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		82.45 2	83.62 1	78.96 36	82.99 91	52.71 126	85.04 110	89.99 10	66.08 45	86.77 1	92.75 13	72.05 1	91.46 44	83.35 5	93.53 1	92.72 17

DELS-MVS		82.32 3	82.50 3	81.79 6	86.80 27	56.89 21	92.77 2	86.30 64	77.83 1	77.88 13	92.13 19	60.24 2	94.78 12	78.97 16	89.61 3	93.69 3
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		80.41 9	79.72 9	82.49 4	89.12 11	57.67 12	89.29 30	91.54 2	59.19 144	71.82 50	90.05 64	59.72 3	96.04 1	78.37 19	88.40 9	93.75 2

GG-mvs-BLEND				77.77 65	86.68 28	50.61 163	68.67 303	88.45 24		68.73 65	87.45 102	59.15 4	90.67 56	54.83 173	87.67 12	92.03 26

gg-mvs-nofinetune		67.43 168	64.53 189	76.13 98	85.95 33	47.79 218	64.38 311	88.28 31	39.34 311	66.62 80	41.27 339	58.69 5	89.00 99	49.64 206	86.62 15	91.59 33

test_part1								88.42 25				58.18 6			86.59 16	91.53 36

ESAPD		80.50 7	80.42 8	80.74 12	89.33 9	55.48 38	89.59 26	88.42 25	56.02 210	82.27 2	93.65 3	58.18 6	95.22 6	79.73 12	86.59 16	91.53 36

CostFormer		73.89 67	72.30 70	78.66 43	82.36 108	56.58 22	75.56 256	85.30 79	66.06 46	70.50 59	76.88 226	57.02 8	89.06 90	68.27 73	68.74 148	90.33 65

DeepPCF-MVS	69.37 1	80.65 6	81.56 5	77.94 64	85.46 48	49.56 184	90.99 11	86.66 57	70.58 13	80.07 7	95.30 1	56.18 9	90.97 51	82.57 8	86.22 21	93.28 7

TSAR-MVS + MP.		78.31 20	78.26 16	78.48 48	81.33 134	56.31 29	81.59 187	86.41 61	69.61 18	81.72 4	88.16 90	55.09 10	88.04 141	74.12 43	86.31 19	91.09 46

TSAR-MVS + GP.		77.82 25	77.59 23	78.49 47	85.25 52	50.27 175	90.02 17	90.57 5	56.58 200	74.26 27	91.60 33	54.26 11	92.16 36	75.87 31	79.91 70	93.05 12

EPP-MVSNet		71.14 100	70.07 100	74.33 138	79.18 166	46.52 230	83.81 136	86.49 58	56.32 208	57.95 182	84.90 127	54.23 12	89.14 89	58.14 145	69.65 143	87.33 119

MCST-MVS		83.01 1	83.30 2	82.15 5	92.84 2	57.58 13	93.77 1	91.10 4	75.95 2	77.10 16	93.09 9	54.15 13	95.57 3	85.80 1	85.87 23	93.31 6

alignmvs		78.08 22	77.98 19	78.39 52	83.53 74	53.22 109	89.77 23	85.45 73	66.11 43	76.59 20	91.99 25	54.07 14	89.05 92	77.34 26	77.00 91	92.89 14

WTY-MVS		77.47 29	77.52 24	77.30 76	88.33 16	46.25 236	88.46 36	90.32 6	71.40 10	72.32 47	91.72 30	53.44 15	92.37 32	66.28 84	75.42 104	93.28 7

IB-MVS	68.87 2	74.01 65	72.03 77	79.94 24	83.04 88	55.50 37	90.24 16	88.65 18	67.14 34	61.38 136	81.74 175	53.21 16	94.28 14	60.45 128	62.41 204	90.03 72
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
HPM-MVS++		80.50 7	80.71 7	79.88 25	87.34 24	55.20 49	89.93 20	87.55 45	66.04 48	79.46 9	93.00 12	53.10 17	91.76 41	80.40 10	89.56 4	92.68 18

MVSTER		73.25 74	72.33 68	76.01 102	85.54 44	53.76 81	83.52 140	87.16 48	67.06 35	63.88 116	81.66 176	52.77 18	90.44 60	64.66 99	64.69 176	83.84 175

CNVR-MVS		81.76 4	81.90 4	81.33 9	90.04 5	57.70 11	91.71 3	88.87 16	70.31 14	77.64 15	93.87 2	52.58 19	93.91 17	84.17 2	87.92 11	92.39 19

tpmp4_e23		70.01 119	67.13 146	78.65 44	81.93 116	57.90 10	73.99 267	81.35 171	60.61 123	65.28 95	73.78 253	52.48 20	88.60 116	48.40 213	66.35 167	89.44 80

FIs		70.00 120	70.24 98	69.30 233	77.93 187	38.55 292	83.99 134	87.72 42	66.86 37	57.66 189	84.17 132	52.28 21	85.31 205	52.72 191	68.80 147	84.02 167

tpm2		70.82 106	68.44 118	77.98 62	80.78 141	56.11 31	74.21 266	81.28 175	60.24 127	68.04 70	75.27 245	52.26 22	88.50 123	55.82 164	68.03 153	89.33 81

DWT-MVSNet_test		75.47 52	73.87 55	80.29 16	87.33 25	57.05 18	82.86 159	87.96 35	72.59 6	67.29 74	87.79 95	51.61 23	91.52 43	54.75 176	72.63 125	92.29 21

UniMVSNet_NR-MVSNet		68.82 142	68.29 121	70.40 220	75.71 212	42.59 269	84.23 126	86.78 54	66.31 40	58.51 175	82.45 164	51.57 24	84.64 219	53.11 183	55.96 257	83.96 172

PAPM		76.76 36	76.07 38	78.81 39	80.20 156	59.11 5	86.86 65	86.23 65	68.60 21	70.18 60	88.84 81	51.57 24	87.16 162	65.48 90	86.68 14	90.15 70

mvs_anonymous		72.29 87	70.74 89	76.94 86	82.85 96	54.72 64	78.43 235	81.54 168	63.77 79	61.69 135	79.32 194	51.11 26	85.31 205	62.15 116	75.79 101	90.79 52

HY-MVS	67.03 5	73.90 66	73.14 59	76.18 97	84.70 58	47.36 221	75.56 256	86.36 63	66.27 41	70.66 57	83.91 135	51.05 27	89.31 87	67.10 78	72.61 126	91.88 28

canonicalmvs		78.17 21	77.86 21	79.12 35	84.30 62	54.22 74	87.71 42	84.57 99	67.70 30	77.70 14	92.11 22	50.90 28	89.95 75	78.18 23	77.54 85	93.20 9

MVS_Test		75.85 49	74.93 49	78.62 45	84.08 67	55.20 49	83.99 134	85.17 84	68.07 25	73.38 34	82.76 157	50.44 29	89.00 99	65.90 86	80.61 59	91.64 31

FC-MVSNet-test		67.49 166	67.91 125	66.21 265	76.06 206	33.06 316	80.82 200	87.18 47	64.44 67	54.81 213	82.87 155	50.40 30	82.60 248	48.05 215	66.55 161	82.98 190

PatchFormer-LS_test		74.17 62	72.30 70	79.77 26	86.61 29	57.26 16	82.02 169	84.80 94	71.85 9	64.73 103	87.52 100	50.33 31	90.40 63	54.23 178	68.63 150	91.64 31

nrg030		72.27 89	71.56 80	74.42 136	75.93 209	50.60 164	86.97 61	83.21 139	62.75 95	67.15 76	84.38 130	50.07 32	86.66 176	71.19 59	62.37 205	85.99 140

tpmrst		71.04 103	69.77 103	74.86 128	83.19 83	55.86 33	75.64 255	78.73 215	67.88 26	64.99 101	73.73 254	49.96 33	79.56 276	65.92 85	67.85 156	89.14 89

CANet		80.90 5	81.17 6	80.09 23	87.62 22	54.21 75	91.60 6	86.47 59	73.13 5	79.89 8	93.10 7	49.88 34	92.98 23	84.09 3	84.75 35	93.08 11

Vis-MVSNet (Re-imp)		65.52 195	65.63 171	65.17 278	77.49 192	30.54 324	75.49 259	77.73 238	59.34 140	52.26 235	86.69 111	49.38 35	80.53 264	37.07 258	75.28 105	84.42 162

EPNet		78.36 19	78.49 15	77.97 63	85.49 45	52.04 139	89.36 29	84.07 117	73.22 4	77.03 17	91.72 30	49.32 36	90.17 72	73.46 49	82.77 44	91.69 30
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		68.36 152	67.48 141	70.97 209	79.93 157	51.34 154	76.58 245	78.75 214	67.73 28	63.54 122	74.86 247	48.33 37	72.36 319	53.93 180	63.71 183	89.21 86

APDe-MVS		78.44 16	78.20 17	79.19 32	88.56 12	54.55 70	89.76 24	87.77 40	55.91 212	78.56 12	92.49 16	48.20 38	92.65 28	79.49 14	83.04 43	90.39 63

MG-MVS		78.42 17	76.99 29	82.73 2	93.17 1	64.46 1	89.93 20	88.51 23	64.83 62	73.52 33	88.09 91	48.07 39	92.19 35	62.24 114	84.53 37	91.53 36

DeepC-MVS	67.15 4	76.90 35	76.27 35	78.80 40	80.70 143	55.02 54	86.39 69	86.71 55	66.96 36	67.91 71	89.97 66	48.03 40	91.41 45	75.60 33	84.14 39	89.96 73
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
test_prior3		77.59 27	77.33 26	78.39 52	86.35 31	54.91 59	89.04 32	85.45 73	61.88 104	73.55 31	91.46 37	48.01 41	89.70 81	74.73 38	85.46 26	90.55 55

test_prior2							89.04 32		61.88 104	73.55 31	91.46 37	48.01 41		74.73 38	85.46 26

Regformer-1		77.80 26	77.44 25	78.88 38	87.78 20	52.44 131	87.60 44	90.08 8	68.86 20	72.49 45	91.79 27	47.69 43	94.90 10	73.57 47	77.05 88	89.31 82

1112_ss		70.05 116	69.37 107	72.10 178	80.77 142	42.78 267	85.12 100	76.75 249	59.69 132	61.19 138	92.12 20	47.48 44	83.84 232	53.04 185	68.21 151	89.66 76

tfpn1000		62.79 231	61.74 207	65.95 271	80.50 155	35.93 301	76.53 247	83.99 121	51.24 256	49.82 255	83.44 150	47.32 45	83.02 247	21.84 327	60.99 209	78.89 247

Effi-MVS+		75.24 53	73.61 57	80.16 20	81.92 117	57.42 14	85.21 94	76.71 250	60.68 122	73.32 35	89.34 74	47.30 46	91.63 42	68.28 72	79.72 72	91.42 40

UniMVSNet (Re)		67.71 161	66.80 149	70.45 218	74.44 223	42.93 265	82.42 165	84.90 91	63.69 82	59.63 157	80.99 184	47.18 47	85.23 207	51.17 199	56.75 251	83.19 187

test12				79.24 31	86.89 26	56.08 32		85.16 85		72.27 48		47.15 48	91.10 48		85.93 22	90.54 58

PVSNet_Blended_VisFu		73.40 73	72.44 67	76.30 93	81.32 135	54.70 65	85.81 78	78.82 212	63.70 81	64.53 106	85.38 122	47.11 49	87.38 159	67.75 75	77.55 84	86.81 129

tfpn_ndepth		64.50 202	63.34 199	67.99 249	81.84 119	38.30 294	79.26 230	83.57 132	53.69 229	52.86 231	84.51 129	46.96 50	84.79 216	24.28 310	63.09 196	80.87 224

NCCC		79.57 13	79.23 13	80.59 13	89.50 8	56.99 19	91.38 8	88.17 32	67.71 29	73.81 29	92.75 13	46.88 51	93.28 21	78.79 17	84.07 40	91.50 39

VNet		77.99 24	77.92 20	78.19 57	87.43 23	50.12 176	90.93 12	91.41 3	67.48 32	75.12 22	90.15 63	46.77 52	91.00 49	73.52 48	78.46 78	93.44 4

PVSNet_BlendedMVS		73.42 72	73.30 58	73.76 153	85.91 34	51.83 144	86.18 74	84.24 108	65.40 55	69.09 63	80.86 186	46.70 53	88.13 137	75.43 34	65.92 170	81.33 216

PVSNet_Blended		76.53 40	76.54 31	76.50 91	85.91 34	51.83 144	88.89 34	84.24 108	67.82 27	69.09 63	89.33 76	46.70 53	88.13 137	75.43 34	81.48 54	89.55 78

conf0.01		63.04 222	61.74 207	66.95 259	80.60 146	35.92 302	76.01 248	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 320	58.77 229	81.89 203

conf0.002		63.04 222	61.74 207	66.95 259	80.60 146	35.92 302	76.01 248	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 320	58.77 229	81.89 203

thresconf0.02		62.84 225	61.74 207	66.14 266	80.60 146	35.92 302	76.01 248	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 320	58.77 229	79.44 242

tfpn_n400		62.84 225	61.74 207	66.14 266	80.60 146	35.92 302	76.01 248	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 320	58.77 229	79.44 242

tfpnconf		62.84 225	61.74 207	66.14 266	80.60 146	35.92 302	76.01 248	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 320	58.77 229	79.44 242

tfpnview11		62.84 225	61.74 207	66.14 266	80.60 146	35.92 302	76.01 248	84.09 110	52.62 238	50.87 247	83.60 142	46.49 55	83.04 241	22.59 320	58.77 229	79.44 242

Regformer-3		76.02 48	75.47 43	77.70 66	85.49 45	51.47 150	85.12 100	90.19 7	68.52 22	69.36 61	90.66 51	46.45 61	94.81 11	70.25 64	73.16 117	86.81 129

tpm cat1		66.28 189	62.78 200	76.77 90	81.40 132	57.14 17	70.03 297	77.19 245	53.00 235	58.76 174	70.73 280	46.17 62	86.73 174	43.27 239	64.46 178	86.44 135

IS-MVSNet		68.80 144	67.55 139	72.54 173	78.50 180	43.43 260	81.03 196	79.35 206	59.12 150	57.27 198	86.71 110	46.05 63	87.70 152	44.32 235	75.60 103	86.49 133

Regformer-2		77.15 30	76.82 30	78.14 58	87.78 20	51.84 143	87.60 44	89.12 13	67.23 33	71.93 49	91.79 27	46.03 64	93.53 20	72.85 55	77.05 88	89.05 90

SMA-MVS		78.93 14	78.55 14	80.10 22	84.42 60	55.81 34	87.58 49	86.47 59	61.29 112	79.34 10	93.10 7	46.02 65	92.41 31	79.97 11	88.72 6	92.08 24

EI-MVSNet		69.70 125	68.70 116	72.68 170	75.00 218	48.90 195	79.54 221	87.16 48	61.05 116	63.88 116	83.74 138	45.87 66	90.44 60	57.42 153	64.68 177	78.70 249

IterMVS-LS		66.63 183	65.36 180	70.42 219	75.10 217	48.90 195	81.45 191	76.69 251	61.05 116	55.71 210	77.10 225	45.86 67	83.65 235	57.44 152	57.88 245	78.70 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS		76.91 33	75.48 42	81.23 10	84.56 59	55.21 48	80.23 209	91.64 1	58.65 163	65.37 94	91.48 36	45.72 68	95.05 8	72.11 57	89.52 5	93.44 4

PAPM_NR		71.80 93	69.98 101	77.26 80	81.54 127	53.34 98	78.60 233	85.25 82	53.46 230	60.53 149	88.66 82	45.69 69	89.24 88	56.49 157	79.62 73	89.19 87

DeepC-MVS_fast	67.50 3	78.00 23	77.63 22	79.13 34	88.52 13	55.12 51	89.95 19	85.98 67	68.31 23	71.33 54	92.75 13	45.52 70	90.37 64	71.15 60	85.14 31	91.91 27
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
agg_prior1		76.68 39	76.24 36	78.00 61	85.64 40	54.92 57	87.55 50	83.61 130	57.99 176	72.53 43	91.05 39	45.36 71	88.10 139	77.76 25	84.68 36	90.99 49

Test_1112_low_res		67.18 173	66.23 158	70.02 230	78.75 172	41.02 281	83.43 145	73.69 283	57.29 187	58.45 179	82.39 166	45.30 72	80.88 259	50.50 201	66.26 168	88.16 104

NR-MVSNet		67.25 171	65.99 164	71.04 208	73.27 236	43.91 256	85.32 93	84.75 96	66.05 47	53.65 224	82.11 172	45.05 73	85.97 195	47.55 217	56.18 255	83.24 185

train_agg		76.91 33	76.40 33	78.45 50	85.68 37	55.42 40	87.59 47	84.00 119	57.84 179	72.99 36	90.98 41	44.99 74	88.58 117	78.19 20	85.32 29	91.34 44

test_8					85.72 36	55.31 44	87.60 44	83.88 124	57.84 179	72.84 39	90.99 40	44.99 74	88.34 128

segment_acmp												44.97 76

TEST9					85.68 37	55.42 40	87.59 47	84.00 119	57.72 182	72.99 36	90.98 41	44.87 77	88.58 117

MVSFormer		73.53 71	72.19 73	77.57 69	83.02 89	55.24 46	81.63 184	81.44 169	50.28 260	76.67 18	90.91 45	44.82 78	86.11 188	60.83 123	80.09 66	91.36 42

lupinMVS		78.38 18	78.11 18	79.19 32	83.02 89	55.24 46	91.57 7	84.82 92	69.12 19	76.67 18	92.02 23	44.82 78	90.23 70	80.83 9	80.09 66	92.08 24

WR-MVS		67.58 163	66.76 152	70.04 229	75.92 210	45.06 249	86.23 73	85.28 80	64.31 68	58.50 177	81.00 183	44.80 80	82.00 253	49.21 208	55.57 262	83.06 188

Fast-Effi-MVS+		72.73 79	71.15 88	77.48 72	82.75 99	54.76 61	86.77 66	80.64 185	63.05 91	65.93 87	84.01 133	44.42 81	89.03 96	56.45 160	76.36 98	88.64 99

agg_prior3		76.73 38	76.15 37	78.48 48	85.66 39	55.59 35	87.54 51	83.95 123	57.78 181	71.78 51	90.81 49	44.33 82	88.52 122	78.19 20	85.32 29	91.34 44

view600		64.79 197	63.45 195	68.82 239	82.13 110	40.75 283	79.41 225	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 315	62.85 199	80.71 227

view800		64.79 197	63.45 195	68.82 239	82.13 110	40.75 283	79.41 225	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 315	62.85 199	80.71 227

conf0.05thres1000		64.79 197	63.45 195	68.82 239	82.13 110	40.75 283	79.41 225	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 315	62.85 199	80.71 227

tfpn		64.79 197	63.45 195	68.82 239	82.13 110	40.75 283	79.41 225	88.29 27	56.54 201	53.26 226	81.30 179	44.26 83	85.01 211	22.97 315	62.85 199	80.71 227

Regformer-4		75.06 55	74.59 51	76.47 92	85.49 45	50.33 171	85.12 100	88.61 20	66.42 38	68.48 66	90.66 51	44.15 87	92.68 27	69.24 67	73.16 117	86.39 137

PCF-MVS	61.03 10	70.10 114	68.40 119	75.22 117	77.15 200	51.99 140	79.30 229	82.12 157	56.47 206	61.88 134	86.48 115	43.98 88	87.24 161	55.37 168	72.79 124	86.43 136
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CDS-MVSNet		70.48 112	69.43 105	73.64 155	77.56 191	48.83 197	83.51 144	77.45 242	63.27 88	62.33 129	85.54 121	43.85 89	83.29 239	57.38 154	74.00 111	88.79 96
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EI-MVSNet-Vis-set		73.19 75	72.60 64	74.99 121	82.56 105	49.80 180	82.55 163	89.00 15	66.17 42	65.89 88	88.98 77	43.83 90	92.29 33	65.38 97	69.01 146	82.87 192

APD-MVS		76.15 45	75.68 39	77.54 70	88.52 13	53.44 90	87.26 58	85.03 88	53.79 227	74.91 23	91.68 32	43.80 91	90.31 65	74.36 41	81.82 51	88.87 94
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		76.39 42	75.38 45	79.42 29	85.33 50	56.47 26	88.15 37	84.97 89	65.15 61	66.06 86	89.88 67	43.79 92	92.16 36	75.03 37	80.03 69	89.64 77

tfpn111		66.40 188	64.99 184	70.63 215	83.29 79	43.15 262	81.67 180	87.78 36	59.04 151	55.92 207	82.18 169	43.73 93	86.83 171	26.34 305	64.92 173	81.89 203

conf200view11		66.80 182	65.42 178	70.95 210	83.29 79	43.15 262	81.67 180	87.78 36	59.04 151	55.92 207	82.18 169	43.73 93	87.80 146	28.80 290	66.36 163	81.89 203

thres100view900		66.87 180	65.42 178	71.24 203	83.29 79	43.15 262	81.67 180	87.78 36	59.04 151	55.92 207	82.18 169	43.73 93	87.80 146	28.80 290	66.36 163	82.78 193

thres600view7		66.46 186	65.12 182	70.47 217	83.41 75	43.80 258	82.15 168	87.78 36	59.37 139	56.02 206	82.21 168	43.73 93	86.90 169	26.51 303	64.94 172	80.71 227

v148		68.24 155	66.35 156	73.88 148	71.76 271	51.47 150	84.23 126	81.90 163	63.69 82	58.94 167	76.44 230	43.72 97	87.78 150	60.63 125	55.86 259	82.39 198

SD-MVS		76.18 44	74.85 50	80.18 19	85.39 49	56.90 20	85.75 82	82.45 152	56.79 195	74.48 26	91.81 26	43.72 97	90.75 55	74.61 40	78.65 77	92.91 13

XXY-MVS		70.18 113	69.28 111	72.89 167	77.64 189	42.88 266	85.06 109	87.50 46	62.58 96	62.66 128	82.34 167	43.64 99	89.83 77	58.42 141	63.70 184	85.96 142

tfpn200view9		67.57 164	66.13 161	71.89 191	84.05 68	45.07 246	83.40 147	87.71 43	60.79 119	57.79 186	82.76 157	43.53 100	87.80 146	28.80 290	66.36 163	82.78 193

thres400		67.40 169	66.13 161	71.19 205	84.05 68	45.07 246	83.40 147	87.71 43	60.79 119	57.79 186	82.76 157	43.53 100	87.80 146	28.80 290	66.36 163	80.71 227

PAPR		75.20 54	74.13 53	78.41 51	88.31 17	55.10 53	84.31 125	85.66 70	63.76 80	67.55 72	90.73 50	43.48 102	89.40 86	66.36 83	77.03 90	90.73 53

MP-MVS		74.99 56	74.33 52	76.95 85	82.89 95	53.05 119	85.63 85	83.50 133	57.86 178	67.25 75	90.24 59	43.38 103	88.85 110	76.03 30	82.23 49	88.96 92
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EI-MVSNet-UG-set		72.37 84	71.73 78	74.29 139	81.60 123	49.29 189	81.85 175	88.64 19	65.29 60	65.05 98	88.29 87	43.18 104	91.83 40	63.74 101	67.97 154	81.75 209

thres200		68.71 148	67.27 144	73.02 163	84.73 57	46.76 227	85.03 111	87.73 41	62.34 98	59.87 152	83.45 149	43.15 105	88.32 131	31.25 284	67.91 155	83.98 170

PHI-MVS		77.49 28	77.00 28	78.95 37	85.33 50	50.69 162	88.57 35	88.59 22	58.14 173	73.60 30	93.31 5	43.14 106	93.79 18	73.81 44	88.53 8	92.37 20

ab-mvs		70.65 109	69.11 112	75.29 114	80.87 140	46.23 237	73.48 269	85.24 83	59.99 129	66.65 79	80.94 185	43.13 107	88.69 112	63.58 102	68.07 152	90.95 50

divwei89l23v2f112		69.50 130	67.67 130	74.98 122	72.72 251	53.41 93	85.08 106	82.14 153	64.79 64	60.88 142	78.19 209	43.11 108	89.04 93	62.51 110	59.62 219	82.48 196

v1		69.49 132	67.67 130	74.98 122	72.69 252	53.41 93	85.08 106	82.13 156	64.80 63	60.87 144	78.19 209	43.11 108	89.04 93	62.51 110	59.61 220	82.49 195

CDPH-MVS		76.05 47	75.19 46	78.62 45	86.51 30	54.98 56	87.32 54	84.59 98	58.62 164	70.75 56	90.85 47	43.10 110	90.63 58	70.50 62	84.51 38	90.24 66

v1141		69.50 130	67.67 130	74.98 122	72.73 250	53.41 93	85.08 106	82.14 153	64.79 64	60.88 142	78.19 209	43.09 111	89.04 93	62.51 110	59.61 220	82.47 197

v18		64.36 204	61.80 206	72.05 179	72.97 239	53.31 101	81.16 194	77.76 237	59.14 148	48.50 260	68.97 288	42.91 112	84.38 221	56.62 156	48.17 287	78.47 253

v8		67.25 171	64.99 184	74.04 144	72.89 245	53.31 101	82.37 166	80.11 191	61.54 109	54.29 218	76.02 241	42.89 113	88.41 125	58.43 140	56.36 252	80.39 235

v1neww		69.43 133	67.62 134	74.89 125	72.90 243	53.31 101	85.12 100	81.11 176	64.29 69	61.00 139	78.53 202	42.88 114	88.98 102	62.66 108	60.06 214	82.37 199

v7new		69.43 133	67.62 134	74.89 125	72.90 243	53.31 101	85.12 100	81.11 176	64.29 69	61.00 139	78.53 202	42.88 114	88.98 102	62.66 108	60.06 214	82.37 199

v6		69.43 133	67.61 136	74.88 127	72.87 247	53.30 105	85.12 100	81.10 178	64.29 69	60.99 141	78.52 204	42.88 114	88.98 102	62.67 107	60.06 214	82.37 199

diffmvs		70.02 117	68.35 120	75.03 118	79.19 165	51.48 149	78.50 234	76.65 252	59.71 131	67.10 77	80.32 189	42.81 117	87.12 163	58.48 139	72.37 128	86.49 133

v16		64.25 206	61.66 215	72.03 180	72.91 242	53.28 107	80.93 197	77.81 233	58.86 159	48.30 261	68.80 291	42.70 118	84.37 222	56.44 161	48.14 288	78.44 256

v17		64.19 207	61.58 216	72.03 180	72.89 245	53.28 107	80.91 198	77.80 234	58.87 158	48.22 262	68.77 292	42.69 119	84.37 222	56.43 162	47.66 291	78.43 257

v15		63.83 210	61.13 221	71.93 188	72.60 255	53.21 110	80.44 204	78.22 222	58.80 161	47.57 267	68.22 294	42.50 120	84.18 224	55.82 164	46.02 303	78.39 259

SteuartSystems-ACMMP		77.08 31	76.33 34	79.34 30	80.98 137	55.31 44	89.76 24	86.91 52	62.94 93	71.65 52	91.56 34	42.33 121	92.56 29	77.14 27	83.69 42	90.15 70
Skip Steuart: Steuart Systems R&D Blog.
HyFIR lowres test		69.94 122	67.58 137	77.04 82	77.11 201	57.29 15	81.49 190	79.11 210	58.27 170	58.86 171	80.41 188	42.33 121	86.96 168	61.91 117	68.68 149	86.87 124

V14		63.72 212	60.99 223	71.91 190	72.58 258	53.18 111	80.24 208	78.19 223	58.53 168	47.35 273	68.10 295	42.28 123	84.18 224	55.68 166	45.97 304	78.36 262

FMVSNet3		68.84 141	67.40 142	73.19 162	85.05 53	48.53 204	85.71 84	85.36 77	60.90 118	57.58 190	79.15 197	42.16 124	86.77 172	47.25 220	63.40 185	84.27 164

VPA-MVSNet		71.12 101	70.66 90	72.49 175	78.75 172	44.43 252	87.64 43	90.02 9	63.97 75	65.02 99	81.58 177	42.14 125	87.42 158	63.42 103	63.38 188	85.63 149

jason		77.01 32	76.45 32	78.69 42	79.69 158	54.74 62	90.56 15	83.99 121	68.26 24	74.10 28	90.91 45	42.14 125	89.99 74	79.30 15	79.12 74	91.36 42
jason: jason.
V9		63.60 213	60.84 224	71.87 192	72.51 260	53.12 115	80.04 213	78.15 225	58.25 171	47.14 275	67.98 296	42.08 127	84.18 224	55.47 167	45.92 306	78.32 263

CLD-MVS		75.60 50	75.39 44	76.24 95	80.69 144	52.40 132	90.69 13	86.20 66	74.40 3	65.01 100	88.93 78	42.05 128	90.58 59	76.57 29	73.96 112	85.73 145
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_0304		79.84 12	79.71 10	80.25 17	85.64 40	54.62 68	90.58 14	84.48 100	72.51 8	79.22 11	93.09 9	42.01 129	93.28 21	84.00 4	85.84 24	92.87 15

TAMVS		69.51 129	68.16 123	73.56 158	76.30 204	48.71 198	82.57 162	77.17 246	62.10 101	61.32 137	84.23 131	41.90 130	83.46 237	54.80 175	73.09 121	88.50 103

TransMVSNet (Re)		62.82 229	60.76 226	69.02 235	73.98 229	41.61 277	86.36 70	79.30 208	56.90 190	52.53 232	76.44 230	41.85 131	87.60 154	38.83 251	40.61 320	77.86 272

VPNet		72.07 90	71.42 84	74.04 144	78.64 177	47.17 225	89.91 22	87.97 34	72.56 7	64.66 104	85.04 125	41.83 132	88.33 130	61.17 121	60.97 210	86.62 131

v12		63.47 215	60.68 227	71.85 193	72.45 261	53.08 117	79.83 215	78.13 227	57.95 177	46.89 277	67.87 298	41.81 133	84.17 227	55.30 169	45.87 307	78.29 265

v13		63.36 217	60.54 230	71.82 194	72.41 262	53.03 120	79.64 220	78.10 228	57.66 183	46.67 280	67.75 299	41.68 134	84.17 227	55.11 170	45.82 308	78.25 268

v2v482		69.55 128	67.64 133	75.26 116	72.32 265	53.83 80	84.93 114	81.94 159	65.37 57	60.80 146	79.25 195	41.62 135	88.98 102	63.03 105	59.51 223	82.98 190

API-MVS		74.17 62	72.07 75	80.49 14	90.02 6	58.55 7	87.30 56	84.27 105	57.51 185	65.77 91	87.77 97	41.61 136	95.97 2	51.71 196	82.63 45	86.94 123

CHOSEN 1792x2688		76.24 43	74.03 54	82.88 1	83.09 86	62.84 2	85.73 83	85.39 76	69.79 16	64.87 102	83.49 148	41.52 137	93.69 19	70.55 61	81.82 51	92.12 23

LFMVS		78.52 15	77.14 27	82.67 3	89.58 7	58.90 6	91.27 9	88.05 33	63.22 89	74.63 25	90.83 48	41.38 138	94.40 13	75.42 36	79.90 71	94.72 1

MAR-MVS		76.76 36	75.60 41	80.21 18	90.87 3	54.68 66	89.14 31	89.11 14	62.95 92	70.54 58	92.33 17	41.05 139	94.95 9	57.90 148	86.55 18	91.00 48
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
DU-MVS		66.84 181	65.74 168	70.16 223	73.27 236	42.59 269	81.50 188	82.92 145	63.53 86	58.51 175	82.11 172	40.75 140	84.64 219	53.11 183	55.96 257	83.24 185

Baseline_NR-MVSNet		65.49 196	64.27 191	69.13 234	74.37 226	41.65 276	83.39 149	78.85 211	59.56 133	59.62 158	76.88 226	40.75 140	87.44 157	49.99 203	55.05 263	78.28 266

HFP-MVS		74.37 59	73.13 61	78.10 59	84.30 62	53.68 82	85.58 86	84.36 102	56.82 193	65.78 89	90.56 53	40.70 142	90.90 52	69.18 68	80.88 55	89.71 74

#test#		74.86 57	73.78 56	78.10 59	84.30 62	53.68 82	86.95 62	84.36 102	59.00 155	65.78 89	90.56 53	40.70 142	90.90 52	71.48 58	80.88 55	89.71 74

v11		63.44 216	60.66 228	71.79 195	72.61 254	53.02 121	79.80 216	78.08 229	58.30 169	47.27 274	67.91 297	40.67 144	84.14 229	54.93 172	46.39 301	78.23 269

ACMMP_Plus		76.43 41	75.66 40	78.73 41	81.92 117	54.67 67	84.06 131	85.35 78	61.10 115	72.99 36	91.50 35	40.25 145	91.00 49	76.84 28	86.98 13	90.51 59

v1144		68.81 143	66.82 148	74.80 131	72.34 264	53.46 88	84.68 119	81.77 165	64.25 72	60.28 151	77.91 213	40.23 146	88.95 106	60.37 129	59.52 222	81.97 202

WR-MVS_H		58.91 256	58.04 246	61.54 297	69.07 290	33.83 313	76.91 242	81.99 158	51.40 255	48.17 263	74.67 248	40.23 146	74.15 305	31.78 280	48.10 289	76.64 283

原ACMM1				76.13 98	84.89 56	54.59 69		85.26 81	51.98 250	66.70 78	87.07 107	40.15 148	89.70 81	51.23 198	85.06 33	84.10 165

MVP-Stereo		70.97 104	70.44 92	72.59 172	76.03 208	51.36 153	85.02 112	86.99 51	60.31 126	56.53 202	78.92 199	40.11 149	90.00 73	60.00 134	90.01 2	76.41 286
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v10		66.61 184	64.20 192	73.83 151	72.59 257	53.37 96	81.88 174	79.91 193	61.11 114	54.09 220	75.60 243	40.06 150	88.26 135	56.47 158	56.10 256	79.86 240

v7		68.76 147	66.79 150	74.68 132	72.60 255	53.37 96	84.72 118	80.88 181	63.80 78	60.43 150	78.21 208	40.05 151	88.89 108	60.34 130	60.07 213	81.77 208

MP-MVS-pluss		75.54 51	75.03 47	77.04 82	81.37 133	52.65 128	84.34 124	84.46 101	61.16 113	69.14 62	91.76 29	39.98 152	88.99 101	78.19 20	84.89 34	89.48 79
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		66.94 179	65.61 172	70.93 211	73.45 233	43.38 261	83.02 156	84.25 106	65.31 59	58.33 181	81.90 174	39.92 153	85.52 201	49.43 207	54.89 265	83.89 174

Patchmatch-test		53.33 290	48.17 294	68.81 243	73.31 234	42.38 273	42.98 341	58.23 331	32.53 332	38.79 313	70.77 279	39.66 154	73.51 312	25.18 307	52.06 278	90.55 55

Test By Simon												39.38 155

v144192		67.86 158	65.76 167	74.16 141	71.68 272	53.09 116	84.14 128	80.83 183	62.85 94	59.21 165	77.28 222	39.30 156	88.00 142	58.67 138	57.88 245	81.40 215

BH-w/o		70.02 117	68.51 117	74.56 134	82.77 98	50.39 169	86.60 67	78.14 226	59.77 130	59.65 156	85.57 120	39.27 157	87.30 160	49.86 204	74.94 109	85.99 140

CR-MVSNet		62.47 234	59.04 242	72.77 168	73.97 230	56.57 23	60.52 322	71.72 294	60.04 128	57.49 193	65.86 307	38.94 158	80.31 266	42.86 242	59.93 217	81.42 213

Patchmtry		56.56 272	52.95 277	67.42 254	72.53 259	50.59 165	59.05 324	71.72 294	37.86 317	46.92 276	65.86 307	38.94 158	80.06 271	36.94 260	46.72 300	71.60 317

sam_mvs1												38.86 160				88.13 107

UA-Net		67.32 170	66.23 158	70.59 216	78.85 170	41.23 280	73.60 268	75.45 267	61.54 109	66.61 81	84.53 128	38.73 161	86.57 181	42.48 244	74.24 110	83.98 170

cdsmvs_eth3d_5k		18.33 331	24.44 324	0.00 349	0.00 362	0.00 363	0.00 355	89.40 12	0.00 357	0.00 360	92.02 23	38.55 162	0.00 360	0.00 359	0.00 357	0.00 360

patchmatchnet-post											59.74 322	38.41 163	79.91 274

CHOSEN 280x420		57.53 268	56.38 259	60.97 301	74.01 228	48.10 216	46.30 337	54.31 336	48.18 272	50.88 246	77.43 220	38.37 164	59.16 340	54.83 173	63.14 194	75.66 292

V42		67.66 162	65.60 173	73.86 149	70.69 282	53.63 84	81.50 188	78.61 218	63.85 77	59.49 161	77.49 218	37.98 165	87.65 153	62.33 113	58.43 235	80.29 236

tpmvs		62.45 235	59.42 237	71.53 200	83.93 70	54.32 71	70.03 297	77.61 239	51.91 251	53.48 225	68.29 293	37.91 166	86.66 176	33.36 273	58.27 236	73.62 307

PatchmatchNet		67.07 177	63.63 194	77.40 74	83.10 84	58.03 8	72.11 285	77.77 236	58.85 160	59.37 162	70.83 278	37.84 167	84.93 215	42.96 241	69.83 142	89.26 83
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pcd_1.5k_mvsjas		3.15 338	4.20 339	0.00 349	0.00 362	0.00 363	0.00 355	0.00 363	0.00 357	0.00 360	0.00 361	37.77 168	0.00 360	0.00 359	0.00 357	0.00 360

PS-MVSNAJss		68.78 146	67.17 145	73.62 157	73.01 238	48.33 212	84.95 113	84.81 93	59.30 143	58.91 170	79.84 192	37.77 168	88.86 109	62.83 106	63.12 195	83.67 178

PS-MVSNAJ		80.06 10	79.52 11	81.68 7	85.58 43	60.97 3	91.69 4	87.02 50	70.62 12	80.75 6	93.22 6	37.77 168	92.50 30	82.75 6	86.25 20	91.57 34

pm-mvs1		64.12 208	62.56 201	68.78 244	71.68 272	38.87 291	82.89 158	81.57 167	55.54 217	53.89 221	77.82 214	37.73 171	86.74 173	48.46 212	53.49 274	80.72 226

RPMNet		58.49 263	53.74 273	72.77 168	73.97 230	56.57 23	60.52 322	72.39 290	35.72 324	57.49 193	58.87 327	37.73 171	80.31 266	27.01 302	59.93 217	81.42 213

xiu_mvs_v2_base		79.86 11	79.31 12	81.53 8	85.03 54	60.73 4	91.65 5	86.86 53	70.30 15	80.77 5	93.07 11	37.63 173	92.28 34	82.73 7	85.71 25	91.57 34

Patchmatch-RL test		58.72 258	54.32 269	71.92 189	63.91 315	44.25 254	61.73 318	55.19 334	57.38 186	49.31 257	54.24 333	37.60 174	80.89 258	62.19 115	47.28 295	90.63 54

HPM-MVS		72.60 81	71.50 81	75.89 104	82.02 114	51.42 152	80.70 202	83.05 142	56.12 209	64.03 113	89.53 72	37.55 175	88.37 126	70.48 63	80.04 68	87.88 110
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_post											16.22 352	37.52 176	84.72 217

PatchT		56.60 271	52.97 276	67.48 253	72.94 241	46.16 238	57.30 328	73.78 282	38.77 313	54.37 217	57.26 330	37.52 176	78.06 285	32.02 278	52.79 275	78.23 269

v1192		67.96 157	65.74 168	74.63 133	71.79 270	53.43 92	84.06 131	80.99 180	63.19 90	59.56 159	77.46 219	37.50 178	88.65 113	58.20 144	58.93 228	81.79 207

HQP2-MVS												37.35 179

HQP-MVS		72.34 85	71.44 83	75.03 118	79.02 167	51.56 147	88.00 38	83.68 127	65.45 52	64.48 107	85.13 123	37.35 179	88.62 114	66.70 80	73.12 119	84.91 157

region2R		73.75 69	72.55 65	77.33 75	83.90 72	52.98 122	85.54 89	84.09 110	56.83 192	65.10 97	90.45 55	37.34 181	90.24 69	68.89 70	80.83 58	88.77 97

TESTMET0.1,1		72.86 78	72.33 68	74.46 135	81.98 115	50.77 160	85.13 97	85.47 72	66.09 44	67.30 73	83.69 140	37.27 182	83.57 236	65.06 98	78.97 76	89.05 90

ACMMPR		73.76 68	72.61 63	77.24 81	83.92 71	52.96 123	85.58 86	84.29 104	56.82 193	65.12 96	90.45 55	37.24 183	90.18 71	69.18 68	80.84 57	88.58 101

sss		70.49 111	70.13 99	71.58 199	81.59 124	39.02 290	80.78 201	84.71 97	59.34 140	66.61 81	88.09 91	37.17 184	85.52 201	61.82 118	71.02 136	90.20 68

EPNet_dtu		66.25 190	66.71 153	64.87 280	78.66 176	34.12 311	82.80 160	75.51 265	61.75 106	64.47 110	86.90 108	37.06 185	72.46 318	43.65 238	69.63 144	88.02 109
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1921920		67.45 167	65.23 181	74.10 143	71.51 275	52.90 124	83.75 138	80.44 188	62.48 97	59.12 166	77.13 223	36.98 186	87.90 143	57.53 151	58.14 240	81.49 212

旧先验1					81.57 126	47.48 220		71.83 293			88.66 82	36.94 187			78.34 79	88.67 98

test-LLR		69.65 126	69.01 113	71.60 197	78.67 174	48.17 214	85.13 97	79.72 196	59.18 146	63.13 124	82.58 162	36.91 188	80.24 268	60.56 126	75.17 106	86.39 137

test0.0.03 1		62.54 232	62.44 202	62.86 290	72.28 267	29.51 327	82.93 157	78.78 213	59.18 146	53.07 230	82.41 165	36.91 188	77.39 292	37.45 254	58.96 227	81.66 210

MDTV_nov1_ep13_2view						43.62 259	71.13 291		54.95 221	59.29 164		36.76 190		46.33 227		87.32 120

GBi-Net		67.09 175	65.47 175	71.96 185	82.71 100	46.36 232	83.52 140	83.31 134	58.55 165	57.58 190	76.23 234	36.72 191	86.20 184	47.25 220	63.40 185	83.32 182

test1		67.09 175	65.47 175	71.96 185	82.71 100	46.36 232	83.52 140	83.31 134	58.55 165	57.58 190	76.23 234	36.72 191	86.20 184	47.25 220	63.40 185	83.32 182

FMVSNet2		67.57 164	65.79 166	72.90 165	82.71 100	47.97 217	85.15 96	84.93 90	58.55 165	56.71 200	78.26 207	36.72 191	86.67 175	46.15 228	62.94 198	84.07 166

BH-untuned		68.28 154	66.40 155	73.91 147	81.62 122	50.01 178	85.56 88	77.39 243	57.63 184	57.47 195	83.69 140	36.36 194	87.08 164	44.81 233	73.08 122	84.65 159

EPMVS		68.45 151	65.44 177	77.47 73	84.91 55	56.17 30	71.89 288	81.91 162	61.72 107	60.85 145	72.49 267	36.21 195	87.06 165	47.32 219	71.62 132	89.17 88

MSLP-MVS++		74.21 61	72.25 72	80.11 21	81.45 131	56.47 26	86.32 71	79.65 199	58.19 172	66.36 83	92.29 18	36.11 196	90.66 57	67.39 76	82.49 46	93.18 10

zzz-MVS		74.15 64	73.11 62	77.27 78	81.54 127	53.57 85	84.02 133	81.31 172	59.41 137	68.39 67	90.96 43	36.07 197	89.01 97	73.80 45	82.45 47	89.23 84

MTAPA		72.73 79	71.22 86	77.27 78	81.54 127	53.57 85	67.06 306	81.31 172	59.41 137	68.39 67	90.96 43	36.07 197	89.01 97	73.80 45	82.45 47	89.23 84

test_normal		71.31 98	68.95 115	78.39 52	72.30 266	54.25 73	81.67 180	84.05 118	65.94 50	51.31 241	78.09 212	36.06 199	90.43 62	73.00 54	78.09 81	90.50 60

HQP_MVS		70.96 105	69.91 102	74.12 142	77.95 185	49.57 182	85.76 80	82.59 149	63.60 84	62.15 130	83.28 152	36.04 200	88.30 132	65.46 91	72.34 129	84.49 160

plane_prior6					78.42 182	49.39 187						36.04 200

DI_MVS_plusplus_test		71.30 99	68.98 114	78.26 56	72.76 248	54.08 78	81.72 179	83.22 138	65.75 51	51.94 237	78.47 206	36.01 202	90.31 65	73.33 50	77.60 83	90.40 62

sam_mvs												35.99 203

PGM-MVS		72.60 81	71.20 87	76.80 89	82.95 92	52.82 125	83.07 154	82.14 153	56.51 205	63.18 123	89.81 68	35.68 204	89.76 80	67.30 77	80.19 65	87.83 111

XVS		72.92 77	71.62 79	76.81 87	83.41 75	52.48 129	84.88 115	83.20 140	58.03 174	63.91 114	89.63 71	35.50 205	89.78 78	65.50 88	80.50 61	88.16 104

X-MVStestdata		65.85 194	62.20 203	76.81 87	83.41 75	52.48 129	84.88 115	83.20 140	58.03 174	63.91 114	4.82 357	35.50 205	89.78 78	65.50 88	80.50 61	88.16 104

v1240		66.99 178	64.68 187	73.93 146	71.38 278	52.66 127	83.39 149	79.98 192	61.97 102	58.44 180	77.11 224	35.25 207	87.81 145	56.46 159	58.15 238	81.33 216

dp		64.41 203	61.58 216	72.90 165	82.40 106	54.09 77	72.53 279	76.59 254	60.39 125	55.68 211	70.39 281	35.18 208	76.90 296	39.34 250	61.71 207	87.73 114

CP-MVS		72.59 83	71.46 82	76.00 103	82.93 94	52.32 136	86.93 64	82.48 151	55.15 219	63.65 119	90.44 57	35.03 209	88.53 121	68.69 71	77.83 82	87.15 122

CP-MVSNet		58.54 262	57.57 249	61.46 299	68.50 294	33.96 312	76.90 243	78.60 219	51.67 254	47.83 264	76.60 229	34.99 210	72.79 316	35.45 265	47.58 292	77.64 276

MDTV_nov1_ep13			61.56 218		81.68 121	55.12 51	72.41 281	78.18 224	59.19 144	58.85 172	69.29 285	34.69 211	86.16 187	36.76 262	62.96 197

3Dnovator	64.70 6	74.46 58	72.48 66	80.41 15	82.84 97	55.40 43	83.08 153	88.61 20	67.61 31	59.85 153	88.66 82	34.57 212	93.97 15	58.42 141	88.70 7	91.85 29

Vis-MVSNet		70.61 110	69.34 108	74.42 136	80.95 139	48.49 206	86.03 77	77.51 241	58.74 162	65.55 93	87.78 96	34.37 213	85.95 196	52.53 192	80.61 59	88.80 95
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_post1							70.84 292				14.72 355	34.33 214	83.86 231	48.80 209

OPM-MVS		70.75 108	69.58 104	74.26 140	75.55 213	51.34 154	86.05 76	83.29 137	61.94 103	62.95 126	85.77 118	34.15 215	88.44 124	65.44 94	71.07 135	82.99 189

1121		68.79 145	66.77 151	74.82 129	83.08 87	53.46 88	80.23 209	71.53 298	45.47 288	66.31 84	87.19 104	34.02 216	85.13 208	52.78 188	80.36 63	85.87 144

DP-MVS Recon		71.99 91	70.31 94	77.01 84	90.65 4	53.44 90	89.37 28	82.97 144	56.33 207	63.56 121	89.47 73	34.02 216	92.15 38	54.05 179	72.41 127	85.43 151

PEN-MVS		58.35 264	57.15 251	61.94 294	67.55 301	34.39 310	77.01 241	78.35 221	51.87 252	47.72 265	76.73 228	33.91 218	73.75 310	34.03 272	47.17 296	77.68 274

QAPM		71.88 92	69.33 109	79.52 27	82.20 109	54.30 72	86.30 72	88.77 17	56.61 199	59.72 155	87.48 101	33.90 219	95.36 4	47.48 218	81.49 53	88.90 93

新几何1				73.30 161	83.10 84	53.48 87		71.43 299	45.55 286	66.14 85	87.17 105	33.88 220	80.54 263	48.50 211	80.33 64	85.88 143

1314		71.11 102	69.41 106	76.22 96	79.32 162	50.49 167	80.23 209	85.14 87	59.44 136	58.93 168	88.89 80	33.83 221	89.60 85	61.49 119	77.42 87	88.57 102

mPP-MVS		71.79 94	70.38 93	76.04 101	82.65 103	52.06 138	84.45 122	81.78 164	55.59 216	62.05 133	89.68 70	33.48 222	88.28 134	65.45 93	78.24 80	87.77 113

OMC-MVS		65.97 193	65.06 183	68.71 245	72.97 239	42.58 271	78.61 232	75.35 268	54.72 222	59.31 163	86.25 116	33.30 223	77.88 288	57.99 146	67.05 158	85.66 147

BH-RMVSNet		70.08 115	68.01 124	76.27 94	84.21 66	51.22 158	87.29 57	79.33 207	58.96 157	63.63 120	86.77 109	33.29 224	90.30 68	44.63 234	73.96 112	87.30 121

v748		61.35 241	58.24 245	70.69 214	66.28 303	47.35 222	76.58 245	79.17 209	53.09 234	46.37 284	71.50 275	33.18 225	86.33 183	46.78 224	51.19 282	78.39 259

JIA-IIPM		52.33 294	47.77 297	66.03 270	71.20 279	46.92 226	40.00 345	76.48 255	37.10 318	46.73 278	37.02 341	32.96 226	77.88 288	35.97 263	52.45 277	73.29 311

PS-CasMVS		58.12 266	57.03 253	61.37 300	68.24 297	33.80 314	76.73 244	78.01 231	51.20 257	47.54 269	76.20 237	32.85 227	72.76 317	35.17 267	47.37 294	77.55 278

DTE-MVSNet		57.03 269	55.73 263	60.95 302	65.94 305	32.57 319	75.71 254	77.09 248	51.16 258	46.65 281	76.34 232	32.84 228	73.22 314	30.94 285	44.87 311	77.06 280

pmmvs4		63.34 218	61.07 222	70.16 223	70.14 284	50.53 166	79.97 214	71.41 300	55.08 220	54.12 219	78.58 201	32.79 229	82.09 252	50.33 202	57.22 249	77.86 272

TR-MVS		69.71 124	67.85 128	75.27 115	82.94 93	48.48 207	87.40 53	80.86 182	57.15 189	64.61 105	87.08 106	32.67 230	89.64 84	46.38 226	71.55 133	87.68 115

VDD-MVS		76.08 46	74.97 48	79.44 28	84.27 65	53.33 100	91.13 10	85.88 68	65.33 58	72.37 46	89.34 74	32.52 231	92.76 26	77.90 24	75.96 99	92.22 22

3Dnovator+	62.71 7	72.29 87	70.50 91	77.65 68	83.40 78	51.29 156	87.32 54	86.40 62	59.01 154	58.49 178	88.32 86	32.40 232	91.27 46	57.04 155	82.15 50	90.38 64

tfpnnormal		61.47 240	59.09 241	68.62 247	76.29 205	41.69 275	81.14 195	85.16 85	54.48 225	51.32 240	73.63 258	32.32 233	86.89 170	21.78 329	55.71 261	77.29 279

MS-PatchMatch		72.34 85	71.26 85	75.61 107	82.38 107	55.55 36	88.00 38	89.95 11	65.38 56	56.51 203	80.74 187	32.28 234	92.89 24	57.95 147	88.10 10	78.39 259

semantic-postprocess				60.08 303	70.68 283	45.07 246		74.25 276	43.54 300	50.02 254	73.73 254	32.22 235	56.74 341	51.06 200	53.60 273	78.42 258

v7n		62.50 233	59.27 239	72.20 177	67.25 302	49.83 179	77.87 238	80.12 190	52.50 247	48.80 259	73.07 262	32.10 236	87.90 143	46.83 223	54.92 264	78.86 248

IterMVS		63.77 211	61.67 214	70.08 225	72.68 253	51.24 157	80.44 204	75.51 265	60.51 124	51.41 239	73.70 257	32.08 237	78.91 277	54.30 177	54.35 268	80.08 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test1		63.23 219	59.16 240	75.43 111	78.58 178	57.92 9	61.61 319	77.53 240	56.71 196	57.75 188	70.98 277	31.97 238	78.19 281	40.97 247	56.36 252	90.18 69

ACMMP		70.81 107	69.29 110	75.39 113	81.52 130	51.92 141	83.43 145	83.03 143	56.67 198	58.80 173	88.91 79	31.92 239	88.58 117	65.89 87	73.39 116	85.67 146
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
APD-MVS_3200maxsize		69.62 127	68.23 122	73.80 152	81.58 125	48.22 213	81.91 173	79.50 201	48.21 271	64.24 112	89.75 69	31.91 240	87.55 155	63.08 104	73.85 114	85.64 148

VDDNet		74.37 59	72.13 74	81.09 11	79.58 159	56.52 25	90.02 17	86.70 56	52.61 244	71.23 55	87.20 103	31.75 241	93.96 16	74.30 42	75.77 102	92.79 16

pmmvs5		62.80 230	61.18 220	67.66 252	69.53 289	42.37 274	82.65 161	75.19 269	54.30 226	52.03 236	78.51 205	31.64 242	80.67 261	48.60 210	58.15 238	79.95 239

LCM-MVSNet-Re		58.82 257	56.54 255	65.68 272	79.31 163	29.09 330	61.39 321	45.79 342	60.73 121	37.65 314	72.47 268	31.42 243	81.08 257	49.66 205	70.41 139	86.87 124

testdata				67.08 257	77.59 190	45.46 243		69.20 310	44.47 293	71.50 53	88.34 85	31.21 244	70.76 325	52.20 193	75.88 100	85.03 154

GA-MVS		69.04 138	66.70 154	76.06 100	75.11 216	52.36 134	83.12 152	80.23 189	63.32 87	60.65 148	79.22 196	30.98 245	88.37 126	61.25 120	66.41 162	87.46 117

OpenMVS	61.00 11	69.99 121	67.55 139	77.30 76	78.37 183	54.07 79	84.36 123	85.76 69	57.22 188	56.71 200	87.67 98	30.79 246	92.83 25	43.04 240	84.06 41	85.01 155

Effi-MVS+-dtu		66.24 191	64.96 186	70.08 225	75.17 214	49.64 181	82.01 170	74.48 273	62.15 99	57.83 184	76.08 240	30.59 247	83.79 233	65.40 95	60.93 211	76.81 281

mvs-test1		69.04 138	67.57 138	73.44 160	75.17 214	51.68 146	86.57 68	74.48 273	62.15 99	62.07 132	85.79 117	30.59 247	87.48 156	65.40 95	65.94 169	81.18 220

test222					79.36 160	50.97 159	77.99 237	67.84 311	42.54 305	62.84 127	86.53 113	30.26 249			76.91 92	85.23 152

MVS_111021_LR		69.07 137	67.91 125	72.54 173	77.27 195	49.56 184	79.77 217	73.96 280	59.33 142	60.73 147	87.82 94	30.19 250	81.53 254	69.94 65	72.19 131	86.53 132

114514_t		69.87 123	67.88 127	75.85 105	88.38 15	52.35 135	86.94 63	83.68 127	53.70 228	55.68 211	85.60 119	30.07 251	91.20 47	55.84 163	71.02 136	83.99 169

CPTT-MVS		67.15 174	65.84 165	71.07 207	80.96 138	50.32 172	81.94 172	74.10 277	46.18 284	57.91 183	87.64 99	29.57 252	81.31 256	64.10 100	70.18 141	81.56 211

CANet_DTU		73.71 70	73.14 59	75.40 112	82.61 104	50.05 177	84.67 120	79.36 205	69.72 17	75.39 21	90.03 65	29.41 253	85.93 197	67.99 74	79.11 75	90.22 67

AdaColmap		67.86 158	65.48 174	75.00 120	88.15 19	54.99 55	86.10 75	76.63 253	49.30 267	57.80 185	86.65 112	29.39 254	88.94 107	45.10 232	70.21 140	81.06 221

CVMVSNet		60.85 244	60.44 231	62.07 292	75.00 218	32.73 318	79.54 221	73.49 286	36.98 319	56.28 205	83.74 138	29.28 255	69.53 328	46.48 225	63.23 191	83.94 173

v52		59.82 247	56.41 257	70.06 227	61.49 325	48.67 199	69.46 301	75.80 262	52.55 245	47.49 271	68.82 290	28.60 256	85.70 199	52.13 194	51.34 281	75.80 289

V4		59.82 247	56.41 257	70.05 228	61.49 325	48.67 199	69.46 301	75.79 263	52.55 245	47.49 271	68.83 289	28.60 256	85.70 199	52.13 194	51.35 280	75.80 289

PMMVS		72.98 76	72.05 76	75.78 106	83.57 73	48.60 201	84.08 129	82.85 146	61.62 108	68.24 69	90.33 58	28.35 258	87.78 150	72.71 56	76.69 93	90.95 50

abl_6		68.03 156	66.15 160	73.66 154	78.54 179	48.48 207	79.77 217	78.04 230	47.39 275	63.70 118	88.25 88	28.21 259	89.06 90	60.17 133	71.25 134	83.45 180

our_test_3		59.11 254	55.08 267	71.18 206	71.42 276	53.29 106	81.96 171	74.52 272	48.32 270	42.08 299	69.28 286	28.14 260	82.15 250	34.35 271	45.68 309	78.11 271

Fast-Effi-MVS+-dtu		66.53 185	64.10 193	73.84 150	72.41 262	52.30 137	84.73 117	75.66 264	59.51 134	56.34 204	79.11 198	28.11 261	85.85 198	57.74 150	63.29 190	83.35 181

HPM-MVS_fast		67.86 158	66.28 157	72.61 171	80.67 145	48.34 211	81.18 193	75.95 261	50.81 259	59.55 160	88.05 93	27.86 262	85.98 193	58.83 137	73.58 115	83.51 179

FMVSNet1		64.57 201	62.11 204	71.96 185	77.32 194	46.36 232	83.52 140	83.31 134	52.43 248	54.42 216	76.23 234	27.80 263	86.20 184	42.59 243	61.34 208	83.32 182

CNLPA		60.59 245	58.44 244	67.05 258	79.21 164	47.26 224	79.75 219	64.34 322	42.46 306	51.90 238	83.94 134	27.79 264	75.41 301	37.12 256	59.49 224	78.47 253

TAPA-MVS	56.12 14	61.82 239	60.18 233	66.71 261	78.48 181	37.97 296	75.19 261	76.41 256	46.82 279	57.04 199	86.52 114	27.67 265	77.03 294	26.50 304	67.02 159	85.14 153
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pmmvs6		59.64 250	57.15 251	67.09 256	66.01 304	36.86 300	80.50 203	78.64 216	45.05 290	49.05 258	73.94 252	27.28 266	86.10 190	43.96 237	49.94 285	78.31 264

test-mter		68.36 152	67.29 143	71.60 197	78.67 174	48.17 214	85.13 97	79.72 196	53.38 231	63.13 124	82.58 162	27.23 267	80.24 268	60.56 126	75.17 106	86.39 137

Anonymous20240521		58.32 265	56.96 254	62.41 291	71.99 269	28.83 331	73.05 276	81.66 166	49.54 266	44.80 288	76.15 238	27.02 268	80.79 260	31.63 281	56.78 250	77.60 277

XVG-OURS-SEG-HR		62.02 237	59.54 236	69.46 232	65.30 308	45.88 239	65.06 308	73.57 285	46.45 282	57.42 196	83.35 151	26.95 269	78.09 284	53.77 181	64.03 180	84.42 162

test_djsdf		63.84 209	61.56 218	70.70 213	68.78 291	44.69 250	81.63 184	81.44 169	50.28 260	52.27 234	76.26 233	26.72 270	86.11 188	60.83 123	55.84 260	81.29 219

Anonymous20231206		59.08 255	57.59 248	63.55 285	68.77 292	32.14 321	80.26 207	79.78 195	50.00 263	49.39 256	72.39 270	26.64 271	78.36 280	33.12 276	57.94 242	80.14 237

pcd1.5k->3k		27.74 323	27.68 323	27.93 341	73.75 232	0.00 363	0.00 355	85.50 71	0.00 357	0.00 360	0.00 361	26.52 272	0.00 360	0.00 359	63.37 189	83.79 176

ppachtmachnet_test		58.56 260	54.34 268	71.24 203	71.42 276	54.74 62	81.84 176	72.27 291	49.02 269	45.86 287	68.99 287	26.27 273	83.30 238	30.12 286	43.23 316	75.69 291

test20.03		55.22 280	54.07 270	58.68 307	63.14 318	25.00 337	77.69 239	74.78 271	52.64 237	43.43 294	72.39 270	26.21 274	74.76 304	29.31 288	47.05 298	76.28 287

FMVSNet5		58.61 259	56.45 256	65.10 279	77.20 199	39.74 288	74.77 262	77.12 247	50.27 262	43.28 296	67.71 300	26.15 275	76.90 296	36.78 261	54.78 266	78.65 251

ACMP	61.11 9	66.24 191	64.33 190	72.00 184	74.89 220	49.12 190	83.18 151	79.83 194	55.41 218	52.29 233	82.68 161	25.83 276	86.10 190	60.89 122	63.94 182	80.78 225
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MIMVSNet		63.12 221	60.29 232	71.61 196	75.92 210	46.65 228	65.15 307	81.94 159	59.14 148	54.65 214	69.47 284	25.74 277	80.63 262	41.03 246	69.56 145	87.55 116

LPG-MVS_test		66.44 187	64.58 188	72.02 182	74.42 224	48.60 201	83.07 154	80.64 185	54.69 223	53.75 222	83.83 136	25.73 278	86.98 166	60.33 131	64.71 174	80.48 233

LGP-MVS_train				72.02 182	74.42 224	48.60 201		80.64 185	54.69 223	53.75 222	83.83 136	25.73 278	86.98 166	60.33 131	64.71 174	80.48 233

ACMM	58.35 12	64.35 205	62.01 205	71.38 201	74.21 227	48.51 205	82.25 167	79.66 198	47.61 273	54.54 215	80.11 190	25.26 280	86.00 192	51.26 197	63.16 193	79.64 241
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test4		68.64 150	65.68 170	77.53 71	67.78 300	53.34 98	79.42 224	82.84 147	65.96 49	46.54 282	76.15 238	25.16 281	88.83 111	69.74 66	77.53 86	90.43 61

XVG-OURS		61.88 238	59.34 238	69.49 231	65.37 307	46.27 235	64.80 310	73.49 286	47.04 277	57.41 197	82.85 156	25.15 282	78.18 282	53.00 186	64.98 171	84.01 168

PVSNet_0	57.04 13	61.19 242	57.24 250	73.02 163	77.45 193	50.31 173	79.43 223	77.36 244	63.96 76	47.51 270	72.45 269	25.03 283	83.78 234	52.76 190	19.22 345	84.96 156

DP-MVS		59.24 252	56.12 260	68.63 246	88.24 18	50.35 170	82.51 164	64.43 321	41.10 308	46.70 279	78.77 200	24.75 284	88.57 120	22.26 326	56.29 254	66.96 327

cascas		69.01 140	66.13 161	77.66 67	79.36 160	55.41 42	86.99 60	83.75 126	56.69 197	58.92 169	81.35 178	24.31 285	92.10 39	53.23 182	70.61 138	85.46 150

CMPMVS	40.41 21	55.34 279	52.64 280	63.46 286	60.88 328	43.84 257	61.58 320	71.06 301	30.43 336	36.33 316	74.63 249	24.14 286	75.44 300	48.05 215	66.62 160	71.12 320
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UGNet		68.71 148	67.11 147	73.50 159	80.55 154	47.61 219	84.08 129	78.51 220	59.45 135	65.68 92	82.73 160	23.78 287	85.08 210	52.80 187	76.40 94	87.80 112
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
YYNet1		53.82 288	49.96 289	65.41 276	70.09 286	48.95 193	72.30 282	71.66 296	44.25 295	31.89 333	63.07 315	23.73 288	73.95 308	33.26 274	39.40 322	73.34 310

1111		48.00 302	46.30 301	53.08 320	55.68 335	20.86 346	70.41 293	76.03 258	36.88 320	34.86 322	59.55 324	23.72 289	68.13 329	20.82 331	38.76 325	70.25 321

.test1245		38.91 314	41.99 311	29.67 339	55.68 335	20.86 346	70.41 293	76.03 258	36.88 320	34.86 322	59.55 324	23.72 289	68.13 329	20.82 331	0.00 357	0.02 357

MDA-MVSNet_test_wron		53.82 288	49.95 290	65.43 275	70.13 285	49.05 191	72.30 282	71.65 297	44.23 296	31.85 334	63.13 314	23.68 291	74.01 307	33.25 275	39.35 323	73.23 312

PLC	52.38 18	60.89 243	58.97 243	66.68 263	81.77 120	45.70 241	78.96 231	74.04 279	43.66 299	47.63 266	83.19 154	23.52 292	77.78 291	37.47 253	60.46 212	76.55 285
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ADS-MVSNet2		55.21 281	51.44 284	66.51 264	80.60 146	49.56 184	55.03 330	65.44 318	44.72 291	51.00 243	61.19 318	22.83 293	75.41 301	28.54 295	53.63 271	74.57 299

ADS-MVSNet		56.17 275	51.95 283	68.84 237	80.60 146	53.07 118	55.03 330	70.02 308	44.72 291	51.00 243	61.19 318	22.83 293	78.88 278	28.54 295	53.63 271	74.57 299

test_0402		56.45 273	53.03 275	66.69 262	76.78 202	50.31 173	81.76 177	69.61 309	42.79 304	43.88 291	72.13 272	22.82 295	86.46 182	16.57 343	50.94 283	63.31 336

UnsupCasMVSNet_eth		57.56 267	55.15 265	64.79 281	64.57 313	33.12 315	73.17 273	83.87 125	58.98 156	41.75 302	70.03 282	22.54 296	79.92 272	46.12 229	35.31 329	81.32 218

xiu_mvs_v1_base_debu		71.60 95	70.29 95	75.55 108	77.26 196	53.15 112	85.34 90	79.37 202	55.83 213	72.54 40	90.19 60	22.38 297	86.66 176	73.28 51	76.39 95	86.85 126

xiu_mvs_v1_base		71.60 95	70.29 95	75.55 108	77.26 196	53.15 112	85.34 90	79.37 202	55.83 213	72.54 40	90.19 60	22.38 297	86.66 176	73.28 51	76.39 95	86.85 126

xiu_mvs_v1_base_debi		71.60 95	70.29 95	75.55 108	77.26 196	53.15 112	85.34 90	79.37 202	55.83 213	72.54 40	90.19 60	22.38 297	86.66 176	73.28 51	76.39 95	86.85 126

LS3D		56.40 274	53.82 271	64.12 282	81.12 136	45.69 242	73.42 270	66.14 316	35.30 329	43.24 297	79.88 191	22.18 300	79.62 275	19.10 338	64.00 181	67.05 326

PVSNet	62.49 8	69.27 136	67.81 129	73.64 155	84.41 61	51.85 142	84.63 121	77.80 234	66.42 38	59.80 154	84.95 126	22.14 301	80.44 265	55.03 171	75.11 108	88.62 100

MDA-MVSNet-bldmvs		51.56 295	47.75 298	63.00 288	71.60 274	47.32 223	69.70 300	72.12 292	43.81 298	27.65 339	63.38 313	21.97 302	75.96 298	27.30 301	32.19 337	65.70 330

pmmvs-eth3d		55.97 277	52.78 279	65.54 274	61.02 327	46.44 231	75.36 260	67.72 312	49.61 265	43.65 293	67.58 301	21.63 303	77.04 293	44.11 236	44.33 313	73.15 313

anonymousdsp		60.46 246	57.65 247	68.88 236	63.63 316	45.09 245	72.93 277	78.63 217	46.52 281	51.12 242	72.80 266	21.46 304	83.07 240	57.79 149	53.97 269	78.47 253

MVS-HIRNet		49.01 298	44.71 303	61.92 295	76.06 206	46.61 229	63.23 314	54.90 335	24.77 340	33.56 329	36.60 342	21.28 305	75.88 299	29.49 287	62.54 203	63.26 337

testing_2		63.60 213	59.86 234	74.82 129	61.87 322	52.39 133	73.06 275	82.76 148	61.49 111	39.96 308	67.39 302	21.06 306	88.34 128	67.07 79	64.10 179	83.72 177

testpf		45.92 308	45.81 302	46.27 327	69.56 288	27.86 334	23.18 350	73.91 281	44.10 297	36.99 315	57.16 331	20.56 307	71.77 320	42.17 245	44.64 312	39.18 346

UnsupCasMVSNet_bld		53.86 287	50.53 287	63.84 283	63.52 317	34.75 309	71.38 289	81.92 161	46.53 280	38.95 312	57.93 328	20.55 308	80.20 270	39.91 249	34.09 336	76.57 284

EU-MVSNet		52.63 292	50.72 286	58.37 309	62.69 320	28.13 333	72.60 278	75.97 260	30.94 335	40.76 307	72.11 273	20.16 309	70.80 324	35.11 268	46.11 302	76.19 288

N_pmnet		41.25 311	39.77 313	45.66 329	68.50 294	0.82 361	72.51 280	0.38 362	35.61 325	35.26 321	61.51 317	20.07 310	67.74 331	23.51 313	40.63 319	68.42 325

MSDG		59.44 251	55.14 266	72.32 176	74.69 221	50.71 161	74.39 265	73.58 284	44.44 294	43.40 295	77.52 217	19.45 311	90.87 54	31.31 283	57.49 248	75.38 294

K. test v3		54.04 285	49.42 292	67.92 251	68.55 293	42.57 272	75.51 258	63.07 325	52.07 249	39.21 310	64.59 311	19.34 312	82.21 249	37.11 257	25.31 341	78.97 246

lessismore_v0				67.98 250	64.76 312	41.25 279		45.75 343		36.03 318	65.63 309	19.29 313	84.11 230	35.67 264	21.24 344	78.59 252

test2356		53.94 286	52.37 282	58.64 308	61.58 323	27.53 336	78.20 236	74.33 275	46.92 278	44.01 290	66.04 306	18.91 314	74.11 306	28.80 290	52.55 276	74.28 301

OpenMVS_ROB	53.19 17	59.20 253	56.00 261	68.83 238	71.13 280	44.30 253	83.64 139	75.02 270	46.42 283	46.48 283	73.03 263	18.69 315	88.14 136	27.74 299	61.80 206	74.05 304

LTVRE_ROB	45.45 19	52.73 291	49.74 291	61.69 296	69.78 287	34.99 308	44.52 339	67.60 313	43.11 303	43.79 292	74.03 251	18.54 316	81.45 255	28.39 297	57.94 242	68.62 324
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
SixPastTwentyTwo		54.37 282	50.10 288	67.21 255	70.70 281	41.46 278	74.73 263	64.69 320	47.56 274	39.12 311	69.49 283	18.49 317	84.69 218	31.87 279	34.20 335	75.48 293

new-patchmatchnet		48.21 300	46.55 299	53.18 319	57.73 332	18.19 352	70.24 295	71.02 302	45.70 285	33.70 328	60.23 320	18.00 318	69.86 327	27.97 298	34.35 333	71.49 319

F-COLMAP		55.96 278	53.65 274	62.87 289	72.76 248	42.77 268	74.70 264	70.37 305	40.03 309	41.11 305	79.36 193	17.77 319	73.70 311	32.80 277	53.96 270	72.15 314

LP		47.05 305	42.23 310	61.53 298	72.04 268	49.37 188	49.48 334	65.50 317	34.57 330	34.29 326	52.30 335	17.73 320	75.32 303	17.56 341	36.57 327	59.91 338

jajsoiax		63.21 220	60.84 224	70.32 221	68.33 296	44.45 251	81.23 192	81.05 179	53.37 232	50.96 245	77.81 215	17.49 321	85.49 203	59.31 135	58.05 241	81.02 222

RPSCF		45.77 309	44.13 306	50.68 322	57.67 333	29.66 326	54.92 332	45.25 344	26.69 339	45.92 286	75.92 242	17.43 322	45.70 350	27.44 300	45.95 305	76.67 282

PatchMatch-RL		56.66 270	53.75 272	65.37 277	77.91 188	45.28 244	69.78 299	60.38 329	41.35 307	47.57 267	73.73 254	16.83 323	76.91 295	36.99 259	59.21 226	73.92 305

mvs_tets		62.96 224	60.55 229	70.19 222	68.22 298	44.24 255	80.90 199	80.74 184	52.99 236	50.82 253	77.56 216	16.74 324	85.44 204	59.04 136	57.94 242	80.89 223

ACMH+	54.58 15	58.55 261	55.24 264	68.50 248	74.68 222	45.80 240	80.27 206	70.21 307	47.15 276	42.77 298	75.48 244	16.73 325	85.98 193	35.10 269	54.78 266	73.72 306

ACMH	53.70 16	59.78 249	55.94 262	71.28 202	76.59 203	48.35 210	80.15 212	76.11 257	49.74 264	41.91 301	73.45 261	16.50 326	90.31 65	31.42 282	57.63 247	75.17 295
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MIMVSNet1		50.35 297	47.81 296	57.96 310	61.53 324	27.80 335	67.40 305	74.06 278	43.25 302	33.31 332	65.38 310	16.03 327	71.34 323	21.80 328	47.55 293	74.75 297

DSMNet-mixed		38.35 315	35.36 316	47.33 326	48.11 343	14.91 354	37.87 346	36.60 350	19.18 344	34.37 324	59.56 323	15.53 328	53.01 344	20.14 335	46.89 299	74.07 303

EG-PatchMatch MVS		62.40 236	59.59 235	70.81 212	73.29 235	49.05 191	85.81 78	84.78 95	51.85 253	44.19 289	73.48 260	15.52 329	89.85 76	40.16 248	67.24 157	73.54 308

testgi		54.25 284	52.57 281	59.29 305	62.76 319	21.65 343	72.21 284	70.47 303	53.25 233	41.94 300	77.33 221	14.28 330	77.95 287	29.18 289	51.72 279	78.28 266

COLMAP_ROB	43.60 20	50.90 296	48.05 295	59.47 304	67.81 299	40.57 287	71.25 290	62.72 327	36.49 323	36.19 317	73.51 259	13.48 331	73.92 309	20.71 333	50.26 284	63.92 334
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		52.39 293	48.73 293	63.35 287	65.21 309	38.42 293	68.54 304	64.95 319	38.19 314	39.57 309	71.43 276	13.23 332	79.92 272	37.16 255	40.32 321	71.72 316

Anonymous20231211		46.87 307	43.27 309	57.67 311	57.88 331	30.12 325	73.14 274	64.16 323	33.43 331	34.34 325	59.42 326	12.15 333	77.99 286	19.64 337	35.23 331	64.90 332

tmp_tt		9.44 333	10.68 334	5.73 346	2.49 360	4.21 360	10.48 354	18.04 358	0.34 356	12.59 348	20.49 350	11.39 334	7.03 359	13.84 346	6.46 355	5.95 355

ITE_SJBPF				51.84 321	58.03 330	31.94 322		53.57 339	36.67 322	41.32 304	75.23 246	11.17 335	51.57 345	25.81 306	48.04 290	72.02 315

AllTest		47.32 303	44.66 304	55.32 316	65.08 310	37.50 298	62.96 316	54.25 337	35.45 327	33.42 330	72.82 264	9.98 336	59.33 338	24.13 311	43.84 314	69.13 322

TestCases				55.32 316	65.08 310	37.50 298		54.25 337	35.45 327	33.42 330	72.82 264	9.98 336	59.33 338	24.13 311	43.84 314	69.13 322

test1235678		47.09 304	43.82 307	56.91 313	53.18 338	24.90 338	71.93 286	70.31 306	39.54 310	31.44 335	56.59 332	9.50 338	71.55 321	22.63 319	39.24 324	74.28 301

USDC		54.36 283	51.23 285	63.76 284	64.29 314	37.71 297	62.84 317	73.48 288	56.85 191	35.47 320	71.94 274	9.23 339	78.43 279	38.43 252	48.57 286	75.13 296

XVG-ACMP-BASELINE		56.03 276	52.85 278	65.58 273	61.91 321	40.95 282	63.36 312	72.43 289	45.20 289	46.02 285	74.09 250	9.20 340	78.12 283	45.13 231	58.27 236	77.66 275

pmmvs3		45.53 310	41.55 312	57.44 312	48.97 342	39.68 289	70.06 296	57.66 332	28.32 338	34.06 327	57.29 329	8.50 341	66.85 333	34.86 270	34.26 334	65.80 329

no-one		37.21 317	31.48 321	54.40 318	39.62 350	31.91 323	45.68 338	67.42 314	35.54 326	14.59 346	35.91 344	7.35 342	73.20 315	22.98 314	14.23 346	58.09 340

ANet_high		34.39 319	29.59 322	48.78 324	30.34 354	22.28 341	55.53 329	63.79 324	38.11 315	15.47 345	36.56 343	6.94 343	59.98 337	13.93 345	5.64 356	64.08 333

FPMVS		35.40 318	33.67 318	40.57 331	46.34 344	28.74 332	41.05 343	57.05 333	20.37 343	22.27 342	53.38 334	6.87 344	44.94 351	8.62 349	47.11 297	48.01 344

new_pmnet		33.56 320	31.89 320	38.59 332	49.01 341	20.42 348	51.01 333	37.92 349	20.58 341	23.45 340	46.79 338	6.66 345	49.28 347	20.00 336	31.57 339	46.09 345

TinyColmap		48.15 301	44.49 305	59.13 306	65.73 306	38.04 295	63.34 313	62.86 326	38.78 312	29.48 337	67.23 304	6.46 346	73.30 313	24.59 309	41.90 318	66.04 328

ambc				62.06 293	53.98 337	29.38 328	35.08 347	79.65 199		41.37 303	59.96 321	6.27 347	82.15 250	35.34 266	38.22 326	74.65 298

TDRefinement		40.91 312	38.37 314	48.55 325	50.45 340	33.03 317	58.98 325	50.97 340	28.50 337	29.89 336	67.39 302	6.21 348	54.51 342	17.67 340	35.25 330	58.11 339

PM-MVS		46.92 306	43.76 308	56.41 314	52.18 339	32.26 320	63.21 315	38.18 348	37.99 316	40.78 306	66.20 305	5.09 349	65.42 334	48.19 214	41.99 317	71.54 318

LF4IMVS		33.04 321	32.55 319	34.52 336	40.96 347	22.03 342	44.45 340	35.62 351	20.42 342	28.12 338	62.35 316	5.03 350	31.88 356	21.61 330	34.42 332	49.63 343

testus		48.97 299	46.53 300	56.31 315	57.39 334	24.08 339	73.40 271	70.45 304	43.37 301	35.52 319	63.95 312	4.77 351	71.36 322	24.88 308	45.02 310	73.50 309

test12356		37.84 316	35.07 317	46.18 328	45.03 345	8.02 359	57.70 327	62.67 328	31.83 334	22.78 341	50.25 336	4.46 352	66.95 332	17.25 342	23.62 343	63.57 335

testmv		39.64 313	36.01 315	50.55 323	42.18 346	21.56 344	64.81 309	66.88 315	32.22 333	22.25 343	47.47 337	4.33 353	64.81 335	17.71 339	26.22 340	65.29 331

EMVS		18.42 330	17.66 331	20.71 343	34.13 353	12.64 356	46.94 336	29.94 355	10.46 352	5.58 355	14.93 354	4.23 354	38.83 354	5.24 355	7.51 353	10.67 354

E-PMN		19.16 329	18.40 330	21.44 342	36.19 352	13.63 355	47.59 335	30.89 354	10.73 350	5.91 354	16.59 351	3.66 355	39.77 353	5.95 353	8.14 350	10.92 353

PMMVS2		26.71 325	22.98 328	37.87 333	36.89 351	8.51 358	42.51 342	29.32 356	19.09 345	13.01 347	37.54 340	2.23 356	53.11 343	14.54 344	11.71 347	51.99 342

Gipuma		27.47 324	24.26 325	37.12 334	60.55 329	29.17 329	11.68 353	60.00 330	14.18 347	10.52 350	15.12 353	2.20 357	63.01 336	8.39 350	35.65 328	19.18 351
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		28.07 322	23.85 326	40.71 330	27.46 356	18.93 351	30.82 348	46.19 341	12.76 349	16.40 344	34.70 346	1.90 358	48.69 348	20.25 334	24.22 342	54.51 341

DeepMVS_CX				13.10 344	21.34 359	8.99 357		10.02 360	10.59 351	7.53 353	30.55 347	1.82 359	14.55 357	6.83 352	7.52 352	15.75 352

PMVS	19.57 22	25.07 327	22.43 329	32.99 337	23.12 357	22.98 340	40.98 344	35.19 352	15.99 346	11.95 349	35.87 345	1.47 360	49.29 346	5.41 354	31.90 338	26.70 350
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	16.60 23	17.34 332	13.39 333	29.16 340	28.43 355	19.72 350	13.73 352	23.63 357	7.23 354	7.96 352	21.41 349	0.80 361	36.08 355	6.97 351	10.39 348	31.69 348
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		25.11 326	23.09 327	31.17 338	40.18 348	21.30 345	57.99 326	33.28 353	13.77 348	9.94 351	30.29 348	0.45 362	43.74 352	13.61 347	8.28 349	28.46 349

wuyk23d		9.11 334	8.77 336	10.15 345	40.18 348	16.76 353	20.28 351	1.01 361	2.58 355	2.66 357	0.98 358	0.23 363	12.49 358	4.08 356	6.90 354	1.19 356

wuykxyi23d		19.94 328	14.87 332	35.13 335	22.47 358	19.80 349	25.80 349	38.64 347	7.61 353	4.88 356	13.58 356	0.23 363	48.42 349	13.11 348	7.53 351	37.18 347

sosnet-low-res		0.00 339	0.00 340	0.00 349	0.00 362	0.00 363	0.00 355	0.00 363	0.00 357	0.00 360	0.00 361	0.00 365	0.00 360	0.00 359	0.00 357	0.00 360

sosnet		0.00 339	0.00 340	0.00 349	0.00 362	0.00 363	0.00 355	0.00 363	0.00 357	0.00 360	0.00 361	0.00 365	0.00 360	0.00 359	0.00 357	0.00 360

uncertanet		0.00 339	0.00 340	0.00 349	0.00 362	0.00 363	0.00 355	0.00 363	0.00 357	0.00 360	0.00 361	0.00 365	0.00 360	0.00 359	0.00 357	0.00 360

Regformer		0.00 339	0.00 340	0.00 349	0.00 362	0.00 363	0.00 355	0.00 363	0.00 357	0.00 360	0.00 361	0.00 365	0.00 360	0.00 359	0.00 357	0.00 360

testmvs		6.14 336	8.18 337	0.01 347	0.01 361	0.00 363	73.40 271	0.00 363	0.00 357	0.02 358	0.15 359	0.00 365	0.00 360	0.02 357	0.00 357	0.02 357

test123		6.01 337	8.01 338	0.01 347	0.00 362	0.01 362	71.93 286	0.00 363	0.00 357	0.02 358	0.11 360	0.00 365	0.00 360	0.02 357	0.00 357	0.02 357

ab-mvs-re		7.68 335	10.24 335	0.00 349	0.00 362	0.00 363	0.00 355	0.00 363	0.00 357	0.00 360	92.12 20	0.00 365	0.00 360	0.00 359	0.00 357	0.00 360

uanet		0.00 339	0.00 340	0.00 349	0.00 362	0.00 363	0.00 355	0.00 363	0.00 357	0.00 360	0.00 361	0.00 365	0.00 360	0.00 359	0.00 357	0.00 360

GSMVS																88.13 107

test_part3							89.59 26		56.02 210		93.65 3		95.22 6	79.73 12

test_part2					89.33 9	55.48 38				82.27 2

MTGPA								81.31 172

MTMP								15.34 359

gm-plane-assit					83.24 82	54.21 75			70.91 11		88.23 89		95.25 5	66.37 82

test9_res														78.72 18	85.44 28	91.39 41

agg_prior2														75.65 32	85.11 32	91.01 47

agg_prior					85.64 40	54.92 57		83.61 130		72.53 43			88.10 139

test_prior4						56.39 28	87.15 59

test_prior				78.39 52	86.35 31	54.91 59		85.45 73					89.70 81			90.55 55

旧先验2							81.73 178		45.53 287	74.66 24			70.48 326	58.31 143

新几何2							81.61 186

无先验							85.19 95	78.00 232	49.08 268				85.13 208	52.78 188		87.45 118

原ACMM2							83.77 137

testdata2													77.81 290	45.64 230

testdata1							77.55 240		64.14 73

plane_prior7					77.95 185	48.46 209

plane_prior5								82.59 149					88.30 132	65.46 91	72.34 129	84.49 160

plane_prior4											83.28 152

plane_prior3						48.95 193			64.01 74	62.15 130

plane_prior2							85.76 80		63.60 84

plane_prior1					78.31 184

plane_prior						49.57 182	87.43 52		64.57 66						72.84 123

n2								0.00 363

nn								0.00 363

door-mid								41.31 346

test11								84.25 106

door								43.27 345

HQP5-MVS						51.56 147

HQP-NCC					79.02 167		88.00 38		65.45 52	64.48 107

ACMP_Plane					79.02 167		88.00 38		65.45 52	64.48 107

BP-MVS														66.70 80

HQP4-MVS										64.47 110			88.61 115			84.91 157

HQP3-MVS								83.68 127							73.12 119

NP-MVS					78.76 171	50.43 168					85.12 124

ACMMP++_ref															63.20 192

ACMMP++															59.38 225

High-resolution multi-view benchmark