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.

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 31	71.25 64	95.06 1	94.23 7	78.38 38	92.78 4	95.74 6	82.45 3	97.49 4	89.42 19	96.68 2	94.95 12

FOURS1					95.00 10	72.39 41	95.06 1	93.84 20	74.49 151	91.30 18

CP-MVS		87.11 38	86.92 43	87.68 37	94.20 38	73.86 7	93.98 3	92.82 68	76.62 83	83.68 112	94.46 36	67.93 122	95.95 62	84.20 78	94.39 61	93.23 124

APDe-MVS		89.15 9	89.63 8	87.73 31	94.49 22	71.69 54	93.83 4	93.96 18	75.70 112	91.06 19	96.03 1	76.84 17	97.03 21	89.09 21	95.65 31	94.47 55
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
SteuartSystems-ACMMP		88.72 14	88.86 14	88.32 9	92.14 78	72.96 25	93.73 5	93.67 25	80.19 12	88.10 42	94.80 27	73.76 37	97.11 18	87.51 46	95.82 25	94.90 15
Skip Steuart: Steuart Systems R&D Blog.
lecture		88.09 17	88.59 16	86.58 62	93.26 56	69.77 96	93.70 6	94.16 9	77.13 66	89.76 26	95.52 14	72.26 53	96.27 48	86.87 50	94.65 52	93.70 99

test0726					95.27 5	71.25 64	93.60 7	94.11 11	77.33 58	92.81 3	95.79 3	80.98 11

SED-MVS		90.08 2	90.85 2	87.77 28	95.30 2	70.98 71	93.57 8	94.06 15	77.24 61	93.10 1	95.72 8	82.99 1	97.44 7	89.07 25	96.63 4	94.88 16

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 8				94.02 61	82.45 3	96.87 24	83.77 82	96.48 8	94.88 16

DVP-MVS		89.60 3	90.35 3	87.33 45	95.27 5	71.25 64	93.49 10	92.73 69	77.33 58	92.12 12	95.78 4	80.98 11	97.40 9	89.08 22	96.41 12	93.33 121
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
test_0728_SECOND				87.71 35	95.34 1	71.43 60	93.49 10	94.23 7					97.49 4	89.08 22	96.41 12	94.21 68

MED-MVS test				87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	75.24 124	92.25 9	95.03 20		97.39 11	88.15 39	95.96 19	94.75 30

MED-MVS		89.59 4	90.16 4	87.86 26	94.57 17	71.43 60	93.28 12	94.36 3	76.30 96	92.25 9	95.03 20	81.59 7	97.39 11	88.15 39	95.96 19	94.75 30

TestfortrainingZip a		89.27 7	89.82 7	87.60 39	94.57 17	70.90 77	93.28 12	94.36 3	75.24 124	92.25 9	95.03 20	81.59 7	97.39 11	86.12 57	95.96 19	94.52 52

TestfortrainingZip							93.28 12

3Dnovator+	77.84 4	85.48 73	84.47 92	88.51 7	91.08 93	73.49 16	93.18 16	93.78 23	80.79 8	76.66 252	93.37 83	60.40 234	96.75 30	77.20 160	93.73 70	95.29 6

HFP-MVS		87.58 26	87.47 31	87.94 19	94.58 16	73.54 15	93.04 17	93.24 38	76.78 77	84.91 82	94.44 39	70.78 75	96.61 36	84.53 72	94.89 46	93.66 100

ACMMPR		87.44 29	87.23 36	88.08 15	94.64 13	73.59 12	93.04 17	93.20 39	76.78 77	84.66 89	94.52 32	68.81 109	96.65 34	84.53 72	94.90 45	94.00 80

ZNCC-MVS		87.94 22	87.85 24	88.20 12	94.39 28	73.33 19	93.03 19	93.81 22	76.81 75	85.24 77	94.32 44	71.76 60	96.93 23	85.53 61	95.79 26	94.32 64

region2R		87.42 31	87.20 37	88.09 14	94.63 14	73.55 13	93.03 19	93.12 45	76.73 80	84.45 94	94.52 32	69.09 103	96.70 31	84.37 74	94.83 49	94.03 78

MSP-MVS		89.51 5	89.91 6	88.30 10	94.28 34	73.46 17	92.90 21	94.11 11	80.27 10	91.35 17	94.16 54	78.35 15	96.77 28	89.59 17	94.22 66	94.67 36
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
CS-MVS		86.69 44	86.95 42	85.90 78	90.76 103	67.57 164	92.83 22	93.30 37	79.67 19	84.57 93	92.27 107	71.47 65	95.02 100	84.24 77	93.46 73	95.13 9

XVS		87.18 37	86.91 44	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	94.17 53	67.45 127	96.60 37	83.06 87	94.50 57	94.07 76

X-MVStestdata		80.37 197	77.83 237	88.00 17	94.42 24	73.33 19	92.78 23	92.99 54	79.14 26	83.67 113	12.47 483	67.45 127	96.60 37	83.06 87	94.50 57	94.07 76

mPP-MVS		86.67 46	86.32 53	87.72 33	94.41 26	73.55 13	92.74 25	92.22 98	76.87 74	82.81 134	94.25 49	66.44 140	96.24 49	82.88 92	94.28 64	93.38 117

ACMMP		85.89 65	85.39 76	87.38 44	93.59 49	72.63 33	92.74 25	93.18 44	76.78 77	80.73 170	93.82 72	64.33 164	96.29 46	82.67 99	90.69 116	93.23 124
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
MP-MVS		87.71 23	87.64 26	87.93 21	94.36 30	73.88 6	92.71 27	92.65 75	77.57 49	83.84 109	94.40 41	72.24 54	96.28 47	85.65 59	95.30 39	93.62 107
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
MM		89.16 8	89.23 10	88.97 4	90.79 102	73.65 10	92.66 28	91.17 147	86.57 1	87.39 57	94.97 25	71.70 62	97.68 1	92.19 1	95.63 32	95.57 1

SF-MVS		88.46 15	88.74 15	87.64 38	92.78 70	71.95 51	92.40 29	94.74 2	75.71 110	89.16 29	95.10 18	75.65 24	96.19 51	87.07 49	96.01 17	94.79 23

SMA-MVS		89.08 10	89.23 10	88.61 6	94.25 35	73.73 9	92.40 29	93.63 26	74.77 145	92.29 7	95.97 2	74.28 33	97.24 16	88.58 33	96.91 1	94.87 18
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
GST-MVS		87.42 31	87.26 34	87.89 24	94.12 40	72.97 24	92.39 31	93.43 33	76.89 73	84.68 86	93.99 65	70.67 77	96.82 26	84.18 79	95.01 41	93.90 86

HPM-MVS++		89.02 11	89.15 12	88.63 5	95.01 9	76.03 1	92.38 32	92.85 64	80.26 11	87.78 48	94.27 47	75.89 22	96.81 27	87.45 47	96.44 9	93.05 140

SR-MVS		86.73 43	86.67 48	86.91 55	94.11 41	72.11 49	92.37 33	92.56 80	74.50 150	86.84 64	94.65 31	67.31 129	95.77 64	84.80 68	92.85 78	92.84 152

SPE-MVS-test		86.29 54	86.48 51	85.71 80	91.02 95	67.21 180	92.36 34	93.78 23	78.97 33	83.51 118	91.20 151	70.65 78	95.15 91	81.96 102	94.89 46	94.77 25

EC-MVSNet		86.01 58	86.38 52	84.91 112	89.31 147	66.27 194	92.32 35	93.63 26	79.37 23	84.17 102	91.88 121	69.04 107	95.43 77	83.93 81	93.77 69	93.01 143

EPP-MVSNet		83.40 120	83.02 120	84.57 123	90.13 114	64.47 251	92.32 35	90.73 162	74.45 153	79.35 191	91.10 154	69.05 106	95.12 92	72.78 215	87.22 184	94.13 72

PHI-MVS		86.43 49	86.17 59	87.24 46	90.88 99	70.96 73	92.27 37	94.07 14	72.45 202	85.22 78	91.90 120	69.47 95	96.42 44	83.28 86	95.94 23	94.35 61

HPM-MVS		87.11 38	86.98 41	87.50 43	93.88 43	72.16 47	92.19 38	93.33 36	76.07 102	83.81 110	93.95 68	69.77 92	96.01 58	85.15 62	94.66 51	94.32 64
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
MTMP							92.18 39	32.83 488

HPM-MVS_fast		85.35 79	84.95 85	86.57 63	93.69 46	70.58 84	92.15 40	91.62 132	73.89 168	82.67 136	94.09 57	62.60 186	95.54 70	80.93 111	92.93 77	93.57 110

CPTT-MVS		83.73 108	83.33 116	84.92 111	93.28 53	70.86 78	92.09 41	90.38 172	68.75 301	79.57 185	92.83 97	60.60 230	93.04 211	80.92 112	91.56 102	90.86 226

APD-MVS_3200maxsize		85.97 61	85.88 65	86.22 67	92.69 72	69.53 99	91.93 42	92.99 54	73.54 178	85.94 69	94.51 35	65.80 152	95.61 67	83.04 89	92.51 83	93.53 114

SR-MVS-dyc-post		85.77 67	85.61 72	86.23 66	93.06 64	70.63 82	91.88 43	92.27 91	73.53 179	85.69 73	94.45 37	65.00 160	95.56 68	82.75 94	91.87 95	92.50 164

RE-MVS-def			85.48 75		93.06 64	70.63 82	91.88 43	92.27 91	73.53 179	85.69 73	94.45 37	63.87 168		82.75 94	91.87 95	92.50 164

APD-MVS		87.44 29	87.52 30	87.19 47	94.24 36	72.39 41	91.86 45	92.83 65	73.01 196	88.58 34	94.52 32	73.36 38	96.49 42	84.26 75	95.01 41	92.70 154
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
SD-MVS		88.06 18	88.50 18	86.71 60	92.60 75	72.71 29	91.81 46	93.19 40	77.87 42	90.32 23	94.00 63	74.83 26	93.78 158	87.63 45	94.27 65	93.65 104
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
NormalMVS		86.29 54	85.88 65	87.52 41	93.26 56	72.47 38	91.65 47	92.19 103	79.31 24	84.39 96	92.18 111	64.64 162	95.53 71	80.70 116	94.65 52	94.56 49

SymmetryMVS		85.38 78	84.81 86	87.07 50	91.47 87	72.47 38	91.65 47	88.06 265	79.31 24	84.39 96	92.18 111	64.64 162	95.53 71	80.70 116	90.91 113	93.21 127

ME-MVS		88.98 12	89.39 9	87.75 30	94.54 20	71.43 60	91.61 49	94.25 6	76.30 96	90.62 21	95.03 20	78.06 16	97.07 20	88.15 39	95.96 19	94.75 30

reproduce_model		87.28 35	87.39 33	86.95 54	93.10 62	71.24 68	91.60 50	93.19 40	74.69 146	88.80 33	95.61 11	70.29 81	96.44 43	86.20 56	93.08 75	93.16 131

DPE-MVS		89.48 6	89.98 5	88.01 16	94.80 11	72.69 31	91.59 51	94.10 13	75.90 105	92.29 7	95.66 10	81.67 6	97.38 14	87.44 48	96.34 15	93.95 83
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
QAPM		80.88 173	79.50 196	85.03 104	88.01 206	68.97 114	91.59 51	92.00 111	66.63 331	75.15 296	92.16 113	57.70 253	95.45 75	63.52 303	88.76 152	90.66 235

IS-MVSNet		83.15 127	82.81 124	84.18 151	89.94 123	63.30 283	91.59 51	88.46 258	79.04 30	79.49 186	92.16 113	65.10 157	94.28 130	67.71 270	91.86 97	94.95 12

reproduce-ours		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 136	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 138

our_new_method		87.47 27	87.61 27	87.07 50	93.27 54	71.60 55	91.56 54	93.19 40	74.98 136	88.96 30	95.54 12	71.20 70	96.54 40	86.28 54	93.49 71	93.06 138

9.14			88.26 19		92.84 69		91.52 56	94.75 1	73.93 167	88.57 35	94.67 30	75.57 25	95.79 63	86.77 51	95.76 27

MGCNet		87.69 24	87.55 29	88.12 13	89.45 138	71.76 53	91.47 57	89.54 204	82.14 3	86.65 66	94.28 46	68.28 118	97.46 6	90.81 6	95.31 38	95.15 8

TSAR-MVS + MP.		88.02 21	88.11 20	87.72 33	93.68 47	72.13 48	91.41 58	92.35 87	74.62 149	88.90 32	93.85 71	75.75 23	96.00 59	87.80 43	94.63 54	95.04 10
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
DeepC-MVS_fast	79.65 3	86.91 41	86.62 50	87.76 29	93.52 50	72.37 43	91.26 59	93.04 46	76.62 83	84.22 100	93.36 84	71.44 66	96.76 29	80.82 113	95.33 37	94.16 70
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HQP_MVS		83.64 112	83.14 117	85.14 98	90.08 116	68.71 123	91.25 60	92.44 82	79.12 28	78.92 197	91.00 161	60.42 232	95.38 82	78.71 142	86.32 200	91.33 209

plane_prior2							91.25 60		79.12 28

NCCC		88.06 18	88.01 22	88.24 11	94.41 26	73.62 11	91.22 62	92.83 65	81.50 5	85.79 72	93.47 80	73.02 45	97.00 22	84.90 64	94.94 44	94.10 74

API-MVS		81.99 148	81.23 152	84.26 148	90.94 97	70.18 91	91.10 63	89.32 216	71.51 222	78.66 202	88.28 242	65.26 155	95.10 97	64.74 297	91.23 107	87.51 345

EPNet		83.72 109	82.92 123	86.14 72	84.22 330	69.48 101	91.05 64	85.27 322	81.30 6	76.83 247	91.65 131	66.09 147	95.56 68	76.00 179	93.85 68	93.38 117
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ACMMP_NAP		88.05 20	88.08 21	87.94 19	93.70 45	73.05 22	90.86 65	93.59 28	76.27 98	88.14 41	95.09 19	71.06 72	96.67 33	87.67 44	96.37 14	94.09 75

CSCG		86.41 51	86.19 58	87.07 50	92.91 67	72.48 37	90.81 66	93.56 29	73.95 165	83.16 125	91.07 156	75.94 21	95.19 89	79.94 124	94.38 62	93.55 112

MSLP-MVS++		85.43 75	85.76 69	84.45 130	91.93 81	70.24 85	90.71 67	92.86 63	77.46 55	84.22 100	92.81 99	67.16 131	92.94 213	80.36 119	94.35 63	90.16 256

3Dnovator	76.31 5	83.38 121	82.31 135	86.59 61	87.94 208	72.94 28	90.64 68	92.14 108	77.21 63	75.47 278	92.83 97	58.56 246	94.72 115	73.24 211	92.71 81	92.13 186

OpenMVS	72.83 10	79.77 208	78.33 224	84.09 157	85.17 307	69.91 93	90.57 69	90.97 153	66.70 325	72.17 342	91.91 119	54.70 282	93.96 144	61.81 326	90.95 112	88.41 326

balanced_conf03		86.78 42	86.99 40	86.15 70	91.24 90	67.61 162	90.51 70	92.90 61	77.26 60	87.44 56	91.63 133	71.27 69	96.06 54	85.62 60	95.01 41	94.78 24

CNVR-MVS		88.93 13	89.13 13	88.33 8	94.77 12	73.82 8	90.51 70	93.00 51	80.90 7	88.06 43	94.06 59	76.43 19	96.84 25	88.48 36	95.99 18	94.34 62

MVSFormer		82.85 134	82.05 142	85.24 95	87.35 239	70.21 86	90.50 72	90.38 172	68.55 304	81.32 156	89.47 205	61.68 204	93.46 182	78.98 139	90.26 123	92.05 188

test_djsdf		80.30 200	79.32 201	83.27 196	83.98 336	65.37 219	90.50 72	90.38 172	68.55 304	76.19 265	88.70 228	56.44 268	93.46 182	78.98 139	80.14 301	90.97 222

save fliter					93.80 44	72.35 44	90.47 74	91.17 147	74.31 156

nrg030		83.88 102	83.53 111	84.96 107	86.77 267	69.28 109	90.46 75	92.67 72	74.79 144	82.95 128	91.33 146	72.70 50	93.09 206	80.79 115	79.28 312	92.50 164

sasdasda		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 147	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

canonicalmvs		85.91 63	85.87 67	86.04 74	89.84 125	69.44 105	90.45 76	93.00 51	76.70 81	88.01 45	91.23 147	73.28 40	93.91 152	81.50 105	88.80 150	94.77 25

plane_prior						68.71 123	90.38 78		77.62 47						86.16 205

DeepC-MVS	79.81 2	87.08 40	86.88 45	87.69 36	91.16 91	72.32 45	90.31 79	93.94 19	77.12 67	82.82 133	94.23 50	72.13 56	97.09 19	84.83 67	95.37 35	93.65 104
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
Vis-MVSNet		83.46 118	82.80 125	85.43 90	90.25 112	68.74 121	90.30 80	90.13 184	76.33 95	80.87 167	92.89 95	61.00 221	94.20 136	72.45 224	90.97 111	93.35 120
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PGM-MVS		86.68 45	86.27 55	87.90 22	94.22 37	73.38 18	90.22 81	93.04 46	75.53 115	83.86 108	94.42 40	67.87 124	96.64 35	82.70 98	94.57 56	93.66 100

LPG-MVS_test		82.08 145	81.27 151	84.50 127	89.23 152	68.76 119	90.22 81	91.94 115	75.37 121	76.64 253	91.51 139	54.29 285	94.91 102	78.44 144	83.78 245	89.83 277

Anonymous20231211		78.97 232	77.69 245	82.81 222	90.54 106	64.29 255	90.11 83	91.51 137	65.01 352	76.16 269	88.13 251	50.56 331	93.03 212	69.68 253	77.56 333	91.11 215

ACMM	73.20 8	80.78 183	79.84 185	83.58 185	89.31 147	68.37 134	89.99 84	91.60 134	70.28 259	77.25 236	89.66 198	53.37 296	93.53 174	74.24 200	82.85 266	88.85 310
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMP	74.13 6	81.51 164	80.57 164	84.36 136	89.42 139	68.69 126	89.97 85	91.50 140	74.46 152	75.04 300	90.41 176	53.82 291	94.54 121	77.56 156	82.91 265	89.86 276
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
fmvsm_s_conf0.5_n_11		86.06 56	86.75 47	84.00 170	87.78 218	66.09 196	89.96 86	90.80 160	77.37 57	86.72 65	94.20 52	72.51 51	92.78 222	89.08 22	92.33 87	93.13 135

LFMVS		81.82 152	81.23 152	83.57 186	91.89 82	63.43 281	89.84 87	81.85 375	77.04 70	83.21 121	93.10 88	52.26 305	93.43 184	71.98 227	89.95 130	93.85 88

MCST-MVS		87.37 34	87.25 35	87.73 31	94.53 21	72.46 40	89.82 88	93.82 21	73.07 194	84.86 85	92.89 95	76.22 20	96.33 45	84.89 66	95.13 40	94.40 58

MAR-MVS		81.84 151	80.70 161	85.27 94	91.32 89	71.53 58	89.82 88	90.92 154	69.77 273	78.50 206	86.21 304	62.36 192	94.52 123	65.36 291	92.05 93	89.77 280
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
MP-MVS-pluss		87.67 25	87.72 25	87.54 40	93.64 48	72.04 50	89.80 90	93.50 30	75.17 132	86.34 68	95.29 17	70.86 74	96.00 59	88.78 31	96.04 16	94.58 45
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
UA-Net		85.08 84	84.96 84	85.45 89	92.07 79	68.07 145	89.78 91	90.86 158	82.48 2	84.60 92	93.20 87	69.35 97	95.22 88	71.39 232	90.88 114	93.07 137

alignmvs		85.48 73	85.32 79	85.96 77	89.51 134	69.47 102	89.74 92	92.47 81	76.17 100	87.73 52	91.46 142	70.32 80	93.78 158	81.51 104	88.95 147	94.63 42

VDDNet		81.52 162	80.67 162	84.05 165	90.44 108	64.13 258	89.73 93	85.91 315	71.11 231	83.18 124	93.48 78	50.54 332	93.49 178	73.40 208	88.25 163	94.54 51

CANet		86.45 48	86.10 61	87.51 42	90.09 115	70.94 75	89.70 94	92.59 79	81.78 4	81.32 156	91.43 143	70.34 79	97.23 17	84.26 75	93.36 74	94.37 60

test_fmvsmconf0.1_n		85.61 71	85.65 71	85.50 88	82.99 367	69.39 107	89.65 95	90.29 179	73.31 186	87.77 49	94.15 55	71.72 61	93.23 193	90.31 9	90.67 117	93.89 87

114514_t		80.68 184	79.51 195	84.20 150	94.09 42	67.27 176	89.64 96	91.11 150	58.75 419	74.08 315	90.72 166	58.10 249	95.04 99	69.70 252	89.42 140	90.30 252

MVSMamba_PlusPlus		85.99 59	85.96 64	86.05 73	91.09 92	67.64 161	89.63 97	92.65 75	72.89 199	84.64 90	91.71 128	71.85 58	96.03 55	84.77 69	94.45 60	94.49 54

test_fmvsmconf_n		85.92 62	86.04 63	85.57 87	85.03 314	69.51 100	89.62 98	90.58 165	73.42 182	87.75 50	94.02 61	72.85 48	93.24 192	90.37 8	90.75 115	93.96 81

fmvsm_l_conf0.5_n_3		86.02 57	86.32 53	85.14 98	87.20 249	68.54 130	89.57 99	90.44 170	75.31 123	87.49 54	94.39 42	72.86 47	92.72 223	89.04 27	90.56 118	94.16 70

DeepPCF-MVS	80.84 1	88.10 16	88.56 17	86.73 59	92.24 77	69.03 110	89.57 99	93.39 35	77.53 53	89.79 25	94.12 56	78.98 14	96.58 39	85.66 58	95.72 28	94.58 45

fmvsm_s_conf0.5_n_10		86.38 52	86.76 46	85.24 95	87.33 244	67.30 174	89.50 101	90.98 152	76.25 99	90.56 22	94.75 29	68.38 115	94.24 135	90.80 7	92.32 89	94.19 69

test_fmvsmconf0.01_n		84.73 89	84.52 91	85.34 92	80.25 409	69.03 110	89.47 102	89.65 200	73.24 190	86.98 62	94.27 47	66.62 136	93.23 193	90.26 10	89.95 130	93.78 96

fmvsm_s_conf0.5_n		83.80 104	83.71 106	84.07 159	86.69 270	67.31 173	89.46 103	83.07 358	71.09 232	86.96 63	93.70 75	69.02 108	91.47 282	88.79 30	84.62 231	93.44 116

MGCFI-Net		85.06 85	85.51 74	83.70 181	89.42 139	63.01 289	89.43 104	92.62 78	76.43 87	87.53 53	91.34 145	72.82 49	93.42 185	81.28 108	88.74 153	94.66 39

fmvsm_s_conf0.5_n_a		83.63 113	83.41 113	84.28 144	86.14 283	68.12 143	89.43 104	82.87 363	70.27 260	87.27 59	93.80 73	69.09 103	91.58 270	88.21 38	83.65 252	93.14 134

UGNet		80.83 175	79.59 194	84.54 124	88.04 203	68.09 144	89.42 106	88.16 260	76.95 71	76.22 264	89.46 207	49.30 349	93.94 147	68.48 265	90.31 121	91.60 199
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
tt0805		78.73 237	77.83 237	81.43 258	85.17 307	60.30 335	89.41 107	90.90 155	71.21 229	77.17 243	88.73 227	46.38 372	93.21 195	72.57 218	78.96 314	90.79 228

fmvsm_s_conf0.1_n		83.56 115	83.38 114	84.10 153	84.86 316	67.28 175	89.40 108	83.01 359	70.67 244	87.08 60	93.96 67	68.38 115	91.45 283	88.56 34	84.50 232	93.56 111

BP-MVS1		84.32 91	83.71 106	86.17 68	87.84 213	67.85 154	89.38 109	89.64 201	77.73 45	83.98 106	92.12 116	56.89 264	95.43 77	84.03 80	91.75 98	95.24 7

AdaColmap		80.58 191	79.42 197	84.06 162	93.09 63	68.91 115	89.36 110	88.97 238	69.27 284	75.70 274	89.69 196	57.20 261	95.77 64	63.06 309	88.41 160	87.50 346

fmvsm_s_conf0.1_n_a		83.32 124	82.99 121	84.28 144	83.79 340	68.07 145	89.34 111	82.85 364	69.80 271	87.36 58	94.06 59	68.34 117	91.56 273	87.95 42	83.46 258	93.21 127

PS-MVSNAJss		82.07 146	81.31 150	84.34 138	86.51 275	67.27 176	89.27 112	91.51 137	71.75 215	79.37 190	90.22 184	63.15 178	94.27 131	77.69 155	82.36 273	91.49 205

jajsoiax		79.29 223	77.96 231	83.27 196	84.68 321	66.57 190	89.25 113	90.16 183	69.20 289	75.46 280	89.49 204	45.75 383	93.13 204	76.84 167	80.80 291	90.11 260

fmvsm_s_conf0.5_n_8		86.56 47	87.17 38	84.73 120	87.76 221	65.62 212	89.20 114	92.21 100	79.94 17	89.74 27	94.86 26	68.63 112	94.20 136	90.83 5	91.39 104	94.38 59

fmvsm_s_conf0.5_n_5		85.22 81	85.55 73	84.25 149	86.26 278	67.40 170	89.18 115	89.31 217	72.50 201	88.31 37	93.86 70	69.66 93	91.96 255	89.81 13	91.05 109	93.38 117

mvs_tets		79.13 227	77.77 241	83.22 200	84.70 320	66.37 192	89.17 116	90.19 182	69.38 281	75.40 283	89.46 207	44.17 395	93.15 202	76.78 171	80.70 293	90.14 257

HQP-NCC					89.33 144		89.17 116		76.41 88	77.23 238

ACMP_Plane					89.33 144		89.17 116		76.41 88	77.23 238

HQP-MVS		82.61 138	82.02 143	84.37 135	89.33 144	66.98 183	89.17 116	92.19 103	76.41 88	77.23 238	90.23 183	60.17 235	95.11 94	77.47 157	85.99 209	91.03 219

LS3D		76.95 282	74.82 301	83.37 193	90.45 107	67.36 172	89.15 120	86.94 295	61.87 392	69.52 372	90.61 172	51.71 319	94.53 122	46.38 436	86.71 195	88.21 331

GDP-MVS		83.52 116	82.64 128	86.16 69	88.14 197	68.45 132	89.13 121	92.69 70	72.82 200	83.71 111	91.86 123	55.69 272	95.35 86	80.03 122	89.74 134	94.69 33

OPM-MVS		83.50 117	82.95 122	85.14 98	88.79 172	70.95 74	89.13 121	91.52 136	77.55 52	80.96 164	91.75 127	60.71 224	94.50 124	79.67 130	86.51 198	89.97 272
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
fmvsm_s_conf0.5_n_3		86.36 53	87.46 32	83.09 205	87.08 258	65.21 223	89.09 123	90.21 181	79.67 19	89.98 24	95.02 24	73.17 42	91.71 267	91.30 3	91.60 99	92.34 171

TSAR-MVS + GP.		85.71 69	85.33 78	86.84 56	91.34 88	72.50 36	89.07 124	87.28 285	76.41 88	85.80 71	90.22 184	74.15 35	95.37 85	81.82 103	91.88 94	92.65 158

test_prior4						72.60 34	89.01 125

GeoE		81.71 154	81.01 157	83.80 180	89.51 134	64.45 252	88.97 126	88.73 251	71.27 228	78.63 203	89.76 195	66.32 142	93.20 198	69.89 250	86.02 208	93.74 97

Anonymous20240529		80.19 203	78.89 212	84.10 153	90.60 104	64.75 243	88.95 127	90.90 155	65.97 339	80.59 172	91.17 153	49.97 339	93.73 164	69.16 258	82.70 270	93.81 92

VDD-MVS		83.01 132	82.36 134	84.96 107	91.02 95	66.40 191	88.91 128	88.11 261	77.57 49	84.39 96	93.29 85	52.19 306	93.91 152	77.05 163	88.70 154	94.57 47

Effi-MVS+		83.62 114	83.08 118	85.24 95	88.38 188	67.45 167	88.89 129	89.15 228	75.50 116	82.27 139	88.28 242	69.61 94	94.45 127	77.81 152	87.84 172	93.84 90

fmvsm_s_conf0.5_n_6		85.55 72	86.20 56	83.60 183	87.32 246	65.13 226	88.86 130	91.63 131	75.41 119	88.23 40	93.45 81	68.56 113	92.47 234	89.52 18	92.78 79	93.20 129

ACMH+	68.96 14	76.01 301	74.01 312	82.03 246	88.60 179	65.31 222	88.86 130	87.55 279	70.25 261	67.75 388	87.47 267	41.27 414	93.19 200	58.37 359	75.94 357	87.60 342

test_prior2							88.85 132		75.41 119	84.91 82	93.54 76	74.28 33		83.31 85	95.86 24

Elysia		81.53 160	80.16 175	85.62 84	85.51 298	68.25 139	88.84 133	92.19 103	71.31 225	80.50 173	89.83 190	46.89 366	94.82 108	76.85 165	89.57 136	93.80 94

StellarMVS		81.53 160	80.16 175	85.62 84	85.51 298	68.25 139	88.84 133	92.19 103	71.31 225	80.50 173	89.83 190	46.89 366	94.82 108	76.85 165	89.57 136	93.80 94

DP-MVS Recon		83.11 130	82.09 141	86.15 70	94.44 23	70.92 76	88.79 135	92.20 101	70.53 249	79.17 193	91.03 159	64.12 166	96.03 55	68.39 267	90.14 125	91.50 204

fmvsm_s_conf0.5_n_4		85.39 77	85.75 70	84.30 142	86.70 269	65.83 205	88.77 136	89.78 193	75.46 118	88.35 36	93.73 74	69.19 102	93.06 208	91.30 3	88.44 159	94.02 79

Effi-MVS+-dtu		80.03 205	78.57 217	84.42 132	85.13 311	68.74 121	88.77 136	88.10 262	74.99 135	74.97 302	83.49 370	57.27 259	93.36 186	73.53 205	80.88 289	91.18 213

TEST9					93.26 56	72.96 25	88.75 138	91.89 117	68.44 307	85.00 80	93.10 88	74.36 32	95.41 80

train_agg		86.43 49	86.20 56	87.13 49	93.26 56	72.96 25	88.75 138	91.89 117	68.69 302	85.00 80	93.10 88	74.43 30	95.41 80	84.97 63	95.71 29	93.02 142

ETV-MVS		84.90 88	84.67 88	85.59 86	89.39 142	68.66 127	88.74 140	92.64 77	79.97 16	84.10 103	85.71 313	69.32 98	95.38 82	80.82 113	91.37 105	92.72 153

PVSNet_Blended_VisFu		82.62 137	81.83 147	84.96 107	90.80 101	69.76 97	88.74 140	91.70 128	69.39 280	78.96 195	88.46 237	65.47 154	94.87 107	74.42 197	88.57 155	90.24 254

casdiffmvs_mvg		85.99 59	86.09 62	85.70 81	87.65 229	67.22 179	88.69 142	93.04 46	79.64 21	85.33 76	92.54 104	73.30 39	94.50 124	83.49 83	91.14 108	95.37 2
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_8					93.13 60	72.57 35	88.68 143	91.84 121	68.69 302	84.87 84	93.10 88	74.43 30	95.16 90

test_fmvsm_n_1920		85.29 80	85.34 77	85.13 101	86.12 284	69.93 92	88.65 144	90.78 161	69.97 267	88.27 38	93.98 66	71.39 67	91.54 277	88.49 35	90.45 120	93.91 84

ACMH	67.68 16	75.89 302	73.93 314	81.77 251	88.71 176	66.61 189	88.62 145	89.01 235	69.81 270	66.78 402	86.70 289	41.95 411	91.51 280	55.64 382	78.14 325	87.17 355
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
fmvsm_s_conf0.5_n_9		87.39 33	87.95 23	85.70 81	89.48 137	67.88 153	88.59 146	89.05 232	80.19 12	90.70 20	95.40 15	74.56 28	93.92 151	91.54 2	92.07 92	95.31 5

CDPH-MVS		85.76 68	85.29 81	87.17 48	93.49 51	71.08 69	88.58 147	92.42 85	68.32 309	84.61 91	93.48 78	72.32 52	96.15 53	79.00 138	95.43 34	94.28 66

fmvsm_l_conf0.5_n_9		85.84 66	86.63 49	83.46 188	87.12 257	66.01 199	88.56 148	89.43 208	75.59 114	89.32 28	94.32 44	72.89 46	91.21 293	90.11 11	92.33 87	93.16 131

DP-MVS		76.78 285	74.57 304	83.42 190	93.29 52	69.46 104	88.55 149	83.70 344	63.98 367	70.20 360	88.89 224	54.01 290	94.80 111	46.66 433	81.88 279	86.01 382

fmvsm_l_conf0.5_n		84.47 90	84.54 89	84.27 146	85.42 301	68.81 116	88.49 150	87.26 288	68.08 311	88.03 44	93.49 77	72.04 57	91.77 263	88.90 29	89.14 146	92.24 178

viewdifsd2359ckpt09		83.34 122	82.55 130	85.70 81	87.64 230	67.72 159	88.43 151	91.68 129	71.91 214	81.65 152	90.68 168	67.10 132	94.75 113	76.17 175	87.70 176	94.62 44

WR-MVS_H		78.51 244	78.49 218	78.56 327	88.02 204	56.38 387	88.43 151	92.67 72	77.14 65	73.89 317	87.55 264	66.25 143	89.24 334	58.92 352	73.55 390	90.06 266

F-COLMAP		76.38 296	74.33 310	82.50 236	89.28 149	66.95 186	88.41 153	89.03 233	64.05 365	66.83 401	88.61 232	46.78 368	92.89 215	57.48 366	78.55 316	87.67 340

GBi-Net		78.40 245	77.40 252	81.40 260	87.60 231	63.01 289	88.39 154	89.28 218	71.63 217	75.34 286	87.28 269	54.80 278	91.11 294	62.72 311	79.57 305	90.09 262

test1		78.40 245	77.40 252	81.40 260	87.60 231	63.01 289	88.39 154	89.28 218	71.63 217	75.34 286	87.28 269	54.80 278	91.11 294	62.72 311	79.57 305	90.09 262

FMVSNet1		77.44 272	76.12 280	81.40 260	86.81 265	63.01 289	88.39 154	89.28 218	70.49 254	74.39 312	87.28 269	49.06 353	91.11 294	60.91 333	78.52 317	90.09 262

tttt0517		79.40 219	77.91 233	83.90 176	88.10 200	63.84 264	88.37 157	84.05 340	71.45 223	76.78 249	89.12 214	49.93 342	94.89 105	70.18 246	83.18 263	92.96 146

fmvsm_l_conf0.5_n_a		84.13 95	84.16 94	84.06 162	85.38 302	68.40 133	88.34 158	86.85 298	67.48 318	87.48 55	93.40 82	70.89 73	91.61 268	88.38 37	89.22 143	92.16 185

v7n		78.97 232	77.58 248	83.14 203	83.45 350	65.51 214	88.32 159	91.21 145	73.69 173	72.41 338	86.32 303	57.93 250	93.81 157	69.18 257	75.65 360	90.11 260

COLMAP_ROB	66.92 17	73.01 342	70.41 358	80.81 278	87.13 252	65.63 211	88.30 160	84.19 339	62.96 377	63.80 430	87.69 259	38.04 433	92.56 229	46.66 433	74.91 377	84.24 409
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
FIs		82.07 146	82.42 131	81.04 272	88.80 171	58.34 353	88.26 161	93.49 31	76.93 72	78.47 209	91.04 157	69.92 89	92.34 242	69.87 251	84.97 225	92.44 169

EIA-MVS		83.31 125	82.80 125	84.82 115	89.59 130	65.59 213	88.21 162	92.68 71	74.66 148	78.96 195	86.42 300	69.06 105	95.26 87	75.54 186	90.09 126	93.62 107

PLC	70.83 11	78.05 256	76.37 278	83.08 207	91.88 83	67.80 156	88.19 163	89.46 207	64.33 360	69.87 369	88.38 239	53.66 292	93.58 166	58.86 353	82.73 268	87.86 337
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
MG-MVS		83.41 119	83.45 112	83.28 195	92.74 71	62.28 306	88.17 164	89.50 206	75.22 126	81.49 154	92.74 103	66.75 134	95.11 94	72.85 214	91.58 101	92.45 168

TAPA-MVS	73.13 9	79.15 226	77.94 232	82.79 226	89.59 130	62.99 293	88.16 165	91.51 137	65.77 340	77.14 244	91.09 155	60.91 222	93.21 195	50.26 414	87.05 188	92.17 184
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
test_fmvsmvis_n_1920		84.02 98	83.87 100	84.49 129	84.12 332	69.37 108	88.15 166	87.96 268	70.01 265	83.95 107	93.23 86	68.80 110	91.51 280	88.61 32	89.96 129	92.57 159

h-mvs33		83.15 127	82.19 138	86.02 76	90.56 105	70.85 79	88.15 166	89.16 227	76.02 103	84.67 87	91.39 144	61.54 207	95.50 73	82.71 96	75.48 364	91.72 198

KinetiMVS		83.31 125	82.61 129	85.39 91	87.08 258	67.56 165	88.06 168	91.65 130	77.80 44	82.21 141	91.79 124	57.27 259	94.07 142	77.77 153	89.89 132	94.56 49

PS-CasMVS		78.01 258	78.09 229	77.77 345	87.71 224	54.39 412	88.02 169	91.22 144	77.50 54	73.26 325	88.64 231	60.73 223	88.41 352	61.88 324	73.88 387	90.53 241

OMC-MVS		82.69 136	81.97 145	84.85 114	88.75 174	67.42 168	87.98 170	90.87 157	74.92 139	79.72 183	91.65 131	62.19 196	93.96 144	75.26 190	86.42 199	93.16 131

v8		79.97 207	79.02 209	82.80 223	84.09 333	64.50 250	87.96 171	90.29 179	74.13 163	75.24 293	86.81 282	62.88 185	93.89 155	74.39 198	75.40 369	90.00 268

FC-MVSNet-test		81.52 162	82.02 143	80.03 296	88.42 187	55.97 393	87.95 172	93.42 34	77.10 68	77.38 233	90.98 163	69.96 88	91.79 262	68.46 266	84.50 232	92.33 172

CP-MVSNet		78.22 249	78.34 223	77.84 343	87.83 214	54.54 410	87.94 173	91.17 147	77.65 46	73.48 323	88.49 236	62.24 195	88.43 351	62.19 320	74.07 383	90.55 240

PAPM_NR		83.02 131	82.41 132	84.82 115	92.47 76	66.37 192	87.93 174	91.80 123	73.82 169	77.32 235	90.66 169	67.90 123	94.90 104	70.37 242	89.48 139	93.19 130

PEN-MVS		77.73 264	77.69 245	77.84 343	87.07 260	53.91 415	87.91 175	91.18 146	77.56 51	73.14 327	88.82 226	61.23 216	89.17 336	59.95 340	72.37 398	90.43 245

ECVR-MVS		79.61 210	79.26 203	80.67 281	90.08 116	54.69 408	87.89 176	77.44 422	74.88 141	80.27 176	92.79 100	48.96 355	92.45 235	68.55 264	92.50 84	94.86 19

v10		79.74 209	78.67 214	82.97 215	84.06 334	64.95 233	87.88 177	90.62 164	73.11 193	75.11 297	86.56 296	61.46 210	94.05 143	73.68 203	75.55 362	89.90 274

test2506		77.30 276	76.49 273	79.74 303	90.08 116	52.02 426	87.86 178	63.10 469	74.88 141	80.16 179	92.79 100	38.29 432	92.35 241	68.74 263	92.50 84	94.86 19

SSM_0404		81.91 149	80.84 160	85.13 101	89.24 151	68.26 137	87.84 179	89.25 222	71.06 234	80.62 171	90.39 177	59.57 237	94.65 119	72.45 224	87.19 185	92.47 167

casdiffmvs		85.11 83	85.14 82	85.01 105	87.20 249	65.77 209	87.75 180	92.83 65	77.84 43	84.36 99	92.38 106	72.15 55	93.93 150	81.27 109	90.48 119	95.33 4
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
TranMVSNet+NR-MVSNet		80.84 174	80.31 171	82.42 237	87.85 212	62.33 304	87.74 181	91.33 142	80.55 9	77.99 221	89.86 188	65.23 156	92.62 224	67.05 279	75.24 374	92.30 174

EI-MVSNet-Vis-set		84.19 94	83.81 103	85.31 93	88.18 194	67.85 154	87.66 182	89.73 198	80.05 15	82.95 128	89.59 202	70.74 76	94.82 108	80.66 118	84.72 229	93.28 123

UniMVSNet (Re)		81.60 158	81.11 154	83.09 205	88.38 188	64.41 253	87.60 183	93.02 50	78.42 37	78.56 205	88.16 246	69.78 91	93.26 191	69.58 254	76.49 346	91.60 199

CNLPA		78.08 254	76.79 266	81.97 248	90.40 109	71.07 70	87.59 184	84.55 332	66.03 338	72.38 339	89.64 199	57.56 255	86.04 378	59.61 344	83.35 259	88.79 313

DTE-MVSNet		76.99 280	76.80 265	77.54 352	86.24 279	53.06 424	87.52 185	90.66 163	77.08 69	72.50 336	88.67 230	60.48 231	89.52 328	57.33 369	70.74 410	90.05 267

无先验							87.48 186	88.98 236	60.00 405				94.12 140	67.28 275		88.97 305

viewdifsd2359ckpt13		82.91 133	82.29 136	84.77 118	86.96 261	66.90 187	87.47 187	91.62 132	72.19 207	81.68 151	90.71 167	66.92 133	93.28 188	75.90 180	87.15 186	94.12 73

mvsmamba		80.60 188	79.38 198	84.27 146	89.74 128	67.24 178	87.47 187	86.95 294	70.02 264	75.38 284	88.93 222	51.24 323	92.56 229	75.47 188	89.22 143	93.00 144

FMVSNet2		78.20 251	77.21 256	81.20 267	87.60 231	62.89 295	87.47 187	89.02 234	71.63 217	75.29 292	87.28 269	54.80 278	91.10 297	62.38 317	79.38 310	89.61 284

E6		84.22 92	84.12 95	84.52 125	87.60 231	65.36 220	87.45 190	92.30 90	76.51 85	83.53 116	92.26 108	69.26 100	93.49 178	79.88 125	88.26 161	94.69 33

E5		84.22 92	84.12 95	84.51 126	87.60 231	65.36 220	87.45 190	92.31 89	76.51 85	83.53 116	92.26 108	69.25 101	93.50 177	79.88 125	88.26 161	94.69 33

RRT-MVS		82.60 140	82.10 140	84.10 153	87.98 207	62.94 294	87.45 190	91.27 143	77.42 56	79.85 181	90.28 180	56.62 267	94.70 117	79.87 127	88.15 165	94.67 36

EI-MVSNet-UG-set		83.81 103	83.38 114	85.09 103	87.87 211	67.53 166	87.44 193	89.66 199	79.74 18	82.23 140	89.41 211	70.24 82	94.74 114	79.95 123	83.92 244	92.99 145

SSM_0407		81.58 159	80.48 167	84.87 113	88.81 167	67.96 149	87.37 194	89.25 222	71.06 234	79.48 187	90.39 177	59.57 237	94.48 126	72.45 224	85.93 211	92.18 181

thisisatest0530		79.40 219	77.76 242	84.31 140	87.69 228	65.10 229	87.36 195	84.26 338	70.04 263	77.42 232	88.26 244	49.94 340	94.79 112	70.20 245	84.70 230	93.03 141

CANet_DTU		80.61 186	79.87 184	82.83 220	85.60 296	63.17 288	87.36 195	88.65 254	76.37 93	75.88 271	88.44 238	53.51 294	93.07 207	73.30 209	89.74 134	92.25 176

test1111		79.43 217	79.18 206	80.15 294	89.99 121	53.31 421	87.33 197	77.05 426	75.04 134	80.23 178	92.77 102	48.97 354	92.33 243	68.87 261	92.40 86	94.81 22

baseline		84.93 86	84.98 83	84.80 117	87.30 247	65.39 218	87.30 198	92.88 62	77.62 47	84.04 105	92.26 108	71.81 59	93.96 144	81.31 107	90.30 122	95.03 11

UniMVSNet_ETH3D		79.10 228	78.24 226	81.70 252	86.85 263	60.24 336	87.28 199	88.79 244	74.25 159	76.84 246	90.53 175	49.48 345	91.56 273	67.98 268	82.15 274	93.29 122

anonymousdsp		78.60 241	77.15 257	82.98 214	80.51 407	67.08 181	87.24 200	89.53 205	65.66 342	75.16 295	87.19 275	52.52 300	92.25 245	77.17 161	79.34 311	89.61 284

UniMVSNet_NR-MVSNet		81.88 150	81.54 149	82.92 216	88.46 184	63.46 279	87.13 201	92.37 86	80.19 12	78.38 210	89.14 213	71.66 64	93.05 209	70.05 247	76.46 347	92.25 176

DPM-MVS		84.93 86	84.29 93	86.84 56	90.20 113	73.04 23	87.12 202	93.04 46	69.80 271	82.85 132	91.22 150	73.06 44	96.02 57	76.72 172	94.63 54	91.46 208

v1144		80.03 205	79.03 208	83.01 211	83.78 341	64.51 248	87.11 203	90.57 167	71.96 213	78.08 219	86.20 305	61.41 211	93.94 147	74.93 192	77.23 334	90.60 238

v2v482		80.23 201	79.29 202	83.05 209	83.62 346	64.14 257	87.04 204	89.97 188	73.61 175	78.18 216	87.22 273	61.10 219	93.82 156	76.11 176	76.78 343	91.18 213

fmvsm_s_conf0.1_n_2		83.80 104	83.79 104	83.83 177	85.62 295	64.94 236	87.03 205	86.62 304	74.32 155	87.97 47	94.33 43	60.67 226	92.60 226	89.72 14	87.79 173	93.96 81

DU-MVS		81.12 170	80.52 166	82.90 217	87.80 215	63.46 279	87.02 206	91.87 119	79.01 31	78.38 210	89.07 215	65.02 158	93.05 209	70.05 247	76.46 347	92.20 179

LuminaMVS		80.68 184	79.62 193	83.83 177	85.07 313	68.01 148	86.99 207	88.83 242	70.36 255	81.38 155	87.99 253	50.11 337	92.51 233	79.02 136	86.89 192	90.97 222

fmvsm_s_conf0.5_n_2		84.04 97	84.11 97	83.81 179	86.17 282	65.00 231	86.96 208	87.28 285	74.35 154	88.25 39	94.23 50	61.82 202	92.60 226	89.85 12	88.09 166	93.84 90

v144192		79.47 215	78.37 222	82.78 227	83.35 351	63.96 260	86.96 208	90.36 175	69.99 266	77.50 230	85.67 316	60.66 227	93.77 160	74.27 199	76.58 344	90.62 236

Fast-Effi-MVS+-dtu		78.02 257	76.49 273	82.62 233	83.16 360	66.96 185	86.94 210	87.45 283	72.45 202	71.49 350	84.17 354	54.79 281	91.58 270	67.61 271	80.31 298	89.30 293

v1192		79.59 212	78.43 221	83.07 208	83.55 348	64.52 247	86.93 211	90.58 165	70.83 240	77.78 226	85.90 309	59.15 241	93.94 147	73.96 202	77.19 336	90.76 230

EPNet_dtu		75.46 308	74.86 300	77.23 356	82.57 377	54.60 409	86.89 212	83.09 357	71.64 216	66.25 411	85.86 311	55.99 270	88.04 356	54.92 386	86.55 197	89.05 300
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
viewmacassd2359aftdt		83.76 107	83.66 108	84.07 159	86.59 273	64.56 245	86.88 213	91.82 122	75.72 109	83.34 120	92.15 115	68.24 119	92.88 216	79.05 134	89.15 145	94.77 25

原ACMM2							86.86 214

VPA-MVSNet		80.60 188	80.55 165	80.76 279	88.07 202	60.80 326	86.86 214	91.58 135	75.67 113	80.24 177	89.45 209	63.34 171	90.25 315	70.51 241	79.22 313	91.23 212

v1921920		79.22 224	78.03 230	82.80 223	83.30 353	63.94 262	86.80 216	90.33 176	69.91 269	77.48 231	85.53 320	58.44 247	93.75 162	73.60 204	76.85 341	90.71 234

IterMVS-LS		80.06 204	79.38 198	82.11 244	85.89 288	63.20 286	86.79 217	89.34 211	74.19 160	75.45 281	86.72 285	66.62 136	92.39 238	72.58 217	76.86 340	90.75 231
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TransMVSNet (Re)		75.39 312	74.56 305	77.86 342	85.50 300	57.10 375	86.78 218	86.09 314	72.17 209	71.53 349	87.34 268	63.01 182	89.31 332	56.84 375	61.83 442	87.17 355

Baseline_NR-MVSNet		78.15 253	78.33 224	77.61 349	85.79 290	56.21 391	86.78 218	85.76 318	73.60 176	77.93 222	87.57 262	65.02 158	88.99 339	67.14 278	75.33 371	87.63 341

PAPR		81.66 157	80.89 159	83.99 172	90.27 111	64.00 259	86.76 220	91.77 126	68.84 300	77.13 245	89.50 203	67.63 125	94.88 106	67.55 272	88.52 157	93.09 136

Vis-MVSNet (Re-imp)		78.36 247	78.45 219	78.07 339	88.64 178	51.78 432	86.70 221	79.63 404	74.14 162	75.11 297	90.83 165	61.29 215	89.75 324	58.10 362	91.60 99	92.69 156

guyue		81.13 169	80.64 163	82.60 234	86.52 274	63.92 263	86.69 222	87.73 276	73.97 164	80.83 169	89.69 196	56.70 265	91.33 288	78.26 151	85.40 222	92.54 161

viewmanbaseed2359cas		83.66 110	83.55 110	84.00 170	86.81 265	64.53 246	86.65 223	91.75 127	74.89 140	83.15 126	91.68 129	68.74 111	92.83 220	79.02 136	89.24 142	94.63 42

pmmvs6		74.69 317	73.39 321	78.61 324	81.38 396	57.48 370	86.64 224	87.95 269	64.99 353	70.18 361	86.61 292	50.43 333	89.52 328	62.12 322	70.18 413	88.83 311

v1240		78.99 231	77.78 240	82.64 232	83.21 356	63.54 276	86.62 225	90.30 178	69.74 276	77.33 234	85.68 315	57.04 262	93.76 161	73.13 212	76.92 338	90.62 236

MTAPA		87.23 36	87.00 39	87.90 22	94.18 39	74.25 5	86.58 226	92.02 109	79.45 22	85.88 70	94.80 27	68.07 120	96.21 50	86.69 52	95.34 36	93.23 124

旧先验2							86.56 227		58.10 424	87.04 61			88.98 340	74.07 201

E4		84.10 96	83.99 99	84.45 130	87.58 237	64.99 232	86.54 228	92.25 94	76.38 92	83.37 119	92.09 117	69.88 90	93.58 166	79.78 128	88.03 169	94.77 25

FMVSNet3		77.88 261	76.85 264	80.97 275	86.84 264	62.36 303	86.52 229	88.77 245	71.13 230	75.34 286	86.66 291	54.07 288	91.10 297	62.72 311	79.57 305	89.45 288

dcpmvs_2		85.63 70	86.15 60	84.06 162	91.71 84	64.94 236	86.47 230	91.87 119	73.63 174	86.60 67	93.02 93	76.57 18	91.87 261	83.36 84	92.15 90	95.35 3

AstraMVS		80.81 176	80.14 177	82.80 223	86.05 287	63.96 260	86.46 231	85.90 316	73.71 172	80.85 168	90.56 173	54.06 289	91.57 272	79.72 129	83.97 243	92.86 150

pm-mvs1		77.25 277	76.68 271	78.93 319	84.22 330	58.62 350	86.41 232	88.36 259	71.37 224	73.31 324	88.01 252	61.22 217	89.15 337	64.24 301	73.01 395	89.03 301

EI-MVSNet		80.52 192	79.98 180	82.12 242	84.28 328	63.19 287	86.41 232	88.95 239	74.18 161	78.69 200	87.54 265	66.62 136	92.43 236	72.57 218	80.57 295	90.74 232

CVMVSNet		72.99 343	72.58 332	74.25 388	84.28 328	50.85 440	86.41 232	83.45 350	44.56 460	73.23 326	87.54 265	49.38 347	85.70 381	65.90 287	78.44 319	86.19 377

E2		84.00 99	83.87 100	84.39 133	87.70 226	64.95 233	86.40 235	92.23 95	75.85 106	83.21 121	91.78 125	70.09 85	93.55 171	79.52 131	88.05 167	94.66 39

E3		84.00 99	83.87 100	84.39 133	87.70 226	64.95 233	86.40 235	92.23 95	75.85 106	83.21 121	91.78 125	70.09 85	93.55 171	79.52 131	88.05 167	94.66 39

MonoMVSNet		76.49 292	75.80 281	78.58 326	81.55 392	58.45 351	86.36 237	86.22 310	74.87 143	74.73 306	83.73 363	51.79 318	88.73 345	70.78 236	72.15 401	88.55 323

NR-MVSNet		80.23 201	79.38 198	82.78 227	87.80 215	63.34 282	86.31 238	91.09 151	79.01 31	72.17 342	89.07 215	67.20 130	92.81 221	66.08 286	75.65 360	92.20 179

viewcassd2359sk11		83.89 101	83.74 105	84.34 138	87.76 221	64.91 239	86.30 239	92.22 98	75.47 117	83.04 127	91.52 138	70.15 83	93.53 174	79.26 133	87.96 170	94.57 47

v148		78.72 238	77.80 239	81.47 257	82.73 373	61.96 311	86.30 239	88.08 263	73.26 188	76.18 266	85.47 322	62.46 190	92.36 240	71.92 228	73.82 388	90.09 262

新几何2							86.29 241

E3new		83.78 106	83.60 109	84.31 140	87.76 221	64.89 240	86.24 242	92.20 101	75.15 133	82.87 130	91.23 147	70.11 84	93.52 176	79.05 134	87.79 173	94.51 53

test_yl		81.17 167	80.47 168	83.24 198	89.13 156	63.62 268	86.21 243	89.95 189	72.43 205	81.78 149	89.61 200	57.50 256	93.58 166	70.75 237	86.90 190	92.52 162

DCV-MVSNet		81.17 167	80.47 168	83.24 198	89.13 156	63.62 268	86.21 243	89.95 189	72.43 205	81.78 149	89.61 200	57.50 256	93.58 166	70.75 237	86.90 190	92.52 162

PVSNet_BlendedMVS		80.60 188	80.02 179	82.36 239	88.85 163	65.40 216	86.16 245	92.00 111	69.34 282	78.11 217	86.09 308	66.02 149	94.27 131	71.52 229	82.06 276	87.39 347

MVS_Test		83.15 127	83.06 119	83.41 192	86.86 262	63.21 285	86.11 246	92.00 111	74.31 156	82.87 130	89.44 210	70.03 87	93.21 195	77.39 159	88.50 158	93.81 92

BH-untuned		79.47 215	78.60 216	82.05 245	89.19 154	65.91 203	86.07 247	88.52 257	72.18 208	75.42 282	87.69 259	61.15 218	93.54 173	60.38 337	86.83 193	86.70 369

MVS_111021_HR		85.14 82	84.75 87	86.32 65	91.65 85	72.70 30	85.98 248	90.33 176	76.11 101	82.08 143	91.61 136	71.36 68	94.17 139	81.02 110	92.58 82	92.08 187

jason		81.39 165	80.29 172	84.70 121	86.63 272	69.90 94	85.95 249	86.77 299	63.24 372	81.07 162	89.47 205	61.08 220	92.15 248	78.33 147	90.07 128	92.05 188
jason: jason.
test_0402		72.79 346	70.44 357	79.84 300	88.13 198	65.99 201	85.93 250	84.29 336	65.57 343	67.40 395	85.49 321	46.92 365	92.61 225	35.88 462	74.38 382	80.94 441

OurMVSNet-221017-0		74.26 321	72.42 334	79.80 301	83.76 342	59.59 343	85.92 251	86.64 302	66.39 333	66.96 399	87.58 261	39.46 423	91.60 269	65.76 289	69.27 416	88.22 330

hse-mvs2		81.72 153	80.94 158	84.07 159	88.72 175	67.68 160	85.87 252	87.26 288	76.02 103	84.67 87	88.22 245	61.54 207	93.48 180	82.71 96	73.44 392	91.06 217

EG-PatchMatch MVS		74.04 325	71.82 339	80.71 280	84.92 315	67.42 168	85.86 253	88.08 263	66.04 337	64.22 425	83.85 358	35.10 443	92.56 229	57.44 367	80.83 290	82.16 434

AUN-MVS		79.21 225	77.60 247	84.05 165	88.71 176	67.61 162	85.84 254	87.26 288	69.08 292	77.23 238	88.14 250	53.20 298	93.47 181	75.50 187	73.45 391	91.06 217

thres100view900		76.50 289	75.55 288	79.33 312	89.52 133	56.99 376	85.83 255	83.23 353	73.94 166	76.32 262	87.12 277	51.89 315	91.95 256	48.33 424	83.75 248	89.07 295

CLD-MVS		82.31 142	81.65 148	84.29 143	88.47 183	67.73 158	85.81 256	92.35 87	75.78 108	78.33 212	86.58 295	64.01 167	94.35 128	76.05 178	87.48 180	90.79 228
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
VortexMVS		78.57 243	77.89 235	80.59 282	85.89 288	62.76 296	85.61 257	89.62 202	72.06 211	74.99 301	85.38 324	55.94 271	90.77 308	74.99 191	76.58 344	88.23 329

SixPastTwentyTwo		73.37 334	71.26 349	79.70 304	85.08 312	57.89 361	85.57 258	83.56 347	71.03 236	65.66 414	85.88 310	42.10 409	92.57 228	59.11 350	63.34 437	88.65 319

xiu_mvs_v1_base_debu		80.80 179	79.72 190	84.03 167	87.35 239	70.19 88	85.56 259	88.77 245	69.06 293	81.83 145	88.16 246	50.91 326	92.85 217	78.29 148	87.56 177	89.06 297

xiu_mvs_v1_base		80.80 179	79.72 190	84.03 167	87.35 239	70.19 88	85.56 259	88.77 245	69.06 293	81.83 145	88.16 246	50.91 326	92.85 217	78.29 148	87.56 177	89.06 297

xiu_mvs_v1_base_debi		80.80 179	79.72 190	84.03 167	87.35 239	70.19 88	85.56 259	88.77 245	69.06 293	81.83 145	88.16 246	50.91 326	92.85 217	78.29 148	87.56 177	89.06 297

V42		79.38 221	78.24 226	82.83 220	81.10 401	65.50 215	85.55 262	89.82 192	71.57 221	78.21 214	86.12 307	60.66 227	93.18 201	75.64 183	75.46 366	89.81 279

lupinMVS		81.39 165	80.27 173	84.76 119	87.35 239	70.21 86	85.55 262	86.41 306	62.85 379	81.32 156	88.61 232	61.68 204	92.24 246	78.41 146	90.26 123	91.83 191

Fast-Effi-MVS+		80.81 176	79.92 181	83.47 187	88.85 163	64.51 248	85.53 264	89.39 210	70.79 241	78.49 207	85.06 333	67.54 126	93.58 166	67.03 280	86.58 196	92.32 173

thres600view7		76.50 289	75.44 289	79.68 305	89.40 141	57.16 373	85.53 264	83.23 353	73.79 170	76.26 263	87.09 278	51.89 315	91.89 259	48.05 429	83.72 251	90.00 268

DELS-MVS		85.41 76	85.30 80	85.77 79	88.49 182	67.93 152	85.52 266	93.44 32	78.70 34	83.63 115	89.03 217	74.57 27	95.71 66	80.26 121	94.04 67	93.66 100
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
fmvsm_s_conf0.5_n_7		83.34 122	84.03 98	81.28 264	85.73 292	65.13 226	85.40 267	89.90 191	74.96 138	82.13 142	93.89 69	66.65 135	87.92 357	86.56 53	91.05 109	90.80 227

IMVS_0407		80.61 186	79.90 183	82.75 230	87.13 252	63.59 272	85.33 268	89.33 212	70.51 250	77.82 223	89.03 217	61.84 200	92.91 214	72.56 220	85.56 218	91.74 194

IMVS_0403		80.80 179	80.12 178	82.87 219	87.13 252	63.59 272	85.19 269	89.33 212	70.51 250	78.49 207	89.03 217	63.26 174	93.27 190	72.56 220	85.56 218	91.74 194

tfpn200view9		76.42 294	75.37 293	79.55 310	89.13 156	57.65 367	85.17 270	83.60 345	73.41 183	76.45 258	86.39 301	52.12 307	91.95 256	48.33 424	83.75 248	89.07 295

thres400		76.50 289	75.37 293	79.86 299	89.13 156	57.65 367	85.17 270	83.60 345	73.41 183	76.45 258	86.39 301	52.12 307	91.95 256	48.33 424	83.75 248	90.00 268

MVS_111021_LR		82.61 138	82.11 139	84.11 152	88.82 166	71.58 57	85.15 272	86.16 312	74.69 146	80.47 175	91.04 157	62.29 193	90.55 311	80.33 120	90.08 127	90.20 255

baseline1		76.98 281	76.75 269	77.66 347	88.13 198	55.66 398	85.12 273	81.89 373	73.04 195	76.79 248	88.90 223	62.43 191	87.78 360	63.30 307	71.18 408	89.55 286

mmtdpeth		74.16 323	73.01 327	77.60 351	83.72 343	61.13 319	85.10 274	85.10 325	72.06 211	77.21 242	80.33 411	43.84 397	85.75 380	77.14 162	52.61 461	85.91 385

viewdifsd2359ckpt07		82.83 135	82.78 127	82.99 212	86.51 275	62.58 297	85.09 275	90.83 159	75.22 126	82.28 138	91.63 133	69.43 96	92.03 251	77.71 154	86.32 200	94.34 62

WR-MVS		79.49 214	79.22 205	80.27 290	88.79 172	58.35 352	85.06 276	88.61 256	78.56 35	77.65 228	88.34 240	63.81 170	90.66 310	64.98 295	77.22 335	91.80 193

ET-MVSNet_ETH3D		78.63 240	76.63 272	84.64 122	86.73 268	69.47 102	85.01 277	84.61 331	69.54 278	66.51 409	86.59 293	50.16 336	91.75 264	76.26 174	84.24 240	92.69 156

OpenMVS_ROB	64.09 19	70.56 368	68.19 374	77.65 348	80.26 408	59.41 346	85.01 277	82.96 362	58.76 418	65.43 416	82.33 389	37.63 435	91.23 291	45.34 443	76.03 356	82.32 431

BH-RMVSNet		79.61 210	78.44 220	83.14 203	89.38 143	65.93 202	84.95 279	87.15 291	73.56 177	78.19 215	89.79 194	56.67 266	93.36 186	59.53 345	86.74 194	90.13 258

BH-w/o		78.21 250	77.33 255	80.84 277	88.81 167	65.13 226	84.87 280	87.85 273	69.75 274	74.52 310	84.74 340	61.34 213	93.11 205	58.24 361	85.84 214	84.27 408

TDRefinement		67.49 394	64.34 406	76.92 358	73.47 455	61.07 322	84.86 281	82.98 361	59.77 407	58.30 450	85.13 331	26.06 458	87.89 358	47.92 430	60.59 447	81.81 437

Anonymous202405211		78.25 248	77.01 259	81.99 247	91.03 94	60.67 329	84.77 282	83.90 342	70.65 248	80.00 180	91.20 151	41.08 416	91.43 284	65.21 292	85.26 223	93.85 88

TAMVS		78.89 235	77.51 251	83.03 210	87.80 215	67.79 157	84.72 283	85.05 327	67.63 314	76.75 250	87.70 258	62.25 194	90.82 304	58.53 357	87.13 187	90.49 243

sc_t1		72.19 353	69.51 364	80.23 291	84.81 317	61.09 321	84.68 284	80.22 398	60.70 399	71.27 351	83.58 368	36.59 438	89.24 334	60.41 336	63.31 438	90.37 248

1314		76.53 288	75.30 296	80.21 292	83.93 337	62.32 305	84.66 285	88.81 243	60.23 403	70.16 363	84.07 356	55.30 275	90.73 309	67.37 274	83.21 262	87.59 344

MVS		78.19 252	76.99 261	81.78 250	85.66 293	66.99 182	84.66 285	90.47 169	55.08 440	72.02 344	85.27 326	63.83 169	94.11 141	66.10 285	89.80 133	84.24 409

tfpnnormal		74.39 319	73.16 325	78.08 338	86.10 286	58.05 356	84.65 287	87.53 280	70.32 258	71.22 353	85.63 317	54.97 276	89.86 321	43.03 448	75.02 376	86.32 374

TR-MVS		77.44 272	76.18 279	81.20 267	88.24 192	63.24 284	84.61 288	86.40 307	67.55 316	77.81 225	86.48 299	54.10 287	93.15 202	57.75 365	82.72 269	87.20 354

AllTest		70.96 362	68.09 377	79.58 308	85.15 309	63.62 268	84.58 289	79.83 401	62.31 386	60.32 443	86.73 283	32.02 448	88.96 342	50.28 412	71.57 406	86.15 378

FA-MVS(test-final)		80.96 172	79.91 182	84.10 153	88.30 191	65.01 230	84.55 290	90.01 187	73.25 189	79.61 184	87.57 262	58.35 248	94.72 115	71.29 233	86.25 203	92.56 160

EU-MVSNet		68.53 389	67.61 388	71.31 416	78.51 429	47.01 454	84.47 291	84.27 337	42.27 463	66.44 410	84.79 339	40.44 419	83.76 399	58.76 355	68.54 421	83.17 421

VNet		82.21 143	82.41 132	81.62 253	90.82 100	60.93 323	84.47 291	89.78 193	76.36 94	84.07 104	91.88 121	64.71 161	90.26 314	70.68 239	88.89 148	93.66 100

xiu_mvs_v2_base		81.69 155	81.05 155	83.60 183	89.15 155	68.03 147	84.46 293	90.02 186	70.67 244	81.30 159	86.53 298	63.17 177	94.19 138	75.60 185	88.54 156	88.57 322

VPNet		78.69 239	78.66 215	78.76 322	88.31 190	55.72 397	84.45 294	86.63 303	76.79 76	78.26 213	90.55 174	59.30 240	89.70 326	66.63 281	77.05 337	90.88 225

FE-MVSNET2		72.88 345	71.28 347	77.67 346	78.30 430	57.78 365	84.43 295	88.92 241	69.56 277	64.61 422	81.67 397	46.73 370	88.54 350	59.33 346	67.99 422	86.69 370

PVSNet_Blended		80.98 171	80.34 170	82.90 217	88.85 163	65.40 216	84.43 295	92.00 111	67.62 315	78.11 217	85.05 334	66.02 149	94.27 131	71.52 229	89.50 138	89.01 302

MVP-Stereo		76.12 298	74.46 308	81.13 270	85.37 303	69.79 95	84.42 297	87.95 269	65.03 351	67.46 392	85.33 325	53.28 297	91.73 266	58.01 363	83.27 261	81.85 436
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
CDS-MVSNet		79.07 229	77.70 244	83.17 202	87.60 231	68.23 141	84.40 298	86.20 311	67.49 317	76.36 261	86.54 297	61.54 207	90.79 305	61.86 325	87.33 182	90.49 243
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
K. test v3		71.19 359	68.51 371	79.21 315	83.04 363	57.78 365	84.35 299	76.91 427	72.90 198	62.99 433	82.86 382	39.27 424	91.09 299	61.65 327	52.66 460	88.75 315

PS-MVSNAJ		81.69 155	81.02 156	83.70 181	89.51 134	68.21 142	84.28 300	90.09 185	70.79 241	81.26 160	85.62 318	63.15 178	94.29 129	75.62 184	88.87 149	88.59 321

patch_mono-2		83.65 111	84.54 89	80.99 273	90.06 120	65.83 205	84.21 301	88.74 250	71.60 220	85.01 79	92.44 105	74.51 29	83.50 403	82.15 101	92.15 90	93.64 106

viewdifsd2359ckpt11		80.37 197	79.73 188	82.30 240	83.70 344	62.39 301	84.20 302	86.67 300	73.22 191	80.90 165	90.62 170	63.00 183	91.56 273	76.81 169	78.44 319	92.95 147

viewmsd2359difaftdt		80.37 197	79.73 188	82.30 240	83.70 344	62.39 301	84.20 302	86.67 300	73.22 191	80.90 165	90.62 170	63.00 183	91.56 273	76.81 169	78.44 319	92.95 147

test222					91.50 86	68.26 137	84.16 304	83.20 356	54.63 441	79.74 182	91.63 133	58.97 242			91.42 103	86.77 367

testdata1							84.14 305		75.71 110

c3_l		78.75 236	77.91 233	81.26 265	82.89 370	61.56 316	84.09 306	89.13 230	69.97 267	75.56 276	84.29 348	66.36 141	92.09 250	73.47 207	75.48 364	90.12 259

MVSTER		79.01 230	77.88 236	82.38 238	83.07 361	64.80 242	84.08 307	88.95 239	69.01 296	78.69 200	87.17 276	54.70 282	92.43 236	74.69 193	80.57 295	89.89 275

diffmvs_AUTHOR		82.38 141	82.27 137	82.73 231	83.26 354	63.80 265	83.89 308	89.76 195	73.35 185	82.37 137	90.84 164	66.25 143	90.79 305	82.77 93	87.93 171	93.59 109

ab-mvs		79.51 213	78.97 210	81.14 269	88.46 184	60.91 324	83.84 309	89.24 224	70.36 255	79.03 194	88.87 225	63.23 176	90.21 316	65.12 293	82.57 271	92.28 175

reproduce_monomvs		75.40 311	74.38 309	78.46 332	83.92 338	57.80 364	83.78 310	86.94 295	73.47 181	72.25 341	84.47 342	38.74 428	89.27 333	75.32 189	70.53 411	88.31 327

PAPM		77.68 268	76.40 277	81.51 256	87.29 248	61.85 312	83.78 310	89.59 203	64.74 354	71.23 352	88.70 228	62.59 187	93.66 165	52.66 398	87.03 189	89.01 302

SD_0403		74.65 318	74.77 302	74.29 387	86.20 281	47.42 451	83.71 312	85.12 324	69.30 283	68.50 383	87.95 254	59.40 239	86.05 377	49.38 418	83.35 259	89.40 289

diffmvs		82.10 144	81.88 146	82.76 229	83.00 364	63.78 267	83.68 313	89.76 195	72.94 197	82.02 144	89.85 189	65.96 151	90.79 305	82.38 100	87.30 183	93.71 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
miper_ehance_all_eth		78.59 242	77.76 242	81.08 271	82.66 375	61.56 316	83.65 314	89.15 228	68.87 299	75.55 277	83.79 361	66.49 139	92.03 251	73.25 210	76.39 349	89.64 283

1112_ss		77.40 274	76.43 275	80.32 289	89.11 160	60.41 334	83.65 314	87.72 277	62.13 389	73.05 328	86.72 285	62.58 188	89.97 320	62.11 323	80.80 291	90.59 239

PCF-MVS	73.52 7	80.38 195	78.84 213	85.01 105	87.71 224	68.99 113	83.65 314	91.46 141	63.00 376	77.77 227	90.28 180	66.10 146	95.09 98	61.40 329	88.22 164	90.94 224
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
XVG-ACMP-BASELINE		76.11 299	74.27 311	81.62 253	83.20 357	64.67 244	83.60 317	89.75 197	69.75 274	71.85 345	87.09 278	32.78 447	92.11 249	69.99 249	80.43 297	88.09 333

tt0320		70.49 370	68.03 378	77.89 341	84.78 318	59.12 347	83.55 318	80.44 393	58.13 423	67.43 394	80.41 410	39.26 425	87.54 363	55.12 384	63.18 439	86.99 362

cl22		78.07 255	77.01 259	81.23 266	82.37 382	61.83 313	83.55 318	87.98 267	68.96 298	75.06 299	83.87 357	61.40 212	91.88 260	73.53 205	76.39 349	89.98 271

XVG-OURS-SEG-HR		80.81 176	79.76 187	83.96 174	85.60 296	68.78 118	83.54 320	90.50 168	70.66 247	76.71 251	91.66 130	60.69 225	91.26 289	76.94 164	81.58 281	91.83 191

viewmambaseed2359dif		80.41 193	79.84 185	82.12 242	82.95 369	62.50 300	83.39 321	88.06 265	67.11 320	80.98 163	90.31 179	66.20 145	91.01 301	74.62 194	84.90 226	92.86 150

IB-MVS	68.01 15	75.85 303	73.36 323	83.31 194	84.76 319	66.03 197	83.38 322	85.06 326	70.21 262	69.40 373	81.05 401	45.76 382	94.66 118	65.10 294	75.49 363	89.25 294
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
HY-MVS	69.67 12	77.95 259	77.15 257	80.36 287	87.57 238	60.21 337	83.37 323	87.78 275	66.11 335	75.37 285	87.06 280	63.27 173	90.48 312	61.38 330	82.43 272	90.40 247

tt0320-xc		70.11 374	67.45 391	78.07 339	85.33 304	59.51 345	83.28 324	78.96 411	58.77 417	67.10 398	80.28 412	36.73 437	87.42 364	56.83 376	59.77 449	87.29 351

test_vis1_n_1920		75.52 307	75.78 282	74.75 383	79.84 415	57.44 371	83.26 325	85.52 320	62.83 380	79.34 192	86.17 306	45.10 388	79.71 425	78.75 141	81.21 285	87.10 361

Anonymous20240521		68.80 385	67.22 394	73.55 394	74.33 447	54.11 413	83.18 326	85.61 319	58.15 422	61.68 437	80.94 404	30.71 453	81.27 419	57.00 373	73.34 394	85.28 394

eth_miper_zixun_eth		77.92 260	76.69 270	81.61 255	83.00 364	61.98 310	83.15 327	89.20 226	69.52 279	74.86 304	84.35 347	61.76 203	92.56 229	71.50 231	72.89 396	90.28 253

FE-MVS		77.78 263	75.68 284	84.08 158	88.09 201	66.00 200	83.13 328	87.79 274	68.42 308	78.01 220	85.23 328	45.50 386	95.12 92	59.11 350	85.83 215	91.11 215

cl____		77.72 265	76.76 267	80.58 283	82.49 379	60.48 332	83.09 329	87.87 271	69.22 287	74.38 313	85.22 329	62.10 197	91.53 278	71.09 234	75.41 368	89.73 282

DIV-MVS_self_test		77.72 265	76.76 267	80.58 283	82.48 380	60.48 332	83.09 329	87.86 272	69.22 287	74.38 313	85.24 327	62.10 197	91.53 278	71.09 234	75.40 369	89.74 281

thres200		75.55 306	74.47 307	78.82 321	87.78 218	57.85 362	83.07 331	83.51 348	72.44 204	75.84 272	84.42 343	52.08 310	91.75 264	47.41 431	83.64 253	86.86 365

testing3		68.56 388	67.67 387	71.22 417	87.33 244	42.87 467	83.06 332	71.54 447	70.36 255	69.08 377	84.38 345	30.33 454	85.69 382	37.50 460	75.45 367	85.09 400

XVG-OURS		80.41 193	79.23 204	83.97 173	85.64 294	69.02 112	83.03 333	90.39 171	71.09 232	77.63 229	91.49 141	54.62 284	91.35 286	75.71 182	83.47 257	91.54 202

miper_enhance_ethall		77.87 262	76.86 263	80.92 276	81.65 389	61.38 318	82.68 334	88.98 236	65.52 344	75.47 278	82.30 390	65.76 153	92.00 254	72.95 213	76.39 349	89.39 290

mvs_anonymous		79.42 218	79.11 207	80.34 288	84.45 327	57.97 359	82.59 335	87.62 278	67.40 319	76.17 268	88.56 235	68.47 114	89.59 327	70.65 240	86.05 207	93.47 115

baseline2		75.70 304	73.83 317	81.30 263	83.26 354	61.79 314	82.57 336	80.65 387	66.81 322	66.88 400	83.42 371	57.86 252	92.19 247	63.47 304	79.57 305	89.91 273

cascas		76.72 286	74.64 303	82.99 212	85.78 291	65.88 204	82.33 337	89.21 225	60.85 398	72.74 332	81.02 402	47.28 362	93.75 162	67.48 273	85.02 224	89.34 292

blend_shiyan4		72.29 351	69.65 363	80.21 292	78.24 431	62.16 308	82.29 338	87.27 287	65.41 347	68.43 385	76.42 444	39.91 422	91.23 291	63.21 308	65.66 431	87.22 353

WB-MVSnew		71.96 356	71.65 341	72.89 402	84.67 324	51.88 430	82.29 338	77.57 419	62.31 386	73.67 321	83.00 378	53.49 295	81.10 420	45.75 440	82.13 275	85.70 388

RPSCF		73.23 339	71.46 343	78.54 328	82.50 378	59.85 339	82.18 340	82.84 365	58.96 415	71.15 354	89.41 211	45.48 387	84.77 393	58.82 354	71.83 404	91.02 221

thisisatest0515		77.33 275	75.38 292	83.18 201	85.27 306	63.80 265	82.11 341	83.27 352	65.06 350	75.91 270	83.84 359	49.54 344	94.27 131	67.24 276	86.19 204	91.48 206

pmmvs-eth3d		70.50 369	67.83 383	78.52 330	77.37 435	66.18 195	81.82 342	81.51 378	58.90 416	63.90 429	80.42 409	42.69 404	86.28 375	58.56 356	65.30 433	83.11 423

MS-PatchMatch		73.83 328	72.67 330	77.30 355	83.87 339	66.02 198	81.82 342	84.66 330	61.37 396	68.61 381	82.82 383	47.29 361	88.21 353	59.27 347	84.32 239	77.68 451

FE-MVSNET3		76.43 293	75.32 295	79.76 302	83.00 364	60.72 327	81.74 344	88.76 249	68.99 297	72.98 329	84.19 353	56.41 269	90.27 313	62.39 316	79.40 309	88.31 327

pmmvs5		71.55 357	70.20 361	75.61 368	77.83 432	56.39 386	81.74 344	80.89 383	57.76 426	67.46 392	84.49 341	49.26 350	85.32 388	57.08 371	75.29 372	85.11 399

Test_1112_low_res		76.40 295	75.44 289	79.27 313	89.28 149	58.09 355	81.69 346	87.07 292	59.53 410	72.48 337	86.67 290	61.30 214	89.33 331	60.81 335	80.15 300	90.41 246

IterMVS		74.29 320	72.94 328	78.35 333	81.53 393	63.49 278	81.58 347	82.49 367	68.06 312	69.99 366	83.69 365	51.66 320	85.54 384	65.85 288	71.64 405	86.01 382
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
IterMVS-SCA-FT		75.43 309	73.87 316	80.11 295	82.69 374	64.85 241	81.57 348	83.47 349	69.16 290	70.49 357	84.15 355	51.95 313	88.15 354	69.23 256	72.14 402	87.34 349

test_vis1_n		69.85 378	69.21 367	71.77 410	72.66 461	55.27 404	81.48 349	76.21 431	52.03 448	75.30 291	83.20 375	28.97 455	76.22 445	74.60 195	78.41 323	83.81 415

pmmvs4		74.03 327	71.91 338	80.39 286	81.96 385	68.32 135	81.45 350	82.14 370	59.32 411	69.87 369	85.13 331	52.40 303	88.13 355	60.21 339	74.74 379	84.73 405

GA-MVS		76.87 283	75.17 298	81.97 248	82.75 372	62.58 297	81.44 351	86.35 309	72.16 210	74.74 305	82.89 381	46.20 377	92.02 253	68.85 262	81.09 286	91.30 211

UWE-MVS		72.13 354	71.49 342	74.03 390	86.66 271	47.70 449	81.40 352	76.89 428	63.60 371	75.59 275	84.22 352	39.94 421	85.62 383	48.98 421	86.13 206	88.77 314

test_fmvs1_n		70.86 364	70.24 360	72.73 404	72.51 462	55.28 403	81.27 353	79.71 403	51.49 451	78.73 199	84.87 336	27.54 457	77.02 437	76.06 177	79.97 303	85.88 386

testing91		76.54 287	75.66 286	79.18 316	88.43 186	55.89 394	81.08 354	83.00 360	73.76 171	75.34 286	84.29 348	46.20 377	90.07 318	64.33 299	84.50 232	91.58 201

testing222		74.04 325	72.66 331	78.19 335	87.89 210	55.36 401	81.06 355	79.20 409	71.30 227	74.65 308	83.57 369	39.11 427	88.67 347	51.43 406	85.75 216	90.53 241

test_fmvs1		70.93 363	70.52 355	72.16 408	73.71 451	55.05 405	80.82 356	78.77 412	51.21 452	78.58 204	84.41 344	31.20 452	76.94 438	75.88 181	80.12 302	84.47 407

CostFormer		75.24 313	73.90 315	79.27 313	82.65 376	58.27 354	80.80 357	82.73 366	61.57 393	75.33 290	83.13 376	55.52 273	91.07 300	64.98 295	78.34 324	88.45 324

testing99		76.09 300	75.12 299	79.00 317	88.16 195	55.50 400	80.79 358	81.40 380	73.30 187	75.17 294	84.27 351	44.48 392	90.02 319	64.28 300	84.22 241	91.48 206

MIMVSNet1		68.58 387	66.78 397	73.98 391	80.07 412	51.82 431	80.77 359	84.37 333	64.40 358	59.75 446	82.16 393	36.47 439	83.63 401	42.73 449	70.33 412	86.48 373

CL-MVSNet_self_test		72.37 349	71.46 343	75.09 377	79.49 422	53.53 417	80.76 360	85.01 328	69.12 291	70.51 356	82.05 394	57.92 251	84.13 397	52.27 400	66.00 430	87.60 342

testing11		75.14 314	74.01 312	78.53 329	88.16 195	56.38 387	80.74 361	80.42 394	70.67 244	72.69 335	83.72 364	43.61 399	89.86 321	62.29 319	83.76 247	89.36 291

MSDG		73.36 336	70.99 351	80.49 285	84.51 326	65.80 207	80.71 362	86.13 313	65.70 341	65.46 415	83.74 362	44.60 390	90.91 303	51.13 407	76.89 339	84.74 404

tpm2		73.26 338	71.46 343	78.63 323	83.34 352	56.71 381	80.65 363	80.40 395	56.63 434	73.55 322	82.02 395	51.80 317	91.24 290	56.35 380	78.42 322	87.95 334

XXY-MVS		75.41 310	75.56 287	74.96 378	83.59 347	57.82 363	80.59 364	83.87 343	66.54 332	74.93 303	88.31 241	63.24 175	80.09 424	62.16 321	76.85 341	86.97 363

test_cas_vis1_n_1920		73.76 329	73.74 318	73.81 393	75.90 439	59.77 340	80.51 365	82.40 368	58.30 421	81.62 153	85.69 314	44.35 394	76.41 443	76.29 173	78.61 315	85.23 395

EGC-MVSNET		52.07 435	47.05 439	67.14 436	83.51 349	60.71 328	80.50 366	67.75 458	0.07 486	0.43 487	75.85 448	24.26 463	81.54 416	28.82 469	62.25 441	59.16 469

SDMVSNet		80.38 195	80.18 174	80.99 273	89.03 161	64.94 236	80.45 367	89.40 209	75.19 130	76.61 255	89.98 186	60.61 229	87.69 361	76.83 168	83.55 254	90.33 250

HyFIR lowres test		77.53 271	75.40 291	83.94 175	89.59 130	66.62 188	80.36 368	88.64 255	56.29 436	76.45 258	85.17 330	57.64 254	93.28 188	61.34 331	83.10 264	91.91 190

D2MVS		74.82 316	73.21 324	79.64 307	79.81 416	62.56 299	80.34 369	87.35 284	64.37 359	68.86 378	82.66 385	46.37 373	90.10 317	67.91 269	81.24 284	86.25 375

testing3-2		75.12 315	75.19 297	74.91 379	90.40 109	45.09 462	80.29 370	78.42 414	78.37 40	76.54 257	87.75 256	44.36 393	87.28 366	57.04 372	83.49 256	92.37 170

TinyColmap		67.30 397	64.81 404	74.76 382	81.92 387	56.68 382	80.29 370	81.49 379	60.33 401	56.27 457	83.22 373	24.77 462	87.66 362	45.52 441	69.47 415	79.95 446

FE-MVSNET		67.25 398	65.33 402	73.02 401	75.86 440	52.54 425	80.26 372	80.56 389	63.80 370	60.39 441	79.70 420	41.41 413	84.66 395	43.34 447	62.62 440	81.86 435

LCM-MVSNet-Re		77.05 279	76.94 262	77.36 353	87.20 249	51.60 433	80.06 373	80.46 392	75.20 129	67.69 389	86.72 285	62.48 189	88.98 340	63.44 305	89.25 141	91.51 203

test_fmvs2		68.35 391	67.48 390	70.98 419	69.50 465	51.95 428	80.05 374	76.38 430	49.33 454	74.65 308	84.38 345	23.30 466	75.40 454	74.51 196	75.17 375	85.60 389

FMVSNet5		69.50 379	67.96 379	74.15 389	82.97 368	55.35 402	80.01 375	82.12 371	62.56 384	63.02 431	81.53 398	36.92 436	81.92 414	48.42 423	74.06 384	85.17 398

SCA		74.22 322	72.33 335	79.91 298	84.05 335	62.17 307	79.96 376	79.29 408	66.30 334	72.38 339	80.13 414	51.95 313	88.60 348	59.25 348	77.67 332	88.96 306

tpmrst		72.39 347	72.13 337	73.18 400	80.54 406	49.91 444	79.91 377	79.08 410	63.11 374	71.69 347	79.95 416	55.32 274	82.77 409	65.66 290	73.89 386	86.87 364

PatchmatchNet		73.12 340	71.33 346	78.49 331	83.18 358	60.85 325	79.63 378	78.57 413	64.13 361	71.73 346	79.81 419	51.20 324	85.97 379	57.40 368	76.36 354	88.66 318
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
PatchMatch-RL		72.38 348	70.90 352	76.80 360	88.60 179	67.38 171	79.53 379	76.17 432	62.75 382	69.36 374	82.00 396	45.51 385	84.89 392	53.62 393	80.58 294	78.12 450

CMPMVS	51.72 21	70.19 373	68.16 375	76.28 362	73.15 458	57.55 369	79.47 380	83.92 341	48.02 456	56.48 456	84.81 338	43.13 401	86.42 374	62.67 314	81.81 280	84.89 402
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ETVMVS		72.25 352	71.05 350	75.84 365	87.77 220	51.91 429	79.39 381	74.98 435	69.26 285	73.71 319	82.95 379	40.82 418	86.14 376	46.17 437	84.43 237	89.47 287

GG-mvs-BLEND				75.38 374	81.59 391	55.80 396	79.32 382	69.63 452		67.19 396	73.67 453	43.24 400	88.90 344	50.41 409	84.50 232	81.45 438

LTVRE_ROB	69.57 13	76.25 297	74.54 306	81.41 259	88.60 179	64.38 254	79.24 383	89.12 231	70.76 243	69.79 371	87.86 255	49.09 352	93.20 198	56.21 381	80.16 299	86.65 371
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
tpm		72.37 349	71.71 340	74.35 386	82.19 383	52.00 427	79.22 384	77.29 424	64.56 356	72.95 331	83.68 366	51.35 321	83.26 406	58.33 360	75.80 358	87.81 338

mvs5depth		69.45 380	67.45 391	75.46 373	73.93 449	55.83 395	79.19 385	83.23 353	66.89 321	71.63 348	83.32 372	33.69 446	85.09 389	59.81 342	55.34 457	85.46 391

ppachtmachnet_test		70.04 375	67.34 393	78.14 336	79.80 417	61.13 319	79.19 385	80.59 388	59.16 413	65.27 417	79.29 423	46.75 369	87.29 365	49.33 419	66.72 425	86.00 384

USDC		70.33 371	68.37 372	76.21 363	80.60 405	56.23 390	79.19 385	86.49 305	60.89 397	61.29 438	85.47 322	31.78 450	89.47 330	53.37 395	76.21 355	82.94 427

sd_testset		77.70 267	77.40 252	78.60 325	89.03 161	60.02 338	79.00 388	85.83 317	75.19 130	76.61 255	89.98 186	54.81 277	85.46 386	62.63 315	83.55 254	90.33 250

PM-MVS		66.41 404	64.14 407	73.20 399	73.92 450	56.45 384	78.97 389	64.96 466	63.88 369	64.72 421	80.24 413	19.84 470	83.44 404	66.24 282	64.52 435	79.71 447

tpmvs		71.09 361	69.29 366	76.49 361	82.04 384	56.04 392	78.92 390	81.37 381	64.05 365	67.18 397	78.28 432	49.74 343	89.77 323	49.67 417	72.37 398	83.67 417

test_post1							78.90 391				5.43 485	48.81 357	85.44 387	59.25 348

mamv4		76.81 284	78.23 228	72.54 406	86.12 284	65.75 210	78.76 392	82.07 372	64.12 362	72.97 330	91.02 160	67.97 121	68.08 471	83.04 89	78.02 326	83.80 416

CHOSEN 1792x2688		77.63 270	75.69 283	83.44 189	89.98 122	68.58 129	78.70 393	87.50 281	56.38 435	75.80 273	86.84 281	58.67 245	91.40 285	61.58 328	85.75 216	90.34 249

Syy-MVS		68.05 392	67.85 381	68.67 430	84.68 321	40.97 473	78.62 394	73.08 444	66.65 329	66.74 403	79.46 421	52.11 309	82.30 411	32.89 465	76.38 352	82.75 428

myMVS_eth3d		67.02 399	66.29 399	69.21 425	84.68 321	42.58 468	78.62 394	73.08 444	66.65 329	66.74 403	79.46 421	31.53 451	82.30 411	39.43 457	76.38 352	82.75 428

WBMVS		73.43 333	72.81 329	75.28 375	87.91 209	50.99 439	78.59 396	81.31 382	65.51 346	74.47 311	84.83 337	46.39 371	86.68 370	58.41 358	77.86 327	88.17 332

test-LLR		72.94 344	72.43 333	74.48 384	81.35 397	58.04 357	78.38 397	77.46 420	66.66 326	69.95 367	79.00 426	48.06 358	79.24 426	66.13 283	84.83 227	86.15 378

TESTMET0.1,1		69.89 377	69.00 369	72.55 405	79.27 425	56.85 377	78.38 397	74.71 439	57.64 427	68.09 386	77.19 439	37.75 434	76.70 439	63.92 302	84.09 242	84.10 412

test-mter		71.41 358	70.39 359	74.48 384	81.35 397	58.04 357	78.38 397	77.46 420	60.32 402	69.95 367	79.00 426	36.08 441	79.24 426	66.13 283	84.83 227	86.15 378

UBG		73.08 341	72.27 336	75.51 371	88.02 204	51.29 437	78.35 400	77.38 423	65.52 344	73.87 318	82.36 388	45.55 384	86.48 373	55.02 385	84.39 238	88.75 315

Anonymous20231206		68.60 386	67.80 384	71.02 418	80.23 410	50.75 441	78.30 401	80.47 391	56.79 433	66.11 413	82.63 386	46.35 374	78.95 428	43.62 446	75.70 359	83.36 420

tpm cat1		70.57 367	68.31 373	77.35 354	82.41 381	57.95 360	78.08 402	80.22 398	52.04 447	68.54 382	77.66 437	52.00 312	87.84 359	51.77 401	72.07 403	86.25 375

myMVS_eth3d28		73.62 330	73.53 320	73.90 392	88.20 193	47.41 452	78.06 403	79.37 406	74.29 158	73.98 316	84.29 348	44.67 389	83.54 402	51.47 404	87.39 181	90.74 232

our_test_3		69.14 382	67.00 395	75.57 369	79.80 417	58.80 348	77.96 404	77.81 417	59.55 409	62.90 434	78.25 433	47.43 360	83.97 398	51.71 402	67.58 424	83.93 414

KD-MVS_self_test		68.81 384	67.59 389	72.46 407	74.29 448	45.45 457	77.93 405	87.00 293	63.12 373	63.99 428	78.99 428	42.32 406	84.77 393	56.55 379	64.09 436	87.16 357

WTY-MVS		75.65 305	75.68 284	75.57 369	86.40 277	56.82 378	77.92 406	82.40 368	65.10 349	76.18 266	87.72 257	63.13 181	80.90 421	60.31 338	81.96 277	89.00 304

UWE-MVS-28		65.32 409	64.93 403	66.49 438	78.70 427	38.55 475	77.86 407	64.39 467	62.00 391	64.13 426	83.60 367	41.44 412	76.00 447	31.39 467	80.89 288	84.92 401

test20.03		67.45 395	66.95 396	68.94 426	75.48 444	44.84 463	77.50 408	77.67 418	66.66 326	63.01 432	83.80 360	47.02 364	78.40 430	42.53 451	68.86 420	83.58 418

EPMVS		69.02 383	68.16 375	71.59 411	79.61 420	49.80 446	77.40 409	66.93 460	62.82 381	70.01 364	79.05 424	45.79 381	77.86 434	56.58 378	75.26 373	87.13 358

test_fmvs3		63.36 416	61.82 419	67.98 434	62.51 474	46.96 455	77.37 410	74.03 441	45.24 459	67.50 391	78.79 429	12.16 478	72.98 463	72.77 216	66.02 429	83.99 413

gg-mvs-nofinetune		69.95 376	67.96 379	75.94 364	83.07 361	54.51 411	77.23 411	70.29 450	63.11 374	70.32 359	62.33 464	43.62 398	88.69 346	53.88 392	87.76 175	84.62 406

IMVS_0404		77.16 278	76.42 276	79.37 311	87.13 252	63.59 272	77.12 412	89.33 212	70.51 250	66.22 412	89.03 217	50.36 334	82.78 408	72.56 220	85.56 218	91.74 194

MDTV_nov1_ep13			69.97 362		83.18 358	53.48 418	77.10 413	80.18 400	60.45 400	69.33 375	80.44 408	48.89 356	86.90 368	51.60 403	78.51 318

icg_test_0407_2		78.92 234	78.93 211	78.90 320	87.13 252	63.59 272	76.58 414	89.33 212	70.51 250	77.82 223	89.03 217	61.84 200	81.38 418	72.56 220	85.56 218	91.74 194

LF4IMVS		64.02 414	62.19 418	69.50 424	70.90 463	53.29 422	76.13 415	77.18 425	52.65 446	58.59 448	80.98 403	23.55 465	76.52 441	53.06 397	66.66 426	78.68 449

sss		73.60 331	73.64 319	73.51 395	82.80 371	55.01 406	76.12 416	81.69 376	62.47 385	74.68 307	85.85 312	57.32 258	78.11 432	60.86 334	80.93 287	87.39 347

testgi		66.67 402	66.53 398	67.08 437	75.62 443	41.69 472	75.93 417	76.50 429	66.11 335	65.20 420	86.59 293	35.72 442	74.71 456	43.71 445	73.38 393	84.84 403

CR-MVSNet		73.37 334	71.27 348	79.67 306	81.32 399	65.19 224	75.92 418	80.30 396	59.92 406	72.73 333	81.19 399	52.50 301	86.69 369	59.84 341	77.71 329	87.11 359

RPMNet		73.51 332	70.49 356	82.58 235	81.32 399	65.19 224	75.92 418	92.27 91	57.60 428	72.73 333	76.45 442	52.30 304	95.43 77	48.14 428	77.71 329	87.11 359

MIMVSNet		70.69 366	69.30 365	74.88 380	84.52 325	56.35 389	75.87 420	79.42 405	64.59 355	67.76 387	82.41 387	41.10 415	81.54 416	46.64 435	81.34 282	86.75 368

test0.0.03 1		68.00 393	67.69 386	68.90 427	77.55 433	47.43 450	75.70 421	72.95 446	66.66 326	66.56 405	82.29 391	48.06 358	75.87 449	44.97 444	74.51 381	83.41 419

dmvs_re		71.14 360	70.58 354	72.80 403	81.96 385	59.68 341	75.60 422	79.34 407	68.55 304	69.27 376	80.72 407	49.42 346	76.54 440	52.56 399	77.79 328	82.19 433

dmvs_testset		62.63 417	64.11 408	58.19 448	78.55 428	24.76 486	75.28 423	65.94 463	67.91 313	60.34 442	76.01 445	53.56 293	73.94 461	31.79 466	67.65 423	75.88 455

PMMVS		69.34 381	68.67 370	71.35 415	75.67 442	62.03 309	75.17 424	73.46 442	50.00 453	68.68 379	79.05 424	52.07 311	78.13 431	61.16 332	82.77 267	73.90 457

UnsupCasMVSNet_eth		67.33 396	65.99 400	71.37 413	73.48 454	51.47 435	75.16 425	85.19 323	65.20 348	60.78 440	80.93 406	42.35 405	77.20 436	57.12 370	53.69 459	85.44 392

MDTV_nov1_ep13_2view						37.79 476	75.16 425		55.10 439	66.53 406		49.34 348		53.98 391		87.94 335

pmmvs3		57.79 424	54.26 429	68.37 431	64.02 473	56.72 380	75.12 427	65.17 464	40.20 465	52.93 461	69.86 461	20.36 469	75.48 452	45.45 442	55.25 458	72.90 459

dp		66.80 400	65.43 401	70.90 420	79.74 419	48.82 448	75.12 427	74.77 437	59.61 408	64.08 427	77.23 438	42.89 402	80.72 422	48.86 422	66.58 427	83.16 422

Patchmtry		70.74 365	69.16 368	75.49 372	80.72 403	54.07 414	74.94 429	80.30 396	58.34 420	70.01 364	81.19 399	52.50 301	86.54 371	53.37 395	71.09 409	85.87 387

ttmdpeth		59.91 422	57.10 426	68.34 432	67.13 469	46.65 456	74.64 430	67.41 459	48.30 455	62.52 436	85.04 335	20.40 468	75.93 448	42.55 450	45.90 470	82.44 430

SSC-MVS3.2		73.35 337	73.39 321	73.23 396	85.30 305	49.01 447	74.58 431	81.57 377	75.21 128	73.68 320	85.58 319	52.53 299	82.05 413	54.33 390	77.69 331	88.63 320

PVSNet	64.34 18	72.08 355	70.87 353	75.69 367	86.21 280	56.44 385	74.37 432	80.73 386	62.06 390	70.17 362	82.23 392	42.86 403	83.31 405	54.77 387	84.45 236	87.32 350

WB-MVS		54.94 427	54.72 428	55.60 454	73.50 453	20.90 488	74.27 433	61.19 471	59.16 413	50.61 463	74.15 451	47.19 363	75.78 450	17.31 479	35.07 473	70.12 461

MDA-MVSNet-bldmvs		66.68 401	63.66 411	75.75 366	79.28 424	60.56 331	73.92 434	78.35 415	64.43 357	50.13 465	79.87 418	44.02 396	83.67 400	46.10 438	56.86 451	83.03 425

SSC-MVS		53.88 430	53.59 430	54.75 456	72.87 459	19.59 489	73.84 435	60.53 473	57.58 429	49.18 467	73.45 454	46.34 375	75.47 453	16.20 482	32.28 475	69.20 462

UnsupCasMVSNet_bld		63.70 415	61.53 421	70.21 422	73.69 452	51.39 436	72.82 436	81.89 373	55.63 438	57.81 452	71.80 457	38.67 429	78.61 429	49.26 420	52.21 462	80.63 443

PatchT		68.46 390	67.85 381	70.29 421	80.70 404	43.93 465	72.47 437	74.88 436	60.15 404	70.55 355	76.57 441	49.94 340	81.59 415	50.58 408	74.83 378	85.34 393

miper_lstm_enhance		74.11 324	73.11 326	77.13 357	80.11 411	59.62 342	72.23 438	86.92 297	66.76 324	70.40 358	82.92 380	56.93 263	82.92 407	69.06 259	72.63 397	88.87 309

MVS-HIRNet		59.14 423	57.67 425	63.57 442	81.65 389	43.50 466	71.73 439	65.06 465	39.59 467	51.43 462	57.73 470	38.34 431	82.58 410	39.53 455	73.95 385	64.62 466

MVStest1		56.63 426	52.76 432	68.25 433	61.67 475	53.25 423	71.67 440	68.90 457	38.59 468	50.59 464	83.05 377	25.08 460	70.66 465	36.76 461	38.56 471	80.83 442

APD_test1		53.31 432	49.93 437	63.42 443	65.68 470	50.13 443	71.59 441	66.90 461	34.43 473	40.58 472	71.56 458	8.65 483	76.27 444	34.64 464	55.36 456	63.86 467

Patchmatch-RL test		70.24 372	67.78 385	77.61 349	77.43 434	59.57 344	71.16 442	70.33 449	62.94 378	68.65 380	72.77 455	50.62 330	85.49 385	69.58 254	66.58 427	87.77 339

test123		6.12 454	8.11 457	0.14 469	0.06 493	0.09 494	71.05 443	0.03 494	0.04 488	0.25 489	1.30 488	0.05 491	0.03 489	0.21 488	0.01 487	0.29 484

ANet_high		50.57 437	46.10 441	63.99 441	48.67 486	39.13 474	70.99 444	80.85 384	61.39 395	31.18 475	57.70 471	17.02 473	73.65 462	31.22 468	15.89 483	79.18 448

KD-MVS_2432*1600		66.22 406	63.89 409	73.21 397	75.47 445	53.42 419	70.76 445	84.35 334	64.10 363	66.52 407	78.52 430	34.55 444	84.98 390	50.40 410	50.33 464	81.23 439

miper_refine_blended		66.22 406	63.89 409	73.21 397	75.47 445	53.42 419	70.76 445	84.35 334	64.10 363	66.52 407	78.52 430	34.55 444	84.98 390	50.40 410	50.33 464	81.23 439

test_vis1_rt		60.28 421	58.42 424	65.84 439	67.25 468	55.60 399	70.44 447	60.94 472	44.33 461	59.00 447	66.64 462	24.91 461	68.67 469	62.80 310	69.48 414	73.25 458

testmvs		6.04 455	8.02 458	0.10 470	0.08 492	0.03 495	69.74 448	0.04 493	0.05 487	0.31 488	1.68 487	0.02 492	0.04 488	0.24 487	0.02 486	0.25 485

N_pmnet		52.79 433	53.26 431	51.40 458	78.99 426	7.68 492	69.52 449	3.89 491	51.63 450	57.01 454	74.98 450	40.83 417	65.96 473	37.78 459	64.67 434	80.56 445

FPMVS		53.68 431	51.64 433	59.81 447	65.08 471	51.03 438	69.48 450	69.58 453	41.46 464	40.67 471	72.32 456	16.46 474	70.00 468	24.24 475	65.42 432	58.40 471

DSMNet-mixed		57.77 425	56.90 427	60.38 446	67.70 467	35.61 477	69.18 451	53.97 478	32.30 476	57.49 453	79.88 417	40.39 420	68.57 470	38.78 458	72.37 398	76.97 452

new-patchmatchnet		61.73 419	61.73 420	61.70 444	72.74 460	24.50 487	69.16 452	78.03 416	61.40 394	56.72 455	75.53 449	38.42 430	76.48 442	45.95 439	57.67 450	84.13 411

YYNet1		65.03 410	62.91 415	71.38 412	75.85 441	56.60 383	69.12 453	74.66 440	57.28 431	54.12 459	77.87 435	45.85 380	74.48 457	49.95 415	61.52 444	83.05 424

MDA-MVSNet_test_wron		65.03 410	62.92 414	71.37 413	75.93 438	56.73 379	69.09 454	74.73 438	57.28 431	54.03 460	77.89 434	45.88 379	74.39 458	49.89 416	61.55 443	82.99 426

PVSNet_0	57.27 20	61.67 420	59.27 423	68.85 428	79.61 420	57.44 371	68.01 455	73.44 443	55.93 437	58.54 449	70.41 460	44.58 391	77.55 435	47.01 432	35.91 472	71.55 460

dongtai		45.42 441	45.38 442	45.55 460	73.36 456	26.85 484	67.72 456	34.19 486	54.15 442	49.65 466	56.41 473	25.43 459	62.94 476	19.45 477	28.09 477	46.86 476

ADS-MVSNet2		66.20 408	63.33 412	74.82 381	79.92 413	58.75 349	67.55 457	75.19 434	53.37 444	65.25 418	75.86 446	42.32 406	80.53 423	41.57 452	68.91 418	85.18 396

ADS-MVSNet		64.36 413	62.88 416	68.78 429	79.92 413	47.17 453	67.55 457	71.18 448	53.37 444	65.25 418	75.86 446	42.32 406	73.99 460	41.57 452	68.91 418	85.18 396

mvsany_test1		62.30 418	61.26 422	65.41 440	69.52 464	54.86 407	66.86 459	49.78 480	46.65 457	68.50 383	83.21 374	49.15 351	66.28 472	56.93 374	60.77 445	75.11 456

LCM-MVSNet		54.25 428	49.68 438	67.97 435	53.73 483	45.28 460	66.85 460	80.78 385	35.96 472	39.45 473	62.23 466	8.70 482	78.06 433	48.24 427	51.20 463	80.57 444

test_vis3_rt		49.26 438	47.02 440	56.00 451	54.30 480	45.27 461	66.76 461	48.08 481	36.83 470	44.38 469	53.20 474	7.17 485	64.07 474	56.77 377	55.66 454	58.65 470

testf1		45.72 439	41.96 443	57.00 449	56.90 477	45.32 458	66.14 462	59.26 474	26.19 477	30.89 476	60.96 468	4.14 486	70.64 466	26.39 473	46.73 468	55.04 472

APD_test2		45.72 439	41.96 443	57.00 449	56.90 477	45.32 458	66.14 462	59.26 474	26.19 477	30.89 476	60.96 468	4.14 486	70.64 466	26.39 473	46.73 468	55.04 472

kuosan		39.70 445	40.40 446	37.58 463	64.52 472	26.98 482	65.62 464	33.02 487	46.12 458	42.79 470	48.99 476	24.10 464	46.56 484	12.16 485	26.30 478	39.20 477

JIA-IIPM		66.32 405	62.82 417	76.82 359	77.09 436	61.72 315	65.34 465	75.38 433	58.04 425	64.51 423	62.32 465	42.05 410	86.51 372	51.45 405	69.22 417	82.21 432

PMVS	37.38 22	44.16 443	40.28 447	55.82 453	40.82 488	42.54 470	65.12 466	63.99 468	34.43 473	24.48 479	57.12 472	3.92 488	76.17 446	17.10 480	55.52 455	48.75 474
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
mamba_0408		79.37 222	77.52 249	84.93 110	88.81 167	67.96 149	65.03 467	88.66 252	70.96 238	79.48 187	89.80 192	58.69 243	94.65 119	70.35 243	85.93 211	92.18 181

SSM_04072		77.67 269	77.52 249	78.12 337	88.81 167	67.96 149	65.03 467	88.66 252	70.96 238	79.48 187	89.80 192	58.69 243	74.23 459	70.35 243	85.93 211	92.18 181

new_pmnet		50.91 436	50.29 436	52.78 457	68.58 466	34.94 479	63.71 469	56.63 477	39.73 466	44.95 468	65.47 463	21.93 467	58.48 477	34.98 463	56.62 452	64.92 465

mvsany_test3		53.99 429	51.45 434	61.61 445	55.51 479	44.74 464	63.52 470	45.41 484	43.69 462	58.11 451	76.45 442	17.99 471	63.76 475	54.77 387	47.59 466	76.34 454

Patchmatch-test		64.82 412	63.24 413	69.57 423	79.42 423	49.82 445	63.49 471	69.05 455	51.98 449	59.95 445	80.13 414	50.91 326	70.98 464	40.66 454	73.57 389	87.90 336

ambc				75.24 376	73.16 457	50.51 442	63.05 472	87.47 282		64.28 424	77.81 436	17.80 472	89.73 325	57.88 364	60.64 446	85.49 390

test_f		52.09 434	50.82 435	55.90 452	53.82 482	42.31 471	59.42 473	58.31 476	36.45 471	56.12 458	70.96 459	12.18 477	57.79 478	53.51 394	56.57 453	67.60 463

CHOSEN 280x420		66.51 403	64.71 405	71.90 409	81.45 394	63.52 277	57.98 474	68.95 456	53.57 443	62.59 435	76.70 440	46.22 376	75.29 455	55.25 383	79.68 304	76.88 453

E-PMN		31.77 446	30.64 449	35.15 464	52.87 484	27.67 481	57.09 475	47.86 482	24.64 479	16.40 484	33.05 480	11.23 479	54.90 480	14.46 483	18.15 481	22.87 480

EMVS		30.81 448	29.65 450	34.27 465	50.96 485	25.95 485	56.58 476	46.80 483	24.01 480	15.53 485	30.68 481	12.47 476	54.43 481	12.81 484	17.05 482	22.43 481

PMMVS2		40.82 444	38.86 448	46.69 459	53.84 481	16.45 490	48.61 477	49.92 479	37.49 469	31.67 474	60.97 467	8.14 484	56.42 479	28.42 470	30.72 476	67.19 464

wuyk23d		16.82 452	15.94 455	19.46 467	58.74 476	31.45 480	39.22 478	3.74 492	6.84 483	6.04 486	2.70 486	1.27 490	24.29 486	10.54 486	14.40 485	2.63 483

tmp_tt		18.61 451	21.40 454	10.23 468	4.82 491	10.11 491	34.70 479	30.74 489	1.48 485	23.91 481	26.07 482	28.42 456	13.41 487	27.12 471	15.35 484	7.17 482

Gipuma		45.18 442	41.86 445	55.16 455	77.03 437	51.52 434	32.50 480	80.52 390	32.46 475	27.12 478	35.02 479	9.52 481	75.50 451	22.31 476	60.21 448	38.45 478
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
MVE	26.22 23	30.37 449	25.89 453	43.81 461	44.55 487	35.46 478	28.87 481	39.07 485	18.20 481	18.58 483	40.18 478	2.68 489	47.37 483	17.07 481	23.78 480	48.60 475
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
test_method		31.52 447	29.28 451	38.23 462	27.03 490	6.50 493	20.94 482	62.21 470	4.05 484	22.35 482	52.50 475	13.33 475	47.58 482	27.04 472	34.04 474	60.62 468

mmdepth		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

monomultidepth		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

test_blank		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

uanet_test		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

DCPMVS		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

cdsmvs_eth3d_5k		19.96 450	26.61 452	0.00 471	0.00 494	0.00 496	0.00 483	89.26 221	0.00 489	0.00 490	88.61 232	61.62 206	0.00 490	0.00 489	0.00 488	0.00 486

pcd_1.5k_mvsjas		5.26 456	7.02 459	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	63.15 178	0.00 490	0.00 489	0.00 488	0.00 486

sosnet-low-res		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

sosnet		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

uncertanet		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

Regformer		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

ab-mvs-re		7.23 453	9.64 456	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	86.72 285	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

uanet		0.00 457	0.00 460	0.00 471	0.00 494	0.00 496	0.00 483	0.00 495	0.00 489	0.00 490	0.00 489	0.00 493	0.00 490	0.00 489	0.00 488	0.00 486

WAC-MVS						42.58 468								39.46 456

MSC_two_6792asdad				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 56

PC_three_1452									68.21 310	92.02 15	94.00 63	82.09 5	95.98 61	84.58 71	96.68 2	94.95 12

No_MVS				89.16 1	94.34 31	75.53 2		92.99 54					97.53 2	89.67 15	96.44 9	94.41 56

test_one_0601					95.07 7	71.46 59		94.14 10	78.27 41	92.05 14	95.74 6	80.83 13

eth-test2					0.00 494

eth-test					0.00 494

ZD-MVS					94.38 29	72.22 46		92.67 72	70.98 237	87.75 50	94.07 58	74.01 36	96.70 31	84.66 70	94.84 48

IU-MVS					95.30 2	71.25 64		92.95 60	66.81 322	92.39 6				88.94 28	96.63 4	94.85 21

test_241102_TWO								94.06 15	77.24 61	92.78 4	95.72 8	81.26 10	97.44 7	89.07 25	96.58 6	94.26 67

test_241102_ONE					95.30 2	70.98 71		94.06 15	77.17 64	93.10 1	95.39 16	82.99 1	97.27 15

test_0728_THIRD									78.38 38	92.12 12	95.78 4	81.46 9	97.40 9	89.42 19	96.57 7	94.67 36

GSMVS																88.96 306

test_part2					95.06 8	72.65 32				91.80 16

sam_mvs1												51.32 322				88.96 306

sam_mvs												50.01 338

MTGPA								92.02 109

test_post											5.46 484	50.36 334	84.24 396

patchmatchnet-post											74.00 452	51.12 325	88.60 348

gm-plane-assit					81.40 395	53.83 416			62.72 383		80.94 404		92.39 238	63.40 306

test9_res														84.90 64	95.70 30	92.87 149

agg_prior2														82.91 91	95.45 33	92.70 154

agg_prior					92.85 68	71.94 52		91.78 125		84.41 95			94.93 101

TestCases				79.58 308	85.15 309	63.62 268		79.83 401	62.31 386	60.32 443	86.73 283	32.02 448	88.96 342	50.28 412	71.57 406	86.15 378

test_prior				86.33 64	92.61 74	69.59 98		92.97 59					95.48 74			93.91 84

新几何1				83.42 190	93.13 60	70.71 80		85.48 321	57.43 430	81.80 148	91.98 118	63.28 172	92.27 244	64.60 298	92.99 76	87.27 352

旧先验1					91.96 80	65.79 208		86.37 308			93.08 92	69.31 99			92.74 80	88.74 317

原ACMM1				84.35 137	93.01 66	68.79 117		92.44 82	63.96 368	81.09 161	91.57 137	66.06 148	95.45 75	67.19 277	94.82 50	88.81 312

testdata2													91.01 301	62.37 318

segment_acmp												73.08 43

testdata				79.97 297	90.90 98	64.21 256		84.71 329	59.27 412	85.40 75	92.91 94	62.02 199	89.08 338	68.95 260	91.37 105	86.63 372

test12				86.80 58	92.63 73	70.70 81		91.79 124		82.71 135		71.67 63	96.16 52		94.50 57	93.54 113

plane_prior7					90.08 116	68.51 131

plane_prior6					89.84 125	68.70 125						60.42 232

plane_prior5								92.44 82					95.38 82	78.71 142	86.32 200	91.33 209

plane_prior4											91.00 161

plane_prior3						68.60 128			78.44 36	78.92 197

plane_prior1					89.90 124

n2								0.00 495

nn								0.00 495

door-mid								69.98 451

lessismore_v0				78.97 318	81.01 402	57.15 374		65.99 462		61.16 439	82.82 383	39.12 426	91.34 287	59.67 343	46.92 467	88.43 325

LGP-MVS_train				84.50 127	89.23 152	68.76 119		91.94 115	75.37 121	76.64 253	91.51 139	54.29 285	94.91 102	78.44 144	83.78 245	89.83 277

test11								92.23 95

door								69.44 454

HQP5-MVS						66.98 183

BP-MVS														77.47 157

HQP4-MVS										77.24 237			95.11 94			91.03 219

HQP3-MVS								92.19 103							85.99 209

HQP2-MVS												60.17 235

NP-MVS					89.62 129	68.32 135					90.24 182

ACMMP++_ref															81.95 278

ACMMP++															81.25 283

Test By Simon												64.33 164

ITE_SJBPF				78.22 334	81.77 388	60.57 330		83.30 351	69.25 286	67.54 390	87.20 274	36.33 440	87.28 366	54.34 389	74.62 380	86.80 366

DeepMVS_CX				27.40 466	40.17 489	26.90 483		24.59 490	17.44 482	23.95 480	48.61 477	9.77 480	26.48 485	18.06 478	24.47 479	28.83 479

High-resolution multi-view benchmark