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.

PS-MVSNAJ		88.14 10	87.61 17	89.71 4	92.06 71	76.72 1	95.75 11	93.26 65	83.86 11	89.55 5	96.06 11	53.55 166	97.89 30	91.10 6	93.31 35	94.54 66

xiu_mvs_v2_base		87.92 14	87.38 22	89.55 7	91.41 94	76.43 2	95.74 12	93.12 73	83.53 13	89.55 5	95.95 12	53.45 171	97.68 32	91.07 7	92.62 42	94.54 66

MG-MVS		87.11 23	86.27 29	89.62 5	97.79 1	76.27 3	94.96 31	94.49 31	78.74 51	83.87 43	92.94 84	64.34 54	96.94 75	75.19 100	94.09 22	95.66 27

CHOSEN 1792x2688		84.98 47	83.45 56	89.57 6	89.94 113	75.14 4	92.07 107	92.32 97	81.87 25	75.68 103	88.27 140	60.18 88	98.60 14	80.46 70	90.27 71	94.96 55

MVS		84.66 51	82.86 66	90.06 1	90.93 99	74.56 5	87.91 214	95.54 10	68.55 207	72.35 139	94.71 50	59.78 90	98.90 7	81.29 67	94.69 17	96.74 7

DELS-MVS		90.05 4	90.09 5	89.94 2	93.14 49	73.88 6	97.01 2	94.40 35	88.32 2	85.71 24	94.91 45	74.11 7	98.91 6	87.26 26	95.94 3	97.03 5
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
MCST-MVS		91.08 1	91.46 2	89.94 2	97.66 2	73.37 7	97.13 1	95.58 9	89.33 1	85.77 23	96.26 7	72.84 9	99.38 1	92.64 4	95.93 4	97.08 4

LFMVS		84.34 54	82.73 69	89.18 8	94.76 19	73.25 8	94.99 30	91.89 116	71.90 152	82.16 50	93.49 74	47.98 215	97.05 62	82.55 55	84.82 105	97.25 2

PAPM		85.89 39	85.46 41	87.18 30	88.20 149	72.42 9	92.41 97	92.77 84	82.11 21	80.34 62	93.07 81	68.27 20	95.02 127	78.39 83	93.59 32	94.09 84

canonicalmvs		86.85 28	86.25 31	88.66 10	91.80 79	71.92 10	93.54 63	91.71 123	80.26 31	87.55 13	95.25 32	63.59 63	96.93 77	88.18 19	84.34 111	97.11 3

OpenMVS	70.45 11	78.54 143	75.92 155	86.41 55	85.93 183	71.68 11	92.74 84	92.51 94	66.49 225	64.56 215	91.96 100	43.88 241	98.10 25	54.61 239	90.65 67	89.44 164

QAPM		79.95 116	77.39 138	87.64 20	89.63 118	71.41 12	93.30 68	93.70 47	65.34 234	67.39 201	91.75 105	47.83 216	98.96 5	57.71 231	89.81 72	92.54 126

3Dnovator	73.91 6	82.69 78	80.82 88	88.31 14	89.57 119	71.26 13	92.60 91	94.39 36	78.84 48	67.89 194	92.48 93	48.42 210	98.52 15	68.80 152	94.40 19	95.15 46

MVSFormer		83.75 65	82.88 65	86.37 56	89.24 127	71.18 14	89.07 197	90.69 155	65.80 229	87.13 15	94.34 60	64.99 49	92.67 212	72.83 113	91.80 52	95.27 40

lupinMVS		87.74 16	87.77 15	87.63 22	89.24 127	71.18 14	96.57 4	92.90 81	82.70 16	87.13 15	95.27 30	64.99 49	95.80 106	89.34 12	91.80 52	95.93 24

alignmvs		87.28 20	86.97 25	88.24 15	91.30 95	71.14 16	95.61 16	93.56 52	79.30 38	87.07 17	95.25 32	68.43 19	96.93 77	87.87 21	84.33 112	96.65 8

CSCG		86.87 27	86.26 30	88.72 9	95.05 18	70.79 17	93.83 56	95.33 11	68.48 209	77.63 88	94.35 59	73.04 8	98.45 16	84.92 41	93.71 30	96.92 6

CNVR-MVS		90.32 3	90.89 4	88.61 11	96.76 4	70.65 18	96.47 6	94.83 20	84.83 9	89.07 7	96.80 2	70.86 14	99.06 3	92.64 4	95.71 5	96.12 18

API-MVS		82.28 82	80.53 93	87.54 23	96.13 8	70.59 19	93.63 59	91.04 148	65.72 231	75.45 108	92.83 88	56.11 129	98.89 8	64.10 191	89.75 74	93.15 110

jason		86.40 32	86.17 32	87.11 33	86.16 177	70.54 20	95.71 15	92.19 107	82.00 24	84.58 34	94.34 60	61.86 76	95.53 122	87.76 22	90.89 64	95.27 40
jason: jason.
PatchmatchNet		77.46 154	74.63 174	85.96 65	89.55 121	70.35 21	79.97 283	89.55 197	72.23 144	70.94 149	76.91 264	57.03 113	92.79 208	54.27 241	81.17 129	94.74 60
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IB-MVS	77.80 4	82.18 83	80.46 94	87.35 27	89.14 129	70.28 22	95.59 17	95.17 13	78.85 47	70.19 156	85.82 169	70.66 16	97.67 33	72.19 122	66.52 223	94.09 84
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
MVS_0304		88.39 9	88.35 12	88.50 12	93.01 51	70.11 23	95.90 10	92.20 105	86.27 6	88.70 9	95.92 13	56.76 118	99.02 4	92.68 3	93.76 28	96.37 15

Patchmatch-test1		75.00 196	71.80 209	84.58 106	86.63 170	70.08 24	81.06 272	89.19 208	71.60 165	70.01 158	77.16 262	45.53 232	88.63 271	51.79 250	73.27 180	95.02 54

DWT-MVSNet_test		83.95 60	82.80 67	87.41 25	92.90 54	70.07 25	89.12 196	94.42 33	82.15 20	77.64 87	91.77 103	70.81 15	96.22 92	65.03 183	81.36 128	95.94 23

VNet		86.20 35	85.65 40	87.84 18	93.92 34	69.99 26	95.73 14	95.94 8	78.43 53	86.00 21	93.07 81	58.22 102	97.00 67	85.22 39	84.33 112	96.52 13

MS-PatchMatch		77.90 151	76.50 148	82.12 163	85.99 179	69.95 27	91.75 130	92.70 85	73.97 113	62.58 233	84.44 183	41.11 251	95.78 107	63.76 192	92.17 49	80.62 285

PatchFormer-LS_test		83.14 70	81.81 78	87.12 32	92.34 64	69.92 28	88.64 202	93.32 62	82.07 23	74.87 112	91.62 107	68.91 17	96.08 99	66.07 174	78.45 148	95.37 33

MVS_Test		84.16 57	83.20 62	87.05 35	91.56 84	69.82 29	89.99 178	92.05 110	77.77 59	82.84 47	86.57 161	63.93 57	96.09 97	74.91 106	89.18 75	95.25 43

VDDNet		80.50 104	78.26 123	87.21 29	86.19 176	69.79 30	94.48 34	91.31 138	60.42 264	79.34 71	90.91 111	38.48 260	96.56 89	82.16 56	81.05 130	95.27 40

MVS_111021_HR		86.19 36	85.80 37	87.37 26	93.17 48	69.79 30	93.99 47	93.76 45	79.08 45	78.88 76	93.99 66	62.25 73	98.15 24	85.93 35	91.15 62	94.15 80

CANet		89.61 6	89.99 6	88.46 13	94.39 24	69.71 32	96.53 5	93.78 42	86.89 4	89.68 4	95.78 15	65.94 40	99.10 2	92.99 1	93.91 25	96.58 11

EPMVS		78.49 144	75.98 154	86.02 63	91.21 96	69.68 33	80.23 279	91.20 141	75.25 93	72.48 135	78.11 252	54.65 152	93.69 188	57.66 232	83.04 119	94.69 61

GG-mvs-BLEND				86.53 49	91.91 76	69.67 34	75.02 299	94.75 23		78.67 81	90.85 112	77.91 2	94.56 142	72.25 119	93.74 29	95.36 34

DI_MVS_plusplus_test		79.78 120	77.50 136	86.62 43	80.90 229	69.46 35	90.69 162	91.97 114	77.00 68	59.07 246	82.34 201	46.82 222	95.88 104	82.14 57	86.59 94	94.53 68

Effi-MVS+		83.82 63	82.76 68	86.99 37	89.56 120	69.40 36	91.35 144	86.12 264	72.59 137	83.22 45	92.81 89	59.60 92	96.01 102	81.76 60	87.80 84	95.56 30

WTY-MVS		86.32 33	85.81 36	87.85 17	92.82 57	69.37 37	95.20 23	95.25 12	82.71 15	81.91 51	94.73 49	67.93 26	97.63 38	79.55 73	82.25 124	96.54 12

test_normal		79.66 121	77.36 140	86.54 47	80.72 233	69.21 38	90.68 163	92.16 109	76.99 69	58.63 250	82.03 210	46.70 224	95.86 105	81.74 61	86.63 93	94.56 63

cascas		78.18 146	75.77 157	85.41 84	87.14 164	69.11 39	92.96 77	91.15 143	66.71 223	70.47 151	86.07 166	37.49 270	96.48 90	70.15 140	79.80 135	90.65 151

NCCC		89.07 8	89.46 8	87.91 16	96.60 5	69.05 40	96.38 7	94.64 28	84.42 10	86.74 18	96.20 8	66.56 35	98.76 11	89.03 16	94.56 18	95.92 25

MVSTER		82.47 79	82.05 74	83.74 120	92.68 60	69.01 41	91.90 120	93.21 66	79.83 32	72.14 140	85.71 171	74.72 6	94.72 138	75.72 96	72.49 187	87.50 181

FMVSNet3		77.73 152	76.04 153	82.80 135	91.20 97	68.99 42	91.87 121	91.99 112	73.35 127	67.04 203	83.19 193	56.62 123	92.14 225	59.80 222	69.34 203	87.28 193

MSLP-MVS++		86.27 34	85.91 35	87.35 27	92.01 72	68.97 43	95.04 29	92.70 85	79.04 46	81.50 54	96.50 4	58.98 99	96.78 81	83.49 50	93.93 24	96.29 16

test12				87.09 34	94.60 23	68.86 44		92.91 80		82.67 48		65.44 45	97.55 41		93.69 31	94.84 58

nrg030		80.93 99	79.86 99	84.13 114	83.69 207	68.83 45	93.23 70	91.20 141	75.55 85	75.06 111	88.22 143	63.04 70	94.74 137	81.88 59	66.88 220	88.82 166

SD-MVS		87.49 18	87.49 19	87.50 24	93.60 39	68.82 46	93.90 53	92.63 90	76.86 71	87.90 12	95.76 16	66.17 36	97.63 38	89.06 15	91.48 58	96.05 20

xiu_mvs_v1_base_debu		82.16 84	81.12 85	85.26 89	86.42 171	68.72 47	92.59 93	90.44 160	73.12 130	84.20 38	94.36 55	38.04 264	95.73 110	84.12 45	86.81 89	91.33 142

xiu_mvs_v1_base		82.16 84	81.12 85	85.26 89	86.42 171	68.72 47	92.59 93	90.44 160	73.12 130	84.20 38	94.36 55	38.04 264	95.73 110	84.12 45	86.81 89	91.33 142

xiu_mvs_v1_base_debi		82.16 84	81.12 85	85.26 89	86.42 171	68.72 47	92.59 93	90.44 160	73.12 130	84.20 38	94.36 55	38.04 264	95.73 110	84.12 45	86.81 89	91.33 142

MDTV_nov1_ep13			72.61 200		89.06 130	68.48 50	80.33 277	90.11 179	71.84 157	71.81 144	75.92 269	53.01 173	93.92 181	48.04 263	73.38 179

CostFormer		82.33 81	81.15 84	85.86 67	89.01 132	68.46 51	82.39 262	93.01 76	75.59 84	80.25 63	81.57 216	72.03 12	94.96 129	79.06 78	77.48 156	94.16 79

mvs_anonymous		81.36 94	79.99 97	85.46 80	90.39 107	68.40 52	86.88 232	90.61 159	74.41 100	70.31 155	84.67 180	63.79 59	92.32 223	73.13 110	85.70 100	95.67 26

tpmp4_e23		78.85 134	76.55 147	85.77 72	89.25 125	68.39 53	81.63 269	91.38 136	70.40 186	75.21 110	79.22 248	67.37 30	94.79 133	58.98 227	75.51 167	94.13 81

gg-mvs-nofinetune		77.18 164	74.31 180	85.80 70	91.42 92	68.36 54	71.78 302	94.72 24	49.61 300	77.12 95	45.92 323	77.41 3	93.98 178	67.62 159	93.16 37	95.05 50

DeepC-MVS_fast	79.48 2	87.95 13	88.00 13	87.79 19	95.86 12	68.32 55	95.74 12	94.11 38	83.82 12	83.49 44	96.19 9	64.53 53	98.44 17	83.42 51	94.88 12	96.61 9
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
PAPR		85.15 45	84.47 47	87.18 30	96.02 10	68.29 56	91.85 123	93.00 78	76.59 76	79.03 75	95.00 39	61.59 77	97.61 40	78.16 84	89.00 76	95.63 28

tpmrst		80.57 102	79.14 115	84.84 99	90.10 110	68.28 57	81.70 266	89.72 194	77.63 62	75.96 102	79.54 246	64.94 51	92.71 210	75.43 98	77.28 159	93.55 99

tpm2		79.80 119	77.95 128	85.34 87	88.28 147	68.26 58	81.56 270	91.42 134	70.11 189	77.59 90	80.50 232	67.40 29	94.26 162	67.34 161	77.35 157	93.51 100

HPM-MVS++		89.37 7	89.95 7	87.64 20	95.10 17	68.23 59	95.24 22	94.49 31	82.43 17	88.90 8	96.35 5	71.89 13	98.63 13	88.76 18	96.40 2	96.06 19

HyFIR lowres test		81.03 98	79.56 104	85.43 83	87.81 156	68.11 60	90.18 174	90.01 183	70.65 183	72.95 126	86.06 167	63.61 62	94.50 146	75.01 104	79.75 136	93.67 96

TSAR-MVS + MP.		88.11 11	88.64 9	86.54 47	91.73 80	68.04 61	90.36 170	93.55 53	82.89 14	91.29 2	92.89 87	72.27 10	96.03 100	87.99 20	94.77 13	95.54 31

CR-MVSNet		73.79 206	70.82 215	82.70 138	83.15 213	67.96 62	70.25 305	84.00 280	73.67 122	69.97 160	72.41 284	57.82 106	89.48 267	52.99 248	73.13 181	90.64 152

RPMNet		69.58 241	65.21 251	82.70 138	83.15 213	67.96 62	70.25 305	86.15 263	46.83 308	69.97 160	65.10 309	56.48 126	89.48 267	35.79 306	73.13 181	90.64 152

V42		76.46 176	74.55 177	82.19 161	79.14 269	67.82 64	90.26 173	89.42 201	73.75 119	68.63 182	81.89 212	51.31 188	94.09 170	71.69 124	64.84 238	84.66 237

tpm cat1		75.30 191	72.21 205	84.58 106	88.52 140	67.77 65	78.16 294	88.02 236	61.88 257	68.45 189	76.37 265	60.65 85	94.03 176	53.77 244	74.11 175	91.93 136

HY-MVS	76.49 5	84.28 55	83.36 61	87.02 36	92.22 69	67.74 66	84.65 244	94.50 30	79.15 42	82.23 49	87.93 145	66.88 32	96.94 75	80.53 69	82.20 125	96.39 14

VDD-MVS		83.06 72	81.81 78	86.81 38	90.86 102	67.70 67	95.40 19	91.50 131	75.46 86	81.78 52	92.34 97	40.09 254	97.13 61	86.85 30	82.04 126	95.60 29

FMVSNet2		76.07 180	74.01 185	82.26 159	88.85 133	67.66 68	91.33 145	91.61 126	70.84 176	65.98 208	82.25 203	48.03 212	92.00 230	58.46 228	68.73 209	87.10 195

CLD-MVS		82.73 75	82.35 73	83.86 118	87.90 155	67.65 69	95.45 18	92.18 108	85.06 8	72.58 132	92.27 98	52.46 179	95.78 107	84.18 44	79.06 141	88.16 176
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
Test4		76.45 177	73.45 192	85.45 82	76.07 291	67.61 70	88.38 206	90.83 151	76.71 74	53.06 279	79.65 245	31.61 293	94.35 156	78.47 81	86.22 98	94.40 72

1314		80.70 101	78.95 116	85.94 66	87.77 157	67.56 71	87.91 214	92.55 93	72.17 148	67.44 198	93.09 78	50.27 194	97.04 64	71.68 125	87.64 85	93.23 108

ACMMP_Plus		86.05 37	85.80 37	86.80 39	91.58 83	67.53 72	91.79 125	93.49 56	74.93 96	84.61 33	95.30 28	59.42 93	97.92 28	86.13 33	94.92 9	94.94 56

PVSNet_BlendedMVS		83.38 67	83.43 57	83.22 131	93.76 35	67.53 72	94.06 43	93.61 50	79.13 43	81.00 57	85.14 175	63.19 67	97.29 52	87.08 27	73.91 178	84.83 236

PVSNet_Blended		86.73 31	86.86 27	86.31 59	93.76 35	67.53 72	96.33 8	93.61 50	82.34 18	81.00 57	93.08 79	63.19 67	97.29 52	87.08 27	91.38 59	94.13 81

test_prior3		87.38 19	87.70 16	86.42 53	94.71 21	67.35 75	95.10 27	93.10 74	75.40 89	85.25 30	95.61 21	67.94 24	96.84 79	87.47 23	94.77 13	95.05 50

test_prior				86.42 53	94.71 21	67.35 75		93.10 74					96.84 79			95.05 50

TEST9					94.18 27	67.28 77	94.16 36	93.51 54	71.75 162	85.52 26	95.33 26	68.01 23	97.27 54

train_agg		87.21 22	87.42 21	86.60 44	94.18 27	67.28 77	94.16 36	93.51 54	71.87 154	85.52 26	95.33 26	68.19 21	97.27 54	89.09 13	94.90 10	95.25 43

test_8					94.19 26	67.19 79	94.15 38	93.42 59	71.87 154	85.38 28	95.35 25	68.19 21	96.95 74

CDPH-MVS		85.71 41	85.46 41	86.46 51	94.75 20	67.19 79	93.89 54	92.83 83	70.90 175	83.09 46	95.28 29	63.62 61	97.36 48	80.63 68	94.18 21	94.84 58

test_prior4						67.18 81	93.92 52

v2v482		77.42 155	75.65 162	82.73 137	80.38 247	67.13 82	91.85 123	90.23 172	75.09 94	69.37 170	83.39 191	53.79 164	94.44 147	71.77 123	65.00 237	86.63 205

DP-MVS Recon		82.73 75	81.65 80	85.98 64	97.31 3	67.06 83	95.15 25	91.99 112	69.08 199	76.50 101	93.89 68	54.48 155	98.20 22	70.76 136	85.66 101	92.69 121

tpmvs		72.88 212	69.76 220	82.22 160	90.98 98	67.05 84	78.22 293	88.30 231	63.10 248	64.35 219	74.98 272	55.09 142	94.27 160	43.25 276	69.57 202	85.34 232

gm-plane-assit					88.42 145	67.04 85			78.62 52		91.83 102		97.37 47	76.57 93

agg_prior1		87.02 24	87.26 23	86.28 60	94.16 31	66.97 86	94.08 42	93.31 63	71.85 156	84.49 35	95.39 24	68.91 17	96.75 83	88.84 17	94.32 20	95.13 47

agg_prior					94.16 31	66.97 86		93.31 63		84.49 35			96.75 83

agg_prior3		86.93 26	87.08 24	86.48 50	94.21 25	66.95 88	94.14 39	93.40 60	71.80 159	84.86 32	95.13 36	66.16 37	97.25 56	89.09 13	94.90 10	95.25 43

diffmvs		80.18 109	78.55 120	85.07 93	88.56 139	66.93 89	86.70 235	88.62 225	70.42 185	78.69 80	85.26 173	56.93 117	94.77 134	68.68 153	83.09 118	93.51 100

ADS-MVSNet		68.54 248	64.38 258	81.03 190	88.06 151	66.90 90	68.01 312	84.02 279	57.57 276	64.48 216	69.87 299	38.68 256	89.21 270	40.87 287	67.89 215	86.97 196

v1neww		77.39 156	75.71 159	82.44 144	80.69 235	66.83 91	91.94 117	90.18 175	74.19 107	69.60 164	82.51 197	54.99 146	94.44 147	71.68 125	65.60 226	86.05 214

v7new		77.39 156	75.71 159	82.44 144	80.69 235	66.83 91	91.94 117	90.18 175	74.19 107	69.60 164	82.51 197	54.99 146	94.44 147	71.68 125	65.60 226	86.05 214

CANet_DTU		84.09 58	83.52 53	85.81 69	90.30 108	66.82 93	91.87 121	89.01 217	85.27 7	86.09 20	93.74 70	47.71 218	96.98 71	77.90 87	89.78 73	93.65 97

v18		71.94 218	69.43 221	79.50 217	80.74 232	66.82 93	88.16 208	86.66 250	68.95 200	55.55 262	72.66 279	55.03 144	90.15 254	64.78 185	52.30 290	81.54 267

v8		75.35 190	73.26 194	81.61 175	80.67 237	66.82 93	89.54 188	89.27 205	71.65 163	63.30 227	80.30 236	54.99 146	94.06 172	67.33 162	62.33 252	83.94 242

v6		77.39 156	75.71 159	82.44 144	80.67 237	66.82 93	91.94 117	90.18 175	74.19 107	69.60 164	82.50 200	55.00 145	94.44 147	71.68 125	65.60 226	86.05 214

v17		71.77 221	69.20 224	79.46 219	80.62 242	66.81 97	87.93 212	86.63 252	68.71 204	55.25 264	72.49 281	54.72 151	90.11 257	64.50 188	51.97 292	81.47 268

v16		71.81 219	69.26 223	79.47 218	80.66 239	66.81 97	87.93 212	86.63 252	68.70 205	55.35 263	72.51 280	54.75 150	90.12 256	64.51 187	52.28 291	81.47 268

3Dnovator+	73.60 7	82.10 87	80.60 92	86.60 44	90.89 101	66.80 99	95.20 23	93.44 58	74.05 110	67.42 199	92.49 92	49.46 201	97.65 37	70.80 135	91.68 54	95.33 35

PAPM_NR		82.97 73	81.84 77	86.37 56	94.10 33	66.76 100	87.66 219	92.84 82	69.96 191	74.07 119	93.57 72	63.10 69	97.50 43	70.66 137	90.58 68	94.85 57

v1141		77.28 161	75.57 163	82.42 150	80.63 241	66.73 101	91.96 113	90.42 163	74.41 100	69.46 167	82.12 207	55.09 142	94.40 152	70.99 132	65.05 233	86.12 211

v1		77.29 160	75.57 163	82.42 150	80.61 245	66.73 101	91.96 113	90.42 163	74.41 100	69.46 167	82.12 207	55.14 140	94.40 152	71.00 130	65.04 234	86.13 210

divwei89l23v2f112		77.28 161	75.57 163	82.42 150	80.62 242	66.72 103	91.96 113	90.42 163	74.41 100	69.46 167	82.12 207	55.11 141	94.40 152	71.00 130	65.04 234	86.12 211

v10		74.77 198	72.54 202	81.46 176	80.33 252	66.71 104	89.15 195	89.08 214	70.94 174	63.08 228	79.86 241	52.52 177	94.04 175	65.70 179	62.17 253	83.64 244

v15		71.40 223	68.75 226	79.35 220	80.39 246	66.70 105	87.57 221	86.64 251	68.66 206	54.68 266	72.00 288	54.50 153	89.98 259	63.69 193	50.66 297	81.38 272

v7		76.83 172	75.01 172	82.29 156	80.35 248	66.70 105	91.68 132	89.97 184	73.47 126	69.22 172	82.22 204	52.52 177	94.43 151	69.73 142	65.96 225	85.74 225

V14		71.29 225	68.61 228	79.31 221	80.34 250	66.65 107	87.39 223	86.61 254	68.41 210	54.49 268	71.91 289	54.25 158	89.96 260	63.50 194	50.62 298	81.33 274

DeepC-MVS	77.85 3	85.52 42	85.24 43	86.37 56	88.80 136	66.64 108	92.15 101	93.68 48	81.07 28	76.91 98	93.64 71	62.59 72	98.44 17	85.50 37	92.84 40	94.03 88
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
v1144		76.73 174	74.88 173	82.27 157	80.23 255	66.60 109	91.68 132	90.21 174	73.69 120	69.06 175	81.89 212	52.73 176	94.40 152	69.21 148	65.23 230	85.80 221

V9		71.16 226	68.46 230	79.27 223	80.26 253	66.60 109	87.21 226	86.56 255	68.17 211	54.26 271	71.81 291	54.00 160	89.93 261	63.28 197	50.57 299	81.27 275

PVSNet_Blended_VisFu		83.97 59	83.50 54	85.39 85	90.02 111	66.59 111	93.77 57	91.73 121	77.43 66	77.08 97	89.81 127	63.77 60	96.97 72	79.67 72	88.21 81	92.60 124

v11		71.05 229	68.32 233	79.23 224	80.34 250	66.57 112	87.01 229	86.55 256	68.11 212	54.40 269	71.66 293	52.94 174	89.91 262	62.71 205	51.12 295	81.21 276

v144192		76.05 181	74.03 184	82.12 163	79.50 264	66.55 113	91.39 140	89.71 195	72.30 143	68.17 190	81.33 220	51.75 184	94.03 176	67.94 155	64.19 243	85.77 222

v12		71.02 230	68.29 235	79.22 225	80.18 256	66.53 114	87.01 229	86.54 257	67.90 213	54.00 274	71.70 292	53.66 165	89.91 262	63.09 199	50.51 300	81.21 276

testing_2		71.09 228	67.32 240	82.40 153	69.82 308	66.52 115	83.64 249	90.77 153	72.21 145	45.12 305	71.07 298	27.60 305	93.74 186	75.71 97	69.96 198	86.95 198

v13		70.90 231	68.15 236	79.15 229	80.08 257	66.45 116	86.83 233	86.50 258	67.62 219	53.78 276	71.61 294	53.51 169	89.87 264	62.89 203	50.50 301	81.14 278

VPNet		78.82 135	77.53 135	82.70 138	84.52 195	66.44 117	93.93 51	92.23 100	80.46 30	72.60 131	88.38 138	49.18 204	93.13 196	72.47 118	63.97 246	88.55 170

SteuartSystems-ACMMP		86.82 30	86.90 26	86.58 46	90.42 105	66.38 118	96.09 9	93.87 40	77.73 60	84.01 42	95.66 19	63.39 64	97.94 27	87.40 25	93.55 33	95.42 32
Skip Steuart: Steuart Systems R&D Blog.
v1921920		75.63 188	73.49 191	82.06 167	79.38 265	66.35 119	91.07 155	89.48 198	71.98 151	67.99 191	81.22 223	49.16 206	93.90 182	66.56 168	64.56 242	85.92 220

MVP-Stereo		77.12 165	76.23 151	79.79 210	81.72 223	66.34 120	89.29 191	90.88 150	70.56 184	62.01 236	82.88 194	49.34 202	94.13 168	65.55 180	93.80 26	78.88 297
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
GA-MVS		78.33 145	76.23 151	84.65 104	83.65 208	66.30 121	91.44 138	90.14 178	76.01 81	70.32 154	84.02 185	42.50 244	94.72 138	70.98 133	77.00 160	92.94 117

APDe-MVS		87.54 17	87.84 14	86.65 42	96.07 9	66.30 121	94.84 32	93.78 42	69.35 196	88.39 10	96.34 6	67.74 28	97.66 36	90.62 9	93.44 34	96.01 22

v1192		75.98 183	73.92 186	82.15 162	79.73 260	66.24 123	91.22 150	89.75 189	72.67 136	68.49 188	81.42 218	49.86 198	94.27 160	67.08 163	65.02 236	85.95 218

dp		75.01 195	72.09 206	83.76 119	89.28 124	66.22 124	79.96 284	89.75 189	71.16 172	67.80 196	77.19 260	51.81 183	92.54 215	50.39 254	71.44 195	92.51 127

EPNet		87.84 15	88.38 10	86.23 61	93.30 44	66.05 125	95.26 21	94.84 19	87.09 3	88.06 11	94.53 52	66.79 33	97.34 50	83.89 48	91.68 54	95.29 38
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
v1240		75.21 193	72.98 196	81.88 169	79.20 267	66.00 126	90.75 161	89.11 213	71.63 164	67.41 200	81.22 223	47.36 220	93.87 183	65.46 181	64.72 240	85.77 222

PCF-MVS	73.15 9	79.29 127	77.63 133	84.29 112	86.06 178	65.96 127	87.03 227	91.10 145	69.86 192	69.79 163	90.64 114	57.54 109	96.59 86	64.37 190	82.29 123	90.32 154
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
MAR-MVS		84.18 56	83.43 57	86.44 52	96.25 7	65.93 128	94.28 35	94.27 37	74.41 100	79.16 74	95.61 21	53.99 161	98.88 9	69.62 144	93.26 36	94.50 69
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
Fast-Effi-MVS+		81.14 96	80.01 96	84.51 109	90.24 109	65.86 129	94.12 40	89.15 211	73.81 118	75.37 109	88.26 141	57.26 110	94.53 145	66.97 165	84.92 104	93.15 110

AdaColmap		78.94 133	77.00 143	84.76 100	96.34 6	65.86 129	92.66 90	87.97 238	62.18 253	70.56 150	92.37 96	43.53 242	97.35 49	64.50 188	82.86 120	91.05 148

Regformer-1		87.24 21	87.60 18	86.15 62	95.14 15	65.83 131	93.95 49	95.12 14	82.11 21	84.25 37	95.73 17	67.88 27	98.35 19	85.60 36	88.64 78	94.26 73

thres200		79.66 121	78.33 121	83.66 126	92.54 62	65.82 132	93.06 74	96.31 6	74.90 97	73.30 124	88.66 133	59.67 91	95.61 116	47.84 264	78.67 145	89.56 163

BH-RMVSNet		79.46 126	77.65 132	84.89 97	91.68 82	65.66 133	93.55 62	88.09 235	72.93 133	73.37 123	91.12 110	46.20 230	96.12 96	56.28 235	85.61 102	92.91 118

MP-MVS-pluss		85.24 44	85.13 44	85.56 77	91.42 92	65.59 134	91.54 137	92.51 94	74.56 99	80.62 59	95.64 20	59.15 96	97.00 67	86.94 29	93.80 26	94.07 86
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
PHI-MVS		86.83 29	86.85 28	86.78 40	93.47 43	65.55 135	95.39 20	95.10 16	71.77 161	85.69 25	96.52 3	62.07 74	98.77 10	86.06 34	95.60 6	96.03 21

114514_t		79.17 129	77.67 131	83.68 124	95.32 14	65.53 136	92.85 82	91.60 127	63.49 243	67.92 193	90.63 116	46.65 225	95.72 114	67.01 164	83.54 117	89.79 159

ab-mvs		80.18 109	78.31 122	85.80 70	88.44 144	65.49 137	83.00 259	92.67 87	71.82 158	77.36 92	85.01 176	54.50 153	96.59 86	76.35 95	75.63 166	95.32 37

TSAR-MVS + GP.		87.96 12	88.37 11	86.70 41	93.51 42	65.32 138	95.15 25	93.84 41	78.17 55	85.93 22	94.80 48	75.80 4	98.21 21	89.38 11	88.78 77	96.59 10

pmmvs4		73.92 205	71.81 208	80.25 199	79.17 268	65.24 139	87.43 222	87.26 247	67.64 218	63.46 225	83.91 186	48.96 208	91.53 241	62.94 202	65.49 229	83.96 241

APD-MVS		85.93 38	85.99 33	85.76 73	95.98 11	65.21 140	93.59 61	92.58 92	66.54 224	86.17 19	95.88 14	63.83 58	97.00 67	86.39 32	92.94 38	95.06 49
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
MPTG		84.73 49	84.47 47	85.50 78	91.89 77	65.16 141	91.55 136	92.23 100	75.32 91	80.53 60	95.21 34	56.06 130	97.16 59	84.86 42	92.55 44	94.18 76

MTAPA		83.91 61	83.38 60	85.50 78	91.89 77	65.16 141	81.75 265	92.23 100	75.32 91	80.53 60	95.21 34	56.06 130	97.16 59	84.86 42	92.55 44	94.18 76

GBi-Net		75.65 186	73.83 187	81.10 187	88.85 133	65.11 143	90.01 175	90.32 166	70.84 176	67.04 203	80.25 237	48.03 212	91.54 238	59.80 222	69.34 203	86.64 202

test1		75.65 186	73.83 187	81.10 187	88.85 133	65.11 143	90.01 175	90.32 166	70.84 176	67.04 203	80.25 237	48.03 212	91.54 238	59.80 222	69.34 203	86.64 202

FMVSNet1		72.71 214	69.91 218	81.10 187	83.60 209	65.11 143	90.01 175	90.32 166	63.92 241	63.56 224	80.25 237	36.35 278	91.54 238	54.46 240	66.75 221	86.64 202

Regformer-3		85.80 40	85.92 34	85.46 80	94.17 29	65.09 146	92.95 78	95.11 15	81.13 27	81.68 53	95.04 37	65.82 42	98.32 20	83.02 52	84.36 109	92.97 116

HFP-MVS		84.73 49	84.40 49	85.72 74	93.75 37	65.01 147	93.50 64	93.19 69	72.19 146	79.22 72	94.93 42	59.04 97	97.67 33	81.55 62	92.21 46	94.49 70

#test#		84.98 47	84.74 46	85.72 74	93.75 37	65.01 147	94.09 41	93.19 69	73.55 124	79.22 72	94.93 42	59.04 97	97.67 33	82.66 54	92.21 46	94.49 70

PVSNet	73.49 8	80.05 113	78.63 118	84.31 111	90.92 100	64.97 149	92.47 96	91.05 147	79.18 41	72.43 137	90.51 118	37.05 276	94.06 172	68.06 154	86.00 99	93.90 94

Regformer-2		87.00 25	87.43 20	85.71 76	95.14 15	64.73 150	93.95 49	94.95 17	81.69 26	84.03 41	95.73 17	67.35 31	98.19 23	85.40 38	88.64 78	94.20 75

tpm		78.58 142	77.03 142	83.22 131	85.94 182	64.56 151	83.21 257	91.14 144	78.31 54	73.67 122	79.68 243	64.01 55	92.09 228	66.07 174	71.26 196	93.03 114

thres400		78.68 139	77.43 137	82.43 147	92.21 70	64.49 152	92.05 108	96.28 7	73.48 125	71.75 145	88.26 141	60.07 89	95.32 125	45.16 273	77.58 154	87.48 182

VPA-MVSNet		79.03 130	78.00 127	82.11 166	85.95 180	64.48 153	93.22 71	94.66 26	75.05 95	74.04 120	84.95 177	52.17 181	93.52 191	74.90 107	67.04 219	88.32 175

CDS-MVSNet		81.43 93	80.74 89	83.52 127	86.26 175	64.45 154	92.09 105	90.65 158	75.83 83	73.95 121	89.81 127	63.97 56	92.91 204	71.27 129	82.82 121	93.20 109
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v148		76.19 178	74.47 179	81.36 177	80.05 259	64.44 155	91.75 130	90.23 172	73.68 121	67.13 202	80.84 228	55.92 133	93.86 185	68.95 150	61.73 258	85.76 224

XXY-MVS		77.94 150	76.44 149	82.43 147	82.60 217	64.44 155	92.01 109	91.83 119	73.59 123	70.00 159	85.82 169	54.43 156	94.76 135	69.63 143	68.02 214	88.10 177

MIMVSNet		71.64 222	68.44 231	81.23 180	81.97 222	64.44 155	73.05 301	88.80 221	69.67 193	64.59 214	74.79 273	32.79 287	87.82 280	53.99 242	76.35 163	91.42 141

Patchmtry		67.53 255	63.93 259	78.34 233	82.12 220	64.38 158	68.72 309	84.00 280	48.23 305	59.24 243	72.41 284	57.82 106	89.27 269	46.10 270	56.68 280	81.36 273

ACMMPR		84.37 52	84.06 50	85.28 88	93.56 40	64.37 159	93.50 64	93.15 72	72.19 146	78.85 78	94.86 46	56.69 122	97.45 44	81.55 62	92.20 48	94.02 89

BH-w/o		80.49 105	79.30 111	84.05 116	90.83 103	64.36 160	93.60 60	89.42 201	74.35 105	69.09 174	90.15 122	55.23 137	95.61 116	64.61 186	86.43 97	92.17 134

region2R		84.36 53	84.03 51	85.36 86	93.54 41	64.31 161	93.43 67	92.95 79	72.16 149	78.86 77	94.84 47	56.97 115	97.53 42	81.38 65	92.11 50	94.24 74

1121		81.25 95	80.05 95	84.87 98	92.30 66	64.31 161	87.91 214	91.39 135	59.44 270	79.94 65	92.91 85	57.09 111	97.01 65	66.63 166	92.81 41	93.29 106

新几何1				84.73 101	92.32 65	64.28 163		91.46 133	59.56 269	79.77 67	92.90 86	56.95 116	96.57 88	63.40 195	92.91 39	93.34 103

原ACMM1				84.42 110	93.21 47	64.27 164		93.40 60	65.39 232	79.51 70	92.50 91	58.11 104	96.69 85	65.27 182	93.96 23	92.32 130

MP-MVS		85.02 46	84.97 45	85.17 92	92.60 61	64.27 164	93.24 69	92.27 99	73.13 129	79.63 69	94.43 53	61.90 75	97.17 58	85.00 40	92.56 43	94.06 87

PGM-MVS		83.25 68	82.70 70	84.92 96	92.81 58	64.07 166	90.44 167	92.20 105	71.28 171	77.23 94	94.43 53	55.17 139	97.31 51	79.33 75	91.38 59	93.37 102

HSP-MVS		90.38 2	91.89 1	85.84 68	92.83 55	64.03 167	93.06 74	94.52 29	82.19 19	93.65 1	96.15 10	85.89 1	97.19 57	91.02 8	97.75 1	96.29 16

CP-MVS		83.71 66	83.40 59	84.65 104	93.14 49	63.84 168	94.59 33	92.28 98	71.03 173	77.41 91	94.92 44	55.21 138	96.19 93	81.32 66	90.70 66	93.91 93

OPM-MVS		79.00 131	78.09 125	81.73 171	83.52 210	63.83 169	91.64 135	90.30 170	76.36 79	71.97 142	89.93 126	46.30 229	95.17 126	75.10 101	77.70 152	86.19 209

XVS		83.87 62	83.47 55	85.05 94	93.22 45	63.78 170	92.92 80	92.66 88	73.99 111	78.18 82	94.31 62	55.25 135	97.41 45	79.16 76	91.58 56	93.95 91

X-MVStestdata		76.86 170	74.13 183	85.05 94	93.22 45	63.78 170	92.92 80	92.66 88	73.99 111	78.18 82	10.19 338	55.25 135	97.41 45	79.16 76	91.58 56	93.95 91

TESTMET0.1,1		82.41 80	81.98 76	83.72 123	88.08 150	63.74 172	92.70 86	93.77 44	79.30 38	77.61 89	87.57 149	58.19 103	94.08 171	73.91 109	86.68 92	93.33 105

BH-untuned		78.68 139	77.08 141	83.48 129	89.84 115	63.74 172	92.70 86	88.59 226	71.57 166	66.83 206	88.65 134	51.75 184	95.39 123	59.03 225	84.77 106	91.32 145

MSDG		69.54 242	65.73 246	80.96 191	85.11 189	63.71 174	84.19 246	83.28 287	56.95 281	54.50 267	84.03 184	31.50 294	96.03 100	42.87 280	69.13 206	83.14 255

thres600view7		78.00 148	76.66 146	82.03 168	91.93 74	63.69 175	91.30 147	96.33 5	72.43 140	70.46 152	87.89 146	60.31 87	94.92 132	42.64 282	76.64 161	87.48 182

PatchT		69.11 245	65.37 250	80.32 197	82.07 221	63.68 176	67.96 314	87.62 241	50.86 298	69.37 170	65.18 308	57.09 111	88.53 275	41.59 285	66.60 222	88.74 167

HQP5-MVS						63.66 177

HQP-MVS		81.14 96	80.64 91	82.64 141	87.54 158	63.66 177	94.06 43	91.70 124	79.80 33	74.18 115	90.30 120	51.63 186	95.61 116	77.63 88	78.90 142	88.63 168

EI-MVSNet-Vis-set		83.77 64	83.67 52	84.06 115	92.79 59	63.56 179	91.76 128	94.81 22	79.65 36	77.87 84	94.09 64	63.35 65	97.90 29	79.35 74	79.36 138	90.74 150

TAMVS		80.37 106	79.45 107	83.13 133	85.14 188	63.37 180	91.23 149	90.76 154	74.81 98	72.65 130	88.49 135	60.63 86	92.95 200	69.41 146	81.95 127	93.08 113

ACMH	63.93 17	68.62 246	64.81 252	80.03 203	85.22 187	63.25 181	87.72 218	84.66 274	60.83 262	51.57 285	79.43 247	27.29 306	94.96 129	41.76 283	64.84 238	81.88 265
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
Regformer-4		85.45 43	85.69 39	84.73 101	94.17 29	63.23 182	92.95 78	94.83 20	80.66 29	81.29 55	95.04 37	65.12 47	98.08 26	82.74 53	84.36 109	92.88 120

EI-MVSNet-UG-set		83.14 70	82.96 63	83.67 125	92.28 67	63.19 183	91.38 142	94.68 25	79.22 40	76.60 99	93.75 69	62.64 71	97.76 31	78.07 85	78.01 149	90.05 157

NP-MVS					87.41 161	63.04 184					90.30 120

IterMVS		72.65 216	70.83 214	78.09 240	82.17 219	62.96 185	87.64 220	86.28 260	71.56 167	60.44 238	78.85 250	45.42 234	86.66 285	63.30 196	61.83 255	84.65 238
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EG-PatchMatch MVS		68.55 247	65.41 249	77.96 241	78.69 274	62.93 186	89.86 184	89.17 209	60.55 263	50.27 290	77.73 255	22.60 314	94.06 172	47.18 267	72.65 186	76.88 305

DP-MVS		69.90 237	66.48 243	80.14 200	95.36 13	62.93 186	89.56 186	76.11 305	50.27 299	57.69 256	85.23 174	39.68 255	95.73 110	33.35 310	71.05 197	81.78 266

mPP-MVS		82.96 74	82.44 71	84.52 108	92.83 55	62.92 188	92.76 83	91.85 118	71.52 168	75.61 106	94.24 63	53.48 170	96.99 70	78.97 79	90.73 65	93.64 98

ACMMP		81.49 92	80.67 90	83.93 117	91.71 81	62.90 189	92.13 102	92.22 104	71.79 160	71.68 146	93.49 74	50.32 192	96.96 73	78.47 81	84.22 116	91.93 136
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
HPM-MVS		83.25 68	82.95 64	84.17 113	92.25 68	62.88 190	90.91 156	91.86 117	70.30 188	77.12 95	93.96 67	56.75 120	96.28 91	82.04 58	91.34 61	93.34 103

MVS_111021_LR		82.02 88	81.52 81	83.51 128	88.42 145	62.88 190	89.77 185	88.93 219	76.78 73	75.55 107	93.10 77	50.31 193	95.38 124	83.82 49	87.02 88	92.26 133

IterMVS-LS		76.49 175	75.18 171	80.43 196	84.49 196	62.74 192	90.64 164	88.80 221	72.40 141	65.16 211	81.72 215	60.98 80	92.27 224	67.74 157	64.65 241	86.29 207
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		78.97 132	78.22 124	81.25 179	85.33 185	62.73 193	89.53 189	93.21 66	72.39 142	72.14 140	90.13 123	60.99 79	94.72 138	67.73 158	72.49 187	86.29 207

CHOSEN 280x420		77.35 159	76.95 144	78.55 232	87.07 165	62.68 194	69.71 308	82.95 290	68.80 202	71.48 147	87.27 157	66.03 39	84.00 297	76.47 94	82.81 122	88.95 165

HQP_MVS		80.34 107	79.75 101	82.12 163	86.94 166	62.42 195	93.13 72	91.31 138	78.81 49	72.53 133	89.14 131	50.66 190	95.55 120	76.74 91	78.53 146	88.39 173

plane_prior						62.42 195	93.85 55		79.38 37						78.80 144

plane_prior6					87.23 162	62.32 197						50.66 190

PVSNet_0	68.08 15	71.81 219	68.32 233	82.27 157	84.68 192	62.31 198	88.68 201	90.31 169	75.84 82	57.93 253	80.65 231	37.85 267	94.19 163	69.94 141	29.05 326	90.31 155

WR-MVS		76.76 173	75.74 158	79.82 209	84.60 193	62.27 199	92.60 91	92.51 94	76.06 80	67.87 195	85.34 172	56.76 118	90.24 251	62.20 209	63.69 248	86.94 199

NR-MVSNet		76.05 181	74.59 175	80.44 195	82.96 215	62.18 200	90.83 158	91.73 121	77.12 67	60.96 237	86.35 162	59.28 95	91.80 232	60.74 216	61.34 261	87.35 191

view600		76.93 166	75.50 166	81.23 180	91.44 88	62.00 201	89.94 179	96.56 1	70.68 179	68.54 184	87.31 152	60.79 81	94.19 163	38.90 295	75.31 168	87.48 182

view800		76.93 166	75.50 166	81.23 180	91.44 88	62.00 201	89.94 179	96.56 1	70.68 179	68.54 184	87.31 152	60.79 81	94.19 163	38.90 295	75.31 168	87.48 182

conf0.05thres1000		76.93 166	75.50 166	81.23 180	91.44 88	62.00 201	89.94 179	96.56 1	70.68 179	68.54 184	87.31 152	60.79 81	94.19 163	38.90 295	75.31 168	87.48 182

tfpn		76.93 166	75.50 166	81.23 180	91.44 88	62.00 201	89.94 179	96.56 1	70.68 179	68.54 184	87.31 152	60.79 81	94.19 163	38.90 295	75.31 168	87.48 182

plane_prior3						61.95 205			79.09 44	72.53 133

Vis-MVSNet		80.92 100	79.98 98	83.74 120	88.48 142	61.80 206	93.44 66	88.26 234	73.96 114	77.73 85	91.76 104	49.94 197	94.76 135	65.84 177	90.37 70	94.65 62
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
CNLPA		74.31 201	72.30 204	80.32 197	91.49 87	61.66 207	90.85 157	80.72 297	56.67 284	63.85 222	90.64 114	46.75 223	90.84 245	53.79 243	75.99 165	88.47 172

test222					89.77 116	61.60 208	89.55 187	89.42 201	56.83 283	77.28 93	92.43 94	52.76 175			91.14 63	93.09 112

plane_prior7					86.94 166	61.51 209

UGNet		79.87 117	78.68 117	83.45 130	89.96 112	61.51 209	92.13 102	90.79 152	76.83 72	78.85 78	86.33 164	38.16 262	96.17 94	67.93 156	87.17 87	92.67 122
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
test-LLR		80.10 112	79.56 104	81.72 172	86.93 168	61.17 211	92.70 86	91.54 128	71.51 169	75.62 104	86.94 158	53.83 162	92.38 220	72.21 120	84.76 107	91.60 139

test-mter		79.96 115	79.38 110	81.72 172	86.93 168	61.17 211	92.70 86	91.54 128	73.85 116	75.62 104	86.94 158	49.84 199	92.38 220	72.21 120	84.76 107	91.60 139

TR-MVS		78.77 137	77.37 139	82.95 134	90.49 104	60.88 213	93.67 58	90.07 180	70.08 190	74.51 114	91.37 108	45.69 231	95.70 115	60.12 220	80.32 133	92.29 131

UniMVSNet (Re)		77.58 153	76.78 145	79.98 204	84.11 203	60.80 214	91.76 128	93.17 71	76.56 77	69.93 162	84.78 179	63.32 66	92.36 222	64.89 184	62.51 251	86.78 201

1112_ss		80.56 103	79.83 100	82.77 136	88.65 138	60.78 215	92.29 98	88.36 230	72.58 138	72.46 136	94.95 40	65.09 48	93.42 193	66.38 170	77.71 151	94.10 83

v7n		71.31 224	68.65 227	79.28 222	76.40 287	60.77 216	86.71 234	89.45 199	64.17 240	58.77 249	78.24 251	44.59 239	93.54 190	57.76 230	61.75 257	83.52 247

test_0402		64.54 268	61.09 273	74.92 263	84.10 204	60.75 217	87.95 211	79.71 300	52.03 294	52.41 281	77.20 259	32.21 291	91.64 235	23.14 325	61.03 262	72.36 312

旧先验1					91.94 73	60.74 218		91.50 131			94.36 55	65.23 46			91.84 51	94.55 64

ADS-MVSNet2		66.90 258	63.44 261	77.26 251	88.06 151	60.70 219	68.01 312	75.56 310	57.57 276	64.48 216	69.87 299	38.68 256	84.10 293	40.87 287	67.89 215	86.97 196

semantic-postprocess				76.32 256	81.48 224	60.67 220		85.99 266	66.17 227	59.50 242	78.88 249	45.51 233	83.65 299	62.58 207	61.93 254	84.63 239

TranMVSNet+NR-MVSNet		75.86 184	74.52 178	79.89 207	82.44 218	60.64 221	91.37 143	91.37 137	76.63 75	67.65 197	86.21 165	52.37 180	91.55 237	61.84 211	60.81 264	87.48 182

pmmvs5		73.35 208	71.52 210	78.86 230	78.64 275	60.61 222	91.08 154	86.90 248	67.69 215	63.32 226	83.64 187	44.33 240	90.53 246	62.04 210	66.02 224	85.46 229

MDA-MVSNet_test_wron		63.78 274	60.16 275	74.64 264	78.15 278	60.41 223	83.49 251	84.03 278	56.17 287	39.17 318	71.59 296	37.22 272	83.24 304	42.87 280	48.73 304	80.26 288

Test_1112_low_res		79.56 124	78.60 119	82.43 147	88.24 148	60.39 224	92.09 105	87.99 237	72.10 150	71.84 143	87.42 151	64.62 52	93.04 197	65.80 178	77.30 158	93.85 95

LP		56.71 289	51.64 293	71.91 287	80.08 257	60.33 225	61.72 320	75.61 309	43.87 317	43.76 310	60.30 316	30.46 299	84.05 294	22.94 326	46.06 309	71.34 314

UniMVSNet_NR-MVSNet		78.15 147	77.55 134	79.98 204	84.46 197	60.26 226	92.25 99	93.20 68	77.50 64	68.88 178	86.61 160	66.10 38	92.13 226	66.38 170	62.55 249	87.54 180

DU-MVS		76.86 170	75.84 156	79.91 206	82.96 215	60.26 226	91.26 148	91.54 128	76.46 78	68.88 178	86.35 162	56.16 127	92.13 226	66.38 170	62.55 249	87.35 191

EPP-MVSNet		81.79 90	81.52 81	82.61 142	88.77 137	60.21 228	93.02 76	93.66 49	68.52 208	72.90 127	90.39 119	72.19 11	94.96 129	74.93 105	79.29 140	92.67 122

YYNet1		63.76 275	60.14 276	74.62 265	78.06 279	60.19 229	83.46 253	83.99 282	56.18 286	39.25 317	71.56 297	37.18 273	83.34 302	42.90 279	48.70 305	80.32 287

v52		69.80 239	67.01 242	78.15 238	71.84 301	60.10 230	82.02 263	87.39 242	64.48 237	57.80 254	75.97 268	41.47 249	92.90 205	63.00 200	59.13 270	81.45 270

V4		69.80 239	67.02 241	78.15 238	71.86 300	60.10 230	82.02 263	87.39 242	64.48 237	57.78 255	75.98 267	41.49 248	92.90 205	63.00 200	59.16 269	81.44 271

IS-MVSNet		80.14 111	79.41 108	82.33 154	87.91 154	60.08 232	91.97 112	88.27 233	72.90 134	71.44 148	91.73 106	61.44 78	93.66 189	62.47 208	86.53 95	93.24 107

HPM-MVS_fast		80.25 108	79.55 106	82.33 154	91.55 85	59.95 233	91.32 146	89.16 210	65.23 235	74.71 113	93.07 81	47.81 217	95.74 109	74.87 108	88.23 80	91.31 146

MDTV_nov1_ep13_2view						59.90 234	80.13 281		67.65 217	72.79 128		54.33 157		59.83 221		92.58 125

CPTT-MVS		79.59 123	79.16 114	80.89 193	91.54 86	59.80 235	92.10 104	88.54 228	60.42 264	72.96 125	93.28 76	48.27 211	92.80 207	78.89 80	86.50 96	90.06 156

ACMP	71.68 10	75.58 189	74.23 182	79.62 213	84.97 190	59.64 236	90.80 159	89.07 215	70.39 187	62.95 229	87.30 156	38.28 261	93.87 183	72.89 112	71.45 194	85.36 231
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
pmmvs-eth3d		65.53 265	62.32 269	75.19 261	69.39 310	59.59 237	82.80 260	83.43 284	62.52 252	51.30 287	72.49 281	32.86 286	87.16 284	55.32 238	50.73 296	78.83 298

sss		82.71 77	82.38 72	83.73 122	89.25 125	59.58 238	92.24 100	94.89 18	77.96 57	79.86 66	92.38 95	56.70 121	97.05 62	77.26 90	80.86 132	94.55 64

Fast-Effi-MVS+-dtu		75.04 194	73.37 193	80.07 202	80.86 230	59.52 239	91.20 152	85.38 271	71.90 152	65.20 210	84.84 178	41.46 250	92.97 199	66.50 169	72.96 183	87.73 179

FIs		79.47 125	79.41 108	79.67 211	85.95 180	59.40 240	91.68 132	93.94 39	78.06 56	68.96 177	88.28 139	66.61 34	91.77 233	66.20 173	74.99 172	87.82 178

LPG-MVS_test		75.82 185	74.58 176	79.56 215	84.31 200	59.37 241	90.44 167	89.73 192	69.49 194	64.86 212	88.42 136	38.65 258	94.30 158	72.56 116	72.76 184	85.01 234

LGP-MVS_train				79.56 215	84.31 200	59.37 241		89.73 192	69.49 194	64.86 212	88.42 136	38.65 258	94.30 158	72.56 116	72.76 184	85.01 234

Baseline_NR-MVSNet		73.99 204	72.83 197	77.48 246	80.78 231	59.29 243	91.79 125	84.55 275	68.85 201	68.99 176	80.70 229	56.16 127	92.04 229	62.67 206	60.98 263	81.11 279

PS-MVSNAJss		77.26 163	76.31 150	80.13 201	80.64 240	59.16 244	90.63 166	91.06 146	72.80 135	68.58 183	84.57 182	53.55 166	93.96 179	72.97 111	71.96 190	87.27 194

TransMVSNet (Re)		70.07 236	67.66 238	77.31 250	80.62 242	59.13 245	91.78 127	84.94 273	65.97 228	60.08 240	80.44 233	50.78 189	91.87 231	48.84 260	45.46 310	80.94 281

Patchmatch-test		65.86 263	60.94 274	80.62 194	83.75 206	58.83 246	58.91 325	75.26 312	44.50 315	50.95 289	77.09 263	58.81 100	87.90 279	35.13 307	64.03 244	95.12 48

v748		70.55 234	67.97 237	78.27 235	75.75 292	58.78 247	86.29 238	89.25 206	65.12 236	56.66 260	77.17 261	45.05 237	92.95 200	58.13 229	58.33 275	83.10 256

APD-MVS_3200maxsize		81.64 91	81.32 83	82.59 143	92.36 63	58.74 248	91.39 140	91.01 149	63.35 244	79.72 68	94.62 51	51.82 182	96.14 95	79.71 71	87.93 83	92.89 119

PLC	68.80 14	75.23 192	73.68 189	79.86 208	92.93 53	58.68 249	90.64 164	88.30 231	60.90 261	64.43 218	90.53 117	42.38 245	94.57 141	56.52 233	76.54 162	86.33 206
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
abl_6		79.82 118	79.20 113	81.70 174	89.85 114	58.34 250	88.47 204	90.07 180	62.56 251	77.71 86	93.08 79	47.65 219	96.78 81	77.94 86	85.45 103	89.99 158

DeepPCF-MVS	81.17 1	89.72 5	91.38 3	84.72 103	93.00 52	58.16 251	96.72 3	94.41 34	86.50 5	90.25 3	97.83 1	75.46 5	98.67 12	92.78 2	95.49 7	97.32 1

FMVSNet5		68.04 251	65.66 247	75.18 262	84.43 198	57.89 252	83.54 250	86.26 261	61.83 258	53.64 277	73.30 276	37.15 274	85.08 289	48.99 259	61.77 256	82.56 262

ACMM	69.62 13	74.34 200	72.73 198	79.17 227	84.25 202	57.87 253	90.36 170	89.93 185	63.17 247	65.64 209	86.04 168	37.79 268	94.10 169	65.89 176	71.52 193	85.55 228
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OpenMVS_ROB	61.12 18	66.39 260	62.92 265	76.80 254	76.51 286	57.77 254	89.22 192	83.41 285	55.48 288	53.86 275	77.84 254	26.28 309	93.95 180	34.90 308	68.76 208	78.68 299

UA-Net		80.02 114	79.65 102	81.11 186	89.33 123	57.72 255	86.33 237	89.00 218	77.44 65	81.01 56	89.15 130	59.33 94	95.90 103	61.01 215	84.28 114	89.73 161

testdata				81.34 178	89.02 131	57.72 255		89.84 187	58.65 274	85.32 29	94.09 64	57.03 113	93.28 194	69.34 147	90.56 69	93.03 114

pm-mvs1		72.89 211	71.09 213	78.26 236	79.10 271	57.62 257	90.80 159	89.30 204	67.66 216	62.91 230	81.78 214	49.11 207	92.95 200	60.29 219	58.89 273	84.22 240

XVG-OURS		74.25 202	72.46 203	79.63 212	78.45 276	57.59 258	80.33 277	87.39 242	63.86 242	68.76 180	89.62 129	40.50 253	91.72 234	69.00 149	74.25 174	89.58 162

OMC-MVS		78.67 141	77.91 130	80.95 192	85.76 184	57.40 259	88.49 203	88.67 223	73.85 116	72.43 137	92.10 99	49.29 203	94.55 143	72.73 115	77.89 150	90.91 149

XVG-OURS-SEG-HR		74.70 199	73.08 195	79.57 214	78.25 277	57.33 260	80.49 275	87.32 245	63.22 246	68.76 180	90.12 125	44.89 238	91.59 236	70.55 138	74.09 176	89.79 159

mvs-test1		78.74 138	77.95 128	81.14 185	83.22 211	57.13 261	93.96 48	87.78 239	75.42 87	72.68 129	90.80 113	45.08 235	94.54 144	75.08 102	77.49 155	91.74 138

ACMH+	65.35 16	67.65 253	64.55 254	76.96 252	84.59 194	57.10 262	88.08 209	80.79 296	58.59 275	53.00 280	81.09 227	26.63 308	92.95 200	46.51 268	61.69 259	80.82 282

MDA-MVSNet-bldmvs		61.54 280	57.70 281	73.05 275	79.53 263	57.00 263	83.08 258	81.23 293	57.57 276	34.91 321	72.45 283	32.79 287	86.26 288	35.81 305	41.95 314	75.89 307

MVS-HIRNet		60.25 282	55.55 288	74.35 267	84.37 199	56.57 264	71.64 303	74.11 314	34.44 324	45.54 304	42.24 326	31.11 297	89.81 265	40.36 290	76.10 164	76.67 306

PMMVS		81.98 89	82.04 75	81.78 170	89.76 117	56.17 265	91.13 153	90.69 155	77.96 57	80.09 64	93.57 72	46.33 228	94.99 128	81.41 64	87.46 86	94.17 78

LS3D		69.17 244	66.40 244	77.50 245	91.92 75	56.12 266	85.12 241	80.37 298	46.96 306	56.50 261	87.51 150	37.25 271	93.71 187	32.52 315	79.40 137	82.68 261

F-COLMAP		70.66 232	68.44 231	77.32 249	86.37 174	55.91 267	88.00 210	86.32 259	56.94 282	57.28 258	88.07 144	33.58 285	92.49 217	51.02 252	68.37 211	83.55 245

PatchMatch-RL		72.06 217	69.98 216	78.28 234	89.51 122	55.70 268	83.49 251	83.39 286	61.24 260	63.72 223	82.76 195	34.77 283	93.03 198	53.37 247	77.59 153	86.12 211

FC-MVSNet-test		77.99 149	78.08 126	77.70 242	84.89 191	55.51 269	90.27 172	93.75 46	76.87 70	66.80 207	87.59 148	65.71 44	90.23 252	62.89 203	73.94 177	87.37 189

USDC		67.43 257	64.51 255	76.19 257	77.94 280	55.29 270	78.38 291	85.00 272	73.17 128	48.36 295	80.37 234	21.23 316	92.48 218	52.15 249	64.02 245	80.81 283

Effi-MVS+-dtu		76.14 179	75.28 170	78.72 231	83.22 211	55.17 271	89.87 183	87.78 239	75.42 87	67.98 192	81.43 217	45.08 235	92.52 216	75.08 102	71.63 191	88.48 171

jajsoiax		73.05 209	71.51 211	77.67 243	77.46 282	54.83 272	88.81 199	90.04 182	69.13 198	62.85 231	83.51 189	31.16 296	92.75 209	70.83 134	69.80 199	85.43 230

anonymousdsp		71.14 227	69.37 222	76.45 255	72.95 297	54.71 273	84.19 246	88.88 220	61.92 256	62.15 235	79.77 242	38.14 263	91.44 243	68.90 151	67.45 218	83.21 253

mvs_tets		72.71 214	71.11 212	77.52 244	77.41 283	54.52 274	88.45 205	89.76 188	68.76 203	62.70 232	83.26 192	29.49 300	92.71 210	70.51 139	69.62 201	85.34 232

JIA-IIPM		66.06 262	62.45 268	76.88 253	81.42 227	54.45 275	57.49 326	88.67 223	49.36 301	63.86 221	46.86 322	56.06 130	90.25 249	49.53 258	68.83 207	85.95 218

Patchmatch-RL test		68.17 250	64.49 256	79.19 226	71.22 303	53.93 276	70.07 307	71.54 321	69.22 197	56.79 259	62.89 312	56.58 124	88.61 272	69.53 145	52.61 289	95.03 53

test_djsdf		73.76 207	72.56 201	77.39 248	77.00 285	53.93 276	89.07 197	90.69 155	65.80 229	63.92 220	82.03 210	43.14 243	92.67 212	72.83 113	68.53 210	85.57 227

pmmvs6		67.57 254	64.76 253	76.00 259	72.82 299	53.37 278	88.71 200	86.78 249	53.19 291	57.58 257	78.03 253	35.33 281	92.41 219	55.56 237	54.88 285	82.21 263

TinyColmap		60.32 281	56.42 287	72.00 286	78.78 272	53.18 279	78.36 292	75.64 308	52.30 293	41.59 316	75.82 270	14.76 325	88.35 276	35.84 304	54.71 286	74.46 309

COLMAP_ROB	57.96 20	62.98 277	59.65 277	72.98 276	81.44 226	53.00 280	83.75 248	75.53 311	48.34 304	48.81 294	81.40 219	24.14 310	90.30 248	32.95 312	60.52 266	75.65 308
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
XVG-ACMP-BASELINE		68.04 251	65.53 248	75.56 260	74.06 296	52.37 281	78.43 290	85.88 268	62.03 254	58.91 248	81.21 225	20.38 317	91.15 244	60.69 217	68.18 212	83.16 254

Vis-MVSNet (Re-imp)		79.24 128	79.57 103	78.24 237	88.46 143	52.29 282	90.41 169	89.12 212	74.24 106	69.13 173	91.91 101	65.77 43	90.09 258	59.00 226	88.09 82	92.33 129

TAPA-MVS	70.22 12	74.94 197	73.53 190	79.17 227	90.40 106	52.07 283	89.19 194	89.61 196	62.69 250	70.07 157	92.67 90	48.89 209	94.32 157	38.26 300	79.97 134	91.12 147
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
UnsupCasMVSNet_bld		61.60 279	57.71 280	73.29 274	68.73 312	51.64 284	78.61 289	89.05 216	57.20 280	46.11 299	61.96 314	28.70 302	88.60 273	50.08 256	38.90 319	79.63 293

LTVRE_ROB	59.60 19	66.27 261	63.54 260	74.45 266	84.00 205	51.55 285	67.08 315	83.53 283	58.78 273	54.94 265	80.31 235	34.54 284	93.23 195	40.64 289	68.03 213	78.58 300
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
WR-MVS_H		70.59 233	69.94 217	72.53 279	81.03 228	51.43 286	87.35 224	92.03 111	67.38 220	60.23 239	80.70 229	55.84 134	83.45 301	46.33 269	58.58 274	82.72 260

AllTest		61.66 278	58.06 279	72.46 280	79.57 261	51.42 287	80.17 280	68.61 324	51.25 296	45.88 300	81.23 221	19.86 318	86.58 286	38.98 293	57.01 278	79.39 294

TestCases				72.46 280	79.57 261	51.42 287		68.61 324	51.25 296	45.88 300	81.23 221	19.86 318	86.58 286	38.98 293	57.01 278	79.39 294

CP-MVSNet		70.50 235	69.91 218	72.26 282	80.71 234	51.00 289	87.23 225	90.30 170	67.84 214	59.64 241	82.69 196	50.23 195	82.30 308	51.28 251	59.28 268	83.46 249

pmmvs3		55.51 292	51.50 295	67.53 296	57.90 325	50.93 290	80.37 276	73.66 315	40.63 321	44.15 309	64.75 310	16.30 321	78.97 319	44.77 275	40.98 317	72.69 311

PS-CasMVS		69.86 238	69.13 225	72.07 285	80.35 248	50.57 291	87.02 228	89.75 189	67.27 221	59.19 244	82.28 202	46.58 226	82.24 309	50.69 253	59.02 271	83.39 251

CMPMVS	48.56 21	66.77 259	64.41 257	73.84 270	70.65 306	50.31 292	77.79 295	85.73 270	45.54 311	44.76 306	82.14 206	35.40 280	90.14 255	63.18 198	74.54 173	81.07 280
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
UnsupCasMVSNet_eth		65.79 264	63.10 263	73.88 269	70.71 305	50.29 293	81.09 271	89.88 186	72.58 138	49.25 293	74.77 274	32.57 289	87.43 282	55.96 236	41.04 316	83.90 243

SixPastTwentyTwo		64.92 266	61.78 272	74.34 268	78.74 273	49.76 294	83.42 254	79.51 301	62.86 249	50.27 290	77.35 256	30.92 298	90.49 247	45.89 271	47.06 307	82.78 257

PEN-MVS		69.46 243	68.56 229	72.17 284	79.27 266	49.71 295	86.90 231	89.24 207	67.24 222	59.08 245	82.51 197	47.23 221	83.54 300	48.42 262	57.12 276	83.25 252

EPNet_dtu		78.80 136	79.26 112	77.43 247	88.06 151	49.71 295	91.96 113	91.95 115	77.67 61	76.56 100	91.28 109	58.51 101	90.20 253	56.37 234	80.95 131	92.39 128
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
K. test v3		63.09 276	59.61 278	73.53 272	76.26 288	49.38 297	83.27 255	77.15 304	64.35 239	47.77 296	72.32 286	28.73 301	87.79 281	49.93 257	36.69 321	83.41 250

DTE-MVSNet		68.46 249	67.33 239	71.87 288	77.94 280	49.00 298	86.16 239	88.58 227	66.36 226	58.19 251	82.21 205	46.36 227	83.87 298	44.97 274	55.17 283	82.73 259

LCM-MVSNet-Re		72.93 210	71.84 207	76.18 258	88.49 141	48.02 299	80.07 282	70.17 322	73.96 114	52.25 282	80.09 240	49.98 196	88.24 277	67.35 160	84.23 115	92.28 132

test0.0.03 1		72.76 213	72.71 199	72.88 277	80.25 254	47.99 300	91.22 150	89.45 199	71.51 169	62.51 234	87.66 147	53.83 162	85.06 290	50.16 255	67.84 217	85.58 226

lessismore_v0				73.72 271	72.93 298	47.83 301		61.72 332		45.86 302	73.76 275	28.63 303	89.81 265	47.75 266	31.37 325	83.53 246

Anonymous20231206		67.53 255	65.78 245	72.79 278	74.95 293	47.59 302	88.23 207	87.32 245	61.75 259	58.07 252	77.29 258	37.79 268	87.29 283	42.91 278	63.71 247	83.48 248

OurMVSNet-221017-0		64.68 267	62.17 270	72.21 283	76.08 290	47.35 303	80.67 274	81.02 295	56.19 285	51.60 284	79.66 244	27.05 307	88.56 274	53.60 245	53.63 288	80.71 284

ITE_SJBPF				70.43 290	74.44 294	47.06 304		77.32 303	60.16 266	54.04 273	83.53 188	23.30 313	84.01 296	43.07 277	61.58 260	80.21 289

TDRefinement		55.28 293	51.58 294	66.39 300	59.53 324	46.15 305	76.23 297	72.80 316	44.60 314	42.49 312	76.28 266	15.29 323	82.39 307	33.20 311	43.75 312	70.62 316

RPSCF		64.24 270	61.98 271	71.01 289	76.10 289	45.00 306	75.83 298	75.94 307	46.94 307	58.96 247	84.59 181	31.40 295	82.00 310	47.76 265	60.33 267	86.04 217

new-patchmatchnet		59.30 286	56.48 286	67.79 295	65.86 314	44.19 307	82.47 261	81.77 291	59.94 267	43.65 311	66.20 305	27.67 304	81.68 311	39.34 292	41.40 315	77.50 304

MIMVSNet1		60.16 283	57.33 283	68.67 293	69.71 309	44.13 308	78.92 288	84.21 276	55.05 289	44.63 307	71.85 290	23.91 311	81.54 312	32.63 314	55.03 284	80.35 286

CVMVSNet		74.04 203	74.27 181	73.33 273	85.33 185	43.94 309	89.53 189	88.39 229	54.33 290	70.37 153	90.13 123	49.17 205	84.05 294	61.83 212	79.36 138	91.99 135

testpf		57.17 287	56.93 284	57.88 309	79.13 270	42.40 310	34.23 332	85.97 267	52.64 292	47.66 298	66.50 303	36.33 279	79.65 316	53.60 245	56.31 281	51.60 325

Anonymous20231211		53.57 295	49.43 297	66.00 301	65.01 315	42.08 311	80.95 273	72.60 317	38.46 322	41.65 315	64.48 311	15.72 322	84.23 292	25.78 322	40.24 318	71.68 313

no-one		44.13 302	38.39 303	61.34 306	45.91 333	41.94 312	61.67 321	75.07 313	45.05 313	20.07 327	40.68 329	11.58 328	79.82 315	30.18 318	15.30 329	62.26 322

PM-MVS		59.40 284	56.59 285	67.84 294	63.63 316	41.86 313	76.76 296	63.22 330	59.01 272	51.07 288	72.27 287	11.72 327	83.25 303	61.34 213	50.28 302	78.39 301

ambc				69.61 291	61.38 322	41.35 314	49.07 329	85.86 269		50.18 292	66.40 304	10.16 330	88.14 278	45.73 272	44.20 311	79.32 296

new_pmnet		49.31 297	46.44 299	57.93 308	62.84 318	40.74 315	68.47 311	62.96 331	36.48 323	35.09 320	57.81 318	14.97 324	72.18 324	32.86 313	46.44 308	60.88 323

testgi		64.48 269	62.87 266	69.31 292	71.24 302	40.62 316	85.49 240	79.92 299	65.36 233	54.18 272	83.49 190	23.74 312	84.55 291	41.60 284	60.79 265	82.77 258

test20.03		63.83 273	62.65 267	67.38 297	70.58 307	39.94 317	86.57 236	84.17 277	63.29 245	51.86 283	77.30 257	37.09 275	82.47 306	38.87 299	54.13 287	79.73 292

LF4IMVS		54.01 294	52.12 292	59.69 307	62.41 319	39.91 318	68.59 310	68.28 326	42.96 318	44.55 308	75.18 271	14.09 326	68.39 327	41.36 286	51.68 293	70.78 315

test2356		64.16 271	63.28 262	66.81 299	69.37 311	39.86 319	87.76 217	86.02 265	59.83 268	53.54 278	73.23 277	34.94 282	80.67 313	39.66 291	65.20 231	79.89 290

Gipuma		34.91 306	31.44 308	45.30 318	70.99 304	39.64 320	19.85 335	72.56 318	20.10 331	16.16 331	21.47 334	5.08 338	71.16 326	13.07 332	43.70 313	25.08 331
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
EU-MVSNet		64.01 272	63.01 264	67.02 298	74.40 295	38.86 321	83.27 255	86.19 262	45.11 312	54.27 270	81.15 226	36.91 277	80.01 314	48.79 261	57.02 277	82.19 264

testus		59.36 285	57.51 282	64.90 302	66.72 313	37.56 322	84.98 242	81.09 294	57.46 279	47.72 297	72.76 278	11.43 329	78.78 320	36.56 301	58.91 272	78.36 302

FPMVS		45.64 301	43.10 302	53.23 315	51.42 328	36.46 323	64.97 317	71.91 319	29.13 326	27.53 324	61.55 315	9.83 331	65.01 331	16.00 331	55.58 282	58.22 324

test1235678		55.73 291	52.74 291	64.68 303	60.16 323	35.56 324	81.65 267	81.46 292	51.27 295	38.93 319	62.82 313	17.44 320	78.58 321	30.87 317	50.09 303	79.89 290

ANet_high		40.27 304	35.20 305	55.47 312	34.74 337	34.47 325	63.84 319	71.56 320	48.42 303	18.80 329	41.08 327	9.52 332	64.45 332	20.18 328	8.66 336	67.49 320

1111		56.66 290	54.98 289	61.69 305	61.99 320	31.38 326	79.81 285	83.17 288	45.66 309	41.94 313	65.44 306	41.50 246	79.56 317	27.64 319	47.68 306	74.14 310

.test1245		46.52 300	49.68 296	37.02 322	61.99 320	31.38 326	79.81 285	83.17 288	45.66 309	41.94 313	65.44 306	41.50 246	79.56 317	27.64 319	0.01 338	0.13 337

LCM-MVSNet		40.54 303	35.79 304	54.76 314	36.92 336	30.81 328	51.41 327	69.02 323	22.07 328	24.63 325	45.37 324	4.56 339	65.81 329	33.67 309	34.50 323	67.67 319

testmv		46.98 299	43.53 301	57.35 310	47.75 331	30.41 329	74.99 300	77.69 302	42.84 319	28.03 323	53.36 319	8.18 334	71.18 325	24.36 324	34.55 322	70.46 317

DSMNet-mixed		56.78 288	54.44 290	63.79 304	63.21 317	29.44 330	64.43 318	64.10 329	42.12 320	51.32 286	71.60 295	31.76 292	75.04 323	36.23 303	65.20 231	86.87 200

PNet_i23d		32.77 307	29.98 309	41.11 320	48.05 329	29.17 331	65.82 316	50.02 335	21.42 329	14.74 332	37.19 330	1.11 343	55.11 334	19.75 329	11.77 331	39.06 327

wuykxyi23d		29.03 310	23.09 315	46.84 317	31.67 339	28.82 332	43.46 330	57.72 333	14.39 334	7.52 337	20.84 335	0.64 344	60.29 333	21.57 327	10.04 333	51.40 326

PMVS	26.43 22	31.84 308	28.16 310	42.89 319	25.87 340	27.58 333	50.92 328	49.78 336	21.37 330	14.17 333	40.81 328	2.01 341	66.62 328	9.61 334	38.88 320	34.49 330
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	24.84 23	24.35 312	19.77 316	38.09 321	34.56 338	26.92 334	26.57 333	38.87 339	11.73 335	11.37 334	27.44 331	1.37 342	50.42 335	11.41 333	14.60 330	36.93 328
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PMMVS2		37.93 305	33.61 306	50.92 316	46.31 332	24.76 335	60.55 324	50.05 334	28.94 327	20.93 326	47.59 321	4.41 340	65.13 330	25.14 323	18.55 328	62.87 321

DeepMVS_CX				34.71 323	51.45 327	24.73 336		28.48 342	31.46 325	17.49 330	52.75 320	5.80 337	42.60 338	18.18 330	19.42 327	36.81 329

test12356		47.51 298	44.82 300	55.56 311	52.53 326	21.09 337	71.45 304	76.03 306	44.14 316	30.69 322	58.18 317	9.01 333	76.14 322	26.95 321	34.43 324	69.46 318

wuyk23d		11.30 316	10.95 317	12.33 328	48.05 329	19.89 338	25.89 334	1.92 344	3.58 336	3.12 338	1.37 339	0.64 344	15.77 340	6.23 337	7.77 337	1.35 335

E-PMN		24.61 311	24.00 312	26.45 325	43.74 334	18.44 339	60.86 322	39.66 337	15.11 332	9.53 335	22.10 333	6.52 336	46.94 336	8.31 335	10.14 332	13.98 333

EMVS		23.76 313	23.20 314	25.46 326	41.52 335	16.90 340	60.56 323	38.79 340	14.62 333	8.99 336	20.24 337	7.35 335	45.82 337	7.25 336	9.46 334	13.64 334

tmp_tt		22.26 314	23.75 313	17.80 327	5.23 341	12.06 341	35.26 331	39.48 338	2.82 337	18.94 328	44.20 325	22.23 315	24.64 339	36.30 302	9.31 335	16.69 332

N_pmnet		50.55 296	49.11 298	54.88 313	77.17 284	4.02 342	84.36 245	2.00 343	48.59 302	45.86 302	68.82 301	32.22 290	82.80 305	31.58 316	51.38 294	77.81 303

test123		6.92 319	9.21 320	0.08 329	0.03 343	0.05 343	81.65 267	0.01 346	0.02 339	0.14 340	0.85 341	0.03 346	0.02 341	0.12 339	0.00 340	0.16 336

testmvs		7.23 318	9.62 319	0.06 330	0.04 342	0.02 344	84.98 242	0.02 345	0.03 338	0.18 339	1.21 340	0.01 347	0.02 341	0.14 338	0.01 338	0.13 337

cdsmvs_eth3d_5k		19.86 315	26.47 311	0.00 331	0.00 344	0.00 345	0.00 336	93.45 57	0.00 340	0.00 341	95.27 30	49.56 200	0.00 343	0.00 340	0.00 340	0.00 339

pcd_1.5k_mvsjas		4.46 320	5.95 321	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	53.55 166	0.00 343	0.00 340	0.00 340	0.00 339

pcd1.5k->3k		31.17 309	31.85 307	29.12 324	81.48 224	0.00 345	0.00 336	91.79 120	0.00 340	0.00 341	0.00 342	41.05 252	0.00 343	0.00 340	72.34 189	87.36 190

sosnet-low-res		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 340	0.00 339

sosnet		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 340	0.00 339

uncertanet		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 340	0.00 339

Regformer		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 340	0.00 339

ab-mvs-re		7.91 317	10.55 318	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	94.95 40	0.00 348	0.00 343	0.00 340	0.00 340	0.00 339

uanet		0.00 321	0.00 322	0.00 331	0.00 344	0.00 345	0.00 336	0.00 347	0.00 340	0.00 341	0.00 342	0.00 348	0.00 343	0.00 340	0.00 340	0.00 339

ESAPD								94.66 26

sam_mvs1												57.85 105

sam_mvs												54.91 149

MTGPA								92.23 100

test_post1							78.95 287				20.70 336	53.05 172	91.50 242	60.43 218

test_post											23.01 332	56.49 125	92.67 212

patchmatchnet-post											67.62 302	57.62 108	90.25 249

MTMP								32.52 341

test9_res														89.41 10	94.96 8	95.29 38

agg_prior2														86.41 31	94.75 16	95.33 35

test_prior2							95.10 27		75.40 89	85.25 30	95.61 21	67.94 24		87.47 23	94.77 13

旧先验2							92.00 111		59.37 271	87.54 14			93.47 192	75.39 99

新几何2							91.41 139

无先验							92.71 85	92.61 91	62.03 254				97.01 65	66.63 166		93.97 90

原ACMM2							92.01 109

testdata2													96.09 97	61.26 214

segment_acmp												65.94 40

testdata1							89.21 193		77.55 63

plane_prior5								91.31 138					95.55 120	76.74 91	78.53 146	88.39 173

plane_prior4											89.14 131

plane_prior2							93.13 72		78.81 49

plane_prior1					87.15 163

n2								0.00 347

nn								0.00 347

door-mid								66.01 328

test11								93.01 76

door								66.57 327

HQP-NCC					87.54 158		94.06 43		79.80 33	74.18 115

ACMP_Plane					87.54 158		94.06 43		79.80 33	74.18 115

BP-MVS														77.63 88

HQP4-MVS										74.18 115			95.61 116			88.63 168

HQP3-MVS								91.70 124							78.90 142

HQP2-MVS												51.63 186

ACMMP++_ref															71.63 191

ACMMP++															69.72 200

Test By Simon												54.21 159

High-resolution multi-view benchmark