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 38	82.99 100	52.71 130	85.04 118	89.99 10	66.08 45	86.77 1	92.75 13	72.05 1	91.46 49	83.35 5	93.53 1	92.72 18

DELS-MVS		82.32 3	82.50 3	81.79 6	86.80 31	56.89 23	92.77 2	86.30 72	77.83 1	77.88 15	92.13 19	60.24 2	94.78 10	78.97 15	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 8	79.72 8	82.49 4	89.12 12	57.67 14	89.29 31	91.54 2	59.19 149	71.82 53	90.05 64	59.72 3	96.04 1	78.37 18	88.40 9	93.75 2

GG-mvs-BLEND				77.77 70	86.68 32	50.61 171	68.67 313	88.45 29		68.73 71	87.45 105	59.15 4	90.67 64	54.83 181	87.67 12	92.03 28

gg-mvs-nofinetune		67.43 179	64.53 200	76.13 107	85.95 37	47.79 229	64.38 321	88.28 36	39.34 322	66.62 86	41.27 349	58.69 5	89.00 108	49.64 216	86.62 15	91.59 37

CostFormer		73.89 72	72.30 75	78.66 47	82.36 117	56.58 24	75.56 268	85.30 87	66.06 46	70.50 64	76.88 237	57.02 6	89.06 99	68.27 78	68.74 155	90.33 69

ESAPD		79.82 12	79.66 10	80.29 17	89.27 11	55.08 55	88.70 37	87.92 41	55.55 224	81.21 5	93.69 3	56.51 7	94.27 13	78.36 19	85.70 24	91.51 42

DeepPCF-MVS	69.37 1	80.65 6	81.56 5	77.94 69	85.46 52	49.56 193	90.99 13	86.66 64	70.58 13	80.07 9	95.30 1	56.18 8	90.97 58	82.57 8	86.22 19	93.28 7

TSAR-MVS + MP.		78.31 21	78.26 16	78.48 52	81.33 144	56.31 31	81.59 197	86.41 68	69.61 18	81.72 4	88.16 93	55.09 9	88.04 151	74.12 44	86.31 17	91.09 50

TSAR-MVS + GP.		77.82 26	77.59 23	78.49 51	85.25 56	50.27 184	90.02 19	90.57 5	56.58 208	74.26 30	91.60 33	54.26 10	92.16 38	75.87 32	79.91 71	93.05 12

EPP-MVSNet		71.14 108	70.07 108	74.33 149	79.18 176	46.52 242	83.81 145	86.49 66	56.32 216	57.95 193	84.90 132	54.23 11	89.14 98	58.14 151	69.65 150	87.33 128

MCST-MVS		83.01 1	83.30 2	82.15 5	92.84 2	57.58 15	93.77 1	91.10 4	75.95 2	77.10 18	93.09 9	54.15 12	95.57 3	85.80 1	85.87 21	93.31 6

alignmvs		78.08 23	77.98 19	78.39 56	83.53 79	53.22 112	89.77 25	85.45 81	66.11 43	76.59 22	91.99 25	54.07 13	89.05 101	77.34 27	77.00 93	92.89 15

casdiffmvs1		78.37 19	77.57 24	80.75 12	83.13 91	58.05 8	84.81 125	86.65 65	63.81 81	79.75 11	84.50 135	53.76 14	91.28 52	80.50 10	81.23 55	92.91 13

WTY-MVS		77.47 31	77.52 25	77.30 82	88.33 17	46.25 248	88.46 39	90.32 6	71.40 10	72.32 50	91.72 30	53.44 15	92.37 34	66.28 90	75.42 107	93.28 7

IB-MVS	68.87 2	74.01 70	72.03 83	79.94 26	83.04 97	55.50 39	90.24 18	88.65 22	67.14 34	61.38 146	81.74 185	53.21 16	94.28 12	60.45 134	62.41 212	90.03 76
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 27	87.34 27	55.20 50	89.93 22	87.55 51	66.04 48	79.46 12	93.00 12	53.10 17	91.76 44	80.40 11	89.56 4	92.68 19

MVSTER		73.25 80	72.33 73	76.01 112	85.54 48	53.76 83	83.52 149	87.16 54	67.06 35	63.88 126	81.66 186	52.77 18	90.44 68	64.66 105	64.69 184	83.84 185

CNVR-MVS		81.76 4	81.90 4	81.33 9	90.04 5	57.70 13	91.71 3	88.87 19	70.31 14	77.64 17	93.87 2	52.58 19	93.91 16	84.17 2	87.92 11	92.39 21

tpmp4_e23		70.01 128	67.13 156	78.65 48	81.93 125	57.90 12	73.99 279	81.35 181	60.61 128	65.28 103	73.78 264	52.48 20	88.60 126	48.40 224	66.35 175	89.44 85

FIs		70.00 129	70.24 106	69.30 245	77.93 200	38.55 304	83.99 143	87.72 48	66.86 37	57.66 200	84.17 138	52.28 21	85.31 216	52.72 201	68.80 154	84.02 176

tpm2		70.82 114	68.44 127	77.98 66	80.78 151	56.11 33	74.21 278	81.28 185	60.24 132	68.04 76	75.27 256	52.26 22	88.50 133	55.82 172	68.03 160	89.33 86

thisisatest0515		73.64 76	72.20 78	77.97 67	81.63 131	53.01 125	86.69 71	88.81 20	62.53 101	64.06 121	85.65 123	52.15 23	92.50 31	58.43 145	69.84 148	88.39 112

DWT-MVSNet_test		75.47 56	73.87 60	80.29 17	87.33 28	57.05 20	82.86 169	87.96 40	72.59 6	67.29 81	87.79 98	51.61 24	91.52 47	54.75 184	72.63 131	92.29 23

UniMVSNet_NR-MVSNet		68.82 152	68.29 129	70.40 232	75.71 225	42.59 281	84.23 135	86.78 60	66.31 40	58.51 186	82.45 174	51.57 25	84.64 230	53.11 191	55.96 267	83.96 182

PAPM		76.76 38	76.07 40	78.81 43	80.20 166	59.11 5	86.86 69	86.23 73	68.60 21	70.18 65	88.84 84	51.57 25	87.16 172	65.48 96	86.68 14	90.15 74

tttt0517		68.33 164	66.29 167	74.46 145	78.08 197	49.06 200	80.88 210	89.08 17	54.40 235	54.75 226	80.77 198	51.31 27	90.33 73	49.35 218	58.01 252	83.99 178

mvs_anonymous		72.29 94	70.74 96	76.94 93	82.85 105	54.72 66	78.43 246	81.54 178	63.77 83	61.69 145	79.32 205	51.11 28	85.31 216	62.15 122	75.79 104	90.79 56

HY-MVS	67.03 5	73.90 71	73.14 64	76.18 106	84.70 62	47.36 232	75.56 268	86.36 71	66.27 41	70.66 62	83.91 141	51.05 29	89.31 96	67.10 84	72.61 132	91.88 32

thisisatest0530		70.47 121	68.56 125	76.20 105	79.78 168	51.52 157	83.49 154	88.58 27	57.62 192	58.60 185	82.79 166	51.03 30	91.48 48	52.84 195	62.36 214	85.59 158

canonicalmvs		78.17 22	77.86 21	79.12 37	84.30 66	54.22 76	87.71 45	84.57 110	67.70 30	77.70 16	92.11 22	50.90 31	89.95 84	78.18 23	77.54 87	93.20 9

casdiffmvs		77.54 29	76.52 33	80.60 13	83.43 80	58.01 10	85.16 103	86.39 70	65.71 53	76.20 23	83.87 142	50.75 32	91.33 51	77.37 26	79.79 73	92.46 20

MVS_Test		75.85 51	74.93 51	78.62 49	84.08 71	55.20 50	83.99 143	85.17 93	68.07 25	73.38 37	82.76 167	50.44 33	89.00 108	65.90 92	80.61 60	91.64 35

FC-MVSNet-test		67.49 177	67.91 133	66.21 277	76.06 219	33.06 328	80.82 211	87.18 53	64.44 70	54.81 225	82.87 164	50.40 34	82.60 259	48.05 226	66.55 169	82.98 200

PatchFormer-LS_test		74.17 67	72.30 75	79.77 28	86.61 33	57.26 18	82.02 179	84.80 104	71.85 9	64.73 111	87.52 103	50.33 35	90.40 71	54.23 186	68.63 157	91.64 35

nrg030		72.27 96	71.56 87	74.42 147	75.93 222	50.60 172	86.97 65	83.21 150	62.75 99	67.15 83	84.38 136	50.07 36	86.66 187	71.19 60	62.37 213	85.99 148

tpmrst		71.04 111	69.77 111	74.86 138	83.19 90	55.86 36	75.64 267	78.73 225	67.88 26	64.99 109	73.73 265	49.96 37	79.56 286	65.92 91	67.85 163	89.14 94

CANet		80.90 5	81.17 6	80.09 25	87.62 25	54.21 77	91.60 6	86.47 67	73.13 5	79.89 10	93.10 7	49.88 38	92.98 22	84.09 3	84.75 34	93.08 11

Vis-MVSNet (Re-imp)		65.52 207	65.63 182	65.17 290	77.49 205	30.54 336	75.49 271	77.73 248	59.34 144	52.26 248	86.69 115	49.38 39	80.53 274	37.07 269	75.28 108	84.42 171

EPNet		78.36 20	78.49 15	77.97 67	85.49 49	52.04 143	89.36 30	84.07 128	73.22 4	77.03 19	91.72 30	49.32 40	90.17 81	73.46 50	82.77 44	91.69 34
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tpm		68.36 162	67.48 150	70.97 221	79.93 167	51.34 162	76.58 257	78.75 224	67.73 28	63.54 132	74.86 258	48.33 41	72.36 328	53.93 188	63.71 191	89.21 91

APDe-MVS		78.44 16	78.20 17	79.19 34	88.56 13	54.55 72	89.76 26	87.77 46	55.91 219	78.56 14	92.49 16	48.20 42	92.65 29	79.49 13	83.04 43	90.39 67

MG-MVS		78.42 17	76.99 30	82.73 2	93.17 1	64.46 1	89.93 22	88.51 28	64.83 65	73.52 36	88.09 94	48.07 43	92.19 37	62.24 120	84.53 36	91.53 41

DeepC-MVS	67.15 4	76.90 37	76.27 37	78.80 44	80.70 153	55.02 56	86.39 74	86.71 62	66.96 36	67.91 77	89.97 66	48.03 44	91.41 50	75.60 34	84.14 38	89.96 77
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 28	77.33 27	78.39 56	86.35 35	54.91 61	89.04 33	85.45 81	61.88 109	73.55 34	91.46 37	48.01 45	89.70 90	74.73 39	85.46 25	90.55 59

test_prior2							89.04 33		61.88 109	73.55 34	91.46 37	48.01 45		74.73 39	85.46 25

Regformer-1		77.80 27	77.44 26	78.88 42	87.78 23	52.44 135	87.60 47	90.08 8	68.86 20	72.49 48	91.79 27	47.69 47	94.90 8	73.57 48	77.05 90	89.31 87

1112_ss		70.05 126	69.37 115	72.10 190	80.77 152	42.78 279	85.12 108	76.75 260	59.69 136	61.19 148	92.12 20	47.48 48	83.84 243	53.04 193	68.21 158	89.66 80

tfpn1000		62.79 243	61.74 219	65.95 283	80.50 165	35.93 313	76.53 259	83.99 132	51.24 266	49.82 268	83.44 157	47.32 49	83.02 258	21.84 337	60.99 218	78.89 258

Effi-MVS+		75.24 57	73.61 62	80.16 22	81.92 126	57.42 16	85.21 101	76.71 261	60.68 127	73.32 38	89.34 75	47.30 50	91.63 45	68.28 77	79.72 74	91.42 44

UniMVSNet (Re)		67.71 172	66.80 159	70.45 230	74.44 236	42.93 277	82.42 175	84.90 101	63.69 86	59.63 167	80.99 195	47.18 51	85.23 218	51.17 209	56.75 261	83.19 197

test12				79.24 33	86.89 30	56.08 34		85.16 94		72.27 51		47.15 52	91.10 55		85.93 20	90.54 62

PVSNet_Blended_VisFu		73.40 79	72.44 72	76.30 101	81.32 145	54.70 67	85.81 84	78.82 222	63.70 85	64.53 114	85.38 127	47.11 53	87.38 169	67.75 81	77.55 86	86.81 138

tfpn_ndepth		64.50 214	63.34 211	67.99 261	81.84 128	38.30 306	79.26 241	83.57 143	53.69 239	52.86 244	84.51 134	46.96 54	84.79 227	24.28 320	63.09 204	80.87 235

NCCC		79.57 13	79.23 13	80.59 14	89.50 8	56.99 21	91.38 8	88.17 37	67.71 29	73.81 32	92.75 13	46.88 55	93.28 20	78.79 16	84.07 39	91.50 43

VNet		77.99 25	77.92 20	78.19 61	87.43 26	50.12 185	90.93 14	91.41 3	67.48 32	75.12 25	90.15 63	46.77 56	91.00 56	73.52 49	78.46 80	93.44 4

PVSNet_BlendedMVS		73.42 78	73.30 63	73.76 164	85.91 38	51.83 150	86.18 79	84.24 119	65.40 57	69.09 69	80.86 197	46.70 57	88.13 147	75.43 35	65.92 178	81.33 227

PVSNet_Blended		76.53 42	76.54 32	76.50 99	85.91 38	51.83 150	88.89 35	84.24 119	67.82 27	69.09 69	89.33 77	46.70 57	88.13 147	75.43 35	81.48 54	89.55 83

SMA-MVS		79.10 14	78.76 14	80.12 23	84.42 64	55.87 35	87.58 52	86.76 61	61.48 117	80.26 8	93.10 7	46.53 59	92.41 33	79.97 12	88.77 6	92.08 26

conf0.01		63.04 234	61.74 219	66.95 271	80.60 156	35.92 314	76.01 260	84.09 121	52.62 248	50.87 260	83.60 149	46.49 60	83.04 252	22.59 330	58.77 239	81.89 214

conf0.002		63.04 234	61.74 219	66.95 271	80.60 156	35.92 314	76.01 260	84.09 121	52.62 248	50.87 260	83.60 149	46.49 60	83.04 252	22.59 330	58.77 239	81.89 214

thresconf0.02		62.84 237	61.74 219	66.14 278	80.60 156	35.92 314	76.01 260	84.09 121	52.62 248	50.87 260	83.60 149	46.49 60	83.04 252	22.59 330	58.77 239	79.44 253

tfpn_n400		62.84 237	61.74 219	66.14 278	80.60 156	35.92 314	76.01 260	84.09 121	52.62 248	50.87 260	83.60 149	46.49 60	83.04 252	22.59 330	58.77 239	79.44 253

tfpnconf		62.84 237	61.74 219	66.14 278	80.60 156	35.92 314	76.01 260	84.09 121	52.62 248	50.87 260	83.60 149	46.49 60	83.04 252	22.59 330	58.77 239	79.44 253

tfpnview11		62.84 237	61.74 219	66.14 278	80.60 156	35.92 314	76.01 260	84.09 121	52.62 248	50.87 260	83.60 149	46.49 60	83.04 252	22.59 330	58.77 239	79.44 253

Regformer-3		76.02 50	75.47 45	77.70 72	85.49 49	51.47 158	85.12 108	90.19 7	68.52 22	69.36 67	90.66 51	46.45 66	94.81 9	70.25 67	73.16 123	86.81 138

tpm cat1		66.28 200	62.78 212	76.77 98	81.40 142	57.14 19	70.03 307	77.19 256	53.00 245	58.76 184	70.73 291	46.17 67	86.73 185	43.27 250	64.46 186	86.44 143

diffmvs1		72.81 85	71.73 84	76.06 109	79.03 177	51.59 153	79.01 242	76.46 266	65.23 63	69.81 66	83.07 163	46.12 68	86.94 178	70.95 62	75.87 103	89.66 80

IS-MVSNet		68.80 154	67.55 148	72.54 185	78.50 192	43.43 272	81.03 206	79.35 216	59.12 155	57.27 209	86.71 114	46.05 69	87.70 162	44.32 246	75.60 106	86.49 142

Regformer-2		77.15 32	76.82 31	78.14 62	87.78 23	51.84 149	87.60 47	89.12 15	67.23 33	71.93 52	91.79 27	46.03 70	93.53 19	72.85 56	77.05 90	89.05 96

EI-MVSNet		69.70 135	68.70 124	72.68 182	75.00 231	48.90 205	79.54 232	87.16 54	61.05 121	63.88 126	83.74 145	45.87 71	90.44 68	57.42 160	64.68 185	78.70 260

IterMVS-LS		66.63 194	65.36 191	70.42 231	75.10 230	48.90 205	81.45 201	76.69 262	61.05 121	55.71 222	77.10 236	45.86 72	83.65 246	57.44 159	57.88 256	78.70 260
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MVS		76.91 35	75.48 44	81.23 10	84.56 63	55.21 49	80.23 220	91.64 1	58.65 168	65.37 102	91.48 36	45.72 73	95.05 6	72.11 58	89.52 5	93.44 4

PAPM_NR		71.80 101	69.98 109	77.26 86	81.54 137	53.34 101	78.60 245	85.25 91	53.46 240	60.53 159	88.66 85	45.69 74	89.24 97	56.49 164	79.62 75	89.19 92

DeepC-MVS_fast	67.50 3	78.00 24	77.63 22	79.13 36	88.52 14	55.12 52	89.95 21	85.98 75	68.31 23	71.33 57	92.75 13	45.52 75	90.37 72	71.15 61	85.14 30	91.91 31
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 41	76.24 38	78.00 65	85.64 44	54.92 59	87.55 53	83.61 141	57.99 181	72.53 46	91.05 39	45.36 76	88.10 149	77.76 25	84.68 35	90.99 53

Test_1112_low_res		67.18 184	66.23 169	70.02 242	78.75 184	41.02 293	83.43 155	73.69 295	57.29 195	58.45 190	82.39 176	45.30 77	80.88 270	50.50 211	66.26 176	88.16 113

NR-MVSNet		67.25 182	65.99 175	71.04 220	73.27 249	43.91 268	85.32 100	84.75 106	66.05 47	53.65 237	82.11 182	45.05 78	85.97 206	47.55 228	56.18 265	83.24 195

train_agg		76.91 35	76.40 35	78.45 54	85.68 41	55.42 41	87.59 50	84.00 130	57.84 186	72.99 39	90.98 41	44.99 79	88.58 127	78.19 20	85.32 28	91.34 48

test_8					85.72 40	55.31 45	87.60 47	83.88 135	57.84 186	72.84 42	90.99 40	44.99 79	88.34 138

segment_acmp												44.97 81

TEST9					85.68 41	55.42 41	87.59 50	84.00 130	57.72 189	72.99 39	90.98 41	44.87 82	88.58 127

MVSFormer		73.53 77	72.19 79	77.57 75	83.02 98	55.24 47	81.63 194	81.44 179	50.28 270	76.67 20	90.91 45	44.82 83	86.11 199	60.83 129	80.09 67	91.36 46

lupinMVS		78.38 18	78.11 18	79.19 34	83.02 98	55.24 47	91.57 7	84.82 102	69.12 19	76.67 20	92.02 23	44.82 83	90.23 79	80.83 9	80.09 67	92.08 26

WR-MVS		67.58 174	66.76 162	70.04 241	75.92 223	45.06 261	86.23 78	85.28 89	64.31 71	58.50 188	81.00 194	44.80 85	82.00 264	49.21 219	55.57 272	83.06 198

Fast-Effi-MVS+		72.73 86	71.15 95	77.48 78	82.75 108	54.76 63	86.77 70	80.64 195	63.05 95	65.93 95	84.01 139	44.42 86	89.03 105	56.45 167	76.36 100	88.64 106

agg_prior3		76.73 40	76.15 39	78.48 52	85.66 43	55.59 37	87.54 54	83.95 134	57.78 188	71.78 54	90.81 49	44.33 87	88.52 132	78.19 20	85.32 28	91.34 48

view600		64.79 209	63.45 207	68.82 251	82.13 119	40.75 295	79.41 236	88.29 32	56.54 209	53.26 239	81.30 190	44.26 88	85.01 222	22.97 325	62.85 207	80.71 238

view800		64.79 209	63.45 207	68.82 251	82.13 119	40.75 295	79.41 236	88.29 32	56.54 209	53.26 239	81.30 190	44.26 88	85.01 222	22.97 325	62.85 207	80.71 238

conf0.05thres1000		64.79 209	63.45 207	68.82 251	82.13 119	40.75 295	79.41 236	88.29 32	56.54 209	53.26 239	81.30 190	44.26 88	85.01 222	22.97 325	62.85 207	80.71 238

tfpn		64.79 209	63.45 207	68.82 251	82.13 119	40.75 295	79.41 236	88.29 32	56.54 209	53.26 239	81.30 190	44.26 88	85.01 222	22.97 325	62.85 207	80.71 238

Regformer-4		75.06 59	74.59 55	76.47 100	85.49 49	50.33 180	85.12 108	88.61 24	66.42 38	68.48 72	90.66 51	44.15 92	92.68 28	69.24 72	73.16 123	86.39 145

diffmvs		71.81 100	70.47 99	75.80 116	78.97 181	51.56 154	78.25 247	76.39 268	65.75 51	67.61 78	81.37 188	44.14 93	86.84 181	67.81 80	73.97 117	89.00 98

PCF-MVS	61.03 10	70.10 124	68.40 128	75.22 128	77.15 213	51.99 144	79.30 240	82.12 168	56.47 214	61.88 144	86.48 119	43.98 94	87.24 171	55.37 176	72.79 130	86.43 144
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CDS-MVSNet		70.48 120	69.43 113	73.64 166	77.56 204	48.83 207	83.51 153	77.45 252	63.27 92	62.33 139	85.54 126	43.85 95	83.29 250	57.38 161	74.00 116	88.79 103
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EI-MVSNet-Vis-set		73.19 81	72.60 69	74.99 131	82.56 114	49.80 189	82.55 173	89.00 18	66.17 42	65.89 96	88.98 80	43.83 96	92.29 35	65.38 103	69.01 153	82.87 202

APD-MVS		76.15 47	75.68 41	77.54 76	88.52 14	53.44 93	87.26 62	85.03 98	53.79 237	74.91 26	91.68 32	43.80 97	90.31 74	74.36 42	81.82 51	88.87 101
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MVS_111021_HR		76.39 44	75.38 47	79.42 31	85.33 54	56.47 28	88.15 40	84.97 99	65.15 64	66.06 94	89.88 67	43.79 98	92.16 38	75.03 38	80.03 70	89.64 82

tfpn111		66.40 199	64.99 195	70.63 227	83.29 86	43.15 274	81.67 190	87.78 42	59.04 156	55.92 219	82.18 179	43.73 99	86.83 182	26.34 315	64.92 181	81.89 214

conf200view11		66.80 193	65.42 189	70.95 222	83.29 86	43.15 274	81.67 190	87.78 42	59.04 156	55.92 219	82.18 179	43.73 99	87.80 156	28.80 300	66.36 171	81.89 214

thres100view900		66.87 191	65.42 189	71.24 215	83.29 86	43.15 274	81.67 190	87.78 42	59.04 156	55.92 219	82.18 179	43.73 99	87.80 156	28.80 300	66.36 171	82.78 203

thres600view7		66.46 197	65.12 193	70.47 229	83.41 81	43.80 270	82.15 178	87.78 42	59.37 143	56.02 218	82.21 178	43.73 99	86.90 179	26.51 313	64.94 180	80.71 238

v148		68.24 166	66.35 166	73.88 159	71.76 283	51.47 158	84.23 135	81.90 174	63.69 86	58.94 177	76.44 242	43.72 103	87.78 160	60.63 131	55.86 269	82.39 208

SD-MVS		76.18 46	74.85 52	80.18 21	85.39 53	56.90 22	85.75 88	82.45 163	56.79 203	74.48 29	91.81 26	43.72 103	90.75 63	74.61 41	78.65 79	92.91 13

XXY-MVS		70.18 122	69.28 119	72.89 179	77.64 202	42.88 278	85.06 117	87.50 52	62.58 100	62.66 138	82.34 177	43.64 105	89.83 86	58.42 147	63.70 192	85.96 150

tfpn200view9		67.57 175	66.13 172	71.89 203	84.05 72	45.07 258	83.40 157	87.71 49	60.79 124	57.79 197	82.76 167	43.53 106	87.80 156	28.80 300	66.36 171	82.78 203

thres400		67.40 180	66.13 172	71.19 217	84.05 72	45.07 258	83.40 157	87.71 49	60.79 124	57.79 197	82.76 167	43.53 106	87.80 156	28.80 300	66.36 171	80.71 238

PAPR		75.20 58	74.13 57	78.41 55	88.31 18	55.10 54	84.31 134	85.66 78	63.76 84	67.55 79	90.73 50	43.48 108	89.40 95	66.36 89	77.03 92	90.73 57

MP-MVS		74.99 60	74.33 56	76.95 92	82.89 104	53.05 122	85.63 92	83.50 144	57.86 185	67.25 82	90.24 59	43.38 109	88.85 120	76.03 31	82.23 49	88.96 99
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
EI-MVSNet-UG-set		72.37 91	71.73 84	74.29 150	81.60 133	49.29 198	81.85 185	88.64 23	65.29 62	65.05 106	88.29 90	43.18 110	91.83 43	63.74 107	67.97 161	81.75 220

thres200		68.71 158	67.27 154	73.02 175	84.73 61	46.76 239	85.03 119	87.73 47	62.34 103	59.87 162	83.45 156	43.15 111	88.32 141	31.25 294	67.91 162	83.98 180

PHI-MVS		77.49 30	77.00 29	78.95 39	85.33 54	50.69 170	88.57 38	88.59 26	58.14 178	73.60 33	93.31 5	43.14 112	93.79 17	73.81 45	88.53 8	92.37 22

ab-mvs		70.65 117	69.11 120	75.29 125	80.87 150	46.23 249	73.48 281	85.24 92	59.99 134	66.65 85	80.94 196	43.13 113	88.69 122	63.58 108	68.07 159	90.95 54

divwei89l23v2f112		69.50 140	67.67 139	74.98 132	72.72 264	53.41 96	85.08 114	82.14 164	64.79 67	60.88 152	78.19 220	43.11 114	89.04 102	62.51 116	59.62 229	82.48 206

v1		69.49 142	67.67 139	74.98 132	72.69 265	53.41 96	85.08 114	82.13 167	64.80 66	60.87 154	78.19 220	43.11 114	89.04 102	62.51 116	59.61 230	82.49 205

CDPH-MVS		76.05 49	75.19 48	78.62 49	86.51 34	54.98 58	87.32 57	84.59 109	58.62 169	70.75 61	90.85 47	43.10 116	90.63 66	70.50 65	84.51 37	90.24 70

v1141		69.50 140	67.67 139	74.98 132	72.73 263	53.41 96	85.08 114	82.14 164	64.79 67	60.88 152	78.19 220	43.09 117	89.04 102	62.51 116	59.61 230	82.47 207

v18		64.36 216	61.80 218	72.05 191	72.97 252	53.31 104	81.16 204	77.76 247	59.14 153	48.50 273	68.97 299	42.91 118	84.38 232	56.62 163	48.17 297	78.47 264

v8		67.25 182	64.99 195	74.04 155	72.89 258	53.31 104	82.37 176	80.11 201	61.54 114	54.29 231	76.02 252	42.89 119	88.41 135	58.43 145	56.36 262	80.39 246

v1neww		69.43 143	67.62 143	74.89 135	72.90 256	53.31 104	85.12 108	81.11 186	64.29 72	61.00 149	78.53 213	42.88 120	88.98 111	62.66 114	60.06 223	82.37 209

v7new		69.43 143	67.62 143	74.89 135	72.90 256	53.31 104	85.12 108	81.11 186	64.29 72	61.00 149	78.53 213	42.88 120	88.98 111	62.66 114	60.06 223	82.37 209

v6		69.43 143	67.61 145	74.88 137	72.87 260	53.30 108	85.12 108	81.10 188	64.29 72	60.99 151	78.52 215	42.88 120	88.98 111	62.67 113	60.06 223	82.37 209

v16		64.25 218	61.66 227	72.03 192	72.91 255	53.28 110	80.93 207	77.81 243	58.86 164	48.30 274	68.80 302	42.70 123	84.37 233	56.44 169	48.14 298	78.44 267

v17		64.19 219	61.58 228	72.03 192	72.89 258	53.28 110	80.91 208	77.80 244	58.87 163	48.22 275	68.77 303	42.69 124	84.37 233	56.43 170	47.66 301	78.43 268

v15		63.83 222	61.13 233	71.93 200	72.60 268	53.21 113	80.44 215	78.22 232	58.80 166	47.57 280	68.22 305	42.50 125	84.18 235	55.82 172	46.02 313	78.39 270

SteuartSystems-ACMMP		77.08 33	76.33 36	79.34 32	80.98 147	55.31 45	89.76 26	86.91 58	62.94 97	71.65 55	91.56 34	42.33 126	92.56 30	77.14 28	83.69 41	90.15 74
Skip Steuart: Steuart Systems R&D Blog.
HyFIR lowres test		69.94 131	67.58 146	77.04 88	77.11 214	57.29 17	81.49 200	79.11 220	58.27 175	58.86 181	80.41 200	42.33 126	86.96 177	61.91 123	68.68 156	86.87 133

V14		63.72 224	60.99 235	71.91 202	72.58 271	53.18 114	80.24 219	78.19 233	58.53 173	47.35 286	68.10 306	42.28 128	84.18 235	55.68 174	45.97 314	78.36 273

FMVSNet3		68.84 151	67.40 151	73.19 174	85.05 57	48.53 215	85.71 91	85.36 85	60.90 123	57.58 201	79.15 208	42.16 129	86.77 183	47.25 231	63.40 193	84.27 173

VPA-MVSNet		71.12 109	70.66 97	72.49 187	78.75 184	44.43 264	87.64 46	90.02 9	63.97 78	65.02 107	81.58 187	42.14 130	87.42 168	63.42 109	63.38 196	85.63 157

jason		77.01 34	76.45 34	78.69 46	79.69 169	54.74 64	90.56 17	83.99 132	68.26 24	74.10 31	90.91 45	42.14 130	89.99 83	79.30 14	79.12 76	91.36 46
jason: jason.
V9		63.60 225	60.84 236	71.87 204	72.51 273	53.12 118	80.04 224	78.15 235	58.25 176	47.14 288	67.98 307	42.08 132	84.18 235	55.47 175	45.92 316	78.32 274

CLD-MVS		75.60 54	75.39 46	76.24 103	80.69 154	52.40 136	90.69 15	86.20 74	74.40 3	65.01 108	88.93 81	42.05 133	90.58 67	76.57 30	73.96 118	85.73 153
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
0601test		75.85 51	74.83 53	78.91 40	88.08 21	51.94 145	91.30 9	89.28 13	57.91 183	71.19 59	89.20 78	42.03 134	92.77 25	69.41 70	75.07 112	92.01 29

Anonymous20240521		75.85 51	74.83 53	78.91 40	88.08 21	51.94 145	91.30 9	89.28 13	57.91 183	71.19 59	89.20 78	42.03 134	92.77 25	69.41 70	75.07 112	92.01 29

MVS_0304		79.84 11	79.71 9	80.25 19	85.64 44	54.62 70	90.58 16	84.48 111	72.51 8	79.22 13	93.09 9	42.01 136	93.28 20	84.00 4	85.84 22	92.87 16

TAMVS		69.51 139	68.16 131	73.56 169	76.30 217	48.71 209	82.57 172	77.17 257	62.10 106	61.32 147	84.23 137	41.90 137	83.46 248	54.80 183	73.09 127	88.50 111

TransMVSNet (Re)		62.82 241	60.76 238	69.02 247	73.98 242	41.61 289	86.36 75	79.30 218	56.90 198	52.53 245	76.44 242	41.85 138	87.60 164	38.83 262	40.61 330	77.86 283

VPNet		72.07 97	71.42 91	74.04 155	78.64 189	47.17 237	89.91 24	87.97 39	72.56 7	64.66 112	85.04 130	41.83 139	88.33 140	61.17 127	60.97 219	86.62 140

v12		63.47 227	60.68 239	71.85 205	72.45 274	53.08 120	79.83 226	78.13 237	57.95 182	46.89 290	67.87 309	41.81 140	84.17 238	55.30 177	45.87 317	78.29 276

v13		63.36 229	60.54 242	71.82 206	72.41 275	53.03 123	79.64 231	78.10 238	57.66 190	46.67 293	67.75 310	41.68 141	84.17 238	55.11 178	45.82 318	78.25 279

v2v482		69.55 138	67.64 142	75.26 127	72.32 278	53.83 82	84.93 122	81.94 170	65.37 59	60.80 156	79.25 206	41.62 142	88.98 111	63.03 111	59.51 233	82.98 200

API-MVS		74.17 67	72.07 81	80.49 15	90.02 6	58.55 7	87.30 59	84.27 116	57.51 193	65.77 99	87.77 100	41.61 143	95.97 2	51.71 206	82.63 45	86.94 132

CHOSEN 1792x2688		76.24 45	74.03 58	82.88 1	83.09 94	62.84 2	85.73 90	85.39 84	69.79 16	64.87 110	83.49 155	41.52 144	93.69 18	70.55 64	81.82 51	92.12 25

LFMVS		78.52 15	77.14 28	82.67 3	89.58 7	58.90 6	91.27 11	88.05 38	63.22 93	74.63 28	90.83 48	41.38 145	94.40 11	75.42 37	79.90 72	94.72 1

MAR-MVS		76.76 38	75.60 43	80.21 20	90.87 3	54.68 68	89.14 32	89.11 16	62.95 96	70.54 63	92.33 17	41.05 146	94.95 7	57.90 154	86.55 16	91.00 52
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
GST-MVS		74.87 61	73.90 59	77.77 70	83.30 85	53.45 92	85.75 88	85.29 88	59.22 148	66.50 89	89.85 68	40.94 147	90.76 62	70.94 63	83.35 42	89.10 95

DU-MVS		66.84 192	65.74 179	70.16 235	73.27 249	42.59 281	81.50 198	82.92 156	63.53 90	58.51 186	82.11 182	40.75 148	84.64 230	53.11 191	55.96 267	83.24 195

Baseline_NR-MVSNet		65.49 208	64.27 202	69.13 246	74.37 239	41.65 288	83.39 159	78.85 221	59.56 137	59.62 168	76.88 237	40.75 148	87.44 167	49.99 213	55.05 273	78.28 277

HFP-MVS		74.37 64	73.13 66	78.10 63	84.30 66	53.68 84	85.58 93	84.36 113	56.82 201	65.78 97	90.56 53	40.70 150	90.90 59	69.18 73	80.88 56	89.71 78

#test#		74.86 62	73.78 61	78.10 63	84.30 66	53.68 84	86.95 66	84.36 113	59.00 160	65.78 97	90.56 53	40.70 150	90.90 59	71.48 59	80.88 56	89.71 78

v11		63.44 228	60.66 240	71.79 207	72.61 267	53.02 124	79.80 227	78.08 239	58.30 174	47.27 287	67.91 308	40.67 152	84.14 240	54.93 180	46.39 311	78.23 280

ACMMP_Plus		76.43 43	75.66 42	78.73 45	81.92 126	54.67 69	84.06 140	85.35 86	61.10 120	72.99 39	91.50 35	40.25 153	91.00 56	76.84 29	86.98 13	90.51 63

v1144		68.81 153	66.82 158	74.80 141	72.34 277	53.46 90	84.68 128	81.77 176	64.25 75	60.28 161	77.91 224	40.23 154	88.95 115	60.37 135	59.52 232	81.97 213

WR-MVS_H		58.91 268	58.04 258	61.54 308	69.07 302	33.83 325	76.91 254	81.99 169	51.40 265	48.17 276	74.67 259	40.23 154	74.15 314	31.78 291	48.10 299	76.64 293

原ACMM1				76.13 107	84.89 60	54.59 71		85.26 90	51.98 260	66.70 84	87.07 110	40.15 156	89.70 90	51.23 208	85.06 32	84.10 174

MVP-Stereo		70.97 112	70.44 100	72.59 184	76.03 221	51.36 161	85.02 120	86.99 57	60.31 131	56.53 214	78.92 210	40.11 157	90.00 82	60.00 140	90.01 2	76.41 296
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
v10		66.61 195	64.20 203	73.83 162	72.59 270	53.37 99	81.88 184	79.91 203	61.11 119	54.09 233	75.60 254	40.06 158	88.26 145	56.47 165	56.10 266	79.86 251

v7		68.76 157	66.79 160	74.68 142	72.60 268	53.37 99	84.72 127	80.88 191	63.80 82	60.43 160	78.21 219	40.05 159	88.89 118	60.34 136	60.07 222	81.77 219

MP-MVS-pluss		75.54 55	75.03 49	77.04 88	81.37 143	52.65 132	84.34 133	84.46 112	61.16 118	69.14 68	91.76 29	39.98 160	88.99 110	78.19 20	84.89 33	89.48 84
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
TranMVSNet+NR-MVSNet		66.94 190	65.61 183	70.93 223	73.45 246	43.38 273	83.02 166	84.25 117	65.31 61	58.33 192	81.90 184	39.92 161	85.52 212	49.43 217	54.89 275	83.89 184

Patchmatch-test		53.33 301	48.17 305	68.81 255	73.31 247	42.38 285	42.98 351	58.23 342	32.53 342	38.79 325	70.77 290	39.66 162	73.51 321	25.18 317	52.06 288	90.55 59

Test By Simon												39.38 163

v144192		67.86 169	65.76 178	74.16 152	71.68 284	53.09 119	84.14 137	80.83 193	62.85 98	59.21 175	77.28 233	39.30 164	88.00 152	58.67 144	57.88 256	81.40 226

BH-w/o		70.02 127	68.51 126	74.56 144	82.77 107	50.39 177	86.60 72	78.14 236	59.77 135	59.65 166	85.57 125	39.27 165	87.30 170	49.86 214	74.94 114	85.99 148

CR-MVSNet		62.47 246	59.04 254	72.77 180	73.97 243	56.57 25	60.52 332	71.72 306	60.04 133	57.49 204	65.86 318	38.94 166	80.31 276	42.86 253	59.93 227	81.42 224

Patchmtry		56.56 283	52.95 288	67.42 266	72.53 272	50.59 173	59.05 334	71.72 306	37.86 328	46.92 289	65.86 318	38.94 166	80.06 281	36.94 271	46.72 310	71.60 327

sam_mvs1												38.86 168				88.13 116

UA-Net		67.32 181	66.23 169	70.59 228	78.85 182	41.23 292	73.60 280	75.45 279	61.54 114	66.61 87	84.53 133	38.73 169	86.57 192	42.48 255	74.24 115	83.98 180

cdsmvs_eth3d_5k		18.33 342	24.44 335	0.00 359	0.00 373	0.00 373	0.00 365	89.40 12	0.00 367	0.00 371	92.02 23	38.55 170	0.00 369	0.00 368	0.00 366	0.00 369

patchmatchnet-post											59.74 333	38.41 171	79.91 284

CHOSEN 280x420		57.53 279	56.38 270	60.97 312	74.01 241	48.10 227	46.30 347	54.31 347	48.18 281	50.88 259	77.43 231	38.37 172	59.16 349	54.83 181	63.14 202	75.66 302

V42		67.66 173	65.60 184	73.86 160	70.69 294	53.63 86	81.50 198	78.61 228	63.85 80	59.49 171	77.49 229	37.98 173	87.65 163	62.33 119	58.43 245	80.29 247

tpmvs		62.45 247	59.42 249	71.53 212	83.93 74	54.32 73	70.03 307	77.61 249	51.91 261	53.48 238	68.29 304	37.91 174	86.66 187	33.36 284	58.27 246	73.62 317

PatchmatchNet		67.07 188	63.63 206	77.40 80	83.10 92	58.03 9	72.11 295	77.77 246	58.85 165	59.37 172	70.83 289	37.84 175	84.93 226	42.96 252	69.83 149	89.26 88
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
pcd_1.5k_mvsjas		3.15 349	4.20 350	0.00 359	0.00 373	0.00 373	0.00 365	0.00 374	0.00 367	0.00 371	0.00 371	37.77 176	0.00 369	0.00 368	0.00 366	0.00 369

PS-MVSNAJss		68.78 156	67.17 155	73.62 168	73.01 251	48.33 223	84.95 121	84.81 103	59.30 147	58.91 180	79.84 203	37.77 176	88.86 119	62.83 112	63.12 203	83.67 188

PS-MVSNAJ		80.06 9	79.52 11	81.68 7	85.58 47	60.97 3	91.69 4	87.02 56	70.62 12	80.75 7	93.22 6	37.77 176	92.50 31	82.75 6	86.25 18	91.57 39

pm-mvs1		64.12 220	62.56 213	68.78 256	71.68 284	38.87 303	82.89 168	81.57 177	55.54 225	53.89 234	77.82 225	37.73 179	86.74 184	48.46 223	53.49 284	80.72 237

RPMNet		58.49 275	53.74 284	72.77 180	73.97 243	56.57 25	60.52 332	72.39 302	35.72 335	57.49 204	58.87 337	37.73 179	80.31 276	27.01 312	59.93 227	81.42 224

xiu_mvs_v2_base		79.86 10	79.31 12	81.53 8	85.03 58	60.73 4	91.65 5	86.86 59	70.30 15	80.77 6	93.07 11	37.63 181	92.28 36	82.73 7	85.71 23	91.57 39

Patchmatch-RL test		58.72 270	54.32 280	71.92 201	63.91 327	44.25 266	61.73 328	55.19 345	57.38 194	49.31 270	54.24 343	37.60 182	80.89 269	62.19 121	47.28 305	90.63 58

HPM-MVS		72.60 88	71.50 88	75.89 114	82.02 123	51.42 160	80.70 213	83.05 153	56.12 217	64.03 122	89.53 73	37.55 183	88.37 136	70.48 66	80.04 69	87.88 119
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
test_post											16.22 362	37.52 184	84.72 228

PatchT		56.60 282	52.97 287	67.48 265	72.94 254	46.16 250	57.30 338	73.78 294	38.77 324	54.37 230	57.26 340	37.52 184	78.06 295	32.02 289	52.79 285	78.23 280

v1192		67.96 168	65.74 179	74.63 143	71.79 282	53.43 95	84.06 140	80.99 190	63.19 94	59.56 169	77.46 230	37.50 186	88.65 123	58.20 150	58.93 238	81.79 218

HQP2-MVS												37.35 187

HQP-MVS		72.34 92	71.44 90	75.03 129	79.02 178	51.56 154	88.00 41	83.68 138	65.45 54	64.48 115	85.13 128	37.35 187	88.62 124	66.70 86	73.12 125	84.91 166

region2R		73.75 74	72.55 70	77.33 81	83.90 76	52.98 126	85.54 96	84.09 121	56.83 200	65.10 105	90.45 55	37.34 189	90.24 78	68.89 75	80.83 59	88.77 104

TESTMET0.1,1		72.86 84	72.33 73	74.46 145	81.98 124	50.77 168	85.13 105	85.47 80	66.09 44	67.30 80	83.69 147	37.27 190	83.57 247	65.06 104	78.97 78	89.05 96

ACMMPR		73.76 73	72.61 68	77.24 87	83.92 75	52.96 127	85.58 93	84.29 115	56.82 201	65.12 104	90.45 55	37.24 191	90.18 80	69.18 73	80.84 58	88.58 108

sss		70.49 119	70.13 107	71.58 211	81.59 134	39.02 302	80.78 212	84.71 107	59.34 144	66.61 87	88.09 94	37.17 192	85.52 212	61.82 124	71.02 142	90.20 72

EPNet_dtu		66.25 201	66.71 163	64.87 292	78.66 188	34.12 323	82.80 170	75.51 277	61.75 111	64.47 118	86.90 111	37.06 193	72.46 327	43.65 249	69.63 151	88.02 118
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1921920		67.45 178	65.23 192	74.10 154	71.51 287	52.90 128	83.75 147	80.44 198	62.48 102	59.12 176	77.13 234	36.98 194	87.90 153	57.53 158	58.14 250	81.49 223

旧先验1					81.57 136	47.48 231		71.83 305			88.66 85	36.94 195			78.34 81	88.67 105

test-LLR		69.65 136	69.01 121	71.60 209	78.67 186	48.17 225	85.13 105	79.72 206	59.18 151	63.13 134	82.58 172	36.91 196	80.24 278	60.56 132	75.17 109	86.39 145

test0.0.03 1		62.54 244	62.44 214	62.86 302	72.28 280	29.51 338	82.93 167	78.78 223	59.18 151	53.07 243	82.41 175	36.91 196	77.39 301	37.45 265	58.96 237	81.66 221

MDTV_nov1_ep13_2view						43.62 271	71.13 301		54.95 229	59.29 174		36.76 198		46.33 238		87.32 129

GBi-Net		67.09 186	65.47 186	71.96 197	82.71 109	46.36 244	83.52 149	83.31 145	58.55 170	57.58 201	76.23 246	36.72 199	86.20 195	47.25 231	63.40 193	83.32 192

test1		67.09 186	65.47 186	71.96 197	82.71 109	46.36 244	83.52 149	83.31 145	58.55 170	57.58 201	76.23 246	36.72 199	86.20 195	47.25 231	63.40 193	83.32 192

FMVSNet2		67.57 175	65.79 177	72.90 177	82.71 109	47.97 228	85.15 104	84.93 100	58.55 170	56.71 211	78.26 218	36.72 199	86.67 186	46.15 239	62.94 206	84.07 175

BH-untuned		68.28 165	66.40 165	73.91 158	81.62 132	50.01 187	85.56 95	77.39 253	57.63 191	57.47 206	83.69 147	36.36 202	87.08 173	44.81 244	73.08 128	84.65 168

EPMVS		68.45 161	65.44 188	77.47 79	84.91 59	56.17 32	71.89 298	81.91 173	61.72 112	60.85 155	72.49 278	36.21 203	87.06 174	47.32 230	71.62 137	89.17 93

MSLP-MVS++		74.21 66	72.25 77	80.11 24	81.45 141	56.47 28	86.32 76	79.65 209	58.19 177	66.36 90	92.29 18	36.11 204	90.66 65	67.39 82	82.49 46	93.18 10

zzz-MVS		74.15 69	73.11 67	77.27 84	81.54 137	53.57 87	84.02 142	81.31 182	59.41 141	68.39 73	90.96 43	36.07 205	89.01 106	73.80 46	82.45 47	89.23 89

MTAPA		72.73 86	71.22 93	77.27 84	81.54 137	53.57 87	67.06 316	81.31 182	59.41 141	68.39 73	90.96 43	36.07 205	89.01 106	73.80 46	82.45 47	89.23 89

test_normal		71.31 106	68.95 123	78.39 56	72.30 279	54.25 75	81.67 190	84.05 129	65.94 50	51.31 254	78.09 223	36.06 207	90.43 70	73.00 55	78.09 83	90.50 64

HQP_MVS		70.96 113	69.91 110	74.12 153	77.95 198	49.57 191	85.76 86	82.59 160	63.60 88	62.15 140	83.28 160	36.04 208	88.30 142	65.46 97	72.34 134	84.49 169

plane_prior6					78.42 194	49.39 196						36.04 208

DI_MVS_plusplus_test		71.30 107	68.98 122	78.26 60	72.76 261	54.08 80	81.72 189	83.22 149	65.75 51	51.94 250	78.47 217	36.01 210	90.31 74	73.33 51	77.60 85	90.40 66

sam_mvs												35.99 211

PGM-MVS		72.60 88	71.20 94	76.80 96	82.95 101	52.82 129	83.07 164	82.14 164	56.51 213	63.18 133	89.81 69	35.68 212	89.76 89	67.30 83	80.19 66	87.83 120

XVS		72.92 83	71.62 86	76.81 94	83.41 81	52.48 133	84.88 123	83.20 151	58.03 179	63.91 124	89.63 72	35.50 213	89.78 87	65.50 94	80.50 62	88.16 113

X-MVStestdata		65.85 206	62.20 215	76.81 94	83.41 81	52.48 133	84.88 123	83.20 151	58.03 179	63.91 124	4.82 367	35.50 213	89.78 87	65.50 94	80.50 62	88.16 113

v1240		66.99 189	64.68 198	73.93 157	71.38 290	52.66 131	83.39 159	79.98 202	61.97 107	58.44 191	77.11 235	35.25 215	87.81 155	56.46 166	58.15 248	81.33 227

dp		64.41 215	61.58 228	72.90 177	82.40 115	54.09 79	72.53 289	76.59 264	60.39 130	55.68 223	70.39 292	35.18 216	76.90 305	39.34 261	61.71 216	87.73 123

CP-MVS		72.59 90	71.46 89	76.00 113	82.93 103	52.32 140	86.93 68	82.48 162	55.15 227	63.65 129	90.44 57	35.03 217	88.53 131	68.69 76	77.83 84	87.15 131

CP-MVSNet		58.54 274	57.57 261	61.46 310	68.50 306	33.96 324	76.90 255	78.60 229	51.67 264	47.83 277	76.60 241	34.99 218	72.79 325	35.45 276	47.58 302	77.64 287

MDTV_nov1_ep13			61.56 230		81.68 130	55.12 52	72.41 291	78.18 234	59.19 149	58.85 182	69.29 296	34.69 219	86.16 198	36.76 273	62.96 205

3Dnovator	64.70 6	74.46 63	72.48 71	80.41 16	82.84 106	55.40 44	83.08 163	88.61 24	67.61 31	59.85 163	88.66 85	34.57 220	93.97 14	58.42 147	88.70 7	91.85 33

Vis-MVSNet		70.61 118	69.34 116	74.42 147	80.95 149	48.49 217	86.03 82	77.51 251	58.74 167	65.55 101	87.78 99	34.37 221	85.95 207	52.53 202	80.61 60	88.80 102
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
test_post1							70.84 302				14.72 365	34.33 222	83.86 242	48.80 220

OPM-MVS		70.75 116	69.58 112	74.26 151	75.55 226	51.34 162	86.05 81	83.29 148	61.94 108	62.95 136	85.77 122	34.15 223	88.44 134	65.44 100	71.07 141	82.99 199

1121		68.79 155	66.77 161	74.82 139	83.08 95	53.46 90	80.23 220	71.53 310	45.47 298	66.31 91	87.19 107	34.02 224	85.13 219	52.78 197	80.36 64	85.87 152

DP-MVS Recon		71.99 98	70.31 102	77.01 90	90.65 4	53.44 93	89.37 29	82.97 155	56.33 215	63.56 131	89.47 74	34.02 224	92.15 40	54.05 187	72.41 133	85.43 160

PEN-MVS		58.35 276	57.15 263	61.94 305	67.55 313	34.39 322	77.01 253	78.35 231	51.87 262	47.72 278	76.73 239	33.91 226	73.75 319	34.03 283	47.17 306	77.68 285

QAPM		71.88 99	69.33 117	79.52 29	82.20 118	54.30 74	86.30 77	88.77 21	56.61 207	59.72 165	87.48 104	33.90 227	95.36 4	47.48 229	81.49 53	88.90 100

新几何1				73.30 173	83.10 92	53.48 89		71.43 311	45.55 296	66.14 92	87.17 108	33.88 228	80.54 273	48.50 222	80.33 65	85.88 151

1314		71.11 110	69.41 114	76.22 104	79.32 173	50.49 175	80.23 220	85.14 96	59.44 140	58.93 178	88.89 83	33.83 229	89.60 94	61.49 125	77.42 89	88.57 109

mPP-MVS		71.79 102	70.38 101	76.04 111	82.65 112	52.06 142	84.45 131	81.78 175	55.59 223	62.05 143	89.68 71	33.48 230	88.28 144	65.45 99	78.24 82	87.77 122

OMC-MVS		65.97 205	65.06 194	68.71 257	72.97 252	42.58 283	78.61 244	75.35 280	54.72 231	59.31 173	86.25 120	33.30 231	77.88 297	57.99 152	67.05 166	85.66 155

BH-RMVSNet		70.08 125	68.01 132	76.27 102	84.21 70	51.22 166	87.29 60	79.33 217	58.96 162	63.63 130	86.77 113	33.29 232	90.30 77	44.63 245	73.96 118	87.30 130

v748		61.35 253	58.24 257	70.69 226	66.28 315	47.35 233	76.58 257	79.17 219	53.09 244	46.37 297	71.50 286	33.18 233	86.33 194	46.78 235	51.19 292	78.39 270

JIA-IIPM		52.33 305	47.77 308	66.03 282	71.20 291	46.92 238	40.00 355	76.48 265	37.10 329	46.73 291	37.02 351	32.96 234	77.88 297	35.97 274	52.45 287	73.29 321

PS-CasMVS		58.12 277	57.03 265	61.37 311	68.24 309	33.80 326	76.73 256	78.01 241	51.20 267	47.54 282	76.20 249	32.85 235	72.76 326	35.17 278	47.37 304	77.55 288

DTE-MVSNet		57.03 280	55.73 274	60.95 313	65.94 317	32.57 331	75.71 266	77.09 259	51.16 268	46.65 294	76.34 244	32.84 236	73.22 323	30.94 295	44.87 321	77.06 290

pmmvs4		63.34 230	61.07 234	70.16 235	70.14 296	50.53 174	79.97 225	71.41 312	55.08 228	54.12 232	78.58 212	32.79 237	82.09 263	50.33 212	57.22 260	77.86 283

TR-MVS		69.71 133	67.85 137	75.27 126	82.94 102	48.48 218	87.40 56	80.86 192	57.15 197	64.61 113	87.08 109	32.67 238	89.64 93	46.38 237	71.55 139	87.68 124

VDD-MVS		76.08 48	74.97 50	79.44 30	84.27 69	53.33 103	91.13 12	85.88 76	65.33 60	72.37 49	89.34 75	32.52 239	92.76 27	77.90 24	75.96 101	92.22 24

3Dnovator+	62.71 7	72.29 94	70.50 98	77.65 74	83.40 84	51.29 164	87.32 57	86.40 69	59.01 159	58.49 189	88.32 89	32.40 240	91.27 53	57.04 162	82.15 50	90.38 68

tfpnnormal		61.47 252	59.09 253	68.62 259	76.29 218	41.69 287	81.14 205	85.16 94	54.48 234	51.32 253	73.63 269	32.32 241	86.89 180	21.78 339	55.71 271	77.29 289

MS-PatchMatch		72.34 92	71.26 92	75.61 118	82.38 116	55.55 38	88.00 41	89.95 11	65.38 58	56.51 215	80.74 199	32.28 242	92.89 23	57.95 153	88.10 10	78.39 270

semantic-postprocess				60.08 314	70.68 295	45.07 258		74.25 288	43.54 311	50.02 267	73.73 265	32.22 243	56.74 350	51.06 210	53.60 283	78.42 269

v7n		62.50 245	59.27 251	72.20 189	67.25 314	49.83 188	77.87 250	80.12 200	52.50 257	48.80 272	73.07 273	32.10 244	87.90 153	46.83 234	54.92 274	78.86 259

IterMVS		63.77 223	61.67 226	70.08 237	72.68 266	51.24 165	80.44 215	75.51 277	60.51 129	51.41 252	73.70 268	32.08 245	78.91 287	54.30 185	54.35 278	80.08 249
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Patchmatch-test1		63.23 231	59.16 252	75.43 122	78.58 190	57.92 11	61.61 329	77.53 250	56.71 204	57.75 199	70.98 288	31.97 246	78.19 291	40.97 258	56.36 262	90.18 73

ACMMP		70.81 115	69.29 118	75.39 124	81.52 140	51.92 147	83.43 155	83.03 154	56.67 206	58.80 183	88.91 82	31.92 247	88.58 127	65.89 93	73.39 122	85.67 154
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		69.62 137	68.23 130	73.80 163	81.58 135	48.22 224	81.91 183	79.50 211	48.21 280	64.24 120	89.75 70	31.91 248	87.55 165	63.08 110	73.85 120	85.64 156

VDDNet		74.37 64	72.13 80	81.09 11	79.58 170	56.52 27	90.02 19	86.70 63	52.61 254	71.23 58	87.20 106	31.75 249	93.96 15	74.30 43	75.77 105	92.79 17

pmmvs5		62.80 242	61.18 232	67.66 264	69.53 301	42.37 286	82.65 171	75.19 281	54.30 236	52.03 249	78.51 216	31.64 250	80.67 271	48.60 221	58.15 248	79.95 250

LCM-MVSNet-Re		58.82 269	56.54 266	65.68 284	79.31 174	29.09 341	61.39 331	45.79 353	60.73 126	37.65 326	72.47 279	31.42 251	81.08 268	49.66 215	70.41 145	86.87 133

testdata				67.08 269	77.59 203	45.46 255		69.20 322	44.47 304	71.50 56	88.34 88	31.21 252	70.76 334	52.20 203	75.88 102	85.03 163

GA-MVS		69.04 148	66.70 164	76.06 109	75.11 229	52.36 138	83.12 162	80.23 199	63.32 91	60.65 158	79.22 207	30.98 253	88.37 136	61.25 126	66.41 170	87.46 126

OpenMVS	61.00 11	69.99 130	67.55 148	77.30 82	78.37 195	54.07 81	84.36 132	85.76 77	57.22 196	56.71 211	87.67 101	30.79 254	92.83 24	43.04 251	84.06 40	85.01 164

Effi-MVS+-dtu		66.24 202	64.96 197	70.08 237	75.17 227	49.64 190	82.01 180	74.48 285	62.15 104	57.83 195	76.08 251	30.59 255	83.79 244	65.40 101	60.93 220	76.81 291

mvs-test1		69.04 148	67.57 147	73.44 171	75.17 227	51.68 152	86.57 73	74.48 285	62.15 104	62.07 142	85.79 121	30.59 255	87.48 166	65.40 101	65.94 177	81.18 231

test222					79.36 171	50.97 167	77.99 249	67.84 323	42.54 316	62.84 137	86.53 117	30.26 257			76.91 94	85.23 161

MVS_111021_LR		69.07 147	67.91 133	72.54 185	77.27 208	49.56 193	79.77 228	73.96 292	59.33 146	60.73 157	87.82 97	30.19 258	81.53 265	69.94 68	72.19 136	86.53 141

114514_t		69.87 132	67.88 135	75.85 115	88.38 16	52.35 139	86.94 67	83.68 138	53.70 238	55.68 223	85.60 124	30.07 259	91.20 54	55.84 171	71.02 142	83.99 178

CPTT-MVS		67.15 185	65.84 176	71.07 219	80.96 148	50.32 181	81.94 182	74.10 289	46.18 294	57.91 194	87.64 102	29.57 260	81.31 267	64.10 106	70.18 147	81.56 222

CANet_DTU		73.71 75	73.14 64	75.40 123	82.61 113	50.05 186	84.67 129	79.36 215	69.72 17	75.39 24	90.03 65	29.41 261	85.93 208	67.99 79	79.11 77	90.22 71

AdaColmap		67.86 169	65.48 185	75.00 130	88.15 20	54.99 57	86.10 80	76.63 263	49.30 276	57.80 196	86.65 116	29.39 262	88.94 117	45.10 243	70.21 146	81.06 232

CVMVSNet		60.85 256	60.44 243	62.07 303	75.00 231	32.73 330	79.54 232	73.49 298	36.98 330	56.28 217	83.74 145	29.28 263	69.53 337	46.48 236	63.23 199	83.94 183

v52		59.82 259	56.41 268	70.06 239	61.49 337	48.67 210	69.46 311	75.80 274	52.55 255	47.49 284	68.82 301	28.60 264	85.70 210	52.13 204	51.34 291	75.80 299

V4		59.82 259	56.41 268	70.05 240	61.49 337	48.67 210	69.46 311	75.79 275	52.55 255	47.49 284	68.83 300	28.60 264	85.70 210	52.13 204	51.35 290	75.80 299

PMMVS		72.98 82	72.05 82	75.78 117	83.57 78	48.60 212	84.08 138	82.85 157	61.62 113	68.24 75	90.33 58	28.35 266	87.78 160	72.71 57	76.69 95	90.95 54

abl_6		68.03 167	66.15 171	73.66 165	78.54 191	48.48 218	79.77 228	78.04 240	47.39 284	63.70 128	88.25 91	28.21 267	89.06 99	60.17 139	71.25 140	83.45 190

our_test_3		59.11 266	55.08 278	71.18 218	71.42 288	53.29 109	81.96 181	74.52 284	48.32 279	42.08 311	69.28 297	28.14 268	82.15 261	34.35 282	45.68 319	78.11 282

Fast-Effi-MVS+-dtu		66.53 196	64.10 204	73.84 161	72.41 275	52.30 141	84.73 126	75.66 276	59.51 138	56.34 216	79.11 209	28.11 269	85.85 209	57.74 157	63.29 198	83.35 191

Anonymous20231211		66.08 204	63.67 205	73.31 172	83.07 96	48.75 208	86.01 83	84.67 108	45.27 299	56.54 213	76.67 240	28.06 270	88.95 115	52.78 197	59.95 226	82.23 212

Anonymous20240529		69.71 133	67.28 153	77.00 91	83.78 77	50.36 178	88.87 36	85.10 97	47.22 285	64.03 122	83.37 158	27.93 271	92.10 41	57.78 156	67.44 164	88.53 110

HPM-MVS_fast		67.86 169	66.28 168	72.61 183	80.67 155	48.34 222	81.18 203	75.95 273	50.81 269	59.55 170	88.05 96	27.86 272	85.98 204	58.83 143	73.58 121	83.51 189

FMVSNet1		64.57 213	62.11 216	71.96 197	77.32 207	46.36 244	83.52 149	83.31 145	52.43 258	54.42 229	76.23 246	27.80 273	86.20 195	42.59 254	61.34 217	83.32 192

CNLPA		60.59 257	58.44 256	67.05 270	79.21 175	47.26 235	79.75 230	64.34 334	42.46 317	51.90 251	83.94 140	27.79 274	75.41 310	37.12 267	59.49 234	78.47 264

TAPA-MVS	56.12 14	61.82 251	60.18 245	66.71 273	78.48 193	37.97 308	75.19 273	76.41 267	46.82 289	57.04 210	86.52 118	27.67 275	77.03 303	26.50 314	67.02 167	85.14 162
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
pmmvs6		59.64 262	57.15 263	67.09 268	66.01 316	36.86 312	80.50 214	78.64 226	45.05 301	49.05 271	73.94 263	27.28 276	86.10 201	43.96 248	49.94 295	78.31 275

test-mter		68.36 162	67.29 152	71.60 209	78.67 186	48.17 225	85.13 105	79.72 206	53.38 241	63.13 134	82.58 172	27.23 277	80.24 278	60.56 132	75.17 109	86.39 145

XVG-OURS-SEG-HR		62.02 249	59.54 248	69.46 244	65.30 320	45.88 251	65.06 318	73.57 297	46.45 292	57.42 207	83.35 159	26.95 278	78.09 294	53.77 189	64.03 188	84.42 171

test_djsdf		63.84 221	61.56 230	70.70 225	68.78 303	44.69 262	81.63 194	81.44 179	50.28 270	52.27 247	76.26 245	26.72 279	86.11 199	60.83 129	55.84 270	81.29 230

Anonymous20231206		59.08 267	57.59 260	63.55 297	68.77 304	32.14 333	80.26 218	79.78 205	50.00 273	49.39 269	72.39 281	26.64 280	78.36 290	33.12 287	57.94 253	80.14 248

pcd1.5k->3k		27.74 334	27.68 334	27.93 351	73.75 245	0.00 373	0.00 365	85.50 79	0.00 367	0.00 371	0.00 371	26.52 281	0.00 369	0.00 368	63.37 197	83.79 186

ppachtmachnet_test		58.56 272	54.34 279	71.24 215	71.42 288	54.74 64	81.84 186	72.27 303	49.02 278	45.86 300	68.99 298	26.27 282	83.30 249	30.12 296	43.23 326	75.69 301

test20.03		55.22 291	54.07 281	58.68 318	63.14 330	25.00 347	77.69 251	74.78 283	52.64 247	43.43 306	72.39 281	26.21 283	74.76 313	29.31 298	47.05 308	76.28 297

FMVSNet5		58.61 271	56.45 267	65.10 291	77.20 212	39.74 300	74.77 274	77.12 258	50.27 272	43.28 308	67.71 311	26.15 284	76.90 305	36.78 272	54.78 276	78.65 262

ACMP	61.11 9	66.24 202	64.33 201	72.00 196	74.89 233	49.12 199	83.18 161	79.83 204	55.41 226	52.29 246	82.68 171	25.83 285	86.10 201	60.89 128	63.94 190	80.78 236
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
MIMVSNet		63.12 233	60.29 244	71.61 208	75.92 223	46.65 240	65.15 317	81.94 170	59.14 153	54.65 227	69.47 295	25.74 286	80.63 272	41.03 257	69.56 152	87.55 125

LPG-MVS_test		66.44 198	64.58 199	72.02 194	74.42 237	48.60 212	83.07 164	80.64 195	54.69 232	53.75 235	83.83 143	25.73 287	86.98 175	60.33 137	64.71 182	80.48 244

LGP-MVS_train				72.02 194	74.42 237	48.60 212		80.64 195	54.69 232	53.75 235	83.83 143	25.73 287	86.98 175	60.33 137	64.71 182	80.48 244

ACMM	58.35 12	64.35 217	62.01 217	71.38 213	74.21 240	48.51 216	82.25 177	79.66 208	47.61 282	54.54 228	80.11 201	25.26 289	86.00 203	51.26 207	63.16 201	79.64 252
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Test4		68.64 160	65.68 181	77.53 77	67.78 312	53.34 101	79.42 235	82.84 158	65.96 49	46.54 295	76.15 250	25.16 290	88.83 121	69.74 69	77.53 88	90.43 65

XVG-OURS		61.88 250	59.34 250	69.49 243	65.37 319	46.27 247	64.80 320	73.49 298	47.04 287	57.41 208	82.85 165	25.15 291	78.18 292	53.00 194	64.98 179	84.01 177

PVSNet_0	57.04 13	61.19 254	57.24 262	73.02 175	77.45 206	50.31 182	79.43 234	77.36 255	63.96 79	47.51 283	72.45 280	25.03 292	83.78 245	52.76 200	19.22 354	84.96 165

DP-MVS		59.24 264	56.12 271	68.63 258	88.24 19	50.35 179	82.51 174	64.43 333	41.10 319	46.70 292	78.77 211	24.75 293	88.57 130	22.26 336	56.29 264	66.96 337

cascas		69.01 150	66.13 172	77.66 73	79.36 171	55.41 43	86.99 64	83.75 137	56.69 205	58.92 179	81.35 189	24.31 294	92.10 41	53.23 190	70.61 144	85.46 159

CMPMVS	40.41 21	55.34 290	52.64 291	63.46 298	60.88 340	43.84 269	61.58 330	71.06 313	30.43 346	36.33 328	74.63 260	24.14 295	75.44 309	48.05 226	66.62 168	71.12 330
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UGNet		68.71 158	67.11 157	73.50 170	80.55 164	47.61 230	84.08 138	78.51 230	59.45 139	65.68 100	82.73 170	23.78 296	85.08 221	52.80 196	76.40 96	87.80 121
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		53.82 299	49.96 300	65.41 288	70.09 298	48.95 203	72.30 292	71.66 308	44.25 306	31.89 344	63.07 326	23.73 297	73.95 317	33.26 285	39.40 332	73.34 320

1111		48.00 313	46.30 312	53.08 330	55.68 346	20.86 356	70.41 303	76.03 270	36.88 331	34.86 334	59.55 335	23.72 298	68.13 338	20.82 341	38.76 335	70.25 331

.test1245		38.91 324	41.99 322	29.67 349	55.68 346	20.86 356	70.41 303	76.03 270	36.88 331	34.86 334	59.55 335	23.72 298	68.13 338	20.82 341	0.00 366	0.02 366

MDA-MVSNet_test_wron		53.82 299	49.95 301	65.43 287	70.13 297	49.05 201	72.30 292	71.65 309	44.23 307	31.85 345	63.13 325	23.68 300	74.01 316	33.25 286	39.35 333	73.23 322

PLC	52.38 18	60.89 255	58.97 255	66.68 275	81.77 129	45.70 253	78.96 243	74.04 291	43.66 310	47.63 279	83.19 162	23.52 301	77.78 300	37.47 264	60.46 221	76.55 295
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ADS-MVSNet2		55.21 292	51.44 295	66.51 276	80.60 156	49.56 193	55.03 340	65.44 330	44.72 302	51.00 256	61.19 329	22.83 302	75.41 310	28.54 305	53.63 281	74.57 309

ADS-MVSNet		56.17 286	51.95 294	68.84 249	80.60 156	53.07 121	55.03 340	70.02 320	44.72 302	51.00 256	61.19 329	22.83 302	78.88 288	28.54 305	53.63 281	74.57 309

test_0402		56.45 284	53.03 286	66.69 274	76.78 215	50.31 182	81.76 187	69.61 321	42.79 315	43.88 303	72.13 283	22.82 304	86.46 193	16.57 352	50.94 293	63.31 345

UnsupCasMVSNet_eth		57.56 278	55.15 276	64.79 293	64.57 325	33.12 327	73.17 285	83.87 136	58.98 161	41.75 314	70.03 293	22.54 305	79.92 282	46.12 240	35.31 339	81.32 229

xiu_mvs_v1_base_debu		71.60 103	70.29 103	75.55 119	77.26 209	53.15 115	85.34 97	79.37 212	55.83 220	72.54 43	90.19 60	22.38 306	86.66 187	73.28 52	76.39 97	86.85 135

xiu_mvs_v1_base		71.60 103	70.29 103	75.55 119	77.26 209	53.15 115	85.34 97	79.37 212	55.83 220	72.54 43	90.19 60	22.38 306	86.66 187	73.28 52	76.39 97	86.85 135

xiu_mvs_v1_base_debi		71.60 103	70.29 103	75.55 119	77.26 209	53.15 115	85.34 97	79.37 212	55.83 220	72.54 43	90.19 60	22.38 306	86.66 187	73.28 52	76.39 97	86.85 135

LS3D		56.40 285	53.82 282	64.12 294	81.12 146	45.69 254	73.42 282	66.14 328	35.30 340	43.24 309	79.88 202	22.18 309	79.62 285	19.10 347	64.00 189	67.05 336

PVSNet	62.49 8	69.27 146	67.81 138	73.64 166	84.41 65	51.85 148	84.63 130	77.80 244	66.42 38	59.80 164	84.95 131	22.14 310	80.44 275	55.03 179	75.11 111	88.62 107

MDA-MVSNet-bldmvs		51.56 306	47.75 309	63.00 300	71.60 286	47.32 234	69.70 310	72.12 304	43.81 309	27.65 350	63.38 324	21.97 311	75.96 307	27.30 311	32.19 346	65.70 340

pmmvs-eth3d		55.97 288	52.78 290	65.54 286	61.02 339	46.44 243	75.36 272	67.72 324	49.61 275	43.65 305	67.58 312	21.63 312	77.04 302	44.11 247	44.33 323	73.15 323

anonymousdsp		60.46 258	57.65 259	68.88 248	63.63 328	45.09 257	72.93 287	78.63 227	46.52 291	51.12 255	72.80 277	21.46 313	83.07 251	57.79 155	53.97 279	78.47 264

MVS-HIRNet		49.01 309	44.71 315	61.92 306	76.06 219	46.61 241	63.23 324	54.90 346	24.77 350	33.56 340	36.60 352	21.28 314	75.88 308	29.49 297	62.54 211	63.26 346

testing_2		63.60 225	59.86 246	74.82 139	61.87 334	52.39 137	73.06 286	82.76 159	61.49 116	39.96 320	67.39 313	21.06 315	88.34 138	67.07 85	64.10 187	83.72 187

Anonymous202405211		70.11 123	67.88 135	76.79 97	87.20 29	47.24 236	89.49 28	77.38 254	54.88 230	66.14 92	86.84 112	20.93 316	91.54 46	56.45 167	71.62 137	91.59 37

testpf		45.92 318	45.81 313	46.27 337	69.56 300	27.86 344	23.18 360	73.91 293	44.10 308	36.99 327	57.16 341	20.56 317	71.77 329	42.17 256	44.64 322	39.18 355

UnsupCasMVSNet_bld		53.86 298	50.53 298	63.84 295	63.52 329	34.75 321	71.38 299	81.92 172	46.53 290	38.95 324	57.93 338	20.55 318	80.20 280	39.91 260	34.09 345	76.57 294

EU-MVSNet		52.63 303	50.72 297	58.37 320	62.69 332	28.13 343	72.60 288	75.97 272	30.94 345	40.76 319	72.11 284	20.16 319	70.80 333	35.11 279	46.11 312	76.19 298

N_pmnet		41.25 321	39.77 324	45.66 339	68.50 306	0.82 371	72.51 290	0.38 373	35.61 336	35.26 333	61.51 328	20.07 320	67.74 340	23.51 323	40.63 329	68.42 335

MSDG		59.44 263	55.14 277	72.32 188	74.69 234	50.71 169	74.39 277	73.58 296	44.44 305	43.40 307	77.52 228	19.45 321	90.87 61	31.31 293	57.49 259	75.38 304

K. test v3		54.04 296	49.42 303	67.92 263	68.55 305	42.57 284	75.51 270	63.07 336	52.07 259	39.21 322	64.59 322	19.34 322	82.21 260	37.11 268	25.31 350	78.97 257

lessismore_v0				67.98 262	64.76 324	41.25 291		45.75 354		36.03 330	65.63 320	19.29 323	84.11 241	35.67 275	21.24 353	78.59 263

test2356		53.94 297	52.37 293	58.64 319	61.58 335	27.53 346	78.20 248	74.33 287	46.92 288	44.01 302	66.04 317	18.91 324	74.11 315	28.80 300	52.55 286	74.28 311

OpenMVS_ROB	53.19 17	59.20 265	56.00 272	68.83 250	71.13 292	44.30 265	83.64 148	75.02 282	46.42 293	46.48 296	73.03 274	18.69 325	88.14 146	27.74 309	61.80 215	74.05 314

LTVRE_ROB	45.45 19	52.73 302	49.74 302	61.69 307	69.78 299	34.99 320	44.52 349	67.60 325	43.11 314	43.79 304	74.03 262	18.54 326	81.45 266	28.39 307	57.94 253	68.62 334
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		54.37 293	50.10 299	67.21 267	70.70 293	41.46 290	74.73 275	64.69 332	47.56 283	39.12 323	69.49 294	18.49 327	84.69 229	31.87 290	34.20 344	75.48 303

new-patchmatchnet		48.21 311	46.55 310	53.18 329	57.73 343	18.19 362	70.24 305	71.02 314	45.70 295	33.70 339	60.23 331	18.00 328	69.86 336	27.97 308	34.35 342	71.49 329

F-COLMAP		55.96 289	53.65 285	62.87 301	72.76 261	42.77 280	74.70 276	70.37 317	40.03 320	41.11 317	79.36 204	17.77 329	73.70 320	32.80 288	53.96 280	72.15 324

LP		47.05 316	42.23 321	61.53 309	72.04 281	49.37 197	49.48 344	65.50 329	34.57 341	34.29 337	52.30 345	17.73 330	75.32 312	17.56 350	36.57 337	59.91 347

jajsoiax		63.21 232	60.84 236	70.32 233	68.33 308	44.45 263	81.23 202	81.05 189	53.37 242	50.96 258	77.81 226	17.49 331	85.49 214	59.31 141	58.05 251	81.02 233

RPSCF		45.77 319	44.13 318	50.68 332	57.67 344	29.66 337	54.92 342	45.25 355	26.69 349	45.92 299	75.92 253	17.43 332	45.70 359	27.44 310	45.95 315	76.67 292

PatchMatch-RL		56.66 281	53.75 283	65.37 289	77.91 201	45.28 256	69.78 309	60.38 340	41.35 318	47.57 280	73.73 265	16.83 333	76.91 304	36.99 270	59.21 236	73.92 315

mvs_tets		62.96 236	60.55 241	70.19 234	68.22 310	44.24 267	80.90 209	80.74 194	52.99 246	50.82 266	77.56 227	16.74 334	85.44 215	59.04 142	57.94 253	80.89 234

ACMH+	54.58 15	58.55 273	55.24 275	68.50 260	74.68 235	45.80 252	80.27 217	70.21 319	47.15 286	42.77 310	75.48 255	16.73 335	85.98 204	35.10 280	54.78 276	73.72 316

ACMH	53.70 16	59.78 261	55.94 273	71.28 214	76.59 216	48.35 221	80.15 223	76.11 269	49.74 274	41.91 313	73.45 272	16.50 336	90.31 74	31.42 292	57.63 258	75.17 305
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
MIMVSNet1		50.35 308	47.81 307	57.96 321	61.53 336	27.80 345	67.40 315	74.06 290	43.25 313	33.31 343	65.38 321	16.03 337	71.34 332	21.80 338	47.55 303	74.75 307

DSMNet-mixed		38.35 325	35.36 327	47.33 336	48.11 354	14.91 364	37.87 356	36.60 361	19.18 354	34.37 336	59.56 334	15.53 338	53.01 353	20.14 345	46.89 309	74.07 313

EG-PatchMatch MVS		62.40 248	59.59 247	70.81 224	73.29 248	49.05 201	85.81 84	84.78 105	51.85 263	44.19 301	73.48 271	15.52 339	89.85 85	40.16 259	67.24 165	73.54 318

testgi		54.25 295	52.57 292	59.29 316	62.76 331	21.65 353	72.21 294	70.47 315	53.25 243	41.94 312	77.33 232	14.28 340	77.95 296	29.18 299	51.72 289	78.28 277

COLMAP_ROB	43.60 20	50.90 307	48.05 306	59.47 315	67.81 311	40.57 299	71.25 300	62.72 338	36.49 334	36.19 329	73.51 270	13.48 341	73.92 318	20.71 343	50.26 294	63.92 343
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
OurMVSNet-221017-0		52.39 304	48.73 304	63.35 299	65.21 321	38.42 305	68.54 314	64.95 331	38.19 325	39.57 321	71.43 287	13.23 342	79.92 282	37.16 266	40.32 331	71.72 326

tmp_tt		9.44 344	10.68 345	5.73 356	2.49 371	4.21 370	10.48 364	18.04 369	0.34 366	12.59 359	20.49 360	11.39 343	7.03 368	13.84 355	6.46 364	5.95 364

ITE_SJBPF				51.84 331	58.03 342	31.94 334		53.57 350	36.67 333	41.32 316	75.23 257	11.17 344	51.57 354	25.81 316	48.04 300	72.02 325

AllTest		47.32 314	44.66 316	55.32 326	65.08 322	37.50 310	62.96 326	54.25 348	35.45 338	33.42 341	72.82 275	9.98 345	59.33 347	24.13 321	43.84 324	69.13 332

TestCases				55.32 326	65.08 322	37.50 310		54.25 348	35.45 338	33.42 341	72.82 275	9.98 345	59.33 347	24.13 321	43.84 324	69.13 332

test1235678		47.09 315	43.82 319	56.91 323	53.18 349	24.90 348	71.93 296	70.31 318	39.54 321	31.44 346	56.59 342	9.50 347	71.55 330	22.63 329	39.24 334	74.28 311

USDC		54.36 294	51.23 296	63.76 296	64.29 326	37.71 309	62.84 327	73.48 300	56.85 199	35.47 332	71.94 285	9.23 348	78.43 289	38.43 263	48.57 296	75.13 306

XVG-ACMP-BASELINE		56.03 287	52.85 289	65.58 285	61.91 333	40.95 294	63.36 322	72.43 301	45.20 300	46.02 298	74.09 261	9.20 349	78.12 293	45.13 242	58.27 246	77.66 286

pmmvs3		45.53 320	41.55 323	57.44 322	48.97 353	39.68 301	70.06 306	57.66 343	28.32 348	34.06 338	57.29 339	8.50 350	66.85 342	34.86 281	34.26 343	65.80 339

no-one		37.21 327	31.48 332	54.40 328	39.62 361	31.91 335	45.68 348	67.42 326	35.54 337	14.59 357	35.91 354	7.35 351	73.20 324	22.98 324	14.23 355	58.09 349

ANet_high		34.39 329	29.59 333	48.78 334	30.34 365	22.28 351	55.53 339	63.79 335	38.11 326	15.47 356	36.56 353	6.94 352	59.98 346	13.93 354	5.64 365	64.08 342

FPMVS		35.40 328	33.67 329	40.57 341	46.34 355	28.74 342	41.05 353	57.05 344	20.37 353	22.27 353	53.38 344	6.87 353	44.94 360	8.62 358	47.11 307	48.01 353

new_pmnet		33.56 331	31.89 331	38.59 342	49.01 352	20.42 358	51.01 343	37.92 360	20.58 351	23.45 351	46.79 348	6.66 354	49.28 356	20.00 346	31.57 348	46.09 354

TinyColmap		48.15 312	44.49 317	59.13 317	65.73 318	38.04 307	63.34 323	62.86 337	38.78 323	29.48 348	67.23 315	6.46 355	73.30 322	24.59 319	41.90 328	66.04 338

ambc				62.06 304	53.98 348	29.38 339	35.08 357	79.65 209		41.37 315	59.96 332	6.27 356	82.15 261	35.34 277	38.22 336	74.65 308

TDRefinement		40.91 322	38.37 325	48.55 335	50.45 351	33.03 329	58.98 335	50.97 351	28.50 347	29.89 347	67.39 313	6.21 357	54.51 351	17.67 349	35.25 340	58.11 348

PM-MVS		46.92 317	43.76 320	56.41 324	52.18 350	32.26 332	63.21 325	38.18 359	37.99 327	40.78 318	66.20 316	5.09 358	65.42 343	48.19 225	41.99 327	71.54 328

LF4IMVS		33.04 332	32.55 330	34.52 346	40.96 358	22.03 352	44.45 350	35.62 362	20.42 352	28.12 349	62.35 327	5.03 359	31.88 365	21.61 340	34.42 341	49.63 352

testus		48.97 310	46.53 311	56.31 325	57.39 345	24.08 349	73.40 283	70.45 316	43.37 312	35.52 331	63.95 323	4.77 360	71.36 331	24.88 318	45.02 320	73.50 319

test12356		37.84 326	35.07 328	46.18 338	45.03 356	8.02 369	57.70 337	62.67 339	31.83 344	22.78 352	50.25 346	4.46 361	66.95 341	17.25 351	23.62 352	63.57 344

testmv		39.64 323	36.01 326	50.55 333	42.18 357	21.56 354	64.81 319	66.88 327	32.22 343	22.25 354	47.47 347	4.33 362	64.81 344	17.71 348	26.22 349	65.29 341

EMVS		18.42 341	17.66 342	20.71 353	34.13 364	12.64 366	46.94 346	29.94 366	10.46 362	5.58 366	14.93 364	4.23 363	38.83 363	5.24 364	7.51 362	10.67 363

E-PMN		19.16 340	18.40 341	21.44 352	36.19 363	13.63 365	47.59 345	30.89 365	10.73 360	5.91 365	16.59 361	3.66 364	39.77 362	5.95 362	8.14 359	10.92 362

PMMVS2		26.71 336	22.98 339	37.87 343	36.89 362	8.51 368	42.51 352	29.32 367	19.09 355	13.01 358	37.54 350	2.23 365	53.11 352	14.54 353	11.71 356	51.99 351

Gipuma		27.47 335	24.26 336	37.12 344	60.55 341	29.17 340	11.68 363	60.00 341	14.18 357	10.52 361	15.12 363	2.20 366	63.01 345	8.39 359	35.65 338	19.18 360
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
LCM-MVSNet		28.07 333	23.85 337	40.71 340	27.46 367	18.93 361	30.82 358	46.19 352	12.76 359	16.40 355	34.70 356	1.90 367	48.69 357	20.25 344	24.22 351	54.51 350

DeepMVS_CX				13.10 354	21.34 370	8.99 367		10.02 371	10.59 361	7.53 364	30.55 357	1.82 368	14.55 366	6.83 361	7.52 361	15.75 361

PMVS	19.57 22	25.07 338	22.43 340	32.99 347	23.12 368	22.98 350	40.98 354	35.19 363	15.99 356	11.95 360	35.87 355	1.47 369	49.29 355	5.41 363	31.90 347	26.70 359
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	16.60 23	17.34 343	13.39 344	29.16 350	28.43 366	19.72 360	13.73 362	23.63 368	7.23 364	7.96 363	21.41 359	0.80 370	36.08 364	6.97 360	10.39 357	31.69 357
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PNet_i23d		25.11 337	23.09 338	31.17 348	40.18 359	21.30 355	57.99 336	33.28 364	13.77 358	9.94 362	30.29 358	0.45 371	43.74 361	13.61 356	8.28 358	28.46 358

wuyk23d		9.11 345	8.77 347	10.15 355	40.18 359	16.76 363	20.28 361	1.01 372	2.58 365	2.66 368	0.98 368	0.23 372	12.49 367	4.08 365	6.90 363	1.19 365

wuykxyi23d		19.94 339	14.87 343	35.13 345	22.47 369	19.80 359	25.80 359	38.64 358	7.61 363	4.88 367	13.58 366	0.23 372	48.42 358	13.11 357	7.53 360	37.18 356

test_part1				0.00 359		0.00 373	0.00 365	88.42 30				0.00 374	0.00 369	0.00 368	0.00 366	0.00 369

v1.0		34.14 330	45.52 314	0.00 359	89.33 9	0.00 373	0.00 365	88.42 30	56.02 218	82.27 2	93.65 4	0.00 374	0.00 369	0.00 368	0.00 366	0.00 369

sosnet-low-res		0.00 350	0.00 351	0.00 359	0.00 373	0.00 373	0.00 365	0.00 374	0.00 367	0.00 371	0.00 371	0.00 374	0.00 369	0.00 368	0.00 366	0.00 369

sosnet		0.00 350	0.00 351	0.00 359	0.00 373	0.00 373	0.00 365	0.00 374	0.00 367	0.00 371	0.00 371	0.00 374	0.00 369	0.00 368	0.00 366	0.00 369

uncertanet		0.00 350	0.00 351	0.00 359	0.00 373	0.00 373	0.00 365	0.00 374	0.00 367	0.00 371	0.00 371	0.00 374	0.00 369	0.00 368	0.00 366	0.00 369

Regformer		0.00 350	0.00 351	0.00 359	0.00 373	0.00 373	0.00 365	0.00 374	0.00 367	0.00 371	0.00 371	0.00 374	0.00 369	0.00 368	0.00 366	0.00 369

testmvs		6.14 347	8.18 348	0.01 357	0.01 372	0.00 373	73.40 283	0.00 374	0.00 367	0.02 369	0.15 369	0.00 374	0.00 369	0.02 366	0.00 366	0.02 366

test123		6.01 348	8.01 349	0.01 357	0.00 373	0.01 372	71.93 296	0.00 374	0.00 367	0.02 369	0.11 370	0.00 374	0.00 369	0.02 366	0.00 366	0.02 366

ab-mvs-re		7.68 346	10.24 346	0.00 359	0.00 373	0.00 373	0.00 365	0.00 374	0.00 367	0.00 371	92.12 20	0.00 374	0.00 369	0.00 368	0.00 366	0.00 369

uanet		0.00 350	0.00 351	0.00 359	0.00 373	0.00 373	0.00 365	0.00 374	0.00 367	0.00 371	0.00 371	0.00 374	0.00 369	0.00 368	0.00 366	0.00 369

GSMVS																88.13 116

test_part2					89.33 9	55.48 40				82.27 2

MTGPA								81.31 182

MTMP							87.27 61	15.34 370

gm-plane-assit					83.24 89	54.21 77			70.91 11		88.23 92		95.25 5	66.37 88

test9_res														78.72 17	85.44 27	91.39 45

agg_prior2														75.65 33	85.11 31	91.01 51

agg_prior					85.64 44	54.92 59		83.61 141		72.53 46			88.10 149

test_prior4						56.39 30	87.15 63

test_prior				78.39 56	86.35 35	54.91 61		85.45 81					89.70 90			90.55 59

旧先验2							81.73 188		45.53 297	74.66 27			70.48 335	58.31 149

新几何2							81.61 196

无先验							85.19 102	78.00 242	49.08 277				85.13 219	52.78 197		87.45 127

原ACMM2							83.77 146

testdata2													77.81 299	45.64 241

testdata1							77.55 252		64.14 76

plane_prior7					77.95 198	48.46 220

plane_prior5								82.59 160					88.30 142	65.46 97	72.34 134	84.49 169

plane_prior4											83.28 160

plane_prior3						48.95 203			64.01 77	62.15 140

plane_prior2							85.76 86		63.60 88

plane_prior1					78.31 196

plane_prior						49.57 191	87.43 55		64.57 69						72.84 129

n2								0.00 374

nn								0.00 374

door-mid								41.31 357

test11								84.25 117

door								43.27 356

HQP5-MVS						51.56 154

HQP-NCC					79.02 178		88.00 41		65.45 54	64.48 115

ACMP_Plane					79.02 178		88.00 41		65.45 54	64.48 115

BP-MVS														66.70 86

HQP4-MVS										64.47 118			88.61 125			84.91 166

HQP3-MVS								83.68 138							73.12 125

NP-MVS					78.76 183	50.43 176					85.12 129

ACMMP++_ref															63.20 200

ACMMP++															59.38 235

High-resolution multi-view benchmark