76.33 37	78.31 164	86.74 80

tfpn200view9		63.18 288	62.18 289	66.21 300	76.85 230	39.62 365	71.96 294	69.44 325	56.63 189	62.61 293	79.83 280	37.18 294	79.17 252	31.84 411	73.25 244	79.83 303

thres400		63.31 284	62.18 289	66.72 288	76.85 230	39.62 365	71.96 294	69.44 325	56.63 189	62.61 293	79.83 280	37.18 294	79.17 252	31.84 411	73.25 244	81.36 268

GBi-Net		67.21 225	66.55 220	69.19 259	77.63 202	43.33 328	77.31 168	77.83 208	56.62 191	65.04 254	82.70 209	41.85 236	80.33 232	47.18 297	72.76 252	83.92 199

test1		67.21 225	66.55 220	69.19 259	77.63 202	43.33 328	77.31 168	77.83 208	56.62 191	65.04 254	82.70 209	41.85 236	80.33 232	47.18 297	72.76 252	83.92 199

FMVSNet2		66.93 235	66.31 231	68.79 268	77.63 202	42.98 333	76.11 203	77.47 214	56.62 191	65.22 251	82.17 233	41.85 236	80.18 238	47.05 300	72.72 255	83.20 226

fmvsm_l_conf0.5_n_9		73.27 81	73.66 76	72.09 184	73.82 296	52.72 189	77.45 165	74.28 273	56.61 194	77.10 38	88.16 71	56.17 43	77.09 297	78.27 24	81.13 110	86.48 92

DPM-MVS		75.47 55	75.00 57	76.88 57	81.38 99	59.16 64	79.94 107	85.71 23	56.59 195	72.46 110	86.76 106	56.89 36	87.86 45	66.36 120	88.91 25	83.64 216

v1921920		69.47 171	68.17 181	73.36 155	73.06 310	50.10 236	77.39 166	80.56 144	56.58 196	68.59 165	80.37 269	44.72 204	84.98 122	62.47 164	69.82 300	85.00 161

FMVSNet1		66.70 240	65.87 237	69.19 259	77.49 210	43.33 328	77.31 168	77.83 208	56.45 197	64.60 263	82.70 209	38.08 286	80.33 232	46.08 306	72.31 261	83.92 199

v1240		69.24 177	67.91 186	73.25 159	73.02 312	49.82 240	77.21 176	80.54 145	56.43 198	68.34 172	80.51 268	43.33 218	84.99 120	62.03 168	69.77 303	84.95 165

fmvsm_s_conf0.5_n_4		72.04 109	71.85 98	72.58 171	73.74 299	52.49 196	76.69 189	72.42 298	56.42 199	75.32 49	87.04 99	52.13 96	78.01 276	79.29 12	73.65 232	87.26 63

testing222		62.29 300	61.31 300	65.25 321	77.87 191	38.53 375	68.34 336	66.31 351	56.37 200	63.15 282	77.58 327	28.47 386	76.18 320	37.04 379	76.65 192	81.05 279

CDPH-MVS		76.31 43	75.67 50	78.22 37	85.35 48	59.14 67	81.31 91	84.02 52	56.32 201	74.05 77	88.98 59	53.34 78	87.92 43	69.23 95	88.42 28	87.59 48

Vis-MVSNet (Re-imp)		63.69 281	63.88 262	63.14 339	74.75 273	31.04 433	71.16 305	63.64 374	56.32 201	59.80 329	84.99 156	44.51 206	75.46 322	39.12 366	80.62 116	82.92 234

AdaColmap		69.99 151	68.66 165	73.97 126	84.94 54	57.83 86	82.63 71	78.71 180	56.28 203	64.34 264	84.14 179	41.57 243	87.06 65	46.45 303	78.88 148	77.02 341

PS-MVSNAJss		72.24 103	71.21 112	75.31 89	78.50 165	55.93 118	81.63 85	82.12 105	56.24 204	70.02 141	85.68 147	47.05 172	84.34 137	65.27 131	74.41 221	85.67 130

c3_l		68.33 200	67.56 191	70.62 234	70.87 354	46.21 299	74.47 243	78.80 178	56.22 205	66.19 225	78.53 308	51.88 99	81.40 203	62.08 165	69.04 315	84.25 186

Fast-Effi-MVS+		70.28 144	69.12 154	73.73 136	78.50 165	51.50 212	75.01 229	79.46 164	56.16 206	68.59 165	79.55 289	53.97 65	84.05 140	53.34 246	77.53 175	85.65 132

PHI-MVS		75.87 50	75.36 52	77.41 51	80.62 115	55.91 119	84.28 45	85.78 21	56.08 207	73.41 86	86.58 117	50.94 118	88.54 28	70.79 87	89.71 17	87.79 40

baseline1		63.81 280	63.87 263	63.62 334	76.29 241	36.36 396	71.78 297	67.29 341	56.05 208	64.23 269	82.95 207	47.11 171	74.41 327	47.30 296	61.85 377	80.10 297

train_agg		76.27 44	76.15 41	76.64 65	85.58 43	61.59 24	81.62 86	81.26 128	55.86 209	74.93 58	88.81 63	53.70 73	84.68 131	75.24 49	88.33 30	83.65 215

test_8					85.40 46	60.96 34	81.54 89	81.18 132	55.86 209	74.81 63	88.80 65	53.70 73	84.45 135

FMVSNet3		66.32 249	65.61 242	68.46 271	76.48 239	42.34 338	74.98 231	77.15 222	55.83 211	65.04 254	81.16 254	39.91 261	80.14 239	47.18 297	72.76 252	82.90 236

PAPR		71.72 115	70.82 120	74.41 114	81.20 104	51.17 214	79.55 118	83.33 80	55.81 212	66.93 211	84.61 167	50.95 117	86.06 95	55.79 223	79.20 143	86.00 112

eth_miper_zixun_eth		67.63 219	66.28 232	71.67 198	71.60 338	48.33 275	73.68 261	77.88 206	55.80 213	65.91 232	78.62 306	47.35 169	82.88 169	59.45 191	66.25 341	83.81 204

ACMH	55.70 15	65.20 263	63.57 268	70.07 243	78.07 185	52.01 206	79.48 119	79.69 157	55.75 214	56.59 362	80.98 259	27.12 399	80.94 218	42.90 341	71.58 270	77.25 339
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 260	62.73 282	73.40 154	74.89 267	52.78 187	73.09 275	75.13 258	55.69 215	58.48 347	73.73 378	32.86 342	86.32 88	50.63 268	70.11 293	81.10 277
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		61.53 309	60.94 308	63.30 337	68.95 384	36.93 392	67.60 343	72.80 296	55.67 216	59.95 326	76.63 340	45.01 202	72.22 340	39.74 363	62.09 376	80.74 285

TEST9					85.58 43	61.59 24	81.62 86	81.26 128	55.65 217	74.93 58	88.81 63	53.70 73	84.68 131

thres200		62.20 301	61.16 305	65.34 319	75.38 258	39.99 361	69.60 327	69.29 327	55.64 218	61.87 305	76.99 334	37.07 299	78.96 264	31.28 419	73.28 243	77.06 340

guyue		68.10 207	67.23 210	70.71 233	73.67 301	49.27 257	73.65 262	76.04 238	55.62 219	67.84 191	82.26 229	41.24 251	78.91 266	61.01 177	73.72 230	83.94 197

pm-mvs1		65.24 262	64.97 253	66.04 305	72.38 325	39.40 368	72.62 281	75.63 243	55.53 220	62.35 302	83.18 205	47.45 165	76.47 315	49.06 282	66.54 339	82.24 253

testing11		62.81 292	61.90 292	65.54 313	78.38 170	40.76 357	67.59 344	66.78 347	55.48 221	60.13 321	77.11 332	31.67 361	76.79 307	45.53 314	74.45 219	79.06 313

ACMM	61.98 7	70.80 133	69.73 140	74.02 123	80.59 116	58.59 79	82.68 70	82.02 107	55.46 222	67.18 206	84.39 176	38.51 278	83.17 161	60.65 180	76.10 199	80.30 292
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AstraMVS		67.86 214	66.83 215	70.93 227	73.50 303	49.34 254	73.28 269	74.01 278	55.45 223	68.10 182	83.28 201	38.93 275	79.14 256	63.22 156	71.74 267	84.30 185

Anonymous20240529		69.91 153	69.02 155	72.56 172	80.19 122	47.65 285	77.56 160	80.99 138	55.45 223	69.88 145	86.76 106	39.24 271	82.18 188	54.04 239	77.10 185	87.85 36

tt0805		67.77 217	67.24 208	69.34 258	74.87 269	40.08 359	77.36 167	81.37 120	55.31 225	66.33 223	84.65 165	37.35 292	82.55 181	55.65 226	72.28 262	85.39 146

GDP-MVS		72.64 94	71.28 111	76.70 60	77.72 197	54.22 151	79.57 117	84.45 44	55.30 226	71.38 124	86.97 101	39.94 260	87.00 66	67.02 115	79.20 143	88.89 10

CPTT-MVS		72.78 90	72.08 97	74.87 97	84.88 57	61.41 26	84.15 49	77.86 207	55.27 227	67.51 199	88.08 74	41.93 235	81.85 193	69.04 96	80.01 127	81.35 270

XVG-OURS		68.76 190	67.37 200	72.90 165	74.32 287	57.22 95	70.09 322	78.81 177	55.24 228	67.79 194	85.81 145	36.54 303	78.28 272	62.04 167	75.74 204	83.19 227

tfpnnormal		62.47 296	61.63 295	64.99 323	74.81 272	39.01 370	71.22 303	73.72 282	55.22 229	60.21 320	80.09 278	41.26 250	76.98 303	30.02 424	68.09 326	78.97 316

cl____		67.18 228	66.26 233	69.94 245	70.20 365	45.74 303	73.30 266	76.83 227	55.10 230	65.27 245	79.57 288	47.39 167	80.53 227	59.41 193	69.22 313	83.53 218

DIV-MVS_self_test		67.18 228	66.26 233	69.94 245	70.20 365	45.74 303	73.29 268	76.83 227	55.10 230	65.27 245	79.58 287	47.38 168	80.53 227	59.43 192	69.22 313	83.54 217

PC_three_1452									55.09 232	84.46 4	89.84 48	66.68 5	89.41 18	74.24 55	91.38 2	88.42 19

EPNet_dtu		61.90 305	61.97 291	61.68 348	72.89 314	39.78 363	75.85 212	65.62 355	55.09 232	54.56 385	79.36 294	37.59 289	67.02 375	39.80 362	76.95 186	78.25 321
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 120	70.39 128	74.65 104	82.01 86	58.82 76	79.93 108	80.35 150	55.09 232	65.82 237	82.16 234	49.17 141	82.64 179	60.34 182	78.62 157	82.50 248

cl22		67.47 222	66.45 222	70.54 236	69.85 374	46.49 295	73.85 258	77.35 218	55.07 235	65.51 240	77.92 317	47.64 160	81.10 212	61.58 173	69.32 309	84.01 195

miper_ehance_all_eth		68.03 208	67.24 208	70.40 238	70.54 358	46.21 299	73.98 251	78.68 182	55.07 235	66.05 229	77.80 321	52.16 95	81.31 206	61.53 175	69.32 309	83.67 212

fmvsm_s_conf0.5_n_2		69.82 155	69.27 151	71.46 204	72.00 332	51.08 215	73.30 266	67.79 337	55.06 237	75.24 51	87.51 85	44.02 212	77.00 301	75.67 42	72.86 250	86.31 105

Elysia		70.19 147	68.29 177	75.88 75	74.15 291	54.33 149	78.26 135	83.21 85	55.04 238	67.28 202	83.59 194	30.16 370	86.11 93	63.67 149	79.26 140	87.20 65

StellarMVS		70.19 147	68.29 177	75.88 75	74.15 291	54.33 149	78.26 135	83.21 85	55.04 238	67.28 202	83.59 194	30.16 370	86.11 93	63.67 149	79.26 140	87.20 65

PS-MVSNAJ		70.51 137	69.70 141	72.93 164	81.52 94	55.79 122	74.92 233	79.00 172	55.04 238	69.88 145	78.66 303	47.05 172	82.19 187	61.61 171	79.58 132	80.83 282

fmvsm_s_conf0.1_n_2		69.64 163	69.01 157	71.52 202	71.66 337	51.04 216	73.39 265	67.14 343	55.02 241	75.11 53	87.64 84	42.94 224	77.01 300	75.55 44	72.63 256	86.52 91

mmtdpeth		60.40 320	59.12 321	64.27 329	69.59 376	48.99 262	70.67 312	70.06 317	54.96 242	62.78 287	73.26 382	27.00 401	67.66 368	58.44 205	45.29 434	76.16 350

xiu_mvs_v2_base		70.52 136	69.75 139	72.84 166	81.21 103	55.63 126	75.11 226	78.92 174	54.92 243	69.96 144	79.68 286	47.00 176	82.09 189	61.60 172	79.37 135	80.81 283

MAR-MVS		71.51 117	70.15 135	75.60 85	81.84 90	59.39 60	81.38 90	82.90 95	54.90 244	68.08 183	78.70 301	47.73 157	85.51 110	51.68 262	84.17 76	81.88 260
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		62.56 294	61.20 304	66.62 292	70.62 357	44.30 319	70.13 321	73.13 293	54.78 245	61.13 314	76.37 348	25.63 411	75.63 321	58.75 202	60.29 389	79.93 299

XVG-OURS-SEG-HR		68.81 187	67.47 197	72.82 168	74.40 284	56.87 105	70.59 313	79.04 171	54.77 246	66.99 209	86.01 136	39.57 266	78.21 273	62.54 162	73.33 242	83.37 221

testing3		56.54 351	55.92 353	58.41 372	77.52 209	27.93 443	69.72 325	56.36 413	54.75 247	58.63 345	77.80 321	20.88 427	71.75 343	25.31 440	62.25 374	75.53 357

Anonymous20231211		69.28 175	68.47 170	71.73 195	80.28 117	47.18 291	79.98 106	82.37 102	54.61 248	67.24 204	84.01 183	39.43 267	82.41 185	55.45 228	72.83 251	85.62 133

SixPastTwentyTwo		61.65 308	58.80 325	70.20 241	75.80 247	47.22 290	75.59 216	69.68 320	54.61 248	54.11 389	79.26 296	27.07 400	82.96 165	43.27 335	49.79 427	80.41 290

test_0402		63.25 287	61.01 307	69.96 244	80.00 126	54.37 148	76.86 187	72.02 303	54.58 250	58.71 341	80.79 266	35.00 315	84.36 136	26.41 438	64.71 352	71.15 408

tttt0517		67.83 215	65.66 241	74.33 116	76.69 232	50.82 222	77.86 151	73.99 279	54.54 251	64.64 262	82.53 222	35.06 314	85.50 111	55.71 224	69.91 298	86.67 84

BH-w/o		66.85 236	65.83 238	69.90 248	79.29 138	52.46 197	74.66 239	76.65 230	54.51 252	64.85 259	78.12 311	45.59 188	82.95 166	43.26 336	75.54 207	74.27 375

AUN-MVS		68.45 199	66.41 226	74.57 109	79.53 135	57.08 103	73.93 255	75.23 255	54.44 253	66.69 215	81.85 241	37.10 298	82.89 168	62.07 166	66.84 336	83.75 209

LTVRE_ROB	55.42 16	63.15 289	61.23 303	68.92 266	76.57 237	47.80 282	59.92 400	76.39 231	54.35 254	58.67 343	82.46 224	29.44 379	81.49 201	42.12 345	71.14 274	77.46 333
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 86	72.59 90	74.27 118	71.28 349	55.88 120	78.21 141	75.56 246	54.31 255	74.86 62	87.80 82	54.72 56	80.23 236	78.07 26	78.48 159	86.70 81

test_fmvsmconf0.01_n		72.17 105	71.50 103	74.16 121	67.96 391	55.58 129	78.06 146	74.67 266	54.19 256	74.54 69	88.23 69	50.35 125	80.24 235	78.07 26	77.46 177	86.65 86

test_fmvsmconf0.1_n		72.81 89	72.33 93	74.24 119	69.89 372	55.81 121	78.22 140	75.40 251	54.17 257	75.00 57	88.03 78	53.82 69	80.23 236	78.08 25	78.34 163	86.69 82

ETVMVS		59.51 330	58.81 323	61.58 350	77.46 211	34.87 407	64.94 368	59.35 399	54.06 258	61.08 315	76.67 339	29.54 376	71.87 342	32.16 407	74.07 224	78.01 328

ab-mvs		66.65 241	66.42 225	67.37 283	76.17 243	41.73 345	70.41 317	76.14 235	53.99 259	65.98 230	83.51 198	49.48 134	76.24 318	48.60 285	73.46 239	84.14 191

fmvsm_s_conf0.5_n_5		72.69 93	72.80 87	72.37 180	74.11 294	53.21 175	78.12 143	73.31 287	53.98 260	76.81 40	88.05 75	53.38 77	77.37 292	76.64 34	80.78 112	86.53 90

IU-MVS					87.77 4	59.15 65		85.53 27	53.93 261	84.64 3				79.07 13	90.87 5	88.37 21

SSM_0407		70.41 141	68.96 158	74.75 99	78.65 160	53.46 167	77.28 173	80.00 154	53.88 262	68.14 177	84.61 167	43.21 219	86.26 90	58.80 200	76.11 196	84.54 175

SSM_0404		70.84 129	69.41 148	75.12 93	79.20 143	53.86 155	77.89 149	80.00 154	53.88 262	69.40 153	84.61 167	43.21 219	86.56 77	58.80 200	77.68 173	84.95 165

XVG-ACMP-BASELINE		64.36 275	62.23 288	70.74 231	72.35 326	52.45 198	70.80 311	78.45 195	53.84 264	59.87 327	81.10 256	16.24 435	79.32 249	55.64 227	71.76 266	80.47 287

mamba_0408		67.78 216	65.42 245	74.85 98	78.65 160	53.46 167	50.83 435	79.09 169	53.75 265	68.14 177	83.83 187	41.79 239	86.56 77	56.58 214	76.11 196	84.54 175

SSM_04072		64.98 266	65.42 245	63.68 333	78.65 160	53.46 167	50.83 435	79.09 169	53.75 265	68.14 177	83.83 187	41.79 239	53.03 437	56.58 214	76.11 196	84.54 175

VortexMVS		66.41 247	65.50 244	69.16 263	73.75 297	48.14 277	73.41 264	78.28 202	53.73 267	64.98 258	78.33 309	40.62 256	79.07 258	58.88 199	67.50 331	80.26 293

FE-MVS		65.91 252	63.33 273	73.63 143	77.36 214	51.95 208	72.62 281	75.81 240	53.70 268	65.31 243	78.96 299	28.81 384	86.39 85	43.93 327	73.48 238	82.55 244

thisisatest0530		67.92 212	65.78 239	74.33 116	76.29 241	51.03 217	76.89 185	74.25 274	53.67 269	65.59 239	81.76 244	35.15 313	85.50 111	55.94 219	72.47 257	86.47 93

PVSNet_BlendedMVS		68.56 196	67.72 188	71.07 224	77.03 227	50.57 226	74.50 242	81.52 114	53.66 270	64.22 270	79.72 285	49.13 142	82.87 170	55.82 221	73.92 226	79.77 306

patch_mono-2		69.85 154	71.09 115	66.16 301	79.11 148	54.80 143	71.97 293	74.31 271	53.50 271	70.90 128	84.17 178	57.63 31	63.31 393	66.17 121	82.02 100	80.38 291

EG-PatchMatch MVS		64.71 268	62.87 279	70.22 239	77.68 199	53.48 166	77.99 147	78.82 176	53.37 272	56.03 369	77.41 329	24.75 416	84.04 141	46.37 304	73.42 241	73.14 381

SD_0403		63.07 290	63.49 270	61.82 347	75.16 263	31.14 432	71.89 296	73.47 284	53.34 273	58.22 349	81.81 243	45.17 199	73.86 330	37.43 375	74.87 216	80.45 288

DP-MVS		65.68 254	63.66 267	71.75 194	84.93 55	56.87 105	80.74 98	73.16 292	53.06 274	59.09 338	82.35 225	36.79 302	85.94 100	32.82 405	69.96 297	72.45 389

TR-MVS		66.59 244	65.07 252	71.17 220	79.18 145	49.63 250	73.48 263	75.20 257	52.95 275	67.90 185	80.33 272	39.81 264	83.68 149	43.20 337	73.56 236	80.20 294

ET-MVSNet_ETH3D		67.96 211	65.72 240	74.68 102	76.67 234	55.62 128	75.11 226	74.74 264	52.91 276	60.03 324	80.12 276	33.68 332	82.64 179	61.86 169	76.34 193	85.78 121

QAPM		70.05 149	68.81 161	73.78 130	76.54 238	53.43 170	83.23 60	83.48 71	52.89 277	65.90 233	86.29 126	41.55 245	86.49 83	51.01 265	78.40 162	81.42 264

LuminaMVS		68.24 203	66.82 216	72.51 174	73.46 305	53.60 163	76.23 200	78.88 175	52.78 278	68.08 183	80.13 275	32.70 348	81.41 202	63.16 157	75.97 200	82.53 245

icg_test_0407_2		66.41 247	66.75 217	65.37 318	77.06 222	49.73 242	63.79 377	78.60 184	52.70 279	66.19 225	82.58 214	45.17 199	63.65 392	59.20 195	75.46 209	82.74 239

IMVS_0407		68.90 185	67.93 185	71.82 191	77.06 222	49.73 242	74.40 246	78.60 184	52.70 279	66.19 225	82.58 214	45.17 199	83.00 163	59.20 195	75.46 209	82.74 239

IMVS_0404		64.63 270	64.22 258	65.88 309	77.06 222	49.73 242	64.40 371	78.60 184	52.70 279	53.16 399	82.58 214	34.82 317	65.16 386	59.20 195	75.46 209	82.74 239

IMVS_0403		69.09 181	68.14 182	71.95 186	77.06 222	49.73 242	74.51 241	78.60 184	52.70 279	66.69 215	82.58 214	46.43 180	83.38 156	59.20 195	75.46 209	82.74 239

OpenMVS	61.03 9	68.85 186	67.56 191	72.70 170	74.26 289	53.99 154	81.21 92	81.34 125	52.70 279	62.75 290	85.55 150	38.86 276	84.14 139	48.41 287	83.01 85	79.97 298

pmmvs6		63.69 281	62.82 281	66.27 299	70.63 356	39.27 369	73.13 274	75.47 250	52.69 284	59.75 331	82.30 227	39.71 265	77.03 299	47.40 294	64.35 357	82.53 245

IterMVS		62.79 293	61.27 301	67.35 284	69.37 380	52.04 205	71.17 304	68.24 335	52.63 285	59.82 328	76.91 336	37.32 293	72.36 336	52.80 250	63.19 367	77.66 331
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 205	66.36 228	73.63 143	75.61 252	55.35 135	80.77 97	78.56 189	52.48 286	64.27 267	84.10 181	27.45 396	81.84 194	63.45 153	70.56 283	83.69 211

jajsoiax		68.25 202	66.45 222	73.66 140	75.62 251	55.49 131	80.82 96	78.51 191	52.33 287	64.33 265	84.11 180	28.28 388	81.81 195	63.48 152	70.62 281	83.67 212

TAMVS		66.78 239	65.27 250	71.33 216	79.16 147	53.67 160	73.84 259	69.59 322	52.32 288	65.28 244	81.72 245	44.49 208	77.40 291	42.32 344	78.66 156	82.92 234

CDS-MVSNet		66.80 238	65.37 247	71.10 223	78.98 150	53.13 178	73.27 270	71.07 309	52.15 289	64.72 260	80.23 274	43.56 216	77.10 296	45.48 316	78.88 148	83.05 233
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		68.47 197	66.56 219	74.21 120	79.60 132	52.95 180	74.94 232	75.48 249	52.09 290	60.10 322	83.27 202	36.54 303	84.70 130	59.32 194	77.69 172	84.99 163

viewmambaseed2359dif		68.91 184	68.18 180	71.11 222	70.21 364	48.05 281	72.28 288	75.90 239	51.96 291	70.93 127	84.47 174	51.37 110	78.59 268	61.55 174	74.97 214	86.68 83

PVSNet_Blended		68.59 192	67.72 188	71.19 218	77.03 227	50.57 226	72.51 284	81.52 114	51.91 292	64.22 270	77.77 324	49.13 142	82.87 170	55.82 221	79.58 132	80.14 296

mvs_anonymous		68.03 208	67.51 195	69.59 253	72.08 330	44.57 317	71.99 292	75.23 255	51.67 293	67.06 208	82.57 218	54.68 57	77.94 277	56.56 216	75.71 205	86.26 107

xiu_mvs_v1_base_debu		68.58 193	67.28 204	72.48 175	78.19 179	57.19 97	75.28 221	75.09 259	51.61 294	70.04 138	81.41 251	32.79 343	79.02 260	63.81 146	77.31 178	81.22 273

xiu_mvs_v1_base		68.58 193	67.28 204	72.48 175	78.19 179	57.19 97	75.28 221	75.09 259	51.61 294	70.04 138	81.41 251	32.79 343	79.02 260	63.81 146	77.31 178	81.22 273

xiu_mvs_v1_base_debi		68.58 193	67.28 204	72.48 175	78.19 179	57.19 97	75.28 221	75.09 259	51.61 294	70.04 138	81.41 251	32.79 343	79.02 260	63.81 146	77.31 178	81.22 273

MVSTER		67.16 230	65.58 243	71.88 189	70.37 363	49.70 246	70.25 320	78.45 195	51.52 297	69.16 160	80.37 269	38.45 279	82.50 182	60.19 183	71.46 271	83.44 220

CNLPA		65.43 258	64.02 260	69.68 251	78.73 158	58.07 83	77.82 154	70.71 312	51.49 298	61.57 310	83.58 197	38.23 284	70.82 348	43.90 328	70.10 294	80.16 295

原ACMM1				74.69 101	85.39 47	59.40 59		83.42 74	51.47 299	70.27 136	86.61 115	48.61 148	86.51 82	53.85 242	87.96 39	78.16 322

miper_enhance_ethall		67.11 231	66.09 235	70.17 242	69.21 382	45.98 301	72.85 278	78.41 198	51.38 300	65.65 238	75.98 355	51.17 114	81.25 207	60.82 179	69.32 309	83.29 224

MSDG		61.81 307	59.23 319	69.55 256	72.64 317	52.63 192	70.45 316	75.81 240	51.38 300	53.70 392	76.11 350	29.52 377	81.08 214	37.70 373	65.79 345	74.93 366

test20.03		53.87 372	54.02 370	53.41 404	61.47 426	28.11 442	61.30 392	59.21 400	51.34 302	52.09 403	77.43 328	33.29 337	58.55 414	29.76 425	60.27 390	73.58 380

MVSFormer		71.50 118	70.38 129	74.88 96	78.76 156	57.15 100	82.79 67	78.48 192	51.26 303	69.49 150	83.22 203	43.99 213	83.24 159	66.06 122	79.37 135	84.23 187

test_djsdf		69.45 172	67.74 187	74.58 108	74.57 280	54.92 141	82.79 67	78.48 192	51.26 303	65.41 242	83.49 199	38.37 280	83.24 159	66.06 122	69.25 312	85.56 134

dmvs_testset		50.16 390	51.90 380	44.94 425	66.49 402	11.78 465	61.01 397	51.50 427	51.17 305	50.30 415	67.44 419	39.28 269	60.29 404	22.38 444	57.49 399	62.76 430

PAPM		67.92 212	66.69 218	71.63 200	78.09 184	49.02 261	77.09 179	81.24 130	51.04 306	60.91 316	83.98 184	47.71 158	84.99 120	40.81 354	79.32 138	80.90 281

Syy-MVS		56.00 358	56.23 351	55.32 390	74.69 275	26.44 449	65.52 358	57.49 408	50.97 307	56.52 363	72.18 386	39.89 262	68.09 364	24.20 441	64.59 355	71.44 404

myMVS_eth3d		54.86 368	54.61 362	55.61 389	74.69 275	27.31 446	65.52 358	57.49 408	50.97 307	56.52 363	72.18 386	21.87 425	68.09 364	27.70 432	64.59 355	71.44 404

miper_lstm_enhance		62.03 304	60.88 309	65.49 316	66.71 400	46.25 297	56.29 419	75.70 242	50.68 309	61.27 312	75.48 362	40.21 259	68.03 366	56.31 218	65.25 348	82.18 254

gg-mvs-nofinetune		57.86 342	56.43 348	62.18 345	72.62 318	35.35 406	66.57 348	56.33 414	50.65 310	57.64 354	57.10 441	30.65 364	76.36 316	37.38 376	78.88 148	74.82 368

TAPA-MVS	59.36 10	66.60 242	65.20 251	70.81 229	76.63 235	48.75 267	76.52 194	80.04 153	50.64 311	65.24 249	84.93 157	39.15 272	78.54 269	36.77 381	76.88 187	85.14 155
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 350	56.83 343	56.61 384	69.23 381	41.02 352	58.37 406	64.18 367	50.59 312	57.45 356	71.42 394	35.54 310	58.94 412	37.23 377	67.45 332	69.87 417

MVP-Stereo		65.41 259	63.80 264	70.22 239	77.62 206	55.53 130	76.30 197	78.53 190	50.59 312	56.47 365	78.65 304	39.84 263	82.68 177	44.10 326	72.12 264	72.44 390
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 128	69.49 145	75.35 88	77.63 202	55.71 123	76.04 207	81.81 110	50.30 314	69.66 148	85.40 154	52.51 87	84.89 126	51.82 259	80.24 124	85.45 141
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs5depth		55.64 361	53.81 372	61.11 356	59.39 436	40.98 356	65.89 353	68.28 334	50.21 315	58.11 351	75.42 363	17.03 431	67.63 370	43.79 330	46.21 431	74.73 370

baseline2		63.42 283	61.26 302	69.89 249	72.55 320	47.62 286	71.54 298	68.38 333	50.11 316	54.82 381	75.55 360	43.06 222	80.96 217	48.13 290	67.16 335	81.11 276

test-LLR		58.15 340	58.13 333	58.22 374	68.57 386	44.80 313	65.46 360	57.92 405	50.08 317	55.44 373	69.82 407	32.62 351	57.44 419	49.66 276	73.62 233	72.41 391

test0.0.03 1		53.32 377	53.59 374	52.50 410	62.81 421	29.45 437	59.51 402	54.11 422	50.08 317	54.40 387	74.31 372	32.62 351	55.92 428	30.50 422	63.95 360	72.15 396

fmvsm_s_conf0.5_n		69.58 165	68.84 160	71.79 193	72.31 328	52.90 182	77.90 148	62.43 386	49.97 319	72.85 103	85.90 139	52.21 93	76.49 313	75.75 41	70.26 291	85.97 113

COLMAP_ROB	52.97 17	61.27 313	58.81 323	68.64 269	74.63 277	52.51 195	78.42 134	73.30 288	49.92 320	50.96 407	81.51 250	23.06 419	79.40 247	31.63 415	65.85 343	74.01 378
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		69.54 167	68.74 163	71.93 187	72.47 323	53.82 157	78.25 137	62.26 388	49.78 321	73.12 96	86.21 128	52.66 85	76.79 307	75.02 50	68.88 317	85.18 154

WBMVS		60.54 317	60.61 311	60.34 359	78.00 188	35.95 403	64.55 370	64.89 360	49.63 322	63.39 277	78.70 301	33.85 330	67.65 369	42.10 346	70.35 288	77.43 334

tpmvs		58.47 335	56.95 341	63.03 341	70.20 365	41.21 351	67.90 341	67.23 342	49.62 323	54.73 383	70.84 398	34.14 324	76.24 318	36.64 385	61.29 381	71.64 400

fmvsm_s_conf0.1_n		69.41 173	68.60 166	71.83 190	71.07 351	52.88 185	77.85 152	62.44 385	49.58 324	72.97 99	86.22 127	51.68 105	76.48 314	75.53 45	70.10 294	86.14 108

UBG		59.62 329	59.53 317	59.89 360	78.12 183	35.92 404	64.11 375	60.81 396	49.45 325	61.34 311	75.55 360	33.05 338	67.39 373	38.68 368	74.62 217	76.35 349

thisisatest0515		65.83 253	63.50 269	72.82 168	73.75 297	49.50 251	71.32 301	73.12 294	49.39 326	63.82 272	76.50 347	34.95 316	84.84 129	53.20 248	75.49 208	84.13 192

fmvsm_s_conf0.1_n_a		69.32 174	68.44 172	71.96 185	70.91 353	53.78 158	78.12 143	62.30 387	49.35 327	73.20 91	86.55 120	51.99 98	76.79 307	74.83 52	68.68 322	85.32 149

HY-MVS	56.14 13	64.55 272	63.89 261	66.55 293	74.73 274	41.02 352	69.96 323	74.43 268	49.29 328	61.66 308	80.92 261	47.43 166	76.68 311	44.91 321	71.69 268	81.94 258

MIMVSNet1		55.17 366	54.31 367	57.77 380	70.03 369	32.01 428	65.68 356	64.81 361	49.19 329	46.75 425	76.00 352	25.53 412	64.04 389	28.65 429	62.13 375	77.26 338

SCA		60.49 318	58.38 329	66.80 287	74.14 293	48.06 279	63.35 380	63.23 378	49.13 330	59.33 337	72.10 388	37.45 290	74.27 328	44.17 323	62.57 371	78.05 324

test_fmvsmvis_n_1920		70.84 129	70.38 129	72.22 183	71.16 350	55.39 133	75.86 211	72.21 301	49.03 331	73.28 89	86.17 130	51.83 102	77.29 294	75.80 40	78.05 167	83.98 196

testgi		51.90 382	52.37 378	50.51 417	60.39 434	23.55 456	58.42 405	58.15 403	49.03 331	51.83 404	79.21 297	22.39 420	55.59 429	29.24 428	62.64 370	72.40 393

sc_t1		59.76 325	57.84 336	65.54 313	74.87 269	42.95 335	69.61 326	64.16 369	48.90 333	58.68 342	77.12 331	28.19 389	72.35 337	43.75 332	55.28 408	81.31 271

MIMVSNet		57.35 344	57.07 339	58.22 374	74.21 290	37.18 387	62.46 385	60.88 395	48.88 334	55.29 376	75.99 354	31.68 360	62.04 398	31.87 410	72.35 259	75.43 359

gm-plane-assit					71.40 346	41.72 347			48.85 335		73.31 380		82.48 184	48.90 283

fmvsm_l_conf0.5_n		70.99 127	70.82 120	71.48 203	71.45 342	54.40 147	77.18 177	70.46 314	48.67 336	75.17 52	86.86 103	53.77 71	76.86 305	76.33 37	77.51 176	83.17 231

UWE-MVS		60.18 321	59.78 315	61.39 353	77.67 200	33.92 419	69.04 333	63.82 372	48.56 337	64.27 267	77.64 326	27.20 398	70.40 353	33.56 402	76.24 194	79.83 303

cascas		65.98 251	63.42 271	73.64 142	77.26 217	52.58 193	72.26 289	77.21 221	48.56 337	61.21 313	74.60 370	32.57 354	85.82 103	50.38 270	76.75 190	82.52 247

PLC	56.13 14	65.09 264	63.21 276	70.72 232	81.04 106	54.87 142	78.57 131	77.47 214	48.51 339	55.71 370	81.89 240	33.71 331	79.71 242	41.66 350	70.37 286	77.58 332
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 268	62.50 284	71.34 215	79.72 131	55.71 123	79.82 110	74.72 265	48.50 340	56.62 361	84.62 166	33.59 334	82.34 186	29.65 426	75.23 213	75.97 351

anonymousdsp		67.00 234	64.82 254	73.57 146	70.09 368	56.13 113	76.35 196	77.35 218	48.43 341	64.99 257	80.84 265	33.01 340	80.34 231	64.66 136	67.64 330	84.23 187

无先验							79.66 115	74.30 272	48.40 342				80.78 224	53.62 243		79.03 315

114514_t		70.83 131	69.56 143	74.64 105	86.21 31	54.63 144	82.34 76	81.81 110	48.22 343	63.01 285	85.83 142	40.92 255	87.10 63	57.91 206	79.79 128	82.18 254

tpm		57.34 345	58.16 331	54.86 393	71.80 336	34.77 409	67.47 346	56.04 417	48.20 344	60.10 322	76.92 335	37.17 296	53.41 436	40.76 355	65.01 349	76.40 348

test_fmvsm_n_1920		71.73 114	71.14 114	73.50 148	72.52 321	56.53 107	75.60 215	76.16 233	48.11 345	77.22 35	85.56 148	53.10 81	77.43 289	74.86 51	77.14 183	86.55 89

MDA-MVSNet-bldmvs		53.87 372	50.81 385	63.05 340	66.25 404	48.58 272	56.93 417	63.82 372	48.09 346	41.22 437	70.48 403	30.34 367	68.00 367	34.24 397	45.92 433	72.57 387

XXY-MVS		60.68 314	61.67 294	57.70 381	70.43 361	38.45 376	64.19 373	66.47 348	48.05 347	63.22 278	80.86 263	49.28 139	60.47 402	45.25 320	67.28 334	74.19 376

F-COLMAP		63.05 291	60.87 310	69.58 255	76.99 229	53.63 162	78.12 143	76.16 233	47.97 348	52.41 402	81.61 247	27.87 391	78.11 274	40.07 357	66.66 338	77.00 342

tt0320-xc		58.33 337	56.41 349	64.08 330	75.79 248	41.34 349	68.30 337	62.72 382	47.90 349	56.29 366	74.16 375	28.53 385	71.04 347	41.50 353	52.50 419	79.88 301

fmvsm_l_conf0.5_n_a		70.50 138	70.27 131	71.18 219	71.30 348	54.09 152	76.89 185	69.87 318	47.90 349	74.37 72	86.49 121	53.07 82	76.69 310	75.41 46	77.11 184	82.76 238

Patchmatch-RL test		58.16 339	55.49 356	66.15 302	67.92 392	48.89 266	60.66 398	51.07 430	47.86 351	59.36 334	62.71 435	34.02 327	72.27 339	56.41 217	59.40 392	77.30 336

D2MVS		62.30 299	60.29 313	68.34 274	66.46 403	48.42 274	65.70 355	73.42 285	47.71 352	58.16 350	75.02 366	30.51 365	77.71 286	53.96 241	71.68 269	78.90 317

ANet_high		41.38 409	37.47 416	53.11 406	39.73 462	24.45 454	56.94 416	69.69 319	47.65 353	26.04 454	52.32 444	12.44 443	62.38 397	21.80 445	10.61 463	72.49 388

CostFormer		64.04 278	62.51 283	68.61 270	71.88 334	45.77 302	71.30 302	70.60 313	47.55 354	64.31 266	76.61 343	41.63 242	79.62 245	49.74 274	69.00 316	80.42 289

PatchmatchNet		59.84 324	58.24 330	64.65 325	73.05 311	46.70 294	69.42 329	62.18 389	47.55 354	58.88 340	71.96 390	34.49 321	69.16 358	42.99 339	63.60 362	78.07 323
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 365	53.89 371	59.21 366	57.80 440	27.47 445	57.75 412	74.32 270	47.38 356	50.90 408	70.00 406	28.45 387	70.30 354	40.44 356	57.92 397	79.87 302

ITE_SJBPF				62.09 346	66.16 405	44.55 318		64.32 365	47.36 357	55.31 375	80.34 271	19.27 428	62.68 396	36.29 389	62.39 373	79.04 314

KD-MVS_2432*1600		53.45 374	51.50 383	59.30 363	62.82 419	37.14 388	55.33 420	71.79 305	47.34 358	55.09 378	70.52 401	21.91 423	70.45 351	35.72 392	42.97 437	70.31 413

miper_refine_blended		53.45 374	51.50 383	59.30 363	62.82 419	37.14 388	55.33 420	71.79 305	47.34 358	55.09 378	70.52 401	21.91 423	70.45 351	35.72 392	42.97 437	70.31 413

OurMVSNet-221017-0		61.37 312	58.63 327	69.61 252	72.05 331	48.06 279	73.93 255	72.51 297	47.23 360	54.74 382	80.92 261	21.49 426	81.24 208	48.57 286	56.22 405	79.53 308

tpmrst		58.24 338	58.70 326	56.84 383	66.97 397	34.32 414	69.57 328	61.14 394	47.17 361	58.58 346	71.60 393	41.28 249	60.41 403	49.20 280	62.84 369	75.78 354

tt0320		58.59 334	56.81 344	63.92 332	75.46 255	41.32 350	68.63 335	64.06 370	47.05 362	56.19 367	74.19 373	30.34 367	71.36 344	39.92 361	55.45 407	79.09 312

PVSNet	50.76 19	58.40 336	57.39 337	61.42 351	75.53 254	44.04 323	61.43 390	63.45 376	47.04 363	56.91 359	73.61 379	27.00 401	64.76 387	39.12 366	72.40 258	75.47 358

WB-MVSnew		59.66 327	59.69 316	59.56 361	75.19 262	35.78 405	69.34 330	64.28 366	46.88 364	61.76 307	75.79 356	40.61 257	65.20 385	32.16 407	71.21 273	77.70 330

UWE-MVS-28		52.25 381	52.35 379	51.93 414	66.99 396	22.79 457	63.48 379	48.31 438	46.78 365	52.73 401	76.11 350	27.78 393	57.82 418	20.58 447	68.41 324	75.17 360

FMVSNet5		55.86 359	54.93 359	58.66 371	71.05 352	36.35 397	64.18 374	62.48 384	46.76 366	50.66 412	74.73 369	25.80 409	64.04 389	33.11 403	65.57 346	75.59 356

jason		69.65 162	68.39 174	73.43 153	78.27 177	56.88 104	77.12 178	73.71 283	46.53 367	69.34 155	83.22 203	43.37 217	79.18 251	64.77 135	79.20 143	84.23 187
jason: jason.
MS-PatchMatch		62.42 297	61.46 297	65.31 320	75.21 261	52.10 202	72.05 291	74.05 277	46.41 368	57.42 357	74.36 371	34.35 323	77.57 288	45.62 312	73.67 231	66.26 427

1112_ss		64.00 279	63.36 272	65.93 307	79.28 140	42.58 337	71.35 300	72.36 300	46.41 368	60.55 319	77.89 319	46.27 183	73.28 332	46.18 305	69.97 296	81.92 259

lupinMVS		69.57 166	68.28 179	73.44 152	78.76 156	57.15 100	76.57 192	73.29 289	46.19 370	69.49 150	82.18 231	43.99 213	79.23 250	64.66 136	79.37 135	83.93 198

testdata				64.66 324	81.52 94	52.93 181		65.29 358	46.09 371	73.88 80	87.46 88	38.08 286	66.26 380	53.31 247	78.48 159	74.78 369

UnsupCasMVSNet_eth		53.16 379	52.47 377	55.23 391	59.45 435	33.39 422	59.43 403	69.13 328	45.98 372	50.35 414	72.32 385	29.30 380	58.26 416	42.02 348	44.30 435	74.05 377

AllTest		57.08 347	54.65 361	64.39 327	71.44 343	49.03 259	69.92 324	67.30 339	45.97 373	47.16 422	79.77 282	17.47 429	67.56 371	33.65 399	59.16 393	76.57 346

TestCases				64.39 327	71.44 343	49.03 259		67.30 339	45.97 373	47.16 422	79.77 282	17.47 429	67.56 371	33.65 399	59.16 393	76.57 346

WTY-MVS		59.75 326	60.39 312	57.85 379	72.32 327	37.83 381	61.05 396	64.18 367	45.95 375	61.91 304	79.11 298	47.01 175	60.88 401	42.50 343	69.49 308	74.83 367

IterMVS-SCA-FT		62.49 295	61.52 296	65.40 317	71.99 333	50.80 223	71.15 306	69.63 321	45.71 376	60.61 318	77.93 316	37.45 290	65.99 382	55.67 225	63.50 364	79.42 309

WB-MVS		43.26 403	43.41 403	42.83 429	63.32 418	10.32 467	58.17 408	45.20 445	45.42 377	40.44 440	67.26 422	34.01 328	58.98 411	11.96 458	24.88 452	59.20 433

旧先验2							76.08 204		45.32 378	76.55 42			65.56 384	58.75 202

OpenMVS_ROB	52.78 18	60.03 322	58.14 332	65.69 312	70.47 360	44.82 312	75.33 220	70.86 311	45.04 379	56.06 368	76.00 352	26.89 403	79.65 243	35.36 394	67.29 333	72.60 386

TinyColmap		54.14 369	51.72 381	61.40 352	66.84 399	41.97 342	66.52 349	68.51 332	44.81 380	42.69 436	75.77 357	11.66 445	72.94 333	31.96 409	56.77 403	69.27 421

MDTV_nov1_ep13			57.00 340		72.73 316	38.26 377	65.02 367	64.73 363	44.74 381	55.46 372	72.48 384	32.61 353	70.47 350	37.47 374	67.75 329

新几何1				70.76 230	85.66 41	61.13 30		66.43 349	44.68 382	70.29 135	86.64 111	41.29 248	75.23 323	49.72 275	81.75 106	75.93 352

Patchmtry		57.16 346	56.47 347	59.23 365	69.17 383	34.58 412	62.98 382	63.15 379	44.53 383	56.83 360	74.84 367	35.83 308	68.71 361	40.03 358	60.91 382	74.39 374

ppachtmachnet_test		58.06 341	55.38 357	66.10 304	69.51 377	48.99 262	68.01 340	66.13 352	44.50 384	54.05 390	70.74 399	32.09 359	72.34 338	36.68 384	56.71 404	76.99 344

PatchT		53.17 378	53.44 375	52.33 411	68.29 390	25.34 453	58.21 407	54.41 421	44.46 385	54.56 385	69.05 413	33.32 336	60.94 400	36.93 380	61.76 379	70.73 411

EPMVS		53.96 370	53.69 373	54.79 394	66.12 406	31.96 429	62.34 387	49.05 434	44.42 386	55.54 371	71.33 396	30.22 369	56.70 422	41.65 351	62.54 372	75.71 355

pmmvs4		61.48 311	59.39 318	67.76 278	71.57 339	53.86 155	71.42 299	65.34 357	44.20 387	59.46 333	77.92 317	35.90 307	74.71 325	43.87 329	64.87 351	74.71 371

dp		51.89 383	51.60 382	52.77 408	68.44 389	32.45 427	62.36 386	54.57 420	44.16 388	49.31 417	67.91 415	28.87 383	56.61 424	33.89 398	54.89 410	69.24 422

PatchMatch-RL		56.25 356	54.55 363	61.32 354	77.06 222	56.07 115	65.57 357	54.10 423	44.13 389	53.49 398	71.27 397	25.20 413	66.78 376	36.52 387	63.66 361	61.12 431

our_test_3		56.49 352	54.42 364	62.68 343	69.51 377	45.48 308	66.08 352	61.49 392	44.11 390	50.73 411	69.60 410	33.05 338	68.15 363	38.38 370	56.86 401	74.40 373

USDC		56.35 355	54.24 368	62.69 342	64.74 411	40.31 358	65.05 366	73.83 281	43.93 391	47.58 420	77.71 325	15.36 438	75.05 324	38.19 372	61.81 378	72.70 385

PM-MVS		52.33 380	50.19 389	58.75 370	62.10 424	45.14 311	65.75 354	40.38 452	43.60 392	53.52 396	72.65 383	9.16 453	65.87 383	50.41 269	54.18 413	65.24 429

pmmvs-eth3d		58.81 333	56.31 350	66.30 298	67.61 393	52.42 199	72.30 287	64.76 362	43.55 393	54.94 380	74.19 373	28.95 381	72.60 335	43.31 334	57.21 400	73.88 379

SSC-MVS		41.96 408	41.99 407	41.90 430	62.46 423	9.28 469	57.41 415	44.32 448	43.38 394	38.30 446	66.45 425	32.67 350	58.42 415	10.98 459	21.91 455	57.99 437

new-patchmatchnet		47.56 397	47.73 397	47.06 420	58.81 438	9.37 468	48.78 439	59.21 400	43.28 395	44.22 432	68.66 414	25.67 410	57.20 421	31.57 417	49.35 428	74.62 372

Test_1112_low_res		62.32 298	61.77 293	64.00 331	79.08 149	39.53 367	68.17 338	70.17 315	43.25 396	59.03 339	79.90 279	44.08 210	71.24 346	43.79 330	68.42 323	81.25 272

RPMNet		61.53 309	58.42 328	70.86 228	69.96 370	52.07 203	65.31 364	81.36 121	43.20 397	59.36 334	70.15 405	35.37 311	85.47 113	36.42 388	64.65 353	75.06 362

tpm2		62.07 302	60.10 314	67.99 276	72.79 315	43.86 324	71.05 309	66.85 346	43.14 398	62.77 288	75.39 364	38.32 282	80.80 223	41.69 349	68.88 317	79.32 310

JIA-IIPM		51.56 384	47.68 398	63.21 338	64.61 412	50.73 224	47.71 441	58.77 402	42.90 399	48.46 419	51.72 445	24.97 414	70.24 355	36.06 391	53.89 414	68.64 423

1314		64.61 271	63.21 276	68.80 267	71.87 335	47.46 288	73.95 253	78.39 200	42.88 400	59.97 325	76.60 344	38.11 285	79.39 248	54.84 232	72.32 260	79.55 307

HyFIR lowres test		65.67 255	63.01 278	73.67 139	79.97 127	55.65 125	69.07 332	75.52 247	42.68 401	63.53 275	77.95 315	40.43 258	81.64 196	46.01 307	71.91 265	83.73 210

CR-MVSNet		59.91 323	57.90 335	65.96 306	69.96 370	52.07 203	65.31 364	63.15 379	42.48 402	59.36 334	74.84 367	35.83 308	70.75 349	45.50 315	64.65 353	75.06 362

test222					83.14 72	58.68 78	72.57 283	63.45 376	41.78 403	67.56 198	86.12 131	37.13 297			78.73 153	74.98 365

TDRefinement		53.44 376	50.72 386	61.60 349	64.31 414	46.96 292	70.89 310	65.27 359	41.78 403	44.61 431	77.98 314	11.52 447	66.36 379	28.57 430	51.59 421	71.49 403

sss		56.17 357	56.57 346	54.96 392	66.93 398	36.32 399	57.94 409	61.69 391	41.67 405	58.64 344	75.32 365	38.72 277	56.25 426	42.04 347	66.19 342	72.31 394

PVSNet_0	43.31 20	47.46 398	45.64 401	52.92 407	67.60 394	44.65 315	54.06 425	54.64 419	41.59 406	46.15 427	58.75 438	30.99 363	58.66 413	32.18 406	24.81 453	55.46 441

MVS		67.37 223	66.33 229	70.51 237	75.46 255	50.94 218	73.95 253	81.85 109	41.57 407	62.54 295	78.57 307	47.98 153	85.47 113	52.97 249	82.05 99	75.14 361

Anonymous20240521		55.30 363	54.41 365	57.96 378	60.92 433	41.73 345	71.09 308	71.06 310	41.18 408	48.65 418	73.31 380	16.93 432	59.25 409	42.54 342	64.01 358	72.90 383

Anonymous20231206		55.10 367	55.30 358	54.48 395	69.81 375	33.94 418	62.91 383	62.13 390	41.08 409	55.18 377	75.65 358	32.75 346	56.59 425	30.32 423	67.86 327	72.91 382

MDA-MVSNet_test_wron		50.71 389	48.95 391	56.00 388	61.17 428	41.84 343	51.90 431	56.45 411	40.96 410	44.79 430	67.84 416	30.04 373	55.07 433	36.71 383	50.69 424	71.11 409

YYNet1		50.73 388	48.96 390	56.03 387	61.10 429	41.78 344	51.94 430	56.44 412	40.94 411	44.84 429	67.80 417	30.08 372	55.08 432	36.77 381	50.71 423	71.22 406

dongtai		34.52 418	34.94 418	33.26 439	61.06 430	16.00 464	52.79 429	23.78 465	40.71 412	39.33 444	48.65 453	16.91 433	48.34 445	12.18 457	19.05 457	35.44 456

CHOSEN 1792x2688		65.08 265	62.84 280	71.82 191	81.49 96	56.26 111	66.32 351	74.20 276	40.53 413	63.16 281	78.65 304	41.30 247	77.80 283	45.80 309	74.09 223	81.40 267

pmmvs5		56.47 353	55.68 355	58.86 369	61.41 427	36.71 394	66.37 350	62.75 381	40.38 414	53.70 392	76.62 341	34.56 319	67.05 374	40.02 359	65.27 347	72.83 384

test_vis1_n_1920		58.86 332	59.06 322	58.25 373	63.76 415	43.14 332	67.49 345	66.36 350	40.22 415	65.89 234	71.95 391	31.04 362	59.75 407	59.94 186	64.90 350	71.85 398

MDTV_nov1_ep13_2view						25.89 451	61.22 393		40.10 416	51.10 406		32.97 341		38.49 369		78.61 319

tpm cat1		59.25 331	56.95 341	66.15 302	72.19 329	46.96 292	68.09 339	65.76 353	40.03 417	57.81 353	70.56 400	38.32 282	74.51 326	38.26 371	61.50 380	77.00 342

test-mter		56.42 354	55.82 354	58.22 374	68.57 386	44.80 313	65.46 360	57.92 405	39.94 418	55.44 373	69.82 407	21.92 422	57.44 419	49.66 276	73.62 233	72.41 391

UnsupCasMVSNet_bld		50.07 391	48.87 392	53.66 400	60.97 432	33.67 420	57.62 413	64.56 364	39.47 419	47.38 421	64.02 433	27.47 395	59.32 408	34.69 396	43.68 436	67.98 425

TESTMET0.1,1		55.28 364	54.90 360	56.42 385	66.56 401	43.67 326	65.46 360	56.27 415	39.18 420	53.83 391	67.44 419	24.21 417	55.46 430	48.04 291	73.11 247	70.13 415

mamv4		56.85 349	58.00 334	53.43 403	72.46 324	54.47 145	57.56 414	54.74 418	38.81 421	57.42 357	79.45 292	47.57 162	38.70 456	60.88 178	53.07 416	67.11 426

ADS-MVSNet2		51.33 386	48.76 393	59.07 368	66.02 407	44.60 316	50.90 433	59.76 398	36.90 422	50.74 409	66.18 427	26.38 404	63.11 394	27.17 434	54.76 411	69.50 419

ADS-MVSNet		48.48 395	47.77 396	50.63 416	66.02 407	29.92 436	50.90 433	50.87 432	36.90 422	50.74 409	66.18 427	26.38 404	52.47 439	27.17 434	54.76 411	69.50 419

RPSCF		55.80 360	54.22 369	60.53 358	65.13 410	42.91 336	64.30 372	57.62 407	36.84 424	58.05 352	82.28 228	28.01 390	56.24 427	37.14 378	58.61 395	82.44 250

test_cas_vis1_n_1920		56.91 348	56.71 345	57.51 382	59.13 437	45.40 309	63.58 378	61.29 393	36.24 425	67.14 207	71.85 392	29.89 374	56.69 423	57.65 208	63.58 363	70.46 412

Patchmatch-test		49.08 393	48.28 395	51.50 415	64.40 413	30.85 434	45.68 445	48.46 437	35.60 426	46.10 428	72.10 388	34.47 322	46.37 448	27.08 436	60.65 386	77.27 337

CHOSEN 280x420		47.83 396	46.36 400	52.24 413	67.37 395	49.78 241	38.91 453	43.11 450	35.00 427	43.27 435	63.30 434	28.95 381	49.19 444	36.53 386	60.80 384	57.76 438

N_pmnet		39.35 413	40.28 410	36.54 436	63.76 415	1.62 473	49.37 438	0.76 472	34.62 428	43.61 434	66.38 426	26.25 406	42.57 452	26.02 439	51.77 420	65.44 428

kuosan		29.62 425	30.82 424	26.02 444	52.99 443	16.22 463	51.09 432	22.71 466	33.91 429	33.99 448	40.85 454	15.89 436	33.11 461	7.59 465	18.37 458	28.72 458

PMMVS		53.96 370	53.26 376	56.04 386	62.60 422	50.92 220	61.17 394	56.09 416	32.81 430	53.51 397	66.84 424	34.04 326	59.93 406	44.14 325	68.18 325	57.27 439

CMPMVS	42.80 21	57.81 343	55.97 352	63.32 336	60.98 431	47.38 289	64.66 369	69.50 324	32.06 431	46.83 424	77.80 321	29.50 378	71.36 344	48.68 284	73.75 229	71.21 407
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 399	42.95 404	53.39 405	52.33 447	29.15 438	57.77 410	48.20 439	31.81 432	49.86 416	77.21 330	8.69 454	59.16 410	27.31 433	33.40 449	71.84 399

CVMVSNet		59.63 328	59.14 320	61.08 357	74.47 281	38.84 372	75.20 224	68.74 331	31.15 433	58.24 348	76.51 345	32.39 356	68.58 362	49.77 273	65.84 344	75.81 353

FPMVS		42.18 407	41.11 409	45.39 422	58.03 439	41.01 354	49.50 437	53.81 424	30.07 434	33.71 449	64.03 431	11.69 444	52.08 442	14.01 453	55.11 409	43.09 450

EU-MVSNet		55.61 362	54.41 365	59.19 367	65.41 409	33.42 421	72.44 285	71.91 304	28.81 435	51.27 405	73.87 377	24.76 415	69.08 359	43.04 338	58.20 396	75.06 362

test_vis1_n		49.89 392	48.69 394	53.50 402	53.97 441	37.38 386	61.53 389	47.33 442	28.54 436	59.62 332	67.10 423	13.52 440	52.27 440	49.07 281	57.52 398	70.84 410

test_fmvs1_n		51.37 385	50.35 388	54.42 397	52.85 444	37.71 383	61.16 395	51.93 425	28.15 437	63.81 273	69.73 409	13.72 439	53.95 434	51.16 264	60.65 386	71.59 401

LF4IMVS		42.95 404	42.26 406	45.04 423	48.30 452	32.50 426	54.80 422	48.49 436	28.03 438	40.51 439	70.16 404	9.24 452	43.89 451	31.63 415	49.18 429	58.72 435

test_fmvs1		51.32 387	50.48 387	53.81 399	53.57 442	37.51 385	60.63 399	51.16 428	28.02 439	63.62 274	69.23 412	16.41 434	53.93 435	51.01 265	60.70 385	69.99 416

MVS-HIRNet		45.52 400	44.48 402	48.65 419	68.49 388	34.05 417	59.41 404	44.50 447	27.03 440	37.96 447	50.47 449	26.16 407	64.10 388	26.74 437	59.52 391	47.82 448

PMVS	28.69 22	36.22 416	33.29 421	45.02 424	36.82 464	35.98 402	54.68 423	48.74 435	26.31 441	21.02 457	51.61 446	2.88 466	60.10 405	9.99 462	47.58 430	38.99 455
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 401	41.95 408	53.86 398	52.58 446	43.55 327	62.11 388	46.90 444	26.05 442	40.63 438	60.19 437	11.08 450	57.91 417	31.83 414	46.15 432	60.11 432

test_fmvs2		48.69 394	47.49 399	52.29 412	48.63 451	33.06 424	57.76 411	48.05 440	25.71 443	59.76 330	69.60 410	11.57 446	52.23 441	49.45 279	56.86 401	71.58 402

PMMVS2		27.40 426	25.91 429	31.87 441	39.46 463	6.57 470	31.17 456	28.52 461	23.96 444	20.45 458	48.94 452	4.20 462	37.94 457	16.51 450	19.97 456	51.09 443

MVStest1		42.65 405	39.29 412	52.71 409	47.26 454	34.58 412	54.41 424	50.84 433	23.35 445	39.31 445	74.08 376	12.57 442	55.09 431	23.32 442	28.47 451	68.47 424

Gipuma		34.77 417	31.91 422	43.33 427	62.05 425	37.87 379	20.39 458	67.03 344	23.23 446	18.41 459	25.84 459	4.24 460	62.73 395	14.71 452	51.32 422	29.38 457
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 410	39.45 411	47.03 421	46.65 455	37.86 380	47.76 440	38.65 453	23.10 447	44.21 433	51.22 447	11.20 449	44.08 450	39.27 365	53.02 417	59.14 434

new_pmnet		34.13 419	34.29 420	33.64 438	52.63 445	18.23 462	44.43 448	33.90 458	22.81 448	30.89 451	53.18 443	10.48 451	35.72 460	20.77 446	39.51 441	46.98 449

mvsany_test1		39.38 412	38.16 415	43.02 428	49.05 449	34.28 415	44.16 449	25.94 463	22.74 449	46.57 426	62.21 436	23.85 418	41.16 455	33.01 404	35.91 445	53.63 442

LCM-MVSNet		40.30 411	35.88 417	53.57 401	42.24 457	29.15 438	45.21 447	60.53 397	22.23 450	28.02 452	50.98 448	3.72 463	61.78 399	31.22 420	38.76 443	69.78 418

test_fmvs3		44.30 402	42.55 405	49.55 418	42.83 456	27.15 448	53.03 427	44.93 446	22.03 451	53.69 394	64.94 430	4.21 461	49.63 443	47.47 292	49.82 426	71.88 397

APD_test1		37.39 415	34.94 418	44.72 426	48.88 450	33.19 423	52.95 428	44.00 449	19.49 452	27.28 453	58.59 439	3.18 465	52.84 438	18.92 448	41.17 440	48.14 447

mvsany_test3		32.62 420	30.57 425	38.77 434	36.16 465	24.20 455	38.10 454	20.63 467	19.14 453	40.36 441	57.43 440	5.06 458	36.63 459	29.59 427	28.66 450	55.49 440

E-PMN		23.77 427	22.73 431	26.90 442	42.02 458	20.67 459	42.66 450	35.70 456	17.43 454	10.28 464	25.05 460	6.42 456	42.39 453	10.28 461	14.71 460	17.63 459

EMVS		22.97 428	21.84 432	26.36 443	40.20 461	19.53 461	41.95 451	34.64 457	17.09 455	9.73 465	22.83 461	7.29 455	42.22 454	9.18 463	13.66 461	17.32 460

test_vis3_rt		32.09 421	30.20 426	37.76 435	35.36 466	27.48 444	40.60 452	28.29 462	16.69 456	32.52 450	40.53 455	1.96 467	37.40 458	33.64 401	42.21 439	48.39 445

test_f		31.86 422	31.05 423	34.28 437	32.33 468	21.86 458	32.34 455	30.46 460	16.02 457	39.78 443	55.45 442	4.80 459	32.36 462	30.61 421	37.66 444	48.64 444

DSMNet-mixed		39.30 414	38.72 413	41.03 431	51.22 448	19.66 460	45.53 446	31.35 459	15.83 458	39.80 442	67.42 421	22.19 421	45.13 449	22.43 443	52.69 418	58.31 436

testf1		31.46 423	28.89 427	39.16 432	41.99 459	28.78 440	46.45 443	37.56 454	14.28 459	21.10 455	48.96 450	1.48 469	47.11 446	13.63 454	34.56 446	41.60 451

APD_test2		31.46 423	28.89 427	39.16 432	41.99 459	28.78 440	46.45 443	37.56 454	14.28 459	21.10 455	48.96 450	1.48 469	47.11 446	13.63 454	34.56 446	41.60 451

MVE	17.77 23	21.41 429	17.77 434	32.34 440	34.34 467	25.44 452	16.11 459	24.11 464	11.19 461	13.22 461	31.92 457	1.58 468	30.95 463	10.47 460	17.03 459	40.62 454
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 447	17.97 469	10.91 466		10.60 470	7.46 462	11.07 463	28.36 458	3.28 464	11.29 466	8.01 464	9.74 465	13.89 461

wuyk23d		13.32 432	12.52 435	15.71 446	47.54 453	26.27 450	31.06 457	1.98 471	4.93 463	5.18 466	1.94 466	0.45 471	18.54 465	6.81 466	12.83 462	2.33 463

test_method		19.68 430	18.10 433	24.41 445	13.68 470	3.11 472	12.06 461	42.37 451	2.00 464	11.97 462	36.38 456	5.77 457	29.35 464	15.06 451	23.65 454	40.76 453

tmp_tt		9.43 433	11.14 436	4.30 448	2.38 471	4.40 471	13.62 460	16.08 469	0.39 465	15.89 460	13.06 462	15.80 437	5.54 467	12.63 456	10.46 464	2.95 462

EGC-MVSNET		42.47 406	38.48 414	54.46 396	74.33 286	48.73 268	70.33 319	51.10 429	0.03 466	0.18 467	67.78 418	13.28 441	66.49 378	18.91 449	50.36 425	48.15 446

testmvs		4.52 436	6.03 439	0.01 450	0.01 472	0.00 475	53.86 426	0.00 473	0.01 467	0.04 468	0.27 467	0.00 473	0.00 468	0.04 467	0.00 466	0.03 465

test123		4.73 435	6.30 438	0.02 449	0.01 472	0.01 474	56.36 418	0.00 473	0.01 467	0.04 468	0.21 468	0.01 472	0.00 468	0.03 468	0.00 466	0.04 464

mmdepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 475	0.00 462	0.00 473	0.00 469	0.00 470	0.00 469	0.00 473	0.00 468	0.00 469	0.00 466	0.00 466

monomultidepth		0.00 438	0.00 441	0.00 451	0.00 474	0.00 475	0.00 462	0.00 473	0.00 469	0.00 470	0.00 469	0.00 473	0.00 468	0.00 469	0.00 466	0.00 466

test_blank		0.00 438	0.00 441	0.00 451	0.00 474	0.00 475	0.00 462	0.00 473	0.00 469	0.00 470	0.00 469	0.00 473	0.00 468	0.00 469	0.00 466	0.00 466

uanet_test		0.00 438	0.00 441	0.00 451	0.00 474	0.00 475	0.00 462	0.00 473	0.00 469	0.00 470	0.00 469	0.00 473	0.00 468	0.00 469	0.00 466	0.00 466

DCPMVS		0.00 438	0.00 441	0.00 451	0.00 474	0.00 475	0.00 462	0.00 473	0.00 469	0.00 470	0.00 469	0.00 473	0.00 468	0.00 469	0.00 466	0.00 466

cdsmvs_eth3d_5k		17.50 431	23.34 430	0.00 451	0.00 474	0.00 475	0.00 462	78.63 183	0.00 469	0.00 470	82.18 231	49.25 140	0.00 468	0.00 469	0.00 466	0.00 466

pcd_1.5k_mvsjas		3.92 437	5.23 440	0.00 451	0.00 474	0.00 475	0.00 462	0.00 473	0.00 469	0.00 470	0.00 469	47.05 172	0.00 468	0.00 469	0.00 466	0.00 466

sosnet-low-res		0.00 438	0.00 441	0.00 451	0.00 474	0.00 475	0.00 462	0.00 473	0.00 469	0.00 470	0.00 469	0.00 473	0.00 468	0.00 469	0.00 466	0.00 466

sosnet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 475	0.00 462	0.00 473	0.00 469	0.00 470	0.00 469	0.00 473	0.00 468	0.00 469	0.00 466	0.00 466

uncertanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 475	0.00 462	0.00 473	0.00 469	0.00 470	0.00 469	0.00 473	0.00 468	0.00 469	0.00 466	0.00 466

Regformer		0.00 438	0.00 441	0.00 451	0.00 474	0.00 475	0.00 462	0.00 473	0.00 469	0.00 470	0.00 469	0.00 473	0.00 468	0.00 469	0.00 466	0.00 466

ab-mvs-re		6.49 434	8.65 437	0.00 451	0.00 474	0.00 475	0.00 462	0.00 473	0.00 469	0.00 470	77.89 319	0.00 473	0.00 468	0.00 469	0.00 466	0.00 466

uanet		0.00 438	0.00 441	0.00 451	0.00 474	0.00 475	0.00 462	0.00 473	0.00 469	0.00 470	0.00 469	0.00 473	0.00 468	0.00 469	0.00 466	0.00 466

WAC-MVS						27.31 446								27.77 431

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 36

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 36

eth-test2					0.00 474

eth-test					0.00 474

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 65	91.15 4	88.23 25

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 46

GSMVS																78.05 324

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 318				78.05 324

sam_mvs												33.43 335

ambc				65.13 322	63.72 417	37.07 390	47.66 442	78.78 179		54.37 388	71.42 394	11.24 448	80.94 218	45.64 311	53.85 415	77.38 335

MTGPA								80.97 139

test_post1							68.67 334				3.64 464	32.39 356	69.49 357	44.17 323

test_post											3.55 465	33.90 329	66.52 377

patchmatchnet-post											64.03 431	34.50 320	74.27 328

GG-mvs-BLEND				62.34 344	71.36 347	37.04 391	69.20 331	57.33 410		54.73 383	65.48 429	30.37 366	77.82 282	34.82 395	74.93 215	72.17 395

MTMP							86.03 19	17.08 468

test9_res														75.28 48	88.31 32	83.81 204

agg_prior2														73.09 66	87.93 40	84.33 182

agg_prior					85.04 50	59.96 50		81.04 137		74.68 67			84.04 141

test_prior4						62.51 14	82.08 82

test_prior				76.69 61	84.20 61	57.27 94		84.88 40					86.43 84			86.38 94

新几何2							76.12 202

旧先验1					83.04 74	53.15 176		67.52 338			87.85 81	44.08 210			80.76 114	78.03 327

原ACMM2							79.02 122

testdata2													72.18 341	46.95 301

segment_acmp												54.23 61

test12				77.76 46	84.52 58	58.41 80		83.36 77		72.93 101		54.61 58	88.05 39		88.12 34	86.81 77

plane_prior7					81.41 97	55.96 117

plane_prior6					81.20 104	56.24 112						45.26 197

plane_prior5								84.01 53					87.21 59	68.16 100	80.58 118	84.65 173

plane_prior4											86.10 132

plane_prior1					81.27 102

n2								0.00 473

nn								0.00 473

door-mid								47.19 443

lessismore_v0				69.91 247	71.42 345	47.80 282		50.90 431		50.39 413	75.56 359	27.43 397	81.33 205	45.91 308	34.10 448	80.59 286

test11								83.47 72

door								47.60 441

HQP5-MVS						54.94 139

BP-MVS														67.04 113

HQP4-MVS										67.85 187			86.93 67			84.32 183

HQP3-MVS								83.90 58							80.35 122

HQP2-MVS												45.46 191

NP-MVS					80.98 107	56.05 116					85.54 151

ACMMP++_ref															74.07 224

ACMMP++															72.16 263

Test By Simon												48.33 151

High-resolution multi-view benchmark