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.

HSP-MVS		90.38 2	91.89 1	85.84 73	92.83 60	64.03 177	93.06 81	94.52 33	82.19 19	93.65 1	96.15 13	85.89 1	97.19 63	91.02 10	97.75 1	96.29 16

GG-mvs-BLEND				86.53 53	91.91 82	69.67 35	75.02 323	94.75 28		78.67 85	90.85 116	77.91 2	94.56 152	72.25 125	93.74 32	95.36 40

gg-mvs-nofinetune		77.18 173	74.31 190	85.80 75	91.42 101	68.36 57	71.78 326	94.72 29	49.61 324	77.12 100	45.92 346	77.41 3	93.98 190	67.62 166	93.16 40	95.05 56

test_part1								94.26 42				77.03 4			95.18 9	96.11 20

ESAPD		89.08 8	89.53 8	87.72 21	96.29 7	68.16 63	94.96 32	94.26 42	68.52 215	90.78 4	97.23 2	77.03 4	98.90 7	91.52 6	95.18 9	96.11 20

TSAR-MVS + GP.		87.96 14	88.37 12	86.70 45	93.51 46	65.32 144	95.15 26	93.84 48	78.17 55	85.93 25	94.80 51	75.80 6	98.21 24	89.38 13	88.78 80	96.59 10

DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 109	93.00 57	58.16 267	96.72 3	94.41 38	86.50 5	90.25 6	97.83 1	75.46 7	98.67 14	92.78 2	95.49 8	97.32 1

MVSTER		82.47 83	82.05 78	83.74 126	92.68 65	69.01 43	91.90 128	93.21 81	79.83 32	72.14 147	85.71 191	74.72 8	94.72 148	75.72 102	72.49 199	87.50 202

DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 54	73.88 6	97.01 2	94.40 39	88.32 2	85.71 27	94.91 48	74.11 9	98.91 6	87.26 29	95.94 4	97.03 5
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
CSCG		86.87 29	86.26 32	88.72 9	95.05 20	70.79 18	93.83 62	95.33 15	68.48 218	77.63 93	94.35 62	73.04 10	98.45 19	84.92 44	93.71 33	96.92 6

MCST-MVS		91.08 1	91.46 2	89.94 2	97.66 2	73.37 7	97.13 1	95.58 13	89.33 1	85.77 26	96.26 10	72.84 11	99.38 1	92.64 4	95.93 5	97.08 4

TSAR-MVS + MP.		88.11 13	88.64 10	86.54 51	91.73 89	68.04 66	90.36 183	93.55 60	82.89 14	91.29 3	92.89 90	72.27 12	96.03 107	87.99 23	94.77 16	95.54 37

EPP-MVSNet		81.79 94	81.52 85	82.61 149	88.77 157	60.21 243	93.02 83	93.66 56	68.52 215	72.90 134	90.39 124	72.19 13	94.96 139	74.93 111	79.29 145	92.67 130

CostFormer		82.33 85	81.15 88	85.86 72	89.01 152	68.46 54	82.39 287	93.01 91	75.59 86	80.25 68	81.57 235	72.03 14	94.96 139	79.06 83	77.48 164	94.16 87

HPM-MVS++		89.37 7	89.95 7	87.64 23	95.10 19	68.23 62	95.24 23	94.49 35	82.43 17	88.90 11	96.35 8	71.89 15	98.63 15	88.76 21	96.40 2	96.06 22

CNVR-MVS		90.32 3	90.89 4	88.61 11	96.76 4	70.65 19	96.47 6	94.83 25	84.83 9	89.07 10	96.80 4	70.86 16	99.06 3	92.64 4	95.71 6	96.12 19

DWT-MVSNet_test		83.95 64	82.80 71	87.41 29	92.90 59	70.07 26	89.12 210	94.42 37	82.15 20	77.64 92	91.77 107	70.81 17	96.22 99	65.03 190	81.36 133	95.94 26

IB-MVS	77.80 4	82.18 87	80.46 98	87.35 31	89.14 149	70.28 23	95.59 18	95.17 17	78.85 47	70.19 167	85.82 188	70.66 18	97.67 36	72.19 128	66.52 242	94.09 92
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
agg_prior1		87.02 26	87.26 25	86.28 64	94.16 34	66.97 91	94.08 46	93.31 77	71.85 163	84.49 39	95.39 27	68.91 19	96.75 89	88.84 20	94.32 23	95.13 53

PatchFormer-LS_test		83.14 74	81.81 82	87.12 36	92.34 69	69.92 29	88.64 218	93.32 76	82.07 23	74.87 118	91.62 111	68.91 19	96.08 106	66.07 181	78.45 153	95.37 39

alignmvs		87.28 22	86.97 27	88.24 15	91.30 104	71.14 17	95.61 17	93.56 59	79.30 38	87.07 20	95.25 35	68.43 21	96.93 83	87.87 24	84.33 116	96.65 8

PAPM		85.89 41	85.46 43	87.18 34	88.20 168	72.42 9	92.41 104	92.77 99	82.11 21	80.34 67	93.07 84	68.27 22	95.02 137	78.39 88	93.59 35	94.09 92

train_agg		87.21 24	87.42 23	86.60 48	94.18 30	67.28 82	94.16 40	93.51 61	71.87 161	85.52 29	95.33 29	68.19 23	97.27 60	89.09 16	94.90 13	95.25 49

test_8					94.19 29	67.19 84	94.15 42	93.42 72	71.87 161	85.38 31	95.35 28	68.19 23	96.95 80

TEST9					94.18 30	67.28 82	94.16 40	93.51 61	71.75 169	85.52 29	95.33 29	68.01 25	97.27 60

test_prior3		87.38 21	87.70 18	86.42 57	94.71 23	67.35 80	95.10 28	93.10 89	75.40 91	85.25 33	95.61 24	67.94 26	96.84 85	87.47 26	94.77 16	95.05 56

test_prior2							95.10 28		75.40 91	85.25 33	95.61 24	67.94 26		87.47 26	94.77 16

WTY-MVS		86.32 35	85.81 38	87.85 17	92.82 62	69.37 38	95.20 24	95.25 16	82.71 15	81.91 55	94.73 52	67.93 28	97.63 41	79.55 78	82.25 128	96.54 12

Regformer-1		87.24 23	87.60 20	86.15 66	95.14 17	65.83 136	93.95 53	95.12 18	82.11 21	84.25 41	95.73 20	67.88 29	98.35 22	85.60 39	88.64 81	94.26 81

APDe-MVS		87.54 19	87.84 16	86.65 46	96.07 11	66.30 125	94.84 36	93.78 49	69.35 202	88.39 13	96.34 9	67.74 30	97.66 39	90.62 11	93.44 37	96.01 25

tpm2		79.80 123	77.95 132	85.34 92	88.28 166	68.26 61	81.56 295	91.42 151	70.11 195	77.59 95	80.50 253	67.40 31	94.26 173	67.34 168	77.35 165	93.51 109

tpmp4_e23		78.85 138	76.55 155	85.77 77	89.25 145	68.39 56	81.63 294	91.38 153	70.40 192	75.21 116	79.22 269	67.37 32	94.79 144	58.98 235	75.51 177	94.13 89

Regformer-2		87.00 27	87.43 22	85.71 81	95.14 17	64.73 158	93.95 53	94.95 22	81.69 26	84.03 45	95.73 20	67.35 33	98.19 26	85.40 41	88.64 81	94.20 83

tfpn1000		75.25 203	74.00 197	79.03 244	90.30 121	57.56 275	88.55 219	93.36 75	64.14 258	65.17 229	89.76 141	67.06 34	91.46 263	34.54 331	73.09 194	88.06 194

HY-MVS	76.49 5	84.28 58	83.36 64	87.02 40	92.22 74	67.74 71	84.65 269	94.50 34	79.15 42	82.23 53	87.93 159	66.88 35	96.94 81	80.53 74	82.20 129	96.39 14

EPNet		87.84 17	88.38 11	86.23 65	93.30 48	66.05 129	95.26 22	94.84 24	87.09 3	88.06 14	94.53 55	66.79 36	97.34 54	83.89 52	91.68 57	95.29 44
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn_ndepth		76.45 187	75.22 180	80.14 211	90.97 109	58.92 261	90.11 188	93.24 80	65.96 238	67.37 213	90.52 122	66.67 37	92.29 237	37.71 316	74.44 184	89.21 174

FIs		79.47 129	79.41 113	79.67 224	85.95 200	59.40 255	91.68 140	93.94 46	78.06 56	68.96 188	88.28 152	66.61 38	91.77 247	66.20 180	74.99 182	87.82 199

NCCC		89.07 9	89.46 9	87.91 16	96.60 5	69.05 42	96.38 7	94.64 32	84.42 10	86.74 21	96.20 11	66.56 39	98.76 13	89.03 19	94.56 21	95.92 28

SD-MVS		87.49 20	87.49 21	87.50 28	93.60 43	68.82 48	93.90 57	92.63 106	76.86 72	87.90 15	95.76 19	66.17 40	97.63 41	89.06 18	91.48 61	96.05 23

agg_prior3		86.93 28	87.08 26	86.48 54	94.21 28	66.95 93	94.14 43	93.40 73	71.80 166	84.86 35	95.13 39	66.16 41	97.25 62	89.09 16	94.90 13	95.25 49

UniMVSNet_NR-MVSNet		78.15 154	77.55 138	79.98 216	84.46 217	60.26 241	92.25 106	93.20 83	77.50 64	68.88 189	86.61 179	66.10 42	92.13 240	66.38 177	62.55 270	87.54 201

CHOSEN 280x420		77.35 168	76.95 151	78.55 253	87.07 185	62.68 208	69.71 332	82.95 312	68.80 209	71.48 155	87.27 175	66.03 43	84.00 320	76.47 100	82.81 126	88.95 175

CANet		89.61 6	89.99 6	88.46 13	94.39 27	69.71 33	96.53 5	93.78 49	86.89 4	89.68 7	95.78 18	65.94 44	99.10 2	92.99 1	93.91 28	96.58 11

segment_acmp												65.94 44

Regformer-3		85.80 42	85.92 36	85.46 85	94.17 32	65.09 152	92.95 85	95.11 19	81.13 27	81.68 57	95.04 40	65.82 46	98.32 23	83.02 56	84.36 113	92.97 124

Vis-MVSNet (Re-imp)		79.24 132	79.57 108	78.24 259	88.46 162	52.29 305	90.41 182	89.12 232	74.24 108	69.13 184	91.91 105	65.77 47	90.09 280	59.00 234	88.09 85	92.33 138

FC-MVSNet-test		77.99 157	78.08 130	77.70 264	84.89 211	55.51 292	90.27 185	93.75 53	76.87 71	66.80 219	87.59 165	65.71 48	90.23 274	62.89 211	73.94 188	87.37 210

SMA-MVS		88.14 11	88.31 14	87.66 22	93.25 49	68.72 49	93.85 59	94.03 45	74.18 112	91.74 2	96.72 5	65.61 49	98.48 18	89.29 15	96.08 3	95.79 30

test12				87.09 38	94.60 25	68.86 46		92.91 95		82.67 52		65.44 50	97.55 44		93.69 34	94.84 64

旧先验1					91.94 79	60.74 233		91.50 148			94.36 58	65.23 51			91.84 54	94.55 72

Regformer-4		85.45 45	85.69 41	84.73 107	94.17 32	63.23 193	92.95 85	94.83 25	80.66 29	81.29 59	95.04 40	65.12 52	98.08 29	82.74 57	84.36 113	92.88 128

conf0.01		74.95 209	73.61 202	78.96 245	89.65 132	56.94 281	87.72 236	93.45 64	65.14 246	65.68 221	89.99 132	65.09 53	91.67 249	35.16 323	70.61 211	88.27 188

conf0.002		74.95 209	73.61 202	78.96 245	89.65 132	56.94 281	87.72 236	93.45 64	65.14 246	65.68 221	89.99 132	65.09 53	91.67 249	35.16 323	70.61 211	88.27 188

thresconf0.02		74.92 212	73.61 202	78.85 248	89.65 132	56.94 281	87.72 236	93.45 64	65.14 246	65.68 221	89.99 132	65.09 53	91.67 249	35.16 323	70.61 211	87.94 195

tfpn_n400		74.92 212	73.61 202	78.85 248	89.65 132	56.94 281	87.72 236	93.45 64	65.14 246	65.68 221	89.99 132	65.09 53	91.67 249	35.16 323	70.61 211	87.94 195

tfpnconf		74.92 212	73.61 202	78.85 248	89.65 132	56.94 281	87.72 236	93.45 64	65.14 246	65.68 221	89.99 132	65.09 53	91.67 249	35.16 323	70.61 211	87.94 195

tfpnview11		74.92 212	73.61 202	78.85 248	89.65 132	56.94 281	87.72 236	93.45 64	65.14 246	65.68 221	89.99 132	65.09 53	91.67 249	35.16 323	70.61 211	87.94 195

1112_ss		80.56 108	79.83 105	82.77 143	88.65 158	60.78 230	92.29 105	88.36 250	72.58 142	72.46 143	94.95 43	65.09 53	93.42 206	66.38 177	77.71 156	94.10 91

MVSFormer		83.75 69	82.88 69	86.37 60	89.24 147	71.18 15	89.07 211	90.69 173	65.80 239	87.13 18	94.34 63	64.99 60	92.67 225	72.83 119	91.80 55	95.27 46

lupinMVS		87.74 18	87.77 17	87.63 26	89.24 147	71.18 15	96.57 4	92.90 96	82.70 16	87.13 18	95.27 33	64.99 60	95.80 113	89.34 14	91.80 55	95.93 27

tpmrst		80.57 107	79.14 120	84.84 104	90.10 124	68.28 60	81.70 291	89.72 214	77.63 62	75.96 108	79.54 267	64.94 62	92.71 223	75.43 104	77.28 167	93.55 108

Test_1112_low_res		79.56 128	78.60 124	82.43 155	88.24 167	60.39 239	92.09 112	87.99 257	72.10 157	71.84 150	87.42 168	64.62 63	93.04 210	65.80 185	77.30 166	93.85 104

DeepC-MVS_fast	79.48 2	87.95 15	88.00 15	87.79 19	95.86 14	68.32 58	95.74 13	94.11 44	83.82 12	83.49 48	96.19 12	64.53 64	98.44 20	83.42 55	94.88 15	96.61 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
casdiffmvs		85.23 47	84.38 52	87.79 19	90.73 115	71.38 13	90.71 174	92.52 110	77.08 68	84.58 37	87.18 176	64.43 65	96.34 97	84.32 47	87.86 87	95.65 33

MG-MVS		87.11 25	86.27 31	89.62 5	97.79 1	76.27 3	94.96 32	94.49 35	78.74 51	83.87 47	92.94 87	64.34 66	96.94 81	75.19 106	94.09 25	95.66 32

tpm		78.58 147	77.03 147	83.22 137	85.94 202	64.56 159	83.21 282	91.14 161	78.31 54	73.67 128	79.68 264	64.01 67	92.09 242	66.07 181	71.26 209	93.03 122

CDS-MVSNet		81.43 98	80.74 93	83.52 133	86.26 195	64.45 164	92.09 112	90.65 176	75.83 85	73.95 127	89.81 139	63.97 68	92.91 217	71.27 135	82.82 125	93.20 117
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_Test		84.16 61	83.20 65	87.05 39	91.56 93	69.82 30	89.99 192	92.05 127	77.77 59	82.84 51	86.57 180	63.93 69	96.09 104	74.91 112	89.18 78	95.25 49

APD-MVS		85.93 40	85.99 35	85.76 78	95.98 13	65.21 146	93.59 68	92.58 108	66.54 233	86.17 22	95.88 17	63.83 70	97.00 73	86.39 35	92.94 41	95.06 55
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mvs_anonymous		81.36 99	79.99 102	85.46 85	90.39 120	68.40 55	86.88 257	90.61 177	74.41 102	70.31 166	84.67 199	63.79 71	92.32 236	73.13 116	85.70 104	95.67 31

PVSNet_Blended_VisFu		83.97 63	83.50 57	85.39 90	90.02 125	66.59 115	93.77 63	91.73 138	77.43 66	77.08 102	89.81 139	63.77 72	96.97 78	79.67 77	88.21 84	92.60 132

CDPH-MVS		85.71 43	85.46 43	86.46 55	94.75 22	67.19 84	93.89 58	92.83 98	70.90 182	83.09 50	95.28 32	63.62 73	97.36 52	80.63 73	94.18 24	94.84 64

HyFIR lowres test		81.03 103	79.56 109	85.43 88	87.81 176	68.11 65	90.18 187	90.01 203	70.65 190	72.95 133	86.06 186	63.61 74	94.50 157	75.01 110	79.75 141	93.67 105

canonicalmvs		86.85 30	86.25 33	88.66 10	91.80 88	71.92 10	93.54 70	91.71 140	80.26 31	87.55 16	95.25 35	63.59 75	96.93 83	88.18 22	84.34 115	97.11 3

SteuartSystems-ACMMP		86.82 32	86.90 28	86.58 50	90.42 118	66.38 122	96.09 9	93.87 47	77.73 60	84.01 46	95.66 22	63.39 76	97.94 30	87.40 28	93.55 36	95.42 38
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		83.77 68	83.67 55	84.06 121	92.79 64	63.56 189	91.76 136	94.81 27	79.65 36	77.87 89	94.09 67	63.35 77	97.90 32	79.35 79	79.36 143	90.74 159

UniMVSNet (Re)		77.58 162	76.78 152	79.98 216	84.11 223	60.80 229	91.76 136	93.17 86	76.56 79	69.93 173	84.78 198	63.32 78	92.36 235	64.89 191	62.51 272	86.78 222

PVSNet_BlendedMVS		83.38 71	83.43 60	83.22 137	93.76 38	67.53 77	94.06 47	93.61 57	79.13 43	81.00 62	85.14 194	63.19 79	97.29 57	87.08 30	73.91 189	84.83 258

PVSNet_Blended		86.73 33	86.86 29	86.31 63	93.76 38	67.53 77	96.33 8	93.61 57	82.34 18	81.00 62	93.08 82	63.19 79	97.29 57	87.08 30	91.38 62	94.13 89

PAPM_NR		82.97 77	81.84 81	86.37 60	94.10 36	66.76 104	87.66 243	92.84 97	69.96 197	74.07 125	93.57 75	63.10 81	97.50 46	70.66 143	90.58 71	94.85 63

nrg030		80.93 104	79.86 104	84.13 120	83.69 227	68.83 47	93.23 77	91.20 158	75.55 87	75.06 117	88.22 157	63.04 82	94.74 147	81.88 63	66.88 239	88.82 178

EI-MVSNet-UG-set		83.14 74	82.96 67	83.67 131	92.28 72	63.19 197	91.38 151	94.68 30	79.22 40	76.60 104	93.75 72	62.64 83	97.76 34	78.07 90	78.01 154	90.05 166

DeepC-MVS	77.85 3	85.52 44	85.24 45	86.37 60	88.80 156	66.64 112	92.15 108	93.68 55	81.07 28	76.91 103	93.64 74	62.59 84	98.44 20	85.50 40	92.84 43	94.03 96
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MVS_111021_HR		86.19 38	85.80 39	87.37 30	93.17 53	69.79 31	93.99 51	93.76 52	79.08 45	78.88 81	93.99 69	62.25 85	98.15 27	85.93 38	91.15 65	94.15 88

PHI-MVS		86.83 31	86.85 30	86.78 44	93.47 47	65.55 141	95.39 21	95.10 20	71.77 168	85.69 28	96.52 6	62.07 86	98.77 12	86.06 37	95.60 7	96.03 24

MP-MVS		85.02 49	84.97 47	85.17 97	92.60 66	64.27 174	93.24 76	92.27 116	73.13 133	79.63 74	94.43 56	61.90 87	97.17 64	85.00 43	92.56 46	94.06 95
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
jason		86.40 34	86.17 34	87.11 37	86.16 197	70.54 21	95.71 16	92.19 124	82.00 24	84.58 37	94.34 63	61.86 88	95.53 129	87.76 25	90.89 67	95.27 46
jason: jason.
PAPR		85.15 48	84.47 49	87.18 34	96.02 12	68.29 59	91.85 131	93.00 93	76.59 77	79.03 80	95.00 42	61.59 89	97.61 43	78.16 89	89.00 79	95.63 34

IS-MVSNet		80.14 115	79.41 113	82.33 162	87.91 174	60.08 247	91.97 120	88.27 253	72.90 138	71.44 156	91.73 110	61.44 90	93.66 201	62.47 216	86.53 99	93.24 115

EI-MVSNet		78.97 136	78.22 128	81.25 189	85.33 205	62.73 207	89.53 203	93.21 81	72.39 146	72.14 147	90.13 129	60.99 91	94.72 148	67.73 165	72.49 199	86.29 228

IterMVS-LS		76.49 185	75.18 181	80.43 207	84.49 216	62.74 206	90.64 177	88.80 242	72.40 145	65.16 230	81.72 234	60.98 92	92.27 238	67.74 164	64.65 260	86.29 228
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
view600		76.93 175	75.50 174	81.23 190	91.44 97	62.00 215	89.94 193	96.56 1	70.68 186	68.54 195	87.31 170	60.79 93	94.19 174	38.90 310	75.31 178	87.48 203

view800		76.93 175	75.50 174	81.23 190	91.44 97	62.00 215	89.94 193	96.56 1	70.68 186	68.54 195	87.31 170	60.79 93	94.19 174	38.90 310	75.31 178	87.48 203

conf0.05thres1000		76.93 175	75.50 174	81.23 190	91.44 97	62.00 215	89.94 193	96.56 1	70.68 186	68.54 195	87.31 170	60.79 93	94.19 174	38.90 310	75.31 178	87.48 203

tfpn		76.93 175	75.50 174	81.23 190	91.44 97	62.00 215	89.94 193	96.56 1	70.68 186	68.54 195	87.31 170	60.79 93	94.19 174	38.90 310	75.31 178	87.48 203

tpm cat1		75.30 202	72.21 223	84.58 112	88.52 159	67.77 70	78.16 318	88.02 256	61.88 277	68.45 200	76.37 287	60.65 97	94.03 188	53.77 252	74.11 186	91.93 145

TAMVS		80.37 111	79.45 112	83.13 139	85.14 208	63.37 190	91.23 158	90.76 172	74.81 100	72.65 137	88.49 148	60.63 98	92.95 213	69.41 152	81.95 131	93.08 121

tfpn111		78.00 155	76.62 154	82.13 172	91.89 83	63.21 194	91.19 162	96.33 5	72.28 148	70.45 161	87.89 160	60.31 99	94.91 143	42.61 296	76.64 170	88.27 188

conf200view11		78.32 152	77.01 148	82.27 165	91.89 83	63.21 194	91.19 162	96.33 5	72.28 148	70.45 161	87.89 160	60.31 99	95.32 132	45.16 282	77.58 159	88.27 188

thres100view900		78.37 150	77.01 148	82.46 151	91.89 83	63.21 194	91.19 162	96.33 5	72.28 148	70.45 161	87.89 160	60.31 99	95.32 132	45.16 282	77.58 159	88.83 176

thres600view7		78.00 155	76.66 153	82.03 178	91.93 80	63.69 185	91.30 156	96.33 5	72.43 144	70.46 160	87.89 160	60.31 99	94.92 142	42.64 295	76.64 170	87.48 203

CHOSEN 1792x2688		84.98 50	83.45 59	89.57 6	89.94 127	75.14 4	92.07 114	92.32 114	81.87 25	75.68 109	88.27 153	60.18 103	98.60 16	80.46 75	90.27 74	94.96 61

tfpn200view9		78.79 141	77.43 141	82.88 141	92.21 75	64.49 161	92.05 115	96.28 10	73.48 128	71.75 152	88.26 154	60.07 104	95.32 132	45.16 282	77.58 159	88.83 176

thres400		78.68 144	77.43 141	82.43 155	92.21 75	64.49 161	92.05 115	96.28 10	73.48 128	71.75 152	88.26 154	60.07 104	95.32 132	45.16 282	77.58 159	87.48 203

MVS		84.66 54	82.86 70	90.06 1	90.93 110	74.56 5	87.91 232	95.54 14	68.55 214	72.35 146	94.71 53	59.78 106	98.90 7	81.29 71	94.69 20	96.74 7

thres200		79.66 125	78.33 125	83.66 132	92.54 67	65.82 137	93.06 81	96.31 9	74.90 99	73.30 130	88.66 146	59.67 107	95.61 123	47.84 272	78.67 150	89.56 172

Effi-MVS+		83.82 67	82.76 72	86.99 41	89.56 140	69.40 37	91.35 153	86.12 284	72.59 141	83.22 49	92.81 93	59.60 108	96.01 109	81.76 64	87.80 88	95.56 36

ACMMP_Plus		86.05 39	85.80 39	86.80 43	91.58 92	67.53 77	91.79 133	93.49 63	74.93 98	84.61 36	95.30 31	59.42 109	97.92 31	86.13 36	94.92 12	94.94 62

UA-Net		80.02 118	79.65 107	81.11 197	89.33 143	57.72 271	86.33 261	89.00 238	77.44 65	81.01 61	89.15 143	59.33 110	95.90 110	61.01 223	84.28 118	89.73 170

NR-MVSNet		76.05 192	74.59 185	80.44 206	82.96 236	62.18 214	90.83 170	91.73 138	77.12 67	60.96 257	86.35 181	59.28 111	91.80 246	60.74 224	61.34 283	87.35 212

MP-MVS-pluss		85.24 46	85.13 46	85.56 82	91.42 101	65.59 140	91.54 145	92.51 111	74.56 101	80.62 64	95.64 23	59.15 112	97.00 73	86.94 32	93.80 29	94.07 94
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		84.73 52	84.40 51	85.72 79	93.75 40	65.01 153	93.50 71	93.19 84	72.19 153	79.22 77	94.93 45	59.04 113	97.67 36	81.55 66	92.21 49	94.49 78

#test#		84.98 50	84.74 48	85.72 79	93.75 40	65.01 153	94.09 45	93.19 84	73.55 127	79.22 77	94.93 45	59.04 113	97.67 36	82.66 58	92.21 49	94.49 78

MSLP-MVS++		86.27 36	85.91 37	87.35 31	92.01 78	68.97 45	95.04 30	92.70 101	79.04 46	81.50 58	96.50 7	58.98 115	96.78 87	83.49 54	93.93 27	96.29 16

Patchmatch-test		65.86 286	60.94 297	80.62 205	83.75 226	58.83 262	58.91 349	75.26 335	44.50 339	50.95 313	77.09 285	58.81 116	87.90 303	35.13 329	64.03 264	95.12 54

EPNet_dtu		78.80 140	79.26 117	77.43 269	88.06 170	49.71 319	91.96 121	91.95 132	77.67 61	76.56 105	91.28 113	58.51 117	90.20 275	56.37 242	80.95 136	92.39 136
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
VNet		86.20 37	85.65 42	87.84 18	93.92 37	69.99 27	95.73 15	95.94 12	78.43 53	86.00 24	93.07 84	58.22 118	97.00 73	85.22 42	84.33 116	96.52 13

TESTMET0.1,1		82.41 84	81.98 80	83.72 129	88.08 169	63.74 182	92.70 93	93.77 51	79.30 38	77.61 94	87.57 166	58.19 119	94.08 182	73.91 115	86.68 96	93.33 113

原ACMM1				84.42 116	93.21 52	64.27 174		93.40 73	65.39 242	79.51 75	92.50 95	58.11 120	96.69 91	65.27 189	93.96 26	92.32 139

diffmvs		81.52 96	80.44 99	84.76 105	87.98 173	65.79 138	86.97 255	88.84 241	76.57 78	78.24 86	85.79 190	58.10 121	94.55 153	77.40 95	84.11 121	93.95 99

sam_mvs1												57.85 122				94.68 68

CR-MVSNet		73.79 224	70.82 233	82.70 145	83.15 234	67.96 67	70.25 329	84.00 302	73.67 125	69.97 171	72.41 308	57.82 123	89.48 291	52.99 256	73.13 192	90.64 161

Patchmtry		67.53 278	63.93 281	78.34 254	82.12 241	64.38 168	68.72 333	84.00 302	48.23 329	59.24 264	72.41 308	57.82 123	89.27 293	46.10 279	56.68 302	81.36 298

patchmatchnet-post											67.62 326	57.62 125	90.25 271

PCF-MVS	73.15 9	79.29 131	77.63 137	84.29 118	86.06 198	65.96 132	87.03 251	91.10 162	69.86 198	69.79 174	90.64 118	57.54 126	96.59 92	64.37 197	82.29 127	90.32 163
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+		81.14 101	80.01 101	84.51 115	90.24 123	65.86 134	94.12 44	89.15 231	73.81 121	75.37 115	88.26 154	57.26 127	94.53 156	66.97 172	84.92 108	93.15 118

1121		81.25 100	80.05 100	84.87 103	92.30 71	64.31 171	87.91 232	91.39 152	59.44 292	79.94 70	92.91 88	57.09 128	97.01 71	66.63 173	92.81 44	93.29 114

PatchT		69.11 266	65.37 271	80.32 208	82.07 242	63.68 186	67.96 338	87.62 261	50.86 322	69.37 181	65.18 332	57.09 128	88.53 299	41.59 299	66.60 241	88.74 179

testdata				81.34 188	89.02 151	57.72 271		89.84 207	58.65 296	85.32 32	94.09 67	57.03 130	93.28 207	69.34 153	90.56 72	93.03 122

PatchmatchNet		77.46 163	74.63 184	85.96 69	89.55 141	70.35 22	79.97 307	89.55 217	72.23 151	70.94 157	76.91 286	57.03 130	92.79 221	54.27 249	81.17 134	94.74 66
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
0601test		84.28 58	83.16 66	87.64 23	94.52 26	69.24 39	95.78 11	95.09 21	69.19 204	81.09 60	92.88 91	57.00 132	97.44 48	81.11 72	81.76 132	96.23 18

region2R		84.36 56	84.03 54	85.36 91	93.54 45	64.31 171	93.43 74	92.95 94	72.16 156	78.86 82	94.84 50	56.97 133	97.53 45	81.38 69	92.11 53	94.24 82

新几何1				84.73 107	92.32 70	64.28 173		91.46 150	59.56 291	79.77 72	92.90 89	56.95 134	96.57 94	63.40 203	92.91 42	93.34 111

MVS_0304		88.39 10	88.35 13	88.50 12	93.01 56	70.11 24	95.90 10	92.20 122	86.27 6	88.70 12	95.92 16	56.76 135	99.02 4	92.68 3	93.76 31	96.37 15

WR-MVS		76.76 183	75.74 166	79.82 221	84.60 213	62.27 213	92.60 98	92.51 111	76.06 82	67.87 206	85.34 192	56.76 135	90.24 273	62.20 217	63.69 268	86.94 220

HPM-MVS		83.25 72	82.95 68	84.17 119	92.25 73	62.88 204	90.91 168	91.86 134	70.30 194	77.12 100	93.96 70	56.75 137	96.28 98	82.04 62	91.34 64	93.34 111
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		82.71 81	82.38 76	83.73 128	89.25 145	59.58 253	92.24 107	94.89 23	77.96 57	79.86 71	92.38 99	56.70 138	97.05 68	77.26 96	80.86 137	94.55 72

ACMMPR		84.37 55	84.06 53	85.28 93	93.56 44	64.37 169	93.50 71	93.15 87	72.19 153	78.85 83	94.86 49	56.69 139	97.45 47	81.55 66	92.20 51	94.02 97

FMVSNet3		77.73 161	76.04 161	82.80 142	91.20 106	68.99 44	91.87 129	91.99 129	73.35 131	67.04 215	83.19 212	56.62 140	92.14 239	59.80 230	69.34 222	87.28 214

Patchmatch-RL test		68.17 272	64.49 278	79.19 239	71.22 327	53.93 299	70.07 331	71.54 343	69.22 203	56.79 281	62.89 335	56.58 141	88.61 296	69.53 151	52.61 312	95.03 59

test_post											23.01 355	56.49 142	92.67 225

RPMNet		69.58 262	65.21 272	82.70 145	83.15 234	67.96 67	70.25 329	86.15 283	46.83 332	69.97 171	65.10 333	56.48 143	89.48 291	35.79 322	73.13 192	90.64 161

DU-MVS		76.86 179	75.84 164	79.91 218	82.96 236	60.26 241	91.26 157	91.54 145	76.46 80	68.88 189	86.35 181	56.16 144	92.13 240	66.38 177	62.55 270	87.35 212

Baseline_NR-MVSNet		73.99 222	72.83 215	77.48 268	80.78 253	59.29 258	91.79 133	84.55 297	68.85 208	68.99 187	80.70 249	56.16 144	92.04 243	62.67 214	60.98 285	81.11 304

API-MVS		82.28 86	80.53 97	87.54 27	96.13 10	70.59 20	93.63 66	91.04 166	65.72 241	75.45 114	92.83 92	56.11 146	98.89 10	64.10 198	89.75 77	93.15 118

zzz-MVS		84.73 52	84.47 49	85.50 83	91.89 83	65.16 147	91.55 144	92.23 117	75.32 93	80.53 65	95.21 37	56.06 147	97.16 65	84.86 45	92.55 47	94.18 84

MTAPA		83.91 65	83.38 63	85.50 83	91.89 83	65.16 147	81.75 290	92.23 117	75.32 93	80.53 65	95.21 37	56.06 147	97.16 65	84.86 45	92.55 47	94.18 84

JIA-IIPM		66.06 285	62.45 291	76.88 275	81.42 249	54.45 298	57.49 350	88.67 244	49.36 325	63.86 241	46.86 345	56.06 147	90.25 271	49.53 266	68.83 226	85.95 240

v148		76.19 189	74.47 189	81.36 187	80.05 281	64.44 165	91.75 138	90.23 191	73.68 124	67.13 214	80.84 248	55.92 150	93.86 197	68.95 157	61.73 279	85.76 246

WR-MVS_H		70.59 252	69.94 236	72.53 302	81.03 250	51.43 309	87.35 248	92.03 128	67.38 229	60.23 260	80.70 249	55.84 151	83.45 324	46.33 278	58.58 296	82.72 283

XVS		83.87 66	83.47 58	85.05 98	93.22 50	63.78 180	92.92 87	92.66 104	73.99 114	78.18 87	94.31 65	55.25 152	97.41 49	79.16 81	91.58 59	93.95 99

X-MVStestdata		76.86 179	74.13 194	85.05 98	93.22 50	63.78 180	92.92 87	92.66 104	73.99 114	78.18 87	10.19 361	55.25 152	97.41 49	79.16 81	91.58 59	93.95 99

BH-w/o		80.49 110	79.30 116	84.05 122	90.83 114	64.36 170	93.60 67	89.42 221	74.35 107	69.09 185	90.15 128	55.23 154	95.61 123	64.61 193	86.43 101	92.17 143

CP-MVS		83.71 70	83.40 62	84.65 110	93.14 54	63.84 178	94.59 37	92.28 115	71.03 180	77.41 96	94.92 47	55.21 155	96.19 100	81.32 70	90.70 69	93.91 102

PGM-MVS		83.25 72	82.70 74	84.92 100	92.81 63	64.07 176	90.44 180	92.20 122	71.28 178	77.23 99	94.43 56	55.17 156	97.31 56	79.33 80	91.38 62	93.37 110

v1		77.29 169	75.57 171	82.42 158	80.61 267	66.73 105	91.96 121	90.42 182	74.41 102	69.46 178	82.12 226	55.14 157	94.40 163	71.00 136	65.04 253	86.13 231

divwei89l23v2f112		77.28 170	75.57 171	82.42 158	80.62 264	66.72 107	91.96 121	90.42 182	74.41 102	69.46 178	82.12 226	55.11 158	94.40 163	71.00 136	65.04 253	86.12 232

v1141		77.28 170	75.57 171	82.42 158	80.63 263	66.73 105	91.96 121	90.42 182	74.41 102	69.46 178	82.12 226	55.09 159	94.40 163	70.99 138	65.05 252	86.12 232

tpmvs		72.88 231	69.76 239	82.22 169	90.98 108	67.05 89	78.22 317	88.30 251	63.10 267	64.35 239	74.98 296	55.09 159	94.27 171	43.25 289	69.57 221	85.34 254

v18		71.94 237	69.43 240	79.50 230	80.74 254	66.82 97	88.16 226	86.66 270	68.95 207	55.55 284	72.66 303	55.03 161	90.15 276	64.78 192	52.30 314	81.54 292

v6		77.39 165	75.71 167	82.44 152	80.67 259	66.82 97	91.94 125	90.18 194	74.19 109	69.60 175	82.50 219	55.00 162	94.44 158	71.68 131	65.60 245	86.05 235

v1neww		77.39 165	75.71 167	82.44 152	80.69 257	66.83 95	91.94 125	90.18 194	74.19 109	69.60 175	82.51 216	54.99 163	94.44 158	71.68 131	65.60 245	86.05 235

v7new		77.39 165	75.71 167	82.44 152	80.69 257	66.83 95	91.94 125	90.18 194	74.19 109	69.60 175	82.51 216	54.99 163	94.44 158	71.68 131	65.60 245	86.05 235

v8		75.35 201	73.26 212	81.61 185	80.67 259	66.82 97	89.54 202	89.27 225	71.65 170	63.30 247	80.30 257	54.99 163	94.06 184	67.33 169	62.33 273	83.94 264

sam_mvs												54.91 166

v16		71.81 238	69.26 242	79.47 231	80.66 261	66.81 101	87.93 230	86.63 272	68.70 212	55.35 286	72.51 304	54.75 167	90.12 278	64.51 194	52.28 315	81.47 293

v17		71.77 240	69.20 243	79.46 232	80.62 264	66.81 101	87.93 230	86.63 272	68.71 211	55.25 287	72.49 305	54.72 168	90.11 279	64.50 195	51.97 316	81.47 293

EPMVS		78.49 149	75.98 162	86.02 67	91.21 105	69.68 34	80.23 303	91.20 158	75.25 95	72.48 142	78.11 274	54.65 169	93.69 200	57.66 240	83.04 123	94.69 67

v15		71.40 242	68.75 245	79.35 233	80.39 268	66.70 109	87.57 245	86.64 271	68.66 213	54.68 289	72.00 312	54.50 170	89.98 281	63.69 201	50.66 321	81.38 297

ab-mvs		80.18 114	78.31 126	85.80 75	88.44 163	65.49 143	83.00 284	92.67 103	71.82 165	77.36 97	85.01 195	54.50 170	96.59 92	76.35 101	75.63 176	95.32 43

DP-MVS Recon		82.73 79	81.65 84	85.98 68	97.31 3	67.06 88	95.15 26	91.99 129	69.08 206	76.50 106	93.89 71	54.48 172	98.20 25	70.76 142	85.66 105	92.69 129

XXY-MVS		77.94 159	76.44 157	82.43 155	82.60 238	64.44 165	92.01 117	91.83 136	73.59 126	70.00 170	85.82 188	54.43 173	94.76 145	69.63 149	68.02 233	88.10 193

MDTV_nov1_ep13_2view						59.90 249	80.13 305		67.65 226	72.79 135		54.33 174		59.83 229		92.58 133

V14		71.29 244	68.61 247	79.31 234	80.34 272	66.65 111	87.39 247	86.61 274	68.41 219	54.49 291	71.91 313	54.25 175	89.96 282	63.50 202	50.62 322	81.33 299

Test By Simon												54.21 176

V9		71.16 245	68.46 249	79.27 236	80.26 275	66.60 113	87.21 250	86.56 275	68.17 220	54.26 294	71.81 315	54.00 177	89.93 283	63.28 205	50.57 323	81.27 300

MAR-MVS		84.18 60	83.43 60	86.44 56	96.25 9	65.93 133	94.28 39	94.27 41	74.41 102	79.16 79	95.61 24	53.99 178	98.88 11	69.62 150	93.26 39	94.50 77
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		80.10 116	79.56 109	81.72 182	86.93 188	61.17 225	92.70 93	91.54 145	71.51 176	75.62 110	86.94 177	53.83 179	92.38 233	72.21 126	84.76 111	91.60 148

test0.0.03 1		72.76 232	72.71 217	72.88 300	80.25 276	47.99 324	91.22 159	89.45 219	71.51 176	62.51 254	87.66 164	53.83 179	85.06 314	50.16 263	67.84 236	85.58 248

v2v482		77.42 164	75.65 170	82.73 144	80.38 269	67.13 87	91.85 131	90.23 191	75.09 96	69.37 181	83.39 210	53.79 181	94.44 158	71.77 129	65.00 256	86.63 226

v12		71.02 249	68.29 254	79.22 238	80.18 278	66.53 118	87.01 253	86.54 277	67.90 222	54.00 297	71.70 316	53.66 182	89.91 284	63.09 207	50.51 324	81.21 301

pcd_1.5k_mvsjas		4.46 342	5.95 343	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	53.55 183	0.00 366	0.00 363	0.00 363	0.00 364

PS-MVSNAJss		77.26 172	76.31 158	80.13 213	80.64 262	59.16 259	90.63 179	91.06 164	72.80 139	68.58 194	84.57 201	53.55 183	93.96 191	72.97 117	71.96 203	87.27 215

PS-MVSNAJ		88.14 11	87.61 19	89.71 4	92.06 77	76.72 1	95.75 12	93.26 79	83.86 11	89.55 8	96.06 14	53.55 183	97.89 33	91.10 8	93.31 38	94.54 74

v13		70.90 250	68.15 255	79.15 242	80.08 279	66.45 120	86.83 258	86.50 278	67.62 228	53.78 299	71.61 318	53.51 186	89.87 286	62.89 211	50.50 325	81.14 303

mPP-MVS		82.96 78	82.44 75	84.52 114	92.83 60	62.92 202	92.76 90	91.85 135	71.52 175	75.61 112	94.24 66	53.48 187	96.99 76	78.97 84	90.73 68	93.64 107

xiu_mvs_v2_base		87.92 16	87.38 24	89.55 7	91.41 103	76.43 2	95.74 13	93.12 88	83.53 13	89.55 8	95.95 15	53.45 188	97.68 35	91.07 9	92.62 45	94.54 74

test_post1							78.95 311				20.70 359	53.05 189	91.50 262	60.43 226

MDTV_nov1_ep13			72.61 218		89.06 150	68.48 53	80.33 301	90.11 198	71.84 164	71.81 151	75.92 293	53.01 190	93.92 193	48.04 271	73.38 190

v11		71.05 248	68.32 252	79.23 237	80.34 272	66.57 116	87.01 253	86.55 276	68.11 221	54.40 292	71.66 317	52.94 191	89.91 284	62.71 213	51.12 319	81.21 301

test222					89.77 130	61.60 222	89.55 201	89.42 221	56.83 305	77.28 98	92.43 98	52.76 192			91.14 66	93.09 120

v1144		76.73 184	74.88 183	82.27 165	80.23 277	66.60 113	91.68 140	90.21 193	73.69 123	69.06 186	81.89 231	52.73 193	94.40 163	69.21 154	65.23 249	85.80 243

v7		76.83 182	75.01 182	82.29 164	80.35 270	66.70 109	91.68 140	89.97 204	73.47 130	69.22 183	82.22 223	52.52 194	94.43 162	69.73 148	65.96 244	85.74 247

v10		74.77 216	72.54 220	81.46 186	80.33 274	66.71 108	89.15 209	89.08 234	70.94 181	63.08 248	79.86 262	52.52 194	94.04 187	65.70 186	62.17 274	83.64 266

CLD-MVS		82.73 79	82.35 77	83.86 124	87.90 175	67.65 74	95.45 19	92.18 125	85.06 8	72.58 139	92.27 102	52.46 196	95.78 114	84.18 48	79.06 146	88.16 192
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		75.86 195	74.52 188	79.89 219	82.44 239	60.64 236	91.37 152	91.37 154	76.63 76	67.65 208	86.21 184	52.37 197	91.55 257	61.84 219	60.81 286	87.48 203

VPA-MVSNet		79.03 134	78.00 131	82.11 176	85.95 200	64.48 163	93.22 78	94.66 31	75.05 97	74.04 126	84.95 196	52.17 198	93.52 203	74.90 113	67.04 238	88.32 187

APD-MVS_3200maxsize		81.64 95	81.32 87	82.59 150	92.36 68	58.74 264	91.39 149	91.01 167	63.35 263	79.72 73	94.62 54	51.82 199	96.14 102	79.71 76	87.93 86	92.89 127

dp		75.01 207	72.09 224	83.76 125	89.28 144	66.22 128	79.96 308	89.75 209	71.16 179	67.80 207	77.19 282	51.81 200	92.54 228	50.39 262	71.44 208	92.51 135

v144192		76.05 192	74.03 195	82.12 173	79.50 286	66.55 117	91.39 149	89.71 215	72.30 147	68.17 201	81.33 239	51.75 201	94.03 188	67.94 162	64.19 262	85.77 244

BH-untuned		78.68 144	77.08 146	83.48 135	89.84 129	63.74 182	92.70 93	88.59 246	71.57 173	66.83 218	88.65 147	51.75 201	95.39 130	59.03 233	84.77 110	91.32 154

HQP2-MVS												51.63 203

HQP-MVS		81.14 101	80.64 95	82.64 148	87.54 178	63.66 187	94.06 47	91.70 141	79.80 33	74.18 121	90.30 125	51.63 203	95.61 123	77.63 93	78.90 147	88.63 180

V42		76.46 186	74.55 187	82.19 170	79.14 291	67.82 69	90.26 186	89.42 221	73.75 122	68.63 193	81.89 231	51.31 205	94.09 181	71.69 130	64.84 257	84.66 259

TransMVSNet (Re)		70.07 256	67.66 258	77.31 272	80.62 264	59.13 260	91.78 135	84.94 295	65.97 237	60.08 261	80.44 254	50.78 206	91.87 245	48.84 268	45.46 334	80.94 306

HQP_MVS		80.34 112	79.75 106	82.12 173	86.94 186	62.42 209	93.13 79	91.31 155	78.81 49	72.53 140	89.14 144	50.66 207	95.55 127	76.74 97	78.53 151	88.39 185

plane_prior6					87.23 182	62.32 211						50.66 207

ACMMP		81.49 97	80.67 94	83.93 123	91.71 90	62.90 203	92.13 109	92.22 121	71.79 167	71.68 154	93.49 77	50.32 209	96.96 79	78.47 86	84.22 120	91.93 145
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 92	81.52 85	83.51 134	88.42 164	62.88 204	89.77 199	88.93 239	76.78 74	75.55 113	93.10 80	50.31 210	95.38 131	83.82 53	87.02 92	92.26 142

1314		80.70 106	78.95 121	85.94 70	87.77 177	67.56 76	87.91 232	92.55 109	72.17 155	67.44 209	93.09 81	50.27 211	97.04 70	71.68 131	87.64 89	93.23 116

CP-MVSNet		70.50 254	69.91 237	72.26 305	80.71 256	51.00 312	87.23 249	90.30 189	67.84 223	59.64 262	82.69 215	50.23 212	82.30 331	51.28 259	59.28 290	83.46 271

LCM-MVSNet-Re		72.93 229	71.84 225	76.18 280	88.49 160	48.02 323	80.07 306	70.17 344	73.96 117	52.25 306	80.09 261	49.98 213	88.24 301	67.35 167	84.23 119	92.28 141

Vis-MVSNet		80.92 105	79.98 103	83.74 126	88.48 161	61.80 220	93.44 73	88.26 254	73.96 117	77.73 90	91.76 108	49.94 214	94.76 145	65.84 184	90.37 73	94.65 70
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1192		75.98 194	73.92 198	82.15 171	79.73 282	66.24 127	91.22 159	89.75 209	72.67 140	68.49 199	81.42 237	49.86 215	94.27 171	67.08 170	65.02 255	85.95 240

test-mter		79.96 119	79.38 115	81.72 182	86.93 188	61.17 225	92.70 93	91.54 145	73.85 119	75.62 110	86.94 177	49.84 216	92.38 233	72.21 126	84.76 111	91.60 148

cdsmvs_eth3d_5k		19.86 337	26.47 333	0.00 353	0.00 367	0.00 368	0.00 360	93.45 64	0.00 363	0.00 364	95.27 33	49.56 217	0.00 366	0.00 363	0.00 363	0.00 364

3Dnovator+	73.60 7	82.10 91	80.60 96	86.60 48	90.89 112	66.80 103	95.20 24	93.44 71	74.05 113	67.42 210	92.49 96	49.46 218	97.65 40	70.80 141	91.68 57	95.33 41

MVP-Stereo		77.12 174	76.23 159	79.79 222	81.72 245	66.34 124	89.29 205	90.88 168	70.56 191	62.01 256	82.88 213	49.34 219	94.13 179	65.55 187	93.80 29	78.88 323
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
OMC-MVS		78.67 146	77.91 134	80.95 203	85.76 204	57.40 276	88.49 220	88.67 244	73.85 119	72.43 144	92.10 103	49.29 220	94.55 153	72.73 121	77.89 155	90.91 158

VPNet		78.82 139	77.53 139	82.70 145	84.52 215	66.44 121	93.93 55	92.23 117	80.46 30	72.60 138	88.38 151	49.18 221	93.13 209	72.47 124	63.97 266	88.55 182

CVMVSNet		74.04 221	74.27 191	73.33 296	85.33 205	43.94 333	89.53 203	88.39 249	54.33 312	70.37 164	90.13 129	49.17 222	84.05 317	61.83 220	79.36 143	91.99 144

v1921920		75.63 199	73.49 209	82.06 177	79.38 287	66.35 123	91.07 167	89.48 218	71.98 158	67.99 202	81.22 243	49.16 223	93.90 194	66.56 175	64.56 261	85.92 242

pm-mvs1		72.89 230	71.09 231	78.26 258	79.10 293	57.62 273	90.80 171	89.30 224	67.66 225	62.91 250	81.78 233	49.11 224	92.95 213	60.29 227	58.89 295	84.22 262

pmmvs4		73.92 223	71.81 226	80.25 210	79.17 290	65.24 145	87.43 246	87.26 267	67.64 227	63.46 245	83.91 205	48.96 225	91.53 261	62.94 210	65.49 248	83.96 263

TAPA-MVS	70.22 12	74.94 211	73.53 208	79.17 240	90.40 119	52.07 306	89.19 208	89.61 216	62.69 269	70.07 168	92.67 94	48.89 226	94.32 168	38.26 315	79.97 139	91.12 156
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
3Dnovator	73.91 6	82.69 82	80.82 92	88.31 14	89.57 139	71.26 14	92.60 98	94.39 40	78.84 48	67.89 205	92.48 97	48.42 227	98.52 17	68.80 159	94.40 22	95.15 52

CPTT-MVS		79.59 127	79.16 119	80.89 204	91.54 95	59.80 250	92.10 111	88.54 248	60.42 284	72.96 132	93.28 79	48.27 228	92.80 220	78.89 85	86.50 100	90.06 165

GBi-Net		75.65 197	73.83 199	81.10 198	88.85 153	65.11 149	90.01 189	90.32 185	70.84 183	67.04 215	80.25 258	48.03 229	91.54 258	59.80 230	69.34 222	86.64 223

test1		75.65 197	73.83 199	81.10 198	88.85 153	65.11 149	90.01 189	90.32 185	70.84 183	67.04 215	80.25 258	48.03 229	91.54 258	59.80 230	69.34 222	86.64 223

FMVSNet2		76.07 191	74.01 196	82.26 168	88.85 153	67.66 73	91.33 154	91.61 143	70.84 183	65.98 220	82.25 222	48.03 229	92.00 244	58.46 236	68.73 228	87.10 216

LFMVS		84.34 57	82.73 73	89.18 8	94.76 21	73.25 8	94.99 31	91.89 133	71.90 159	82.16 54	93.49 77	47.98 232	97.05 68	82.55 59	84.82 109	97.25 2

QAPM		79.95 120	77.39 143	87.64 23	89.63 138	71.41 12	93.30 75	93.70 54	65.34 244	67.39 212	91.75 109	47.83 233	98.96 5	57.71 239	89.81 75	92.54 134

HPM-MVS_fast		80.25 113	79.55 111	82.33 162	91.55 94	59.95 248	91.32 155	89.16 230	65.23 245	74.71 119	93.07 84	47.81 234	95.74 116	74.87 114	88.23 83	91.31 155

CANet_DTU		84.09 62	83.52 56	85.81 74	90.30 121	66.82 97	91.87 129	89.01 237	85.27 7	86.09 23	93.74 73	47.71 235	96.98 77	77.90 92	89.78 76	93.65 106

abl_6		79.82 122	79.20 118	81.70 184	89.85 128	58.34 266	88.47 221	90.07 199	62.56 270	77.71 91	93.08 82	47.65 236	96.78 87	77.94 91	85.45 107	89.99 167

v1240		75.21 205	72.98 214	81.88 179	79.20 289	66.00 131	90.75 173	89.11 233	71.63 171	67.41 211	81.22 243	47.36 237	93.87 195	65.46 188	64.72 259	85.77 244

PEN-MVS		69.46 264	68.56 248	72.17 307	79.27 288	49.71 319	86.90 256	89.24 227	67.24 231	59.08 266	82.51 216	47.23 238	83.54 323	48.42 270	57.12 298	83.25 275

DI_MVS_plusplus_test		79.78 124	77.50 140	86.62 47	80.90 251	69.46 36	90.69 175	91.97 131	77.00 69	59.07 267	82.34 220	46.82 239	95.88 111	82.14 61	86.59 98	94.53 76

CNLPA		74.31 219	72.30 222	80.32 208	91.49 96	61.66 221	90.85 169	80.72 320	56.67 306	63.85 242	90.64 118	46.75 240	90.84 266	53.79 251	75.99 175	88.47 184

test_normal		79.66 125	77.36 145	86.54 51	80.72 255	69.21 40	90.68 176	92.16 126	76.99 70	58.63 271	82.03 229	46.70 241	95.86 112	81.74 65	86.63 97	94.56 71

114514_t		79.17 133	77.67 135	83.68 130	95.32 16	65.53 142	92.85 89	91.60 144	63.49 262	67.92 204	90.63 120	46.65 242	95.72 121	67.01 171	83.54 122	89.79 168

PS-CasMVS		69.86 258	69.13 244	72.07 308	80.35 270	50.57 314	87.02 252	89.75 209	67.27 230	59.19 265	82.28 221	46.58 243	82.24 332	50.69 261	59.02 293	83.39 273

DTE-MVSNet		68.46 270	67.33 260	71.87 311	77.94 304	49.00 322	86.16 263	88.58 247	66.36 235	58.19 272	82.21 224	46.36 244	83.87 321	44.97 286	55.17 306	82.73 282

PMMVS		81.98 93	82.04 79	81.78 180	89.76 131	56.17 288	91.13 165	90.69 173	77.96 57	80.09 69	93.57 75	46.33 245	94.99 138	81.41 68	87.46 90	94.17 86

OPM-MVS		79.00 135	78.09 129	81.73 181	83.52 230	63.83 179	91.64 143	90.30 189	76.36 81	71.97 149	89.93 138	46.30 246	95.17 136	75.10 107	77.70 157	86.19 230

BH-RMVSNet		79.46 130	77.65 136	84.89 101	91.68 91	65.66 139	93.55 69	88.09 255	72.93 137	73.37 129	91.12 114	46.20 247	96.12 103	56.28 243	85.61 106	92.91 126

TR-MVS		78.77 142	77.37 144	82.95 140	90.49 117	60.88 228	93.67 65	90.07 199	70.08 196	74.51 120	91.37 112	45.69 248	95.70 122	60.12 228	80.32 138	92.29 140

Patchmatch-test1		75.00 208	71.80 227	84.58 112	86.63 190	70.08 25	81.06 297	89.19 228	71.60 172	70.01 169	77.16 284	45.53 249	88.63 295	51.79 258	73.27 191	95.02 60

semantic-postprocess				76.32 278	81.48 246	60.67 235		85.99 286	66.17 236	59.50 263	78.88 270	45.51 250	83.65 322	62.58 215	61.93 275	84.63 261

IterMVS		72.65 235	70.83 232	78.09 262	82.17 240	62.96 199	87.64 244	86.28 280	71.56 174	60.44 259	78.85 271	45.42 251	86.66 309	63.30 204	61.83 276	84.65 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		76.14 190	75.28 179	78.72 252	83.22 232	55.17 294	89.87 197	87.78 259	75.42 89	67.98 203	81.43 236	45.08 252	92.52 229	75.08 108	71.63 204	88.48 183

mvs-test1		78.74 143	77.95 132	81.14 195	83.22 232	57.13 278	93.96 52	87.78 259	75.42 89	72.68 136	90.80 117	45.08 252	94.54 155	75.08 108	77.49 163	91.74 147

v748		70.55 253	67.97 257	78.27 257	75.75 316	58.78 263	86.29 262	89.25 226	65.12 252	56.66 282	77.17 283	45.05 254	92.95 213	58.13 237	58.33 297	83.10 279

XVG-OURS-SEG-HR		74.70 217	73.08 213	79.57 227	78.25 301	57.33 277	80.49 299	87.32 265	63.22 265	68.76 191	90.12 131	44.89 255	91.59 256	70.55 144	74.09 187	89.79 168

v7n		71.31 243	68.65 246	79.28 235	76.40 311	60.77 231	86.71 259	89.45 219	64.17 257	58.77 270	78.24 273	44.59 256	93.54 202	57.76 238	61.75 278	83.52 269

pmmvs5		73.35 226	71.52 228	78.86 247	78.64 299	60.61 237	91.08 166	86.90 268	67.69 224	63.32 246	83.64 206	44.33 257	90.53 268	62.04 218	66.02 243	85.46 251

OpenMVS	70.45 11	78.54 148	75.92 163	86.41 59	85.93 203	71.68 11	92.74 91	92.51 111	66.49 234	64.56 234	91.96 104	43.88 258	98.10 28	54.61 247	90.65 70	89.44 173

AdaColmap		78.94 137	77.00 150	84.76 105	96.34 6	65.86 134	92.66 97	87.97 258	62.18 273	70.56 158	92.37 100	43.53 259	97.35 53	64.50 195	82.86 124	91.05 157

tfpnnormal		70.10 255	67.36 259	78.32 255	83.45 231	60.97 227	88.85 213	92.77 99	64.85 253	60.83 258	78.53 272	43.52 260	93.48 204	31.73 339	61.70 280	80.52 311

test_djsdf		73.76 225	72.56 219	77.39 270	77.00 309	53.93 299	89.07 211	90.69 173	65.80 239	63.92 240	82.03 229	43.14 261	92.67 225	72.83 119	68.53 229	85.57 249

GA-MVS		78.33 151	76.23 159	84.65 110	83.65 228	66.30 125	91.44 146	90.14 197	76.01 83	70.32 165	84.02 204	42.50 262	94.72 148	70.98 139	77.00 169	92.94 125

PLC	68.80 14	75.23 204	73.68 201	79.86 220	92.93 58	58.68 265	90.64 177	88.30 251	60.90 281	64.43 238	90.53 121	42.38 263	94.57 151	56.52 241	76.54 172	86.33 227
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
Anonymous20240521		69.68 261	68.15 255	74.28 291	82.04 243	49.91 317	85.92 264	90.52 178	63.87 260	57.61 278	81.33 239	41.82 264	89.57 290	46.86 276	63.36 269	83.37 274

1111		56.66 313	54.98 312	61.69 327	61.99 343	31.38 349	79.81 309	83.17 310	45.66 333	41.94 337	65.44 330	41.50 265	79.56 340	27.64 343	47.68 330	74.14 336

.test1245		46.52 322	49.68 319	37.02 344	61.99 343	31.38 349	79.81 309	83.17 310	45.66 333	41.94 337	65.44 330	41.50 265	79.56 340	27.64 343	0.01 361	0.13 362

V4		69.80 259	67.02 262	78.15 260	71.86 324	60.10 245	82.02 288	87.39 262	64.48 254	57.78 276	75.98 290	41.49 267	92.90 218	63.00 208	59.16 291	81.44 296

v52		69.80 259	67.01 263	78.15 260	71.84 325	60.10 245	82.02 288	87.39 262	64.48 254	57.80 275	75.97 291	41.47 268	92.90 218	63.00 208	59.13 292	81.45 295

Fast-Effi-MVS+-dtu		75.04 206	73.37 211	80.07 214	80.86 252	59.52 254	91.20 161	85.38 291	71.90 159	65.20 228	84.84 197	41.46 269	92.97 212	66.50 176	72.96 195	87.73 200

MS-PatchMatch		77.90 160	76.50 156	82.12 173	85.99 199	69.95 28	91.75 138	92.70 101	73.97 116	62.58 253	84.44 202	41.11 270	95.78 114	63.76 200	92.17 52	80.62 310

pcd1.5k->3k		31.17 331	31.85 329	29.12 346	81.48 246	0.00 368	0.00 360	91.79 137	0.00 363	0.00 364	0.00 365	41.05 271	0.00 366	0.00 363	72.34 202	87.36 211

our_test_3		68.29 271	64.69 275	79.11 243	78.92 294	64.85 157	88.40 223	85.06 293	60.32 286	52.68 304	76.12 289	40.81 272	89.80 289	44.25 288	55.65 304	82.67 286

XVG-OURS		74.25 220	72.46 221	79.63 225	78.45 300	57.59 274	80.33 301	87.39 262	63.86 261	68.76 191	89.62 142	40.50 273	91.72 248	69.00 156	74.25 185	89.58 171

VDD-MVS		83.06 76	81.81 82	86.81 42	90.86 113	67.70 72	95.40 20	91.50 148	75.46 88	81.78 56	92.34 101	40.09 274	97.13 67	86.85 33	82.04 130	95.60 35

DP-MVS		69.90 257	66.48 264	80.14 211	95.36 15	62.93 200	89.56 200	76.11 328	50.27 323	57.69 277	85.23 193	39.68 275	95.73 117	33.35 333	71.05 210	81.78 291

ppachtmachnet_test		67.72 275	63.70 282	79.77 223	78.92 294	66.04 130	88.68 216	82.90 313	60.11 288	55.45 285	75.96 292	39.19 276	90.55 267	39.53 306	52.55 313	82.71 284

ADS-MVSNet2		66.90 281	63.44 284	77.26 273	88.06 170	60.70 234	68.01 336	75.56 333	57.57 298	64.48 235	69.87 323	38.68 277	84.10 316	40.87 301	67.89 234	86.97 217

ADS-MVSNet		68.54 269	64.38 280	81.03 201	88.06 170	66.90 94	68.01 336	84.02 301	57.57 298	64.48 235	69.87 323	38.68 277	89.21 294	40.87 301	67.89 234	86.97 217

LPG-MVS_test		75.82 196	74.58 186	79.56 228	84.31 220	59.37 256	90.44 180	89.73 212	69.49 200	64.86 231	88.42 149	38.65 279	94.30 169	72.56 122	72.76 196	85.01 256

LGP-MVS_train				79.56 228	84.31 220	59.37 256		89.73 212	69.49 200	64.86 231	88.42 149	38.65 279	94.30 169	72.56 122	72.76 196	85.01 256

VDDNet		80.50 109	78.26 127	87.21 33	86.19 196	69.79 31	94.48 38	91.31 155	60.42 284	79.34 76	90.91 115	38.48 281	96.56 95	82.16 60	81.05 135	95.27 46

ACMP	71.68 10	75.58 200	74.23 192	79.62 226	84.97 210	59.64 251	90.80 171	89.07 235	70.39 193	62.95 249	87.30 174	38.28 282	93.87 195	72.89 118	71.45 207	85.36 253
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
UGNet		79.87 121	78.68 122	83.45 136	89.96 126	61.51 223	92.13 109	90.79 170	76.83 73	78.85 83	86.33 183	38.16 283	96.17 101	67.93 163	87.17 91	92.67 130
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		71.14 246	69.37 241	76.45 277	72.95 321	54.71 296	84.19 271	88.88 240	61.92 276	62.15 255	79.77 263	38.14 284	91.44 264	68.90 158	67.45 237	83.21 276

xiu_mvs_v1_base_debu		82.16 88	81.12 89	85.26 94	86.42 191	68.72 49	92.59 100	90.44 179	73.12 134	84.20 42	94.36 58	38.04 285	95.73 117	84.12 49	86.81 93	91.33 151

xiu_mvs_v1_base		82.16 88	81.12 89	85.26 94	86.42 191	68.72 49	92.59 100	90.44 179	73.12 134	84.20 42	94.36 58	38.04 285	95.73 117	84.12 49	86.81 93	91.33 151

xiu_mvs_v1_base_debi		82.16 88	81.12 89	85.26 94	86.42 191	68.72 49	92.59 100	90.44 179	73.12 134	84.20 42	94.36 58	38.04 285	95.73 117	84.12 49	86.81 93	91.33 151

PVSNet_0	68.08 15	71.81 238	68.32 252	82.27 165	84.68 212	62.31 212	88.68 216	90.31 188	75.84 84	57.93 274	80.65 252	37.85 288	94.19 174	69.94 147	29.05 349	90.31 164

Anonymous20231206		67.53 278	65.78 266	72.79 301	74.95 317	47.59 326	88.23 225	87.32 265	61.75 279	58.07 273	77.29 280	37.79 289	87.29 307	42.91 291	63.71 267	83.48 270

ACMM	69.62 13	74.34 218	72.73 216	79.17 240	84.25 222	57.87 269	90.36 183	89.93 205	63.17 266	65.64 227	86.04 187	37.79 289	94.10 180	65.89 183	71.52 206	85.55 250
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cascas		78.18 153	75.77 165	85.41 89	87.14 184	69.11 41	92.96 84	91.15 160	66.71 232	70.47 159	86.07 185	37.49 291	96.48 96	70.15 146	79.80 140	90.65 160

LS3D		69.17 265	66.40 265	77.50 267	91.92 81	56.12 289	85.12 266	80.37 321	46.96 330	56.50 283	87.51 167	37.25 292	93.71 199	32.52 338	79.40 142	82.68 285

MDA-MVSNet_test_wron		63.78 297	60.16 298	74.64 286	78.15 302	60.41 238	83.49 276	84.03 300	56.17 309	39.17 341	71.59 320	37.22 293	83.24 327	42.87 293	48.73 328	80.26 314

YYNet1		63.76 298	60.14 299	74.62 287	78.06 303	60.19 244	83.46 278	83.99 304	56.18 308	39.25 340	71.56 321	37.18 294	83.34 325	42.90 292	48.70 329	80.32 313

FMVSNet5		68.04 273	65.66 268	75.18 284	84.43 218	57.89 268	83.54 275	86.26 281	61.83 278	53.64 300	73.30 300	37.15 295	85.08 313	48.99 267	61.77 277	82.56 287

test20.03		63.83 296	62.65 290	67.38 320	70.58 331	39.94 340	86.57 260	84.17 299	63.29 264	51.86 307	77.30 279	37.09 296	82.47 329	38.87 314	54.13 310	79.73 318

PVSNet	73.49 8	80.05 117	78.63 123	84.31 117	90.92 111	64.97 155	92.47 103	91.05 165	79.18 41	72.43 144	90.51 123	37.05 297	94.06 184	68.06 161	86.00 103	93.90 103

EU-MVSNet		64.01 295	63.01 287	67.02 321	74.40 319	38.86 344	83.27 280	86.19 282	45.11 336	54.27 293	81.15 246	36.91 298	80.01 337	48.79 269	57.02 299	82.19 289

Anonymous20231211		73.08 227	70.39 234	81.13 196	90.62 116	63.33 191	91.40 148	90.06 201	51.84 318	64.46 237	80.67 251	36.49 299	94.07 183	63.83 199	64.17 263	85.98 239

FMVSNet1		72.71 233	69.91 237	81.10 198	83.60 229	65.11 149	90.01 189	90.32 185	63.92 259	63.56 244	80.25 258	36.35 300	91.54 258	54.46 248	66.75 240	86.64 223

testpf		57.17 310	56.93 307	57.88 331	79.13 292	42.40 334	34.23 356	85.97 287	52.64 315	47.66 322	66.50 327	36.33 301	79.65 339	53.60 253	56.31 303	51.60 350

Anonymous20240529		76.84 181	74.15 193	84.88 102	91.02 107	64.95 156	93.84 61	91.09 163	53.57 313	73.00 131	87.42 168	35.91 302	97.32 55	69.14 155	72.41 201	92.36 137

CMPMVS	48.56 21	66.77 282	64.41 279	73.84 293	70.65 330	50.31 315	77.79 319	85.73 290	45.54 335	44.76 330	82.14 225	35.40 303	90.14 277	63.18 206	74.54 183	81.07 305
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		67.57 277	64.76 274	76.00 281	72.82 323	53.37 301	88.71 215	86.78 269	53.19 314	57.58 279	78.03 275	35.33 304	92.41 232	55.56 245	54.88 308	82.21 288

test2356		64.16 294	63.28 285	66.81 322	69.37 335	39.86 342	87.76 235	86.02 285	59.83 290	53.54 301	73.23 301	34.94 305	80.67 336	39.66 305	65.20 250	79.89 316

PatchMatch-RL		72.06 236	69.98 235	78.28 256	89.51 142	55.70 291	83.49 276	83.39 308	61.24 280	63.72 243	82.76 214	34.77 306	93.03 211	53.37 255	77.59 158	86.12 232

LTVRE_ROB	59.60 19	66.27 284	63.54 283	74.45 288	84.00 225	51.55 308	67.08 339	83.53 305	58.78 295	54.94 288	80.31 256	34.54 307	93.23 208	40.64 303	68.03 232	78.58 326
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
F-COLMAP		70.66 251	68.44 250	77.32 271	86.37 194	55.91 290	88.00 228	86.32 279	56.94 304	57.28 280	88.07 158	33.58 308	92.49 230	51.02 260	68.37 230	83.55 267

pmmvs-eth3d		65.53 288	62.32 292	75.19 283	69.39 334	59.59 252	82.80 285	83.43 306	62.52 271	51.30 311	72.49 305	32.86 309	87.16 308	55.32 246	50.73 320	78.83 324

MDA-MVSNet-bldmvs		61.54 303	57.70 304	73.05 298	79.53 285	57.00 280	83.08 283	81.23 316	57.57 298	34.91 344	72.45 307	32.79 310	86.26 312	35.81 321	41.95 338	75.89 333

MIMVSNet		71.64 241	68.44 250	81.23 190	81.97 244	64.44 165	73.05 325	88.80 242	69.67 199	64.59 233	74.79 297	32.79 310	87.82 304	53.99 250	76.35 173	91.42 150

UnsupCasMVSNet_eth		65.79 287	63.10 286	73.88 292	70.71 329	50.29 316	81.09 296	89.88 206	72.58 142	49.25 317	74.77 298	32.57 312	87.43 306	55.96 244	41.04 340	83.90 265

N_pmnet		50.55 318	49.11 320	54.88 335	77.17 308	4.02 365	84.36 270	2.00 365	48.59 326	45.86 326	68.82 325	32.22 313	82.80 328	31.58 340	51.38 318	77.81 329

test_0402		64.54 291	61.09 296	74.92 285	84.10 224	60.75 232	87.95 229	79.71 323	52.03 317	52.41 305	77.20 281	32.21 314	91.64 255	23.14 348	61.03 284	72.36 338

DSMNet-mixed		56.78 311	54.44 313	63.79 326	63.21 340	29.44 353	64.43 342	64.10 351	42.12 344	51.32 310	71.60 319	31.76 315	75.04 346	36.23 319	65.20 250	86.87 221

Test4		76.45 187	73.45 210	85.45 87	76.07 315	67.61 75	88.38 224	90.83 169	76.71 75	53.06 302	79.65 266	31.61 316	94.35 167	78.47 86	86.22 102	94.40 80

MSDG		69.54 263	65.73 267	80.96 202	85.11 209	63.71 184	84.19 271	83.28 309	56.95 303	54.50 290	84.03 203	31.50 317	96.03 107	42.87 293	69.13 225	83.14 278

RPSCF		64.24 293	61.98 294	71.01 312	76.10 313	45.00 330	75.83 322	75.94 330	46.94 331	58.96 268	84.59 200	31.40 318	82.00 333	47.76 273	60.33 289	86.04 238

jajsoiax		73.05 228	71.51 229	77.67 265	77.46 306	54.83 295	88.81 214	90.04 202	69.13 205	62.85 251	83.51 208	31.16 319	92.75 222	70.83 140	69.80 218	85.43 252

MVS-HIRNet		60.25 305	55.55 311	74.35 289	84.37 219	56.57 287	71.64 327	74.11 337	34.44 347	45.54 328	42.24 349	31.11 320	89.81 287	40.36 304	76.10 174	76.67 332

SixPastTwentyTwo		64.92 289	61.78 295	74.34 290	78.74 297	49.76 318	83.42 279	79.51 324	62.86 268	50.27 314	77.35 278	30.92 321	90.49 269	45.89 280	47.06 331	82.78 280

LP		56.71 312	51.64 316	71.91 310	80.08 279	60.33 240	61.72 344	75.61 332	43.87 341	43.76 334	60.30 339	30.46 322	84.05 317	22.94 349	46.06 333	71.34 339

mvs_tets		72.71 233	71.11 230	77.52 266	77.41 307	54.52 297	88.45 222	89.76 208	68.76 210	62.70 252	83.26 211	29.49 323	92.71 223	70.51 145	69.62 220	85.34 254

Anonymous202405211		77.96 158	75.33 178	85.87 71	93.73 42	64.52 160	94.85 35	85.36 292	62.52 271	76.11 107	90.18 127	29.43 324	97.29 57	68.51 160	77.24 168	95.81 29

K. test v3		63.09 299	59.61 301	73.53 295	76.26 312	49.38 321	83.27 280	77.15 327	64.35 256	47.77 320	72.32 310	28.73 325	87.79 305	49.93 265	36.69 344	83.41 272

UnsupCasMVSNet_bld		61.60 302	57.71 303	73.29 297	68.73 336	51.64 307	78.61 313	89.05 236	57.20 302	46.11 323	61.96 337	28.70 326	88.60 297	50.08 264	38.90 342	79.63 319

lessismore_v0				73.72 294	72.93 322	47.83 325		61.72 354		45.86 326	73.76 299	28.63 327	89.81 287	47.75 274	31.37 348	83.53 268

new-patchmatchnet		59.30 309	56.48 309	67.79 318	65.86 338	44.19 331	82.47 286	81.77 314	59.94 289	43.65 335	66.20 329	27.67 328	81.68 334	39.34 307	41.40 339	77.50 330

testing_2		71.09 247	67.32 261	82.40 161	69.82 332	66.52 119	83.64 274	90.77 171	72.21 152	45.12 329	71.07 322	27.60 329	93.74 198	75.71 103	69.96 217	86.95 219

ACMH	63.93 17	68.62 267	64.81 273	80.03 215	85.22 207	63.25 192	87.72 236	84.66 296	60.83 282	51.57 309	79.43 268	27.29 330	94.96 139	41.76 297	64.84 257	81.88 290
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		64.68 290	62.17 293	72.21 306	76.08 314	47.35 327	80.67 298	81.02 318	56.19 307	51.60 308	79.66 265	27.05 331	88.56 298	53.60 253	53.63 311	80.71 309

ACMH+	65.35 16	67.65 276	64.55 276	76.96 274	84.59 214	57.10 279	88.08 227	80.79 319	58.59 297	53.00 303	81.09 247	26.63 332	92.95 213	46.51 277	61.69 281	80.82 307

OpenMVS_ROB	61.12 18	66.39 283	62.92 288	76.80 276	76.51 310	57.77 270	89.22 206	83.41 307	55.48 310	53.86 298	77.84 276	26.28 333	93.95 192	34.90 330	68.76 227	78.68 325

COLMAP_ROB	57.96 20	62.98 300	59.65 300	72.98 299	81.44 248	53.00 303	83.75 273	75.53 334	48.34 328	48.81 318	81.40 238	24.14 334	90.30 270	32.95 335	60.52 288	75.65 334
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	57.33 306	68.67 316	69.71 333	44.13 332	78.92 312	84.21 298	55.05 311	44.63 331	71.85 314	23.91 335	81.54 335	32.63 337	55.03 307	80.35 312

testgi		64.48 292	62.87 289	69.31 315	71.24 326	40.62 339	85.49 265	79.92 322	65.36 243	54.18 295	83.49 209	23.74 336	84.55 315	41.60 298	60.79 287	82.77 281

ITE_SJBPF				70.43 313	74.44 318	47.06 328		77.32 326	60.16 287	54.04 296	83.53 207	23.30 337	84.01 319	43.07 290	61.58 282	80.21 315

EG-PatchMatch MVS		68.55 268	65.41 270	77.96 263	78.69 298	62.93 200	89.86 198	89.17 229	60.55 283	50.27 314	77.73 277	22.60 338	94.06 184	47.18 275	72.65 198	76.88 331

tmp_tt		22.26 336	23.75 335	17.80 349	5.23 364	12.06 364	35.26 355	39.48 360	2.82 360	18.94 351	44.20 348	22.23 339	24.64 362	36.30 318	9.31 358	16.69 357

USDC		67.43 280	64.51 277	76.19 279	77.94 304	55.29 293	78.38 315	85.00 294	73.17 132	48.36 319	80.37 255	21.23 340	92.48 231	52.15 257	64.02 265	80.81 308

XVG-ACMP-BASELINE		68.04 273	65.53 269	75.56 282	74.06 320	52.37 304	78.43 314	85.88 288	62.03 274	58.91 269	81.21 245	20.38 341	91.15 265	60.69 225	68.18 231	83.16 277

AllTest		61.66 301	58.06 302	72.46 303	79.57 283	51.42 310	80.17 304	68.61 346	51.25 320	45.88 324	81.23 241	19.86 342	86.58 310	38.98 308	57.01 300	79.39 320

TestCases				72.46 303	79.57 283	51.42 310		68.61 346	51.25 320	45.88 324	81.23 241	19.86 342	86.58 310	38.98 308	57.01 300	79.39 320

test1235678		55.73 314	52.74 314	64.68 325	60.16 346	35.56 347	81.65 292	81.46 315	51.27 319	38.93 342	62.82 336	17.44 344	78.58 344	30.87 341	50.09 327	79.89 316

pmmvs3		55.51 315	51.50 318	67.53 319	57.90 348	50.93 313	80.37 300	73.66 338	40.63 345	44.15 333	64.75 334	16.30 345	78.97 342	44.77 287	40.98 341	72.69 337

TDRefinement		55.28 316	51.58 317	66.39 323	59.53 347	46.15 329	76.23 321	72.80 339	44.60 338	42.49 336	76.28 288	15.29 346	82.39 330	33.20 334	43.75 336	70.62 341

new_pmnet		49.31 319	46.44 321	57.93 330	62.84 341	40.74 338	68.47 335	62.96 353	36.48 346	35.09 343	57.81 341	14.97 347	72.18 347	32.86 336	46.44 332	60.88 348

TinyColmap		60.32 304	56.42 310	72.00 309	78.78 296	53.18 302	78.36 316	75.64 331	52.30 316	41.59 339	75.82 294	14.76 348	88.35 300	35.84 320	54.71 309	74.46 335

LF4IMVS		54.01 317	52.12 315	59.69 329	62.41 342	39.91 341	68.59 334	68.28 348	42.96 342	44.55 332	75.18 295	14.09 349	68.39 350	41.36 300	51.68 317	70.78 340

PM-MVS		59.40 307	56.59 308	67.84 317	63.63 339	41.86 336	76.76 320	63.22 352	59.01 294	51.07 312	72.27 311	11.72 350	83.25 326	61.34 221	50.28 326	78.39 327

no-one		44.13 324	38.39 325	61.34 328	45.91 356	41.94 335	61.67 345	75.07 336	45.05 337	20.07 350	40.68 352	11.58 351	79.82 338	30.18 342	15.30 352	62.26 347

testus		59.36 308	57.51 305	64.90 324	66.72 337	37.56 345	84.98 267	81.09 317	57.46 301	47.72 321	72.76 302	11.43 352	78.78 343	36.56 317	58.91 294	78.36 328

ambc				69.61 314	61.38 345	41.35 337	49.07 353	85.86 289		50.18 316	66.40 328	10.16 353	88.14 302	45.73 281	44.20 335	79.32 322

FPMVS		45.64 323	43.10 324	53.23 337	51.42 351	36.46 346	64.97 341	71.91 341	29.13 349	27.53 347	61.55 338	9.83 354	65.01 354	16.00 354	55.58 305	58.22 349

ANet_high		40.27 326	35.20 327	55.47 334	34.74 360	34.47 348	63.84 343	71.56 342	48.42 327	18.80 352	41.08 350	9.52 355	64.45 355	20.18 351	8.66 359	67.49 345

test12356		47.51 320	44.82 322	55.56 333	52.53 349	21.09 360	71.45 328	76.03 329	44.14 340	30.69 345	58.18 340	9.01 356	76.14 345	26.95 345	34.43 347	69.46 343

testmv		46.98 321	43.53 323	57.35 332	47.75 354	30.41 352	74.99 324	77.69 325	42.84 343	28.03 346	53.36 342	8.18 357	71.18 348	24.36 347	34.55 345	70.46 342

EMVS		23.76 335	23.20 336	25.46 348	41.52 358	16.90 363	60.56 347	38.79 362	14.62 356	8.99 359	20.24 360	7.35 358	45.82 360	7.25 359	9.46 357	13.64 359

E-PMN		24.61 333	24.00 334	26.45 347	43.74 357	18.44 362	60.86 346	39.66 359	15.11 355	9.53 358	22.10 356	6.52 359	46.94 359	8.31 358	10.14 355	13.98 358

DeepMVS_CX				34.71 345	51.45 350	24.73 359		28.48 364	31.46 348	17.49 353	52.75 343	5.80 360	42.60 361	18.18 353	19.42 350	36.81 354

Gipuma		34.91 328	31.44 330	45.30 340	70.99 328	39.64 343	19.85 359	72.56 340	20.10 354	16.16 354	21.47 357	5.08 361	71.16 349	13.07 355	43.70 337	25.08 356
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		40.54 325	35.79 326	54.76 336	36.92 359	30.81 351	51.41 351	69.02 345	22.07 351	24.63 348	45.37 347	4.56 362	65.81 352	33.67 332	34.50 346	67.67 344

PMMVS2		37.93 327	33.61 328	50.92 338	46.31 355	24.76 358	60.55 348	50.05 356	28.94 350	20.93 349	47.59 344	4.41 363	65.13 353	25.14 346	18.55 351	62.87 346

PMVS	26.43 22	31.84 330	28.16 332	42.89 341	25.87 363	27.58 356	50.92 352	49.78 358	21.37 353	14.17 356	40.81 351	2.01 364	66.62 351	9.61 357	38.88 343	34.49 355
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	24.84 23	24.35 334	19.77 338	38.09 343	34.56 361	26.92 357	26.57 357	38.87 361	11.73 358	11.37 357	27.44 354	1.37 365	50.42 358	11.41 356	14.60 353	36.93 353
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		32.77 329	29.98 331	41.11 342	48.05 352	29.17 354	65.82 340	50.02 357	21.42 352	14.74 355	37.19 353	1.11 366	55.11 357	19.75 352	11.77 354	39.06 352

wuyk23d		11.30 338	10.95 339	12.33 350	48.05 352	19.89 361	25.89 358	1.92 366	3.58 359	3.12 361	1.37 362	0.64 367	15.77 363	6.23 360	7.77 360	1.35 360

wuykxyi23d		29.03 332	23.09 337	46.84 339	31.67 362	28.82 355	43.46 354	57.72 355	14.39 357	7.52 360	20.84 358	0.64 367	60.29 356	21.57 350	10.04 356	51.40 351

test123		6.92 341	9.21 342	0.08 351	0.03 366	0.05 366	81.65 292	0.01 368	0.02 362	0.14 363	0.85 364	0.03 369	0.02 364	0.12 362	0.00 363	0.16 361

testmvs		7.23 340	9.62 341	0.06 352	0.04 365	0.02 367	84.98 267	0.02 367	0.03 361	0.18 362	1.21 363	0.01 370	0.02 364	0.14 361	0.01 361	0.13 362

sosnet-low-res		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 363	0.00 364

sosnet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 363	0.00 364

uncertanet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 363	0.00 364

Regformer		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 363	0.00 364

ab-mvs-re		7.91 339	10.55 340	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	94.95 43	0.00 371	0.00 366	0.00 363	0.00 363	0.00 364

uanet		0.00 343	0.00 344	0.00 353	0.00 367	0.00 368	0.00 360	0.00 369	0.00 363	0.00 364	0.00 365	0.00 371	0.00 366	0.00 363	0.00 363	0.00 364

GSMVS																94.68 68

test_part3							94.96 32		68.52 215		97.23 2		98.90 7	91.52 6

test_part2					96.29 7	68.16 63				90.78 4

MTGPA								92.23 117

MTMP							93.77 63	32.52 363

gm-plane-assit					88.42 164	67.04 90			78.62 52		91.83 106		97.37 51	76.57 99

test9_res														89.41 12	94.96 11	95.29 44

agg_prior2														86.41 34	94.75 19	95.33 41

agg_prior					94.16 34	66.97 91		93.31 77		84.49 39			96.75 89

test_prior4						67.18 86	93.92 56

test_prior				86.42 57	94.71 23	67.35 80		93.10 89					96.84 85			95.05 56

旧先验2							92.00 119		59.37 293	87.54 17			93.47 205	75.39 105

新几何2							91.41 147

无先验							92.71 92	92.61 107	62.03 274				97.01 71	66.63 173		93.97 98

原ACMM2							92.01 117

testdata2													96.09 104	61.26 222

testdata1							89.21 207		77.55 63

plane_prior7					86.94 186	61.51 223

plane_prior5								91.31 155					95.55 127	76.74 97	78.53 151	88.39 185

plane_prior4											89.14 144

plane_prior3						61.95 219			79.09 44	72.53 140

plane_prior2							93.13 79		78.81 49

plane_prior1					87.15 183

plane_prior						62.42 209	93.85 59		79.38 37						78.80 149

n2								0.00 369

nn								0.00 369

door-mid								66.01 350

test11								93.01 91

door								66.57 349

HQP5-MVS						63.66 187

HQP-NCC					87.54 178		94.06 47		79.80 33	74.18 121

ACMP_Plane					87.54 178		94.06 47		79.80 33	74.18 121

BP-MVS														77.63 93

HQP4-MVS										74.18 121			95.61 123			88.63 180

HQP3-MVS								91.70 141							78.90 147

NP-MVS					87.41 181	63.04 198					90.30 125

ACMMP++_ref															71.63 204

ACMMP++															69.72 219

High-resolution multi-view benchmark