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.

HPM-MVS++		89.02 3	89.15 3	88.63 1	95.01 2	76.03 1	92.38 14	92.85 34	80.26 14	87.78 12	94.27 16	75.89 8	96.81 8	87.45 9	96.44 1	93.05 63

test_part1								94.09 2				81.79 1			96.38 2	93.74 36

ACMMP_Plus		88.05 9	88.08 9	87.94 11	93.70 24	73.05 18	90.86 33	93.59 9	76.27 66	88.14 8	95.09 3	71.06 37	96.67 13	87.67 6	96.37 3	94.09 22

MP-MVS-pluss		87.67 12	87.72 11	87.54 26	93.64 27	72.04 39	89.80 55	93.50 11	75.17 83	86.34 17	95.29 2	70.86 38	96.00 33	88.78 3	96.04 4	94.58 7
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
CNVR-MVS		88.93 4	89.13 4	88.33 3	94.77 3	73.82 6	90.51 39	93.00 26	80.90 10	88.06 10	94.06 24	76.43 5	96.84 7	88.48 4	95.99 5	94.34 15

PHI-MVS		86.43 31	86.17 33	87.24 30	90.88 68	70.96 48	92.27 17	94.07 3	72.45 136	85.22 26	91.90 58	69.47 51	96.42 22	83.28 34	95.94 6	94.35 14

test_prior3		86.73 26	86.86 25	86.33 46	92.61 47	69.59 72	88.85 79	92.97 31	75.41 76	84.91 30	93.54 29	74.28 18	95.48 43	83.31 32	95.86 7	93.91 30

test_prior2							88.85 79		75.41 76	84.91 30	93.54 29	74.28 18		83.31 32	95.86 7

SteuartSystems-ACMMP		88.72 5	88.86 5	88.32 4	92.14 53	72.96 19	93.73 3	93.67 8	80.19 15	88.10 9	94.80 4	73.76 21	97.11 3	87.51 8	95.82 9	94.90 4
Skip Steuart: Steuart Systems R&D Blog.
DeepPCF-MVS	80.84 1	88.10 6	88.56 6	86.73 39	92.24 51	69.03 80	89.57 62	93.39 15	77.53 39	89.79 5	94.12 22	78.98 2	96.58 20	85.66 12	95.72 10	94.58 7

train_agg		86.43 31	86.20 31	87.13 33	93.26 34	72.96 19	88.75 84	91.89 69	68.69 196	85.00 28	93.10 39	74.43 14	95.41 48	84.97 15	95.71 11	93.02 64

agg_prior3		86.16 37	85.85 38	87.10 34	93.31 31	72.86 23	88.77 82	91.68 79	68.29 202	84.26 43	92.83 47	72.83 26	95.42 47	84.97 15	95.71 11	93.02 64

test9_res														84.90 17	95.70 13	92.87 69

APDe-MVS		89.15 2	89.63 2	87.73 20	94.49 9	71.69 42	93.83 2	93.96 4	75.70 72	91.06 3	96.03 1	76.84 4	97.03 5	89.09 2	95.65 14	94.47 11

agg_prior2														82.91 39	95.45 15	92.70 70

CDPH-MVS		85.76 41	85.29 46	87.17 32	93.49 30	71.08 46	88.58 90	92.42 48	68.32 201	84.61 37	93.48 31	72.32 30	96.15 30	79.00 60	95.43 16	94.28 18

DeepC-MVS	79.81 2	87.08 24	86.88 24	87.69 24	91.16 63	72.32 36	90.31 45	93.94 5	77.12 44	82.82 60	94.23 18	72.13 32	97.09 4	84.83 20	95.37 17	93.65 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MPTG		87.53 14	87.41 15	87.90 15	94.18 20	74.25 2	90.23 47	92.02 60	79.45 19	85.88 19	94.80 4	68.07 58	96.21 27	86.69 10	95.34 18	93.23 55

MTAPA		87.23 20	87.00 20	87.90 15	94.18 20	74.25 2	86.58 160	92.02 60	79.45 19	85.88 19	94.80 4	68.07 58	96.21 27	86.69 10	95.34 18	93.23 55

agg_prior1		86.22 36	86.09 35	86.62 42	92.85 42	71.94 40	88.59 89	91.78 75	68.96 193	84.41 40	93.18 38	74.94 10	94.93 65	84.75 22	95.33 20	93.01 66

DeepC-MVS_fast	79.65 3	86.91 25	86.62 26	87.76 19	93.52 29	72.37 35	91.26 27	93.04 23	76.62 57	84.22 44	93.36 35	71.44 35	96.76 10	80.82 51	95.33 20	94.16 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MP-MVS		87.71 11	87.64 12	87.93 14	94.36 15	73.88 4	92.71 13	92.65 42	77.57 35	83.84 48	94.40 15	72.24 31	96.28 25	85.65 13	95.30 22	93.62 44

MCST-MVS		87.37 18	87.25 16	87.73 20	94.53 8	72.46 32	89.82 53	93.82 6	73.07 124	84.86 35	92.89 45	76.22 6	96.33 23	84.89 19	95.13 23	94.40 12

APD-MVS		87.44 15	87.52 13	87.19 31	94.24 17	72.39 33	91.86 22	92.83 35	73.01 125	88.58 7	94.52 7	73.36 22	96.49 21	84.26 27	95.01 24	92.70 70
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
NCCC		88.06 7	88.01 10	88.24 5	94.41 13	73.62 7	91.22 30	92.83 35	81.50 7	85.79 22	93.47 33	73.02 25	97.00 6	84.90 17	94.94 25	94.10 21

ACMMPR		87.44 15	87.23 17	88.08 7	94.64 4	73.59 8	93.04 5	93.20 19	76.78 52	84.66 36	94.52 7	68.81 56	96.65 14	84.53 23	94.90 26	94.00 28

HFP-MVS		87.58 13	87.47 14	87.94 11	94.58 6	73.54 11	93.04 5	93.24 17	76.78 52	84.91 30	94.44 12	70.78 39	96.61 16	84.53 23	94.89 27	93.66 37

#test#		87.33 19	87.13 19	87.94 11	94.58 6	73.54 11	92.34 15	93.24 17	75.23 80	84.91 30	94.44 12	70.78 39	96.61 16	83.75 31	94.89 27	93.66 37

region2R		87.42 17	87.20 18	88.09 6	94.63 5	73.55 9	93.03 7	93.12 22	76.73 55	84.45 39	94.52 7	69.09 54	96.70 12	84.37 26	94.83 29	94.03 25

原ACMM1				84.35 85	93.01 40	68.79 86		92.44 45	63.96 244	81.09 79	91.57 66	66.06 76	95.45 45	67.19 167	94.82 30	88.81 198

HPM-MVS		87.11 22	86.98 21	87.50 28	93.88 23	72.16 37	92.19 18	93.33 16	76.07 69	83.81 49	93.95 26	69.77 49	96.01 32	85.15 14	94.66 31	94.32 17

TSAR-MVS + MP.		88.02 10	88.11 8	87.72 22	93.68 26	72.13 38	91.41 26	92.35 50	74.62 89	88.90 6	93.85 27	75.75 9	96.00 33	87.80 5	94.63 32	95.04 2

PGM-MVS		86.68 27	86.27 30	87.90 15	94.22 18	73.38 15	90.22 48	93.04 23	75.53 74	83.86 47	94.42 14	67.87 62	96.64 15	82.70 41	94.57 33	93.66 37

XVS		87.18 21	86.91 23	88.00 9	94.42 11	73.33 16	92.78 9	92.99 28	79.14 21	83.67 51	94.17 19	67.45 65	96.60 18	83.06 36	94.50 34	94.07 23

X-MVStestdata		80.37 117	77.83 151	88.00 9	94.42 11	73.33 16	92.78 9	92.99 28	79.14 21	83.67 51	12.47 342	67.45 65	96.60 18	83.06 36	94.50 34	94.07 23

test12				86.80 38	92.63 46	70.70 56		91.79 74		82.71 62		71.67 33	96.16 29		94.50 34	93.54 47

CP-MVS		87.11 22	86.92 22	87.68 25	94.20 19	73.86 5	93.98 1	92.82 37	76.62 57	83.68 50	94.46 11	67.93 60	95.95 35	84.20 29	94.39 37	93.23 55

CSCG		86.41 33	86.19 32	87.07 35	92.91 41	72.48 31	90.81 34	93.56 10	73.95 97	83.16 56	91.07 76	75.94 7	95.19 55	79.94 58	94.38 38	93.55 46

MSLP-MVS++		85.43 46	85.76 40	84.45 81	91.93 56	70.24 59	90.71 36	92.86 33	77.46 41	84.22 44	92.81 50	67.16 68	92.94 152	80.36 54	94.35 39	90.16 148

mPP-MVS		86.67 28	86.32 29	87.72 22	94.41 13	73.55 9	92.74 11	92.22 53	76.87 50	82.81 61	94.25 17	66.44 72	96.24 26	82.88 40	94.28 40	93.38 50

SD-MVS		88.06 7	88.50 7	86.71 40	92.60 49	72.71 24	91.81 23	93.19 20	77.87 32	90.32 4	94.00 25	74.83 11	93.78 112	87.63 7	94.27 41	93.65 42

MVS_0304		86.37 35	85.81 39	88.02 8	90.13 76	72.39 33	89.66 60	92.75 38	81.64 6	82.66 64	92.04 54	64.44 87	97.35 1	84.76 21	94.25 42	94.33 16

HSP-MVS		89.28 1	89.76 1	87.85 18	94.28 16	73.46 14	92.90 8	92.73 39	80.27 13	91.35 2	94.16 20	78.35 3	96.77 9	89.59 1	94.22 43	93.33 53

DELS-MVS		85.41 47	85.30 45	85.77 55	88.49 131	67.93 108	85.52 196	93.44 13	78.70 28	83.63 53	89.03 117	74.57 12	95.71 38	80.26 56	94.04 44	93.66 37
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
EPNet		83.72 57	82.92 63	86.14 51	84.22 208	69.48 75	91.05 32	85.27 224	81.30 8	76.83 140	91.65 62	66.09 75	95.56 41	76.00 89	93.85 45	93.38 50
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
3Dnovator+	77.84 4	85.48 44	84.47 52	88.51 2	91.08 64	73.49 13	93.18 4	93.78 7	80.79 11	76.66 143	93.37 34	60.40 161	96.75 11	77.20 78	93.73 46	95.29 1

CANet		86.45 30	86.10 34	87.51 27	90.09 78	70.94 50	89.70 59	92.59 43	81.78 4	81.32 74	91.43 71	70.34 42	97.23 2	84.26 27	93.36 47	94.37 13

新几何1				83.42 112	93.13 36	70.71 55		85.48 222	57.43 293	81.80 71	91.98 56	63.28 97	92.27 171	64.60 188	92.99 48	87.27 240

1121		80.84 97	79.77 102	84.05 94	93.11 38	70.78 54	84.66 208	85.42 223	57.37 294	81.76 72	92.02 55	63.41 95	94.12 93	67.28 165	92.93 49	87.26 241

HPM-MVS_fast		85.35 48	84.95 49	86.57 44	93.69 25	70.58 57	92.15 19	91.62 80	73.89 100	82.67 63	94.09 23	62.60 121	95.54 42	80.93 49	92.93 49	93.57 45

旧先验1					91.96 55	65.79 141		86.37 214			93.08 43	69.31 53			92.74 51	88.74 201

3Dnovator	76.31 5	83.38 64	82.31 70	86.59 43	87.94 146	72.94 22	90.64 37	92.14 57	77.21 42	75.47 168	92.83 47	58.56 168	94.72 75	73.24 119	92.71 52	92.13 89

MVS_111021_HR		85.14 51	84.75 51	86.32 48	91.65 59	72.70 25	85.98 175	90.33 118	76.11 68	82.08 67	91.61 65	71.36 36	94.17 92	81.02 48	92.58 53	92.08 90

APD-MVS_3200maxsize		85.97 38	85.88 36	86.22 49	92.69 45	69.53 74	91.93 21	92.99 28	73.54 111	85.94 18	94.51 10	65.80 79	95.61 39	83.04 38	92.51 54	93.53 48

MAR-MVS		81.84 82	80.70 88	85.27 61	91.32 62	71.53 44	89.82 53	90.92 99	69.77 174	78.50 101	86.21 202	62.36 128	94.52 79	65.36 181	92.05 55	89.77 174
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
TSAR-MVS + GP.		85.71 42	85.33 43	86.84 37	91.34 61	72.50 30	89.07 74	87.28 204	76.41 59	85.80 21	90.22 92	74.15 20	95.37 52	81.82 45	91.88 56	92.65 73

IS-MVSNet		83.15 65	82.81 64	84.18 90	89.94 82	63.30 200	91.59 24	88.46 184	79.04 25	79.49 89	92.16 52	65.10 83	94.28 84	67.71 160	91.86 57	94.95 3

Vis-MVSNet (Re-imp)		78.36 157	78.45 137	78.07 242	88.64 127	51.78 303	86.70 157	79.63 287	74.14 95	75.11 182	90.83 83	61.29 144	89.75 231	58.10 239	91.60 58	92.69 72

MG-MVS		83.41 62	83.45 55	83.28 117	92.74 44	62.28 217	88.17 105	89.50 145	75.22 81	81.49 73	92.74 51	66.75 69	95.11 58	72.85 121	91.58 59	92.45 77

CPTT-MVS		83.73 56	83.33 57	84.92 72	93.28 33	70.86 53	92.09 20	90.38 113	68.75 195	79.57 88	92.83 47	60.60 157	93.04 150	80.92 50	91.56 60	90.86 118

test222					91.50 60	68.26 102	84.16 225	83.20 248	54.63 305	79.74 86	91.63 64	58.97 166			91.42 61	86.77 252

testdata				79.97 206	90.90 67	64.21 182		84.71 228	59.27 280	85.40 23	92.91 44	62.02 134	89.08 244	68.95 154	91.37 62	86.63 256

abl_6		85.23 49	84.95 49	86.07 52	92.23 52	70.48 58	90.80 35	92.08 58	73.51 112	85.26 25	94.16 20	62.75 114	95.92 36	82.46 44	91.30 63	91.81 97

API-MVS		81.99 80	81.23 82	84.26 88	90.94 66	70.18 65	91.10 31	89.32 150	71.51 152	78.66 99	88.28 135	65.26 81	95.10 61	64.74 187	91.23 64	87.51 234

Vis-MVSNet		83.46 61	82.80 65	85.43 58	90.25 75	68.74 90	90.30 46	90.13 127	76.33 65	80.87 82	92.89 45	61.00 150	94.20 89	72.45 128	90.97 65	93.35 52
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
OpenMVS	72.83 10	79.77 131	78.33 143	84.09 92	85.17 192	69.91 66	90.57 38	90.97 98	66.70 213	72.17 210	91.91 57	54.70 197	93.96 98	61.81 208	90.95 66	88.41 218

UA-Net		85.08 52	84.96 48	85.45 57	92.07 54	68.07 106	89.78 56	90.86 101	82.48 2	84.60 38	93.20 37	69.35 52	95.22 54	71.39 140	90.88 67	93.07 62

ACMMP		85.89 40	85.39 42	87.38 29	93.59 28	72.63 27	92.74 11	93.18 21	76.78 52	80.73 83	93.82 28	64.33 88	96.29 24	82.67 42	90.69 68	93.23 55
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
Regformer-1		86.41 33	86.33 28	86.64 41	89.33 100	70.93 51	88.43 92	91.39 89	82.14 3	86.65 16	90.09 94	74.39 16	95.01 64	83.97 30	90.63 69	93.97 29

Regformer-2		86.63 29	86.53 27	86.95 36	89.33 100	71.24 45	88.43 92	92.05 59	82.50 1	86.88 15	90.09 94	74.45 13	95.61 39	84.38 25	90.63 69	94.01 27

UGNet		80.83 99	79.59 107	84.54 78	88.04 143	68.09 105	89.42 63	88.16 186	76.95 48	76.22 154	89.46 108	49.30 255	93.94 100	68.48 157	90.31 71	91.60 99
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
MVSFormer		82.85 70	82.05 73	85.24 62	87.35 163	70.21 60	90.50 40	90.38 113	68.55 198	81.32 74	89.47 106	61.68 135	93.46 128	78.98 61	90.26 72	92.05 91

lupinMVS		81.39 91	80.27 96	84.76 75	87.35 163	70.21 60	85.55 192	86.41 212	62.85 252	81.32 74	88.61 125	61.68 135	92.24 173	78.41 67	90.26 72	91.83 95

DP-MVS Recon		83.11 67	82.09 72	86.15 50	94.44 10	70.92 52	88.79 81	92.20 54	70.53 164	79.17 92	91.03 79	64.12 90	96.03 31	68.39 159	90.14 74	91.50 103

MVS_111021_LR		82.61 73	82.11 71	84.11 91	88.82 120	71.58 43	85.15 201	86.16 217	74.69 88	80.47 84	91.04 77	62.29 129	90.55 222	80.33 55	90.08 75	90.20 147

jason		81.39 91	80.29 95	84.70 76	86.63 177	69.90 67	85.95 176	86.77 208	63.24 246	81.07 80	89.47 106	61.08 149	92.15 174	78.33 68	90.07 76	92.05 91
jason: jason.
LFMVS		81.82 83	81.23 82	83.57 109	91.89 57	63.43 198	89.84 52	81.85 266	77.04 47	83.21 54	93.10 39	52.26 216	93.43 132	71.98 134	89.95 77	93.85 33

MVS		78.19 161	76.99 165	81.78 167	85.66 186	66.99 122	84.66 208	90.47 111	55.08 304	72.02 215	85.27 222	63.83 93	94.11 95	66.10 175	89.80 78	84.24 283

CANet_DTU		80.61 107	79.87 100	82.83 144	85.60 188	63.17 206	87.36 128	88.65 180	76.37 63	75.88 160	88.44 131	53.51 207	93.07 147	73.30 118	89.74 79	92.25 84

PVSNet_Blended		80.98 94	80.34 93	82.90 138	88.85 117	65.40 147	84.43 218	92.00 63	67.62 207	78.11 118	85.05 227	66.02 77	94.27 85	71.52 138	89.50 80	89.01 192

PAPM_NR		83.02 68	82.41 67	84.82 74	92.47 50	66.37 131	87.93 112	91.80 73	73.82 105	77.32 132	90.66 85	67.90 61	94.90 69	70.37 144	89.48 81	93.19 59

114514_t		80.68 106	79.51 111	84.20 89	94.09 22	67.27 119	89.64 61	91.11 96	58.75 284	74.08 191	90.72 84	58.10 171	95.04 63	69.70 149	89.42 82	90.30 145

LCM-MVSNet-Re		77.05 190	76.94 166	77.36 251	87.20 169	51.60 304	80.06 260	80.46 278	75.20 82	67.69 265	86.72 176	62.48 126	88.98 247	63.44 192	89.25 83	91.51 102

alignmvs		85.48 44	85.32 44	85.96 54	89.51 95	69.47 76	89.74 57	92.47 44	76.17 67	87.73 13	91.46 70	70.32 43	93.78 112	81.51 46	88.95 84	94.63 6

VNet		82.21 76	82.41 67	81.62 177	90.82 69	60.93 224	84.47 214	89.78 138	76.36 64	84.07 46	91.88 59	64.71 86	90.26 224	70.68 141	88.89 85	93.66 37

PS-MVSNAJ		81.69 84	81.02 86	83.70 105	89.51 95	68.21 104	84.28 223	90.09 128	70.79 159	81.26 78	85.62 215	63.15 102	94.29 83	75.62 96	88.87 86	88.59 210

canonicalmvs		85.91 39	85.87 37	86.04 53	89.84 84	69.44 78	90.45 43	93.00 26	76.70 56	88.01 11	91.23 73	73.28 23	93.91 103	81.50 47	88.80 87	94.77 5

QAPM		80.88 95	79.50 112	85.03 67	88.01 145	68.97 84	91.59 24	92.00 63	66.63 217	75.15 181	92.16 52	57.70 173	95.45 45	63.52 191	88.76 88	90.66 127

VDD-MVS		83.01 69	82.36 69	84.96 69	91.02 65	66.40 130	88.91 76	88.11 187	77.57 35	84.39 42	93.29 36	52.19 217	93.91 103	77.05 81	88.70 89	94.57 9

Test4		77.83 172	75.90 189	83.62 106	80.24 287	65.25 153	85.27 198	90.67 103	69.03 191	66.48 277	83.75 241	43.07 286	93.00 151	75.93 90	88.66 90	92.62 74

test_normal		79.81 130	78.45 137	83.89 103	82.70 257	65.40 147	85.82 182	89.48 146	69.39 179	70.12 236	85.66 213	57.15 181	93.71 121	77.08 80	88.62 91	92.56 75

PVSNet_Blended_VisFu		82.62 72	81.83 77	84.96 69	90.80 70	69.76 69	88.74 86	91.70 78	69.39 179	78.96 94	88.46 130	65.47 80	94.87 71	74.42 105	88.57 92	90.24 146

DI_MVS_plusplus_test		79.89 129	78.58 134	83.85 104	82.89 253	65.32 151	86.12 172	89.55 143	69.64 178	70.55 227	85.82 210	57.24 179	93.81 110	76.85 83	88.55 93	92.41 79

xiu_mvs_v2_base		81.69 84	81.05 85	83.60 107	89.15 111	68.03 107	84.46 216	90.02 132	70.67 162	81.30 77	86.53 192	63.17 101	94.19 90	75.60 97	88.54 94	88.57 212

PAPR		81.66 86	80.89 87	83.99 99	90.27 74	64.00 188	86.76 156	91.77 77	68.84 194	77.13 139	89.50 104	67.63 63	94.88 70	67.55 162	88.52 95	93.09 61

MVS_Test		83.15 65	83.06 60	83.41 114	86.86 173	63.21 203	86.11 173	92.00 63	74.31 92	82.87 59	89.44 111	70.03 45	93.21 137	77.39 77	88.50 96	93.81 35

AdaColmap		80.58 110	79.42 113	84.06 93	93.09 39	68.91 85	89.36 64	88.97 167	69.27 183	75.70 167	89.69 100	57.20 180	95.77 37	63.06 195	88.41 97	87.50 235

VDDNet		81.52 88	80.67 89	84.05 94	90.44 72	64.13 184	89.73 58	85.91 220	71.11 155	83.18 55	93.48 31	50.54 245	93.49 127	73.40 117	88.25 98	94.54 10

PCF-MVS	73.52 7	80.38 116	78.84 129	85.01 68	87.71 156	68.99 83	83.65 232	91.46 88	63.00 249	77.77 125	90.28 89	66.10 74	95.09 62	61.40 211	88.22 99	90.94 116
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Effi-MVS+		83.62 59	83.08 59	85.24 62	88.38 136	67.45 114	88.89 77	89.15 157	75.50 75	82.27 65	88.28 135	69.61 50	94.45 81	77.81 72	87.84 100	93.84 34

gg-mvs-nofinetune		69.95 263	67.96 265	75.94 263	83.07 247	54.51 292	77.23 283	70.29 325	63.11 247	70.32 231	62.33 326	43.62 283	88.69 253	53.88 261	87.76 101	84.62 281

Regformer-3		85.23 49	85.07 47	85.70 56	88.95 115	69.01 82	88.29 101	89.91 136	80.95 9	85.01 27	90.01 96	72.45 29	94.19 90	82.50 43	87.57 102	93.90 32

Regformer-4		85.68 43	85.45 41	86.35 45	88.95 115	69.67 71	88.29 101	91.29 91	81.73 5	85.36 24	90.01 96	72.62 28	95.35 53	83.28 34	87.57 102	94.03 25

xiu_mvs_v1_base_debu		80.80 102	79.72 104	84.03 96	87.35 163	70.19 62	85.56 189	88.77 176	69.06 188	81.83 68	88.16 137	50.91 239	92.85 154	78.29 69	87.56 104	89.06 185

xiu_mvs_v1_base		80.80 102	79.72 104	84.03 96	87.35 163	70.19 62	85.56 189	88.77 176	69.06 188	81.83 68	88.16 137	50.91 239	92.85 154	78.29 69	87.56 104	89.06 185

xiu_mvs_v1_base_debi		80.80 102	79.72 104	84.03 96	87.35 163	70.19 62	85.56 189	88.77 176	69.06 188	81.83 68	88.16 137	50.91 239	92.85 154	78.29 69	87.56 104	89.06 185

CLD-MVS		82.31 75	81.65 78	84.29 87	88.47 132	67.73 112	85.81 183	92.35 50	75.78 70	78.33 109	86.58 189	64.01 91	94.35 82	76.05 88	87.48 107	90.79 119
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
CDS-MVSNet		79.07 146	77.70 155	83.17 121	87.60 158	68.23 103	84.40 220	86.20 216	67.49 210	76.36 150	86.54 191	61.54 138	90.79 219	61.86 207	87.33 108	90.49 138
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
EPP-MVSNet		83.40 63	83.02 61	84.57 77	90.13 76	64.47 178	92.32 16	90.73 102	74.45 91	79.35 91	91.10 74	69.05 55	95.12 57	72.78 122	87.22 109	94.13 20

TAMVS		78.89 150	77.51 158	83.03 129	87.80 151	67.79 111	84.72 207	85.05 227	67.63 206	76.75 141	87.70 148	62.25 130	90.82 218	58.53 235	87.13 110	90.49 138

TAPA-MVS	73.13 9	79.15 144	77.94 149	82.79 148	89.59 91	62.99 210	88.16 106	91.51 85	65.77 225	77.14 138	91.09 75	60.91 151	93.21 137	50.26 275	87.05 111	92.17 88
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
PAPM		77.68 175	76.40 174	81.51 180	87.29 168	61.85 221	83.78 231	89.59 142	64.74 234	71.23 222	88.70 121	62.59 122	93.66 122	52.66 266	87.03 112	89.01 192

BH-untuned		79.47 138	78.60 132	82.05 162	89.19 110	65.91 138	86.07 174	88.52 183	72.18 142	75.42 171	87.69 149	61.15 147	93.54 125	60.38 218	86.83 113	86.70 254

BH-RMVSNet		79.61 133	78.44 139	83.14 123	89.38 99	65.93 137	84.95 204	87.15 205	73.56 110	78.19 116	89.79 99	56.67 183	93.36 133	59.53 226	86.74 114	90.13 150

LS3D		76.95 192	74.82 207	83.37 115	90.45 71	67.36 118	89.15 72	86.94 207	61.87 262	69.52 245	90.61 86	51.71 232	94.53 78	46.38 299	86.71 115	88.21 220

Fast-Effi-MVS+		80.81 100	79.92 99	83.47 110	88.85 117	64.51 172	85.53 194	89.39 148	70.79 159	78.49 102	85.06 226	67.54 64	93.58 123	67.03 170	86.58 116	92.32 81

EPNet_dtu		75.46 217	74.86 206	77.23 254	82.57 260	54.60 290	86.89 149	83.09 249	71.64 147	66.25 279	85.86 208	55.99 186	88.04 260	54.92 256	86.55 117	89.05 189
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
OPM-MVS		83.50 60	82.95 62	85.14 64	88.79 123	70.95 49	89.13 73	91.52 84	77.55 38	80.96 81	91.75 60	60.71 153	94.50 80	79.67 59	86.51 118	89.97 166

OMC-MVS		82.69 71	81.97 76	84.85 73	88.75 125	67.42 115	87.98 108	90.87 100	74.92 86	79.72 87	91.65 62	62.19 132	93.96 98	75.26 101	86.42 119	93.16 60

HQP_MVS		83.64 58	83.14 58	85.14 64	90.08 79	68.71 92	91.25 28	92.44 45	79.12 23	78.92 95	91.00 80	60.42 159	95.38 50	78.71 63	86.32 120	91.33 106

plane_prior5								92.44 45					95.38 50	78.71 63	86.32 120	91.33 106

plane_prior						68.71 92	90.38 44		77.62 34						86.16 122

mvs_anonymous		79.42 140	79.11 125	80.34 199	84.45 204	57.97 249	82.59 240	87.62 198	67.40 212	76.17 158	88.56 128	68.47 57	89.59 234	70.65 142	86.05 123	93.47 49

HQP3-MVS								92.19 55							85.99 124

HQP-MVS		82.61 73	82.02 74	84.37 83	89.33 100	66.98 123	89.17 68	92.19 55	76.41 59	77.23 135	90.23 91	60.17 162	95.11 58	77.47 75	85.99 124	91.03 112

BH-w/o		78.21 159	77.33 161	80.84 192	88.81 121	65.13 157	84.87 205	87.85 195	69.75 175	74.52 189	84.74 233	61.34 142	93.11 145	58.24 238	85.84 126	84.27 282

CHOSEN 1792x2688		77.63 178	75.69 190	83.44 111	89.98 81	68.58 97	78.70 274	87.50 201	56.38 299	75.80 162	86.84 172	58.67 167	91.40 202	61.58 210	85.75 127	90.34 144

cascas		76.72 195	74.64 208	82.99 131	85.78 185	65.88 139	82.33 242	89.21 156	60.85 268	72.74 201	81.02 275	47.28 264	93.75 116	67.48 163	85.02 128	89.34 180

FIs		82.07 78	82.42 66	81.04 190	88.80 122	58.34 243	88.26 103	93.49 12	76.93 49	78.47 103	91.04 77	69.92 47	92.34 170	69.87 148	84.97 129	92.44 78

test-LLR		72.94 243	72.43 230	74.48 275	81.35 275	58.04 247	78.38 275	77.46 297	66.66 214	69.95 240	79.00 290	48.06 261	79.24 301	66.13 173	84.83 130	86.15 261

test-mter		71.41 251	70.39 249	74.48 275	81.35 275	58.04 247	78.38 275	77.46 297	60.32 271	69.95 240	79.00 290	36.08 315	79.24 301	66.13 173	84.83 130	86.15 261

diffmvs		79.51 135	78.59 133	82.25 159	83.31 240	62.66 212	84.17 224	88.11 187	67.64 205	76.09 159	87.47 156	64.01 91	91.15 208	71.71 137	84.82 132	92.94 68

EI-MVSNet-Vis-set		84.19 53	83.81 53	85.31 59	88.18 140	67.85 109	87.66 116	89.73 140	80.05 17	82.95 57	89.59 103	70.74 41	94.82 72	80.66 53	84.72 133	93.28 54

GG-mvs-BLEND				75.38 269	81.59 270	55.80 285	79.32 267	69.63 327		67.19 270	73.67 313	43.24 284	88.90 252	50.41 272	84.50 134	81.45 304

FC-MVSNet-test		81.52 88	82.02 74	80.03 205	88.42 135	55.97 280	87.95 110	93.42 14	77.10 45	77.38 130	90.98 82	69.96 46	91.79 183	68.46 158	84.50 134	92.33 80

PVSNet	64.34 18	72.08 248	70.87 246	75.69 265	86.21 181	56.44 273	74.37 298	80.73 275	62.06 261	70.17 234	82.23 256	42.86 288	83.31 288	54.77 257	84.45 136	87.32 239

MS-PatchMatch		73.83 228	72.67 227	77.30 253	83.87 227	66.02 135	81.82 245	84.66 229	61.37 266	68.61 258	82.82 250	47.29 263	88.21 257	59.27 227	84.32 137	77.68 315

TESTMET0.1,1		69.89 264	69.00 255	72.55 285	79.27 299	56.85 265	78.38 275	74.71 313	57.64 291	68.09 262	77.19 301	37.75 309	76.70 312	63.92 190	84.09 138	84.10 286

EI-MVSNet-UG-set		83.81 55	83.38 56	85.09 66	87.87 147	67.53 113	87.44 127	89.66 141	79.74 18	82.23 66	89.41 112	70.24 44	94.74 74	79.95 57	83.92 139	92.99 67

LPG-MVS_test		82.08 77	81.27 81	84.50 79	89.23 108	68.76 88	90.22 48	91.94 67	75.37 78	76.64 144	91.51 67	54.29 200	94.91 67	78.44 65	83.78 140	89.83 170

LGP-MVS_train				84.50 79	89.23 108	68.76 88		91.94 67	75.37 78	76.64 144	91.51 67	54.29 200	94.91 67	78.44 65	83.78 140	89.83 170

conf200view11		76.55 196	75.55 193	79.57 216	89.52 93	56.99 263	85.83 180	83.23 245	73.94 98	76.32 151	87.12 167	51.89 224	91.95 177	48.33 282	83.75 142	89.78 173

thres100view900		76.50 198	75.55 193	79.33 218	89.52 93	56.99 263	85.83 180	83.23 245	73.94 98	76.32 151	87.12 167	51.89 224	91.95 177	48.33 282	83.75 142	89.07 183

tfpn200view9		76.42 201	75.37 198	79.55 217	89.13 112	57.65 255	85.17 199	83.60 238	73.41 114	76.45 146	86.39 195	52.12 218	91.95 177	48.33 282	83.75 142	89.07 183

thres400		76.50 198	75.37 198	79.86 207	89.13 112	57.65 255	85.17 199	83.60 238	73.41 114	76.45 146	86.39 195	52.12 218	91.95 177	48.33 282	83.75 142	90.00 159

thres600view7		76.50 198	75.44 195	79.68 211	89.40 98	57.16 260	85.53 194	83.23 245	73.79 106	76.26 153	87.09 169	51.89 224	91.89 181	48.05 288	83.72 146	90.00 159

thres200		75.55 216	74.47 212	78.82 230	87.78 154	57.85 252	83.07 238	83.51 241	72.44 138	75.84 161	84.42 235	52.08 220	91.75 186	47.41 290	83.64 147	86.86 250

XVG-OURS		80.41 112	79.23 123	83.97 100	85.64 187	69.02 81	83.03 239	90.39 112	71.09 156	77.63 127	91.49 69	54.62 199	91.35 203	75.71 94	83.47 148	91.54 101

view600		76.20 205	75.21 201	79.16 223	89.64 86	55.82 281	85.74 184	82.06 261	73.88 101	75.74 163	87.85 143	51.84 227	91.66 194	46.75 292	83.42 149	90.00 159

view800		76.20 205	75.21 201	79.16 223	89.64 86	55.82 281	85.74 184	82.06 261	73.88 101	75.74 163	87.85 143	51.84 227	91.66 194	46.75 292	83.42 149	90.00 159

conf0.05thres1000		76.20 205	75.21 201	79.16 223	89.64 86	55.82 281	85.74 184	82.06 261	73.88 101	75.74 163	87.85 143	51.84 227	91.66 194	46.75 292	83.42 149	90.00 159

tfpn		76.20 205	75.21 201	79.16 223	89.64 86	55.82 281	85.74 184	82.06 261	73.88 101	75.74 163	87.85 143	51.84 227	91.66 194	46.75 292	83.42 149	90.00 159

CNLPA		78.08 163	76.79 169	81.97 164	90.40 73	71.07 47	87.59 118	84.55 230	66.03 224	72.38 208	89.64 102	57.56 175	86.04 273	59.61 224	83.35 153	88.79 199

MVP-Stereo		76.12 209	74.46 213	81.13 189	85.37 191	69.79 68	84.42 219	87.95 193	65.03 232	67.46 267	85.33 221	53.28 209	91.73 187	58.01 240	83.27 154	81.85 302
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
1314		76.53 197	75.30 200	80.21 203	83.93 226	62.32 216	84.66 208	88.81 175	60.23 272	70.16 235	84.07 238	55.30 191	90.73 220	67.37 164	83.21 155	87.59 233

mvs-test1		80.88 95	79.40 114	85.29 60	85.13 195	69.75 70	89.28 65	88.10 189	74.99 84	76.44 149	86.72 176	57.27 177	94.26 88	73.53 115	83.18 156	91.87 94

HyFIR lowres test		77.53 179	75.40 197	83.94 102	89.59 91	66.62 127	80.36 258	88.64 181	56.29 300	76.45 146	85.17 223	57.64 174	93.28 135	61.34 213	83.10 157	91.91 93

ACMP	74.13 6	81.51 90	80.57 90	84.36 84	89.42 97	68.69 95	89.97 51	91.50 87	74.46 90	75.04 184	90.41 88	53.82 205	94.54 77	77.56 74	82.91 158	89.86 169
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ACMM	73.20 8	80.78 105	79.84 101	83.58 108	89.31 105	68.37 99	89.99 50	91.60 81	70.28 168	77.25 133	89.66 101	53.37 208	93.53 126	74.24 108	82.85 159	88.85 196
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
PMMVS		69.34 266	68.67 257	71.35 292	75.67 312	62.03 219	75.17 292	73.46 318	50.00 321	68.68 255	79.05 288	52.07 221	78.13 306	61.16 214	82.77 160	73.90 323

PLC	70.83 11	78.05 164	76.37 175	83.08 126	91.88 58	67.80 110	88.19 104	89.46 147	64.33 239	69.87 242	88.38 132	53.66 206	93.58 123	58.86 231	82.73 161	87.86 227
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TR-MVS		77.44 186	76.18 181	81.20 186	88.24 139	63.24 202	84.61 212	86.40 213	67.55 209	77.81 123	86.48 194	54.10 202	93.15 142	57.75 242	82.72 162	87.20 242

pcd1.5k->3k		34.07 316	35.26 316	30.50 331	86.92 172	0.00 352	0.00 342	91.58 82	0.00 346	0.00 348	0.00 348	56.23 185	0.00 349	0.00 346	82.60 163	91.49 104

ab-mvs		79.51 135	78.97 128	81.14 188	88.46 133	60.91 225	83.84 230	89.24 155	70.36 166	79.03 93	88.87 119	63.23 100	90.21 226	65.12 182	82.57 164	92.28 83

HY-MVS	69.67 12	77.95 168	77.15 163	80.36 198	87.57 162	60.21 231	83.37 237	87.78 196	66.11 221	75.37 173	87.06 171	63.27 98	90.48 223	61.38 212	82.43 165	90.40 143

PS-MVSNAJss		82.07 78	81.31 80	84.34 86	86.51 178	67.27 119	89.27 66	91.51 85	71.75 146	79.37 90	90.22 92	63.15 102	94.27 85	77.69 73	82.36 166	91.49 104

PVSNet_BlendedMVS		80.60 108	80.02 97	82.36 158	88.85 117	65.40 147	86.16 171	92.00 63	69.34 182	78.11 118	86.09 205	66.02 77	94.27 85	71.52 138	82.06 167	87.39 236

WTY-MVS		75.65 215	75.68 191	75.57 267	86.40 179	56.82 266	77.92 280	82.40 255	65.10 231	76.18 156	87.72 147	63.13 105	80.90 295	60.31 219	81.96 168	89.00 194

ACMMP++_ref															81.95 169

DP-MVS		76.78 194	74.57 209	83.42 112	93.29 32	69.46 77	88.55 91	83.70 237	63.98 243	70.20 232	88.89 118	54.01 204	94.80 73	46.66 296	81.88 170	86.01 266

CMPMVS	51.72 21	70.19 262	68.16 262	76.28 261	73.15 321	57.55 257	79.47 266	83.92 235	48.02 323	56.48 316	84.81 231	43.13 285	86.42 271	62.67 199	81.81 171	84.89 277
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
tfpn_ndepth		73.70 229	72.75 226	76.52 258	87.78 154	54.92 289	84.32 222	80.28 282	67.57 208	72.50 204	84.82 230	50.12 248	89.44 238	45.73 302	81.66 172	85.20 272

XVG-OURS-SEG-HR		80.81 100	79.76 103	83.96 101	85.60 188	68.78 87	83.54 235	90.50 110	70.66 163	76.71 142	91.66 61	60.69 154	91.26 205	76.94 82	81.58 173	91.83 95

tfpn1000		73.44 234	72.49 229	76.29 260	87.81 150	53.69 297	84.05 229	78.81 291	67.99 204	72.09 214	86.27 201	49.95 250	89.04 245	44.09 307	81.38 174	86.15 261

MIMVSNet		70.69 256	69.30 252	74.88 272	84.52 202	56.35 276	75.87 290	79.42 288	64.59 235	67.76 263	82.41 253	41.10 297	81.54 294	46.64 298	81.34 175	86.75 253

ACMMP++															81.25 176

GA-MVS		76.87 193	75.17 205	81.97 164	82.75 255	62.58 213	81.44 252	86.35 215	72.16 144	74.74 187	82.89 248	46.20 270	92.02 176	68.85 155	81.09 177	91.30 108

sss		73.60 231	73.64 219	73.51 282	82.80 254	55.01 288	76.12 286	81.69 267	62.47 257	74.68 188	85.85 209	57.32 176	78.11 307	60.86 216	80.93 178	87.39 236

Effi-MVS+-dtu		80.03 126	78.57 135	84.42 82	85.13 195	68.74 90	88.77 82	88.10 189	74.99 84	74.97 185	83.49 245	57.27 177	93.36 133	73.53 115	80.88 179	91.18 110

EG-PatchMatch MVS		74.04 226	71.82 236	80.71 195	84.92 198	67.42 115	85.86 179	88.08 191	66.04 223	64.22 290	83.85 239	35.10 317	92.56 163	57.44 244	80.83 180	82.16 301

jajsoiax		79.29 142	77.96 148	83.27 118	84.68 201	66.57 129	89.25 67	90.16 126	69.20 185	75.46 169	89.49 105	45.75 275	93.13 144	76.84 84	80.80 181	90.11 151

1112_ss		77.40 188	76.43 173	80.32 200	89.11 114	60.41 230	83.65 232	87.72 197	62.13 260	73.05 199	86.72 176	62.58 123	89.97 228	62.11 205	80.80 181	90.59 132

mvs_tets		79.13 145	77.77 154	83.22 120	84.70 200	66.37 131	89.17 68	90.19 125	69.38 181	75.40 172	89.46 108	44.17 281	93.15 142	76.78 85	80.70 183	90.14 149

PatchMatch-RL		72.38 246	70.90 245	76.80 257	88.60 128	67.38 117	79.53 265	76.17 303	62.75 254	69.36 248	82.00 262	45.51 276	84.89 281	53.62 262	80.58 184	78.12 313

EI-MVSNet		80.52 111	79.98 98	82.12 160	84.28 205	63.19 205	86.41 165	88.95 169	74.18 94	78.69 97	87.54 154	66.62 70	92.43 165	72.57 127	80.57 185	90.74 122

MVSTER		79.01 147	77.88 150	82.38 157	83.07 247	64.80 164	84.08 228	88.95 169	69.01 192	78.69 97	87.17 166	54.70 197	92.43 165	74.69 104	80.57 185	89.89 168

testing_2		75.73 214	73.34 222	82.89 140	77.37 305	65.22 154	84.10 227	90.54 109	69.09 187	60.46 301	81.15 273	40.48 299	92.84 157	76.36 86	80.54 187	90.60 130

XVG-ACMP-BASELINE		76.11 210	74.27 215	81.62 177	83.20 243	64.67 166	83.60 234	89.75 139	69.75 175	71.85 216	87.09 169	32.78 318	92.11 175	69.99 147	80.43 188	88.09 222

Fast-Effi-MVS+-dtu		78.02 165	76.49 172	82.62 154	83.16 246	66.96 125	86.94 147	87.45 203	72.45 136	71.49 221	84.17 236	54.79 196	91.58 200	67.61 161	80.31 189	89.30 181

LTVRE_ROB	69.57 13	76.25 204	74.54 211	81.41 182	88.60 128	64.38 181	79.24 268	89.12 158	70.76 161	69.79 244	87.86 142	49.09 257	93.20 139	56.21 252	80.16 190	86.65 255
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
Test_1112_low_res		76.40 202	75.44 195	79.27 219	89.28 106	58.09 245	81.69 248	87.07 206	59.53 278	72.48 206	86.67 182	61.30 143	89.33 239	60.81 217	80.15 191	90.41 142

test_djsdf		80.30 118	79.32 117	83.27 118	83.98 225	65.37 150	90.50 40	90.38 113	68.55 198	76.19 155	88.70 121	56.44 184	93.46 128	78.98 61	80.14 192	90.97 115

CHOSEN 280x420		66.51 280	64.71 279	71.90 287	81.45 272	63.52 194	57.98 332	68.95 332	53.57 311	62.59 297	76.70 302	46.22 269	75.29 319	55.25 255	79.68 193	76.88 321

GBi-Net		78.40 155	77.40 159	81.40 183	87.60 158	63.01 207	88.39 96	89.28 151	71.63 148	75.34 174	87.28 159	54.80 193	91.11 209	62.72 196	79.57 194	90.09 153

test1		78.40 155	77.40 159	81.40 183	87.60 158	63.01 207	88.39 96	89.28 151	71.63 148	75.34 174	87.28 159	54.80 193	91.11 209	62.72 196	79.57 194	90.09 153

FMVSNet3		77.88 171	76.85 167	80.97 191	86.84 174	62.36 214	86.52 162	88.77 176	71.13 154	75.34 174	86.66 183	54.07 203	91.10 212	62.72 196	79.57 194	89.45 179

FMVSNet2		78.20 160	77.21 162	81.20 186	87.60 158	62.89 211	87.47 126	89.02 160	71.63 148	75.29 178	87.28 159	54.80 193	91.10 212	62.38 200	79.38 197	89.61 177

anonymousdsp		78.60 153	77.15 163	82.98 132	80.51 285	67.08 121	87.24 135	89.53 144	65.66 227	75.16 180	87.19 165	52.52 210	92.25 172	77.17 79	79.34 198	89.61 177

nrg030		83.88 54	83.53 54	84.96 69	86.77 176	69.28 79	90.46 42	92.67 40	74.79 87	82.95 57	91.33 72	72.70 27	93.09 146	80.79 52	79.28 199	92.50 76

VPA-MVSNet		80.60 108	80.55 91	80.76 194	88.07 142	60.80 227	86.86 150	91.58 82	75.67 73	80.24 85	89.45 110	63.34 96	90.25 225	70.51 143	79.22 200	91.23 109

F-COLMAP		76.38 203	74.33 214	82.50 155	89.28 106	66.95 126	88.41 95	89.03 159	64.05 241	66.83 273	88.61 125	46.78 267	92.89 153	57.48 243	78.55 201	87.67 230

FMVSNet1		77.44 186	76.12 182	81.40 183	86.81 175	63.01 207	88.39 96	89.28 151	70.49 165	74.39 190	87.28 159	49.06 258	91.11 209	60.91 215	78.52 202	90.09 153

MDTV_nov1_ep13			69.97 251		83.18 244	53.48 298	77.10 284	80.18 284	60.45 269	69.33 249	80.44 279	48.89 259	86.90 266	51.60 268	78.51 203

CVMVSNet		72.99 242	72.58 228	74.25 278	84.28 205	50.85 310	86.41 165	83.45 243	44.56 325	73.23 197	87.54 154	49.38 253	85.70 275	65.90 177	78.44 204	86.19 260

tpm2		73.26 238	71.46 238	78.63 232	83.34 239	56.71 269	80.65 256	80.40 279	56.63 298	73.55 193	82.02 261	51.80 231	91.24 206	56.35 251	78.42 205	87.95 224

CostFormer		75.24 220	73.90 218	79.27 219	82.65 259	58.27 244	80.80 253	82.73 253	61.57 263	75.33 177	83.13 247	55.52 189	91.07 215	64.98 185	78.34 206	88.45 216

ACMH	67.68 16	75.89 212	73.93 217	81.77 168	88.71 126	66.61 128	88.62 88	89.01 162	69.81 173	66.78 274	86.70 181	41.95 295	91.51 201	55.64 253	78.14 207	87.17 243
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CR-MVSNet		73.37 235	71.27 241	79.67 212	81.32 277	65.19 155	75.92 288	80.30 280	59.92 275	72.73 202	81.19 271	52.50 211	86.69 267	59.84 222	77.71 208	87.11 246

RPMNet		71.62 249	68.94 256	79.67 212	81.32 277	65.19 155	75.92 288	78.30 294	57.60 292	72.73 202	76.45 304	52.30 215	86.69 267	48.14 287	77.71 208	87.11 246

Patchmatch-test1		73.49 232	71.85 235	78.41 238	84.05 224	62.17 218	79.96 262	79.29 289	66.30 220	72.38 208	79.58 287	51.95 223	85.08 280	55.46 254	77.67 210	87.99 223

tpmp4_e23		73.45 233	71.17 243	80.31 201	83.55 235	59.56 235	81.88 244	82.33 256	57.94 289	70.51 229	81.62 269	51.19 237	91.63 198	53.96 260	77.51 211	89.75 175

v7		80.24 119	79.26 122	83.15 122	84.07 222	64.94 161	87.56 122	90.67 103	72.26 141	78.28 110	86.51 193	61.45 140	94.03 97	75.14 102	77.41 212	90.49 138

DWT-MVSNet_test		73.70 229	71.86 234	79.21 221	82.91 252	58.94 238	82.34 241	82.17 258	65.21 229	71.05 225	78.31 292	44.21 280	90.17 227	63.29 194	77.28 213	88.53 215

v1144		80.03 126	79.03 126	83.01 130	83.78 231	64.51 172	87.11 142	90.57 108	71.96 145	78.08 120	86.20 203	61.41 141	93.94 100	74.93 103	77.23 214	90.60 130

WR-MVS		79.49 137	79.22 124	80.27 202	88.79 123	58.35 242	85.06 202	88.61 182	78.56 29	77.65 126	88.34 133	63.81 94	90.66 221	64.98 185	77.22 215	91.80 98

v1192		79.59 134	78.43 140	83.07 127	83.55 235	64.52 170	86.93 148	90.58 107	70.83 158	77.78 124	85.90 206	59.15 165	93.94 100	73.96 110	77.19 216	90.76 120

VPNet		78.69 152	78.66 131	78.76 231	88.31 138	55.72 286	84.45 217	86.63 210	76.79 51	78.26 114	90.55 87	59.30 164	89.70 233	66.63 171	77.05 217	90.88 117

v1neww		80.40 113	79.54 108	82.98 132	84.10 216	64.51 172	87.57 119	90.22 122	73.25 117	78.47 103	86.65 184	62.83 110	93.86 106	75.72 92	77.02 218	90.58 133

v7new		80.40 113	79.54 108	82.98 132	84.10 216	64.51 172	87.57 119	90.22 122	73.25 117	78.47 103	86.65 184	62.83 110	93.86 106	75.72 92	77.02 218	90.58 133

v6		80.40 113	79.54 108	82.98 132	84.09 218	64.50 176	87.57 119	90.22 122	73.25 117	78.47 103	86.63 186	62.84 109	93.86 106	75.73 91	77.02 218	90.58 133

v1240		78.99 148	77.78 153	82.64 153	83.21 242	63.54 193	86.62 159	90.30 120	69.74 177	77.33 131	85.68 211	57.04 182	93.76 115	73.13 120	76.92 221	90.62 128

MSDG		73.36 237	70.99 244	80.49 196	84.51 203	65.80 140	80.71 255	86.13 218	65.70 226	65.46 282	83.74 242	44.60 278	90.91 217	51.13 270	76.89 222	84.74 279

IterMVS-LS		80.06 125	79.38 115	82.11 161	85.89 183	63.20 204	86.79 153	89.34 149	74.19 93	75.45 170	86.72 176	66.62 70	92.39 167	72.58 126	76.86 223	90.75 121
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
v1921920		79.22 143	78.03 147	82.80 146	83.30 241	63.94 190	86.80 152	90.33 118	69.91 172	77.48 129	85.53 217	58.44 169	93.75 116	73.60 114	76.85 224	90.71 123

XXY-MVS		75.41 218	75.56 192	74.96 271	83.59 234	57.82 253	80.59 257	83.87 236	66.54 218	74.93 186	88.31 134	63.24 99	80.09 299	62.16 203	76.85 224	86.97 248

v1141		80.19 122	79.31 118	82.85 141	83.84 228	64.12 185	87.14 137	90.08 129	73.13 120	78.27 111	86.39 195	62.67 119	93.75 116	75.40 99	76.83 226	90.68 124

divwei89l23v2f112		80.19 122	79.31 118	82.85 141	83.84 228	64.11 187	87.13 140	90.08 129	73.13 120	78.27 111	86.39 195	62.69 117	93.75 116	75.40 99	76.82 227	90.68 124

v1		80.19 122	79.31 118	82.85 141	83.83 230	64.12 185	87.14 137	90.07 131	73.13 120	78.27 111	86.38 199	62.72 116	93.75 116	75.41 98	76.82 227	90.68 124

v2v482		80.23 120	79.29 121	83.05 128	83.62 233	64.14 183	87.04 144	89.97 133	73.61 108	78.18 117	87.22 163	61.10 148	93.82 109	76.11 87	76.78 229	91.18 110

v144192		79.47 138	78.37 141	82.78 149	83.35 238	63.96 189	86.96 146	90.36 116	69.99 171	77.50 128	85.67 212	60.66 155	93.77 114	74.27 107	76.58 230	90.62 128

UniMVSNet (Re)		81.60 87	81.11 84	83.09 125	88.38 136	64.41 180	87.60 117	93.02 25	78.42 31	78.56 100	88.16 137	69.78 48	93.26 136	69.58 150	76.49 231	91.60 99

UniMVSNet_NR-MVSNet		81.88 81	81.54 79	82.92 137	88.46 133	63.46 196	87.13 140	92.37 49	80.19 15	78.38 107	89.14 114	71.66 34	93.05 148	70.05 145	76.46 232	92.25 84

DU-MVS		81.12 93	80.52 92	82.90 138	87.80 151	63.46 196	87.02 145	91.87 71	79.01 26	78.38 107	89.07 115	65.02 84	93.05 148	70.05 145	76.46 232	92.20 86

PatchmatchNet		73.12 240	71.33 240	78.49 237	83.18 244	60.85 226	79.63 264	78.57 292	64.13 240	71.73 217	79.81 286	51.20 236	85.97 274	57.40 245	76.36 234	88.66 202
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
USDC		70.33 260	68.37 259	76.21 262	80.60 283	56.23 277	79.19 270	86.49 211	60.89 267	61.29 298	85.47 219	31.78 321	89.47 237	53.37 263	76.21 235	82.94 298

OpenMVS_ROB	64.09 19	70.56 258	68.19 261	77.65 247	80.26 286	59.41 237	85.01 203	82.96 251	58.76 283	65.43 283	82.33 254	37.63 311	91.23 207	45.34 305	76.03 236	82.32 299

ACMH+	68.96 14	76.01 211	74.01 216	82.03 163	88.60 128	65.31 152	88.86 78	87.55 199	70.25 169	67.75 264	87.47 156	41.27 296	93.19 140	58.37 236	75.94 237	87.60 232

PatchFormer-LS_test		74.50 222	73.05 224	78.86 229	82.95 251	59.55 236	81.65 249	82.30 257	67.44 211	71.62 219	78.15 294	52.34 214	88.92 251	65.05 184	75.90 238	88.12 221

tpm		72.37 247	71.71 237	74.35 277	82.19 264	52.00 301	79.22 269	77.29 299	64.56 236	72.95 200	83.68 244	51.35 234	83.26 289	58.33 237	75.80 239	87.81 228

Anonymous20231206		68.60 268	67.80 269	71.02 294	80.23 288	50.75 311	78.30 278	80.47 277	56.79 297	66.11 280	82.63 252	46.35 268	78.95 303	43.62 309	75.70 240	83.36 290

v7n		78.97 149	77.58 157	83.14 123	83.45 237	65.51 145	88.32 99	91.21 93	73.69 107	72.41 207	86.32 200	57.93 172	93.81 110	69.18 153	75.65 241	90.11 151

NR-MVSNet		80.23 120	79.38 115	82.78 149	87.80 151	63.34 199	86.31 168	91.09 97	79.01 26	72.17 210	89.07 115	67.20 67	92.81 158	66.08 176	75.65 241	92.20 86

v10		79.74 132	78.67 130	82.97 136	84.06 223	64.95 160	87.88 114	90.62 106	73.11 123	75.11 182	86.56 190	61.46 139	94.05 96	73.68 111	75.55 243	89.90 167

IB-MVS	68.01 15	75.85 213	73.36 221	83.31 116	84.76 199	66.03 134	83.38 236	85.06 226	70.21 170	69.40 246	81.05 274	45.76 274	94.66 76	65.10 183	75.49 244	89.25 182
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
V42		79.38 141	78.24 145	82.83 144	81.10 279	65.50 146	85.55 192	89.82 137	71.57 151	78.21 115	86.12 204	60.66 155	93.18 141	75.64 95	75.46 245	89.81 172

v8		79.97 128	79.02 127	82.80 146	84.09 218	64.50 176	87.96 109	90.29 121	74.13 96	75.24 179	86.81 173	62.88 107	93.89 105	74.39 106	75.40 246	90.00 159

Baseline_NR-MVSNet		78.15 162	78.33 143	77.61 248	85.79 184	56.21 278	86.78 154	85.76 221	73.60 109	77.93 122	87.57 152	65.02 84	88.99 246	67.14 168	75.33 247	87.63 231

pmmvs5		71.55 250	70.20 250	75.61 266	77.83 302	56.39 274	81.74 247	80.89 272	57.76 290	67.46 267	84.49 234	49.26 256	85.32 279	57.08 248	75.29 248	85.11 276

EPMVS		69.02 267	68.16 262	71.59 288	79.61 293	49.80 316	77.40 282	66.93 334	62.82 253	70.01 237	79.05 288	45.79 273	77.86 309	56.58 250	75.26 249	87.13 245

TranMVSNet+NR-MVSNet		80.84 97	80.31 94	82.42 156	87.85 148	62.33 215	87.74 115	91.33 90	80.55 12	77.99 121	89.86 98	65.23 82	92.62 160	67.05 169	75.24 250	92.30 82

tfpnnormal		74.39 223	73.16 223	78.08 241	86.10 182	58.05 246	84.65 211	87.53 200	70.32 167	71.22 223	85.63 214	54.97 192	89.86 229	43.03 310	75.02 251	86.32 258

COLMAP_ROB	66.92 17	73.01 241	70.41 248	80.81 193	87.13 170	65.63 142	88.30 100	84.19 234	62.96 250	63.80 293	87.69 149	38.04 308	92.56 163	46.66 296	74.91 252	84.24 283
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PatchT		68.46 271	67.85 267	70.29 296	80.70 282	43.93 324	72.47 301	74.88 309	60.15 273	70.55 227	76.57 303	49.94 251	81.59 293	50.58 271	74.83 253	85.34 271

pmmvs4		74.03 227	71.91 233	80.39 197	81.96 266	68.32 100	81.45 251	82.14 259	59.32 279	69.87 242	85.13 224	52.40 213	88.13 259	60.21 220	74.74 254	84.73 280

ITE_SJBPF				78.22 240	81.77 268	60.57 228		83.30 244	69.25 184	67.54 266	87.20 164	36.33 314	87.28 265	54.34 258	74.62 255	86.80 251

test0.0.03 1		68.00 272	67.69 271	68.90 301	77.55 303	47.43 318	75.70 291	72.95 320	66.66 214	66.56 275	82.29 255	48.06 261	75.87 316	44.97 306	74.51 256	83.41 289

test_0402		72.79 244	70.44 247	79.84 208	88.13 141	65.99 136	85.93 177	84.29 232	65.57 228	67.40 269	85.49 218	46.92 266	92.61 161	35.88 321	74.38 257	80.94 305

CP-MVSNet		78.22 158	78.34 142	77.84 244	87.83 149	54.54 291	87.94 111	91.17 95	77.65 33	73.48 194	88.49 129	62.24 131	88.43 255	62.19 202	74.07 258	90.55 136

FMVSNet5		69.50 265	67.96 265	74.15 279	82.97 250	55.35 287	80.01 261	82.12 260	62.56 256	63.02 294	81.53 270	36.92 312	81.92 292	48.42 281	74.06 259	85.17 275

MVS-HIRNet		59.14 295	57.67 297	63.57 311	81.65 269	43.50 325	71.73 302	65.06 337	39.59 330	51.43 325	57.73 330	38.34 307	82.58 291	39.53 316	73.95 260	64.62 330

tpmrst		72.39 245	72.13 232	73.18 284	80.54 284	49.91 314	79.91 263	79.08 290	63.11 247	71.69 218	79.95 283	55.32 190	82.77 290	65.66 180	73.89 261	86.87 249

PS-CasMVS		78.01 166	78.09 146	77.77 246	87.71 156	54.39 293	88.02 107	91.22 92	77.50 40	73.26 196	88.64 124	60.73 152	88.41 256	61.88 206	73.88 262	90.53 137

v148		78.72 151	77.80 152	81.47 181	82.73 256	61.96 220	86.30 169	88.08 191	73.26 116	76.18 156	85.47 219	62.46 127	92.36 169	71.92 136	73.82 263	90.09 153

Patchmatch-test		64.82 286	63.24 284	69.57 298	79.42 295	49.82 315	63.49 327	69.05 331	51.98 317	59.95 304	80.13 282	50.91 239	70.98 330	40.66 315	73.57 264	87.90 226

WR-MVS_H		78.51 154	78.49 136	78.56 234	88.02 144	56.38 275	88.43 92	92.67 40	77.14 43	73.89 192	87.55 153	66.25 73	89.24 241	58.92 230	73.55 265	90.06 157

testgi		66.67 279	66.53 275	67.08 306	75.62 313	41.69 329	75.93 287	76.50 302	66.11 221	65.20 286	86.59 188	35.72 316	74.71 320	43.71 308	73.38 266	84.84 278

pm-mvs1		77.25 189	76.68 170	78.93 228	84.22 208	58.62 240	86.41 165	88.36 185	71.37 153	73.31 195	88.01 141	61.22 146	89.15 243	64.24 189	73.01 267	89.03 191

V4		77.95 168	76.37 175	82.67 151	79.40 296	65.52 143	86.43 163	89.94 134	72.28 139	72.14 213	84.95 228	55.72 187	93.44 130	73.64 112	72.86 268	89.05 189

v52		77.94 170	76.37 175	82.67 151	79.39 297	65.52 143	86.43 163	89.94 134	72.28 139	72.15 212	84.94 229	55.70 188	93.44 130	73.64 112	72.84 269	89.06 185

v748		77.97 167	76.65 171	81.92 166	82.29 263	63.28 201	87.53 123	90.35 117	73.50 113	70.76 226	85.55 216	58.28 170	92.81 158	68.81 156	72.76 270	89.67 176

tpmvs		71.09 253	69.29 253	76.49 259	82.04 265	56.04 279	78.92 272	81.37 271	64.05 241	67.18 271	78.28 293	49.74 252	89.77 230	49.67 278	72.37 271	83.67 287

PEN-MVS		77.73 173	77.69 156	77.84 244	87.07 171	53.91 295	87.91 113	91.18 94	77.56 37	73.14 198	88.82 120	61.23 145	89.17 242	59.95 221	72.37 271	90.43 141

DSMNet-mixed		57.77 299	56.90 299	60.38 315	67.70 331	35.61 335	69.18 312	53.97 341	32.30 336	57.49 312	79.88 284	40.39 301	68.57 334	38.78 317	72.37 271	76.97 318

semantic-postprocess				80.11 204	82.69 258	64.85 163		83.47 242	69.16 186	70.49 230	84.15 237	50.83 243	88.15 258	69.23 152	72.14 274	87.34 238

tpm cat1		70.57 257	68.31 260	77.35 252	82.41 262	57.95 250	78.08 279	80.22 283	52.04 316	68.54 259	77.66 299	52.00 222	87.84 262	51.77 267	72.07 275	86.25 259

RPSCF		73.23 239	71.46 238	78.54 235	82.50 261	59.85 232	82.18 243	82.84 252	58.96 281	71.15 224	89.41 112	45.48 277	84.77 282	58.82 232	71.83 276	91.02 114

IterMVS		74.29 224	72.94 225	78.35 239	81.53 271	63.49 195	81.58 250	82.49 254	68.06 203	69.99 239	83.69 243	51.66 233	85.54 276	65.85 178	71.64 277	86.01 266
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
AllTest		70.96 254	68.09 264	79.58 214	85.15 193	63.62 191	84.58 213	79.83 285	62.31 258	60.32 302	86.73 174	32.02 319	88.96 249	50.28 273	71.57 278	86.15 261

TestCases				79.58 214	85.15 193	63.62 191		79.83 285	62.31 258	60.32 302	86.73 174	32.02 319	88.96 249	50.28 273	71.57 278	86.15 261

Patchmtry		70.74 255	69.16 254	75.49 268	80.72 281	54.07 294	74.94 297	80.30 280	58.34 285	70.01 237	81.19 271	52.50 211	86.54 269	53.37 263	71.09 280	85.87 268

test2356		59.50 294	58.08 294	63.74 310	71.23 325	41.88 327	67.59 319	72.42 322	53.72 310	57.65 311	70.74 318	26.31 325	72.40 327	32.03 328	71.06 281	76.93 319

DTE-MVSNet		76.99 191	76.80 168	77.54 250	86.24 180	53.06 300	87.52 124	90.66 105	77.08 46	72.50 204	88.67 123	60.48 158	89.52 235	57.33 246	70.74 282	90.05 158

MIMVSNet1		68.58 269	66.78 274	73.98 280	80.07 289	51.82 302	80.77 254	84.37 231	64.40 238	59.75 305	82.16 257	36.47 313	83.63 286	42.73 311	70.33 283	86.48 257

pmmvs6		74.69 221	73.39 220	78.61 233	81.38 274	57.48 258	86.64 158	87.95 193	64.99 233	70.18 233	86.61 187	50.43 246	89.52 235	62.12 204	70.18 284	88.83 197

TinyColmap		67.30 276	64.81 278	74.76 274	81.92 267	56.68 270	80.29 259	81.49 270	60.33 270	56.27 317	83.22 246	24.77 328	87.66 264	45.52 303	69.47 285	79.95 309

OurMVSNet-221017-0		74.26 225	72.42 231	79.80 209	83.76 232	59.59 233	85.92 178	86.64 209	66.39 219	66.96 272	87.58 151	39.46 302	91.60 199	65.76 179	69.27 286	88.22 219

JIA-IIPM		66.32 282	62.82 288	76.82 256	77.09 308	61.72 222	65.34 324	75.38 305	58.04 288	64.51 288	62.32 327	42.05 294	86.51 270	51.45 269	69.22 287	82.21 300

ADS-MVSNet2		66.20 283	63.33 283	74.82 273	79.92 290	58.75 239	67.55 320	75.19 307	53.37 312	65.25 284	75.86 305	42.32 291	80.53 297	41.57 313	68.91 288	85.18 273

ADS-MVSNet		64.36 288	62.88 287	68.78 303	79.92 290	47.17 319	67.55 320	71.18 323	53.37 312	65.25 284	75.86 305	42.32 291	73.99 324	41.57 313	68.91 288	85.18 273

test20.03		67.45 274	66.95 273	68.94 300	75.48 315	44.84 322	77.50 281	77.67 296	66.66 214	63.01 295	83.80 240	47.02 265	78.40 305	42.53 312	68.86 290	83.58 288

testus		59.00 296	57.91 295	62.25 313	72.25 323	39.09 332	69.74 308	75.02 308	53.04 314	57.21 313	73.72 312	18.76 336	70.33 331	32.86 324	68.57 291	77.35 316

EU-MVSNet		68.53 270	67.61 272	71.31 293	78.51 301	47.01 320	84.47 214	84.27 233	42.27 326	66.44 278	84.79 232	40.44 300	83.76 284	58.76 233	68.54 292	83.17 291

v16		77.69 174	76.36 178	81.68 174	84.15 213	64.63 169	87.33 130	88.99 164	72.69 134	69.31 250	82.08 258	62.80 113	91.79 183	72.70 124	67.23 293	88.63 204

v18		77.67 177	76.35 179	81.64 176	84.09 218	64.47 178	87.27 133	89.01 162	72.59 135	69.39 247	82.04 260	62.85 108	91.80 182	72.72 123	67.20 294	88.63 204

v17		77.68 175	76.35 179	81.69 173	84.15 213	64.65 167	87.33 130	88.99 164	72.70 133	69.25 251	82.07 259	62.82 112	91.79 183	72.69 125	67.15 295	88.63 204

v11		77.45 185	76.06 188	81.59 179	84.22 208	64.52 170	87.11 142	89.02 160	72.76 131	68.76 254	81.90 268	62.09 133	91.71 191	71.98 134	66.73 296	88.56 213

LF4IMVS		64.02 289	62.19 289	69.50 299	70.90 326	53.29 299	76.13 285	77.18 300	52.65 315	58.59 306	80.98 276	23.55 329	76.52 313	53.06 265	66.66 297	78.68 312

Patchmatch-RL test		70.24 261	67.78 270	77.61 248	77.43 304	59.57 234	71.16 303	70.33 324	62.94 251	68.65 256	72.77 314	50.62 244	85.49 277	69.58 150	66.58 298	87.77 229

dp		66.80 277	65.43 277	70.90 295	79.74 292	48.82 317	75.12 295	74.77 311	59.61 277	64.08 291	77.23 300	42.89 287	80.72 296	48.86 280	66.58 298	83.16 292

v15		77.51 182	76.12 182	81.66 175	84.09 218	64.65 167	87.14 137	88.96 168	72.76 131	68.90 252	81.91 267	62.74 115	91.73 187	72.32 129	66.29 300	88.61 207

V14		77.52 180	76.12 182	81.70 172	84.15 213	64.77 165	87.21 136	88.95 169	72.80 130	68.79 253	81.94 266	62.69 117	91.72 189	72.31 130	66.27 301	88.60 208

V9		77.52 180	76.11 185	81.73 171	84.19 212	64.89 162	87.26 134	88.94 172	72.87 129	68.65 256	81.96 265	62.65 120	91.72 189	72.27 131	66.24 302	88.60 208

v13		77.50 184	76.07 187	81.77 168	84.23 207	65.07 158	87.34 129	88.91 174	72.92 126	68.35 261	81.97 263	62.53 125	91.69 193	72.20 133	66.22 303	88.56 213

v12		77.51 182	76.09 186	81.76 170	84.22 208	64.99 159	87.30 132	88.93 173	72.92 126	68.48 260	81.97 263	62.54 124	91.70 192	72.24 132	66.21 304	88.58 211

FPMVS		53.68 304	51.64 304	59.81 316	65.08 332	51.03 308	69.48 311	69.58 328	41.46 327	40.67 330	72.32 315	16.46 339	70.00 332	24.24 336	65.42 305	58.40 332

pmmvs-eth3d		70.50 259	67.83 268	78.52 236	77.37 305	66.18 133	81.82 245	81.51 269	58.90 282	63.90 292	80.42 280	42.69 289	86.28 272	58.56 234	65.30 306	83.11 293

N_pmnet		52.79 305	53.26 303	51.40 324	78.99 300	7.68 349	69.52 310	3.89 350	51.63 319	57.01 314	74.98 308	40.83 298	65.96 337	37.78 319	64.67 307	80.56 308

PM-MVS		66.41 281	64.14 281	73.20 283	73.92 316	56.45 272	78.97 271	64.96 338	63.88 245	64.72 287	80.24 281	19.84 334	83.44 287	66.24 172	64.52 308	79.71 310

SixPastTwentyTwo		73.37 235	71.26 242	79.70 210	85.08 197	57.89 251	85.57 188	83.56 240	71.03 157	65.66 281	85.88 207	42.10 293	92.57 162	59.11 229	63.34 309	88.65 203

test1235678		58.74 297	56.89 300	64.30 308	69.70 327	41.87 328	71.05 304	74.87 310	54.06 307	50.63 326	71.53 317	25.30 327	74.10 323	31.80 329	63.10 310	76.93 319

testpf		56.51 301	57.58 298	53.30 321	71.99 324	41.19 330	46.89 337	69.32 330	58.06 287	52.87 324	69.45 322	27.99 323	72.73 326	59.59 225	62.07 311	45.98 335

TransMVSNet (Re)		75.39 219	74.56 210	77.86 243	85.50 190	57.10 262	86.78 154	86.09 219	72.17 143	71.53 220	87.34 158	63.01 106	89.31 240	56.84 249	61.83 312	87.17 243

MDA-MVSNet_test_wron		65.03 284	62.92 285	71.37 290	75.93 310	56.73 267	69.09 315	74.73 312	57.28 295	54.03 320	77.89 296	45.88 271	74.39 322	49.89 277	61.55 313	82.99 296

YYNet1		65.03 284	62.91 286	71.38 289	75.85 311	56.60 271	69.12 314	74.66 315	57.28 295	54.12 319	77.87 297	45.85 272	74.48 321	49.95 276	61.52 314	83.05 294

1111		57.11 300	56.82 301	57.97 318	69.10 328	28.28 340	68.90 316	74.54 316	54.01 308	53.71 321	74.51 309	23.09 330	67.90 335	32.28 326	61.26 315	77.73 314

ambc				75.24 270	73.16 320	50.51 312	63.05 328	87.47 202		64.28 289	77.81 298	17.80 337	89.73 232	57.88 241	60.64 316	85.49 269

TDRefinement		67.49 273	64.34 280	76.92 255	73.47 319	61.07 223	84.86 206	82.98 250	59.77 276	58.30 308	85.13 224	26.06 326	87.89 261	47.92 289	60.59 317	81.81 303

Gipuma		45.18 311	41.86 312	55.16 320	77.03 309	51.52 305	32.50 340	80.52 276	32.46 334	27.12 336	35.02 337	9.52 345	75.50 317	22.31 337	60.21 318	38.45 337
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
new-patchmatchnet		61.73 291	61.73 291	61.70 314	72.74 322	24.50 345	69.16 313	78.03 295	61.40 264	56.72 315	75.53 307	38.42 306	76.48 314	45.95 301	57.67 319	84.13 285

Anonymous20231211		64.82 286	61.79 290	73.91 281	77.11 307	50.92 309	85.29 197	81.53 268	54.19 306	57.98 309	78.03 295	26.90 324	87.83 263	37.92 318	57.12 320	82.99 296

MDA-MVSNet-bldmvs		66.68 278	63.66 282	75.75 264	79.28 298	60.56 229	73.92 299	78.35 293	64.43 237	50.13 327	79.87 285	44.02 282	83.67 285	46.10 300	56.86 321	83.03 295

new_pmnet		50.91 307	50.29 306	52.78 322	68.58 330	34.94 338	63.71 326	56.63 340	39.73 329	44.95 328	65.47 325	21.93 332	58.48 339	34.98 322	56.62 322	64.92 329

LP		61.36 293	57.78 296	72.09 286	75.54 314	58.53 241	67.16 322	75.22 306	51.90 318	54.13 318	69.97 320	37.73 310	80.45 298	32.74 325	55.63 323	77.29 317

PMVS	37.38 22	44.16 312	40.28 313	55.82 319	40.82 346	42.54 326	65.12 325	63.99 339	34.43 333	24.48 337	57.12 332	3.92 348	76.17 315	17.10 339	55.52 324	48.75 333
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		57.79 298	54.26 302	68.37 304	64.02 333	56.72 268	75.12 295	65.17 336	40.20 328	52.93 323	69.86 321	20.36 333	75.48 318	45.45 304	55.25 325	72.90 324

UnsupCasMVSNet_eth		67.33 275	65.99 276	71.37 290	73.48 318	51.47 306	75.16 293	85.19 225	65.20 230	60.78 300	80.93 278	42.35 290	77.20 311	57.12 247	53.69 326	85.44 270

K. test v3		71.19 252	68.51 258	79.21 221	83.04 249	57.78 254	84.35 221	76.91 301	72.90 128	62.99 296	82.86 249	39.27 303	91.09 214	61.65 209	52.66 327	88.75 200

UnsupCasMVSNet_bld		63.70 290	61.53 292	70.21 297	73.69 317	51.39 307	72.82 300	81.89 265	55.63 302	57.81 310	71.80 316	38.67 305	78.61 304	49.26 279	52.21 328	80.63 306

LCM-MVSNet		54.25 302	49.68 308	67.97 305	53.73 340	45.28 321	66.85 323	80.78 274	35.96 332	39.45 332	62.23 328	8.70 346	78.06 308	48.24 286	51.20 329	80.57 307

testmv		53.85 303	51.03 305	62.31 312	61.46 335	38.88 333	70.95 307	74.69 314	51.11 320	41.26 329	66.85 323	14.28 340	72.13 328	29.19 331	49.51 330	75.93 322

lessismore_v0				78.97 227	81.01 280	57.15 261		65.99 335		61.16 299	82.82 250	39.12 304	91.34 204	59.67 223	46.92 331	88.43 217

test12356		49.28 309	48.51 309	51.59 323	62.06 334	19.11 346	60.40 329	72.45 321	47.60 324	40.64 331	65.68 324	13.84 341	68.72 333	27.29 333	46.67 332	66.94 328

PVSNet_0	57.27 20	61.67 292	59.27 293	68.85 302	79.61 293	57.44 259	68.01 318	73.44 319	55.93 301	58.54 307	70.41 319	44.58 279	77.55 310	47.01 291	35.91 333	71.55 325

PMMVS2		40.82 313	38.86 314	46.69 326	53.84 339	16.45 347	48.61 336	49.92 343	37.49 331	31.67 334	60.97 329	8.14 347	56.42 340	28.42 332	30.72 334	67.19 327

no-one		51.08 306	45.79 311	66.95 307	57.92 338	50.49 313	59.63 331	76.04 304	48.04 322	31.85 333	56.10 333	19.12 335	80.08 300	36.89 320	26.52 335	70.29 326

DeepMVS_CX				27.40 332	40.17 347	26.90 343		24.59 349	17.44 341	23.95 338	48.61 334	9.77 344	26.48 344	18.06 338	24.47 336	28.83 338

MVE	26.22 23	30.37 319	25.89 321	43.81 327	44.55 345	35.46 337	28.87 341	39.07 346	18.20 340	18.58 341	40.18 336	2.68 349	47.37 343	17.07 340	23.78 337	48.60 334
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuykxyi23d		39.76 314	33.18 317	59.51 317	46.98 344	44.01 323	57.70 333	67.74 333	24.13 338	13.98 344	34.33 338	1.27 351	71.33 329	34.23 323	18.23 338	63.18 331

E-PMN		31.77 317	30.64 318	35.15 328	52.87 341	27.67 342	57.09 334	47.86 344	24.64 337	16.40 342	33.05 339	11.23 343	54.90 341	14.46 341	18.15 339	22.87 339

PNet_i23d		38.26 315	35.42 315	46.79 325	58.74 336	35.48 336	59.65 330	51.25 342	32.45 335	23.44 340	47.53 335	2.04 350	58.96 338	25.60 335	18.09 340	45.92 336

EMVS		30.81 318	29.65 319	34.27 329	50.96 342	25.95 344	56.58 335	46.80 345	24.01 339	15.53 343	30.68 340	12.47 342	54.43 342	12.81 342	17.05 341	22.43 340

ANet_high		50.57 308	46.10 310	63.99 309	48.67 343	39.13 331	70.99 306	80.85 273	61.39 265	31.18 335	57.70 331	17.02 338	73.65 325	31.22 330	15.89 342	79.18 311

tmp_tt		18.61 321	21.40 322	10.23 334	4.82 348	10.11 348	34.70 339	30.74 348	1.48 343	23.91 339	26.07 341	28.42 322	13.41 346	27.12 334	15.35 343	7.17 341

wuyk23d		16.82 322	15.94 323	19.46 333	58.74 336	31.45 339	39.22 338	3.74 351	6.84 342	6.04 345	2.70 345	1.27 351	24.29 345	10.54 343	14.40 344	2.63 342

.test1245		45.55 310	50.02 307	32.14 330	69.10 328	28.28 340	68.90 316	74.54 316	54.01 308	53.71 321	74.51 309	23.09 330	67.90 335	32.28 326	0.02 345	0.25 344

testmvs		6.04 325	8.02 326	0.10 336	0.08 349	0.03 351	69.74 308	0.04 352	0.05 344	0.31 346	1.68 346	0.02 354	0.04 347	0.24 344	0.02 345	0.25 344

test123		6.12 324	8.11 325	0.14 335	0.06 350	0.09 350	71.05 304	0.03 353	0.04 345	0.25 347	1.30 347	0.05 353	0.03 348	0.21 345	0.01 347	0.29 343

cdsmvs_eth3d_5k		19.96 320	26.61 320	0.00 337	0.00 351	0.00 352	0.00 342	89.26 154	0.00 346	0.00 348	88.61 125	61.62 137	0.00 349	0.00 346	0.00 348	0.00 346

pcd_1.5k_mvsjas		5.26 326	7.02 327	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	63.15 102	0.00 349	0.00 346	0.00 348	0.00 346

sosnet-low-res		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

sosnet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

uncertanet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

Regformer		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

ab-mvs-re		7.23 323	9.64 324	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	86.72 176	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

uanet		0.00 327	0.00 328	0.00 337	0.00 351	0.00 352	0.00 342	0.00 354	0.00 346	0.00 348	0.00 348	0.00 355	0.00 349	0.00 346	0.00 348	0.00 346

test_part2					95.06 1	72.65 26				91.80 1

test11111								94.22 1

sam_mvs1												51.32 235

sam_mvs												50.01 249

MTGPA								92.02 60

test_post1							78.90 273				5.43 344	48.81 260	85.44 278	59.25 228

test_post											5.46 343	50.36 247	84.24 283

patchmatchnet-post											74.00 311	51.12 238	88.60 254

MTMP								32.83 347

gm-plane-assit					81.40 273	53.83 296			62.72 255		80.94 277		92.39 167	63.40 193

TEST9					93.26 34	72.96 19	88.75 84	91.89 69	68.44 200	85.00 28	93.10 39	74.36 17	95.41 48

test_8					93.13 36	72.57 29	88.68 87	91.84 72	68.69 196	84.87 34	93.10 39	74.43 14	95.16 56

agg_prior					92.85 42	71.94 40		91.78 75		84.41 40			94.93 65

test_prior4						72.60 28	89.01 75

test_prior				86.33 46	92.61 47	69.59 72		92.97 31					95.48 43			93.91 30

旧先验2							86.56 161		58.10 286	87.04 14			88.98 247	74.07 109

新几何2							86.29 170

无先验							87.48 125	88.98 166	60.00 274				94.12 93	67.28 165		88.97 195

原ACMM2							86.86 150

testdata2													91.01 216	62.37 201

segment_acmp												73.08 24

testdata1							84.14 226		75.71 71

plane_prior7					90.08 79	68.51 98

plane_prior6					89.84 84	68.70 94						60.42 159

plane_prior4											91.00 80

plane_prior3						68.60 96			78.44 30	78.92 95

plane_prior2							91.25 28		79.12 23

plane_prior1					89.90 83

n2								0.00 354

nn								0.00 354

door-mid								69.98 326

test11								92.23 52

door								69.44 329

HQP5-MVS						66.98 123

HQP-NCC					89.33 100		89.17 68		76.41 59	77.23 135

ACMP_Plane					89.33 100		89.17 68		76.41 59	77.23 135

BP-MVS														77.47 75

HQP4-MVS										77.24 134			95.11 58			91.03 112

HQP2-MVS												60.17 162

NP-MVS					89.62 90	68.32 100					90.24 90

MDTV_nov1_ep13_2view						37.79 334	75.16 293		55.10 303	66.53 276		49.34 254		53.98 259		87.94 225

Test By Simon												64.33 88

High-resolution multi-view benchmark