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	81.64 112	68.35 2	75.77 45	90.38 30	53.98 64	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	70.09 133	86.34 124	54.92 54	88.90 25	72.68 69	84.55 69	87.76 40

UA-Net		73.13 83	72.93 83	73.76 132	83.58 67	51.66 211	78.75 125	77.66 209	67.75 4	72.61 107	89.42 52	49.82 126	83.29 158	53.61 240	83.14 83	86.32 100

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 68

TranMVSNet+NR-MVSNet		70.36 140	70.10 135	71.17 216	78.64 163	42.97 330	76.53 192	81.16 133	66.95 6	68.53 164	85.42 152	51.61 104	83.07 162	52.32 248	69.70 300	87.46 50

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	67.80 189	89.24 56	42.03 228	89.38 19	64.07 137	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 92

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 41
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 84	72.16 94	75.90 74	75.95 246	56.28 110	83.05 62	72.39 295	66.53 10	65.27 241	87.00 99	50.40 121	85.47 113	62.48 159	86.32 60	85.94 112
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 122	71.00 116	71.44 205	79.20 143	44.13 316	76.02 207	82.60 99	66.48 11	68.20 169	84.60 166	56.82 37	82.82 174	54.62 230	70.43 280	87.36 59

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 29
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	85.26 149

NR-MVSNet		69.54 165	68.85 157	71.59 200	78.05 186	43.81 321	74.20 247	80.86 140	65.18 14	62.76 285	84.52 167	52.35 91	83.59 152	50.96 263	70.78 275	87.37 57

MTAPA		76.90 35	76.42 39	78.35 35	86.08 37	63.57 2	74.92 232	80.97 138	65.13 15	75.77 45	90.88 20	48.63 143	86.66 74	77.23 29	88.17 33	84.81 165

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 18

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 29

EI-MVSNet-Vis-set		72.42 100	71.59 100	74.91 95	78.47 167	54.02 153	77.05 179	79.33 165	65.03 18	71.68 118	79.35 291	52.75 83	84.89 126	66.46 117	74.23 220	85.83 118

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	72.65 106	79.46 287	53.65 75	87.87 44	67.45 109	82.91 89	85.89 115

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	49.48 130	86.17 91	68.04 101	87.55 43	87.42 52

SymmetryMVS		75.28 56	74.60 62	77.30 54	83.85 65	59.89 52	84.36 41	75.51 246	64.69 22	74.21 75	87.40 89	49.48 130	86.17 91	68.04 101	83.88 79	85.85 116

WR-MVS		68.47 193	68.47 168	68.44 268	80.20 121	39.84 358	73.75 259	76.07 234	64.68 24	68.11 177	83.63 189	50.39 122	79.14 253	49.78 268	69.66 301	86.34 96

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	72.17 112	90.01 45	47.95 150	88.01 40	71.55 82	86.74 55	86.37 94

X-MVStestdata		70.21 143	67.28 200	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 112	6.49 459	47.95 150	88.01 40	71.55 82	86.74 55	86.37 94

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	69.27 152	86.10 131	45.26 193	87.21 59	68.16 99	80.58 117	84.65 169

plane_prior2							84.22 46		64.52 27

EI-MVSNet-UG-set		71.92 109	71.06 115	74.52 112	77.98 189	53.56 164	76.62 189	79.16 166	64.40 29	71.18 123	78.95 296	52.19 93	84.66 133	65.47 128	73.57 233	85.32 145

DU-MVS		70.01 148	69.53 142	71.44 205	78.05 186	44.13 316	75.01 228	81.51 115	64.37 30	68.20 169	84.52 167	49.12 140	82.82 174	54.62 230	70.43 280	87.37 57

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	85.45 137
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 24

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 43

LFMVS		71.78 111	71.59 100	72.32 180	83.40 71	46.38 292	79.75 112	71.08 304	64.18 34	72.80 103	88.64 67	42.58 223	83.72 148	57.41 206	84.49 72	86.86 73

IS-MVSNet		71.57 115	71.00 116	73.27 157	78.86 153	45.63 303	80.22 103	78.69 180	64.14 37	66.46 216	87.36 92	49.30 134	85.60 106	50.26 267	83.71 82	88.59 14

plane_prior3						56.09 114			63.92 38	69.27 152

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	85.74 125
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	75.59 215	84.17 50	63.76 40	73.15 92	82.79 204	59.58 20	86.80 70	67.24 110	86.04 61	87.89 32
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	72.52 108	87.49 86	47.18 166	85.88 101	69.47 92	80.78 111	83.66 210
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 133	70.20 130	71.89 187	78.55 164	45.29 306	75.94 208	82.92 93	63.68 42	68.16 172	83.59 190	53.89 67	83.49 155	53.97 236	71.12 273	86.89 72

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 85	88.53 29	74.79 53	88.34 29	86.63 85

testing3-2		62.06 299	62.36 282	61.17 351	79.29 138	30.31 431	64.09 372	63.49 371	63.50 44	62.84 282	82.22 226	32.35 354	69.02 356	40.01 356	73.43 238	84.17 186

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	72.77 104	87.25 96	53.13 79	87.93 42	71.97 77	85.57 64	86.66 83

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 65

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 74
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 95	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 32
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 96	72.09 95	73.75 134	81.58 93	49.69 248	77.76 156	77.63 210	63.21 50	73.21 90	89.02 58	42.14 227	83.32 157	61.72 166	82.50 95	88.25 23

plane_prior						56.31 108	83.58 59		63.19 51						80.48 120

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	69.80 143	89.74 51	45.43 189	87.16 61	72.01 75	82.87 91	85.14 151
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		66.60 238	66.45 218	67.04 282	77.11 221	36.56 391	77.03 180	80.42 147	62.95 53	62.51 293	84.03 178	46.69 174	79.07 255	44.22 318	63.08 364	85.51 132

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 76
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	82.28 102	62.90 55	71.77 116	90.26 35	46.61 175	86.55 80	71.71 80	85.66 63	84.97 160

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 86	88.39 30	79.34 9	90.52 13	86.78 77

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 28

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 78	88.35 31	74.02 59	87.05 47	86.13 107

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	49.80 127	88.24 33	74.02 59	87.03 48	86.32 100

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	49.90 124	88.21 34	73.78 61	87.03 48	86.29 104

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 93	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 91	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 96	89.97 46	50.90 117	87.48 53	75.30 47	86.85 53	87.33 60
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 252	65.34 244	66.31 293	76.06 245	34.79 404	76.43 194	79.38 164	62.55 64	61.66 304	83.83 183	45.60 183	79.15 252	41.64 348	60.88 379	85.00 157

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 26
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		66.49 241	66.41 222	66.72 284	77.67 200	36.33 394	76.83 187	79.52 161	62.45 66	62.54 291	83.47 196	46.32 177	78.37 267	45.47 313	63.43 361	85.45 137

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	72.68 105	90.50 27	48.18 148	87.34 54	73.59 63	85.71 62	84.76 168

PS-CasMVS		66.42 242	66.32 226	66.70 286	77.60 208	36.30 396	76.94 182	79.61 159	62.36 68	62.43 296	83.66 188	45.69 181	78.37 267	45.35 315	63.26 362	85.42 140

3Dnovator	64.47 5	72.49 97	71.39 106	75.79 77	77.70 198	58.99 73	80.66 99	83.15 89	62.24 69	65.46 237	86.59 115	42.38 226	85.52 109	59.59 186	84.72 67	82.85 233

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 88	86.78 71	80.66 4	89.64 19	87.80 38
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 111		82.31 77		62.10 71	67.85 183

ACMP_Plane					80.66 111		82.31 77		62.10 71	67.85 183

HQP-MVS		73.45 77	72.80 86	75.40 87	80.66 111	54.94 139	82.31 77	83.90 58	62.10 71	67.85 183	85.54 150	45.46 187	86.93 67	67.04 112	80.35 121	84.32 179

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	71.49 121	86.03 134	53.83 68	86.36 87	67.74 104	86.91 52	88.19 26

VPNet		67.52 217	68.11 179	65.74 307	79.18 145	36.80 389	72.17 286	72.83 291	62.04 75	67.79 190	85.83 141	48.88 142	76.60 308	51.30 259	72.97 247	83.81 200

WR-MVS_H		67.02 229	66.92 210	67.33 281	77.95 190	37.75 378	77.57 159	82.11 105	62.03 76	62.65 288	82.48 219	50.57 120	79.46 243	42.91 336	64.01 354	84.79 166

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 97	88.88 62	53.72 71	89.06 23	68.27 96	88.04 37	87.42 52
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 34

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	72.45 111	90.34 33	48.48 146	88.13 37	72.32 72	86.85 53	85.78 119

fmvsm_s_conf0.5_n_8		74.30 70	74.39 65	74.01 124	75.33 259	52.89 184	78.24 138	77.32 218	61.65 80	78.13 27	88.90 61	52.82 82	81.54 200	78.46 22	78.67 154	87.60 46

Effi-MVS+		73.31 80	72.54 90	75.62 84	77.87 191	53.64 161	79.62 116	79.61 159	61.63 81	72.02 114	82.61 209	56.44 40	85.97 99	63.99 140	79.07 146	87.25 62

MG-MVS		73.96 73	73.89 72	74.16 121	85.65 42	49.69 248	81.59 88	81.29 126	61.45 82	71.05 124	88.11 72	51.77 101	87.73 48	61.05 172	83.09 84	85.05 156

fmvsm_s_conf0.5_n_9		75.16 57	75.22 56	75.01 94	78.34 174	55.37 134	77.30 170	73.95 278	61.40 83	79.46 19	90.14 37	57.07 34	81.15 210	80.00 5	79.31 138	88.51 17

LPG-MVS_test		72.74 90	71.74 99	75.76 78	80.22 119	57.51 92	82.55 73	83.40 75	61.32 84	66.67 213	87.33 93	39.15 268	86.59 75	67.70 105	77.30 179	83.19 223

LGP-MVS_train				75.76 78	80.22 119	57.51 92		83.40 75	61.32 84	66.67 213	87.33 93	39.15 268	86.59 75	67.70 105	77.30 179	83.19 223

CLD-MVS		73.33 79	72.68 88	75.29 91	78.82 155	53.33 173	78.23 139	84.79 42	61.30 86	70.41 130	81.04 253	52.41 89	87.12 62	64.61 136	82.49 96	85.41 141
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT-MVS		71.46 118	70.70 121	73.74 135	77.76 196	49.30 255	76.60 190	80.45 146	61.25 87	68.17 171	84.78 158	44.64 201	84.90 125	64.79 132	77.88 168	87.03 68

fmvsm_s_conf0.5_n_3		73.55 76	74.39 65	71.03 221	74.09 294	51.86 209	77.77 155	75.60 242	61.18 88	78.67 25	88.98 59	55.88 46	77.73 281	78.69 16	78.68 153	83.50 215

MVS_111021_HR		74.02 72	73.46 78	75.69 81	83.01 76	60.63 40	77.29 171	78.40 198	61.18 88	70.58 128	85.97 136	54.18 62	84.00 144	67.52 108	82.98 88	82.45 245

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 97	56.46 39	88.14 36	72.87 67	88.03 38	89.00 8

FIs		70.82 130	71.43 104	68.98 261	78.33 175	38.14 374	76.96 181	83.59 69	61.02 91	67.33 197	86.73 107	55.07 50	81.64 196	54.61 232	79.22 141	87.14 66

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

FC-MVSNet-test		69.80 155	70.58 124	67.46 277	77.61 207	34.73 407	76.05 205	83.19 88	60.84 93	65.88 231	86.46 121	54.52 59	80.76 224	52.52 247	78.12 164	86.91 71

v8		70.33 141	69.28 148	73.49 149	73.15 307	50.22 233	78.62 129	80.78 141	60.79 94	66.45 217	82.11 233	49.35 133	84.98 122	63.58 149	68.71 316	85.28 147

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	84.83 156	60.76 15	86.56 77	67.86 103	87.87 41	86.06 109

Vis-MVSNet		72.18 103	71.37 107	74.61 106	81.29 100	55.41 132	80.90 95	78.28 200	60.73 96	69.23 155	88.09 73	44.36 205	82.65 178	57.68 203	81.75 106	85.77 122
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		71.26 121	70.16 132	74.57 109	74.59 277	52.77 188	75.91 209	81.20 130	60.72 97	69.10 158	85.71 145	41.67 237	83.53 153	63.91 143	78.62 156	87.42 52

BP-MVS1		73.41 78	72.25 93	76.88 57	76.68 233	53.70 159	79.15 121	81.07 134	60.66 98	71.81 115	87.39 91	40.93 250	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	84.83 164
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 101	71.20 112	75.59 86	80.28 117	57.54 90	82.74 69	82.84 97	60.58 100	65.24 245	86.18 128	39.25 266	86.03 97	66.95 115	76.79 187	83.22 221
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	51.51 106	87.78 47	75.65 43	87.55 43	87.10 67

testdata1							72.65 275		60.50 102

UGNet		68.81 183	67.39 195	73.06 160	78.33 175	54.47 145	79.77 111	75.40 249	60.45 103	63.22 274	84.40 171	32.71 343	80.91 220	51.71 257	80.56 119	83.81 200
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 91	71.49 103	76.40 68	81.99 88	59.58 57	76.92 183	76.74 227	60.40 104	74.81 63	85.95 137	45.54 185	85.76 104	70.41 88	70.61 278	83.86 199

hse-mvs2		71.04 123	69.86 136	74.60 107	79.58 133	57.12 102	73.96 251	75.25 252	60.40 104	74.81 63	81.95 235	45.54 185	82.90 167	70.41 88	66.83 333	83.77 204

EPP-MVSNet		72.16 106	71.31 109	74.71 100	78.68 159	49.70 246	82.10 81	81.65 111	60.40 104	65.94 227	85.84 140	51.74 102	86.37 86	55.93 216	79.55 133	88.07 31

UniMVSNet_ETH3D		67.60 216	67.07 209	69.18 258	77.39 213	42.29 335	74.18 248	75.59 243	60.37 107	66.77 209	86.06 133	37.64 284	78.93 262	52.16 250	73.49 235	86.32 100

test_prior2							81.75 84		60.37 107	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	60.37 107	79.89 18	89.38 54	54.97 53	85.58 108	76.12 39	84.94 66	86.33 98
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		69.68 159	70.19 131	68.16 271	79.73 130	41.63 344	70.53 310	77.38 215	60.37 107	70.69 127	86.63 112	51.08 113	77.09 293	53.61 240	81.69 108	85.75 124

sasdasda		74.67 63	74.98 58	73.71 137	78.94 151	50.56 228	80.23 101	83.87 61	60.30 111	77.15 36	86.56 117	59.65 17	82.00 190	66.01 122	82.12 97	88.58 15

canonicalmvs		74.67 63	74.98 58	73.71 137	78.94 151	50.56 228	80.23 101	83.87 61	60.30 111	77.15 36	86.56 117	59.65 17	82.00 190	66.01 122	82.12 97	88.58 15

v7n		69.01 179	67.36 197	73.98 125	72.51 321	52.65 190	78.54 133	81.30 125	60.26 113	62.67 287	81.62 242	43.61 211	84.49 134	57.01 207	68.70 317	84.79 166

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	50.67 118	87.69 49	72.46 70	84.53 70	85.46 135

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	50.67 118	87.69 49	72.46 70	84.53 70	85.46 135

HPM-MVS_fast		74.30 70	73.46 78	76.80 59	84.45 60	59.04 71	83.65 58	81.05 135	60.15 116	70.43 129	89.84 48	41.09 249	85.59 107	67.61 107	82.90 90	85.77 122

VPA-MVSNet		69.02 178	69.47 144	67.69 275	77.42 212	41.00 351	74.04 249	79.68 157	60.06 117	69.26 154	84.81 157	51.06 114	77.58 283	54.44 233	74.43 218	84.48 176

v10		70.21 143	69.02 153	73.81 129	73.51 301	50.92 220	78.74 126	81.39 118	60.05 118	66.39 218	81.83 238	47.58 157	85.41 116	62.80 156	68.86 315	85.09 155

SR-MVS		76.13 48	75.70 49	77.40 53	85.87 40	61.20 29	85.52 28	82.19 103	59.99 119	75.10 54	90.35 32	47.66 155	86.52 81	71.64 81	82.99 86	84.47 177

SSC-MVS3.2		60.57 312	61.39 294	58.12 373	74.29 287	32.63 421	59.52 397	65.53 352	59.90 120	62.45 294	79.75 280	41.96 229	63.90 387	39.47 360	69.65 303	77.84 325

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 28	89.52 21

v2v482		70.50 136	69.45 145	73.66 140	72.62 317	50.03 238	77.58 158	80.51 145	59.90 120	69.52 145	82.14 231	47.53 159	84.88 128	65.07 131	70.17 288	86.09 108

Baseline_NR-MVSNet		67.05 228	67.56 187	65.50 311	75.65 250	37.70 380	75.42 218	74.65 265	59.90 120	68.14 173	83.15 202	49.12 140	77.20 291	52.23 249	69.78 297	81.60 258

API-MVS		72.17 104	71.41 105	74.45 113	81.95 89	57.22 95	84.03 51	80.38 148	59.89 124	68.40 166	82.33 222	49.64 128	87.83 46	51.87 254	84.16 77	78.30 316

Effi-MVS+-dtu		69.64 161	67.53 190	75.95 73	76.10 244	62.29 15	80.20 104	76.06 235	59.83 125	65.26 244	77.09 329	41.56 240	84.02 143	60.60 177	71.09 274	81.53 259

reproduce_model		76.43 42	76.08 42	77.49 50	83.47 70	60.09 47	84.60 37	82.90 94	59.65 126	77.31 34	91.43 13	49.62 129	87.24 55	71.99 76	83.75 81	85.14 151

MVSMamba_PlusPlus		75.75 53	75.44 51	76.67 63	80.84 108	53.06 179	78.62 129	85.13 33	59.65 126	71.53 120	87.47 87	56.92 35	88.17 35	72.18 74	86.63 58	88.80 10

CANet_DTU		68.18 201	67.71 186	69.59 249	74.83 270	46.24 294	78.66 128	76.85 224	59.60 128	63.45 272	82.09 234	35.25 308	77.41 286	59.88 183	78.76 151	85.14 151

EI-MVSNet		69.27 174	68.44 170	71.73 194	74.47 280	49.39 253	75.20 223	78.45 194	59.60 128	69.16 156	76.51 341	51.29 109	82.50 182	59.86 185	71.45 270	83.30 218

IterMVS-LS		69.22 176	68.48 166	71.43 207	74.44 282	49.40 252	76.23 199	77.55 211	59.60 128	65.85 232	81.59 245	51.28 110	81.58 199	59.87 184	69.90 295	83.30 218
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 98	73.34 80	69.81 246	77.77 195	43.21 327	75.84 212	81.18 131	59.59 131	75.45 48	86.64 110	57.74 28	77.94 274	63.92 141	81.90 102	88.30 21

VDDNet		71.81 110	71.33 108	73.26 158	82.80 79	47.60 283	78.74 126	75.27 251	59.59 131	72.94 99	89.40 53	41.51 242	83.91 145	58.75 198	82.99 86	88.26 22

viewmanbaseed2359cas		72.92 87	72.89 84	73.00 161	75.16 263	49.25 257	77.25 174	83.11 91	59.52 133	72.93 100	86.63 112	54.11 63	80.98 215	66.63 116	80.67 114	88.76 12

alignmvs		73.86 74	73.99 70	73.45 151	78.20 178	50.50 230	78.57 131	82.43 100	59.40 134	76.57 41	86.71 109	56.42 41	81.23 209	65.84 125	81.79 103	88.62 13

MVS_Test		72.45 98	72.46 91	72.42 178	74.88 267	48.50 269	76.28 197	83.14 90	59.40 134	72.46 109	84.68 159	55.66 47	81.12 211	65.98 124	79.66 130	87.63 44

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 51	83.82 64	59.34 136	79.37 20	89.76 50	59.84 16	87.62 52	76.69 33	86.74 55	87.68 42
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	81.88 107	59.34 136	71.59 119	86.83 103	45.94 180	83.65 150	65.09 130	85.22 65	81.06 274

PAPM_NR		72.63 94	71.80 98	75.13 92	81.72 92	53.42 171	79.91 109	83.28 83	59.14 138	66.31 220	85.90 138	51.86 99	86.06 95	57.45 205	80.62 115	85.91 114

testing91		64.46 269	63.80 260	66.47 290	78.43 169	40.06 356	67.63 338	69.59 318	59.06 139	63.18 276	78.05 309	34.05 321	76.99 298	48.30 284	75.87 200	82.37 247

myMVS_eth3d28		60.66 311	61.04 302	59.51 358	77.32 215	31.58 426	63.11 377	63.87 367	59.00 140	60.90 313	78.26 306	32.69 345	66.15 377	36.10 386	78.13 163	80.81 279

save fliter					86.17 33	61.30 28	83.98 53	79.66 158	59.00 140

v148		68.24 199	67.19 207	71.40 208	70.43 358	47.77 280	75.76 213	77.03 222	58.91 142	67.36 196	80.10 273	48.60 145	81.89 192	60.01 181	66.52 336	84.53 174

TransMVSNet (Re)		64.72 263	64.33 253	65.87 306	75.22 260	38.56 370	74.66 238	75.08 260	58.90 143	61.79 302	82.63 208	51.18 111	78.07 272	43.63 329	55.87 402	80.99 276

Anonymous202405211		66.84 233	65.99 232	69.40 253	80.19 122	42.21 337	71.11 303	71.31 303	58.80 144	67.90 181	86.39 123	29.83 371	79.65 240	49.60 274	78.78 150	86.33 98

test2506		65.33 257	64.61 251	67.50 276	79.46 136	34.19 412	74.43 244	51.92 422	58.72 145	66.75 210	88.05 75	25.99 404	80.92 219	51.94 253	84.25 74	87.39 55

ECVR-MVS		67.72 214	67.51 191	68.35 269	79.46 136	36.29 397	74.79 235	66.93 341	58.72 145	67.19 201	88.05 75	36.10 301	81.38 204	52.07 251	84.25 74	87.39 55

test1111		67.21 221	67.14 208	67.42 278	79.24 142	34.76 406	73.89 256	65.65 350	58.71 147	66.96 206	87.95 79	36.09 302	80.53 226	52.03 252	83.79 80	86.97 70

LCM-MVSNet-Re		61.88 302	61.35 295	63.46 331	74.58 278	31.48 427	61.42 387	58.14 400	58.71 147	53.02 396	79.55 285	43.07 217	76.80 302	45.69 306	77.96 166	82.11 253

testing99		64.05 273	63.29 271	66.34 292	78.17 182	39.76 360	67.33 343	68.00 332	58.60 149	63.03 279	78.10 308	32.57 350	76.94 300	48.22 285	75.58 204	82.34 248

v1144		70.42 138	69.31 147	73.76 132	73.22 305	50.64 225	77.83 153	81.43 117	58.58 150	69.40 149	81.16 250	47.53 159	85.29 118	64.01 139	70.64 276	85.34 144

TSAR-MVS + GP.		74.90 59	74.15 69	77.17 55	82.00 87	58.77 77	81.80 83	78.57 187	58.58 150	74.32 73	84.51 169	55.94 45	87.22 58	67.11 111	84.48 73	85.52 131

BH-RMVSNet		68.81 183	67.42 194	72.97 162	80.11 125	52.53 194	74.26 246	76.29 230	58.48 152	68.38 167	84.20 173	42.59 222	83.83 146	46.53 298	75.91 199	82.56 239

APD-MVS_3200maxsize		74.96 58	74.39 65	76.67 63	82.20 84	58.24 82	83.67 57	83.29 82	58.41 153	73.71 83	90.14 37	45.62 182	85.99 98	69.64 90	82.85 92	85.78 119

OMC-MVS		71.40 120	70.60 122	73.78 130	76.60 236	53.15 176	79.74 113	79.78 155	58.37 154	68.75 160	86.45 122	45.43 189	80.60 225	62.58 157	77.73 169	87.58 48

nrg030		72.96 86	73.01 82	72.84 165	75.41 257	50.24 232	80.02 105	82.89 96	58.36 155	74.44 70	86.73 107	58.90 24	80.83 221	65.84 125	74.46 216	87.44 51

K. test v3		60.47 315	57.11 334	70.56 231	73.74 298	48.22 272	75.10 227	62.55 379	58.27 156	53.62 391	76.31 345	27.81 388	81.59 198	47.42 289	39.18 438	81.88 256

FA-MVS(test-final)		69.82 153	68.48 166	73.84 128	78.44 168	50.04 237	75.58 217	78.99 172	58.16 157	67.59 193	82.14 231	42.66 221	85.63 105	56.60 209	76.19 193	85.84 117

MVS_111021_LR		69.50 168	68.78 160	71.65 198	78.38 170	59.33 61	74.82 234	70.11 312	58.08 158	67.83 188	84.68 159	41.96 229	76.34 313	65.62 127	77.54 172	79.30 307

SR-MVS-dyc-post		74.57 66	73.90 71	76.58 66	83.49 68	59.87 54	84.29 43	81.36 120	58.07 159	73.14 93	90.07 39	44.74 199	85.84 102	68.20 97	81.76 104	84.03 189

RE-MVS-def			73.71 75		83.49 68	59.87 54	84.29 43	81.36 120	58.07 159	73.14 93	90.07 39	43.06 218		68.20 97	81.76 104	84.03 189

SDMVSNet		68.03 204	68.10 180	67.84 273	77.13 219	48.72 267	65.32 359	79.10 167	58.02 161	65.08 248	82.55 215	47.83 152	73.40 327	63.92 141	73.92 224	81.41 261

sd_testset		64.46 269	64.45 252	64.51 322	77.13 219	42.25 336	62.67 380	72.11 298	58.02 161	65.08 248	82.55 215	41.22 248	69.88 352	47.32 291	73.92 224	81.41 261

GeoE		71.01 124	70.15 133	73.60 145	79.57 134	52.17 201	78.93 124	78.12 202	58.02 161	67.76 192	83.87 182	52.36 90	82.72 176	56.90 208	75.79 201	85.92 113

ZD-MVS					86.64 21	60.38 45		82.70 98	57.95 164	78.10 28	90.06 41	56.12 44	88.84 26	74.05 58	87.00 51

EIA-MVS		71.78 111	70.60 122	75.30 90	79.85 128	53.54 165	77.27 173	83.26 84	57.92 165	66.49 215	79.39 289	52.07 96	86.69 73	60.05 180	79.14 145	85.66 127

test_yl		69.69 157	69.13 150	71.36 209	78.37 172	45.74 299	74.71 236	80.20 150	57.91 166	70.01 138	83.83 183	42.44 224	82.87 170	54.97 226	79.72 128	85.48 133

DCV-MVSNet		69.69 157	69.13 150	71.36 209	78.37 172	45.74 299	74.71 236	80.20 150	57.91 166	70.01 138	83.83 183	42.44 224	82.87 170	54.97 226	79.72 128	85.48 133

MonoMVSNet		64.15 272	63.31 270	66.69 287	70.51 356	44.12 318	74.47 242	74.21 273	57.81 168	63.03 279	76.62 337	38.33 277	77.31 289	54.22 234	60.59 384	78.64 314

dcpmvs_2		74.55 67	75.23 55	72.48 174	82.34 83	53.34 172	77.87 150	81.46 116	57.80 169	75.49 47	86.81 104	62.22 13	77.75 280	71.09 85	82.02 100	86.34 96

fmvsm_s_conf0.5_n_6		72.59 95	72.87 85	71.73 194	75.14 265	51.96 207	76.28 197	77.12 221	57.63 170	73.85 81	86.91 101	51.54 105	77.87 277	77.18 31	80.18 125	85.37 143

Fast-Effi-MVS+-dtu		67.37 219	65.33 245	73.48 150	72.94 312	57.78 88	77.47 163	76.88 223	57.60 171	61.97 299	76.85 333	39.31 264	80.49 229	54.72 229	70.28 286	82.17 252

v1192		69.97 150	68.68 162	73.85 127	73.19 306	50.94 218	77.68 157	81.36 120	57.51 172	68.95 159	80.85 260	45.28 192	85.33 117	62.97 155	70.37 282	85.27 148

ACMH+	57.40 11	66.12 246	64.06 255	72.30 181	77.79 194	52.83 186	80.39 100	78.03 203	57.30 173	57.47 351	82.55 215	27.68 390	84.17 138	45.54 309	69.78 297	79.90 296

diffmvs		70.69 132	70.43 125	71.46 203	69.45 375	48.95 263	72.93 272	78.46 193	57.27 174	71.69 117	83.97 181	51.48 107	77.92 276	70.70 87	77.95 167	87.53 49
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		68.27 197	67.29 199	71.21 213	79.74 129	53.22 174	76.06 204	77.46 214	57.19 175	66.10 224	81.61 243	45.37 191	83.50 154	45.42 314	76.68 189	76.91 341

thres100view900		63.28 282	62.41 281	65.89 304	77.31 216	38.66 369	72.65 275	69.11 325	57.07 176	62.45 294	81.03 254	37.01 296	79.17 249	31.84 407	73.25 242	79.83 299

fmvsm_s_conf0.5_n_7		69.54 165	69.67 140	69.15 260	73.47 303	51.41 213	70.35 314	73.34 284	57.05 177	68.41 165	85.83 141	49.86 125	72.84 330	71.86 78	76.83 186	83.19 223

DP-MVS Recon		72.15 107	70.73 120	76.40 68	86.57 24	57.99 84	81.15 93	82.96 92	57.03 178	66.78 208	85.56 147	44.50 203	88.11 38	51.77 256	80.23 124	83.10 228

thres600view7		63.30 281	62.27 283	66.41 291	77.18 218	38.87 367	72.35 282	69.11 325	56.98 179	62.37 297	80.96 256	37.01 296	79.00 260	31.43 414	73.05 246	81.36 264

V42		68.65 187	67.35 198	72.56 171	68.93 381	50.18 234	72.90 273	79.47 162	56.92 180	69.45 148	80.26 269	46.29 178	82.99 164	64.07 137	67.82 324	84.53 174

MCST-MVS		77.48 29	77.45 28	77.54 48	86.67 20	58.36 81	83.22 61	86.93 5	56.91 181	74.91 60	88.19 70	59.15 23	87.68 51	73.67 62	87.45 45	86.57 86

GA-MVS		65.53 253	63.70 262	71.02 222	70.87 351	48.10 274	70.48 311	74.40 267	56.69 182	64.70 257	76.77 334	33.66 329	81.10 212	55.42 225	70.32 285	83.87 198

v144192		69.71 156	68.51 165	73.33 156	73.10 308	50.13 235	77.54 161	80.64 142	56.65 183	68.57 163	80.55 263	46.87 173	84.96 124	62.98 154	69.66 301	84.89 163

fmvsm_l_conf0.5_n_3		73.23 82	73.13 81	73.55 147	74.40 283	55.13 137	78.97 123	74.96 261	56.64 184	74.76 66	88.75 66	55.02 52	78.77 264	76.33 37	78.31 162	86.74 78

tfpn200view9		63.18 284	62.18 285	66.21 296	76.85 230	39.62 361	71.96 290	69.44 321	56.63 185	62.61 289	79.83 276	37.18 290	79.17 249	31.84 407	73.25 242	79.83 299

thres400		63.31 280	62.18 285	66.72 284	76.85 230	39.62 361	71.96 290	69.44 321	56.63 185	62.61 289	79.83 276	37.18 290	79.17 249	31.84 407	73.25 242	81.36 264

GBi-Net		67.21 221	66.55 216	69.19 255	77.63 202	43.33 324	77.31 167	77.83 206	56.62 187	65.04 250	82.70 205	41.85 232	80.33 231	47.18 293	72.76 250	83.92 195

test1		67.21 221	66.55 216	69.19 255	77.63 202	43.33 324	77.31 167	77.83 206	56.62 187	65.04 250	82.70 205	41.85 232	80.33 231	47.18 293	72.76 250	83.92 195

FMVSNet2		66.93 231	66.31 227	68.79 264	77.63 202	42.98 329	76.11 202	77.47 212	56.62 187	65.22 247	82.17 229	41.85 232	80.18 237	47.05 296	72.72 253	83.20 222

fmvsm_l_conf0.5_n_9		73.27 81	73.66 76	72.09 183	73.82 295	52.72 189	77.45 164	74.28 271	56.61 190	77.10 38	88.16 71	56.17 43	77.09 293	78.27 24	81.13 110	86.48 90

DPM-MVS		75.47 55	75.00 57	76.88 57	81.38 99	59.16 64	79.94 107	85.71 23	56.59 191	72.46 109	86.76 105	56.89 36	87.86 45	66.36 118	88.91 25	83.64 212

v1921920		69.47 169	68.17 177	73.36 155	73.06 309	50.10 236	77.39 165	80.56 143	56.58 192	68.59 161	80.37 265	44.72 200	84.98 122	62.47 160	69.82 296	85.00 157

FMVSNet1		66.70 236	65.87 233	69.19 255	77.49 210	43.33 324	77.31 167	77.83 206	56.45 193	64.60 259	82.70 205	38.08 282	80.33 231	46.08 302	72.31 259	83.92 195

v1240		69.24 175	67.91 182	73.25 159	73.02 311	49.82 240	77.21 175	80.54 144	56.43 194	68.34 168	80.51 264	43.33 214	84.99 120	62.03 164	69.77 299	84.95 161

fmvsm_s_conf0.5_n_4		72.04 108	71.85 97	72.58 170	73.74 298	52.49 196	76.69 188	72.42 294	56.42 195	75.32 49	87.04 98	52.13 95	78.01 273	79.29 12	73.65 230	87.26 61

testing222		62.29 296	61.31 296	65.25 317	77.87 191	38.53 371	68.34 332	66.31 347	56.37 196	63.15 278	77.58 323	28.47 382	76.18 316	37.04 375	76.65 190	81.05 275

CDPH-MVS		76.31 43	75.67 50	78.22 37	85.35 48	59.14 67	81.31 91	84.02 52	56.32 197	74.05 77	88.98 59	53.34 77	87.92 43	69.23 94	88.42 28	87.59 47

Vis-MVSNet (Re-imp)		63.69 277	63.88 258	63.14 335	74.75 272	31.04 429	71.16 301	63.64 370	56.32 197	59.80 325	84.99 154	44.51 202	75.46 318	39.12 362	80.62 115	82.92 230

AdaColmap		69.99 149	68.66 163	73.97 126	84.94 54	57.83 86	82.63 71	78.71 179	56.28 199	64.34 260	84.14 175	41.57 239	87.06 65	46.45 299	78.88 147	77.02 337

PS-MVSNAJss		72.24 102	71.21 111	75.31 89	78.50 165	55.93 118	81.63 85	82.12 104	56.24 200	70.02 137	85.68 146	47.05 168	84.34 137	65.27 129	74.41 219	85.67 126

c3_l		68.33 196	67.56 187	70.62 230	70.87 351	46.21 295	74.47 242	78.80 177	56.22 201	66.19 221	78.53 304	51.88 98	81.40 203	62.08 161	69.04 311	84.25 182

Fast-Effi-MVS+		70.28 142	69.12 152	73.73 136	78.50 165	51.50 212	75.01 228	79.46 163	56.16 202	68.59 161	79.55 285	53.97 65	84.05 140	53.34 242	77.53 173	85.65 128

PHI-MVS		75.87 50	75.36 52	77.41 51	80.62 115	55.91 119	84.28 45	85.78 21	56.08 203	73.41 86	86.58 116	50.94 116	88.54 28	70.79 86	89.71 17	87.79 39

baseline1		63.81 276	63.87 259	63.62 330	76.29 241	36.36 392	71.78 293	67.29 337	56.05 204	64.23 265	82.95 203	47.11 167	74.41 323	47.30 292	61.85 373	80.10 293

train_agg		76.27 44	76.15 41	76.64 65	85.58 43	61.59 24	81.62 86	81.26 127	55.86 205	74.93 58	88.81 63	53.70 72	84.68 131	75.24 49	88.33 30	83.65 211

test_8					85.40 46	60.96 34	81.54 89	81.18 131	55.86 205	74.81 63	88.80 65	53.70 72	84.45 135

FMVSNet3		66.32 245	65.61 238	68.46 267	76.48 239	42.34 334	74.98 230	77.15 220	55.83 207	65.04 250	81.16 250	39.91 257	80.14 238	47.18 293	72.76 250	82.90 232

PAPR		71.72 114	70.82 118	74.41 114	81.20 104	51.17 214	79.55 118	83.33 80	55.81 208	66.93 207	84.61 163	50.95 115	86.06 95	55.79 219	79.20 142	86.00 110

eth_miper_zixun_eth		67.63 215	66.28 228	71.67 197	71.60 337	48.33 271	73.68 260	77.88 204	55.80 209	65.91 228	78.62 302	47.35 165	82.88 169	59.45 187	66.25 337	83.81 200

ACMH	55.70 15	65.20 259	63.57 264	70.07 239	78.07 185	52.01 206	79.48 119	79.69 156	55.75 210	56.59 358	80.98 255	27.12 395	80.94 217	42.90 337	71.58 268	77.25 335
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 256	62.73 278	73.40 154	74.89 266	52.78 187	73.09 271	75.13 256	55.69 211	58.48 343	73.73 374	32.86 338	86.32 88	50.63 264	70.11 289	81.10 273
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		61.53 305	60.94 304	63.30 333	68.95 380	36.93 388	67.60 339	72.80 292	55.67 212	59.95 322	76.63 336	45.01 198	72.22 336	39.74 359	62.09 372	80.74 281

TEST9					85.58 43	61.59 24	81.62 86	81.26 127	55.65 213	74.93 58	88.81 63	53.70 72	84.68 131

thres200		62.20 297	61.16 301	65.34 315	75.38 258	39.99 357	69.60 323	69.29 323	55.64 214	61.87 301	76.99 330	37.07 295	78.96 261	31.28 415	73.28 241	77.06 336

guyue		68.10 203	67.23 206	70.71 229	73.67 300	49.27 256	73.65 261	76.04 236	55.62 215	67.84 187	82.26 225	41.24 247	78.91 263	61.01 173	73.72 228	83.94 193

pm-mvs1		65.24 258	64.97 249	66.04 301	72.38 324	39.40 364	72.62 277	75.63 241	55.53 216	62.35 298	83.18 201	47.45 161	76.47 311	49.06 278	66.54 335	82.24 249

testing11		62.81 288	61.90 288	65.54 309	78.38 170	40.76 353	67.59 340	66.78 343	55.48 217	60.13 317	77.11 328	31.67 357	76.79 303	45.53 310	74.45 217	79.06 309

ACMM	61.98 7	70.80 131	69.73 138	74.02 123	80.59 116	58.59 79	82.68 70	82.02 106	55.46 218	67.18 202	84.39 172	38.51 274	83.17 161	60.65 176	76.10 197	80.30 288
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AstraMVS		67.86 210	66.83 211	70.93 223	73.50 302	49.34 254	73.28 268	74.01 276	55.45 219	68.10 178	83.28 197	38.93 271	79.14 253	63.22 152	71.74 265	84.30 181

Anonymous20240529		69.91 151	69.02 153	72.56 171	80.19 122	47.65 281	77.56 160	80.99 137	55.45 219	69.88 141	86.76 105	39.24 267	82.18 188	54.04 235	77.10 183	87.85 35

tt0805		67.77 213	67.24 204	69.34 254	74.87 268	40.08 355	77.36 166	81.37 119	55.31 221	66.33 219	84.65 161	37.35 288	82.55 181	55.65 222	72.28 260	85.39 142

GDP-MVS		72.64 93	71.28 110	76.70 60	77.72 197	54.22 151	79.57 117	84.45 44	55.30 222	71.38 122	86.97 100	39.94 256	87.00 66	67.02 114	79.20 142	88.89 9

CPTT-MVS		72.78 89	72.08 96	74.87 97	84.88 57	61.41 26	84.15 49	77.86 205	55.27 223	67.51 195	88.08 74	41.93 231	81.85 193	69.04 95	80.01 126	81.35 266

XVG-OURS		68.76 186	67.37 196	72.90 164	74.32 286	57.22 95	70.09 318	78.81 176	55.24 224	67.79 190	85.81 144	36.54 299	78.28 269	62.04 163	75.74 202	83.19 223

tfpnnormal		62.47 292	61.63 291	64.99 319	74.81 271	39.01 366	71.22 299	73.72 280	55.22 225	60.21 316	80.09 274	41.26 246	76.98 299	30.02 420	68.09 322	78.97 312

cl____		67.18 224	66.26 229	69.94 241	70.20 362	45.74 299	73.30 265	76.83 225	55.10 226	65.27 241	79.57 284	47.39 163	80.53 226	59.41 189	69.22 309	83.53 214

DIV-MVS_self_test		67.18 224	66.26 229	69.94 241	70.20 362	45.74 299	73.29 267	76.83 225	55.10 226	65.27 241	79.58 283	47.38 164	80.53 226	59.43 188	69.22 309	83.54 213

PC_three_1452									55.09 228	84.46 4	89.84 48	66.68 5	89.41 18	74.24 55	91.38 2	88.42 18

EPNet_dtu		61.90 301	61.97 287	61.68 344	72.89 313	39.78 359	75.85 211	65.62 351	55.09 228	54.56 381	79.36 290	37.59 285	67.02 371	39.80 358	76.95 184	78.25 317
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 119	70.39 126	74.65 104	82.01 86	58.82 76	79.93 108	80.35 149	55.09 228	65.82 233	82.16 230	49.17 137	82.64 179	60.34 178	78.62 156	82.50 244

cl22		67.47 218	66.45 218	70.54 232	69.85 370	46.49 291	73.85 257	77.35 216	55.07 231	65.51 236	77.92 313	47.64 156	81.10 212	61.58 169	69.32 305	84.01 191

miper_ehance_all_eth		68.03 204	67.24 204	70.40 234	70.54 355	46.21 295	73.98 250	78.68 181	55.07 231	66.05 225	77.80 317	52.16 94	81.31 206	61.53 171	69.32 305	83.67 208

fmvsm_s_conf0.5_n_2		69.82 153	69.27 149	71.46 203	72.00 331	51.08 215	73.30 265	67.79 333	55.06 233	75.24 51	87.51 85	44.02 208	77.00 297	75.67 42	72.86 248	86.31 103

Elysia		70.19 145	68.29 173	75.88 75	74.15 290	54.33 149	78.26 135	83.21 85	55.04 234	67.28 198	83.59 190	30.16 366	86.11 93	63.67 147	79.26 139	87.20 63

StellarMVS		70.19 145	68.29 173	75.88 75	74.15 290	54.33 149	78.26 135	83.21 85	55.04 234	67.28 198	83.59 190	30.16 366	86.11 93	63.67 147	79.26 139	87.20 63

PS-MVSNAJ		70.51 135	69.70 139	72.93 163	81.52 94	55.79 122	74.92 232	79.00 171	55.04 234	69.88 141	78.66 299	47.05 168	82.19 187	61.61 167	79.58 131	80.83 278

fmvsm_s_conf0.1_n_2		69.64 161	69.01 155	71.52 201	71.66 336	51.04 216	73.39 264	67.14 339	55.02 237	75.11 53	87.64 84	42.94 220	77.01 296	75.55 44	72.63 254	86.52 89

mmtdpeth		60.40 316	59.12 317	64.27 325	69.59 372	48.99 261	70.67 308	70.06 313	54.96 238	62.78 283	73.26 378	27.00 397	67.66 364	58.44 201	45.29 430	76.16 346

xiu_mvs_v2_base		70.52 134	69.75 137	72.84 165	81.21 103	55.63 126	75.11 225	78.92 173	54.92 239	69.96 140	79.68 282	47.00 172	82.09 189	61.60 168	79.37 134	80.81 279

MAR-MVS		71.51 116	70.15 133	75.60 85	81.84 90	59.39 60	81.38 90	82.90 94	54.90 240	68.08 179	78.70 297	47.73 153	85.51 110	51.68 258	84.17 76	81.88 256
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		62.56 290	61.20 300	66.62 288	70.62 354	44.30 315	70.13 317	73.13 289	54.78 241	61.13 310	76.37 344	25.63 407	75.63 317	58.75 198	60.29 385	79.93 295

XVG-OURS-SEG-HR		68.81 183	67.47 193	72.82 167	74.40 283	56.87 105	70.59 309	79.04 170	54.77 242	66.99 205	86.01 135	39.57 262	78.21 270	62.54 158	73.33 240	83.37 217

testing3		56.54 347	55.92 349	58.41 368	77.52 209	27.93 439	69.72 321	56.36 409	54.75 243	58.63 341	77.80 317	20.88 423	71.75 339	25.31 436	62.25 370	75.53 353

Anonymous20231211		69.28 173	68.47 168	71.73 194	80.28 117	47.18 287	79.98 106	82.37 101	54.61 244	67.24 200	84.01 179	39.43 263	82.41 185	55.45 224	72.83 249	85.62 129

SixPastTwentyTwo		61.65 304	58.80 321	70.20 237	75.80 247	47.22 286	75.59 215	69.68 316	54.61 244	54.11 385	79.26 292	27.07 396	82.96 165	43.27 331	49.79 423	80.41 286

test_0402		63.25 283	61.01 303	69.96 240	80.00 126	54.37 148	76.86 186	72.02 299	54.58 246	58.71 337	80.79 262	35.00 311	84.36 136	26.41 434	64.71 348	71.15 404

tttt0517		67.83 211	65.66 237	74.33 116	76.69 232	50.82 222	77.86 151	73.99 277	54.54 247	64.64 258	82.53 218	35.06 310	85.50 111	55.71 220	69.91 294	86.67 82

BH-w/o		66.85 232	65.83 234	69.90 244	79.29 138	52.46 197	74.66 238	76.65 228	54.51 248	64.85 255	78.12 307	45.59 184	82.95 166	43.26 332	75.54 205	74.27 371

AUN-MVS		68.45 195	66.41 222	74.57 109	79.53 135	57.08 103	73.93 254	75.23 253	54.44 249	66.69 211	81.85 237	37.10 294	82.89 168	62.07 162	66.84 332	83.75 205

LTVRE_ROB	55.42 16	63.15 285	61.23 299	68.92 262	76.57 237	47.80 278	59.92 396	76.39 229	54.35 250	58.67 339	82.46 220	29.44 375	81.49 201	42.12 341	71.14 272	77.46 329
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 85	72.59 89	74.27 118	71.28 346	55.88 120	78.21 141	75.56 244	54.31 251	74.86 62	87.80 82	54.72 56	80.23 235	78.07 26	78.48 158	86.70 79

test_fmvsmconf0.01_n		72.17 104	71.50 102	74.16 121	67.96 387	55.58 129	78.06 146	74.67 264	54.19 252	74.54 69	88.23 69	50.35 123	80.24 234	78.07 26	77.46 175	86.65 84

test_fmvsmconf0.1_n		72.81 88	72.33 92	74.24 119	69.89 369	55.81 121	78.22 140	75.40 249	54.17 253	75.00 57	88.03 78	53.82 69	80.23 235	78.08 25	78.34 161	86.69 80

ETVMVS		59.51 326	58.81 319	61.58 346	77.46 211	34.87 403	64.94 364	59.35 395	54.06 254	61.08 311	76.67 335	29.54 372	71.87 338	32.16 403	74.07 222	78.01 324

ab-mvs		66.65 237	66.42 221	67.37 279	76.17 243	41.73 341	70.41 313	76.14 233	53.99 255	65.98 226	83.51 194	49.48 130	76.24 314	48.60 281	73.46 237	84.14 187

fmvsm_s_conf0.5_n_5		72.69 92	72.80 86	72.37 179	74.11 293	53.21 175	78.12 143	73.31 285	53.98 256	76.81 40	88.05 75	53.38 76	77.37 288	76.64 34	80.78 111	86.53 88

IU-MVS					87.77 4	59.15 65		85.53 27	53.93 257	84.64 3				79.07 13	90.87 5	88.37 20

mamba_test_0407		70.41 139	68.96 156	74.75 99	78.65 160	53.46 167	77.28 172	80.00 153	53.88 258	68.14 173	84.61 163	43.21 215	86.26 90	58.80 196	76.11 194	84.54 171

mamba_0404		70.84 127	69.41 146	75.12 93	79.20 143	53.86 155	77.89 149	80.00 153	53.88 258	69.40 149	84.61 163	43.21 215	86.56 77	58.80 196	77.68 171	84.95 161

XVG-ACMP-BASELINE		64.36 271	62.23 284	70.74 227	72.35 325	52.45 198	70.80 307	78.45 194	53.84 260	59.87 323	81.10 252	16.24 431	79.32 246	55.64 223	71.76 264	80.47 283

mamba_0408		67.78 212	65.42 241	74.85 98	78.65 160	53.46 167	50.83 431	79.09 168	53.75 261	68.14 173	83.83 183	41.79 235	86.56 77	56.58 210	76.11 194	84.54 171

mamba_test_0407_2		64.98 262	65.42 241	63.68 329	78.65 160	53.46 167	50.83 431	79.09 168	53.75 261	68.14 173	83.83 183	41.79 235	53.03 433	56.58 210	76.11 194	84.54 171

VortexMVS		66.41 243	65.50 240	69.16 259	73.75 296	48.14 273	73.41 263	78.28 200	53.73 263	64.98 254	78.33 305	40.62 252	79.07 255	58.88 195	67.50 327	80.26 289

FE-MVS		65.91 248	63.33 269	73.63 143	77.36 214	51.95 208	72.62 277	75.81 238	53.70 264	65.31 239	78.96 295	28.81 380	86.39 85	43.93 323	73.48 236	82.55 240

thisisatest0530		67.92 208	65.78 235	74.33 116	76.29 241	51.03 217	76.89 184	74.25 272	53.67 265	65.59 235	81.76 240	35.15 309	85.50 111	55.94 215	72.47 255	86.47 91

PVSNet_BlendedMVS		68.56 192	67.72 184	71.07 220	77.03 227	50.57 226	74.50 241	81.52 113	53.66 266	64.22 266	79.72 281	49.13 138	82.87 170	55.82 217	73.92 224	79.77 302

patch_mono-2		69.85 152	71.09 114	66.16 297	79.11 148	54.80 143	71.97 289	74.31 269	53.50 267	70.90 126	84.17 174	57.63 31	63.31 389	66.17 119	82.02 100	80.38 287

EG-PatchMatch MVS		64.71 264	62.87 275	70.22 235	77.68 199	53.48 166	77.99 147	78.82 175	53.37 268	56.03 365	77.41 325	24.75 412	84.04 141	46.37 300	73.42 239	73.14 377

SD_0403		63.07 286	63.49 266	61.82 343	75.16 263	31.14 428	71.89 292	73.47 282	53.34 269	58.22 345	81.81 239	45.17 195	73.86 326	37.43 371	74.87 214	80.45 284

DP-MVS		65.68 250	63.66 263	71.75 193	84.93 55	56.87 105	80.74 98	73.16 288	53.06 270	59.09 334	82.35 221	36.79 298	85.94 100	32.82 401	69.96 293	72.45 385

TR-MVS		66.59 240	65.07 248	71.17 216	79.18 145	49.63 250	73.48 262	75.20 255	52.95 271	67.90 181	80.33 268	39.81 260	83.68 149	43.20 333	73.56 234	80.20 290

ET-MVSNet_ETH3D		67.96 207	65.72 236	74.68 102	76.67 234	55.62 128	75.11 225	74.74 262	52.91 272	60.03 320	80.12 272	33.68 328	82.64 179	61.86 165	76.34 191	85.78 119

QAPM		70.05 147	68.81 159	73.78 130	76.54 238	53.43 170	83.23 60	83.48 71	52.89 273	65.90 229	86.29 125	41.55 241	86.49 83	51.01 261	78.40 160	81.42 260

LuminaMVS		68.24 199	66.82 212	72.51 173	73.46 304	53.60 163	76.23 199	78.88 174	52.78 274	68.08 179	80.13 271	32.70 344	81.41 202	63.16 153	75.97 198	82.53 241

icg_test_0407_2		66.41 243	66.75 213	65.37 314	77.06 222	49.73 242	63.79 373	78.60 183	52.70 275	66.19 221	82.58 210	45.17 195	63.65 388	59.20 191	75.46 207	82.74 235

icg_test_0407		68.90 181	67.93 181	71.82 190	77.06 222	49.73 242	74.40 245	78.60 183	52.70 275	66.19 221	82.58 210	45.17 195	83.00 163	59.20 191	75.46 207	82.74 235

ICG_test_0404		64.63 266	64.22 254	65.88 305	77.06 222	49.73 242	64.40 367	78.60 183	52.70 275	53.16 395	82.58 210	34.82 313	65.16 382	59.20 191	75.46 207	82.74 235

icg_test_0403		69.09 177	68.14 178	71.95 185	77.06 222	49.73 242	74.51 240	78.60 183	52.70 275	66.69 211	82.58 210	46.43 176	83.38 156	59.20 191	75.46 207	82.74 235

OpenMVS	61.03 9	68.85 182	67.56 187	72.70 169	74.26 288	53.99 154	81.21 92	81.34 124	52.70 275	62.75 286	85.55 149	38.86 272	84.14 139	48.41 283	83.01 85	79.97 294

pmmvs6		63.69 277	62.82 277	66.27 295	70.63 353	39.27 365	73.13 270	75.47 248	52.69 280	59.75 327	82.30 223	39.71 261	77.03 295	47.40 290	64.35 353	82.53 241

IterMVS		62.79 289	61.27 297	67.35 280	69.37 376	52.04 205	71.17 300	68.24 331	52.63 281	59.82 324	76.91 332	37.32 289	72.36 332	52.80 246	63.19 363	77.66 327
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 201	66.36 224	73.63 143	75.61 252	55.35 135	80.77 97	78.56 188	52.48 282	64.27 263	84.10 177	27.45 392	81.84 194	63.45 151	70.56 279	83.69 207

jajsoiax		68.25 198	66.45 218	73.66 140	75.62 251	55.49 131	80.82 96	78.51 190	52.33 283	64.33 261	84.11 176	28.28 384	81.81 195	63.48 150	70.62 277	83.67 208

TAMVS		66.78 235	65.27 246	71.33 212	79.16 147	53.67 160	73.84 258	69.59 318	52.32 284	65.28 240	81.72 241	44.49 204	77.40 287	42.32 340	78.66 155	82.92 230

CDS-MVSNet		66.80 234	65.37 243	71.10 219	78.98 150	53.13 178	73.27 269	71.07 305	52.15 285	64.72 256	80.23 270	43.56 212	77.10 292	45.48 312	78.88 147	83.05 229
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		68.47 193	66.56 215	74.21 120	79.60 132	52.95 180	74.94 231	75.48 247	52.09 286	60.10 318	83.27 198	36.54 299	84.70 130	59.32 190	77.69 170	84.99 159

viewmambaseed2359dif		68.91 180	68.18 176	71.11 218	70.21 361	48.05 277	72.28 284	75.90 237	51.96 287	70.93 125	84.47 170	51.37 108	78.59 265	61.55 170	74.97 212	86.68 81

PVSNet_Blended		68.59 188	67.72 184	71.19 214	77.03 227	50.57 226	72.51 280	81.52 113	51.91 288	64.22 266	77.77 320	49.13 138	82.87 170	55.82 217	79.58 131	80.14 292

mvs_anonymous		68.03 204	67.51 191	69.59 249	72.08 329	44.57 313	71.99 288	75.23 253	51.67 289	67.06 204	82.57 214	54.68 57	77.94 274	56.56 212	75.71 203	86.26 105

xiu_mvs_v1_base_debu		68.58 189	67.28 200	72.48 174	78.19 179	57.19 97	75.28 220	75.09 257	51.61 290	70.04 134	81.41 247	32.79 339	79.02 257	63.81 144	77.31 176	81.22 269

xiu_mvs_v1_base		68.58 189	67.28 200	72.48 174	78.19 179	57.19 97	75.28 220	75.09 257	51.61 290	70.04 134	81.41 247	32.79 339	79.02 257	63.81 144	77.31 176	81.22 269

xiu_mvs_v1_base_debi		68.58 189	67.28 200	72.48 174	78.19 179	57.19 97	75.28 220	75.09 257	51.61 290	70.04 134	81.41 247	32.79 339	79.02 257	63.81 144	77.31 176	81.22 269

MVSTER		67.16 226	65.58 239	71.88 188	70.37 360	49.70 246	70.25 316	78.45 194	51.52 293	69.16 156	80.37 265	38.45 275	82.50 182	60.19 179	71.46 269	83.44 216

CNLPA		65.43 254	64.02 256	69.68 247	78.73 158	58.07 83	77.82 154	70.71 308	51.49 294	61.57 306	83.58 193	38.23 280	70.82 344	43.90 324	70.10 290	80.16 291

原ACMM1				74.69 101	85.39 47	59.40 59		83.42 74	51.47 295	70.27 132	86.61 114	48.61 144	86.51 82	53.85 238	87.96 39	78.16 318

miper_enhance_ethall		67.11 227	66.09 231	70.17 238	69.21 378	45.98 297	72.85 274	78.41 197	51.38 296	65.65 234	75.98 351	51.17 112	81.25 207	60.82 175	69.32 305	83.29 220

MSDG		61.81 303	59.23 315	69.55 252	72.64 316	52.63 192	70.45 312	75.81 238	51.38 296	53.70 388	76.11 346	29.52 373	81.08 214	37.70 369	65.79 341	74.93 362

test20.03		53.87 368	54.02 366	53.41 400	61.47 422	28.11 438	61.30 388	59.21 396	51.34 298	52.09 399	77.43 324	33.29 333	58.55 410	29.76 421	60.27 386	73.58 376

MVSFormer		71.50 117	70.38 127	74.88 96	78.76 156	57.15 100	82.79 67	78.48 191	51.26 299	69.49 146	83.22 199	43.99 209	83.24 159	66.06 120	79.37 134	84.23 183

test_djsdf		69.45 170	67.74 183	74.58 108	74.57 279	54.92 141	82.79 67	78.48 191	51.26 299	65.41 238	83.49 195	38.37 276	83.24 159	66.06 120	69.25 308	85.56 130

dmvs_testset		50.16 386	51.90 376	44.94 421	66.49 398	11.78 461	61.01 393	51.50 423	51.17 301	50.30 411	67.44 415	39.28 265	60.29 400	22.38 440	57.49 395	62.76 426

PAPM		67.92 208	66.69 214	71.63 199	78.09 184	49.02 260	77.09 178	81.24 129	51.04 302	60.91 312	83.98 180	47.71 154	84.99 120	40.81 350	79.32 137	80.90 277

Syy-MVS		56.00 354	56.23 347	55.32 386	74.69 274	26.44 445	65.52 354	57.49 404	50.97 303	56.52 359	72.18 382	39.89 258	68.09 360	24.20 437	64.59 351	71.44 400

myMVS_eth3d		54.86 364	54.61 358	55.61 385	74.69 274	27.31 442	65.52 354	57.49 404	50.97 303	56.52 359	72.18 382	21.87 421	68.09 360	27.70 428	64.59 351	71.44 400

miper_lstm_enhance		62.03 300	60.88 305	65.49 312	66.71 396	46.25 293	56.29 415	75.70 240	50.68 305	61.27 308	75.48 358	40.21 255	68.03 362	56.31 214	65.25 344	82.18 250

gg-mvs-nofinetune		57.86 338	56.43 344	62.18 341	72.62 317	35.35 402	66.57 344	56.33 410	50.65 306	57.64 350	57.10 437	30.65 360	76.36 312	37.38 372	78.88 147	74.82 364

TAPA-MVS	59.36 10	66.60 238	65.20 247	70.81 225	76.63 235	48.75 265	76.52 193	80.04 152	50.64 307	65.24 245	84.93 155	39.15 268	78.54 266	36.77 377	76.88 185	85.14 151
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 346	56.83 339	56.61 380	69.23 377	41.02 348	58.37 402	64.18 363	50.59 308	57.45 352	71.42 390	35.54 306	58.94 408	37.23 373	67.45 328	69.87 413

MVP-Stereo		65.41 255	63.80 260	70.22 235	77.62 206	55.53 130	76.30 196	78.53 189	50.59 308	56.47 361	78.65 300	39.84 259	82.68 177	44.10 322	72.12 262	72.44 386
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 126	69.49 143	75.35 88	77.63 202	55.71 123	76.04 206	81.81 109	50.30 310	69.66 144	85.40 153	52.51 86	84.89 126	51.82 255	80.24 123	85.45 137
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs5depth		55.64 357	53.81 368	61.11 352	59.39 432	40.98 352	65.89 349	68.28 330	50.21 311	58.11 347	75.42 359	17.03 427	67.63 366	43.79 326	46.21 427	74.73 366

baseline2		63.42 279	61.26 298	69.89 245	72.55 319	47.62 282	71.54 294	68.38 329	50.11 312	54.82 377	75.55 356	43.06 218	80.96 216	48.13 286	67.16 331	81.11 272

test-LLR		58.15 336	58.13 329	58.22 370	68.57 382	44.80 309	65.46 356	57.92 401	50.08 313	55.44 369	69.82 403	32.62 347	57.44 415	49.66 272	73.62 231	72.41 387

test0.0.03 1		53.32 373	53.59 370	52.50 406	62.81 417	29.45 433	59.51 398	54.11 418	50.08 313	54.40 383	74.31 368	32.62 347	55.92 424	30.50 418	63.95 356	72.15 392

fmvsm_s_conf0.5_n		69.58 163	68.84 158	71.79 192	72.31 327	52.90 182	77.90 148	62.43 382	49.97 315	72.85 102	85.90 138	52.21 92	76.49 309	75.75 41	70.26 287	85.97 111

COLMAP_ROB	52.97 17	61.27 309	58.81 319	68.64 265	74.63 276	52.51 195	78.42 134	73.30 286	49.92 316	50.96 403	81.51 246	23.06 415	79.40 244	31.63 411	65.85 339	74.01 374
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		69.54 165	68.74 161	71.93 186	72.47 322	53.82 157	78.25 137	62.26 384	49.78 317	73.12 95	86.21 127	52.66 84	76.79 303	75.02 50	68.88 313	85.18 150

WBMVS		60.54 313	60.61 307	60.34 355	78.00 188	35.95 399	64.55 366	64.89 356	49.63 318	63.39 273	78.70 297	33.85 326	67.65 365	42.10 342	70.35 284	77.43 330

tpmvs		58.47 331	56.95 337	63.03 337	70.20 362	41.21 347	67.90 337	67.23 338	49.62 319	54.73 379	70.84 394	34.14 320	76.24 314	36.64 381	61.29 377	71.64 396

fmvsm_s_conf0.1_n		69.41 171	68.60 164	71.83 189	71.07 348	52.88 185	77.85 152	62.44 381	49.58 320	72.97 98	86.22 126	51.68 103	76.48 310	75.53 45	70.10 290	86.14 106

UBG		59.62 325	59.53 313	59.89 356	78.12 183	35.92 400	64.11 371	60.81 392	49.45 321	61.34 307	75.55 356	33.05 334	67.39 369	38.68 364	74.62 215	76.35 345

thisisatest0515		65.83 249	63.50 265	72.82 167	73.75 296	49.50 251	71.32 297	73.12 290	49.39 322	63.82 268	76.50 343	34.95 312	84.84 129	53.20 244	75.49 206	84.13 188

fmvsm_s_conf0.1_n_a		69.32 172	68.44 170	71.96 184	70.91 350	53.78 158	78.12 143	62.30 383	49.35 323	73.20 91	86.55 119	51.99 97	76.79 303	74.83 52	68.68 318	85.32 145

HY-MVS	56.14 13	64.55 268	63.89 257	66.55 289	74.73 273	41.02 348	69.96 319	74.43 266	49.29 324	61.66 304	80.92 257	47.43 162	76.68 307	44.91 317	71.69 266	81.94 254

MIMVSNet1		55.17 362	54.31 363	57.77 376	70.03 366	32.01 424	65.68 352	64.81 357	49.19 325	46.75 421	76.00 348	25.53 408	64.04 385	28.65 425	62.13 371	77.26 334

SCA		60.49 314	58.38 325	66.80 283	74.14 292	48.06 275	63.35 376	63.23 374	49.13 326	59.33 333	72.10 384	37.45 286	74.27 324	44.17 319	62.57 367	78.05 320

test_fmvsmvis_n_1920		70.84 127	70.38 127	72.22 182	71.16 347	55.39 133	75.86 210	72.21 297	49.03 327	73.28 89	86.17 129	51.83 100	77.29 290	75.80 40	78.05 165	83.98 192

testgi		51.90 378	52.37 374	50.51 413	60.39 430	23.55 452	58.42 401	58.15 399	49.03 327	51.83 400	79.21 293	22.39 416	55.59 425	29.24 424	62.64 366	72.40 389

sc_t1		59.76 321	57.84 332	65.54 309	74.87 268	42.95 331	69.61 322	64.16 365	48.90 329	58.68 338	77.12 327	28.19 385	72.35 333	43.75 328	55.28 404	81.31 267

MIMVSNet		57.35 340	57.07 335	58.22 370	74.21 289	37.18 383	62.46 381	60.88 391	48.88 330	55.29 372	75.99 350	31.68 356	62.04 394	31.87 406	72.35 257	75.43 355

gm-plane-assit					71.40 343	41.72 343			48.85 331		73.31 376		82.48 184	48.90 279

fmvsm_l_conf0.5_n		70.99 125	70.82 118	71.48 202	71.45 339	54.40 147	77.18 176	70.46 310	48.67 332	75.17 52	86.86 102	53.77 70	76.86 301	76.33 37	77.51 174	83.17 227

UWE-MVS		60.18 317	59.78 311	61.39 349	77.67 200	33.92 415	69.04 329	63.82 368	48.56 333	64.27 263	77.64 322	27.20 394	70.40 349	33.56 398	76.24 192	79.83 299

cascas		65.98 247	63.42 267	73.64 142	77.26 217	52.58 193	72.26 285	77.21 219	48.56 333	61.21 309	74.60 366	32.57 350	85.82 103	50.38 266	76.75 188	82.52 243

PLC	56.13 14	65.09 260	63.21 272	70.72 228	81.04 106	54.87 142	78.57 131	77.47 212	48.51 335	55.71 366	81.89 236	33.71 327	79.71 239	41.66 346	70.37 282	77.58 328
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 264	62.50 280	71.34 211	79.72 131	55.71 123	79.82 110	74.72 263	48.50 336	56.62 357	84.62 162	33.59 330	82.34 186	29.65 422	75.23 211	75.97 347

anonymousdsp		67.00 230	64.82 250	73.57 146	70.09 365	56.13 113	76.35 195	77.35 216	48.43 337	64.99 253	80.84 261	33.01 336	80.34 230	64.66 134	67.64 326	84.23 183

无先验							79.66 115	74.30 270	48.40 338				80.78 223	53.62 239		79.03 311

114514_t		70.83 129	69.56 141	74.64 105	86.21 31	54.63 144	82.34 76	81.81 109	48.22 339	63.01 281	85.83 141	40.92 251	87.10 63	57.91 202	79.79 127	82.18 250

tpm		57.34 341	58.16 327	54.86 389	71.80 335	34.77 405	67.47 342	56.04 413	48.20 340	60.10 318	76.92 331	37.17 292	53.41 432	40.76 351	65.01 345	76.40 344

test_fmvsm_n_1920		71.73 113	71.14 113	73.50 148	72.52 320	56.53 107	75.60 214	76.16 231	48.11 341	77.22 35	85.56 147	53.10 80	77.43 285	74.86 51	77.14 181	86.55 87

MDA-MVSNet-bldmvs		53.87 368	50.81 381	63.05 336	66.25 400	48.58 268	56.93 413	63.82 368	48.09 342	41.22 433	70.48 399	30.34 363	68.00 363	34.24 393	45.92 429	72.57 383

XXY-MVS		60.68 310	61.67 290	57.70 377	70.43 358	38.45 372	64.19 369	66.47 344	48.05 343	63.22 274	80.86 259	49.28 135	60.47 398	45.25 316	67.28 330	74.19 372

F-COLMAP		63.05 287	60.87 306	69.58 251	76.99 229	53.63 162	78.12 143	76.16 231	47.97 344	52.41 398	81.61 243	27.87 387	78.11 271	40.07 353	66.66 334	77.00 338

tt0320-xc		58.33 333	56.41 345	64.08 326	75.79 248	41.34 345	68.30 333	62.72 378	47.90 345	56.29 362	74.16 371	28.53 381	71.04 343	41.50 349	52.50 415	79.88 297

fmvsm_l_conf0.5_n_a		70.50 136	70.27 129	71.18 215	71.30 345	54.09 152	76.89 184	69.87 314	47.90 345	74.37 72	86.49 120	53.07 81	76.69 306	75.41 46	77.11 182	82.76 234

Patchmatch-RL test		58.16 335	55.49 352	66.15 298	67.92 388	48.89 264	60.66 394	51.07 426	47.86 347	59.36 330	62.71 431	34.02 323	72.27 335	56.41 213	59.40 388	77.30 332

D2MVS		62.30 295	60.29 309	68.34 270	66.46 399	48.42 270	65.70 351	73.42 283	47.71 348	58.16 346	75.02 362	30.51 361	77.71 282	53.96 237	71.68 267	78.90 313

ANet_high		41.38 405	37.47 412	53.11 402	39.73 458	24.45 450	56.94 412	69.69 315	47.65 349	26.04 450	52.32 440	12.44 439	62.38 393	21.80 441	10.61 459	72.49 384

CostFormer		64.04 274	62.51 279	68.61 266	71.88 333	45.77 298	71.30 298	70.60 309	47.55 350	64.31 262	76.61 339	41.63 238	79.62 242	49.74 270	69.00 312	80.42 285

PatchmatchNet		59.84 320	58.24 326	64.65 321	73.05 310	46.70 290	69.42 325	62.18 385	47.55 350	58.88 336	71.96 386	34.49 317	69.16 354	42.99 335	63.60 358	78.07 319
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 361	53.89 367	59.21 362	57.80 436	27.47 441	57.75 408	74.32 268	47.38 352	50.90 404	70.00 402	28.45 383	70.30 350	40.44 352	57.92 393	79.87 298

ITE_SJBPF				62.09 342	66.16 401	44.55 314		64.32 361	47.36 353	55.31 371	80.34 267	19.27 424	62.68 392	36.29 385	62.39 369	79.04 310

KD-MVS_2432*1600		53.45 370	51.50 379	59.30 359	62.82 415	37.14 384	55.33 416	71.79 301	47.34 354	55.09 374	70.52 397	21.91 419	70.45 347	35.72 388	42.97 433	70.31 409

miper_refine_blended		53.45 370	51.50 379	59.30 359	62.82 415	37.14 384	55.33 416	71.79 301	47.34 354	55.09 374	70.52 397	21.91 419	70.45 347	35.72 388	42.97 433	70.31 409

OurMVSNet-221017-0		61.37 308	58.63 323	69.61 248	72.05 330	48.06 275	73.93 254	72.51 293	47.23 356	54.74 378	80.92 257	21.49 422	81.24 208	48.57 282	56.22 401	79.53 304

tpmrst		58.24 334	58.70 322	56.84 379	66.97 393	34.32 410	69.57 324	61.14 390	47.17 357	58.58 342	71.60 389	41.28 245	60.41 399	49.20 276	62.84 365	75.78 350

tt0320		58.59 330	56.81 340	63.92 328	75.46 255	41.32 346	68.63 331	64.06 366	47.05 358	56.19 363	74.19 369	30.34 363	71.36 340	39.92 357	55.45 403	79.09 308

PVSNet	50.76 19	58.40 332	57.39 333	61.42 347	75.53 254	44.04 319	61.43 386	63.45 372	47.04 359	56.91 355	73.61 375	27.00 397	64.76 383	39.12 362	72.40 256	75.47 354

WB-MVSnew		59.66 323	59.69 312	59.56 357	75.19 262	35.78 401	69.34 326	64.28 362	46.88 360	61.76 303	75.79 352	40.61 253	65.20 381	32.16 403	71.21 271	77.70 326

UWE-MVS-28		52.25 377	52.35 375	51.93 410	66.99 392	22.79 453	63.48 375	48.31 434	46.78 361	52.73 397	76.11 346	27.78 389	57.82 414	20.58 443	68.41 320	75.17 356

FMVSNet5		55.86 355	54.93 355	58.66 367	71.05 349	36.35 393	64.18 370	62.48 380	46.76 362	50.66 408	74.73 365	25.80 405	64.04 385	33.11 399	65.57 342	75.59 352

jason		69.65 160	68.39 172	73.43 153	78.27 177	56.88 104	77.12 177	73.71 281	46.53 363	69.34 151	83.22 199	43.37 213	79.18 248	64.77 133	79.20 142	84.23 183
jason: jason.
MS-PatchMatch		62.42 293	61.46 293	65.31 316	75.21 261	52.10 202	72.05 287	74.05 275	46.41 364	57.42 353	74.36 367	34.35 319	77.57 284	45.62 308	73.67 229	66.26 423

1112_ss		64.00 275	63.36 268	65.93 303	79.28 140	42.58 333	71.35 296	72.36 296	46.41 364	60.55 315	77.89 315	46.27 179	73.28 328	46.18 301	69.97 292	81.92 255

lupinMVS		69.57 164	68.28 175	73.44 152	78.76 156	57.15 100	76.57 191	73.29 287	46.19 366	69.49 146	82.18 227	43.99 209	79.23 247	64.66 134	79.37 134	83.93 194

testdata				64.66 320	81.52 94	52.93 181		65.29 354	46.09 367	73.88 80	87.46 88	38.08 282	66.26 376	53.31 243	78.48 158	74.78 365

UnsupCasMVSNet_eth		53.16 375	52.47 373	55.23 387	59.45 431	33.39 418	59.43 399	69.13 324	45.98 368	50.35 410	72.32 381	29.30 376	58.26 412	42.02 344	44.30 431	74.05 373

AllTest		57.08 343	54.65 357	64.39 323	71.44 340	49.03 258	69.92 320	67.30 335	45.97 369	47.16 418	79.77 278	17.47 425	67.56 367	33.65 395	59.16 389	76.57 342

TestCases				64.39 323	71.44 340	49.03 258		67.30 335	45.97 369	47.16 418	79.77 278	17.47 425	67.56 367	33.65 395	59.16 389	76.57 342

WTY-MVS		59.75 322	60.39 308	57.85 375	72.32 326	37.83 377	61.05 392	64.18 363	45.95 371	61.91 300	79.11 294	47.01 171	60.88 397	42.50 339	69.49 304	74.83 363

IterMVS-SCA-FT		62.49 291	61.52 292	65.40 313	71.99 332	50.80 223	71.15 302	69.63 317	45.71 372	60.61 314	77.93 312	37.45 286	65.99 378	55.67 221	63.50 360	79.42 305

WB-MVS		43.26 399	43.41 399	42.83 425	63.32 414	10.32 463	58.17 404	45.20 441	45.42 373	40.44 436	67.26 418	34.01 324	58.98 407	11.96 454	24.88 448	59.20 429

旧先验2							76.08 203		45.32 374	76.55 42			65.56 380	58.75 198

OpenMVS_ROB	52.78 18	60.03 318	58.14 328	65.69 308	70.47 357	44.82 308	75.33 219	70.86 307	45.04 375	56.06 364	76.00 348	26.89 399	79.65 240	35.36 390	67.29 329	72.60 382

TinyColmap		54.14 365	51.72 377	61.40 348	66.84 395	41.97 338	66.52 345	68.51 328	44.81 376	42.69 432	75.77 353	11.66 441	72.94 329	31.96 405	56.77 399	69.27 417

MDTV_nov1_ep13			57.00 336		72.73 315	38.26 373	65.02 363	64.73 359	44.74 377	55.46 368	72.48 380	32.61 349	70.47 346	37.47 370	67.75 325

新几何1				70.76 226	85.66 41	61.13 30		66.43 345	44.68 378	70.29 131	86.64 110	41.29 244	75.23 319	49.72 271	81.75 106	75.93 348

Patchmtry		57.16 342	56.47 343	59.23 361	69.17 379	34.58 408	62.98 378	63.15 375	44.53 379	56.83 356	74.84 363	35.83 304	68.71 357	40.03 354	60.91 378	74.39 370

ppachtmachnet_test		58.06 337	55.38 353	66.10 300	69.51 373	48.99 261	68.01 336	66.13 348	44.50 380	54.05 386	70.74 395	32.09 355	72.34 334	36.68 380	56.71 400	76.99 340

PatchT		53.17 374	53.44 371	52.33 407	68.29 386	25.34 449	58.21 403	54.41 417	44.46 381	54.56 381	69.05 409	33.32 332	60.94 396	36.93 376	61.76 375	70.73 407

EPMVS		53.96 366	53.69 369	54.79 390	66.12 402	31.96 425	62.34 383	49.05 430	44.42 382	55.54 367	71.33 392	30.22 365	56.70 418	41.65 347	62.54 368	75.71 351

pmmvs4		61.48 307	59.39 314	67.76 274	71.57 338	53.86 155	71.42 295	65.34 353	44.20 383	59.46 329	77.92 313	35.90 303	74.71 321	43.87 325	64.87 347	74.71 367

dp		51.89 379	51.60 378	52.77 404	68.44 385	32.45 423	62.36 382	54.57 416	44.16 384	49.31 413	67.91 411	28.87 379	56.61 420	33.89 394	54.89 406	69.24 418

PatchMatch-RL		56.25 352	54.55 359	61.32 350	77.06 222	56.07 115	65.57 353	54.10 419	44.13 385	53.49 394	71.27 393	25.20 409	66.78 372	36.52 383	63.66 357	61.12 427

our_test_3		56.49 348	54.42 360	62.68 339	69.51 373	45.48 304	66.08 348	61.49 388	44.11 386	50.73 407	69.60 406	33.05 334	68.15 359	38.38 366	56.86 397	74.40 369

USDC		56.35 351	54.24 364	62.69 338	64.74 407	40.31 354	65.05 362	73.83 279	43.93 387	47.58 416	77.71 321	15.36 434	75.05 320	38.19 368	61.81 374	72.70 381

PM-MVS		52.33 376	50.19 385	58.75 366	62.10 420	45.14 307	65.75 350	40.38 448	43.60 388	53.52 392	72.65 379	9.16 449	65.87 379	50.41 265	54.18 409	65.24 425

pmmvs-eth3d		58.81 329	56.31 346	66.30 294	67.61 389	52.42 199	72.30 283	64.76 358	43.55 389	54.94 376	74.19 369	28.95 377	72.60 331	43.31 330	57.21 396	73.88 375

SSC-MVS		41.96 404	41.99 403	41.90 426	62.46 419	9.28 465	57.41 411	44.32 444	43.38 390	38.30 442	66.45 421	32.67 346	58.42 411	10.98 455	21.91 451	57.99 433

new-patchmatchnet		47.56 393	47.73 393	47.06 416	58.81 434	9.37 464	48.78 435	59.21 396	43.28 391	44.22 428	68.66 410	25.67 406	57.20 417	31.57 413	49.35 424	74.62 368

Test_1112_low_res		62.32 294	61.77 289	64.00 327	79.08 149	39.53 363	68.17 334	70.17 311	43.25 392	59.03 335	79.90 275	44.08 206	71.24 342	43.79 326	68.42 319	81.25 268

RPMNet		61.53 305	58.42 324	70.86 224	69.96 367	52.07 203	65.31 360	81.36 120	43.20 393	59.36 330	70.15 401	35.37 307	85.47 113	36.42 384	64.65 349	75.06 358

tpm2		62.07 298	60.10 310	67.99 272	72.79 314	43.86 320	71.05 305	66.85 342	43.14 394	62.77 284	75.39 360	38.32 278	80.80 222	41.69 345	68.88 313	79.32 306

JIA-IIPM		51.56 380	47.68 394	63.21 334	64.61 408	50.73 224	47.71 437	58.77 398	42.90 395	48.46 415	51.72 441	24.97 410	70.24 351	36.06 387	53.89 410	68.64 419

1314		64.61 267	63.21 272	68.80 263	71.87 334	47.46 284	73.95 252	78.39 199	42.88 396	59.97 321	76.60 340	38.11 281	79.39 245	54.84 228	72.32 258	79.55 303

HyFIR lowres test		65.67 251	63.01 274	73.67 139	79.97 127	55.65 125	69.07 328	75.52 245	42.68 397	63.53 271	77.95 311	40.43 254	81.64 196	46.01 303	71.91 263	83.73 206

CR-MVSNet		59.91 319	57.90 331	65.96 302	69.96 367	52.07 203	65.31 360	63.15 375	42.48 398	59.36 330	74.84 363	35.83 304	70.75 345	45.50 311	64.65 349	75.06 358

test222					83.14 72	58.68 78	72.57 279	63.45 372	41.78 399	67.56 194	86.12 130	37.13 293			78.73 152	74.98 361

TDRefinement		53.44 372	50.72 382	61.60 345	64.31 410	46.96 288	70.89 306	65.27 355	41.78 399	44.61 427	77.98 310	11.52 443	66.36 375	28.57 426	51.59 417	71.49 399

sss		56.17 353	56.57 342	54.96 388	66.93 394	36.32 395	57.94 405	61.69 387	41.67 401	58.64 340	75.32 361	38.72 273	56.25 422	42.04 343	66.19 338	72.31 390

PVSNet_0	43.31 20	47.46 394	45.64 397	52.92 403	67.60 390	44.65 311	54.06 421	54.64 415	41.59 402	46.15 423	58.75 434	30.99 359	58.66 409	32.18 402	24.81 449	55.46 437

MVS		67.37 219	66.33 225	70.51 233	75.46 255	50.94 218	73.95 252	81.85 108	41.57 403	62.54 291	78.57 303	47.98 149	85.47 113	52.97 245	82.05 99	75.14 357

Anonymous20240521		55.30 359	54.41 361	57.96 374	60.92 429	41.73 341	71.09 304	71.06 306	41.18 404	48.65 414	73.31 376	16.93 428	59.25 405	42.54 338	64.01 354	72.90 379

Anonymous20231206		55.10 363	55.30 354	54.48 391	69.81 371	33.94 414	62.91 379	62.13 386	41.08 405	55.18 373	75.65 354	32.75 342	56.59 421	30.32 419	67.86 323	72.91 378

MDA-MVSNet_test_wron		50.71 385	48.95 387	56.00 384	61.17 424	41.84 339	51.90 427	56.45 407	40.96 406	44.79 426	67.84 412	30.04 369	55.07 429	36.71 379	50.69 420	71.11 405

YYNet1		50.73 384	48.96 386	56.03 383	61.10 425	41.78 340	51.94 426	56.44 408	40.94 407	44.84 425	67.80 413	30.08 368	55.08 428	36.77 377	50.71 419	71.22 402

dongtai		34.52 414	34.94 414	33.26 435	61.06 426	16.00 460	52.79 425	23.78 461	40.71 408	39.33 440	48.65 449	16.91 429	48.34 441	12.18 453	19.05 453	35.44 452

CHOSEN 1792x2688		65.08 261	62.84 276	71.82 190	81.49 96	56.26 111	66.32 347	74.20 274	40.53 409	63.16 277	78.65 300	41.30 243	77.80 279	45.80 305	74.09 221	81.40 263

pmmvs5		56.47 349	55.68 351	58.86 365	61.41 423	36.71 390	66.37 346	62.75 377	40.38 410	53.70 388	76.62 337	34.56 315	67.05 370	40.02 355	65.27 343	72.83 380

test_vis1_n_1920		58.86 328	59.06 318	58.25 369	63.76 411	43.14 328	67.49 341	66.36 346	40.22 411	65.89 230	71.95 387	31.04 358	59.75 403	59.94 182	64.90 346	71.85 394

MDTV_nov1_ep13_2view						25.89 447	61.22 389		40.10 412	51.10 402		32.97 337		38.49 365		78.61 315

tpm cat1		59.25 327	56.95 337	66.15 298	72.19 328	46.96 288	68.09 335	65.76 349	40.03 413	57.81 349	70.56 396	38.32 278	74.51 322	38.26 367	61.50 376	77.00 338

test-mter		56.42 350	55.82 350	58.22 370	68.57 382	44.80 309	65.46 356	57.92 401	39.94 414	55.44 369	69.82 403	21.92 418	57.44 415	49.66 272	73.62 231	72.41 387

UnsupCasMVSNet_bld		50.07 387	48.87 388	53.66 396	60.97 428	33.67 416	57.62 409	64.56 360	39.47 415	47.38 417	64.02 429	27.47 391	59.32 404	34.69 392	43.68 432	67.98 421

TESTMET0.1,1		55.28 360	54.90 356	56.42 381	66.56 397	43.67 322	65.46 356	56.27 411	39.18 416	53.83 387	67.44 415	24.21 413	55.46 426	48.04 287	73.11 245	70.13 411

mamv4		56.85 345	58.00 330	53.43 399	72.46 323	54.47 145	57.56 410	54.74 414	38.81 417	57.42 353	79.45 288	47.57 158	38.70 452	60.88 174	53.07 412	67.11 422

ADS-MVSNet2		51.33 382	48.76 389	59.07 364	66.02 403	44.60 312	50.90 429	59.76 394	36.90 418	50.74 405	66.18 423	26.38 400	63.11 390	27.17 430	54.76 407	69.50 415

ADS-MVSNet		48.48 391	47.77 392	50.63 412	66.02 403	29.92 432	50.90 429	50.87 428	36.90 418	50.74 405	66.18 423	26.38 400	52.47 435	27.17 430	54.76 407	69.50 415

RPSCF		55.80 356	54.22 365	60.53 354	65.13 406	42.91 332	64.30 368	57.62 403	36.84 420	58.05 348	82.28 224	28.01 386	56.24 423	37.14 374	58.61 391	82.44 246

test_cas_vis1_n_1920		56.91 344	56.71 341	57.51 378	59.13 433	45.40 305	63.58 374	61.29 389	36.24 421	67.14 203	71.85 388	29.89 370	56.69 419	57.65 204	63.58 359	70.46 408

Patchmatch-test		49.08 389	48.28 391	51.50 411	64.40 409	30.85 430	45.68 441	48.46 433	35.60 422	46.10 424	72.10 384	34.47 318	46.37 444	27.08 432	60.65 382	77.27 333

CHOSEN 280x420		47.83 392	46.36 396	52.24 409	67.37 391	49.78 241	38.91 449	43.11 446	35.00 423	43.27 431	63.30 430	28.95 377	49.19 440	36.53 382	60.80 380	57.76 434

N_pmnet		39.35 409	40.28 406	36.54 432	63.76 411	1.62 469	49.37 434	0.76 468	34.62 424	43.61 430	66.38 422	26.25 402	42.57 448	26.02 435	51.77 416	65.44 424

kuosan		29.62 421	30.82 420	26.02 440	52.99 439	16.22 459	51.09 428	22.71 462	33.91 425	33.99 444	40.85 450	15.89 432	33.11 457	7.59 461	18.37 454	28.72 454

PMMVS		53.96 366	53.26 372	56.04 382	62.60 418	50.92 220	61.17 390	56.09 412	32.81 426	53.51 393	66.84 420	34.04 322	59.93 402	44.14 321	68.18 321	57.27 435

CMPMVS	42.80 21	57.81 339	55.97 348	63.32 332	60.98 427	47.38 285	64.66 365	69.50 320	32.06 427	46.83 420	77.80 317	29.50 374	71.36 340	48.68 280	73.75 227	71.21 403
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 395	42.95 400	53.39 401	52.33 443	29.15 434	57.77 406	48.20 435	31.81 428	49.86 412	77.21 326	8.69 450	59.16 406	27.31 429	33.40 445	71.84 395

CVMVSNet		59.63 324	59.14 316	61.08 353	74.47 280	38.84 368	75.20 223	68.74 327	31.15 429	58.24 344	76.51 341	32.39 352	68.58 358	49.77 269	65.84 340	75.81 349

FPMVS		42.18 403	41.11 405	45.39 418	58.03 435	41.01 350	49.50 433	53.81 420	30.07 430	33.71 445	64.03 427	11.69 440	52.08 438	14.01 449	55.11 405	43.09 446

EU-MVSNet		55.61 358	54.41 361	59.19 363	65.41 405	33.42 417	72.44 281	71.91 300	28.81 431	51.27 401	73.87 373	24.76 411	69.08 355	43.04 334	58.20 392	75.06 358

test_vis1_n		49.89 388	48.69 390	53.50 398	53.97 437	37.38 382	61.53 385	47.33 438	28.54 432	59.62 328	67.10 419	13.52 436	52.27 436	49.07 277	57.52 394	70.84 406

test_fmvs1_n		51.37 381	50.35 384	54.42 393	52.85 440	37.71 379	61.16 391	51.93 421	28.15 433	63.81 269	69.73 405	13.72 435	53.95 430	51.16 260	60.65 382	71.59 397

LF4IMVS		42.95 400	42.26 402	45.04 419	48.30 448	32.50 422	54.80 418	48.49 432	28.03 434	40.51 435	70.16 400	9.24 448	43.89 447	31.63 411	49.18 425	58.72 431

test_fmvs1		51.32 383	50.48 383	53.81 395	53.57 438	37.51 381	60.63 395	51.16 424	28.02 435	63.62 270	69.23 408	16.41 430	53.93 431	51.01 261	60.70 381	69.99 412

MVS-HIRNet		45.52 396	44.48 398	48.65 415	68.49 384	34.05 413	59.41 400	44.50 443	27.03 436	37.96 443	50.47 445	26.16 403	64.10 384	26.74 433	59.52 387	47.82 444

PMVS	28.69 22	36.22 412	33.29 417	45.02 420	36.82 460	35.98 398	54.68 419	48.74 431	26.31 437	21.02 453	51.61 442	2.88 462	60.10 401	9.99 458	47.58 426	38.99 451
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 397	41.95 404	53.86 394	52.58 442	43.55 323	62.11 384	46.90 440	26.05 438	40.63 434	60.19 433	11.08 446	57.91 413	31.83 410	46.15 428	60.11 428

test_fmvs2		48.69 390	47.49 395	52.29 408	48.63 447	33.06 420	57.76 407	48.05 436	25.71 439	59.76 326	69.60 406	11.57 442	52.23 437	49.45 275	56.86 397	71.58 398

PMMVS2		27.40 422	25.91 425	31.87 437	39.46 459	6.57 466	31.17 452	28.52 457	23.96 440	20.45 454	48.94 448	4.20 458	37.94 453	16.51 446	19.97 452	51.09 439

MVStest1		42.65 401	39.29 408	52.71 405	47.26 450	34.58 408	54.41 420	50.84 429	23.35 441	39.31 441	74.08 372	12.57 438	55.09 427	23.32 438	28.47 447	68.47 420

Gipuma		34.77 413	31.91 418	43.33 423	62.05 421	37.87 375	20.39 454	67.03 340	23.23 442	18.41 455	25.84 455	4.24 456	62.73 391	14.71 448	51.32 418	29.38 453
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 406	39.45 407	47.03 417	46.65 451	37.86 376	47.76 436	38.65 449	23.10 443	44.21 429	51.22 443	11.20 445	44.08 446	39.27 361	53.02 413	59.14 430

new_pmnet		34.13 415	34.29 416	33.64 434	52.63 441	18.23 458	44.43 444	33.90 454	22.81 444	30.89 447	53.18 439	10.48 447	35.72 456	20.77 442	39.51 437	46.98 445

mvsany_test1		39.38 408	38.16 411	43.02 424	49.05 445	34.28 411	44.16 445	25.94 459	22.74 445	46.57 422	62.21 432	23.85 414	41.16 451	33.01 400	35.91 441	53.63 438

LCM-MVSNet		40.30 407	35.88 413	53.57 397	42.24 453	29.15 434	45.21 443	60.53 393	22.23 446	28.02 448	50.98 444	3.72 459	61.78 395	31.22 416	38.76 439	69.78 414

test_fmvs3		44.30 398	42.55 401	49.55 414	42.83 452	27.15 444	53.03 423	44.93 442	22.03 447	53.69 390	64.94 426	4.21 457	49.63 439	47.47 288	49.82 422	71.88 393

APD_test1		37.39 411	34.94 414	44.72 422	48.88 446	33.19 419	52.95 424	44.00 445	19.49 448	27.28 449	58.59 435	3.18 461	52.84 434	18.92 444	41.17 436	48.14 443

mvsany_test3		32.62 416	30.57 421	38.77 430	36.16 461	24.20 451	38.10 450	20.63 463	19.14 449	40.36 437	57.43 436	5.06 454	36.63 455	29.59 423	28.66 446	55.49 436

E-PMN		23.77 423	22.73 427	26.90 438	42.02 454	20.67 455	42.66 446	35.70 452	17.43 450	10.28 460	25.05 456	6.42 452	42.39 449	10.28 457	14.71 456	17.63 455

EMVS		22.97 424	21.84 428	26.36 439	40.20 457	19.53 457	41.95 447	34.64 453	17.09 451	9.73 461	22.83 457	7.29 451	42.22 450	9.18 459	13.66 457	17.32 456

test_vis3_rt		32.09 417	30.20 422	37.76 431	35.36 462	27.48 440	40.60 448	28.29 458	16.69 452	32.52 446	40.53 451	1.96 463	37.40 454	33.64 397	42.21 435	48.39 441

test_f		31.86 418	31.05 419	34.28 433	32.33 464	21.86 454	32.34 451	30.46 456	16.02 453	39.78 439	55.45 438	4.80 455	32.36 458	30.61 417	37.66 440	48.64 440

DSMNet-mixed		39.30 410	38.72 409	41.03 427	51.22 444	19.66 456	45.53 442	31.35 455	15.83 454	39.80 438	67.42 417	22.19 417	45.13 445	22.43 439	52.69 414	58.31 432

testf1		31.46 419	28.89 423	39.16 428	41.99 455	28.78 436	46.45 439	37.56 450	14.28 455	21.10 451	48.96 446	1.48 465	47.11 442	13.63 450	34.56 442	41.60 447

APD_test2		31.46 419	28.89 423	39.16 428	41.99 455	28.78 436	46.45 439	37.56 450	14.28 455	21.10 451	48.96 446	1.48 465	47.11 442	13.63 450	34.56 442	41.60 447

MVE	17.77 23	21.41 425	17.77 430	32.34 436	34.34 463	25.44 448	16.11 455	24.11 460	11.19 457	13.22 457	31.92 453	1.58 464	30.95 459	10.47 456	17.03 455	40.62 450
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 443	17.97 465	10.91 462		10.60 466	7.46 458	11.07 459	28.36 454	3.28 460	11.29 462	8.01 460	9.74 461	13.89 457

wuyk23d		13.32 428	12.52 431	15.71 442	47.54 449	26.27 446	31.06 453	1.98 467	4.93 459	5.18 462	1.94 462	0.45 467	18.54 461	6.81 462	12.83 458	2.33 459

test_method		19.68 426	18.10 429	24.41 441	13.68 466	3.11 468	12.06 457	42.37 447	2.00 460	11.97 458	36.38 452	5.77 453	29.35 460	15.06 447	23.65 450	40.76 449

tmp_tt		9.43 429	11.14 432	4.30 444	2.38 467	4.40 467	13.62 456	16.08 465	0.39 461	15.89 456	13.06 458	15.80 433	5.54 463	12.63 452	10.46 460	2.95 458

EGC-MVSNET		42.47 402	38.48 410	54.46 392	74.33 285	48.73 266	70.33 315	51.10 425	0.03 462	0.18 463	67.78 414	13.28 437	66.49 374	18.91 445	50.36 421	48.15 442

testmvs		4.52 432	6.03 435	0.01 446	0.01 468	0.00 471	53.86 422	0.00 469	0.01 463	0.04 464	0.27 463	0.00 469	0.00 464	0.04 463	0.00 462	0.03 461

test123		4.73 431	6.30 434	0.02 445	0.01 468	0.01 470	56.36 414	0.00 469	0.01 463	0.04 464	0.21 464	0.01 468	0.00 464	0.03 464	0.00 462	0.04 460

mmdepth		0.00 434	0.00 437	0.00 447	0.00 470	0.00 471	0.00 458	0.00 469	0.00 465	0.00 466	0.00 465	0.00 469	0.00 464	0.00 465	0.00 462	0.00 462

monomultidepth		0.00 434	0.00 437	0.00 447	0.00 470	0.00 471	0.00 458	0.00 469	0.00 465	0.00 466	0.00 465	0.00 469	0.00 464	0.00 465	0.00 462	0.00 462

test_blank		0.00 434	0.00 437	0.00 447	0.00 470	0.00 471	0.00 458	0.00 469	0.00 465	0.00 466	0.00 465	0.00 469	0.00 464	0.00 465	0.00 462	0.00 462

uanet_test		0.00 434	0.00 437	0.00 447	0.00 470	0.00 471	0.00 458	0.00 469	0.00 465	0.00 466	0.00 465	0.00 469	0.00 464	0.00 465	0.00 462	0.00 462

DCPMVS		0.00 434	0.00 437	0.00 447	0.00 470	0.00 471	0.00 458	0.00 469	0.00 465	0.00 466	0.00 465	0.00 469	0.00 464	0.00 465	0.00 462	0.00 462

cdsmvs_eth3d_5k		17.50 427	23.34 426	0.00 447	0.00 470	0.00 471	0.00 458	78.63 182	0.00 465	0.00 466	82.18 227	49.25 136	0.00 464	0.00 465	0.00 462	0.00 462

pcd_1.5k_mvsjas		3.92 433	5.23 436	0.00 447	0.00 470	0.00 471	0.00 458	0.00 469	0.00 465	0.00 466	0.00 465	47.05 168	0.00 464	0.00 465	0.00 462	0.00 462

sosnet-low-res		0.00 434	0.00 437	0.00 447	0.00 470	0.00 471	0.00 458	0.00 469	0.00 465	0.00 466	0.00 465	0.00 469	0.00 464	0.00 465	0.00 462	0.00 462

sosnet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 471	0.00 458	0.00 469	0.00 465	0.00 466	0.00 465	0.00 469	0.00 464	0.00 465	0.00 462	0.00 462

uncertanet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 471	0.00 458	0.00 469	0.00 465	0.00 466	0.00 465	0.00 469	0.00 464	0.00 465	0.00 462	0.00 462

Regformer		0.00 434	0.00 437	0.00 447	0.00 470	0.00 471	0.00 458	0.00 469	0.00 465	0.00 466	0.00 465	0.00 469	0.00 464	0.00 465	0.00 462	0.00 462

ab-mvs-re		6.49 430	8.65 433	0.00 447	0.00 470	0.00 471	0.00 458	0.00 469	0.00 465	0.00 466	77.89 315	0.00 469	0.00 464	0.00 465	0.00 462	0.00 462

uanet		0.00 434	0.00 437	0.00 447	0.00 470	0.00 471	0.00 458	0.00 469	0.00 465	0.00 466	0.00 465	0.00 469	0.00 464	0.00 465	0.00 462	0.00 462

WAC-MVS						27.31 442								27.77 427

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 35

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 35

eth-test2					0.00 470

eth-test					0.00 470

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 24

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 45

GSMVS																78.05 320

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 314				78.05 320

sam_mvs												33.43 331

ambc				65.13 318	63.72 413	37.07 386	47.66 438	78.78 178		54.37 384	71.42 390	11.24 444	80.94 217	45.64 307	53.85 411	77.38 331

MTGPA								80.97 138

test_post1							68.67 330				3.64 460	32.39 352	69.49 353	44.17 319

test_post											3.55 461	33.90 325	66.52 373

patchmatchnet-post											64.03 427	34.50 316	74.27 324

GG-mvs-BLEND				62.34 340	71.36 344	37.04 387	69.20 327	57.33 406		54.73 379	65.48 425	30.37 362	77.82 278	34.82 391	74.93 213	72.17 391

MTMP							86.03 19	17.08 464

test9_res														75.28 48	88.31 32	83.81 200

agg_prior2														73.09 66	87.93 40	84.33 178

agg_prior					85.04 50	59.96 50		81.04 136		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 92

新几何2							76.12 201

旧先验1					83.04 74	53.15 176		67.52 334			87.85 81	44.08 206			80.76 113	78.03 323

原ACMM2							79.02 122

testdata2													72.18 337	46.95 297

segment_acmp												54.23 61

test12				77.76 46	84.52 58	58.41 80		83.36 77		72.93 100		54.61 58	88.05 39		88.12 34	86.81 75

plane_prior7					81.41 97	55.96 117

plane_prior6					81.20 104	56.24 112						45.26 193

plane_prior5								84.01 53					87.21 59	68.16 99	80.58 117	84.65 169

plane_prior4											86.10 131

plane_prior1					81.27 102

n2								0.00 469

nn								0.00 469

door-mid								47.19 439

lessismore_v0				69.91 243	71.42 342	47.80 278		50.90 427		50.39 409	75.56 355	27.43 393	81.33 205	45.91 304	34.10 444	80.59 282

test11								83.47 72

door								47.60 437

HQP5-MVS						54.94 139

BP-MVS														67.04 112

HQP4-MVS										67.85 183			86.93 67			84.32 179

HQP3-MVS								83.90 58							80.35 121

HQP2-MVS												45.46 187

NP-MVS					80.98 107	56.05 116					85.54 150

ACMMP++_ref															74.07 222

ACMMP++															72.16 261

Test By Simon												48.33 147

High-resolution multi-view benchmark