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.

MSP-MVS		90.38 3	91.87 1	85.88 82	92.83 76	64.03 187	93.06 99	94.33 48	82.19 19	93.65 2	96.15 27	85.89 1	97.19 79	91.02 15	97.75 1	96.43 22

OPU-MVS				89.97 3	97.52 3	73.15 11	96.89 4				97.00 9	83.82 2	99.15 2	95.72 1	97.63 3	97.62 2

DPM-MVS		90.70 2	90.52 5	91.24 1	89.68 150	76.68 2	97.29 1	95.35 10	82.87 14	91.58 8	97.22 4	79.93 3	99.10 7	83.12 72	97.64 2	97.94 1

baseline2		83.68 87	83.42 77	84.48 130	87.37 200	66.00 131	90.06 213	95.93 7	79.71 43	69.08 205	90.39 152	77.92 4	96.28 115	78.91 107	81.38 155	91.16 178

GG-mvs-BLEND				86.53 62	91.91 102	69.67 40	75.02 324	94.75 28		78.67 111	90.85 144	77.91 5	94.56 177	72.25 152	93.74 43	95.36 50

gg-mvs-nofinetune		77.18 190	74.31 207	85.80 87	91.42 118	68.36 64	71.78 326	94.72 29	49.61 328	77.12 125	45.92 344	77.41 6	93.98 204	67.62 192	93.16 52	95.05 70

SED-MVS		89.94 7	90.36 7	88.70 13	96.45 11	69.38 42	96.89 4	94.44 40	71.65 184	92.11 3	97.21 5	76.79 7	99.11 4	92.34 6	95.36 12	97.62 2

test_241102_ONE					96.45 11	69.38 42		94.44 40	71.65 184	92.11 3	97.05 8	76.79 7	99.11 4

test_0728_THIRD									72.48 153	90.55 12	96.93 10	76.24 9	99.08 9	91.53 12	94.99 15	96.43 22

DPE-MVS		88.77 13	89.21 13	87.45 33	96.26 18	67.56 85	94.17 52	94.15 53	68.77 230	90.74 11	97.27 3	76.09 10	98.49 23	90.58 17	94.91 18	96.30 25

TSAR-MVS + GP.		87.96 17	88.37 16	86.70 55	93.51 60	65.32 148	95.15 34	93.84 59	78.17 66	85.93 38	94.80 67	75.80 11	98.21 31	89.38 21	88.78 98	96.59 13

DeepPCF-MVS	81.17 1	89.72 8	91.38 3	84.72 122	93.00 73	58.16 277	96.72 7	94.41 43	86.50 5	90.25 14	97.83 1	75.46 12	98.67 19	92.78 3	95.49 11	97.32 4

MVSTER		82.47 101	82.05 98	83.74 143	92.68 83	69.01 50	91.90 144	93.21 87	79.83 39	72.14 173	85.71 208	74.72 13	94.72 172	75.72 126	72.49 215	87.50 218

test_241102_TWO								94.41 43	71.65 184	92.07 5	97.21 5	74.58 14	99.11 4	92.34 6	95.36 12	96.59 13

ETH3 D test6400		90.27 5	90.44 6	89.75 6	96.82 6	74.33 7	95.89 16	94.80 26	77.13 78	89.13 18	97.38 2	74.49 15	98.48 24	92.32 9	95.98 6	96.46 21

DELS-MVS		90.05 6	90.09 8	89.94 4	93.14 70	73.88 8	97.01 3	94.40 45	88.32 2	85.71 41	94.91 63	74.11 16	98.91 13	87.26 44	95.94 7	97.03 8
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
DVP-MVS		89.41 10	89.73 11	88.45 18	96.40 14	69.99 30	96.64 8	94.52 36	71.92 170	90.55 12	96.93 10	73.77 17	99.08 9	91.91 10	94.90 19	96.29 26

test0726					96.40 14	69.99 30	96.76 6	94.33 48	71.92 170	91.89 6	97.11 7	73.77 17

ET-MVSNet_ETH3D		84.01 78	83.15 84	86.58 59	90.78 133	70.89 20	94.74 45	94.62 34	81.44 27	58.19 292	93.64 96	73.64 19	92.35 255	82.66 76	78.66 174	96.50 19

CSCG		86.87 35	86.26 41	88.72 12	95.05 30	70.79 21	93.83 76	95.33 11	68.48 234	77.63 118	94.35 81	73.04 20	98.45 25	84.92 60	93.71 45	96.92 9

tttt0517		79.50 149	78.53 149	82.41 173	87.22 202	61.43 235	89.75 221	94.76 27	69.29 222	67.91 223	88.06 182	72.92 21	95.63 143	62.91 234	73.90 206	90.16 188

MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 9	97.13 2	95.58 8	89.33 1	85.77 39	96.26 24	72.84 22	99.38 1	92.64 4	95.93 8	97.08 7

thisisatest0515		83.41 88	82.49 94	86.16 75	89.46 156	68.26 68	93.54 85	94.70 30	74.31 114	75.75 134	90.92 142	72.62 23	96.52 112	69.64 172	81.50 154	93.71 119

thisisatest0530		81.15 120	80.07 122	84.39 132	88.26 181	65.63 142	91.40 165	94.62 34	71.27 197	70.93 185	89.18 165	72.47 24	96.04 127	65.62 215	76.89 192	91.49 169

TSAR-MVS + MP.		88.11 16	88.64 14	86.54 61	91.73 108	68.04 73	90.36 205	93.55 73	82.89 13	91.29 9	92.89 113	72.27 25	96.03 128	87.99 35	94.77 23	95.54 46
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EPP-MVSNet		81.79 114	81.52 105	82.61 167	88.77 170	60.21 254	93.02 103	93.66 69	68.52 233	72.90 160	90.39 152	72.19 26	94.96 164	74.93 135	79.29 168	92.67 147

CostFormer		82.33 103	81.15 108	85.86 85	89.01 165	68.46 62	82.39 292	93.01 98	75.59 94	80.25 88	81.57 251	72.03 27	94.96 164	79.06 105	77.48 186	94.16 102

HPM-MVS++		89.37 11	89.95 10	87.64 26	95.10 29	68.23 70	95.24 31	94.49 38	82.43 17	88.90 20	96.35 21	71.89 28	98.63 20	88.76 30	96.40 4	96.06 31

CNVR-MVS		90.32 4	90.89 4	88.61 16	96.76 7	70.65 22	96.47 12	94.83 23	84.83 8	89.07 19	96.80 12	70.86 29	99.06 11	92.64 4	95.71 9	96.12 30

DWT-MVSNet_test		83.95 80	82.80 89	87.41 34	92.90 75	70.07 29	89.12 234	94.42 42	82.15 20	77.64 117	91.77 134	70.81 30	96.22 117	65.03 221	81.36 156	95.94 35

IB-MVS	77.80 4	82.18 105	80.46 121	87.35 36	89.14 162	70.28 27	95.59 25	95.17 14	78.85 56	70.19 193	85.82 206	70.66 31	97.67 51	72.19 155	66.52 255	94.09 106
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
baseline1		81.84 113	81.03 112	84.28 135	91.60 111	66.62 115	91.08 183	91.66 151	81.87 24	74.86 143	91.67 138	69.98 32	94.92 167	71.76 158	64.75 267	91.29 177

agg_prior1		87.02 33	87.26 30	86.28 73	94.16 44	66.97 105	94.08 58	93.31 84	71.85 177	84.49 52	95.39 40	68.91 33	96.75 105	88.84 29	94.32 34	95.13 66

alignmvs		87.28 26	86.97 34	88.24 20	91.30 121	71.14 19	95.61 24	93.56 72	79.30 47	87.07 29	95.25 49	68.43 34	96.93 99	87.87 36	84.33 137	96.65 11

PAPM		85.89 50	85.46 53	87.18 39	88.20 184	72.42 12	92.41 125	92.77 106	82.11 21	80.34 87	93.07 107	68.27 35	95.02 162	78.39 112	93.59 47	94.09 106

train_agg		87.21 28	87.42 28	86.60 57	94.18 40	67.28 94	94.16 53	93.51 74	71.87 175	85.52 43	95.33 42	68.19 36	97.27 77	89.09 25	94.90 19	95.25 63

test_8					94.19 39	67.19 97	94.15 55	93.42 81	71.87 175	85.38 45	95.35 41	68.19 36	96.95 96

ETH3D cwj APD-0.16		87.06 31	87.18 32	86.71 53	91.99 98	67.48 90	92.97 104	94.21 51	71.48 195	85.72 40	96.32 23	68.13 38	98.00 38	89.06 26	94.70 27	94.65 84

TEST9					94.18 40	67.28 94	94.16 53	93.51 74	71.75 182	85.52 43	95.33 42	68.01 39	97.27 77

test_prior3		87.38 25	87.70 22	86.42 66	94.71 33	67.35 92	95.10 36	93.10 96	75.40 99	85.25 48	95.61 37	67.94 40	96.84 101	87.47 39	94.77 23	95.05 70

test_prior2							95.10 36		75.40 99	85.25 48	95.61 37	67.94 40		87.47 39	94.77 23

WTY-MVS		86.32 43	85.81 48	87.85 22	92.82 78	69.37 44	95.20 32	95.25 12	82.71 15	81.91 70	94.73 68	67.93 42	97.63 56	79.55 100	82.25 148	96.54 16

Regformer-1		87.24 27	87.60 25	86.15 76	95.14 27	65.83 137	93.95 65	95.12 15	82.11 21	84.25 54	95.73 33	67.88 43	98.35 29	85.60 54	88.64 100	94.26 96

ETH3D-3000-0.1		87.61 22	87.89 19	86.75 52	93.58 57	67.21 96	94.31 50	94.14 54	72.92 145	87.13 26	96.62 14	67.81 44	97.94 39	90.13 18	94.42 32	95.09 68

APDe-MVS		87.54 23	87.84 20	86.65 56	96.07 21	66.30 124	94.84 44	93.78 60	69.35 221	88.39 21	96.34 22	67.74 45	97.66 54	90.62 16	93.44 49	96.01 34

tpm2		79.80 145	77.95 158	85.34 105	88.28 180	68.26 68	81.56 297	91.42 160	70.11 213	77.59 120	80.50 269	67.40 46	94.26 190	67.34 194	77.35 187	93.51 124

Regformer-2		87.00 34	87.43 27	85.71 92	95.14 27	64.73 166	93.95 65	94.95 20	81.69 26	84.03 60	95.73 33	67.35 47	98.19 33	85.40 56	88.64 100	94.20 98

miper_enhance_ethall		78.86 159	77.97 157	81.54 199	88.00 189	65.17 152	91.41 163	89.15 240	75.19 104	68.79 212	83.98 224	67.17 48	92.82 234	72.73 147	65.30 258	86.62 239

xxxxxxxxxxxxxcwj		87.14 29	87.19 31	86.99 45	93.84 49	67.89 77	95.05 38	84.72 299	78.19 64	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 58

SF-MVS		87.03 32	87.09 33	86.84 47	92.70 82	67.45 91	93.64 80	93.76 63	70.78 207	86.25 31	96.44 18	66.98 49	97.79 47	88.68 31	94.56 29	95.28 58

HY-MVS	76.49 5	84.28 71	83.36 80	87.02 44	92.22 91	67.74 81	84.65 274	94.50 37	79.15 51	82.23 68	87.93 183	66.88 51	96.94 97	80.53 95	82.20 149	96.39 24

RRT_test8_iter05		80.61 128	79.62 131	83.60 150	91.87 106	66.90 107	93.42 92	93.68 67	77.09 80	68.83 211	85.63 209	66.82 52	95.42 154	76.46 124	62.74 279	88.48 207

EPNet		87.84 20	88.38 15	86.23 74	93.30 63	66.05 129	95.26 30	94.84 22	87.09 3	88.06 22	94.53 72	66.79 53	97.34 70	83.89 68	91.68 73	95.29 56
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
9.14			87.63 23		93.86 48		94.41 48	94.18 52	72.76 148	86.21 33	96.51 16	66.64 54	97.88 45	90.08 19	94.04 37

FIs		79.47 150	79.41 137	79.67 241	85.95 221	59.40 264	91.68 156	93.94 57	78.06 67	68.96 208	88.28 175	66.61 55	91.77 267	66.20 209	74.99 199	87.82 215

NCCC		89.07 12	89.46 12	87.91 21	96.60 9	69.05 49	96.38 13	94.64 33	84.42 9	86.74 30	96.20 25	66.56 56	98.76 18	89.03 28	94.56 29	95.92 37

SD-MVS		87.49 24	87.49 26	87.50 32	93.60 56	68.82 55	93.90 69	92.63 114	76.86 82	87.90 23	95.76 32	66.17 57	97.63 56	89.06 26	91.48 77	96.05 32
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
UniMVSNet_NR-MVSNet		78.15 175	77.55 164	79.98 232	84.46 243	60.26 252	92.25 127	93.20 89	77.50 75	68.88 209	86.61 197	66.10 58	92.13 259	66.38 206	62.55 280	87.54 217

CHOSEN 280x420		77.35 188	76.95 175	78.55 256	87.07 205	62.68 219	69.71 331	82.95 315	68.80 229	71.48 182	87.27 194	66.03 59	84.00 324	76.47 123	82.81 146	88.95 199

CANet		89.61 9	89.99 9	88.46 17	94.39 38	69.71 38	96.53 11	93.78 60	86.89 4	89.68 15	95.78 31	65.94 60	99.10 7	92.99 2	93.91 40	96.58 15

segment_acmp												65.94 60

Regformer-3		85.80 51	85.92 46	85.46 99	94.17 42	65.09 158	92.95 106	95.11 16	81.13 30	81.68 72	95.04 54	65.82 62	98.32 30	83.02 73	84.36 134	92.97 141

Vis-MVSNet (Re-imp)		79.24 153	79.57 132	78.24 261	88.46 175	52.29 312	90.41 203	89.12 242	74.24 115	69.13 203	91.91 132	65.77 63	90.09 291	59.00 257	88.09 105	92.33 155

FC-MVSNet-test		77.99 177	78.08 155	77.70 264	84.89 236	55.51 298	90.27 207	93.75 65	76.87 81	66.80 239	87.59 187	65.71 64	90.23 288	62.89 235	73.94 204	87.37 222

SMA-MVS		88.14 14	88.29 17	87.67 25	93.21 67	68.72 57	93.85 72	94.03 56	74.18 116	91.74 7	96.67 13	65.61 65	98.42 28	89.24 24	96.08 5	95.88 39
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
test12				87.09 42	94.60 35	68.86 53		92.91 102		82.67 67		65.44 66	97.55 59		93.69 46	94.84 77

旧先验1					91.94 99	60.74 245		91.50 157			94.36 77	65.23 67			91.84 70	94.55 86

Regformer-4		85.45 54	85.69 51	84.73 120	94.17 42	63.23 204	92.95 106	94.83 23	80.66 34	81.29 75	95.04 54	65.12 68	98.08 36	82.74 75	84.36 134	92.88 145

1112_ss		80.56 130	79.83 128	82.77 162	88.65 171	60.78 242	92.29 126	88.36 264	72.58 151	72.46 169	94.95 58	65.09 69	93.42 220	66.38 206	77.71 179	94.10 105

MVSFormer		83.75 85	82.88 87	86.37 69	89.24 160	71.18 17	89.07 235	90.69 184	65.80 250	87.13 26	94.34 82	64.99 70	92.67 242	72.83 145	91.80 71	95.27 60

lupinMVS		87.74 21	87.77 21	87.63 30	89.24 160	71.18 17	96.57 10	92.90 103	82.70 16	87.13 26	95.27 47	64.99 70	95.80 133	89.34 22	91.80 71	95.93 36

tpmrst		80.57 129	79.14 144	84.84 118	90.10 142	68.28 67	81.70 295	89.72 221	77.63 73	75.96 133	79.54 282	64.94 72	92.71 239	75.43 128	77.28 189	93.55 123

ZD-MVS					96.63 8	65.50 146		93.50 76	70.74 208	85.26 47	95.19 53	64.92 73	97.29 73	87.51 38	93.01 54

miper_ehance_all_eth		77.60 183	76.44 181	81.09 213	85.70 226	64.41 176	90.65 197	88.64 259	72.31 158	67.37 233	82.52 237	64.77 74	92.64 245	70.67 166	65.30 258	86.24 244

Test_1112_low_res		79.56 148	78.60 148	82.43 170	88.24 183	60.39 251	92.09 134	87.99 273	72.10 168	71.84 177	87.42 190	64.62 75	93.04 224	65.80 213	77.30 188	93.85 118

DeepC-MVS_fast	79.48 2	87.95 18	88.00 18	87.79 24	95.86 24	68.32 65	95.74 20	94.11 55	83.82 11	83.49 63	96.19 26	64.53 76	98.44 26	83.42 71	94.88 22	96.61 12
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
testtj		86.62 40	86.66 39	86.50 63	96.95 5	65.70 139	94.41 48	93.45 78	67.74 236	86.19 34	96.39 20	64.38 77	97.91 42	87.33 42	93.14 53	95.90 38

MG-MVS		87.11 30	86.27 40	89.62 8	97.79 1	76.27 4	94.96 42	94.49 38	78.74 60	83.87 62	92.94 110	64.34 78	96.94 97	75.19 130	94.09 36	95.66 42

casdiffmvs		85.37 55	84.87 60	86.84 47	88.25 182	69.07 48	93.04 101	91.76 145	81.27 28	80.84 83	92.07 130	64.23 79	96.06 126	84.98 59	87.43 110	95.39 48

cl-mvsnet2		77.94 179	76.78 176	81.42 201	87.57 194	64.93 164	90.67 196	88.86 252	72.45 154	67.63 228	82.68 236	64.07 80	92.91 232	71.79 156	65.30 258	86.44 240

tpm		78.58 168	77.03 172	83.22 156	85.94 223	64.56 167	83.21 287	91.14 171	78.31 63	73.67 154	79.68 280	64.01 81	92.09 261	66.07 210	71.26 225	93.03 139

CDS-MVSNet		81.43 117	80.74 114	83.52 151	86.26 216	64.45 172	92.09 134	90.65 187	75.83 93	73.95 153	89.81 162	63.97 82	92.91 232	71.27 161	82.82 145	93.20 134
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_Test		84.16 76	83.20 81	87.05 43	91.56 113	69.82 35	89.99 217	92.05 133	77.77 70	82.84 66	86.57 198	63.93 83	96.09 122	74.91 136	89.18 96	95.25 63

APD-MVS		85.93 49	85.99 45	85.76 89	95.98 23	65.21 151	93.59 83	92.58 116	66.54 245	86.17 35	95.88 30	63.83 84	97.00 89	86.39 50	92.94 55	95.06 69
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mvs_anonymous		81.36 118	79.99 125	85.46 99	90.39 138	68.40 63	86.88 265	90.61 188	74.41 111	70.31 192	84.67 217	63.79 85	92.32 256	73.13 142	85.70 124	95.67 41

PVSNet_Blended_VisFu		83.97 79	83.50 72	85.39 103	90.02 143	66.59 117	93.77 77	91.73 146	77.43 77	77.08 127	89.81 162	63.77 86	96.97 94	79.67 99	88.21 103	92.60 149

baseline		85.01 60	84.44 64	86.71 53	88.33 179	68.73 56	90.24 209	91.82 144	81.05 32	81.18 77	92.50 120	63.69 87	96.08 124	84.45 63	86.71 118	95.32 53

CDPH-MVS		85.71 52	85.46 53	86.46 64	94.75 32	67.19 97	93.89 70	92.83 105	70.90 203	83.09 65	95.28 45	63.62 88	97.36 68	80.63 94	94.18 35	94.84 77

HyFIR lowres test		81.03 124	79.56 133	85.43 101	87.81 192	68.11 72	90.18 210	90.01 211	70.65 209	72.95 159	86.06 204	63.61 89	94.50 182	75.01 134	79.75 164	93.67 120

canonicalmvs		86.85 36	86.25 42	88.66 15	91.80 107	71.92 13	93.54 85	91.71 148	80.26 38	87.55 24	95.25 49	63.59 90	96.93 99	88.18 34	84.34 136	97.11 6

cl_fuxian		76.83 195	75.47 193	80.93 217	85.02 234	64.18 185	90.39 204	88.11 270	71.66 183	66.65 240	81.64 249	63.58 91	92.56 246	69.31 178	62.86 278	86.04 250

SteuartSystems-ACMMP		86.82 38	86.90 35	86.58 59	90.42 136	66.38 121	96.09 15	93.87 58	77.73 71	84.01 61	95.66 35	63.39 92	97.94 39	87.40 41	93.55 48	95.42 47
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		83.77 84	83.67 70	84.06 138	92.79 81	63.56 200	91.76 152	94.81 25	79.65 44	77.87 114	94.09 88	63.35 93	97.90 43	79.35 101	79.36 166	90.74 182

UniMVSNet (Re)		77.58 184	76.78 176	79.98 232	84.11 249	60.80 241	91.76 152	93.17 92	76.56 87	69.93 199	84.78 216	63.32 94	92.36 254	64.89 222	62.51 282	86.78 233

PVSNet_BlendedMVS		83.38 89	83.43 75	83.22 156	93.76 51	67.53 87	94.06 59	93.61 70	79.13 52	81.00 81	85.14 212	63.19 95	97.29 73	87.08 45	73.91 205	84.83 272

PVSNet_Blended		86.73 39	86.86 36	86.31 72	93.76 51	67.53 87	96.33 14	93.61 70	82.34 18	81.00 81	93.08 105	63.19 95	97.29 73	87.08 45	91.38 78	94.13 104

PAPM_NR		82.97 94	81.84 101	86.37 69	94.10 46	66.76 112	87.66 255	92.84 104	69.96 215	74.07 151	93.57 98	63.10 97	97.50 61	70.66 167	90.58 88	94.85 76

nrg030		80.93 125	79.86 127	84.13 137	83.69 254	68.83 54	93.23 95	91.20 167	75.55 95	75.06 142	88.22 180	63.04 98	94.74 171	81.88 82	66.88 252	88.82 202

CS-MVS		86.61 41	86.85 37	85.88 82	91.52 116	66.25 126	95.42 27	92.25 124	80.36 37	84.10 59	94.82 66	62.88 99	96.08 124	88.25 33	92.07 69	95.30 55

EI-MVSNet-UG-set		83.14 92	82.96 85	83.67 148	92.28 89	63.19 206	91.38 169	94.68 31	79.22 49	76.60 129	93.75 94	62.64 100	97.76 49	78.07 114	78.01 177	90.05 190

DeepC-MVS	77.85 3	85.52 53	85.24 55	86.37 69	88.80 169	66.64 114	92.15 130	93.68 67	81.07 31	76.91 128	93.64 96	62.59 101	98.44 26	85.50 55	92.84 57	94.03 110
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EIA-MVS		84.84 63	84.88 59	84.69 123	91.30 121	62.36 222	93.85 72	92.04 134	79.45 45	79.33 99	94.28 85	62.42 102	96.35 114	80.05 97	91.25 81	95.38 49

MVS_111021_HR		86.19 46	85.80 49	87.37 35	93.17 69	69.79 36	93.99 63	93.76 63	79.08 54	78.88 107	93.99 91	62.25 103	98.15 34	85.93 53	91.15 82	94.15 103

PHI-MVS		86.83 37	86.85 37	86.78 51	93.47 61	65.55 144	95.39 29	95.10 17	71.77 181	85.69 42	96.52 15	62.07 104	98.77 17	86.06 52	95.60 10	96.03 33

MP-MVS		85.02 59	84.97 58	85.17 110	92.60 84	64.27 183	93.24 94	92.27 123	73.13 139	79.63 96	94.43 75	61.90 105	97.17 80	85.00 58	92.56 60	94.06 109
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
jason		86.40 42	86.17 43	87.11 41	86.16 218	70.54 24	95.71 23	92.19 131	82.00 23	84.58 51	94.34 82	61.86 106	95.53 153	87.76 37	90.89 84	95.27 60
jason: jason.
PAPR		85.15 58	84.47 62	87.18 39	96.02 22	68.29 66	91.85 147	93.00 100	76.59 86	79.03 103	95.00 56	61.59 107	97.61 58	78.16 113	89.00 97	95.63 43

IS-MVSNet		80.14 137	79.41 137	82.33 175	87.91 190	60.08 256	91.97 142	88.27 267	72.90 146	71.44 183	91.73 137	61.44 108	93.66 215	62.47 238	86.53 120	93.24 132

cl-mvsnet_		76.07 202	74.67 198	80.28 225	85.15 230	61.76 229	90.12 211	88.73 255	71.16 198	65.43 245	81.57 251	61.15 109	92.95 227	66.54 203	62.17 284	86.13 248

cl-mvsnet1		76.07 202	74.67 198	80.28 225	85.14 231	61.75 230	90.12 211	88.73 255	71.16 198	65.42 246	81.60 250	61.15 109	92.94 231	66.54 203	62.16 286	86.14 246

EI-MVSNet		78.97 157	78.22 153	81.25 204	85.33 227	62.73 218	89.53 225	93.21 87	72.39 157	72.14 173	90.13 158	60.99 111	94.72 172	67.73 191	72.49 215	86.29 242

IterMVS-LS		76.49 198	75.18 196	80.43 222	84.49 242	62.74 217	90.64 198	88.80 253	72.40 156	65.16 248	81.72 247	60.98 112	92.27 257	67.74 190	64.65 269	86.29 242
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ETV-MVS		86.01 48	86.11 44	85.70 93	90.21 141	67.02 104	93.43 90	91.92 139	81.21 29	84.13 58	94.07 90	60.93 113	95.63 143	89.28 23	89.81 92	94.46 94

tpm cat1		75.30 217	72.21 233	84.58 127	88.52 172	67.77 80	78.16 318	88.02 272	61.88 282	68.45 218	76.37 303	60.65 114	94.03 201	53.77 274	74.11 202	91.93 164

TAMVS		80.37 133	79.45 136	83.13 158	85.14 231	63.37 201	91.23 176	90.76 183	74.81 109	72.65 163	88.49 171	60.63 115	92.95 227	69.41 176	81.95 151	93.08 138

ZNCC-MVS		85.33 56	85.08 57	86.06 77	93.09 72	65.65 141	93.89 70	93.41 82	73.75 127	79.94 91	94.68 70	60.61 116	98.03 37	82.63 78	93.72 44	94.52 90

thres100view900		78.37 171	77.01 173	82.46 169	91.89 103	63.21 205	91.19 180	96.33 1	72.28 160	70.45 189	87.89 184	60.31 117	95.32 157	45.16 302	77.58 182	88.83 200

thres600view7		78.00 176	76.66 178	82.03 190	91.93 100	63.69 195	91.30 174	96.33 1	72.43 155	70.46 188	87.89 184	60.31 117	94.92 167	42.64 314	76.64 193	87.48 219

CHOSEN 1792x2688		84.98 61	83.45 74	89.57 9	89.94 145	75.14 5	92.07 136	92.32 121	81.87 24	75.68 135	88.27 176	60.18 119	98.60 21	80.46 96	90.27 91	94.96 74

tfpn200view9		78.79 162	77.43 166	82.88 160	92.21 92	64.49 169	92.05 137	96.28 4	73.48 134	71.75 179	88.26 177	60.07 120	95.32 157	45.16 302	77.58 182	88.83 200

thres400		78.68 165	77.43 166	82.43 170	92.21 92	64.49 169	92.05 137	96.28 4	73.48 134	71.75 179	88.26 177	60.07 120	95.32 157	45.16 302	77.58 182	87.48 219

diffmvs		84.28 71	83.83 69	85.61 95	87.40 199	68.02 74	90.88 189	89.24 234	80.54 35	81.64 73	92.52 119	59.83 122	94.52 181	87.32 43	85.11 128	94.29 95

MVS		84.66 66	82.86 88	90.06 2	90.93 127	74.56 6	87.91 251	95.54 9	68.55 232	72.35 172	94.71 69	59.78 123	98.90 14	81.29 91	94.69 28	96.74 10

thres200		79.66 146	78.33 150	83.66 149	92.54 85	65.82 138	93.06 99	96.31 3	74.90 108	73.30 156	88.66 169	59.67 124	95.61 145	47.84 293	78.67 173	89.56 197

Effi-MVS+		83.82 83	82.76 90	86.99 45	89.56 153	69.40 41	91.35 171	86.12 289	72.59 150	83.22 64	92.81 117	59.60 125	96.01 130	81.76 83	87.80 107	95.56 45

eth_miper_zixun_eth		75.96 208	74.40 206	80.66 219	84.66 238	63.02 208	89.28 229	88.27 267	71.88 174	65.73 243	81.65 248	59.45 126	92.81 235	68.13 186	60.53 301	86.14 246

ACMMP_NAP		86.05 47	85.80 49	86.80 50	91.58 112	67.53 87	91.79 149	93.49 77	74.93 107	84.61 50	95.30 44	59.42 127	97.92 41	86.13 51	94.92 17	94.94 75

GST-MVS		84.63 67	84.29 66	85.66 94	92.82 78	65.27 149	93.04 101	93.13 94	73.20 137	78.89 104	94.18 87	59.41 128	97.85 46	81.45 87	92.48 63	93.86 117

UA-Net		80.02 140	79.65 130	81.11 209	89.33 157	57.72 281	86.33 268	89.00 248	77.44 76	81.01 80	89.15 166	59.33 129	95.90 131	61.01 245	84.28 139	89.73 194

NR-MVSNet		76.05 205	74.59 201	80.44 221	82.96 263	62.18 226	90.83 191	91.73 146	77.12 79	60.96 278	86.35 199	59.28 130	91.80 266	60.74 246	61.34 295	87.35 223

MP-MVS-pluss		85.24 57	85.13 56	85.56 96	91.42 118	65.59 143	91.54 161	92.51 118	74.56 110	80.62 84	95.64 36	59.15 131	97.00 89	86.94 47	93.80 41	94.07 108
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		84.73 64	84.40 65	85.72 90	93.75 53	65.01 160	93.50 87	93.19 90	72.19 164	79.22 100	94.93 60	59.04 132	97.67 51	81.55 85	92.21 64	94.49 92

#test#		84.98 61	84.74 61	85.72 90	93.75 53	65.01 160	94.09 57	93.19 90	73.55 133	79.22 100	94.93 60	59.04 132	97.67 51	82.66 76	92.21 64	94.49 92

MSLP-MVS++		86.27 44	85.91 47	87.35 36	92.01 97	68.97 52	95.04 40	92.70 108	79.04 55	81.50 74	96.50 17	58.98 134	96.78 103	83.49 70	93.93 39	96.29 26

Patchmatch-test		65.86 286	60.94 298	80.62 220	83.75 253	58.83 270	58.91 345	75.26 333	44.50 340	50.95 319	77.09 300	58.81 135	87.90 307	35.13 334	64.03 273	95.12 67

EPNet_dtu		78.80 161	79.26 141	77.43 269	88.06 186	49.71 325	91.96 143	91.95 138	77.67 72	76.56 130	91.28 140	58.51 136	90.20 289	56.37 264	80.95 159	92.39 153
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VNet		86.20 45	85.65 52	87.84 23	93.92 47	69.99 30	95.73 22	95.94 6	78.43 62	86.00 37	93.07 107	58.22 137	97.00 89	85.22 57	84.33 137	96.52 17

TESTMET0.1,1		82.41 102	81.98 100	83.72 146	88.08 185	63.74 192	92.70 114	93.77 62	79.30 47	77.61 119	87.57 188	58.19 138	94.08 195	73.91 140	86.68 119	93.33 129

原ACMM1				84.42 131	93.21 67	64.27 183		93.40 83	65.39 253	79.51 97	92.50 120	58.11 139	96.69 107	65.27 220	93.96 38	92.32 156

sam_mvs1												57.85 140				94.68 81

CR-MVSNet		73.79 232	70.82 244	82.70 164	83.15 261	67.96 75	70.25 328	84.00 307	73.67 131	69.97 197	72.41 318	57.82 141	89.48 295	52.99 277	73.13 209	90.64 184

Patchmtry		67.53 278	63.93 283	78.34 257	82.12 268	64.38 177	68.72 332	84.00 307	48.23 333	59.24 285	72.41 318	57.82 141	89.27 296	46.10 299	56.68 314	81.36 306

patchmatchnet-post											67.62 331	57.62 143	90.25 284

PCF-MVS	73.15 9	79.29 152	77.63 163	84.29 134	86.06 219	65.96 133	87.03 261	91.10 172	69.86 216	69.79 200	90.64 146	57.54 144	96.59 108	64.37 225	82.29 147	90.32 186
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+		81.14 121	80.01 124	84.51 129	90.24 140	65.86 135	94.12 56	89.15 240	73.81 126	75.37 141	88.26 177	57.26 145	94.53 180	66.97 199	84.92 129	93.15 135

miper_lstm_enhance		73.05 236	71.73 238	77.03 274	83.80 252	58.32 276	81.76 293	88.88 250	69.80 217	61.01 277	78.23 290	57.19 146	87.51 310	65.34 219	59.53 305	85.27 269

1121		81.25 119	80.05 123	84.87 117	92.30 88	64.31 180	87.91 251	91.39 161	59.44 297	79.94 91	92.91 111	57.09 147	97.01 87	66.63 200	92.81 58	93.29 130

PatchT		69.11 265	65.37 274	80.32 223	82.07 269	63.68 196	67.96 337	87.62 277	50.86 326	69.37 201	65.18 334	57.09 147	88.53 302	41.59 317	66.60 254	88.74 203

testdata				81.34 203	89.02 164	57.72 281		89.84 214	58.65 301	85.32 46	94.09 88	57.03 149	93.28 221	69.34 177	90.56 89	93.03 139

PatchmatchNet		77.46 185	74.63 200	85.96 80	89.55 154	70.35 26	79.97 309	89.55 224	72.23 162	70.94 184	76.91 302	57.03 149	92.79 237	54.27 271	81.17 157	94.74 79
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_yl		84.28 71	83.16 82	87.64 26	94.52 36	69.24 45	95.78 17	95.09 18	69.19 224	81.09 78	92.88 114	57.00 151	97.44 63	81.11 92	81.76 152	96.23 28

DCV-MVSNet		84.28 71	83.16 82	87.64 26	94.52 36	69.24 45	95.78 17	95.09 18	69.19 224	81.09 78	92.88 114	57.00 151	97.44 63	81.11 92	81.76 152	96.23 28

region2R		84.36 69	84.03 68	85.36 104	93.54 59	64.31 180	93.43 90	92.95 101	72.16 167	78.86 108	94.84 65	56.97 153	97.53 60	81.38 89	92.11 68	94.24 97

新几何1				84.73 120	92.32 87	64.28 182		91.46 159	59.56 296	79.77 94	92.90 112	56.95 154	96.57 110	63.40 230	92.91 56	93.34 127

WR-MVS		76.76 196	75.74 191	79.82 237	84.60 239	62.27 225	92.60 119	92.51 118	76.06 90	67.87 225	85.34 210	56.76 155	90.24 287	62.20 239	63.69 277	86.94 231

HPM-MVS		83.25 90	82.95 86	84.17 136	92.25 90	62.88 215	90.91 186	91.86 141	70.30 212	77.12 125	93.96 92	56.75 156	96.28 115	82.04 81	91.34 80	93.34 127
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		82.71 99	82.38 96	83.73 145	89.25 159	59.58 262	92.24 128	94.89 21	77.96 68	79.86 93	92.38 125	56.70 157	97.05 84	77.26 119	80.86 160	94.55 86

ACMMPR		84.37 68	84.06 67	85.28 106	93.56 58	64.37 178	93.50 87	93.15 93	72.19 164	78.85 109	94.86 64	56.69 158	97.45 62	81.55 85	92.20 66	94.02 111

FMVSNet3		77.73 182	76.04 186	82.80 161	91.20 124	68.99 51	91.87 145	91.99 136	73.35 136	67.04 235	83.19 232	56.62 159	92.14 258	59.80 253	69.34 234	87.28 225

Patchmatch-RL test		68.17 272	64.49 280	79.19 249	71.22 332	53.93 305	70.07 330	71.54 340	69.22 223	56.79 299	62.89 337	56.58 160	88.61 299	69.53 175	52.61 323	95.03 73

test_post											23.01 351	56.49 161	92.67 242

RPMNet		70.42 257	65.68 270	84.63 126	83.15 261	67.96 75	70.25 328	90.45 189	46.83 336	69.97 197	65.10 335	56.48 162	95.30 160	35.79 333	73.13 209	90.64 184

DU-MVS		76.86 192	75.84 189	79.91 234	82.96 263	60.26 252	91.26 175	91.54 154	76.46 88	68.88 209	86.35 199	56.16 163	92.13 259	66.38 206	62.55 280	87.35 223

Baseline_NR-MVSNet		73.99 229	72.83 224	77.48 268	80.78 277	59.29 267	91.79 149	84.55 302	68.85 228	68.99 207	80.70 265	56.16 163	92.04 262	62.67 236	60.98 297	81.11 307

API-MVS		82.28 104	80.53 119	87.54 31	96.13 20	70.59 23	93.63 81	91.04 176	65.72 252	75.45 140	92.83 116	56.11 165	98.89 15	64.10 226	89.75 95	93.15 135

zzz-MVS		84.73 64	84.47 62	85.50 97	91.89 103	65.16 153	91.55 160	92.23 125	75.32 101	80.53 85	95.21 51	56.06 166	97.16 81	84.86 61	92.55 61	94.18 99

MTAPA		83.91 81	83.38 79	85.50 97	91.89 103	65.16 153	81.75 294	92.23 125	75.32 101	80.53 85	95.21 51	56.06 166	97.16 81	84.86 61	92.55 61	94.18 99

JIA-IIPM		66.06 285	62.45 292	76.88 276	81.42 274	54.45 304	57.49 346	88.67 257	49.36 329	63.86 260	46.86 343	56.06 166	90.25 284	49.53 286	68.83 238	85.95 254

v148		76.19 200	74.47 205	81.36 202	80.05 288	64.44 173	91.75 154	90.23 201	73.68 130	67.13 234	80.84 264	55.92 169	93.86 211	68.95 182	61.73 291	85.76 260

WR-MVS_H		70.59 255	69.94 249	72.53 303	81.03 275	51.43 316	87.35 259	92.03 135	67.38 241	60.23 281	80.70 265	55.84 170	83.45 328	46.33 298	58.58 309	82.72 295

AUN-MVS		78.37 171	77.43 166	81.17 206	86.60 211	57.45 286	89.46 227	91.16 169	74.11 117	74.40 146	90.49 151	55.52 171	94.57 175	74.73 139	60.43 303	91.48 170

XVS		83.87 82	83.47 73	85.05 111	93.22 65	63.78 190	92.92 108	92.66 111	73.99 119	78.18 112	94.31 84	55.25 172	97.41 65	79.16 103	91.58 75	93.95 113

X-MVStestdata		76.86 192	74.13 211	85.05 111	93.22 65	63.78 190	92.92 108	92.66 111	73.99 119	78.18 112	10.19 356	55.25 172	97.41 65	79.16 103	91.58 75	93.95 113

BH-w/o		80.49 132	79.30 140	84.05 139	90.83 132	64.36 179	93.60 82	89.42 229	74.35 113	69.09 204	90.15 157	55.23 174	95.61 145	64.61 223	86.43 122	92.17 162

CP-MVS		83.71 86	83.40 78	84.65 124	93.14 70	63.84 188	94.59 46	92.28 122	71.03 201	77.41 121	94.92 62	55.21 175	96.19 118	81.32 90	90.70 86	93.91 115

PGM-MVS		83.25 90	82.70 92	84.92 114	92.81 80	64.07 186	90.44 201	92.20 130	71.28 196	77.23 124	94.43 75	55.17 176	97.31 72	79.33 102	91.38 78	93.37 126

tpmvs		72.88 240	69.76 252	82.22 180	90.98 126	67.05 102	78.22 317	88.30 265	63.10 270	64.35 258	74.98 310	55.09 177	94.27 188	43.25 308	69.57 233	85.34 267

v8		75.35 216	73.26 221	81.61 198	80.67 279	66.82 109	89.54 224	89.27 233	71.65 184	63.30 267	80.30 273	54.99 178	94.06 197	67.33 195	62.33 283	83.94 278

sam_mvs												54.91 179

EPMVS		78.49 170	75.98 187	86.02 78	91.21 123	69.68 39	80.23 305	91.20 167	75.25 103	72.48 168	78.11 291	54.65 180	93.69 214	57.66 261	83.04 143	94.69 80

ab-mvs		80.18 136	78.31 151	85.80 87	88.44 176	65.49 147	83.00 289	92.67 110	71.82 179	77.36 122	85.01 213	54.50 181	96.59 108	76.35 125	75.63 198	95.32 53

DP-MVS Recon		82.73 97	81.65 104	85.98 79	97.31 4	67.06 101	95.15 34	91.99 136	69.08 227	76.50 131	93.89 93	54.48 182	98.20 32	70.76 165	85.66 125	92.69 146

XXY-MVS		77.94 179	76.44 181	82.43 170	82.60 265	64.44 173	92.01 139	91.83 143	73.59 132	70.00 196	85.82 206	54.43 183	94.76 169	69.63 173	68.02 245	88.10 214

MDTV_nov1_ep13_2view						59.90 258	80.13 307		67.65 239	72.79 161		54.33 184		59.83 252		92.58 150

Test By Simon												54.21 185

MAR-MVS		84.18 75	83.43 75	86.44 65	96.25 19	65.93 134	94.28 51	94.27 50	74.41 111	79.16 102	95.61 37	53.99 186	98.88 16	69.62 174	93.26 51	94.50 91
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
test-LLR		80.10 138	79.56 133	81.72 194	86.93 208	61.17 236	92.70 114	91.54 154	71.51 193	75.62 136	86.94 195	53.83 187	92.38 252	72.21 153	84.76 132	91.60 167

test0.0.03 1		72.76 241	72.71 227	72.88 301	80.25 286	47.99 330	91.22 177	89.45 227	71.51 193	62.51 274	87.66 186	53.83 187	85.06 319	50.16 283	67.84 248	85.58 261

v2v482		77.42 186	75.65 192	82.73 163	80.38 282	67.13 100	91.85 147	90.23 201	75.09 105	69.37 201	83.39 230	53.79 189	94.44 183	71.77 157	65.00 264	86.63 238

SR-MVS		82.81 96	82.58 93	83.50 153	93.35 62	61.16 238	92.23 129	91.28 166	64.48 258	81.27 76	95.28 45	53.71 190	95.86 132	82.87 74	88.77 99	93.49 125

pcd_1.5k_mvsjas		4.46 330	5.95 333	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	53.55 191	0.00 360	0.00 358	0.00 357	0.00 356

PS-MVSNAJss		77.26 189	76.31 183	80.13 229	80.64 280	59.16 268	90.63 200	91.06 174	72.80 147	68.58 216	84.57 219	53.55 191	93.96 205	72.97 143	71.96 219	87.27 226

PS-MVSNAJ		88.14 14	87.61 24	89.71 7	92.06 94	76.72 1	95.75 19	93.26 86	83.86 10	89.55 16	96.06 28	53.55 191	97.89 44	91.10 13	93.31 50	94.54 88

mPP-MVS		82.96 95	82.44 95	84.52 128	92.83 76	62.92 213	92.76 111	91.85 142	71.52 192	75.61 138	94.24 86	53.48 194	96.99 92	78.97 106	90.73 85	93.64 122

xiu_mvs_v2_base		87.92 19	87.38 29	89.55 10	91.41 120	76.43 3	95.74 20	93.12 95	83.53 12	89.55 16	95.95 29	53.45 195	97.68 50	91.07 14	92.62 59	94.54 88

test_post1							78.95 311				20.70 354	53.05 196	91.50 276	60.43 248

MDTV_nov1_ep13			72.61 228		89.06 163	68.48 61	80.33 303	90.11 205	71.84 178	71.81 178	75.92 307	53.01 197	93.92 207	48.04 292	73.38 207

test222					89.77 148	61.60 232	89.55 223	89.42 229	56.83 310	77.28 123	92.43 124	52.76 198			91.14 83	93.09 137

v1144		76.73 197	74.88 197	82.27 177	80.23 287	66.60 116	91.68 156	90.21 203	73.69 129	69.06 206	81.89 244	52.73 199	94.40 184	69.21 179	65.23 261	85.80 257

v10		74.77 223	72.54 230	81.46 200	80.33 285	66.71 113	89.15 233	89.08 244	70.94 202	63.08 268	79.86 278	52.52 200	94.04 200	65.70 214	62.17 284	83.64 280

CLD-MVS		82.73 97	82.35 97	83.86 141	87.90 191	67.65 84	95.45 26	92.18 132	85.06 7	72.58 165	92.27 128	52.46 201	95.78 134	84.18 64	79.06 169	88.16 213
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TranMVSNet+NR-MVSNet		75.86 209	74.52 204	79.89 235	82.44 266	60.64 248	91.37 170	91.37 162	76.63 85	67.65 227	86.21 202	52.37 202	91.55 271	61.84 241	60.81 298	87.48 219

VPA-MVSNet		79.03 155	78.00 156	82.11 188	85.95 221	64.48 171	93.22 96	94.66 32	75.05 106	74.04 152	84.95 214	52.17 203	93.52 217	74.90 137	67.04 251	88.32 212

APD-MVS_3200maxsize		81.64 115	81.32 107	82.59 168	92.36 86	58.74 271	91.39 167	91.01 177	63.35 266	79.72 95	94.62 71	51.82 204	96.14 120	79.71 98	87.93 106	92.89 144

dp		75.01 221	72.09 234	83.76 142	89.28 158	66.22 128	79.96 310	89.75 216	71.16 198	67.80 226	77.19 299	51.81 205	92.54 247	50.39 282	71.44 224	92.51 152

v144192		76.05 205	74.03 212	82.12 185	79.50 294	66.55 118	91.39 167	89.71 222	72.30 159	68.17 219	81.33 256	51.75 206	94.03 201	67.94 188	64.19 271	85.77 258

BH-untuned		78.68 165	77.08 171	83.48 154	89.84 147	63.74 192	92.70 114	88.59 260	71.57 190	66.83 238	88.65 170	51.75 206	95.39 155	59.03 256	84.77 131	91.32 175

HQP2-MVS												51.63 208

HQP-MVS		81.14 121	80.64 117	82.64 166	87.54 195	63.66 197	94.06 59	91.70 149	79.80 40	74.18 147	90.30 154	51.63 208	95.61 145	77.63 117	78.90 170	88.63 204

V42		76.46 199	74.55 203	82.19 181	79.14 300	67.82 79	90.26 208	89.42 229	73.75 127	68.63 215	81.89 244	51.31 210	94.09 194	71.69 159	64.84 265	84.66 273

test1172		81.90 112	81.83 102	82.13 184	93.23 64	57.52 285	91.61 159	90.98 178	64.32 260	80.20 89	95.00 56	51.26 211	95.61 145	81.73 84	88.13 104	93.26 131

SR-MVS-dyc-post		81.06 123	80.70 115	82.15 182	92.02 95	58.56 273	90.90 187	90.45 189	62.76 272	78.89 104	94.46 73	51.26 211	95.61 145	78.77 109	86.77 116	92.28 158

TransMVSNet (Re)		70.07 259	67.66 261	77.31 272	80.62 281	59.13 269	91.78 151	84.94 298	65.97 249	60.08 282	80.44 270	50.78 213	91.87 264	48.84 288	45.46 334	80.94 309

HQP_MVS		80.34 134	79.75 129	82.12 185	86.94 206	62.42 220	93.13 97	91.31 163	78.81 58	72.53 166	89.14 167	50.66 214	95.55 151	76.74 120	78.53 175	88.39 210

plane_prior6					87.23 201	62.32 223						50.66 214

ACMMP		81.49 116	80.67 116	83.93 140	91.71 109	62.90 214	92.13 131	92.22 129	71.79 180	71.68 181	93.49 100	50.32 216	96.96 95	78.47 111	84.22 141	91.93 164
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
MVS_111021_LR		82.02 110	81.52 105	83.51 152	88.42 177	62.88 215	89.77 220	88.93 249	76.78 84	75.55 139	93.10 103	50.31 217	95.38 156	83.82 69	87.02 112	92.26 161

1314		80.70 127	78.95 145	85.94 81	87.77 193	67.56 85	87.91 251	92.55 117	72.17 166	67.44 229	93.09 104	50.27 218	97.04 86	71.68 160	87.64 108	93.23 133

CP-MVSNet		70.50 256	69.91 250	72.26 306	80.71 278	51.00 319	87.23 260	90.30 199	67.84 235	59.64 283	82.69 235	50.23 219	82.30 336	51.28 279	59.28 306	83.46 285

LCM-MVSNet-Re		72.93 238	71.84 236	76.18 281	88.49 173	48.02 329	80.07 308	70.17 341	73.96 122	52.25 312	80.09 277	49.98 220	88.24 304	67.35 193	84.23 140	92.28 158

Vis-MVSNet		80.92 126	79.98 126	83.74 143	88.48 174	61.80 228	93.44 89	88.26 269	73.96 122	77.73 115	91.76 135	49.94 221	94.76 169	65.84 212	90.37 90	94.65 84
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1192		75.98 207	73.92 214	82.15 182	79.73 290	66.24 127	91.22 177	89.75 216	72.67 149	68.49 217	81.42 254	49.86 222	94.27 188	67.08 197	65.02 263	85.95 254

test-mter		79.96 141	79.38 139	81.72 194	86.93 208	61.17 236	92.70 114	91.54 154	73.85 124	75.62 136	86.94 195	49.84 223	92.38 252	72.21 153	84.76 132	91.60 167

cdsmvs_eth3d_5k		19.86 325	26.47 324	0.00 342	0.00 363	0.00 364	0.00 354	93.45 78	0.00 359	0.00 360	95.27 47	49.56 224	0.00 360	0.00 358	0.00 357	0.00 356

3Dnovator+	73.60 7	82.10 109	80.60 118	86.60 57	90.89 130	66.80 111	95.20 32	93.44 80	74.05 118	67.42 230	92.49 122	49.46 225	97.65 55	70.80 164	91.68 73	95.33 51

MVP-Stereo		77.12 191	76.23 184	79.79 238	81.72 271	66.34 123	89.29 228	90.88 180	70.56 210	62.01 276	82.88 233	49.34 226	94.13 192	65.55 217	93.80 41	78.88 325
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
RE-MVS-def			80.48 120		92.02 95	58.56 273	90.90 187	90.45 189	62.76 272	78.89 104	94.46 73	49.30 227		78.77 109	86.77 116	92.28 158

OMC-MVS		78.67 167	77.91 160	80.95 216	85.76 225	57.40 287	88.49 243	88.67 257	73.85 124	72.43 170	92.10 129	49.29 228	94.55 178	72.73 147	77.89 178	90.91 181

VPNet		78.82 160	77.53 165	82.70 164	84.52 241	66.44 120	93.93 67	92.23 125	80.46 36	72.60 164	88.38 174	49.18 229	93.13 223	72.47 151	63.97 275	88.55 206

CVMVSNet		74.04 228	74.27 208	73.33 297	85.33 227	43.94 339	89.53 225	88.39 263	54.33 317	70.37 190	90.13 158	49.17 230	84.05 322	61.83 242	79.36 166	91.99 163

v1921920		75.63 214	73.49 219	82.06 189	79.38 295	66.35 122	91.07 185	89.48 226	71.98 169	67.99 220	81.22 259	49.16 231	93.90 208	66.56 202	64.56 270	85.92 256

pm-mvs1		72.89 239	71.09 242	78.26 260	79.10 301	57.62 283	90.80 192	89.30 232	67.66 238	62.91 270	81.78 246	49.11 232	92.95 227	60.29 250	58.89 308	84.22 276

pmmvs4		73.92 230	71.81 237	80.25 227	79.17 298	65.24 150	87.43 258	87.26 281	67.64 240	63.46 264	83.91 225	48.96 233	91.53 275	62.94 233	65.49 257	83.96 277

TAPA-MVS	70.22 12	74.94 222	73.53 218	79.17 250	90.40 137	52.07 313	89.19 232	89.61 223	62.69 274	70.07 194	92.67 118	48.89 234	94.32 185	38.26 328	79.97 162	91.12 179
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
3Dnovator	73.91 6	82.69 100	80.82 113	88.31 19	89.57 152	71.26 16	92.60 119	94.39 46	78.84 57	67.89 224	92.48 123	48.42 235	98.52 22	68.80 184	94.40 33	95.15 65

CPTT-MVS		79.59 147	79.16 143	80.89 218	91.54 115	59.80 259	92.10 133	88.54 262	60.42 289	72.96 158	93.28 102	48.27 236	92.80 236	78.89 108	86.50 121	90.06 189

GBi-Net		75.65 212	73.83 215	81.10 210	88.85 166	65.11 155	90.01 214	90.32 195	70.84 204	67.04 235	80.25 274	48.03 237	91.54 272	59.80 253	69.34 234	86.64 235

test1		75.65 212	73.83 215	81.10 210	88.85 166	65.11 155	90.01 214	90.32 195	70.84 204	67.04 235	80.25 274	48.03 237	91.54 272	59.80 253	69.34 234	86.64 235

FMVSNet2		76.07 202	74.01 213	82.26 179	88.85 166	67.66 83	91.33 172	91.61 152	70.84 204	65.98 242	82.25 240	48.03 237	92.00 263	58.46 258	68.73 240	87.10 227

LFMVS		84.34 70	82.73 91	89.18 11	94.76 31	73.25 10	94.99 41	91.89 140	71.90 172	82.16 69	93.49 100	47.98 240	97.05 84	82.55 79	84.82 130	97.25 5

QAPM		79.95 142	77.39 169	87.64 26	89.63 151	71.41 15	93.30 93	93.70 66	65.34 255	67.39 232	91.75 136	47.83 241	98.96 12	57.71 260	89.81 92	92.54 151

HPM-MVS_fast		80.25 135	79.55 135	82.33 175	91.55 114	59.95 257	91.32 173	89.16 239	65.23 256	74.71 144	93.07 107	47.81 242	95.74 136	74.87 138	88.23 102	91.31 176

CANet_DTU		84.09 77	83.52 71	85.81 86	90.30 139	66.82 109	91.87 145	89.01 247	85.27 6	86.09 36	93.74 95	47.71 243	96.98 93	77.90 116	89.78 94	93.65 121

abl_6		79.82 144	79.20 142	81.70 196	89.85 146	58.34 275	88.47 244	90.07 206	62.56 275	77.71 116	93.08 105	47.65 244	96.78 103	77.94 115	85.45 127	89.99 191

v1240		75.21 219	72.98 223	81.88 191	79.20 297	66.00 131	90.75 194	89.11 243	71.63 188	67.41 231	81.22 259	47.36 245	93.87 209	65.46 218	64.72 268	85.77 258

PEN-MVS		69.46 263	68.56 257	72.17 308	79.27 296	49.71 325	86.90 264	89.24 234	67.24 243	59.08 288	82.51 238	47.23 246	83.54 327	48.42 290	57.12 310	83.25 288

CNLPA		74.31 226	72.30 232	80.32 223	91.49 117	61.66 231	90.85 190	80.72 321	56.67 311	63.85 261	90.64 146	46.75 247	90.84 279	53.79 273	75.99 197	88.47 209

114514_t		79.17 154	77.67 161	83.68 147	95.32 26	65.53 145	92.85 110	91.60 153	63.49 265	67.92 222	90.63 148	46.65 248	95.72 141	67.01 198	83.54 142	89.79 192

PS-CasMVS		69.86 261	69.13 255	72.07 309	80.35 284	50.57 321	87.02 262	89.75 216	67.27 242	59.19 287	82.28 239	46.58 249	82.24 337	50.69 281	59.02 307	83.39 287

DTE-MVSNet		68.46 270	67.33 263	71.87 311	77.94 313	49.00 328	86.16 269	88.58 261	66.36 247	58.19 292	82.21 241	46.36 250	83.87 325	44.97 305	55.17 317	82.73 294

PMMVS		81.98 111	82.04 99	81.78 192	89.76 149	56.17 294	91.13 182	90.69 184	77.96 68	80.09 90	93.57 98	46.33 251	94.99 163	81.41 88	87.46 109	94.17 101

OPM-MVS		79.00 156	78.09 154	81.73 193	83.52 257	63.83 189	91.64 158	90.30 199	76.36 89	71.97 176	89.93 161	46.30 252	95.17 161	75.10 131	77.70 180	86.19 245

BH-RMVSNet		79.46 151	77.65 162	84.89 115	91.68 110	65.66 140	93.55 84	88.09 271	72.93 144	73.37 155	91.12 141	46.20 253	96.12 121	56.28 265	85.61 126	92.91 143

TR-MVS		78.77 163	77.37 170	82.95 159	90.49 135	60.88 240	93.67 79	90.07 206	70.08 214	74.51 145	91.37 139	45.69 254	95.70 142	60.12 251	80.32 161	92.29 157

IterMVS-SCA-FT		71.55 250	69.97 248	76.32 279	81.48 272	60.67 247	87.64 256	85.99 290	66.17 248	59.50 284	78.88 285	45.53 255	83.65 326	62.58 237	61.93 287	84.63 275

SCA		75.82 210	72.76 225	85.01 113	86.63 210	70.08 28	81.06 299	89.19 237	71.60 189	70.01 195	77.09 300	45.53 255	90.25 284	60.43 248	73.27 208	94.68 81

IterMVS		72.65 245	70.83 243	78.09 262	82.17 267	62.96 210	87.64 256	86.28 286	71.56 191	60.44 280	78.85 286	45.42 257	86.66 314	63.30 231	61.83 288	84.65 274
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		76.14 201	75.28 195	78.72 255	83.22 259	55.17 300	89.87 218	87.78 275	75.42 97	67.98 221	81.43 253	45.08 258	92.52 248	75.08 132	71.63 220	88.48 207

mvs-test1		78.74 164	77.95 158	81.14 207	83.22 259	57.13 289	93.96 64	87.78 275	75.42 97	72.68 162	90.80 145	45.08 258	94.54 179	75.08 132	77.49 185	91.74 166

XVG-OURS-SEG-HR		74.70 224	73.08 222	79.57 244	78.25 310	57.33 288	80.49 301	87.32 279	63.22 268	68.76 213	90.12 160	44.89 260	91.59 270	70.55 168	74.09 203	89.79 192

v7n		71.31 251	68.65 256	79.28 248	76.40 320	60.77 243	86.71 266	89.45 227	64.17 261	58.77 291	78.24 289	44.59 261	93.54 216	57.76 259	61.75 290	83.52 283

pmmvs5		73.35 234	71.52 239	78.86 254	78.64 307	60.61 249	91.08 183	86.90 282	67.69 237	63.32 266	83.64 226	44.33 262	90.53 281	62.04 240	66.02 256	85.46 264

RRT_MVS		77.38 187	76.59 179	79.77 239	90.91 129	63.61 199	91.15 181	90.91 179	72.28 160	72.06 175	87.28 193	43.92 263	89.04 298	73.32 141	67.47 249	86.67 234

OpenMVS	70.45 11	78.54 169	75.92 188	86.41 68	85.93 224	71.68 14	92.74 112	92.51 118	66.49 246	64.56 253	91.96 131	43.88 264	98.10 35	54.61 269	90.65 87	89.44 198

AdaColmap		78.94 158	77.00 174	84.76 119	96.34 16	65.86 135	92.66 118	87.97 274	62.18 278	70.56 186	92.37 126	43.53 265	97.35 69	64.50 224	82.86 144	91.05 180

tfpnnormal		70.10 258	67.36 262	78.32 258	83.45 258	60.97 239	88.85 237	92.77 106	64.85 257	60.83 279	78.53 287	43.52 266	93.48 218	31.73 343	61.70 292	80.52 314

test_djsdf		73.76 233	72.56 229	77.39 270	77.00 318	53.93 305	89.07 235	90.69 184	65.80 250	63.92 259	82.03 243	43.14 267	92.67 242	72.83 145	68.53 241	85.57 262

GA-MVS		78.33 173	76.23 184	84.65 124	83.65 255	66.30 124	91.44 162	90.14 204	76.01 91	70.32 191	84.02 223	42.50 268	94.72 172	70.98 162	77.00 191	92.94 142

PLC	68.80 14	75.23 218	73.68 217	79.86 236	92.93 74	58.68 272	90.64 198	88.30 265	60.90 286	64.43 257	90.53 149	42.38 269	94.57 175	56.52 263	76.54 194	86.33 241
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
D2MVS		73.80 231	72.02 235	79.15 252	79.15 299	62.97 209	88.58 242	90.07 206	72.94 143	59.22 286	78.30 288	42.31 270	92.70 241	65.59 216	72.00 218	81.79 304

Fast-Effi-MVS+-dtu		75.04 220	73.37 220	80.07 230	80.86 276	59.52 263	91.20 179	85.38 294	71.90 172	65.20 247	84.84 215	41.46 271	92.97 226	66.50 205	72.96 211	87.73 216

MS-PatchMatch		77.90 181	76.50 180	82.12 185	85.99 220	69.95 33	91.75 154	92.70 108	73.97 121	62.58 273	84.44 220	41.11 272	95.78 134	63.76 229	92.17 67	80.62 313

our_test_3		68.29 271	64.69 277	79.11 253	78.92 302	64.85 165	88.40 246	85.06 296	60.32 291	52.68 310	76.12 305	40.81 273	89.80 294	44.25 307	55.65 315	82.67 298

XVG-OURS		74.25 227	72.46 231	79.63 242	78.45 309	57.59 284	80.33 303	87.39 278	63.86 263	68.76 213	89.62 164	40.50 274	91.72 268	69.00 181	74.25 201	89.58 195

test_part1		72.36 246	69.25 254	81.68 197	79.94 289	65.07 159	90.68 195	89.53 225	52.32 320	63.42 265	79.21 284	40.43 275	94.01 203	67.14 196	60.59 300	85.96 253

VDD-MVS		83.06 93	81.81 103	86.81 49	90.86 131	67.70 82	95.40 28	91.50 157	75.46 96	81.78 71	92.34 127	40.09 276	97.13 83	86.85 48	82.04 150	95.60 44

DP-MVS		69.90 260	66.48 266	80.14 228	95.36 25	62.93 211	89.56 222	76.11 328	50.27 327	57.69 296	85.23 211	39.68 277	95.73 137	33.35 337	71.05 226	81.78 305

ppachtmachnet_test		67.72 275	63.70 284	79.77 239	78.92 302	66.04 130	88.68 240	82.90 316	60.11 293	55.45 301	75.96 306	39.19 278	90.55 280	39.53 323	52.55 324	82.71 296

ADS-MVSNet2		66.90 281	63.44 286	77.26 273	88.06 186	60.70 246	68.01 335	75.56 331	57.57 303	64.48 254	69.87 327	38.68 279	84.10 321	40.87 319	67.89 246	86.97 228

ADS-MVSNet		68.54 269	64.38 282	81.03 214	88.06 186	66.90 107	68.01 335	84.02 306	57.57 303	64.48 254	69.87 327	38.68 279	89.21 297	40.87 319	67.89 246	86.97 228

LPG-MVS_test		75.82 210	74.58 202	79.56 245	84.31 246	59.37 265	90.44 201	89.73 219	69.49 219	64.86 249	88.42 172	38.65 281	94.30 186	72.56 149	72.76 212	85.01 270

LGP-MVS_train				79.56 245	84.31 246	59.37 265		89.73 219	69.49 219	64.86 249	88.42 172	38.65 281	94.30 186	72.56 149	72.76 212	85.01 270

VDDNet		80.50 131	78.26 152	87.21 38	86.19 217	69.79 36	94.48 47	91.31 163	60.42 289	79.34 98	90.91 143	38.48 283	96.56 111	82.16 80	81.05 158	95.27 60

ACMP	71.68 10	75.58 215	74.23 209	79.62 243	84.97 235	59.64 260	90.80 192	89.07 245	70.39 211	62.95 269	87.30 192	38.28 284	93.87 209	72.89 144	71.45 223	85.36 266
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UGNet		79.87 143	78.68 146	83.45 155	89.96 144	61.51 233	92.13 131	90.79 181	76.83 83	78.85 109	86.33 201	38.16 285	96.17 119	67.93 189	87.17 111	92.67 147
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
anonymousdsp		71.14 252	69.37 253	76.45 278	72.95 328	54.71 302	84.19 276	88.88 250	61.92 281	62.15 275	79.77 279	38.14 286	91.44 277	68.90 183	67.45 250	83.21 289

xiu_mvs_v1_base_debu		82.16 106	81.12 109	85.26 107	86.42 212	68.72 57	92.59 121	90.44 192	73.12 140	84.20 55	94.36 77	38.04 287	95.73 137	84.12 65	86.81 113	91.33 172

xiu_mvs_v1_base		82.16 106	81.12 109	85.26 107	86.42 212	68.72 57	92.59 121	90.44 192	73.12 140	84.20 55	94.36 77	38.04 287	95.73 137	84.12 65	86.81 113	91.33 172

xiu_mvs_v1_base_debi		82.16 106	81.12 109	85.26 107	86.42 212	68.72 57	92.59 121	90.44 192	73.12 140	84.20 55	94.36 77	38.04 287	95.73 137	84.12 65	86.81 113	91.33 172

PVSNet_0	68.08 15	71.81 248	68.32 260	82.27 177	84.68 237	62.31 224	88.68 240	90.31 198	75.84 92	57.93 295	80.65 268	37.85 290	94.19 191	69.94 171	29.05 347	90.31 187

Anonymous20231206		67.53 278	65.78 268	72.79 302	74.95 324	47.59 332	88.23 247	87.32 279	61.75 284	58.07 294	77.29 297	37.79 291	87.29 312	42.91 310	63.71 276	83.48 284

ACMM	69.62 13	74.34 225	72.73 226	79.17 250	84.25 248	57.87 279	90.36 205	89.93 212	63.17 269	65.64 244	86.04 205	37.79 291	94.10 193	65.89 211	71.52 222	85.55 263
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cascas		78.18 174	75.77 190	85.41 102	87.14 204	69.11 47	92.96 105	91.15 170	66.71 244	70.47 187	86.07 203	37.49 293	96.48 113	70.15 170	79.80 163	90.65 183

LS3D		69.17 264	66.40 267	77.50 267	91.92 101	56.12 295	85.12 271	80.37 322	46.96 334	56.50 300	87.51 189	37.25 294	93.71 213	32.52 342	79.40 165	82.68 297

MDA-MVSNet_test_wron		63.78 296	60.16 299	74.64 287	78.15 311	60.41 250	83.49 281	84.03 305	56.17 314	39.17 343	71.59 324	37.22 295	83.24 331	42.87 312	48.73 329	80.26 317

YYNet1		63.76 297	60.14 300	74.62 288	78.06 312	60.19 255	83.46 283	83.99 309	56.18 313	39.25 342	71.56 325	37.18 296	83.34 329	42.90 311	48.70 330	80.32 316

FMVSNet5		68.04 273	65.66 271	75.18 285	84.43 244	57.89 278	83.54 280	86.26 287	61.83 283	53.64 308	73.30 315	37.15 297	85.08 318	48.99 287	61.77 289	82.56 299

test20.03		63.83 295	62.65 291	67.38 321	70.58 336	39.94 344	86.57 267	84.17 304	63.29 267	51.86 313	77.30 296	37.09 298	82.47 334	38.87 327	54.13 321	79.73 320

PVSNet	73.49 8	80.05 139	78.63 147	84.31 133	90.92 128	64.97 162	92.47 124	91.05 175	79.18 50	72.43 170	90.51 150	37.05 299	94.06 197	68.06 187	86.00 123	93.90 116

EU-MVSNet		64.01 294	63.01 288	67.02 322	74.40 326	38.86 348	83.27 285	86.19 288	45.11 338	54.27 304	81.15 262	36.91 300	80.01 341	48.79 289	57.02 311	82.19 302

Anonymous20231211		73.08 235	70.39 246	81.13 208	90.62 134	63.33 202	91.40 165	90.06 209	51.84 323	64.46 256	80.67 267	36.49 301	94.07 196	63.83 228	64.17 272	85.98 252

FMVSNet1		72.71 243	69.91 250	81.10 210	83.60 256	65.11 155	90.01 214	90.32 195	63.92 262	63.56 263	80.25 274	36.35 302	91.54 272	54.46 270	66.75 253	86.64 235

Anonymous20240529		76.84 194	74.15 210	84.88 116	91.02 125	64.95 163	93.84 75	91.09 173	53.57 318	73.00 157	87.42 190	35.91 303	97.32 71	69.14 180	72.41 217	92.36 154

CMPMVS	48.56 21	66.77 282	64.41 281	73.84 294	70.65 335	50.31 322	77.79 319	85.73 293	45.54 337	44.76 335	82.14 242	35.40 304	90.14 290	63.18 232	74.54 200	81.07 308
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		67.57 277	64.76 276	76.00 282	72.82 330	53.37 307	88.71 239	86.78 283	53.19 319	57.58 297	78.03 292	35.33 305	92.41 251	55.56 267	54.88 319	82.21 301

PatchMatch-RL		72.06 247	69.98 247	78.28 259	89.51 155	55.70 297	83.49 281	83.39 313	61.24 285	63.72 262	82.76 234	34.77 306	93.03 225	53.37 276	77.59 181	86.12 249

LTVRE_ROB	59.60 19	66.27 284	63.54 285	74.45 289	84.00 251	51.55 315	67.08 338	83.53 310	58.78 300	54.94 302	80.31 272	34.54 307	93.23 222	40.64 321	68.03 244	78.58 328
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
UniMVSNet_ETH3D		72.74 242	70.53 245	79.36 247	78.62 308	56.64 292	85.01 272	89.20 236	63.77 264	64.84 251	84.44 220	34.05 308	91.86 265	63.94 227	70.89 227	89.57 196

F-COLMAP		70.66 254	68.44 258	77.32 271	86.37 215	55.91 296	88.00 249	86.32 285	56.94 309	57.28 298	88.07 181	33.58 309	92.49 249	51.02 280	68.37 242	83.55 281

pmmvs-eth3d		65.53 288	62.32 293	75.19 284	69.39 339	59.59 261	82.80 290	83.43 311	62.52 276	51.30 317	72.49 316	32.86 310	87.16 313	55.32 268	50.73 327	78.83 326

MDA-MVSNet-bldmvs		61.54 302	57.70 306	73.05 299	79.53 293	57.00 291	83.08 288	81.23 318	57.57 303	34.91 345	72.45 317	32.79 311	86.26 317	35.81 332	41.95 338	75.89 334

MIMVSNet		71.64 249	68.44 258	81.23 205	81.97 270	64.44 173	73.05 325	88.80 253	69.67 218	64.59 252	74.79 311	32.79 311	87.82 308	53.99 272	76.35 195	91.42 171

UnsupCasMVSNet_eth		65.79 287	63.10 287	73.88 293	70.71 334	50.29 323	81.09 298	89.88 213	72.58 151	49.25 324	74.77 312	32.57 313	87.43 311	55.96 266	41.04 340	83.90 279

N_pmnet		50.55 313	49.11 316	54.88 328	77.17 317	4.02 361	84.36 275	2.00 360	48.59 330	45.86 331	68.82 329	32.22 314	82.80 333	31.58 344	51.38 326	77.81 330

test_0402		64.54 291	61.09 297	74.92 286	84.10 250	60.75 244	87.95 250	79.71 324	52.03 322	52.41 311	77.20 298	32.21 315	91.64 269	23.14 346	61.03 296	72.36 338

DSMNet-mixed		56.78 309	54.44 312	63.79 324	63.21 344	29.44 352	64.43 340	64.10 348	42.12 342	51.32 316	71.60 323	31.76 316	75.04 343	36.23 330	65.20 262	86.87 232

MSDG		69.54 262	65.73 269	80.96 215	85.11 233	63.71 194	84.19 276	83.28 314	56.95 308	54.50 303	84.03 222	31.50 317	96.03 128	42.87 312	69.13 237	83.14 291

RPSCF		64.24 293	61.98 295	71.01 312	76.10 322	45.00 336	75.83 322	75.94 329	46.94 335	58.96 289	84.59 218	31.40 318	82.00 338	47.76 294	60.33 304	86.04 250

jajsoiax		73.05 236	71.51 240	77.67 265	77.46 315	54.83 301	88.81 238	90.04 210	69.13 226	62.85 271	83.51 228	31.16 319	92.75 238	70.83 163	69.80 230	85.43 265

MVS-HIRNet		60.25 305	55.55 311	74.35 290	84.37 245	56.57 293	71.64 327	74.11 334	34.44 345	45.54 333	42.24 347	31.11 320	89.81 292	40.36 322	76.10 196	76.67 333

SixPastTwentyTwo		64.92 289	61.78 296	74.34 291	78.74 305	49.76 324	83.42 284	79.51 325	62.86 271	50.27 320	77.35 295	30.92 321	90.49 282	45.89 300	47.06 332	82.78 292

CL-MVSNet_2432*1600		60.87 303	58.60 303	67.68 319	66.13 341	39.93 345	75.63 323	84.70 300	57.32 306	49.57 323	68.45 330	29.55 322	82.87 332	48.09 291	47.94 331	80.25 318

mvs_tets		72.71 243	71.11 241	77.52 266	77.41 316	54.52 303	88.45 245	89.76 215	68.76 231	62.70 272	83.26 231	29.49 323	92.71 239	70.51 169	69.62 232	85.34 267

Anonymous202405211		77.96 178	75.33 194	85.87 84	93.73 55	64.52 168	94.85 43	85.36 295	62.52 276	76.11 132	90.18 156	29.43 324	97.29 73	68.51 185	77.24 190	95.81 40

K. test v3		63.09 298	59.61 302	73.53 296	76.26 321	49.38 327	83.27 285	77.15 327	64.35 259	47.77 327	72.32 320	28.73 325	87.79 309	49.93 285	36.69 344	83.41 286

UnsupCasMVSNet_bld		61.60 301	57.71 305	73.29 298	68.73 340	51.64 314	78.61 313	89.05 246	57.20 307	46.11 328	61.96 338	28.70 326	88.60 300	50.08 284	38.90 342	79.63 321

lessismore_v0				73.72 295	72.93 329	47.83 331		61.72 351		45.86 331	73.76 313	28.63 327	89.81 292	47.75 295	31.37 346	83.53 282

new-patchmatchnet		59.30 308	56.48 309	67.79 318	65.86 342	44.19 337	82.47 291	81.77 317	59.94 294	43.65 339	66.20 333	27.67 328	81.68 339	39.34 324	41.40 339	77.50 331

testing_2		71.09 253	67.32 264	82.40 174	69.82 337	66.52 119	83.64 279	90.77 182	72.21 163	45.12 334	71.07 326	27.60 329	93.74 212	75.71 127	69.96 229	86.95 230

ACMH	63.93 17	68.62 267	64.81 275	80.03 231	85.22 229	63.25 203	87.72 254	84.66 301	60.83 287	51.57 315	79.43 283	27.29 330	94.96 164	41.76 315	64.84 265	81.88 303
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		64.68 290	62.17 294	72.21 307	76.08 323	47.35 333	80.67 300	81.02 319	56.19 312	51.60 314	79.66 281	27.05 331	88.56 301	53.60 275	53.63 322	80.71 312

ACMH+	65.35 16	67.65 276	64.55 278	76.96 275	84.59 240	57.10 290	88.08 248	80.79 320	58.59 302	53.00 309	81.09 263	26.63 332	92.95 227	46.51 297	61.69 293	80.82 310

OpenMVS_ROB	61.12 18	66.39 283	62.92 289	76.80 277	76.51 319	57.77 280	89.22 230	83.41 312	55.48 315	53.86 307	77.84 293	26.28 333	93.95 206	34.90 335	68.76 239	78.68 327

MVS_0304		68.99 266	67.23 265	74.28 292	80.36 283	52.54 310	87.01 263	86.36 284	59.89 295	66.22 241	73.56 314	24.25 334	88.03 306	57.34 262	70.11 228	82.27 300

COLMAP_ROB	57.96 20	62.98 299	59.65 301	72.98 300	81.44 273	53.00 309	83.75 278	75.53 332	48.34 332	48.81 325	81.40 255	24.14 335	90.30 283	32.95 339	60.52 302	75.65 335
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		60.16 306	57.33 307	68.67 316	69.71 338	44.13 338	78.92 312	84.21 303	55.05 316	44.63 336	71.85 322	23.91 336	81.54 340	32.63 341	55.03 318	80.35 315

testgi		64.48 292	62.87 290	69.31 315	71.24 331	40.62 343	85.49 270	79.92 323	65.36 254	54.18 305	83.49 229	23.74 337	84.55 320	41.60 316	60.79 299	82.77 293

ITE_SJBPF				70.43 313	74.44 325	47.06 334		77.32 326	60.16 292	54.04 306	83.53 227	23.30 338	84.01 323	43.07 309	61.58 294	80.21 319

EG-PatchMatch MVS		68.55 268	65.41 273	77.96 263	78.69 306	62.93 211	89.86 219	89.17 238	60.55 288	50.27 320	77.73 294	22.60 339	94.06 197	47.18 296	72.65 214	76.88 332

tmp_tt		22.26 324	23.75 326	17.80 338	5.23 360	12.06 360	35.26 350	39.48 355	2.82 356	18.94 349	44.20 346	22.23 340	24.64 356	36.30 329	9.31 353	16.69 350

USDC		67.43 280	64.51 279	76.19 280	77.94 313	55.29 299	78.38 315	85.00 297	73.17 138	48.36 326	80.37 271	21.23 341	92.48 250	52.15 278	64.02 274	80.81 311

XVG-ACMP-BASELINE		68.04 273	65.53 272	75.56 283	74.06 327	52.37 311	78.43 314	85.88 291	62.03 279	58.91 290	81.21 261	20.38 342	91.15 278	60.69 247	68.18 243	83.16 290

AllTest		61.66 300	58.06 304	72.46 304	79.57 291	51.42 317	80.17 306	68.61 343	51.25 324	45.88 329	81.23 257	19.86 343	86.58 315	38.98 325	57.01 312	79.39 322

TestCases				72.46 304	79.57 291	51.42 317		68.61 343	51.25 324	45.88 329	81.23 257	19.86 343	86.58 315	38.98 325	57.01 312	79.39 322

pmmvs3		55.51 310	51.50 315	67.53 320	57.90 349	50.93 320	80.37 302	73.66 335	40.63 343	44.15 338	64.75 336	16.30 345	78.97 342	44.77 306	40.98 341	72.69 337

TDRefinement		55.28 311	51.58 314	66.39 323	59.53 348	46.15 335	76.23 321	72.80 336	44.60 339	42.49 340	76.28 304	15.29 346	82.39 335	33.20 338	43.75 336	70.62 340

new_pmnet		49.31 314	46.44 317	57.93 326	62.84 345	40.74 342	68.47 334	62.96 350	36.48 344	35.09 344	57.81 340	14.97 347	72.18 344	32.86 340	46.44 333	60.88 344

TinyColmap		60.32 304	56.42 310	72.00 310	78.78 304	53.18 308	78.36 316	75.64 330	52.30 321	41.59 341	75.82 308	14.76 348	88.35 303	35.84 331	54.71 320	74.46 336

LF4IMVS		54.01 312	52.12 313	59.69 325	62.41 346	39.91 346	68.59 333	68.28 345	42.96 341	44.55 337	75.18 309	14.09 349	68.39 346	41.36 318	51.68 325	70.78 339

PM-MVS		59.40 307	56.59 308	67.84 317	63.63 343	41.86 340	76.76 320	63.22 349	59.01 299	51.07 318	72.27 321	11.72 350	83.25 330	61.34 243	50.28 328	78.39 329

ambc				69.61 314	61.38 347	41.35 341	49.07 349	85.86 292		50.18 322	66.40 332	10.16 351	88.14 305	45.73 301	44.20 335	79.32 324

FPMVS		45.64 315	43.10 318	53.23 330	51.42 351	36.46 349	64.97 339	71.91 338	29.13 347	27.53 346	61.55 339	9.83 352	65.01 350	16.00 349	55.58 316	58.22 345

ANet_high		40.27 317	35.20 320	55.47 327	34.74 357	34.47 350	63.84 341	71.56 339	48.42 331	18.80 350	41.08 348	9.52 353	64.45 351	20.18 347	8.66 354	67.49 342

EMVS		23.76 323	23.20 327	25.46 337	41.52 355	16.90 359	60.56 343	38.79 357	14.62 353	8.99 356	20.24 355	7.35 354	45.82 354	7.25 354	9.46 352	13.64 352

E-PMN		24.61 321	24.00 325	26.45 336	43.74 354	18.44 358	60.86 342	39.66 354	15.11 352	9.53 355	22.10 352	6.52 355	46.94 353	8.31 353	10.14 351	13.98 351

DeepMVS_CX				34.71 335	51.45 350	24.73 356		28.48 359	31.46 346	17.49 351	52.75 341	5.80 356	42.60 355	18.18 348	19.42 348	36.81 347

Gipuma		34.91 319	31.44 322	45.30 332	70.99 333	39.64 347	19.85 353	72.56 337	20.10 351	16.16 352	21.47 353	5.08 357	71.16 345	13.07 350	43.70 337	25.08 349
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		40.54 316	35.79 319	54.76 329	36.92 356	30.81 351	51.41 347	69.02 342	22.07 349	24.63 347	45.37 345	4.56 358	65.81 348	33.67 336	34.50 345	67.67 341

PMMVS2		37.93 318	33.61 321	50.92 331	46.31 353	24.76 355	60.55 344	50.05 352	28.94 348	20.93 348	47.59 342	4.41 359	65.13 349	25.14 345	18.55 349	62.87 343

PMVS	26.43 22	31.84 320	28.16 323	42.89 333	25.87 359	27.58 353	50.92 348	49.78 353	21.37 350	14.17 353	40.81 349	2.01 360	66.62 347	9.61 352	38.88 343	34.49 348
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	24.84 23	24.35 322	19.77 328	38.09 334	34.56 358	26.92 354	26.57 351	38.87 356	11.73 354	11.37 354	27.44 350	1.37 361	50.42 352	11.41 351	14.60 350	36.93 346
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		11.30 326	10.95 329	12.33 339	48.05 352	19.89 357	25.89 352	1.92 361	3.58 355	3.12 357	1.37 357	0.64 362	15.77 357	6.23 355	7.77 355	1.35 353

test123		6.92 329	9.21 332	0.08 340	0.03 362	0.05 362	81.65 296	0.01 363	0.02 358	0.14 359	0.85 359	0.03 363	0.02 358	0.12 357	0.00 357	0.16 354

testmvs		7.23 328	9.62 331	0.06 341	0.04 361	0.02 363	84.98 273	0.02 362	0.03 357	0.18 358	1.21 358	0.01 364	0.02 358	0.14 356	0.01 356	0.13 355

uanet_test		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 357	0.00 356

sosnet-low-res		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 357	0.00 356

sosnet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 357	0.00 356

uncertanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 357	0.00 356

Regformer		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 357	0.00 356

ab-mvs-re		7.91 327	10.55 330	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	94.95 58	0.00 365	0.00 360	0.00 358	0.00 357	0.00 356

uanet		0.00 331	0.00 334	0.00 342	0.00 363	0.00 364	0.00 354	0.00 364	0.00 359	0.00 360	0.00 360	0.00 365	0.00 360	0.00 358	0.00 357	0.00 356

IU-MVS					96.46 10	69.91 34		95.18 13	80.75 33	95.28 1				92.34 6	95.36 12	96.47 20

save fliter					93.84 49	67.89 77	95.05 38	92.66 111	78.19 64

test_0728_SECOND				88.70 13	96.45 11	70.43 25	96.64 8	94.37 47					99.15 2	91.91 10	94.90 19	96.51 18

GSMVS																94.68 81

test_part2					96.29 17	68.16 71				90.78 10

MTGPA								92.23 125

MTMP							93.77 77	32.52 358

gm-plane-assit					88.42 177	67.04 103			78.62 61		91.83 133		97.37 67	76.57 122

test9_res														89.41 20	94.96 16	95.29 56

agg_prior2														86.41 49	94.75 26	95.33 51

agg_prior					94.16 44	66.97 105		93.31 84		84.49 52			96.75 105

test_prior4						67.18 99	93.92 68

test_prior				86.42 66	94.71 33	67.35 92		93.10 96					96.84 101			95.05 70

旧先验2							92.00 141		59.37 298	87.54 25			93.47 219	75.39 129

新几何2							91.41 163

无先验							92.71 113	92.61 115	62.03 279				97.01 87	66.63 200		93.97 112

原ACMM2							92.01 139

testdata2													96.09 122	61.26 244

testdata1							89.21 231		77.55 74

plane_prior7					86.94 206	61.51 233

plane_prior5								91.31 163					95.55 151	76.74 120	78.53 175	88.39 210

plane_prior4											89.14 167

plane_prior3						61.95 227			79.09 53	72.53 166

plane_prior2							93.13 97		78.81 58

plane_prior1					87.15 203

plane_prior						62.42 220	93.85 72		79.38 46						78.80 172

n2								0.00 364

nn								0.00 364

door-mid								66.01 347

test11								93.01 98

door								66.57 346

HQP5-MVS						63.66 197

HQP-NCC					87.54 195		94.06 59		79.80 40	74.18 147

ACMP_Plane					87.54 195		94.06 59		79.80 40	74.18 147

BP-MVS														77.63 117

HQP4-MVS										74.18 147			95.61 145			88.63 204

HQP3-MVS								91.70 149							78.90 170

NP-MVS					87.41 198	63.04 207					90.30 154

ACMMP++_ref															71.63 220

ACMMP++															69.72 231

High-resolution multi-view benchmark