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.

MVS_0304		88.08 13	88.08 16	88.08 13	89.67 113	72.04 47	92.26 32	89.26 172	84.19 1	85.01 48	95.18 13	69.93 66	97.20 13	91.63 1	95.60 28	94.99 8

UA-Net		85.08 61	84.96 61	85.45 69	92.07 70	68.07 125	89.78 79	90.86 125	82.48 2	84.60 61	93.20 59	69.35 72	95.22 73	71.39 167	90.88 91	93.07 90

CANet		86.45 37	86.10 44	87.51 36	90.09 101	70.94 66	89.70 82	92.59 66	81.78 3	81.32 105	91.43 99	70.34 61	97.23 12	84.26 45	93.36 63	94.37 34

NCCC		88.06 14	88.01 18	88.24 10	94.41 22	73.62 10	91.22 51	92.83 55	81.50 4	85.79 41	93.47 54	73.02 39	97.00 17	84.90 35	94.94 38	94.10 44

EPNet		83.72 71	82.92 80	86.14 58	84.22 254	69.48 90	91.05 54	85.27 254	81.30 5	76.83 183	91.65 90	66.09 103	95.56 57	76.00 125	93.85 59	93.38 78
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CNVR-MVS		88.93 9	89.13 9	88.33 7	94.77 12	73.82 8	90.51 60	93.00 43	80.90 6	88.06 26	94.06 41	76.43 16	96.84 20	88.48 20	95.99 18	94.34 36

3Dnovator+	77.84 4	85.48 53	84.47 67	88.51 6	91.08 81	73.49 15	93.18 11	93.78 18	80.79 7	76.66 188	93.37 55	60.40 181	96.75 25	77.20 110	93.73 61	95.29 4

TranMVSNet+NR-MVSNet		80.84 120	80.31 119	82.42 180	87.85 182	62.33 239	87.74 147	91.33 112	80.55 8	77.99 160	89.86 132	65.23 112	92.62 185	67.05 211	75.24 294	92.30 116

MSP-MVS		89.51 4	89.91 5	88.30 9	94.28 30	73.46 16	92.90 16	94.11 6	80.27 9	91.35 14	94.16 36	78.35 13	96.77 23	89.59 7	94.22 57	94.67 23
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++		89.02 8	89.15 8	88.63 4	95.01 9	76.03 1	92.38 26	92.85 54	80.26 10	87.78 28	94.27 31	75.89 19	96.81 22	87.45 25	96.44 9	93.05 91

UniMVSNet_NR-MVSNet		81.88 99	81.54 99	82.92 164	88.46 163	63.46 221	87.13 161	92.37 73	80.19 11	78.38 147	89.14 153	71.66 50	93.05 175	70.05 179	76.46 267	92.25 118

SteuartSystems-ACMMP		88.72 10	88.86 10	88.32 8	92.14 69	72.96 24	93.73 5	93.67 20	80.19 11	88.10 25	94.80 16	73.76 33	97.11 14	87.51 24	95.82 21	94.90 12
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		84.19 66	83.81 70	85.31 72	88.18 171	67.85 128	87.66 148	89.73 158	80.05 13	82.95 85	89.59 141	70.74 58	94.82 94	80.66 82	84.72 165	93.28 83

ETV-MVS		84.90 64	84.67 64	85.59 66	89.39 124	68.66 114	88.74 112	92.64 65	79.97 14	84.10 70	85.71 247	69.32 73	95.38 68	80.82 78	91.37 85	92.72 99

EI-MVSNet-UG-set		83.81 69	83.38 73	85.09 79	87.87 181	67.53 136	87.44 154	89.66 159	79.74 15	82.23 94	89.41 150	70.24 63	94.74 97	79.95 86	83.92 175	92.99 95

CS-MVS		86.69 34	86.95 30	85.90 62	90.76 90	67.57 135	92.83 17	93.30 32	79.67 16	84.57 62	92.27 79	71.47 51	95.02 85	84.24 47	93.46 62	95.13 5

casdiffmvs_mvg		85.99 43	86.09 45	85.70 65	87.65 192	67.22 144	88.69 114	93.04 38	79.64 17	85.33 45	92.54 76	73.30 35	94.50 106	83.49 52	91.14 88	95.37 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MTAPA		87.23 27	87.00 28	87.90 21	94.18 35	74.25 5	86.58 180	92.02 85	79.45 18	85.88 39	94.80 16	68.07 82	96.21 41	86.69 29	95.34 32	93.23 84

EC-MVSNet		86.01 42	86.38 37	84.91 87	89.31 130	66.27 159	92.32 29	93.63 21	79.37 19	84.17 69	91.88 86	69.04 78	95.43 64	83.93 50	93.77 60	93.01 94

XVS		87.18 28	86.91 32	88.00 16	94.42 20	73.33 18	92.78 18	92.99 45	79.14 20	83.67 78	94.17 35	67.45 88	96.60 32	83.06 56	94.50 49	94.07 46

X-MVStestdata		80.37 138	77.83 175	88.00 16	94.42 20	73.33 18	92.78 18	92.99 45	79.14 20	83.67 78	12.47 389	67.45 88	96.60 32	83.06 56	94.50 49	94.07 46

HQP_MVS		83.64 73	83.14 75	85.14 76	90.08 102	68.71 110	91.25 49	92.44 69	79.12 22	78.92 134	91.00 113	60.42 179	95.38 68	78.71 95	86.32 148	91.33 142

plane_prior2							91.25 49		79.12 22

IS-MVSNet		83.15 81	82.81 81	84.18 112	89.94 109	63.30 225	91.59 42	88.46 203	79.04 24	79.49 126	92.16 81	65.10 113	94.28 111	67.71 202	91.86 80	94.95 9

DU-MVS		81.12 116	80.52 115	82.90 165	87.80 185	63.46 221	87.02 165	91.87 95	79.01 25	78.38 147	89.07 155	65.02 114	93.05 175	70.05 179	76.46 267	92.20 120

NR-MVSNet		80.23 141	79.38 137	82.78 173	87.80 185	63.34 224	86.31 187	91.09 119	79.01 25	72.17 262	89.07 155	67.20 91	92.81 184	66.08 218	75.65 280	92.20 120

CS-MVS-test		86.29 41	86.48 36	85.71 64	91.02 83	67.21 145	92.36 28	93.78 18	78.97 27	83.51 81	91.20 104	70.65 60	95.15 76	81.96 69	94.89 40	94.77 21

DELS-MVS		85.41 56	85.30 57	85.77 63	88.49 161	67.93 127	85.52 212	93.44 27	78.70 28	83.63 80	89.03 157	74.57 24	95.71 55	80.26 85	94.04 58	93.66 64
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
WR-MVS		79.49 155	79.22 144	80.27 229	88.79 151	58.35 280	85.06 217	88.61 201	78.56 29	77.65 165	88.34 177	63.81 124	90.66 248	64.98 227	77.22 256	91.80 131

plane_prior3						68.60 115			78.44 30	78.92 134

UniMVSNet (Re)		81.60 108	81.11 104	83.09 155	88.38 166	64.41 202	87.60 149	93.02 42	78.42 31	78.56 143	88.16 183	69.78 68	93.26 158	69.58 186	76.49 266	91.60 133

DVP-MVS++		90.23 1	91.01 1	87.89 23	94.34 27	71.25 56	95.06 1	94.23 3	78.38 32	92.78 4	95.74 6	82.45 3	97.49 3	89.42 8	96.68 2	94.95 9

test_0728_THIRD									78.38 32	92.12 9	95.78 4	81.46 7	97.40 7	89.42 8	96.57 7	94.67 23

test_one_0601					95.07 7	71.46 54		94.14 5	78.27 34	92.05 11	95.74 6	80.83 11

SD-MVS		88.06 14	88.50 13	86.71 50	92.60 66	72.71 28	91.81 41	93.19 35	77.87 35	90.32 17	94.00 44	74.83 23	93.78 135	87.63 23	94.27 56	93.65 68
Zhenlong Yuan, Jiakai Cao, Zhaoxin Li, Hao Jiang and Zhaoqi Wang: SD-MVS: Segmentation-driven Deformation Multi-View Stereo with Spherical Refinement and EM optimization. AAAI2024
casdiffmvs		85.11 60	85.14 59	85.01 81	87.20 208	65.77 172	87.75 146	92.83 55	77.84 36	84.36 66	92.38 78	72.15 44	93.93 129	81.27 74	90.48 95	95.33 3
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
CP-MVSNet		78.22 187	78.34 163	77.84 268	87.83 184	54.54 331	87.94 140	91.17 116	77.65 37	73.48 247	88.49 173	62.24 146	88.43 281	62.19 247	74.07 303	90.55 172

plane_prior						68.71 110	90.38 66		77.62 38						86.16 152

baseline		84.93 62	84.98 60	84.80 91	87.30 206	65.39 181	87.30 158	92.88 52	77.62 38	84.04 72	92.26 80	71.81 46	93.96 123	81.31 73	90.30 98	95.03 7

VDD-MVS		83.01 86	82.36 87	84.96 83	91.02 83	66.40 156	88.91 103	88.11 206	77.57 40	84.39 65	93.29 57	52.19 239	93.91 130	77.05 112	88.70 119	94.57 28

MP-MVS		87.71 19	87.64 21	87.93 20	94.36 26	73.88 6	92.71 22	92.65 64	77.57 40	83.84 75	94.40 29	72.24 43	96.28 39	85.65 31	95.30 34	93.62 71
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
PEN-MVS		77.73 202	77.69 183	77.84 268	87.07 211	53.91 336	87.91 142	91.18 115	77.56 42	73.14 251	88.82 163	61.23 164	89.17 268	59.95 266	72.37 318	90.43 176

OPM-MVS		83.50 75	82.95 79	85.14 76	88.79 151	70.95 65	89.13 98	91.52 106	77.55 43	80.96 112	91.75 88	60.71 172	94.50 106	79.67 88	86.51 146	89.97 202
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
DeepPCF-MVS	80.84 1	88.10 12	88.56 12	86.73 49	92.24 68	69.03 98	89.57 86	93.39 30	77.53 44	89.79 18	94.12 38	78.98 12	96.58 34	85.66 30	95.72 24	94.58 26

PS-CasMVS		78.01 196	78.09 168	77.77 270	87.71 189	54.39 333	88.02 136	91.22 113	77.50 45	73.26 249	88.64 168	60.73 171	88.41 282	61.88 251	73.88 307	90.53 173

MSLP-MVS++		85.43 55	85.76 49	84.45 102	91.93 72	70.24 75	90.71 57	92.86 53	77.46 46	84.22 67	92.81 71	67.16 92	92.94 179	80.36 83	94.35 54	90.16 186

DVP-MVS		89.60 3	90.35 3	87.33 39	95.27 5	71.25 56	93.49 9	92.73 59	77.33 47	92.12 9	95.78 4	80.98 9	97.40 7	89.08 11	96.41 12	93.33 81
Zhenlong Yuan, Jinguo Luo, Fei Shen, Zhaoxin Li, Cong Liu, Tianlu Mao, Zhaoqi Wang: DVP-MVS: Synergize Depth-Edge and Visibility Prior for Multi-View Stereo. AAAI2025
test0726					95.27 5	71.25 56	93.60 6	94.11 6	77.33 47	92.81 3	95.79 3	80.98 9

SED-MVS		90.08 2	90.85 2	87.77 25	95.30 2	70.98 62	93.57 7	94.06 10	77.24 49	93.10 1	95.72 8	82.99 1	97.44 5	89.07 13	96.63 4	94.88 13

test_241102_TWO								94.06 10	77.24 49	92.78 4	95.72 8	81.26 8	97.44 5	89.07 13	96.58 6	94.26 40

3Dnovator	76.31 5	83.38 79	82.31 88	86.59 51	87.94 180	72.94 27	90.64 58	92.14 84	77.21 51	75.47 213	92.83 69	58.56 188	94.72 98	73.24 152	92.71 68	92.13 123

test_241102_ONE					95.30 2	70.98 62		94.06 10	77.17 52	93.10 1	95.39 11	82.99 1	97.27 10

WR-MVS_H		78.51 182	78.49 158	78.56 258	88.02 178	56.38 313	88.43 120	92.67 61	77.14 53	73.89 244	87.55 198	66.25 101	89.24 267	58.92 276	73.55 310	90.06 196

DeepC-MVS	79.81 2	87.08 31	86.88 33	87.69 32	91.16 80	72.32 42	90.31 67	93.94 14	77.12 54	82.82 89	94.23 34	72.13 45	97.09 15	84.83 38	95.37 31	93.65 68
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FC-MVSNet-test		81.52 109	82.02 93	80.03 233	88.42 165	55.97 318	87.95 139	93.42 29	77.10 55	77.38 170	90.98 115	69.96 65	91.79 216	68.46 198	84.50 167	92.33 114

DTE-MVSNet		76.99 217	76.80 201	77.54 275	86.24 221	53.06 344	87.52 151	90.66 128	77.08 56	72.50 257	88.67 167	60.48 178	89.52 262	57.33 292	70.74 329	90.05 197

LFMVS		81.82 101	81.23 102	83.57 137	91.89 73	63.43 223	89.84 75	81.85 300	77.04 57	83.21 82	93.10 60	52.26 238	93.43 154	71.98 162	89.95 106	93.85 56

UGNet		80.83 121	79.59 133	84.54 97	88.04 177	68.09 124	89.42 88	88.16 205	76.95 58	76.22 200	89.46 146	49.30 278	93.94 126	68.48 197	90.31 97	91.60 133
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
FIs		82.07 96	82.42 84	81.04 212	88.80 150	58.34 281	88.26 128	93.49 26	76.93 59	78.47 146	91.04 110	69.92 67	92.34 198	69.87 183	84.97 162	92.44 113

GST-MVS		87.42 24	87.26 24	87.89 23	94.12 36	72.97 23	92.39 25	93.43 28	76.89 60	84.68 56	93.99 46	70.67 59	96.82 21	84.18 49	95.01 36	93.90 54

mPP-MVS		86.67 36	86.32 38	87.72 29	94.41 22	73.55 12	92.74 20	92.22 80	76.87 61	82.81 90	94.25 33	66.44 98	96.24 40	82.88 60	94.28 55	93.38 78

ZNCC-MVS		87.94 18	87.85 19	88.20 11	94.39 24	73.33 18	93.03 14	93.81 17	76.81 62	85.24 46	94.32 30	71.76 47	96.93 18	85.53 32	95.79 22	94.32 37

VPNet		78.69 178	78.66 155	78.76 254	88.31 168	55.72 320	84.45 234	86.63 237	76.79 63	78.26 151	90.55 121	59.30 184	89.70 260	66.63 213	77.05 258	90.88 159

HFP-MVS		87.58 21	87.47 23	87.94 18	94.58 16	73.54 14	93.04 12	93.24 33	76.78 64	84.91 52	94.44 27	70.78 57	96.61 31	84.53 42	94.89 40	93.66 64

ACMMPR		87.44 22	87.23 26	88.08 13	94.64 13	73.59 11	93.04 12	93.20 34	76.78 64	84.66 59	94.52 20	68.81 79	96.65 29	84.53 42	94.90 39	94.00 49

ACMMP		85.89 47	85.39 53	87.38 38	93.59 45	72.63 32	92.74 20	93.18 36	76.78 64	80.73 114	93.82 50	64.33 118	96.29 38	82.67 66	90.69 93	93.23 84
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
region2R		87.42 24	87.20 27	88.09 12	94.63 14	73.55 12	93.03 14	93.12 37	76.73 67	84.45 63	94.52 20	69.09 75	96.70 26	84.37 44	94.83 43	94.03 48

canonicalmvs		85.91 46	85.87 48	86.04 59	89.84 111	69.44 94	90.45 65	93.00 43	76.70 68	88.01 27	91.23 102	73.28 36	93.91 130	81.50 72	88.80 117	94.77 21

CP-MVS		87.11 29	86.92 31	87.68 33	94.20 34	73.86 7	93.98 3	92.82 58	76.62 69	83.68 77	94.46 24	67.93 83	95.95 51	84.20 48	94.39 52	93.23 84

DeepC-MVS_fast	79.65 3	86.91 32	86.62 35	87.76 26	93.52 46	72.37 40	91.26 47	93.04 38	76.62 69	84.22 67	93.36 56	71.44 52	96.76 24	80.82 78	95.33 33	94.16 42
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		85.71 50	85.33 55	86.84 46	91.34 78	72.50 35	89.07 99	87.28 227	76.41 71	85.80 40	90.22 127	74.15 31	95.37 71	81.82 70	91.88 77	92.65 104

HQP-NCC					89.33 127		89.17 93		76.41 71	77.23 175

ACMP_Plane					89.33 127		89.17 93		76.41 71	77.23 175

HQP-MVS		82.61 90	82.02 93	84.37 104	89.33 127	66.98 148	89.17 93	92.19 82	76.41 71	77.23 175	90.23 126	60.17 182	95.11 79	77.47 107	85.99 155	91.03 154

CANet_DTU		80.61 130	79.87 127	82.83 167	85.60 230	63.17 230	87.36 155	88.65 199	76.37 75	75.88 207	88.44 175	53.51 229	93.07 174	73.30 150	89.74 109	92.25 118

VNet		82.21 93	82.41 85	81.62 193	90.82 88	60.93 255	84.47 231	89.78 155	76.36 76	84.07 71	91.88 86	64.71 117	90.26 251	70.68 173	88.89 115	93.66 64

Vis-MVSNet		83.46 76	82.80 82	85.43 70	90.25 98	68.74 108	90.30 68	90.13 147	76.33 77	80.87 113	92.89 67	61.00 169	94.20 117	72.45 161	90.97 89	93.35 80
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
ACMMP_NAP		88.05 16	88.08 16	87.94 18	93.70 41	73.05 21	90.86 55	93.59 23	76.27 78	88.14 24	95.09 15	71.06 55	96.67 28	87.67 22	96.37 14	94.09 45

alignmvs		85.48 53	85.32 56	85.96 61	89.51 119	69.47 91	89.74 80	92.47 68	76.17 79	87.73 32	91.46 98	70.32 62	93.78 135	81.51 71	88.95 114	94.63 25

MVS_111021_HR		85.14 59	84.75 63	86.32 54	91.65 76	72.70 29	85.98 195	90.33 140	76.11 80	82.08 95	91.61 93	71.36 54	94.17 119	81.02 75	92.58 69	92.08 124

HPM-MVS		87.11 29	86.98 29	87.50 37	93.88 39	72.16 44	92.19 33	93.33 31	76.07 81	83.81 76	93.95 48	69.77 69	96.01 47	85.15 33	94.66 45	94.32 37
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
h-mvs33		83.15 81	82.19 89	86.02 60	90.56 92	70.85 69	88.15 133	89.16 177	76.02 82	84.67 57	91.39 100	61.54 155	95.50 60	82.71 63	75.48 284	91.72 132

hse-mvs2		81.72 102	80.94 108	84.07 118	88.72 154	67.68 133	85.87 199	87.26 228	76.02 82	84.67 57	88.22 182	61.54 155	93.48 150	82.71 63	73.44 312	91.06 152

DPE-MVS		89.48 5	89.98 4	88.01 15	94.80 11	72.69 30	91.59 42	94.10 8	75.90 84	92.29 7	95.66 10	81.67 6	97.38 9	87.44 26	96.34 15	93.95 51
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CLD-MVS		82.31 92	81.65 98	84.29 109	88.47 162	67.73 131	85.81 203	92.35 74	75.78 85	78.33 149	86.58 229	64.01 121	94.35 109	76.05 124	87.48 132	90.79 161
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
SF-MVS		88.46 11	88.74 11	87.64 34	92.78 61	71.95 49	92.40 23	94.74 2	75.71 86	89.16 19	95.10 14	75.65 21	96.19 42	87.07 27	96.01 17	94.79 20

testdata1							84.14 242		75.71 86

APDe-MVS		89.15 6	89.63 6	87.73 27	94.49 18	71.69 51	93.83 4	93.96 13	75.70 88	91.06 16	96.03 1	76.84 14	97.03 16	89.09 10	95.65 27	94.47 30
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
VPA-MVSNet		80.60 131	80.55 114	80.76 219	88.07 176	60.80 258	86.86 170	91.58 105	75.67 89	80.24 118	89.45 148	63.34 125	90.25 252	70.51 175	79.22 238	91.23 146

PGM-MVS		86.68 35	86.27 39	87.90 21	94.22 33	73.38 17	90.22 69	93.04 38	75.53 90	83.86 74	94.42 28	67.87 85	96.64 30	82.70 65	94.57 48	93.66 64

Effi-MVS+		83.62 74	83.08 76	85.24 74	88.38 166	67.45 137	88.89 104	89.15 178	75.50 91	82.27 93	88.28 179	69.61 70	94.45 108	77.81 104	87.84 127	93.84 58

test_prior2							88.85 106		75.41 92	84.91 52	93.54 51	74.28 29		83.31 54	95.86 20

LPG-MVS_test		82.08 95	81.27 101	84.50 98	89.23 134	68.76 106	90.22 69	91.94 91	75.37 93	76.64 189	91.51 95	54.29 221	94.91 87	78.44 97	83.78 176	89.83 207

LGP-MVS_train				84.50 98	89.23 134	68.76 106		91.94 91	75.37 93	76.64 189	91.51 95	54.29 221	94.91 87	78.44 97	83.78 176	89.83 207

MG-MVS		83.41 77	83.45 72	83.28 145	92.74 62	62.28 241	88.17 131	89.50 163	75.22 95	81.49 104	92.74 75	66.75 93	95.11 79	72.85 155	91.58 82	92.45 112

LCM-MVSNet-Re		77.05 216	76.94 198	77.36 276	87.20 208	51.60 350	80.06 294	80.46 313	75.20 96	67.69 305	86.72 219	62.48 140	88.98 272	63.44 235	89.25 113	91.51 136

SDMVSNet		80.38 136	80.18 122	80.99 213	89.03 143	64.94 190	80.45 290	89.40 165	75.19 97	76.61 191	89.98 130	60.61 176	87.69 290	76.83 116	83.55 183	90.33 180

sd_testset		77.70 205	77.40 188	78.60 257	89.03 143	60.02 269	79.00 307	85.83 249	75.19 97	76.61 191	89.98 130	54.81 212	85.46 306	62.63 244	83.55 183	90.33 180

MP-MVS-pluss		87.67 20	87.72 20	87.54 35	93.64 44	72.04 47	89.80 78	93.50 25	75.17 99	86.34 37	95.29 12	70.86 56	96.00 48	88.78 16	96.04 16	94.58 26
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
test1111		79.43 158	79.18 146	80.15 231	89.99 107	53.31 342	87.33 157	77.05 339	75.04 100	80.23 119	92.77 74	48.97 284	92.33 199	68.87 193	92.40 73	94.81 19

Effi-MVS+-dtu		80.03 145	78.57 157	84.42 103	85.13 239	68.74 108	88.77 109	88.10 207	74.99 101	74.97 233	83.49 289	57.27 201	93.36 155	73.53 146	80.88 215	91.18 147

OMC-MVS		82.69 88	81.97 95	84.85 88	88.75 153	67.42 138	87.98 137	90.87 124	74.92 102	79.72 123	91.65 90	62.19 147	93.96 123	75.26 133	86.42 147	93.16 88

test2506		77.30 213	76.49 209	79.74 239	90.08 102	52.02 345	87.86 145	63.10 377	74.88 103	80.16 120	92.79 72	38.29 346	92.35 197	68.74 195	92.50 71	94.86 16

ECVR-MVS		79.61 151	79.26 142	80.67 221	90.08 102	54.69 329	87.89 143	77.44 336	74.88 103	80.27 117	92.79 72	48.96 285	92.45 191	68.55 196	92.50 71	94.86 16

nrg030		83.88 68	83.53 71	84.96 83	86.77 216	69.28 97	90.46 64	92.67 61	74.79 105	82.95 85	91.33 101	72.70 41	93.09 173	80.79 80	79.28 237	92.50 109

SMA-MVS		89.08 7	89.23 7	88.61 5	94.25 31	73.73 9	92.40 23	93.63 21	74.77 106	92.29 7	95.97 2	74.28 29	97.24 11	88.58 18	96.91 1	94.87 15
Yufeng Yin; Xiaoyan Liu; Zichao Zhang: SMA-MVS: Segmentation-Guided Multi-Scale Anchor Deformation Patch Multi-View Stereo. IEEE Transactions on Circuits and Systems for Video Technology
MVS_111021_LR		82.61 90	82.11 90	84.11 113	88.82 148	71.58 52	85.15 215	86.16 244	74.69 107	80.47 116	91.04 110	62.29 144	90.55 249	80.33 84	90.08 103	90.20 185

EIA-MVS		83.31 80	82.80 82	84.82 89	89.59 115	65.59 174	88.21 129	92.68 60	74.66 108	78.96 132	86.42 234	69.06 76	95.26 72	75.54 131	90.09 102	93.62 71

mvsmamba		81.69 104	80.74 110	84.56 96	87.45 199	66.72 152	91.26 47	85.89 248	74.66 108	78.23 152	90.56 120	54.33 220	94.91 87	80.73 81	83.54 185	92.04 127

TSAR-MVS + MP.		88.02 17	88.11 15	87.72 29	93.68 43	72.13 45	91.41 46	92.35 74	74.62 110	88.90 20	93.85 49	75.75 20	96.00 48	87.80 21	94.63 46	95.04 6
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
SR-MVS		86.73 33	86.67 34	86.91 45	94.11 37	72.11 46	92.37 27	92.56 67	74.50 111	86.84 35	94.65 19	67.31 90	95.77 53	84.80 39	92.85 66	92.84 98

FOURS1					95.00 10	72.39 38	95.06 1	93.84 15	74.49 112	91.30 15

ACMP	74.13 6	81.51 111	80.57 113	84.36 105	89.42 122	68.69 113	89.97 73	91.50 110	74.46 113	75.04 232	90.41 123	53.82 226	94.54 103	77.56 106	82.91 192	89.86 206
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
EPP-MVSNet		83.40 78	83.02 78	84.57 95	90.13 100	64.47 200	92.32 29	90.73 127	74.45 114	79.35 128	91.10 107	69.05 77	95.12 77	72.78 156	87.22 135	94.13 43

save fliter					93.80 40	72.35 41	90.47 63	91.17 116	74.31 115

MVS_Test		83.15 81	83.06 77	83.41 142	86.86 212	63.21 227	86.11 193	92.00 87	74.31 115	82.87 87	89.44 149	70.03 64	93.21 162	77.39 109	88.50 123	93.81 59

UniMVSNet_ETH3D		79.10 168	78.24 166	81.70 192	86.85 213	60.24 267	87.28 159	88.79 192	74.25 117	76.84 182	90.53 122	49.48 274	91.56 222	67.98 200	82.15 201	93.29 82

IterMVS-LS		80.06 144	79.38 137	82.11 184	85.89 225	63.20 228	86.79 173	89.34 167	74.19 118	75.45 216	86.72 219	66.62 94	92.39 194	72.58 158	76.86 261	90.75 164
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 134	79.98 124	82.12 183	84.28 252	63.19 229	86.41 184	88.95 188	74.18 119	78.69 138	87.54 199	66.62 94	92.43 192	72.57 159	80.57 221	90.74 165

Vis-MVSNet (Re-imp)		78.36 185	78.45 159	78.07 266	88.64 157	51.78 349	86.70 177	79.63 322	74.14 120	75.11 229	90.83 116	61.29 163	89.75 258	58.10 285	91.60 81	92.69 102

v8		79.97 148	79.02 149	82.80 170	84.09 257	64.50 199	87.96 138	90.29 143	74.13 121	75.24 226	86.81 216	62.88 136	93.89 132	74.39 139	75.40 289	90.00 198

CSCG		86.41 40	86.19 41	87.07 44	92.91 58	72.48 36	90.81 56	93.56 24	73.95 122	83.16 84	91.07 109	75.94 18	95.19 74	79.94 87	94.38 53	93.55 74

thres100view900		76.50 224	75.55 222	79.33 247	89.52 118	56.99 302	85.83 202	83.23 285	73.94 123	76.32 198	87.12 211	51.89 248	91.95 210	48.33 338	83.75 178	89.07 223

9.14			88.26 14		92.84 60		91.52 45	94.75 1	73.93 124	88.57 22	94.67 18	75.57 22	95.79 52	86.77 28	95.76 23

HPM-MVS_fast		85.35 57	84.95 62	86.57 52	93.69 42	70.58 74	92.15 35	91.62 103	73.89 125	82.67 92	94.09 39	62.60 137	95.54 59	80.93 76	92.93 65	93.57 73

RRT_MVS		80.35 139	79.22 144	83.74 133	87.63 193	65.46 178	91.08 53	88.92 190	73.82 126	76.44 196	90.03 129	49.05 283	94.25 116	76.84 114	79.20 239	91.51 136

PAPM_NR		83.02 85	82.41 85	84.82 89	92.47 67	66.37 157	87.93 141	91.80 98	73.82 126	77.32 172	90.66 118	67.90 84	94.90 90	70.37 176	89.48 111	93.19 87

thres600view7		76.50 224	75.44 223	79.68 241	89.40 123	57.16 299	85.53 210	83.23 285	73.79 128	76.26 199	87.09 212	51.89 248	91.89 213	48.05 343	83.72 181	90.00 198

v7n		78.97 172	77.58 186	83.14 153	83.45 269	65.51 175	88.32 126	91.21 114	73.69 129	72.41 259	86.32 237	57.93 192	93.81 134	69.18 189	75.65 280	90.11 190

dcpmvs_2		85.63 51	86.15 43	84.06 119	91.71 75	64.94 190	86.47 183	91.87 95	73.63 130	86.60 36	93.02 65	76.57 15	91.87 215	83.36 53	92.15 74	95.35 2

v2v482		80.23 141	79.29 141	83.05 158	83.62 265	64.14 206	87.04 164	89.97 151	73.61 131	78.18 155	87.22 207	61.10 167	93.82 133	76.11 122	76.78 264	91.18 147

Baseline_NR-MVSNet		78.15 191	78.33 164	77.61 273	85.79 226	56.21 316	86.78 174	85.76 250	73.60 132	77.93 161	87.57 196	65.02 114	88.99 271	67.14 210	75.33 291	87.63 261

BH-RMVSNet		79.61 151	78.44 160	83.14 153	89.38 125	65.93 165	84.95 220	87.15 230	73.56 133	78.19 154	89.79 134	56.67 205	93.36 155	59.53 270	86.74 142	90.13 188

APD-MVS_3200maxsize		85.97 44	85.88 47	86.22 56	92.69 63	69.53 88	91.93 37	92.99 45	73.54 134	85.94 38	94.51 23	65.80 108	95.61 56	83.04 58	92.51 70	93.53 76

SR-MVS-dyc-post		85.77 48	85.61 51	86.23 55	93.06 55	70.63 72	91.88 38	92.27 76	73.53 135	85.69 42	94.45 25	65.00 116	95.56 57	82.75 61	91.87 78	92.50 109

RE-MVS-def			85.48 52		93.06 55	70.63 72	91.88 38	92.27 76	73.53 135	85.69 42	94.45 25	63.87 122		82.75 61	91.87 78	92.50 109

test_fmvsmconf_n		85.92 45	86.04 46	85.57 67	85.03 242	69.51 89	89.62 85	90.58 130	73.42 137	87.75 30	94.02 42	72.85 40	93.24 159	90.37 2	90.75 92	93.96 50

tfpn200view9		76.42 227	75.37 227	79.55 246	89.13 138	57.65 293	85.17 213	83.60 277	73.41 138	76.45 193	86.39 235	52.12 240	91.95 210	48.33 338	83.75 178	89.07 223

thres400		76.50 224	75.37 227	79.86 236	89.13 138	57.65 293	85.17 213	83.60 277	73.41 138	76.45 193	86.39 235	52.12 240	91.95 210	48.33 338	83.75 178	90.00 198

test_fmvsmconf0.1_n		85.61 52	85.65 50	85.50 68	82.99 284	69.39 95	89.65 83	90.29 143	73.31 140	87.77 29	94.15 37	71.72 48	93.23 160	90.31 3	90.67 94	93.89 55

v148		78.72 177	77.80 177	81.47 197	82.73 289	61.96 245	86.30 188	88.08 208	73.26 141	76.18 202	85.47 255	62.46 141	92.36 196	71.92 163	73.82 308	90.09 192

FA-MVS(test-final)		80.96 118	79.91 126	84.10 114	88.30 169	65.01 188	84.55 230	90.01 150	73.25 142	79.61 124	87.57 196	58.35 190	94.72 98	71.29 168	86.25 150	92.56 106

test_fmvsmconf0.01_n		84.73 65	84.52 66	85.34 71	80.25 324	69.03 98	89.47 87	89.65 160	73.24 143	86.98 34	94.27 31	66.62 94	93.23 160	90.26 4	89.95 106	93.78 61

iter_conf_final		80.63 129	79.35 139	84.46 101	89.36 126	67.70 132	89.85 74	84.49 264	73.19 144	78.30 150	88.94 158	45.98 304	94.56 101	79.59 89	84.48 169	91.11 149

v10		79.74 150	78.67 154	82.97 163	84.06 258	64.95 189	87.88 144	90.62 129	73.11 145	75.11 229	86.56 230	61.46 158	94.05 122	73.68 144	75.55 282	89.90 204

MCST-MVS		87.37 26	87.25 25	87.73 27	94.53 17	72.46 37	89.82 76	93.82 16	73.07 146	84.86 55	92.89 67	76.22 17	96.33 37	84.89 37	95.13 35	94.40 33

baseline1		76.98 218	76.75 205	77.66 271	88.13 172	55.66 321	85.12 216	81.89 298	73.04 147	76.79 184	88.90 160	62.43 142	87.78 289	63.30 237	71.18 327	89.55 216

APD-MVS		87.44 22	87.52 22	87.19 41	94.24 32	72.39 38	91.86 40	92.83 55	73.01 148	88.58 21	94.52 20	73.36 34	96.49 35	84.26 45	95.01 36	92.70 100
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
diffmvs		82.10 94	81.88 96	82.76 175	83.00 282	63.78 213	83.68 247	89.76 156	72.94 149	82.02 96	89.85 133	65.96 107	90.79 245	82.38 67	87.30 134	93.71 63
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
K. test v3		71.19 275	68.51 287	79.21 250	83.04 281	57.78 292	84.35 238	76.91 340	72.90 150	62.99 344	82.86 297	39.27 341	91.09 240	61.65 254	52.66 371	88.75 242

Fast-Effi-MVS+-dtu		78.02 195	76.49 209	82.62 177	83.16 278	66.96 150	86.94 167	87.45 225	72.45 151	71.49 269	84.17 278	54.79 216	91.58 221	67.61 203	80.31 224	89.30 221

PHI-MVS		86.43 38	86.17 42	87.24 40	90.88 87	70.96 64	92.27 31	94.07 9	72.45 151	85.22 47	91.90 85	69.47 71	96.42 36	83.28 55	95.94 19	94.35 35

thres200		75.55 238	74.47 237	78.82 253	87.78 188	57.85 290	83.07 262	83.51 280	72.44 153	75.84 208	84.42 272	52.08 243	91.75 217	47.41 345	83.64 182	86.86 282

test_yl		81.17 114	80.47 116	83.24 148	89.13 138	63.62 214	86.21 190	89.95 152	72.43 154	81.78 101	89.61 139	57.50 198	93.58 143	70.75 171	86.90 139	92.52 107

DCV-MVSNet		81.17 114	80.47 116	83.24 148	89.13 138	63.62 214	86.21 190	89.95 152	72.43 154	81.78 101	89.61 139	57.50 198	93.58 143	70.75 171	86.90 139	92.52 107

BH-untuned		79.47 156	78.60 156	82.05 185	89.19 136	65.91 166	86.07 194	88.52 202	72.18 156	75.42 217	87.69 193	61.15 166	93.54 147	60.38 263	86.83 141	86.70 286

TransMVSNet (Re)		75.39 243	74.56 235	77.86 267	85.50 232	57.10 301	86.78 174	86.09 246	72.17 157	71.53 268	87.34 202	63.01 135	89.31 266	56.84 297	61.83 355	87.17 273

GA-MVS		76.87 220	75.17 230	81.97 188	82.75 288	62.58 236	81.44 279	86.35 242	72.16 158	74.74 236	82.89 296	46.20 303	92.02 208	68.85 194	81.09 213	91.30 145

v1144		80.03 145	79.03 148	83.01 160	83.78 263	64.51 197	87.11 163	90.57 132	71.96 159	78.08 158	86.20 239	61.41 159	93.94 126	74.93 134	77.23 255	90.60 170

PS-MVSNAJss		82.07 96	81.31 100	84.34 107	86.51 219	67.27 142	89.27 91	91.51 107	71.75 160	79.37 127	90.22 127	63.15 131	94.27 112	77.69 105	82.36 200	91.49 139

EPNet_dtu		75.46 240	74.86 231	77.23 279	82.57 293	54.60 330	86.89 169	83.09 288	71.64 161	66.25 325	85.86 245	55.99 207	88.04 286	54.92 305	86.55 145	89.05 228
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
GBi-Net		78.40 183	77.40 188	81.40 200	87.60 194	63.01 231	88.39 122	89.28 169	71.63 162	75.34 220	87.28 203	54.80 213	91.11 235	62.72 240	79.57 231	90.09 192

test1		78.40 183	77.40 188	81.40 200	87.60 194	63.01 231	88.39 122	89.28 169	71.63 162	75.34 220	87.28 203	54.80 213	91.11 235	62.72 240	79.57 231	90.09 192

FMVSNet2		78.20 189	77.21 192	81.20 207	87.60 194	62.89 235	87.47 153	89.02 183	71.63 162	75.29 225	87.28 203	54.80 213	91.10 238	62.38 245	79.38 235	89.61 214

iter_conf05		80.00 147	78.70 153	83.91 130	87.84 183	65.83 168	88.84 107	84.92 259	71.61 165	78.70 137	88.94 158	43.88 319	94.56 101	79.28 90	84.28 172	91.33 142

patch_mono-2		83.65 72	84.54 65	80.99 213	90.06 106	65.83 168	84.21 240	88.74 197	71.60 166	85.01 48	92.44 77	74.51 25	83.50 320	82.15 68	92.15 74	93.64 70

V42		79.38 162	78.24 166	82.83 167	81.10 316	65.50 176	85.55 208	89.82 154	71.57 167	78.21 153	86.12 241	60.66 174	93.18 168	75.64 128	75.46 286	89.81 209

API-MVS		81.99 98	81.23 102	84.26 110	90.94 85	70.18 81	91.10 52	89.32 168	71.51 168	78.66 140	88.28 179	65.26 111	95.10 82	64.74 229	91.23 87	87.51 265

tttt0517		79.40 160	77.91 172	83.90 131	88.10 174	63.84 211	88.37 125	84.05 272	71.45 169	76.78 185	89.12 154	49.93 271	94.89 91	70.18 178	83.18 190	92.96 96

pm-mvs1		77.25 215	76.68 207	78.93 252	84.22 254	58.62 279	86.41 184	88.36 204	71.37 170	73.31 248	88.01 189	61.22 165	89.15 269	64.24 231	73.01 315	89.03 229

GeoE		81.71 103	81.01 107	83.80 132	89.51 119	64.45 201	88.97 101	88.73 198	71.27 171	78.63 141	89.76 135	66.32 100	93.20 165	69.89 182	86.02 154	93.74 62

tt0805		78.73 176	77.83 175	81.43 198	85.17 235	60.30 266	89.41 89	90.90 122	71.21 172	77.17 179	88.73 164	46.38 298	93.21 162	72.57 159	78.96 240	90.79 161

FMVSNet3		77.88 199	76.85 200	80.97 215	86.84 214	62.36 238	86.52 182	88.77 193	71.13 173	75.34 220	86.66 225	54.07 224	91.10 238	62.72 240	79.57 231	89.45 217

VDDNet		81.52 109	80.67 112	84.05 121	90.44 95	64.13 207	89.73 81	85.91 247	71.11 174	83.18 83	93.48 52	50.54 263	93.49 149	73.40 149	88.25 125	94.54 29

XVG-OURS		80.41 135	79.23 143	83.97 127	85.64 229	69.02 100	83.03 264	90.39 135	71.09 175	77.63 166	91.49 97	54.62 219	91.35 230	75.71 127	83.47 186	91.54 135

SixPastTwentyTwo		73.37 258	71.26 268	79.70 240	85.08 240	57.89 289	85.57 204	83.56 279	71.03 176	65.66 327	85.88 244	42.10 331	92.57 187	59.11 274	63.34 353	88.65 245

ZD-MVS					94.38 25	72.22 43		92.67 61	70.98 177	87.75 30	94.07 40	74.01 32	96.70 26	84.66 40	94.84 42

v1192		79.59 153	78.43 161	83.07 157	83.55 267	64.52 196	86.93 168	90.58 130	70.83 178	77.78 163	85.90 243	59.15 185	93.94 126	73.96 143	77.19 257	90.76 163

Fast-Effi-MVS+		80.81 122	79.92 125	83.47 138	88.85 145	64.51 197	85.53 210	89.39 166	70.79 179	78.49 145	85.06 265	67.54 87	93.58 143	67.03 212	86.58 144	92.32 115

PS-MVSNAJ		81.69 104	81.02 106	83.70 134	89.51 119	68.21 123	84.28 239	90.09 148	70.79 179	81.26 109	85.62 252	63.15 131	94.29 110	75.62 129	88.87 116	88.59 246

LTVRE_ROB	69.57 13	76.25 230	74.54 236	81.41 199	88.60 158	64.38 203	79.24 303	89.12 181	70.76 181	69.79 289	87.86 190	49.09 281	93.20 165	56.21 302	80.16 225	86.65 287
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
xiu_mvs_v2_base		81.69 104	81.05 105	83.60 135	89.15 137	68.03 126	84.46 233	90.02 149	70.67 182	81.30 108	86.53 232	63.17 130	94.19 118	75.60 130	88.54 121	88.57 247

XVG-OURS-SEG-HR		80.81 122	79.76 129	83.96 128	85.60 230	68.78 105	83.54 253	90.50 133	70.66 183	76.71 187	91.66 89	60.69 173	91.26 232	76.94 113	81.58 208	91.83 129

Anonymous202405211		78.25 186	77.01 195	81.99 187	91.03 82	60.67 260	84.77 223	83.90 274	70.65 184	80.00 121	91.20 104	41.08 336	91.43 228	65.21 224	85.26 160	93.85 56

DP-MVS Recon		83.11 84	82.09 91	86.15 57	94.44 19	70.92 67	88.79 108	92.20 81	70.53 185	79.17 130	91.03 112	64.12 120	96.03 45	68.39 199	90.14 101	91.50 138

FMVSNet1		77.44 209	76.12 215	81.40 200	86.81 215	63.01 231	88.39 122	89.28 169	70.49 186	74.39 240	87.28 203	49.06 282	91.11 235	60.91 260	78.52 243	90.09 192

testing3		68.56 301	67.67 302	71.22 328	87.33 205	42.87 376	83.06 263	71.54 358	70.36 187	69.08 295	84.38 274	30.33 365	85.69 303	37.50 370	75.45 287	85.09 313

ab-mvs		79.51 154	78.97 150	81.14 209	88.46 163	60.91 256	83.84 245	89.24 174	70.36 187	79.03 131	88.87 162	63.23 129	90.21 253	65.12 225	82.57 198	92.28 117

tfpnnormal		74.39 247	73.16 251	78.08 265	86.10 224	58.05 284	84.65 227	87.53 222	70.32 189	71.22 271	85.63 251	54.97 211	89.86 256	43.03 359	75.02 296	86.32 290

ACMM	73.20 8	80.78 127	79.84 128	83.58 136	89.31 130	68.37 118	89.99 72	91.60 104	70.28 190	77.25 173	89.66 137	53.37 230	93.53 148	74.24 141	82.85 193	88.85 238
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	68.96 14	76.01 233	74.01 241	82.03 186	88.60 158	65.31 183	88.86 105	87.55 221	70.25 191	67.75 304	87.47 201	41.27 334	93.19 167	58.37 282	75.94 277	87.60 262

IB-MVS	68.01 15	75.85 235	73.36 249	83.31 144	84.76 244	66.03 161	83.38 254	85.06 256	70.21 192	69.40 291	81.05 314	45.76 308	94.66 100	65.10 226	75.49 283	89.25 222
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
thisisatest0530		79.40 160	77.76 180	84.31 108	87.69 191	65.10 187	87.36 155	84.26 270	70.04 193	77.42 169	88.26 181	49.94 269	94.79 96	70.20 177	84.70 166	93.03 92

test_fmvsmvis_n_1920		84.02 67	83.87 69	84.49 100	84.12 256	69.37 96	88.15 133	87.96 211	70.01 194	83.95 73	93.23 58	68.80 80	91.51 226	88.61 17	89.96 105	92.57 105

v144192		79.47 156	78.37 162	82.78 173	83.35 270	63.96 209	86.96 166	90.36 139	69.99 195	77.50 167	85.67 250	60.66 174	93.77 137	74.27 140	76.58 265	90.62 168

test_fmvsm_n_1920		85.29 58	85.34 54	85.13 78	86.12 223	69.93 82	88.65 116	90.78 126	69.97 196	88.27 23	93.98 47	71.39 53	91.54 223	88.49 19	90.45 96	93.91 52

c3_l		78.75 175	77.91 172	81.26 204	82.89 286	61.56 250	84.09 243	89.13 180	69.97 196	75.56 211	84.29 277	66.36 99	92.09 206	73.47 148	75.48 284	90.12 189

v1921920		79.22 164	78.03 169	82.80 170	83.30 272	63.94 210	86.80 172	90.33 140	69.91 198	77.48 168	85.53 253	58.44 189	93.75 139	73.60 145	76.85 262	90.71 166

ACMH	67.68 16	75.89 234	73.93 242	81.77 191	88.71 155	66.61 154	88.62 117	89.01 184	69.81 199	66.78 316	86.70 223	41.95 333	91.51 226	55.64 303	78.14 249	87.17 273
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
DPM-MVS		84.93 62	84.29 68	86.84 46	90.20 99	73.04 22	87.12 162	93.04 38	69.80 200	82.85 88	91.22 103	73.06 38	96.02 46	76.72 119	94.63 46	91.46 141

MAR-MVS		81.84 100	80.70 111	85.27 73	91.32 79	71.53 53	89.82 76	90.92 121	69.77 201	78.50 144	86.21 238	62.36 143	94.52 105	65.36 223	92.05 76	89.77 210
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
XVG-ACMP-BASELINE		76.11 232	74.27 240	81.62 193	83.20 275	64.67 195	83.60 251	89.75 157	69.75 202	71.85 265	87.09 212	32.78 358	92.11 205	69.99 181	80.43 223	88.09 253

BH-w/o		78.21 188	77.33 191	80.84 217	88.81 149	65.13 186	84.87 221	87.85 216	69.75 202	74.52 239	84.74 270	61.34 161	93.11 172	58.24 284	85.84 157	84.27 320

v1240		78.99 171	77.78 178	82.64 176	83.21 274	63.54 218	86.62 179	90.30 142	69.74 204	77.33 171	85.68 249	57.04 203	93.76 138	73.13 153	76.92 259	90.62 168

ET-MVSNet_ETH3D		78.63 179	76.63 208	84.64 94	86.73 217	69.47 91	85.01 218	84.61 262	69.54 205	66.51 323	86.59 227	50.16 266	91.75 217	76.26 121	84.24 173	92.69 102

eth_miper_zixun_eth		77.92 198	76.69 206	81.61 195	83.00 282	61.98 244	83.15 258	89.20 176	69.52 206	74.86 235	84.35 276	61.76 151	92.56 188	71.50 166	72.89 316	90.28 183

PVSNet_Blended_VisFu		82.62 89	81.83 97	84.96 83	90.80 89	69.76 86	88.74 112	91.70 102	69.39 207	78.96 132	88.46 174	65.47 110	94.87 93	74.42 138	88.57 120	90.24 184

mvs_tets		79.13 167	77.77 179	83.22 150	84.70 245	66.37 157	89.17 93	90.19 145	69.38 208	75.40 218	89.46 146	44.17 317	93.15 169	76.78 117	80.70 219	90.14 187

PVSNet_BlendedMVS		80.60 131	80.02 123	82.36 182	88.85 145	65.40 179	86.16 192	92.00 87	69.34 209	78.11 156	86.09 242	66.02 105	94.27 112	71.52 164	82.06 202	87.39 267

AdaColmap		80.58 133	79.42 136	84.06 119	93.09 54	68.91 103	89.36 90	88.97 187	69.27 210	75.70 210	89.69 136	57.20 202	95.77 53	63.06 238	88.41 124	87.50 266

ITE_SJBPF				78.22 263	81.77 304	60.57 261		83.30 283	69.25 211	67.54 306	87.20 208	36.33 352	87.28 293	54.34 308	74.62 300	86.80 283

cl____		77.72 203	76.76 203	80.58 222	82.49 295	60.48 263	83.09 260	87.87 214	69.22 212	74.38 241	85.22 261	62.10 148	91.53 224	71.09 169	75.41 288	89.73 212

DIV-MVS_self_test		77.72 203	76.76 203	80.58 222	82.48 296	60.48 263	83.09 260	87.86 215	69.22 212	74.38 241	85.24 259	62.10 148	91.53 224	71.09 169	75.40 289	89.74 211

bld_raw_dy_0_64		77.29 214	75.98 216	81.22 206	85.04 241	65.47 177	88.14 135	77.56 333	69.20 214	73.77 245	89.40 152	42.24 330	88.85 277	76.78 117	81.64 207	89.33 220

jajsoiax		79.29 163	77.96 170	83.27 146	84.68 246	66.57 155	89.25 92	90.16 146	69.20 214	75.46 215	89.49 143	45.75 309	93.13 171	76.84 114	80.80 217	90.11 190

IterMVS-SCA-FT		75.43 241	73.87 244	80.11 232	82.69 290	64.85 192	81.57 276	83.47 281	69.16 216	70.49 275	84.15 279	51.95 246	88.15 284	69.23 188	72.14 321	87.34 269

CL-MVSNet_self_test		72.37 270	71.46 263	75.09 296	79.49 337	53.53 338	80.76 284	85.01 258	69.12 217	70.51 274	82.05 308	57.92 193	84.13 315	52.27 318	66.00 347	87.60 262

AUN-MVS		79.21 165	77.60 185	84.05 121	88.71 155	67.61 134	85.84 201	87.26 228	69.08 218	77.23 175	88.14 187	53.20 232	93.47 151	75.50 132	73.45 311	91.06 152

xiu_mvs_v1_base_debu		80.80 124	79.72 130	84.03 123	87.35 200	70.19 78	85.56 205	88.77 193	69.06 219	81.83 97	88.16 183	50.91 257	92.85 181	78.29 101	87.56 129	89.06 225

xiu_mvs_v1_base		80.80 124	79.72 130	84.03 123	87.35 200	70.19 78	85.56 205	88.77 193	69.06 219	81.83 97	88.16 183	50.91 257	92.85 181	78.29 101	87.56 129	89.06 225

xiu_mvs_v1_base_debi		80.80 124	79.72 130	84.03 123	87.35 200	70.19 78	85.56 205	88.77 193	69.06 219	81.83 97	88.16 183	50.91 257	92.85 181	78.29 101	87.56 129	89.06 225

MVSTER		79.01 170	77.88 174	82.38 181	83.07 279	64.80 193	84.08 244	88.95 188	69.01 222	78.69 138	87.17 210	54.70 217	92.43 192	74.69 135	80.57 221	89.89 205

cl22		78.07 193	77.01 195	81.23 205	82.37 298	61.83 247	83.55 252	87.98 210	68.96 223	75.06 231	83.87 281	61.40 160	91.88 214	73.53 146	76.39 269	89.98 201

miper_ehance_all_eth		78.59 181	77.76 180	81.08 211	82.66 291	61.56 250	83.65 248	89.15 178	68.87 224	75.55 212	83.79 285	66.49 97	92.03 207	73.25 151	76.39 269	89.64 213

PAPR		81.66 107	80.89 109	83.99 126	90.27 97	64.00 208	86.76 176	91.77 101	68.84 225	77.13 181	89.50 142	67.63 86	94.88 92	67.55 204	88.52 122	93.09 89

CPTT-MVS		83.73 70	83.33 74	84.92 86	93.28 49	70.86 68	92.09 36	90.38 136	68.75 226	79.57 125	92.83 69	60.60 177	93.04 177	80.92 77	91.56 83	90.86 160

train_agg		86.43 38	86.20 40	87.13 43	93.26 50	72.96 24	88.75 110	91.89 93	68.69 227	85.00 50	93.10 60	74.43 26	95.41 66	84.97 34	95.71 25	93.02 93

test_8					93.13 52	72.57 34	88.68 115	91.84 97	68.69 227	84.87 54	93.10 60	74.43 26	95.16 75

dmvs_re		71.14 276	70.58 272	72.80 315	81.96 301	59.68 272	75.60 334	79.34 324	68.55 229	69.27 294	80.72 320	49.42 275	76.54 353	52.56 317	77.79 250	82.19 343

MVSFormer		82.85 87	82.05 92	85.24 74	87.35 200	70.21 76	90.50 61	90.38 136	68.55 229	81.32 105	89.47 144	61.68 152	93.46 152	78.98 92	90.26 99	92.05 125

test_djsdf		80.30 140	79.32 140	83.27 146	83.98 260	65.37 182	90.50 61	90.38 136	68.55 229	76.19 201	88.70 165	56.44 206	93.46 152	78.98 92	80.14 227	90.97 157

TEST9					93.26 50	72.96 24	88.75 110	91.89 93	68.44 232	85.00 50	93.10 60	74.36 28	95.41 66

FE-MVS		77.78 201	75.68 219	84.08 117	88.09 175	66.00 163	83.13 259	87.79 217	68.42 233	78.01 159	85.23 260	45.50 311	95.12 77	59.11 274	85.83 158	91.11 149

CDPH-MVS		85.76 49	85.29 58	87.17 42	93.49 47	71.08 60	88.58 118	92.42 72	68.32 234	84.61 60	93.48 52	72.32 42	96.15 44	79.00 91	95.43 30	94.28 39

PC_three_1452									68.21 235	92.02 12	94.00 44	82.09 5	95.98 50	84.58 41	96.68 2	94.95 9

IterMVS		74.29 248	72.94 253	78.35 262	81.53 308	63.49 220	81.58 275	82.49 293	68.06 236	69.99 284	83.69 287	51.66 252	85.54 304	65.85 220	71.64 324	86.01 298
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
dmvs_testset		62.63 328	64.11 319	58.19 356	78.55 342	24.76 392	75.28 335	65.94 372	67.91 237	60.34 351	76.01 353	53.56 228	73.94 371	31.79 375	67.65 340	75.88 363

TAMVS		78.89 174	77.51 187	83.03 159	87.80 185	67.79 130	84.72 224	85.05 257	67.63 238	76.75 186	87.70 192	62.25 145	90.82 244	58.53 281	87.13 136	90.49 174

PVSNet_Blended		80.98 117	80.34 118	82.90 165	88.85 145	65.40 179	84.43 235	92.00 87	67.62 239	78.11 156	85.05 266	66.02 105	94.27 112	71.52 164	89.50 110	89.01 230

TR-MVS		77.44 209	76.18 214	81.20 207	88.24 170	63.24 226	84.61 228	86.40 240	67.55 240	77.81 162	86.48 233	54.10 223	93.15 169	57.75 288	82.72 196	87.20 272

CDS-MVSNet		79.07 169	77.70 182	83.17 152	87.60 194	68.23 122	84.40 237	86.20 243	67.49 241	76.36 197	86.54 231	61.54 155	90.79 245	61.86 252	87.33 133	90.49 174
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvs_anonymous		79.42 159	79.11 147	80.34 227	84.45 251	57.97 287	82.59 266	87.62 220	67.40 242	76.17 204	88.56 172	68.47 81	89.59 261	70.65 174	86.05 153	93.47 77

IU-MVS					95.30 2	71.25 56		92.95 51	66.81 243	92.39 6				88.94 15	96.63 4	94.85 18

baseline2		75.70 236	73.83 245	81.30 203	83.26 273	61.79 248	82.57 267	80.65 309	66.81 243	66.88 314	83.42 290	57.86 194	92.19 203	63.47 234	79.57 231	89.91 203

miper_lstm_enhance		74.11 251	73.11 252	77.13 280	80.11 326	59.62 273	72.23 348	86.92 234	66.76 245	70.40 276	82.92 295	56.93 204	82.92 324	69.06 191	72.63 317	88.87 237

OpenMVS	72.83 10	79.77 149	78.33 164	84.09 116	85.17 235	69.91 83	90.57 59	90.97 120	66.70 246	72.17 262	91.91 84	54.70 217	93.96 123	61.81 253	90.95 90	88.41 250

test-LLR		72.94 266	72.43 256	74.48 302	81.35 312	58.04 285	78.38 314	77.46 334	66.66 247	69.95 285	79.00 335	48.06 288	79.24 339	66.13 215	84.83 163	86.15 294

test20.03		67.45 308	66.95 309	68.94 337	75.48 355	44.84 372	77.50 322	77.67 332	66.66 247	63.01 343	83.80 284	47.02 294	78.40 343	42.53 361	68.86 338	83.58 329

test0.0.03 1		68.00 306	67.69 301	68.90 338	77.55 345	47.43 363	75.70 333	72.95 357	66.66 247	66.56 319	82.29 305	48.06 288	75.87 360	44.97 356	74.51 301	83.41 330

Syy-MVS		68.05 305	67.85 296	68.67 341	84.68 246	40.97 382	78.62 312	73.08 355	66.65 250	66.74 317	79.46 330	52.11 242	82.30 327	32.89 374	76.38 272	82.75 339

myMVS_eth3d		67.02 311	66.29 312	69.21 336	84.68 246	42.58 377	78.62 312	73.08 355	66.65 250	66.74 317	79.46 330	31.53 362	82.30 327	39.43 367	76.38 272	82.75 339

QAPM		80.88 119	79.50 135	85.03 80	88.01 179	68.97 102	91.59 42	92.00 87	66.63 252	75.15 228	92.16 81	57.70 195	95.45 62	63.52 233	88.76 118	90.66 167

XXY-MVS		75.41 242	75.56 221	74.96 297	83.59 266	57.82 291	80.59 287	83.87 275	66.54 253	74.93 234	88.31 178	63.24 128	80.09 337	62.16 248	76.85 262	86.97 280

OurMVSNet-221017-0		74.26 249	72.42 257	79.80 238	83.76 264	59.59 274	85.92 198	86.64 236	66.39 254	66.96 313	87.58 195	39.46 340	91.60 220	65.76 221	69.27 334	88.22 251

SCA		74.22 250	72.33 258	79.91 235	84.05 259	62.17 242	79.96 297	79.29 325	66.30 255	72.38 260	80.13 324	51.95 246	88.60 279	59.25 272	77.67 253	88.96 234

testgi		66.67 314	66.53 311	67.08 346	75.62 354	41.69 381	75.93 329	76.50 341	66.11 256	65.20 333	86.59 227	35.72 354	74.71 367	43.71 357	73.38 313	84.84 315

HY-MVS	69.67 12	77.95 197	77.15 193	80.36 226	87.57 198	60.21 268	83.37 255	87.78 218	66.11 256	75.37 219	87.06 214	63.27 127	90.48 250	61.38 257	82.43 199	90.40 178

EG-PatchMatch MVS		74.04 252	71.82 261	80.71 220	84.92 243	67.42 138	85.86 200	88.08 208	66.04 258	64.22 337	83.85 282	35.10 355	92.56 188	57.44 290	80.83 216	82.16 344

CNLPA		78.08 192	76.79 202	81.97 188	90.40 96	71.07 61	87.59 150	84.55 263	66.03 259	72.38 260	89.64 138	57.56 197	86.04 300	59.61 269	83.35 187	88.79 241

Anonymous20240529		80.19 143	78.89 151	84.10 114	90.60 91	64.75 194	88.95 102	90.90 122	65.97 260	80.59 115	91.17 106	49.97 268	93.73 141	69.16 190	82.70 197	93.81 59

TAPA-MVS	73.13 9	79.15 166	77.94 171	82.79 172	89.59 115	62.99 234	88.16 132	91.51 107	65.77 261	77.14 180	91.09 108	60.91 170	93.21 162	50.26 330	87.05 137	92.17 122
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
MSDG		73.36 260	70.99 269	80.49 224	84.51 250	65.80 170	80.71 285	86.13 245	65.70 262	65.46 328	83.74 286	44.60 314	90.91 243	51.13 323	76.89 260	84.74 316

anonymousdsp		78.60 180	77.15 193	82.98 162	80.51 322	67.08 146	87.24 160	89.53 162	65.66 263	75.16 227	87.19 209	52.52 233	92.25 201	77.17 111	79.34 236	89.61 214

test_0402		72.79 267	70.44 275	79.84 237	88.13 172	65.99 164	85.93 197	84.29 268	65.57 264	67.40 310	85.49 254	46.92 295	92.61 186	35.88 371	74.38 302	80.94 350

miper_enhance_ethall		77.87 200	76.86 199	80.92 216	81.65 305	61.38 252	82.68 265	88.98 185	65.52 265	75.47 213	82.30 304	65.76 109	92.00 209	72.95 154	76.39 269	89.39 218

UnsupCasMVSNet_eth		67.33 309	65.99 313	71.37 324	73.48 364	51.47 352	75.16 337	85.19 255	65.20 266	60.78 350	80.93 319	42.35 326	77.20 349	57.12 293	53.69 370	85.44 305

WTY-MVS		75.65 237	75.68 219	75.57 291	86.40 220	56.82 304	77.92 321	82.40 294	65.10 267	76.18 202	87.72 191	63.13 134	80.90 334	60.31 264	81.96 203	89.00 232

thisisatest0515		77.33 212	75.38 226	83.18 151	85.27 234	63.80 212	82.11 270	83.27 284	65.06 268	75.91 206	83.84 283	49.54 273	94.27 112	67.24 208	86.19 151	91.48 140

MVP-Stereo		76.12 231	74.46 238	81.13 210	85.37 233	69.79 85	84.42 236	87.95 212	65.03 269	67.46 308	85.33 257	53.28 231	91.73 219	58.01 286	83.27 188	81.85 345
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
Anonymous20231211		78.97 172	77.69 183	82.81 169	90.54 93	64.29 204	90.11 71	91.51 107	65.01 270	76.16 205	88.13 188	50.56 262	93.03 178	69.68 185	77.56 254	91.11 149

pmmvs6		74.69 246	73.39 248	78.61 256	81.38 311	57.48 296	86.64 178	87.95 212	64.99 271	70.18 279	86.61 226	50.43 264	89.52 262	62.12 249	70.18 331	88.83 239

PAPM		77.68 206	76.40 212	81.51 196	87.29 207	61.85 246	83.78 246	89.59 161	64.74 272	71.23 270	88.70 165	62.59 138	93.66 142	52.66 316	87.03 138	89.01 230

MIMVSNet		70.69 282	69.30 281	74.88 298	84.52 249	56.35 314	75.87 332	79.42 323	64.59 273	67.76 303	82.41 302	41.10 335	81.54 331	46.64 349	81.34 209	86.75 285

tpm		72.37 270	71.71 262	74.35 304	82.19 299	52.00 346	79.22 304	77.29 337	64.56 274	72.95 253	83.68 288	51.35 253	83.26 323	58.33 283	75.80 278	87.81 258

MDA-MVSNet-bldmvs		66.68 313	63.66 322	75.75 288	79.28 339	60.56 262	73.92 344	78.35 329	64.43 275	50.13 373	79.87 328	44.02 318	83.67 318	46.10 351	56.86 363	83.03 336

MIMVSNet1		68.58 300	66.78 310	73.98 307	80.07 327	51.82 348	80.77 283	84.37 265	64.40 276	59.75 355	82.16 307	36.47 351	83.63 319	42.73 360	70.33 330	86.48 289

D2MVS		74.82 245	73.21 250	79.64 243	79.81 331	62.56 237	80.34 292	87.35 226	64.37 277	68.86 296	82.66 300	46.37 299	90.10 254	67.91 201	81.24 211	86.25 291

PLC	70.83 11	78.05 194	76.37 213	83.08 156	91.88 74	67.80 129	88.19 130	89.46 164	64.33 278	69.87 287	88.38 176	53.66 227	93.58 143	58.86 277	82.73 195	87.86 257
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
PatchmatchNet		73.12 263	71.33 266	78.49 261	83.18 276	60.85 257	79.63 299	78.57 328	64.13 279	71.73 266	79.81 329	51.20 255	85.97 301	57.40 291	76.36 274	88.66 244
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_2432*1600		66.22 318	63.89 320	73.21 311	75.47 356	53.42 340	70.76 354	84.35 266	64.10 280	66.52 321	78.52 339	34.55 356	84.98 309	50.40 326	50.33 374	81.23 348

miper_refine_blended		66.22 318	63.89 320	73.21 311	75.47 356	53.42 340	70.76 354	84.35 266	64.10 280	66.52 321	78.52 339	34.55 356	84.98 309	50.40 326	50.33 374	81.23 348

tpmvs		71.09 277	69.29 282	76.49 284	82.04 300	56.04 317	78.92 309	81.37 304	64.05 282	67.18 312	78.28 341	49.74 272	89.77 257	49.67 333	72.37 318	83.67 328

F-COLMAP		76.38 229	74.33 239	82.50 179	89.28 132	66.95 151	88.41 121	89.03 182	64.05 282	66.83 315	88.61 169	46.78 296	92.89 180	57.48 289	78.55 242	87.67 260

DP-MVS		76.78 221	74.57 234	83.42 140	93.29 48	69.46 93	88.55 119	83.70 276	63.98 284	70.20 278	88.89 161	54.01 225	94.80 95	46.66 347	81.88 205	86.01 298

原ACMM1				84.35 106	93.01 57	68.79 104		92.44 69	63.96 285	81.09 110	91.57 94	66.06 104	95.45 62	67.19 209	94.82 44	88.81 240

PM-MVS		66.41 316	64.14 318	73.20 313	73.92 360	56.45 310	78.97 308	64.96 375	63.88 286	64.72 334	80.24 323	19.84 377	83.44 321	66.24 214	64.52 351	79.71 355

jason		81.39 112	80.29 120	84.70 93	86.63 218	69.90 84	85.95 196	86.77 235	63.24 287	81.07 111	89.47 144	61.08 168	92.15 204	78.33 100	90.07 104	92.05 125
jason: jason.
KD-MVS_self_test		68.81 297	67.59 304	72.46 318	74.29 359	45.45 367	77.93 320	87.00 232	63.12 288	63.99 339	78.99 337	42.32 327	84.77 312	56.55 300	64.09 352	87.16 275

gg-mvs-nofinetune		69.95 290	67.96 294	75.94 287	83.07 279	54.51 332	77.23 325	70.29 361	63.11 289	70.32 277	62.33 372	43.62 320	88.69 278	53.88 310	87.76 128	84.62 318

tpmrst		72.39 268	72.13 259	73.18 314	80.54 321	49.91 359	79.91 298	79.08 326	63.11 289	71.69 267	79.95 326	55.32 209	82.77 325	65.66 222	73.89 306	86.87 281

PCF-MVS	73.52 7	80.38 136	78.84 152	85.01 81	87.71 189	68.99 101	83.65 248	91.46 111	63.00 291	77.77 164	90.28 124	66.10 102	95.09 83	61.40 256	88.22 126	90.94 158
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
COLMAP_ROB	66.92 17	73.01 264	70.41 276	80.81 218	87.13 210	65.63 173	88.30 127	84.19 271	62.96 292	63.80 341	87.69 193	38.04 347	92.56 188	46.66 347	74.91 297	84.24 321
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
Patchmatch-RL test		70.24 287	67.78 300	77.61 273	77.43 346	59.57 275	71.16 351	70.33 360	62.94 293	68.65 298	72.77 363	50.62 261	85.49 305	69.58 186	66.58 344	87.77 259

lupinMVS		81.39 112	80.27 121	84.76 92	87.35 200	70.21 76	85.55 208	86.41 239	62.85 294	81.32 105	88.61 169	61.68 152	92.24 202	78.41 99	90.26 99	91.83 129

test_vis1_n_1920		75.52 239	75.78 217	74.75 301	79.84 330	57.44 297	83.26 256	85.52 252	62.83 295	79.34 129	86.17 240	45.10 313	79.71 338	78.75 94	81.21 212	87.10 279

EPMVS		69.02 296	68.16 291	71.59 322	79.61 335	49.80 361	77.40 323	66.93 369	62.82 296	70.01 282	79.05 333	45.79 307	77.86 347	56.58 299	75.26 293	87.13 276

PatchMatch-RL		72.38 269	70.90 270	76.80 283	88.60 158	67.38 140	79.53 300	76.17 344	62.75 297	69.36 292	82.00 310	45.51 310	84.89 311	53.62 311	80.58 220	78.12 358

gm-plane-assit					81.40 310	53.83 337			62.72 298		80.94 317		92.39 194	63.40 236

FMVSNet5		69.50 293	67.96 294	74.15 306	82.97 285	55.35 323	80.01 296	82.12 297	62.56 299	63.02 342	81.53 311	36.92 350	81.92 329	48.42 337	74.06 304	85.17 311

sss		73.60 256	73.64 247	73.51 310	82.80 287	55.01 327	76.12 328	81.69 301	62.47 300	74.68 237	85.85 246	57.32 200	78.11 345	60.86 261	80.93 214	87.39 267

AllTest		70.96 278	68.09 293	79.58 244	85.15 237	63.62 214	84.58 229	79.83 319	62.31 301	60.32 352	86.73 217	32.02 359	88.96 274	50.28 328	71.57 325	86.15 294

TestCases				79.58 244	85.15 237	63.62 214		79.83 319	62.31 301	60.32 352	86.73 217	32.02 359	88.96 274	50.28 328	71.57 325	86.15 294

1112_ss		77.40 211	76.43 211	80.32 228	89.11 142	60.41 265	83.65 248	87.72 219	62.13 303	73.05 252	86.72 219	62.58 139	89.97 255	62.11 250	80.80 217	90.59 171

PVSNet	64.34 18	72.08 272	70.87 271	75.69 289	86.21 222	56.44 311	74.37 342	80.73 308	62.06 304	70.17 280	82.23 306	42.86 324	83.31 322	54.77 306	84.45 170	87.32 270

LS3D		76.95 219	74.82 232	83.37 143	90.45 94	67.36 141	89.15 97	86.94 233	61.87 305	69.52 290	90.61 119	51.71 251	94.53 104	46.38 350	86.71 143	88.21 252

CostFormer		75.24 244	73.90 243	79.27 248	82.65 292	58.27 282	80.80 282	82.73 292	61.57 306	75.33 223	83.13 294	55.52 208	91.07 241	64.98 227	78.34 248	88.45 248

new-patchmatchnet		61.73 330	61.73 331	61.70 352	72.74 369	24.50 393	69.16 361	78.03 330	61.40 307	56.72 364	75.53 357	38.42 344	76.48 355	45.95 352	57.67 362	84.13 323

ANet_high		50.57 346	46.10 350	63.99 349	48.67 392	39.13 383	70.99 353	80.85 306	61.39 308	31.18 381	57.70 379	17.02 380	73.65 372	31.22 376	15.89 389	79.18 356

MS-PatchMatch		73.83 254	72.67 254	77.30 278	83.87 262	66.02 162	81.82 271	84.66 261	61.37 309	68.61 299	82.82 298	47.29 291	88.21 283	59.27 271	84.32 171	77.68 359

USDC		70.33 286	68.37 288	76.21 286	80.60 320	56.23 315	79.19 305	86.49 238	60.89 310	61.29 348	85.47 255	31.78 361	89.47 264	53.37 313	76.21 275	82.94 338

cascas		76.72 222	74.64 233	82.99 161	85.78 227	65.88 167	82.33 268	89.21 175	60.85 311	72.74 254	81.02 315	47.28 292	93.75 139	67.48 205	85.02 161	89.34 219

MDTV_nov1_ep13			69.97 280		83.18 276	53.48 339	77.10 326	80.18 318	60.45 312	69.33 293	80.44 321	48.89 286	86.90 294	51.60 321	78.51 244

TinyColmap		67.30 310	64.81 315	74.76 300	81.92 303	56.68 308	80.29 293	81.49 303	60.33 313	56.27 366	83.22 291	24.77 371	87.66 291	45.52 353	69.47 333	79.95 354

test-mter		71.41 274	70.39 277	74.48 302	81.35 312	58.04 285	78.38 314	77.46 334	60.32 314	69.95 285	79.00 335	36.08 353	79.24 339	66.13 215	84.83 163	86.15 294

1314		76.53 223	75.30 229	80.21 230	83.93 261	62.32 240	84.66 225	88.81 191	60.23 315	70.16 281	84.07 280	55.30 210	90.73 247	67.37 206	83.21 189	87.59 264

PatchT		68.46 303	67.85 296	70.29 332	80.70 319	43.93 374	72.47 347	74.88 347	60.15 316	70.55 273	76.57 350	49.94 269	81.59 330	50.58 324	74.83 298	85.34 306

无先验							87.48 152	88.98 185	60.00 317				94.12 120	67.28 207		88.97 233

CR-MVSNet		73.37 258	71.27 267	79.67 242	81.32 314	65.19 184	75.92 330	80.30 315	59.92 318	72.73 255	81.19 312	52.50 234	86.69 295	59.84 267	77.71 251	87.11 277

TDRefinement		67.49 307	64.34 317	76.92 281	73.47 365	61.07 254	84.86 222	82.98 289	59.77 319	58.30 359	85.13 263	26.06 369	87.89 287	47.92 344	60.59 360	81.81 346

dp		66.80 312	65.43 314	70.90 331	79.74 334	48.82 362	75.12 339	74.77 348	59.61 320	64.08 338	77.23 347	42.89 323	80.72 335	48.86 336	66.58 344	83.16 333

our_test_3		69.14 295	67.00 308	75.57 291	79.80 332	58.80 277	77.96 319	77.81 331	59.55 321	62.90 345	78.25 342	47.43 290	83.97 316	51.71 320	67.58 341	83.93 326

Test_1112_low_res		76.40 228	75.44 223	79.27 248	89.28 132	58.09 283	81.69 274	87.07 231	59.53 322	72.48 258	86.67 224	61.30 162	89.33 265	60.81 262	80.15 226	90.41 177

pmmvs4		74.03 253	71.91 260	80.39 225	81.96 301	68.32 119	81.45 278	82.14 296	59.32 323	69.87 287	85.13 263	52.40 236	88.13 285	60.21 265	74.74 299	84.73 317

testdata				79.97 234	90.90 86	64.21 205		84.71 260	59.27 324	85.40 44	92.91 66	62.02 150	89.08 270	68.95 192	91.37 85	86.63 288

WB-MVS		54.94 336	54.72 338	55.60 362	73.50 363	20.90 394	74.27 343	61.19 379	59.16 325	50.61 372	74.15 359	47.19 293	75.78 361	17.31 386	35.07 381	70.12 369

ppachtmachnet_test		70.04 289	67.34 306	78.14 264	79.80 332	61.13 253	79.19 305	80.59 310	59.16 325	65.27 330	79.29 332	46.75 297	87.29 292	49.33 334	66.72 342	86.00 300

RPSCF		73.23 262	71.46 263	78.54 259	82.50 294	59.85 270	82.18 269	82.84 291	58.96 327	71.15 272	89.41 150	45.48 312	84.77 312	58.82 278	71.83 323	91.02 156

pmmvs-eth3d		70.50 285	67.83 298	78.52 260	77.37 347	66.18 160	81.82 271	81.51 302	58.90 328	63.90 340	80.42 322	42.69 325	86.28 299	58.56 280	65.30 349	83.11 334

OpenMVS_ROB	64.09 19	70.56 284	68.19 290	77.65 272	80.26 323	59.41 276	85.01 218	82.96 290	58.76 329	65.43 329	82.33 303	37.63 349	91.23 234	45.34 355	76.03 276	82.32 341

114514_t		80.68 128	79.51 134	84.20 111	94.09 38	67.27 142	89.64 84	91.11 118	58.75 330	74.08 243	90.72 117	58.10 191	95.04 84	69.70 184	89.42 112	90.30 182

Patchmtry		70.74 281	69.16 284	75.49 293	80.72 318	54.07 335	74.94 341	80.30 315	58.34 331	70.01 282	81.19 312	52.50 234	86.54 296	53.37 313	71.09 328	85.87 302

test_cas_vis1_n_1920		73.76 255	73.74 246	73.81 308	75.90 351	59.77 271	80.51 288	82.40 294	58.30 332	81.62 103	85.69 248	44.35 316	76.41 356	76.29 120	78.61 241	85.23 308

Anonymous20240521		68.80 298	67.22 307	73.55 309	74.33 358	54.11 334	83.18 257	85.61 251	58.15 333	61.68 347	80.94 317	30.71 364	81.27 333	57.00 295	73.34 314	85.28 307

旧先验2							86.56 181		58.10 334	87.04 33			88.98 272	74.07 142

JIA-IIPM		66.32 317	62.82 328	76.82 282	77.09 348	61.72 249	65.34 372	75.38 345	58.04 335	64.51 335	62.32 373	42.05 332	86.51 297	51.45 322	69.22 335	82.21 342

pmmvs5		71.55 273	70.20 279	75.61 290	77.83 344	56.39 312	81.74 273	80.89 305	57.76 336	67.46 308	84.49 271	49.26 279	85.32 308	57.08 294	75.29 292	85.11 312

TESTMET0.1,1		69.89 291	69.00 285	72.55 317	79.27 340	56.85 303	78.38 314	74.71 350	57.64 337	68.09 302	77.19 348	37.75 348	76.70 352	63.92 232	84.09 174	84.10 324

RPMNet		73.51 257	70.49 274	82.58 178	81.32 314	65.19 184	75.92 330	92.27 76	57.60 338	72.73 255	76.45 351	52.30 237	95.43 64	48.14 342	77.71 251	87.11 277

SSC-MVS		53.88 339	53.59 340	54.75 364	72.87 368	19.59 395	73.84 345	60.53 381	57.58 339	49.18 374	73.45 362	46.34 301	75.47 364	16.20 389	32.28 383	69.20 370

新几何1				83.42 140	93.13 52	70.71 70		85.48 253	57.43 340	81.80 100	91.98 83	63.28 126	92.27 200	64.60 230	92.99 64	87.27 271

YYNet1		65.03 321	62.91 326	71.38 323	75.85 352	56.60 309	69.12 362	74.66 351	57.28 341	54.12 368	77.87 344	45.85 306	74.48 368	49.95 331	61.52 357	83.05 335

MDA-MVSNet_test_wron		65.03 321	62.92 325	71.37 324	75.93 350	56.73 305	69.09 363	74.73 349	57.28 341	54.03 369	77.89 343	45.88 305	74.39 369	49.89 332	61.55 356	82.99 337

Anonymous20231206		68.60 299	67.80 299	71.02 329	80.23 325	50.75 356	78.30 317	80.47 312	56.79 343	66.11 326	82.63 301	46.35 300	78.95 341	43.62 358	75.70 279	83.36 331

tpm2		73.26 261	71.46 263	78.63 255	83.34 271	56.71 307	80.65 286	80.40 314	56.63 344	73.55 246	82.02 309	51.80 250	91.24 233	56.35 301	78.42 246	87.95 254

CHOSEN 1792x2688		77.63 207	75.69 218	83.44 139	89.98 108	68.58 116	78.70 311	87.50 223	56.38 345	75.80 209	86.84 215	58.67 187	91.40 229	61.58 255	85.75 159	90.34 179

HyFIR lowres test		77.53 208	75.40 225	83.94 129	89.59 115	66.62 153	80.36 291	88.64 200	56.29 346	76.45 193	85.17 262	57.64 196	93.28 157	61.34 258	83.10 191	91.91 128

PVSNet_0	57.27 20	61.67 331	59.27 334	68.85 339	79.61 335	57.44 297	68.01 364	73.44 354	55.93 347	58.54 358	70.41 368	44.58 315	77.55 348	47.01 346	35.91 380	71.55 368

UnsupCasMVSNet_bld		63.70 326	61.53 332	70.21 333	73.69 362	51.39 353	72.82 346	81.89 298	55.63 348	57.81 361	71.80 365	38.67 343	78.61 342	49.26 335	52.21 372	80.63 351

MDTV_nov1_ep13_2view						37.79 384	75.16 337		55.10 349	66.53 320		49.34 277		53.98 309		87.94 255

MVS		78.19 190	76.99 197	81.78 190	85.66 228	66.99 147	84.66 225	90.47 134	55.08 350	72.02 264	85.27 258	63.83 123	94.11 121	66.10 217	89.80 108	84.24 321

test222					91.50 77	68.26 121	84.16 241	83.20 287	54.63 351	79.74 122	91.63 92	58.97 186			91.42 84	86.77 284

CHOSEN 280x420		66.51 315	64.71 316	71.90 320	81.45 309	63.52 219	57.98 379	68.95 367	53.57 352	62.59 346	76.70 349	46.22 302	75.29 366	55.25 304	79.68 230	76.88 361

ADS-MVSNet2		66.20 320	63.33 323	74.82 299	79.92 328	58.75 278	67.55 365	75.19 346	53.37 353	65.25 331	75.86 354	42.32 327	80.53 336	41.57 362	68.91 336	85.18 309

ADS-MVSNet		64.36 324	62.88 327	68.78 340	79.92 328	47.17 364	67.55 365	71.18 359	53.37 353	65.25 331	75.86 354	42.32 327	73.99 370	41.57 362	68.91 336	85.18 309

LF4IMVS		64.02 325	62.19 329	69.50 335	70.90 372	53.29 343	76.13 327	77.18 338	52.65 355	58.59 357	80.98 316	23.55 373	76.52 354	53.06 315	66.66 343	78.68 357

tpm cat1		70.57 283	68.31 289	77.35 277	82.41 297	57.95 288	78.08 318	80.22 317	52.04 356	68.54 300	77.66 346	52.00 245	87.84 288	51.77 319	72.07 322	86.25 291

test_vis1_n		69.85 292	69.21 283	71.77 321	72.66 370	55.27 325	81.48 277	76.21 343	52.03 357	75.30 224	83.20 293	28.97 366	76.22 358	74.60 136	78.41 247	83.81 327

Patchmatch-test		64.82 323	63.24 324	69.57 334	79.42 338	49.82 360	63.49 376	69.05 366	51.98 358	59.95 354	80.13 324	50.91 257	70.98 374	40.66 364	73.57 309	87.90 256

N_pmnet		52.79 342	53.26 341	51.40 366	78.99 341	7.68 398	69.52 358	3.89 397	51.63 359	57.01 363	74.98 358	40.83 337	65.96 381	37.78 369	64.67 350	80.56 353

test_fmvs1_n		70.86 280	70.24 278	72.73 316	72.51 371	55.28 324	81.27 280	79.71 321	51.49 360	78.73 136	84.87 267	27.54 368	77.02 350	76.06 123	79.97 229	85.88 301

test_fmvs1		70.93 279	70.52 273	72.16 319	73.71 361	55.05 326	80.82 281	78.77 327	51.21 361	78.58 142	84.41 273	31.20 363	76.94 351	75.88 126	80.12 228	84.47 319

PMMVS		69.34 294	68.67 286	71.35 326	75.67 353	62.03 243	75.17 336	73.46 353	50.00 362	68.68 297	79.05 333	52.07 244	78.13 344	61.16 259	82.77 194	73.90 365

test_fmvs2		68.35 304	67.48 305	70.98 330	69.50 374	51.95 347	80.05 295	76.38 342	49.33 363	74.65 238	84.38 274	23.30 374	75.40 365	74.51 137	75.17 295	85.60 303

CMPMVS	51.72 21	70.19 288	68.16 291	76.28 285	73.15 367	57.55 295	79.47 301	83.92 273	48.02 364	56.48 365	84.81 268	43.13 322	86.42 298	62.67 243	81.81 206	84.89 314
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
mvsany_test1		62.30 329	61.26 333	65.41 348	69.52 373	54.86 328	66.86 367	49.78 388	46.65 365	68.50 301	83.21 292	49.15 280	66.28 380	56.93 296	60.77 358	75.11 364

test_fmvs3		63.36 327	61.82 330	67.98 343	62.51 381	46.96 366	77.37 324	74.03 352	45.24 366	67.50 307	78.79 338	12.16 385	72.98 373	72.77 157	66.02 346	83.99 325

CVMVSNet		72.99 265	72.58 255	74.25 305	84.28 252	50.85 355	86.41 184	83.45 282	44.56 367	73.23 250	87.54 199	49.38 276	85.70 302	65.90 219	78.44 245	86.19 293

test_vis1_rt		60.28 332	58.42 335	65.84 347	67.25 377	55.60 322	70.44 356	60.94 380	44.33 368	59.00 356	66.64 370	24.91 370	68.67 378	62.80 239	69.48 332	73.25 366

mvsany_test3		53.99 338	51.45 343	61.61 353	55.51 385	44.74 373	63.52 375	45.41 392	43.69 369	58.11 360	76.45 351	17.99 378	63.76 383	54.77 306	47.59 376	76.34 362

EU-MVSNet		68.53 302	67.61 303	71.31 327	78.51 343	47.01 365	84.47 231	84.27 269	42.27 370	66.44 324	84.79 269	40.44 338	83.76 317	58.76 279	68.54 339	83.17 332

FPMVS		53.68 340	51.64 342	59.81 355	65.08 379	51.03 354	69.48 359	69.58 364	41.46 371	40.67 377	72.32 364	16.46 381	70.00 377	24.24 383	65.42 348	58.40 379

pmmvs3		57.79 334	54.26 339	68.37 342	64.02 380	56.72 306	75.12 339	65.17 373	40.20 372	52.93 370	69.86 369	20.36 376	75.48 363	45.45 354	55.25 369	72.90 367

new_pmnet		50.91 345	50.29 345	52.78 365	68.58 375	34.94 387	63.71 374	56.63 385	39.73 373	44.95 375	65.47 371	21.93 375	58.48 384	34.98 372	56.62 364	64.92 373

MVS-HIRNet		59.14 333	57.67 336	63.57 350	81.65 305	43.50 375	71.73 349	65.06 374	39.59 374	51.43 371	57.73 378	38.34 345	82.58 326	39.53 365	73.95 305	64.62 374

PMMVS2		40.82 352	38.86 355	46.69 367	53.84 387	16.45 396	48.61 382	49.92 387	37.49 375	31.67 380	60.97 375	8.14 391	56.42 386	28.42 378	30.72 384	67.19 372

test_vis3_rt		49.26 347	47.02 349	56.00 359	54.30 386	45.27 371	66.76 369	48.08 389	36.83 376	44.38 376	53.20 381	7.17 392	64.07 382	56.77 298	55.66 366	58.65 378

test_f		52.09 343	50.82 344	55.90 360	53.82 388	42.31 380	59.42 378	58.31 384	36.45 377	56.12 367	70.96 367	12.18 384	57.79 385	53.51 312	56.57 365	67.60 371

LCM-MVSNet		54.25 337	49.68 347	67.97 344	53.73 389	45.28 370	66.85 368	80.78 307	35.96 378	39.45 379	62.23 374	8.70 389	78.06 346	48.24 341	51.20 373	80.57 352

APD_test1		53.31 341	49.93 346	63.42 351	65.68 378	50.13 358	71.59 350	66.90 370	34.43 379	40.58 378	71.56 366	8.65 390	76.27 357	34.64 373	55.36 368	63.86 375

PMVS	37.38 22	44.16 351	40.28 354	55.82 361	40.82 394	42.54 379	65.12 373	63.99 376	34.43 379	24.48 385	57.12 380	3.92 395	76.17 359	17.10 387	55.52 367	48.75 382
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
Gipuma		45.18 350	41.86 353	55.16 363	77.03 349	51.52 351	32.50 385	80.52 311	32.46 381	27.12 384	35.02 385	9.52 388	75.50 362	22.31 384	60.21 361	38.45 384
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
DSMNet-mixed		57.77 335	56.90 337	60.38 354	67.70 376	35.61 385	69.18 360	53.97 386	32.30 382	57.49 362	79.88 327	40.39 339	68.57 379	38.78 368	72.37 318	76.97 360

testf1		45.72 348	41.96 351	57.00 357	56.90 383	45.32 368	66.14 370	59.26 382	26.19 383	30.89 382	60.96 376	4.14 393	70.64 375	26.39 381	46.73 378	55.04 380

APD_test2		45.72 348	41.96 351	57.00 357	56.90 383	45.32 368	66.14 370	59.26 382	26.19 383	30.89 382	60.96 376	4.14 393	70.64 375	26.39 381	46.73 378	55.04 380

E-PMN		31.77 353	30.64 356	35.15 370	52.87 390	27.67 389	57.09 380	47.86 390	24.64 385	16.40 390	33.05 386	11.23 386	54.90 387	14.46 390	18.15 387	22.87 386

EMVS		30.81 355	29.65 357	34.27 371	50.96 391	25.95 391	56.58 381	46.80 391	24.01 386	15.53 391	30.68 387	12.47 383	54.43 388	12.81 391	17.05 388	22.43 387

MVE	26.22 23	30.37 356	25.89 360	43.81 368	44.55 393	35.46 386	28.87 386	39.07 393	18.20 387	18.58 389	40.18 384	2.68 396	47.37 390	17.07 388	23.78 386	48.60 383
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				27.40 372	40.17 395	26.90 390		24.59 396	17.44 388	23.95 386	48.61 383	9.77 387	26.48 391	18.06 385	24.47 385	28.83 385

wuyk23d		16.82 359	15.94 362	19.46 373	58.74 382	31.45 388	39.22 383	3.74 398	6.84 389	6.04 392	2.70 392	1.27 397	24.29 392	10.54 392	14.40 391	2.63 389

test_method		31.52 354	29.28 358	38.23 369	27.03 396	6.50 399	20.94 387	62.21 378	4.05 390	22.35 388	52.50 382	13.33 382	47.58 389	27.04 380	34.04 382	60.62 376

tmp_tt		18.61 358	21.40 361	10.23 374	4.82 397	10.11 397	34.70 384	30.74 395	1.48 391	23.91 387	26.07 388	28.42 367	13.41 393	27.12 379	15.35 390	7.17 388

EGC-MVSNET		52.07 344	47.05 348	67.14 345	83.51 268	60.71 259	80.50 289	67.75 368	0.07 392	0.43 393	75.85 356	24.26 372	81.54 331	28.82 377	62.25 354	59.16 377

testmvs		6.04 362	8.02 365	0.10 376	0.08 398	0.03 401	69.74 357	0.04 399	0.05 393	0.31 394	1.68 393	0.02 399	0.04 394	0.24 393	0.02 392	0.25 391

test123		6.12 361	8.11 364	0.14 375	0.06 399	0.09 400	71.05 352	0.03 400	0.04 394	0.25 395	1.30 394	0.05 398	0.03 395	0.21 394	0.01 393	0.29 390

test_blank		0.00 364	0.00 367	0.00 377	0.00 400	0.00 402	0.00 388	0.00 401	0.00 395	0.00 396	0.00 395	0.00 400	0.00 396	0.00 395	0.00 394	0.00 392

uanet_test		0.00 364	0.00 367	0.00 377	0.00 400	0.00 402	0.00 388	0.00 401	0.00 395	0.00 396	0.00 395	0.00 400	0.00 396	0.00 395	0.00 394	0.00 392

DCPMVS		0.00 364	0.00 367	0.00 377	0.00 400	0.00 402	0.00 388	0.00 401	0.00 395	0.00 396	0.00 395	0.00 400	0.00 396	0.00 395	0.00 394	0.00 392

cdsmvs_eth3d_5k		19.96 357	26.61 359	0.00 377	0.00 400	0.00 402	0.00 388	89.26 172	0.00 395	0.00 396	88.61 169	61.62 154	0.00 396	0.00 395	0.00 394	0.00 392

pcd_1.5k_mvsjas		5.26 363	7.02 366	0.00 377	0.00 400	0.00 402	0.00 388	0.00 401	0.00 395	0.00 396	0.00 395	63.15 131	0.00 396	0.00 395	0.00 394	0.00 392

sosnet-low-res		0.00 364	0.00 367	0.00 377	0.00 400	0.00 402	0.00 388	0.00 401	0.00 395	0.00 396	0.00 395	0.00 400	0.00 396	0.00 395	0.00 394	0.00 392

sosnet		0.00 364	0.00 367	0.00 377	0.00 400	0.00 402	0.00 388	0.00 401	0.00 395	0.00 396	0.00 395	0.00 400	0.00 396	0.00 395	0.00 394	0.00 392

uncertanet		0.00 364	0.00 367	0.00 377	0.00 400	0.00 402	0.00 388	0.00 401	0.00 395	0.00 396	0.00 395	0.00 400	0.00 396	0.00 395	0.00 394	0.00 392

Regformer		0.00 364	0.00 367	0.00 377	0.00 400	0.00 402	0.00 388	0.00 401	0.00 395	0.00 396	0.00 395	0.00 400	0.00 396	0.00 395	0.00 394	0.00 392

ab-mvs-re		7.23 360	9.64 363	0.00 377	0.00 400	0.00 402	0.00 388	0.00 401	0.00 395	0.00 396	86.72 219	0.00 400	0.00 396	0.00 395	0.00 394	0.00 392

uanet		0.00 364	0.00 367	0.00 377	0.00 400	0.00 402	0.00 388	0.00 401	0.00 395	0.00 396	0.00 395	0.00 400	0.00 396	0.00 395	0.00 394	0.00 392

WAC-MVS						42.58 377								39.46 366

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 45					97.53 1	89.67 5	96.44 9	94.41 31

No_MVS				89.16 1	94.34 27	75.53 2		92.99 45					97.53 1	89.67 5	96.44 9	94.41 31

eth-test2					0.00 400

eth-test					0.00 400

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 42	82.45 3	96.87 19	83.77 51	96.48 8	94.88 13

test_0728_SECOND				87.71 31	95.34 1	71.43 55	93.49 9	94.23 3					97.49 3	89.08 11	96.41 12	94.21 41

GSMVS																88.96 234

test_part2					95.06 8	72.65 31				91.80 13

sam_mvs1												51.32 254				88.96 234

sam_mvs												50.01 267

ambc				75.24 295	73.16 366	50.51 357	63.05 377	87.47 224		64.28 336	77.81 345	17.80 379	89.73 259	57.88 287	60.64 359	85.49 304

MTGPA								92.02 85

test_post1							78.90 310				5.43 391	48.81 287	85.44 307	59.25 272

test_post											5.46 390	50.36 265	84.24 314

patchmatchnet-post											74.00 360	51.12 256	88.60 279

GG-mvs-BLEND				75.38 294	81.59 307	55.80 319	79.32 302	69.63 363		67.19 311	73.67 361	43.24 321	88.90 276	50.41 325	84.50 167	81.45 347

MTMP							92.18 34	32.83 394

test9_res														84.90 35	95.70 26	92.87 97

agg_prior2														82.91 59	95.45 29	92.70 100

agg_prior					92.85 59	71.94 50		91.78 100		84.41 64			94.93 86

test_prior4						72.60 33	89.01 100

test_prior				86.33 53	92.61 65	69.59 87		92.97 50					95.48 61			93.91 52

新几何2							86.29 189

旧先验1					91.96 71	65.79 171		86.37 241			93.08 64	69.31 74			92.74 67	88.74 243

原ACMM2							86.86 170

testdata2													91.01 242	62.37 246

segment_acmp												73.08 37

test12				86.80 48	92.63 64	70.70 71		91.79 99		82.71 91		71.67 49	96.16 43		94.50 49	93.54 75

plane_prior7					90.08 102	68.51 117

plane_prior6					89.84 111	68.70 112						60.42 179

plane_prior5								92.44 69					95.38 68	78.71 95	86.32 148	91.33 142

plane_prior4											91.00 113

plane_prior1					89.90 110

n2								0.00 401

nn								0.00 401

door-mid								69.98 362

lessismore_v0				78.97 251	81.01 317	57.15 300		65.99 371		61.16 349	82.82 298	39.12 342	91.34 231	59.67 268	46.92 377	88.43 249

test11								92.23 79

door								69.44 365

HQP5-MVS						66.98 148

BP-MVS														77.47 107

HQP4-MVS										77.24 174			95.11 79			91.03 154

HQP3-MVS								92.19 82							85.99 155

HQP2-MVS												60.17 182

NP-MVS					89.62 114	68.32 119					90.24 125

ACMMP++_ref															81.95 204

ACMMP++															81.25 210

Test By Simon												64.33 118

High-resolution multi-view benchmark