High-res multi-view results

This table lists the benchmark results for the high-res multi-view scenario. The following metrics are evaluated:

Accuracy [%]: The fraction of the reconstruction which is closer to the ground truth than the evaluation threshold distance (*). Larger is better.
Completeness [%]: The fraction of the ground truth which is closer to the reconstruction than the evaluation threshold distance (*). Larger is better.
F₁ score [%]: The harmonic mean of accuracy and completeness, used to rank methods based on both metrics. Larger is better.
Time [s]: The runtime of the method. Smaller is better.

(*) For exact definitions, detailing how potentially incomplete ground truth is taken into account, see our paper.

The datasets are grouped into different categories, and result averages are computed for a category and method if results of the method are available for all datasets within the category. Note that the category "all" includes both the high-res multi-view and the low-res many-view scenarios.

Methods with suffix _ROB may participate in the Robust Vision Challenge.

Click a dataset result cell to show a visualization of the reconstruction. For training datasets, ground truth and accuracy / completeness visualizations are also available. The visualizations may not work with mobile browsers.

Method	all	high-res multi-view	indoor	outdoor	botani.	boulde.	bridge	door	exhibi.	lectur.	living.	lounge	observ.	old co.	statue	terrac.

HPM-MVS++		89.02 9	89.15 9	88.63 5	95.01 9	76.03 1	92.38 27	92.85 54	80.26 11	87.78 29	94.27 32	75.89 19	96.81 23	87.45 32	96.44 9	93.05 98

MSC_two_6792asdad				89.16 1	94.34 27	75.53 2		92.99 45					97.53 2	89.67 6	96.44 9	94.41 32

No_MVS				89.16 1	94.34 27	75.53 2		92.99 45					97.53 2	89.67 6	96.44 9	94.41 32

OPU-MVS				89.06 3	94.62 15	75.42 4	93.57 7				94.02 44	82.45 3	96.87 20	83.77 58	96.48 8	94.88 14

MTAPA		87.23 28	87.00 29	87.90 22	94.18 35	74.25 5	86.58 186	92.02 85	79.45 19	85.88 46	94.80 17	68.07 89	96.21 42	86.69 36	95.34 33	93.23 89

MP-MVS		87.71 20	87.64 22	87.93 21	94.36 26	73.88 6	92.71 22	92.65 64	77.57 41	83.84 82	94.40 30	72.24 43	96.28 40	85.65 38	95.30 35	93.62 72
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
CP-MVS		87.11 30	86.92 32	87.68 34	94.20 34	73.86 7	93.98 3	92.82 58	76.62 70	83.68 84	94.46 25	67.93 90	95.95 52	84.20 55	94.39 54	93.23 89

CNVR-MVS		88.93 10	89.13 10	88.33 8	94.77 12	73.82 8	90.51 61	93.00 43	80.90 7	88.06 26	94.06 42	76.43 16	96.84 21	88.48 24	95.99 18	94.34 37

SMA-MVS		89.08 8	89.23 7	88.61 6	94.25 31	73.73 9	92.40 24	93.63 21	74.77 107	92.29 7	95.97 2	74.28 29	97.24 12	88.58 21	96.91 1	94.87 16
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
MM		89.16 6	89.23 7	88.97 4	90.79 90	73.65 10	92.66 23	91.17 116	86.57 1	87.39 35	94.97 16	71.70 50	97.68 1	92.19 1	95.63 28	95.57 1

NCCC		88.06 15	88.01 19	88.24 11	94.41 22	73.62 11	91.22 52	92.83 55	81.50 5	85.79 48	93.47 60	73.02 39	97.00 18	84.90 42	94.94 39	94.10 45

ACMMPR		87.44 23	87.23 27	88.08 14	94.64 13	73.59 12	93.04 12	93.20 34	76.78 65	84.66 66	94.52 21	68.81 84	96.65 30	84.53 49	94.90 40	94.00 50

region2R		87.42 25	87.20 28	88.09 13	94.63 14	73.55 13	93.03 14	93.12 37	76.73 68	84.45 70	94.52 21	69.09 78	96.70 27	84.37 51	94.83 45	94.03 49

mPP-MVS		86.67 37	86.32 39	87.72 30	94.41 22	73.55 13	92.74 20	92.22 80	76.87 62	82.81 98	94.25 34	66.44 105	96.24 41	82.88 67	94.28 57	93.38 83

HFP-MVS		87.58 22	87.47 24	87.94 19	94.58 16	73.54 15	93.04 12	93.24 33	76.78 65	84.91 59	94.44 28	70.78 60	96.61 32	84.53 49	94.89 41	93.66 65

3Dnovator+	77.84 4	85.48 54	84.47 69	88.51 7	91.08 81	73.49 16	93.18 11	93.78 18	80.79 8	76.66 196	93.37 62	60.40 188	96.75 26	77.20 118	93.73 63	95.29 5

MSP-MVS		89.51 4	89.91 5	88.30 10	94.28 30	73.46 17	92.90 16	94.11 6	80.27 10	91.35 14	94.16 37	78.35 13	96.77 24	89.59 8	94.22 59	94.67 24
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
PGM-MVS		86.68 36	86.27 40	87.90 22	94.22 33	73.38 18	90.22 70	93.04 38	75.53 91	83.86 81	94.42 29	67.87 92	96.64 31	82.70 72	94.57 50	93.66 65

ZNCC-MVS		87.94 19	87.85 20	88.20 12	94.39 24	73.33 19	93.03 14	93.81 17	76.81 63	85.24 53	94.32 31	71.76 48	96.93 19	85.53 39	95.79 22	94.32 38

XVS		87.18 29	86.91 33	88.00 17	94.42 20	73.33 19	92.78 18	92.99 45	79.14 21	83.67 85	94.17 36	67.45 95	96.60 33	83.06 63	94.50 51	94.07 47

X-MVStestdata		80.37 146	77.83 183	88.00 17	94.42 20	73.33 19	92.78 18	92.99 45	79.14 21	83.67 85	12.47 403	67.45 95	96.60 33	83.06 63	94.50 51	94.07 47

ACMMP_NAP		88.05 17	88.08 17	87.94 19	93.70 41	73.05 22	90.86 56	93.59 23	76.27 79	88.14 24	95.09 15	71.06 57	96.67 29	87.67 29	96.37 14	94.09 46

DPM-MVS		84.93 63	84.29 70	86.84 47	90.20 100	73.04 23	87.12 168	93.04 38	69.80 208	82.85 96	91.22 110	73.06 38	96.02 47	76.72 126	94.63 48	91.46 152

GST-MVS		87.42 25	87.26 25	87.89 24	94.12 36	72.97 24	92.39 26	93.43 28	76.89 61	84.68 63	93.99 48	70.67 62	96.82 22	84.18 56	95.01 37	93.90 55

TEST9					93.26 50	72.96 25	88.75 115	91.89 93	68.44 242	85.00 57	93.10 67	74.36 28	95.41 69

train_agg		86.43 39	86.20 41	87.13 44	93.26 50	72.96 25	88.75 115	91.89 93	68.69 237	85.00 57	93.10 67	74.43 26	95.41 69	84.97 41	95.71 25	93.02 100

SteuartSystems-ACMMP		88.72 11	88.86 11	88.32 9	92.14 69	72.96 25	93.73 5	93.67 20	80.19 12	88.10 25	94.80 17	73.76 33	97.11 15	87.51 31	95.82 21	94.90 13
Skip Steuart: Steuart Systems R&D Blog.
3Dnovator	76.31 5	83.38 85	82.31 95	86.59 52	87.94 185	72.94 28	90.64 59	92.14 84	77.21 52	75.47 222	92.83 76	58.56 195	94.72 101	73.24 159	92.71 70	92.13 132

SD-MVS		88.06 15	88.50 14	86.71 51	92.60 66	72.71 29	91.81 42	93.19 35	77.87 36	90.32 17	94.00 46	74.83 23	93.78 137	87.63 30	94.27 58	93.65 69
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
MVS_111021_HR		85.14 60	84.75 64	86.32 55	91.65 76	72.70 30	85.98 202	90.33 141	76.11 81	82.08 103	91.61 100	71.36 56	94.17 121	81.02 82	92.58 71	92.08 133

DPE-MVS		89.48 5	89.98 4	88.01 16	94.80 11	72.69 31	91.59 43	94.10 8	75.90 85	92.29 7	95.66 10	81.67 6	97.38 10	87.44 33	96.34 15	93.95 52
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					95.06 8	72.65 32				91.80 13

ACMMP		85.89 48	85.39 54	87.38 39	93.59 45	72.63 33	92.74 20	93.18 36	76.78 65	80.73 123	93.82 53	64.33 125	96.29 39	82.67 73	90.69 95	93.23 89
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
test_prior4						72.60 34	89.01 105

test_8					93.13 52	72.57 35	88.68 120	91.84 97	68.69 237	84.87 61	93.10 67	74.43 26	95.16 78

TSAR-MVS + GP.		85.71 51	85.33 56	86.84 47	91.34 78	72.50 36	89.07 104	87.28 228	76.41 72	85.80 47	90.22 134	74.15 31	95.37 74	81.82 77	91.88 79	92.65 111

CSCG		86.41 41	86.19 42	87.07 45	92.91 58	72.48 37	90.81 57	93.56 24	73.95 123	83.16 92	91.07 116	75.94 18	95.19 77	79.94 94	94.38 55	93.55 76

MCST-MVS		87.37 27	87.25 26	87.73 28	94.53 17	72.46 38	89.82 76	93.82 16	73.07 148	84.86 62	92.89 74	76.22 17	96.33 38	84.89 44	95.13 36	94.40 34

FOURS1					95.00 10	72.39 39	95.06 1	93.84 15	74.49 113	91.30 15

APD-MVS		87.44 23	87.52 23	87.19 42	94.24 32	72.39 39	91.86 41	92.83 55	73.01 150	88.58 21	94.52 21	73.36 34	96.49 36	84.26 52	95.01 37	92.70 107
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
DeepC-MVS_fast	79.65 3	86.91 33	86.62 36	87.76 27	93.52 46	72.37 41	91.26 48	93.04 38	76.62 70	84.22 74	93.36 63	71.44 54	96.76 25	80.82 85	95.33 34	94.16 43
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
save fliter					93.80 40	72.35 42	90.47 64	91.17 116	74.31 116

DeepC-MVS	79.81 2	87.08 32	86.88 34	87.69 33	91.16 80	72.32 43	90.31 68	93.94 14	77.12 55	82.82 97	94.23 35	72.13 45	97.09 16	84.83 45	95.37 32	93.65 69
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
ZD-MVS					94.38 25	72.22 44		92.67 61	70.98 182	87.75 31	94.07 41	74.01 32	96.70 27	84.66 47	94.84 44

HPM-MVS		87.11 30	86.98 30	87.50 38	93.88 39	72.16 45	92.19 34	93.33 31	76.07 82	83.81 83	93.95 51	69.77 72	96.01 48	85.15 40	94.66 47	94.32 38
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
TSAR-MVS + MP.		88.02 18	88.11 16	87.72 30	93.68 43	72.13 46	91.41 47	92.35 74	74.62 111	88.90 20	93.85 52	75.75 20	96.00 49	87.80 28	94.63 48	95.04 7
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 34	86.67 35	86.91 46	94.11 37	72.11 47	92.37 28	92.56 67	74.50 112	86.84 42	94.65 20	67.31 97	95.77 55	84.80 46	92.85 68	92.84 105

MVS_0304		88.08 14	88.08 17	88.08 14	89.67 114	72.04 48	92.26 33	89.26 173	84.19 2	85.01 55	95.18 13	69.93 69	97.20 14	91.63 2	95.60 29	94.99 9

MP-MVS-pluss		87.67 21	87.72 21	87.54 36	93.64 44	72.04 48	89.80 78	93.50 25	75.17 100	86.34 44	95.29 12	70.86 59	96.00 49	88.78 19	96.04 16	94.58 27
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
SF-MVS		88.46 12	88.74 12	87.64 35	92.78 61	71.95 50	92.40 24	94.74 2	75.71 87	89.16 19	95.10 14	75.65 21	96.19 43	87.07 34	96.01 17	94.79 21

agg_prior					92.85 59	71.94 51		91.78 100		84.41 71			94.93 89

APDe-MVS		89.15 7	89.63 6	87.73 28	94.49 18	71.69 52	93.83 4	93.96 13	75.70 89	91.06 16	96.03 1	76.84 14	97.03 17	89.09 11	95.65 27	94.47 31
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
MVS_111021_LR		82.61 97	82.11 97	84.11 118	88.82 150	71.58 53	85.15 222	86.16 248	74.69 108	80.47 125	91.04 117	62.29 151	90.55 257	80.33 91	90.08 105	90.20 196

MAR-MVS		81.84 107	80.70 118	85.27 74	91.32 79	71.53 54	89.82 76	90.92 122	69.77 210	78.50 153	86.21 245	62.36 150	94.52 107	65.36 230	92.05 78	89.77 221
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
test_one_0601					95.07 7	71.46 55		94.14 5	78.27 35	92.05 11	95.74 6	80.83 11

test_0728_SECOND				87.71 32	95.34 1	71.43 56	93.49 9	94.23 3					97.49 4	89.08 12	96.41 12	94.21 42

DVP-MVS++		90.23 1	91.01 1	87.89 24	94.34 27	71.25 57	95.06 1	94.23 3	78.38 33	92.78 4	95.74 6	82.45 3	97.49 4	89.42 9	96.68 2	94.95 10

IU-MVS					95.30 2	71.25 57		92.95 51	66.81 255	92.39 6				88.94 16	96.63 4	94.85 19

DVP-MVS		89.60 3	90.35 3	87.33 40	95.27 5	71.25 57	93.49 9	92.73 59	77.33 48	92.12 9	95.78 4	80.98 9	97.40 8	89.08 12	96.41 12	93.33 86
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 57	93.60 6	94.11 6	77.33 48	92.81 3	95.79 3	80.98 9

CDPH-MVS		85.76 50	85.29 59	87.17 43	93.49 47	71.08 61	88.58 123	92.42 72	68.32 244	84.61 67	93.48 58	72.32 42	96.15 45	79.00 99	95.43 31	94.28 40

CNLPA		78.08 200	76.79 210	81.97 196	90.40 97	71.07 62	87.59 156	84.55 268	66.03 271	72.38 274	89.64 145	57.56 204	86.04 312	59.61 279	83.35 201	88.79 253

SED-MVS		90.08 2	90.85 2	87.77 26	95.30 2	70.98 63	93.57 7	94.06 10	77.24 50	93.10 1	95.72 8	82.99 1	97.44 6	89.07 14	96.63 4	94.88 14

test_241102_ONE					95.30 2	70.98 63		94.06 10	77.17 53	93.10 1	95.39 11	82.99 1	97.27 11

PHI-MVS		86.43 39	86.17 43	87.24 41	90.88 87	70.96 65	92.27 32	94.07 9	72.45 153	85.22 54	91.90 92	69.47 74	96.42 37	83.28 62	95.94 19	94.35 36

OPM-MVS		83.50 81	82.95 86	85.14 78	88.79 153	70.95 66	89.13 103	91.52 106	77.55 44	80.96 121	91.75 95	60.71 179	94.50 108	79.67 97	86.51 150	89.97 213
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
CANet		86.45 38	86.10 45	87.51 37	90.09 102	70.94 67	89.70 82	92.59 66	81.78 4	81.32 114	91.43 106	70.34 64	97.23 13	84.26 52	93.36 65	94.37 35

DP-MVS Recon		83.11 91	82.09 98	86.15 58	94.44 19	70.92 68	88.79 113	92.20 81	70.53 192	79.17 139	91.03 119	64.12 127	96.03 46	68.39 206	90.14 103	91.50 148

CPTT-MVS		83.73 74	83.33 80	84.92 89	93.28 49	70.86 69	92.09 37	90.38 137	68.75 236	79.57 134	92.83 76	60.60 184	93.04 179	80.92 84	91.56 85	90.86 170

h-mvs33		83.15 88	82.19 96	86.02 61	90.56 93	70.85 70	88.15 140	89.16 178	76.02 83	84.67 64	91.39 107	61.54 162	95.50 63	82.71 70	75.48 298	91.72 141

新几何1				83.42 148	93.13 52	70.71 71		85.48 257	57.43 354	81.80 109	91.98 90	63.28 133	92.27 202	64.60 237	92.99 66	87.27 284

test12				86.80 49	92.63 64	70.70 72		91.79 99		82.71 99		71.67 51	96.16 44		94.50 51	93.54 77

SR-MVS-dyc-post		85.77 49	85.61 52	86.23 56	93.06 55	70.63 73	91.88 39	92.27 76	73.53 137	85.69 49	94.45 26	65.00 123	95.56 60	82.75 68	91.87 80	92.50 116

RE-MVS-def			85.48 53		93.06 55	70.63 73	91.88 39	92.27 76	73.53 137	85.69 49	94.45 26	63.87 129		82.75 68	91.87 80	92.50 116

HPM-MVS_fast		85.35 58	84.95 63	86.57 53	93.69 42	70.58 75	92.15 36	91.62 103	73.89 126	82.67 100	94.09 40	62.60 144	95.54 62	80.93 83	92.93 67	93.57 74

MSLP-MVS++		85.43 56	85.76 50	84.45 104	91.93 72	70.24 76	90.71 58	92.86 53	77.46 47	84.22 74	92.81 78	67.16 99	92.94 181	80.36 90	94.35 56	90.16 197

MVSFormer		82.85 94	82.05 99	85.24 75	87.35 207	70.21 77	90.50 62	90.38 137	68.55 239	81.32 114	89.47 151	61.68 159	93.46 154	78.98 100	90.26 101	92.05 134

lupinMVS		81.39 120	80.27 129	84.76 95	87.35 207	70.21 77	85.55 215	86.41 243	62.85 307	81.32 114	88.61 175	61.68 159	92.24 204	78.41 107	90.26 101	91.83 138

xiu_mvs_v1_base_debu		80.80 132	79.72 138	84.03 131	87.35 207	70.19 79	85.56 212	88.77 194	69.06 229	81.83 106	88.16 190	50.91 265	92.85 183	78.29 109	87.56 133	89.06 237

xiu_mvs_v1_base		80.80 132	79.72 138	84.03 131	87.35 207	70.19 79	85.56 212	88.77 194	69.06 229	81.83 106	88.16 190	50.91 265	92.85 183	78.29 109	87.56 133	89.06 237

xiu_mvs_v1_base_debi		80.80 132	79.72 138	84.03 131	87.35 207	70.19 79	85.56 212	88.77 194	69.06 229	81.83 106	88.16 190	50.91 265	92.85 183	78.29 109	87.56 133	89.06 237

API-MVS		81.99 105	81.23 109	84.26 115	90.94 85	70.18 82	91.10 53	89.32 169	71.51 171	78.66 149	88.28 186	65.26 118	95.10 85	64.74 236	91.23 89	87.51 278

test_fmvsm_n_1920		85.29 59	85.34 55	85.13 80	86.12 233	69.93 83	88.65 121	90.78 127	69.97 204	88.27 23	93.98 49	71.39 55	91.54 229	88.49 23	90.45 98	93.91 53

OpenMVS	72.83 10	79.77 157	78.33 172	84.09 122	85.17 247	69.91 84	90.57 60	90.97 121	66.70 258	72.17 276	91.91 91	54.70 224	93.96 125	61.81 263	90.95 92	88.41 263

jason		81.39 120	80.29 128	84.70 96	86.63 227	69.90 85	85.95 203	86.77 239	63.24 300	81.07 120	89.47 151	61.08 175	92.15 206	78.33 108	90.07 106	92.05 134
jason: jason.
MVP-Stereo		76.12 239	74.46 247	81.13 217	85.37 245	69.79 86	84.42 243	87.95 213	65.03 281	67.46 322	85.33 264	53.28 239	91.73 222	58.01 296	83.27 202	81.85 359
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PVSNet_Blended_VisFu		82.62 96	81.83 104	84.96 86	90.80 89	69.76 87	88.74 117	91.70 102	69.39 217	78.96 141	88.46 181	65.47 117	94.87 96	74.42 145	88.57 124	90.24 195

test_prior				86.33 54	92.61 65	69.59 88		92.97 50					95.48 64			93.91 53

APD-MVS_3200maxsize		85.97 45	85.88 48	86.22 57	92.69 63	69.53 89	91.93 38	92.99 45	73.54 136	85.94 45	94.51 24	65.80 115	95.61 59	83.04 65	92.51 72	93.53 78

test_fmvsmconf_n		85.92 46	86.04 47	85.57 68	85.03 253	69.51 90	89.62 86	90.58 131	73.42 139	87.75 31	94.02 44	72.85 40	93.24 161	90.37 3	90.75 94	93.96 51

EPNet		83.72 75	82.92 87	86.14 59	84.22 267	69.48 91	91.05 55	85.27 259	81.30 6	76.83 191	91.65 97	66.09 110	95.56 60	76.00 132	93.85 61	93.38 83
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
ET-MVSNet_ETH3D		78.63 187	76.63 216	84.64 97	86.73 224	69.47 92	85.01 225	84.61 267	69.54 215	66.51 337	86.59 234	50.16 274	91.75 220	76.26 128	84.24 183	92.69 109

alignmvs		85.48 54	85.32 57	85.96 62	89.51 120	69.47 92	89.74 80	92.47 68	76.17 80	87.73 33	91.46 105	70.32 65	93.78 137	81.51 78	88.95 118	94.63 26

DP-MVS		76.78 228	74.57 243	83.42 148	93.29 48	69.46 94	88.55 124	83.70 280	63.98 296	70.20 292	88.89 167	54.01 232	94.80 98	46.66 359	81.88 220	86.01 311

canonicalmvs		85.91 47	85.87 49	86.04 60	89.84 112	69.44 95	90.45 66	93.00 43	76.70 69	88.01 28	91.23 109	73.28 36	93.91 132	81.50 79	88.80 121	94.77 22

test_fmvsmconf0.1_n		85.61 53	85.65 51	85.50 69	82.99 298	69.39 96	89.65 83	90.29 144	73.31 142	87.77 30	94.15 38	71.72 49	93.23 162	90.31 4	90.67 96	93.89 56

test_fmvsmvis_n_1920		84.02 70	83.87 72	84.49 103	84.12 269	69.37 97	88.15 140	87.96 212	70.01 202	83.95 80	93.23 65	68.80 85	91.51 232	88.61 20	89.96 107	92.57 112

nrg030		83.88 71	83.53 75	84.96 86	86.77 223	69.28 98	90.46 65	92.67 61	74.79 106	82.95 93	91.33 108	72.70 41	93.09 175	80.79 87	79.28 251	92.50 116

test_fmvsmconf0.01_n		84.73 66	84.52 68	85.34 72	80.25 338	69.03 99	89.47 88	89.65 161	73.24 146	86.98 40	94.27 32	66.62 101	93.23 162	90.26 5	89.95 108	93.78 62

DeepPCF-MVS	80.84 1	88.10 13	88.56 13	86.73 50	92.24 68	69.03 99	89.57 87	93.39 30	77.53 45	89.79 18	94.12 39	78.98 12	96.58 35	85.66 37	95.72 24	94.58 27

XVG-OURS		80.41 143	79.23 151	83.97 135	85.64 239	69.02 101	83.03 271	90.39 136	71.09 179	77.63 174	91.49 104	54.62 226	91.35 238	75.71 134	83.47 199	91.54 145

PCF-MVS	73.52 7	80.38 144	78.84 160	85.01 84	87.71 196	68.99 102	83.65 255	91.46 111	63.00 304	77.77 172	90.28 131	66.10 109	95.09 86	61.40 266	88.22 130	90.94 168
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
QAPM		80.88 127	79.50 143	85.03 83	88.01 184	68.97 103	91.59 43	92.00 87	66.63 264	75.15 239	92.16 88	57.70 202	95.45 65	63.52 242	88.76 122	90.66 177

AdaColmap		80.58 141	79.42 144	84.06 126	93.09 54	68.91 104	89.36 94	88.97 188	69.27 220	75.70 218	89.69 143	57.20 209	95.77 55	63.06 247	88.41 128	87.50 279

fmvsm_l_conf0.5_n		84.47 67	84.54 66	84.27 114	85.42 243	68.81 105	88.49 125	87.26 229	68.08 246	88.03 27	93.49 57	72.04 46	91.77 219	88.90 17	89.14 117	92.24 127

原ACMM1				84.35 108	93.01 57	68.79 106		92.44 69	63.96 297	81.09 119	91.57 101	66.06 111	95.45 65	67.19 216	94.82 46	88.81 252

XVG-OURS-SEG-HR		80.81 130	79.76 137	83.96 136	85.60 240	68.78 107	83.54 260	90.50 134	70.66 190	76.71 195	91.66 96	60.69 180	91.26 240	76.94 121	81.58 222	91.83 138

LPG-MVS_test		82.08 102	81.27 108	84.50 101	89.23 136	68.76 108	90.22 70	91.94 91	75.37 94	76.64 197	91.51 102	54.29 228	94.91 90	78.44 105	83.78 187	89.83 218

LGP-MVS_train				84.50 101	89.23 136	68.76 108		91.94 91	75.37 94	76.64 197	91.51 102	54.29 228	94.91 90	78.44 105	83.78 187	89.83 218

Effi-MVS+-dtu		80.03 153	78.57 165	84.42 105	85.13 251	68.74 110	88.77 114	88.10 208	74.99 102	74.97 244	83.49 301	57.27 208	93.36 157	73.53 153	80.88 229	91.18 158

Vis-MVSNet		83.46 82	82.80 89	85.43 71	90.25 99	68.74 110	90.30 69	90.13 148	76.33 78	80.87 122	92.89 74	61.00 176	94.20 119	72.45 168	90.97 91	93.35 85
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
HQP_MVS		83.64 77	83.14 81	85.14 78	90.08 103	68.71 112	91.25 50	92.44 69	79.12 23	78.92 143	91.00 120	60.42 186	95.38 71	78.71 103	86.32 152	91.33 153

plane_prior						68.71 112	90.38 67		77.62 39						86.16 156

plane_prior6					89.84 112	68.70 114						60.42 186

ACMP	74.13 6	81.51 119	80.57 120	84.36 107	89.42 125	68.69 115	89.97 74	91.50 110	74.46 114	75.04 243	90.41 130	53.82 233	94.54 105	77.56 114	82.91 206	89.86 217
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
ETV-MVS		84.90 65	84.67 65	85.59 67	89.39 127	68.66 116	88.74 117	92.64 65	79.97 15	84.10 77	85.71 254	69.32 76	95.38 71	80.82 85	91.37 87	92.72 106

plane_prior3						68.60 117			78.44 31	78.92 143

CHOSEN 1792x2688		77.63 215	75.69 225	83.44 147	89.98 109	68.58 118	78.70 325	87.50 224	56.38 359	75.80 217	86.84 222	58.67 194	91.40 237	61.58 265	85.75 164	90.34 190

plane_prior7					90.08 103	68.51 119

fmvsm_l_conf0.5_n_a		84.13 69	84.16 71	84.06 126	85.38 244	68.40 120	88.34 132	86.85 238	67.48 253	87.48 34	93.40 61	70.89 58	91.61 223	88.38 25	89.22 116	92.16 131

ACMM	73.20 8	80.78 135	79.84 136	83.58 144	89.31 132	68.37 121	89.99 73	91.60 104	70.28 197	77.25 181	89.66 144	53.37 238	93.53 150	74.24 148	82.85 207	88.85 250
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
pmmvs4		74.03 264	71.91 271	80.39 232	81.96 315	68.32 122	81.45 286	82.14 305	59.32 337	69.87 301	85.13 270	52.40 244	88.13 296	60.21 275	74.74 313	84.73 331

NP-MVS					89.62 115	68.32 122					90.24 132

test222					91.50 77	68.26 124	84.16 248	83.20 291	54.63 365	79.74 131	91.63 99	58.97 193			91.42 86	86.77 297

CDS-MVSNet		79.07 177	77.70 190	83.17 160	87.60 201	68.23 125	84.40 244	86.20 247	67.49 252	76.36 205	86.54 238	61.54 162	90.79 253	61.86 262	87.33 137	90.49 185
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PS-MVSNAJ		81.69 111	81.02 113	83.70 142	89.51 120	68.21 126	84.28 246	90.09 149	70.79 184	81.26 118	85.62 259	63.15 138	94.29 112	75.62 136	88.87 120	88.59 259

fmvsm_s_conf0.5_n_a		83.63 78	83.41 77	84.28 112	86.14 232	68.12 127	89.43 90	82.87 298	70.27 198	87.27 37	93.80 54	69.09 78	91.58 225	88.21 26	83.65 194	93.14 95

UGNet		80.83 129	79.59 141	84.54 100	88.04 182	68.09 128	89.42 91	88.16 206	76.95 59	76.22 208	89.46 153	49.30 286	93.94 128	68.48 204	90.31 99	91.60 142
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
fmvsm_s_conf0.1_n_a		83.32 86	82.99 85	84.28 112	83.79 276	68.07 129	89.34 95	82.85 299	69.80 208	87.36 36	94.06 42	68.34 88	91.56 227	87.95 27	83.46 200	93.21 92

UA-Net		85.08 62	84.96 62	85.45 70	92.07 70	68.07 129	89.78 79	90.86 126	82.48 3	84.60 68	93.20 66	69.35 75	95.22 76	71.39 174	90.88 93	93.07 97

xiu_mvs_v2_base		81.69 111	81.05 112	83.60 143	89.15 139	68.03 131	84.46 240	90.02 150	70.67 187	81.30 117	86.53 239	63.17 137	94.19 120	75.60 137	88.54 125	88.57 260

DELS-MVS		85.41 57	85.30 58	85.77 64	88.49 163	67.93 132	85.52 219	93.44 27	78.70 29	83.63 87	89.03 164	74.57 24	95.71 57	80.26 92	94.04 60	93.66 65
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
EI-MVSNet-Vis-set		84.19 68	83.81 73	85.31 73	88.18 174	67.85 133	87.66 154	89.73 159	80.05 14	82.95 93	89.59 148	70.74 61	94.82 97	80.66 89	84.72 172	93.28 88

PLC	70.83 11	78.05 202	76.37 221	83.08 164	91.88 74	67.80 134	88.19 137	89.46 165	64.33 290	69.87 301	88.38 183	53.66 234	93.58 145	58.86 287	82.73 209	87.86 270
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
TAMVS		78.89 182	77.51 195	83.03 167	87.80 191	67.79 135	84.72 231	85.05 262	67.63 249	76.75 194	87.70 199	62.25 152	90.82 252	58.53 291	87.13 140	90.49 185

CLD-MVS		82.31 99	81.65 105	84.29 111	88.47 164	67.73 136	85.81 210	92.35 74	75.78 86	78.33 158	86.58 236	64.01 128	94.35 111	76.05 131	87.48 136	90.79 171
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
hse-mvs2		81.72 109	80.94 115	84.07 124	88.72 156	67.68 137	85.87 206	87.26 229	76.02 83	84.67 64	88.22 189	61.54 162	93.48 152	82.71 70	73.44 326	91.06 162

AUN-MVS		79.21 173	77.60 193	84.05 129	88.71 157	67.61 138	85.84 208	87.26 229	69.08 228	77.23 183	88.14 194	53.20 240	93.47 153	75.50 139	73.45 325	91.06 162

CS-MVS		86.69 35	86.95 31	85.90 63	90.76 91	67.57 139	92.83 17	93.30 32	79.67 17	84.57 69	92.27 86	71.47 53	95.02 88	84.24 54	93.46 64	95.13 6

EI-MVSNet-UG-set		83.81 72	83.38 78	85.09 81	87.87 187	67.53 140	87.44 160	89.66 160	79.74 16	82.23 102	89.41 157	70.24 66	94.74 100	79.95 93	83.92 186	92.99 102

Effi-MVS+		83.62 79	83.08 82	85.24 75	88.38 169	67.45 141	88.89 109	89.15 179	75.50 92	82.27 101	88.28 186	69.61 73	94.45 110	77.81 112	87.84 131	93.84 59

EG-PatchMatch MVS		74.04 262	71.82 272	80.71 227	84.92 254	67.42 142	85.86 207	88.08 209	66.04 270	64.22 351	83.85 292	35.10 369	92.56 190	57.44 300	80.83 230	82.16 358

OMC-MVS		82.69 95	81.97 102	84.85 91	88.75 155	67.42 142	87.98 143	90.87 125	74.92 103	79.72 132	91.65 97	62.19 154	93.96 125	75.26 140	86.42 151	93.16 94

PatchMatch-RL		72.38 280	70.90 284	76.80 294	88.60 160	67.38 144	79.53 313	76.17 357	62.75 310	69.36 306	82.00 324	45.51 318	84.89 324	53.62 321	80.58 234	78.12 372

LS3D		76.95 226	74.82 241	83.37 151	90.45 95	67.36 145	89.15 102	86.94 236	61.87 319	69.52 304	90.61 126	51.71 259	94.53 106	46.38 362	86.71 147	88.21 265

fmvsm_s_conf0.5_n		83.80 73	83.71 74	84.07 124	86.69 225	67.31 146	89.46 89	83.07 293	71.09 179	86.96 41	93.70 55	69.02 83	91.47 234	88.79 18	84.62 174	93.44 82

fmvsm_s_conf0.1_n		83.56 80	83.38 78	84.10 119	84.86 255	67.28 147	89.40 93	83.01 294	70.67 187	87.08 38	93.96 50	68.38 87	91.45 235	88.56 22	84.50 175	93.56 75

PS-MVSNAJss		82.07 103	81.31 107	84.34 109	86.51 228	67.27 148	89.27 96	91.51 107	71.75 162	79.37 136	90.22 134	63.15 138	94.27 114	77.69 113	82.36 214	91.49 149

114514_t		80.68 137	79.51 142	84.20 116	94.09 38	67.27 148	89.64 84	91.11 119	58.75 344	74.08 255	90.72 124	58.10 198	95.04 87	69.70 191	89.42 114	90.30 193

casdiffmvs_mvg		85.99 44	86.09 46	85.70 66	87.65 199	67.22 150	88.69 119	93.04 38	79.64 18	85.33 52	92.54 83	73.30 35	94.50 108	83.49 59	91.14 90	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
CS-MVS-test		86.29 42	86.48 37	85.71 65	91.02 83	67.21 151	92.36 29	93.78 18	78.97 28	83.51 88	91.20 111	70.65 63	95.15 79	81.96 76	94.89 41	94.77 22

anonymousdsp		78.60 188	77.15 201	82.98 170	80.51 336	67.08 152	87.24 166	89.53 163	65.66 275	75.16 238	87.19 216	52.52 241	92.25 203	77.17 119	79.34 250	89.61 225

MVS		78.19 198	76.99 205	81.78 198	85.66 238	66.99 153	84.66 232	90.47 135	55.08 364	72.02 278	85.27 265	63.83 130	94.11 123	66.10 224	89.80 110	84.24 335

HQP5-MVS						66.98 154

HQP-MVS		82.61 97	82.02 100	84.37 106	89.33 129	66.98 154	89.17 98	92.19 82	76.41 72	77.23 183	90.23 133	60.17 189	95.11 82	77.47 115	85.99 160	91.03 164

Fast-Effi-MVS+-dtu		78.02 203	76.49 217	82.62 185	83.16 292	66.96 156	86.94 173	87.45 226	72.45 153	71.49 283	84.17 288	54.79 223	91.58 225	67.61 210	80.31 238	89.30 233

F-COLMAP		76.38 237	74.33 248	82.50 187	89.28 134	66.95 157	88.41 127	89.03 183	64.05 294	66.83 329	88.61 175	46.78 304	92.89 182	57.48 299	78.55 256	87.67 273

mvsmamba		81.69 111	80.74 117	84.56 99	87.45 206	66.72 158	91.26 48	85.89 252	74.66 109	78.23 160	90.56 127	54.33 227	94.91 90	80.73 88	83.54 198	92.04 136

HyFIR lowres test		77.53 216	75.40 233	83.94 137	89.59 116	66.62 159	80.36 304	88.64 201	56.29 360	76.45 201	85.17 269	57.64 203	93.28 159	61.34 268	83.10 205	91.91 137

ACMH	67.68 16	75.89 243	73.93 252	81.77 199	88.71 157	66.61 160	88.62 122	89.01 185	69.81 207	66.78 330	86.70 230	41.95 344	91.51 232	55.64 313	78.14 263	87.17 286
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
jajsoiax		79.29 171	77.96 178	83.27 154	84.68 258	66.57 161	89.25 97	90.16 147	69.20 225	75.46 224	89.49 150	45.75 317	93.13 173	76.84 122	80.80 231	90.11 201

VDD-MVS		83.01 93	82.36 94	84.96 86	91.02 83	66.40 162	88.91 108	88.11 207	77.57 41	84.39 72	93.29 64	52.19 247	93.91 132	77.05 120	88.70 123	94.57 29

mvs_tets		79.13 175	77.77 187	83.22 158	84.70 257	66.37 163	89.17 98	90.19 146	69.38 218	75.40 227	89.46 153	44.17 326	93.15 171	76.78 125	80.70 233	90.14 198

PAPM_NR		83.02 92	82.41 92	84.82 92	92.47 67	66.37 163	87.93 147	91.80 98	73.82 127	77.32 180	90.66 125	67.90 91	94.90 93	70.37 183	89.48 113	93.19 93

EC-MVSNet		86.01 43	86.38 38	84.91 90	89.31 132	66.27 165	92.32 30	93.63 21	79.37 20	84.17 76	91.88 93	69.04 82	95.43 67	83.93 57	93.77 62	93.01 101

iter_conf05_11		81.63 115	80.44 125	85.20 77	89.46 123	66.20 166	86.21 196	86.97 235	71.53 170	83.35 89	88.53 179	43.22 333	95.94 53	79.82 95	94.85 43	93.47 79

pmmvs-eth3d		70.50 299	67.83 312	78.52 270	77.37 361	66.18 167	81.82 279	81.51 311	58.90 342	63.90 354	80.42 336	42.69 337	86.28 310	58.56 290	65.30 363	83.11 348

bld_raw_dy_0_64		80.78 135	79.36 147	85.06 82	89.46 123	66.03 168	89.63 85	85.46 258	69.76 211	81.88 105	89.06 163	43.39 331	95.70 58	79.82 95	85.74 166	93.47 79

IB-MVS	68.01 15	75.85 244	73.36 259	83.31 152	84.76 256	66.03 168	83.38 261	85.06 261	70.21 200	69.40 305	81.05 328	45.76 316	94.66 103	65.10 233	75.49 297	89.25 234
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
MS-PatchMatch		73.83 265	72.67 264	77.30 289	83.87 275	66.02 170	81.82 279	84.66 266	61.37 323	68.61 313	82.82 312	47.29 299	88.21 294	59.27 281	84.32 181	77.68 373

FE-MVS		77.78 209	75.68 226	84.08 123	88.09 180	66.00 171	83.13 266	87.79 218	68.42 243	78.01 167	85.23 267	45.50 319	95.12 80	59.11 284	85.83 163	91.11 160

test_0402		72.79 278	70.44 289	79.84 244	88.13 177	65.99 172	85.93 204	84.29 272	65.57 276	67.40 324	85.49 261	46.92 303	92.61 188	35.88 385	74.38 316	80.94 364

BH-RMVSNet		79.61 159	78.44 168	83.14 161	89.38 128	65.93 173	84.95 227	87.15 232	73.56 135	78.19 162	89.79 141	56.67 212	93.36 157	59.53 280	86.74 146	90.13 199

BH-untuned		79.47 164	78.60 164	82.05 193	89.19 138	65.91 174	86.07 201	88.52 203	72.18 158	75.42 226	87.69 200	61.15 173	93.54 149	60.38 273	86.83 145	86.70 299

cascas		76.72 229	74.64 242	82.99 169	85.78 237	65.88 175	82.33 275	89.21 176	60.85 325	72.74 267	81.02 329	47.28 300	93.75 141	67.48 212	85.02 168	89.34 232

patch_mono-2		83.65 76	84.54 66	80.99 220	90.06 107	65.83 176	84.21 247	88.74 198	71.60 168	85.01 55	92.44 84	74.51 25	83.50 333	82.15 75	92.15 76	93.64 71

iter_conf05		80.00 155	78.70 161	83.91 138	87.84 189	65.83 176	88.84 112	84.92 264	71.61 167	78.70 146	88.94 165	43.88 328	94.56 104	79.28 98	84.28 182	91.33 153

MSDG		73.36 271	70.99 283	80.49 231	84.51 263	65.80 178	80.71 298	86.13 249	65.70 274	65.46 342	83.74 296	44.60 322	90.91 251	51.13 334	76.89 274	84.74 330

旧先验1					91.96 71	65.79 179		86.37 245			93.08 71	69.31 77			92.74 69	88.74 256

casdiffmvs		85.11 61	85.14 60	85.01 84	87.20 215	65.77 180	87.75 152	92.83 55	77.84 37	84.36 73	92.38 85	72.15 44	93.93 131	81.27 81	90.48 97	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
COLMAP_ROB	66.92 17	73.01 275	70.41 290	80.81 225	87.13 217	65.63 181	88.30 134	84.19 275	62.96 305	63.80 355	87.69 200	38.04 361	92.56 190	46.66 359	74.91 311	84.24 335
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
EIA-MVS		83.31 87	82.80 89	84.82 92	89.59 116	65.59 182	88.21 136	92.68 60	74.66 109	78.96 141	86.42 241	69.06 80	95.26 75	75.54 138	90.09 104	93.62 72

v7n		78.97 180	77.58 194	83.14 161	83.45 283	65.51 183	88.32 133	91.21 114	73.69 131	72.41 273	86.32 244	57.93 199	93.81 136	69.18 196	75.65 294	90.11 201

V42		79.38 170	78.24 174	82.83 175	81.10 330	65.50 184	85.55 215	89.82 155	71.57 169	78.21 161	86.12 248	60.66 181	93.18 170	75.64 135	75.46 300	89.81 220

RRT_MVS		80.35 147	79.22 152	83.74 141	87.63 200	65.46 185	91.08 54	88.92 191	73.82 127	76.44 204	90.03 136	49.05 291	94.25 118	76.84 122	79.20 253	91.51 146

PVSNet_BlendedMVS		80.60 139	80.02 131	82.36 190	88.85 147	65.40 186	86.16 199	92.00 87	69.34 219	78.11 164	86.09 249	66.02 112	94.27 114	71.52 171	82.06 217	87.39 280

PVSNet_Blended		80.98 125	80.34 126	82.90 173	88.85 147	65.40 186	84.43 242	92.00 87	67.62 250	78.11 164	85.05 273	66.02 112	94.27 114	71.52 171	89.50 112	89.01 242

baseline		84.93 63	84.98 61	84.80 94	87.30 213	65.39 188	87.30 164	92.88 52	77.62 39	84.04 79	92.26 87	71.81 47	93.96 125	81.31 80	90.30 100	95.03 8

test_djsdf		80.30 148	79.32 148	83.27 154	83.98 273	65.37 189	90.50 62	90.38 137	68.55 239	76.19 209	88.70 171	56.44 213	93.46 154	78.98 100	80.14 241	90.97 167

ACMH+	68.96 14	76.01 242	74.01 250	82.03 194	88.60 160	65.31 190	88.86 110	87.55 222	70.25 199	67.75 318	87.47 208	41.27 345	93.19 169	58.37 292	75.94 291	87.60 275

CR-MVSNet		73.37 269	71.27 280	79.67 249	81.32 328	65.19 191	75.92 344	80.30 326	59.92 332	72.73 268	81.19 326	52.50 242	86.69 306	59.84 277	77.71 265	87.11 290

RPMNet		73.51 268	70.49 288	82.58 186	81.32 328	65.19 191	75.92 344	92.27 76	57.60 352	72.73 268	76.45 365	52.30 245	95.43 67	48.14 354	77.71 265	87.11 290

BH-w/o		78.21 196	77.33 199	80.84 224	88.81 151	65.13 193	84.87 228	87.85 217	69.75 212	74.52 251	84.74 277	61.34 168	93.11 174	58.24 294	85.84 162	84.27 334

thisisatest0530		79.40 168	77.76 188	84.31 110	87.69 198	65.10 194	87.36 161	84.26 274	70.04 201	77.42 177	88.26 188	49.94 277	94.79 99	70.20 184	84.70 173	93.03 99

FA-MVS(test-final)		80.96 126	79.91 134	84.10 119	88.30 172	65.01 195	84.55 237	90.01 151	73.25 145	79.61 133	87.57 203	58.35 197	94.72 101	71.29 175	86.25 154	92.56 113

v10		79.74 158	78.67 162	82.97 171	84.06 271	64.95 196	87.88 150	90.62 130	73.11 147	75.11 240	86.56 237	61.46 165	94.05 124	73.68 151	75.55 296	89.90 215

SDMVSNet		80.38 144	80.18 130	80.99 220	89.03 145	64.94 197	80.45 303	89.40 166	75.19 98	76.61 199	89.98 137	60.61 183	87.69 301	76.83 124	83.55 196	90.33 191

dcpmvs_2		85.63 52	86.15 44	84.06 126	91.71 75	64.94 197	86.47 189	91.87 95	73.63 132	86.60 43	93.02 72	76.57 15	91.87 217	83.36 60	92.15 76	95.35 3

IterMVS-SCA-FT		75.43 250	73.87 254	80.11 239	82.69 304	64.85 199	81.57 284	83.47 285	69.16 226	70.49 289	84.15 289	51.95 254	88.15 295	69.23 195	72.14 335	87.34 282

MVSTER		79.01 178	77.88 182	82.38 189	83.07 293	64.80 200	84.08 251	88.95 189	69.01 232	78.69 147	87.17 217	54.70 224	92.43 194	74.69 142	80.57 235	89.89 216

Anonymous20240529		80.19 151	78.89 159	84.10 119	90.60 92	64.75 201	88.95 107	90.90 123	65.97 272	80.59 124	91.17 113	49.97 276	93.73 143	69.16 197	82.70 211	93.81 60

XVG-ACMP-BASELINE		76.11 240	74.27 249	81.62 201	83.20 289	64.67 202	83.60 258	89.75 158	69.75 212	71.85 279	87.09 219	32.78 372	92.11 207	69.99 188	80.43 237	88.09 266

v1192		79.59 161	78.43 169	83.07 165	83.55 281	64.52 203	86.93 174	90.58 131	70.83 183	77.78 171	85.90 250	59.15 192	93.94 128	73.96 150	77.19 271	90.76 173

Fast-Effi-MVS+		80.81 130	79.92 133	83.47 146	88.85 147	64.51 204	85.53 217	89.39 167	70.79 184	78.49 154	85.06 272	67.54 94	93.58 145	67.03 219	86.58 148	92.32 122

v1144		80.03 153	79.03 156	83.01 168	83.78 277	64.51 204	87.11 169	90.57 133	71.96 161	78.08 166	86.20 246	61.41 166	93.94 128	74.93 141	77.23 269	90.60 180

v8		79.97 156	79.02 157	82.80 178	84.09 270	64.50 206	87.96 144	90.29 144	74.13 122	75.24 236	86.81 223	62.88 143	93.89 134	74.39 146	75.40 303	90.00 209

EPP-MVSNet		83.40 84	83.02 84	84.57 98	90.13 101	64.47 207	92.32 30	90.73 128	74.45 115	79.35 137	91.10 114	69.05 81	95.12 80	72.78 163	87.22 139	94.13 44

GeoE		81.71 110	81.01 114	83.80 140	89.51 120	64.45 208	88.97 106	88.73 199	71.27 175	78.63 150	89.76 142	66.32 107	93.20 167	69.89 189	86.02 159	93.74 63

UniMVSNet (Re)		81.60 116	81.11 111	83.09 163	88.38 169	64.41 209	87.60 155	93.02 42	78.42 32	78.56 152	88.16 190	69.78 71	93.26 160	69.58 193	76.49 280	91.60 142

LTVRE_ROB	69.57 13	76.25 238	74.54 245	81.41 207	88.60 160	64.38 210	79.24 317	89.12 182	70.76 186	69.79 303	87.86 197	49.09 289	93.20 167	56.21 312	80.16 239	86.65 300
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
Anonymous20231211		78.97 180	77.69 191	82.81 177	90.54 94	64.29 211	90.11 72	91.51 107	65.01 282	76.16 213	88.13 195	50.56 270	93.03 180	69.68 192	77.56 268	91.11 160

testdata				79.97 241	90.90 86	64.21 212		84.71 265	59.27 338	85.40 51	92.91 73	62.02 157	89.08 281	68.95 199	91.37 87	86.63 301

v2v482		80.23 149	79.29 149	83.05 166	83.62 279	64.14 213	87.04 170	89.97 152	73.61 133	78.18 163	87.22 214	61.10 174	93.82 135	76.11 129	76.78 278	91.18 158

VDDNet		81.52 117	80.67 119	84.05 129	90.44 96	64.13 214	89.73 81	85.91 251	71.11 178	83.18 91	93.48 58	50.54 271	93.49 151	73.40 156	88.25 129	94.54 30

PAPR		81.66 114	80.89 116	83.99 134	90.27 98	64.00 215	86.76 182	91.77 101	68.84 235	77.13 189	89.50 149	67.63 93	94.88 95	67.55 211	88.52 126	93.09 96

v144192		79.47 164	78.37 170	82.78 181	83.35 284	63.96 216	86.96 172	90.36 140	69.99 203	77.50 175	85.67 257	60.66 181	93.77 139	74.27 147	76.58 279	90.62 178

v1921920		79.22 172	78.03 177	82.80 178	83.30 286	63.94 217	86.80 178	90.33 141	69.91 206	77.48 176	85.53 260	58.44 196	93.75 141	73.60 152	76.85 276	90.71 176

tttt0517		79.40 168	77.91 180	83.90 139	88.10 179	63.84 218	88.37 131	84.05 276	71.45 172	76.78 193	89.12 160	49.93 279	94.89 94	70.18 185	83.18 204	92.96 103

thisisatest0515		77.33 220	75.38 234	83.18 159	85.27 246	63.80 219	82.11 278	83.27 288	65.06 280	75.91 214	83.84 293	49.54 281	94.27 114	67.24 215	86.19 155	91.48 150

diffmvs		82.10 101	81.88 103	82.76 183	83.00 296	63.78 220	83.68 254	89.76 157	72.94 151	82.02 104	89.85 140	65.96 114	90.79 253	82.38 74	87.30 138	93.71 64
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_yl		81.17 122	80.47 123	83.24 156	89.13 140	63.62 221	86.21 196	89.95 153	72.43 156	81.78 110	89.61 146	57.50 205	93.58 145	70.75 178	86.90 143	92.52 114

DCV-MVSNet		81.17 122	80.47 123	83.24 156	89.13 140	63.62 221	86.21 196	89.95 153	72.43 156	81.78 110	89.61 146	57.50 205	93.58 145	70.75 178	86.90 143	92.52 114

AllTest		70.96 292	68.09 307	79.58 251	85.15 249	63.62 221	84.58 236	79.83 330	62.31 314	60.32 366	86.73 224	32.02 373	88.96 285	50.28 339	71.57 339	86.15 307

TestCases				79.58 251	85.15 249	63.62 221		79.83 330	62.31 314	60.32 366	86.73 224	32.02 373	88.96 285	50.28 339	71.57 339	86.15 307

v1240		78.99 179	77.78 186	82.64 184	83.21 288	63.54 225	86.62 185	90.30 143	69.74 214	77.33 179	85.68 256	57.04 210	93.76 140	73.13 160	76.92 273	90.62 178

CHOSEN 280x420		66.51 329	64.71 330	71.90 334	81.45 323	63.52 226	57.98 393	68.95 381	53.57 366	62.59 360	76.70 363	46.22 310	75.29 380	55.25 314	79.68 244	76.88 375

IterMVS		74.29 258	72.94 263	78.35 272	81.53 322	63.49 227	81.58 283	82.49 302	68.06 247	69.99 298	83.69 298	51.66 260	85.54 317	65.85 227	71.64 338	86.01 311
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
UniMVSNet_NR-MVSNet		81.88 106	81.54 106	82.92 172	88.46 165	63.46 228	87.13 167	92.37 73	80.19 12	78.38 156	89.14 159	71.66 52	93.05 177	70.05 186	76.46 281	92.25 125

DU-MVS		81.12 124	80.52 122	82.90 173	87.80 191	63.46 228	87.02 171	91.87 95	79.01 26	78.38 156	89.07 161	65.02 121	93.05 177	70.05 186	76.46 281	92.20 128

LFMVS		81.82 108	81.23 109	83.57 145	91.89 73	63.43 230	89.84 75	81.85 309	77.04 58	83.21 90	93.10 67	52.26 246	93.43 156	71.98 169	89.95 108	93.85 57

NR-MVSNet		80.23 149	79.38 145	82.78 181	87.80 191	63.34 231	86.31 193	91.09 120	79.01 26	72.17 276	89.07 161	67.20 98	92.81 186	66.08 225	75.65 294	92.20 128

IS-MVSNet		83.15 88	82.81 88	84.18 117	89.94 110	63.30 232	91.59 43	88.46 204	79.04 25	79.49 135	92.16 88	65.10 120	94.28 113	67.71 209	91.86 82	94.95 10

TR-MVS		77.44 217	76.18 222	81.20 214	88.24 173	63.24 233	84.61 235	86.40 244	67.55 251	77.81 170	86.48 240	54.10 230	93.15 171	57.75 298	82.72 210	87.20 285

MVS_Test		83.15 88	83.06 83	83.41 150	86.86 219	63.21 234	86.11 200	92.00 87	74.31 116	82.87 95	89.44 156	70.03 67	93.21 164	77.39 117	88.50 127	93.81 60

IterMVS-LS		80.06 152	79.38 145	82.11 192	85.89 235	63.20 235	86.79 179	89.34 168	74.19 119	75.45 225	86.72 226	66.62 101	92.39 196	72.58 165	76.86 275	90.75 174
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		80.52 142	79.98 132	82.12 191	84.28 265	63.19 236	86.41 190	88.95 189	74.18 120	78.69 147	87.54 206	66.62 101	92.43 194	72.57 166	80.57 235	90.74 175

CANet_DTU		80.61 138	79.87 135	82.83 175	85.60 240	63.17 237	87.36 161	88.65 200	76.37 76	75.88 215	88.44 182	53.51 236	93.07 176	73.30 157	89.74 111	92.25 125

GBi-Net		78.40 191	77.40 196	81.40 208	87.60 201	63.01 238	88.39 128	89.28 170	71.63 164	75.34 229	87.28 210	54.80 220	91.11 243	62.72 249	79.57 245	90.09 203

test1		78.40 191	77.40 196	81.40 208	87.60 201	63.01 238	88.39 128	89.28 170	71.63 164	75.34 229	87.28 210	54.80 220	91.11 243	62.72 249	79.57 245	90.09 203

FMVSNet1		77.44 217	76.12 223	81.40 208	86.81 222	63.01 238	88.39 128	89.28 170	70.49 193	74.39 252	87.28 210	49.06 290	91.11 243	60.91 270	78.52 257	90.09 203

TAPA-MVS	73.13 9	79.15 174	77.94 179	82.79 180	89.59 116	62.99 241	88.16 139	91.51 107	65.77 273	77.14 188	91.09 115	60.91 177	93.21 164	50.26 341	87.05 141	92.17 130
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
FMVSNet2		78.20 197	77.21 200	81.20 214	87.60 201	62.89 242	87.47 159	89.02 184	71.63 164	75.29 235	87.28 210	54.80 220	91.10 246	62.38 254	79.38 249	89.61 225

GA-MVS		76.87 227	75.17 238	81.97 196	82.75 302	62.58 243	81.44 287	86.35 246	72.16 160	74.74 247	82.89 310	46.20 311	92.02 210	68.85 201	81.09 227	91.30 156

D2MVS		74.82 255	73.21 260	79.64 250	79.81 345	62.56 244	80.34 305	87.35 227	64.37 289	68.86 310	82.66 314	46.37 307	90.10 262	67.91 208	81.24 225	86.25 304

FMVSNet3		77.88 207	76.85 208	80.97 222	86.84 221	62.36 245	86.52 188	88.77 194	71.13 177	75.34 229	86.66 232	54.07 231	91.10 246	62.72 249	79.57 245	89.45 229

TranMVSNet+NR-MVSNet		80.84 128	80.31 127	82.42 188	87.85 188	62.33 246	87.74 153	91.33 112	80.55 9	77.99 168	89.86 139	65.23 119	92.62 187	67.05 218	75.24 308	92.30 123

1314		76.53 231	75.30 237	80.21 237	83.93 274	62.32 247	84.66 232	88.81 192	60.23 329	70.16 295	84.07 290	55.30 217	90.73 255	67.37 213	83.21 203	87.59 277

MG-MVS		83.41 83	83.45 76	83.28 153	92.74 62	62.28 248	88.17 138	89.50 164	75.22 96	81.49 113	92.74 82	66.75 100	95.11 82	72.85 162	91.58 84	92.45 119

SCA		74.22 260	72.33 269	79.91 242	84.05 272	62.17 249	79.96 310	79.29 336	66.30 267	72.38 274	80.13 338	51.95 254	88.60 290	59.25 282	77.67 267	88.96 246

PMMVS		69.34 308	68.67 300	71.35 340	75.67 367	62.03 250	75.17 350	73.46 367	50.00 376	68.68 311	79.05 347	52.07 252	78.13 358	61.16 269	82.77 208	73.90 379

eth_miper_zixun_eth		77.92 206	76.69 214	81.61 203	83.00 296	61.98 251	83.15 265	89.20 177	69.52 216	74.86 246	84.35 283	61.76 158	92.56 190	71.50 173	72.89 330	90.28 194

v148		78.72 185	77.80 185	81.47 205	82.73 303	61.96 252	86.30 194	88.08 209	73.26 144	76.18 210	85.47 262	62.46 148	92.36 198	71.92 170	73.82 322	90.09 203

PAPM		77.68 214	76.40 220	81.51 204	87.29 214	61.85 253	83.78 253	89.59 162	64.74 284	71.23 284	88.70 171	62.59 145	93.66 144	52.66 326	87.03 142	89.01 242

cl22		78.07 201	77.01 203	81.23 213	82.37 312	61.83 254	83.55 259	87.98 211	68.96 233	75.06 242	83.87 291	61.40 167	91.88 216	73.53 153	76.39 283	89.98 212

baseline2		75.70 245	73.83 255	81.30 211	83.26 287	61.79 255	82.57 274	80.65 319	66.81 255	66.88 328	83.42 302	57.86 201	92.19 205	63.47 243	79.57 245	89.91 214

JIA-IIPM		66.32 331	62.82 342	76.82 293	77.09 362	61.72 256	65.34 386	75.38 358	58.04 349	64.51 349	62.32 387	42.05 343	86.51 308	51.45 332	69.22 349	82.21 356

miper_ehance_all_eth		78.59 189	77.76 188	81.08 218	82.66 305	61.56 257	83.65 255	89.15 179	68.87 234	75.55 221	83.79 295	66.49 104	92.03 209	73.25 158	76.39 283	89.64 224

c3_l		78.75 183	77.91 180	81.26 212	82.89 300	61.56 257	84.09 250	89.13 181	69.97 204	75.56 220	84.29 284	66.36 106	92.09 208	73.47 155	75.48 298	90.12 200

miper_enhance_ethall		77.87 208	76.86 207	80.92 223	81.65 319	61.38 259	82.68 272	88.98 186	65.52 277	75.47 222	82.30 318	65.76 116	92.00 211	72.95 161	76.39 283	89.39 230

ppachtmachnet_test		70.04 303	67.34 320	78.14 275	79.80 346	61.13 260	79.19 319	80.59 320	59.16 339	65.27 344	79.29 346	46.75 305	87.29 303	49.33 345	66.72 356	86.00 313

TDRefinement		67.49 321	64.34 331	76.92 292	73.47 379	61.07 261	84.86 229	82.98 296	59.77 333	58.30 373	85.13 270	26.06 383	87.89 298	47.92 356	60.59 374	81.81 360

VNet		82.21 100	82.41 92	81.62 201	90.82 88	60.93 262	84.47 238	89.78 156	76.36 77	84.07 78	91.88 93	64.71 124	90.26 259	70.68 180	88.89 119	93.66 65

ab-mvs		79.51 162	78.97 158	81.14 216	88.46 165	60.91 263	83.84 252	89.24 175	70.36 194	79.03 140	88.87 168	63.23 136	90.21 261	65.12 232	82.57 212	92.28 124

PatchmatchNet		73.12 274	71.33 279	78.49 271	83.18 290	60.85 264	79.63 312	78.57 340	64.13 291	71.73 280	79.81 343	51.20 263	85.97 313	57.40 301	76.36 288	88.66 257
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
VPA-MVSNet		80.60 139	80.55 121	80.76 226	88.07 181	60.80 265	86.86 176	91.58 105	75.67 90	80.24 127	89.45 155	63.34 132	90.25 260	70.51 182	79.22 252	91.23 157

EGC-MVSNET		52.07 358	47.05 362	67.14 359	83.51 282	60.71 266	80.50 302	67.75 382	0.07 406	0.43 407	75.85 370	24.26 386	81.54 344	28.82 391	62.25 368	59.16 391

Anonymous202405211		78.25 194	77.01 203	81.99 195	91.03 82	60.67 267	84.77 230	83.90 278	70.65 191	80.00 130	91.20 111	41.08 347	91.43 236	65.21 231	85.26 167	93.85 57

ITE_SJBPF				78.22 273	81.77 318	60.57 268		83.30 287	69.25 222	67.54 320	87.20 215	36.33 366	87.28 304	54.34 318	74.62 314	86.80 296

MDA-MVSNet-bldmvs		66.68 327	63.66 336	75.75 300	79.28 353	60.56 269	73.92 358	78.35 341	64.43 287	50.13 387	79.87 342	44.02 327	83.67 331	46.10 364	56.86 377	83.03 350

cl____		77.72 211	76.76 211	80.58 229	82.49 309	60.48 270	83.09 267	87.87 215	69.22 223	74.38 253	85.22 268	62.10 155	91.53 230	71.09 176	75.41 302	89.73 223

DIV-MVS_self_test		77.72 211	76.76 211	80.58 229	82.48 310	60.48 270	83.09 267	87.86 216	69.22 223	74.38 253	85.24 266	62.10 155	91.53 230	71.09 176	75.40 303	89.74 222

1112_ss		77.40 219	76.43 219	80.32 235	89.11 144	60.41 272	83.65 255	87.72 220	62.13 317	73.05 265	86.72 226	62.58 146	89.97 265	62.11 260	80.80 231	90.59 181

tt0805		78.73 184	77.83 183	81.43 206	85.17 247	60.30 273	89.41 92	90.90 123	71.21 176	77.17 187	88.73 170	46.38 306	93.21 164	72.57 166	78.96 254	90.79 171

UniMVSNet_ETH3D		79.10 176	78.24 174	81.70 200	86.85 220	60.24 274	87.28 165	88.79 193	74.25 118	76.84 190	90.53 129	49.48 282	91.56 227	67.98 207	82.15 215	93.29 87

HY-MVS	69.67 12	77.95 205	77.15 201	80.36 233	87.57 205	60.21 275	83.37 262	87.78 219	66.11 268	75.37 228	87.06 221	63.27 134	90.48 258	61.38 267	82.43 213	90.40 189

sd_testset		77.70 213	77.40 196	78.60 266	89.03 145	60.02 276	79.00 321	85.83 253	75.19 98	76.61 199	89.98 137	54.81 219	85.46 319	62.63 253	83.55 196	90.33 191

RPSCF		73.23 273	71.46 276	78.54 268	82.50 308	59.85 277	82.18 277	82.84 300	58.96 341	71.15 286	89.41 157	45.48 320	84.77 325	58.82 288	71.83 337	91.02 166

test_cas_vis1_n_1920		73.76 266	73.74 256	73.81 321	75.90 365	59.77 278	80.51 301	82.40 303	58.30 346	81.62 112	85.69 255	44.35 325	76.41 370	76.29 127	78.61 255	85.23 322

dmvs_re		71.14 290	70.58 286	72.80 329	81.96 315	59.68 279	75.60 348	79.34 335	68.55 239	69.27 308	80.72 334	49.42 283	76.54 367	52.56 327	77.79 264	82.19 357

miper_lstm_enhance		74.11 261	73.11 262	77.13 291	80.11 340	59.62 280	72.23 362	86.92 237	66.76 257	70.40 290	82.92 309	56.93 211	82.92 337	69.06 198	72.63 331	88.87 249

OurMVSNet-221017-0		74.26 259	72.42 268	79.80 245	83.76 278	59.59 281	85.92 205	86.64 240	66.39 266	66.96 327	87.58 202	39.46 353	91.60 224	65.76 228	69.27 348	88.22 264

Patchmatch-RL test		70.24 301	67.78 314	77.61 284	77.43 360	59.57 282	71.16 365	70.33 374	62.94 306	68.65 312	72.77 377	50.62 269	85.49 318	69.58 193	66.58 358	87.77 272

OpenMVS_ROB	64.09 19	70.56 298	68.19 304	77.65 283	80.26 337	59.41 283	85.01 225	82.96 297	58.76 343	65.43 343	82.33 317	37.63 363	91.23 242	45.34 369	76.03 290	82.32 355

our_test_3		69.14 309	67.00 322	75.57 303	79.80 346	58.80 284	77.96 333	77.81 343	59.55 335	62.90 359	78.25 356	47.43 298	83.97 329	51.71 330	67.58 355	83.93 340

ADS-MVSNet2		66.20 334	63.33 337	74.82 311	79.92 342	58.75 285	67.55 379	75.19 359	53.37 367	65.25 345	75.86 368	42.32 339	80.53 350	41.57 376	68.91 350	85.18 323

pm-mvs1		77.25 222	76.68 215	78.93 261	84.22 267	58.62 286	86.41 190	88.36 205	71.37 173	73.31 261	88.01 196	61.22 172	89.15 280	64.24 240	73.01 329	89.03 241

WR-MVS		79.49 163	79.22 152	80.27 236	88.79 153	58.35 287	85.06 224	88.61 202	78.56 30	77.65 173	88.34 184	63.81 131	90.66 256	64.98 234	77.22 270	91.80 140

FIs		82.07 103	82.42 91	81.04 219	88.80 152	58.34 288	88.26 135	93.49 26	76.93 60	78.47 155	91.04 117	69.92 70	92.34 200	69.87 190	84.97 169	92.44 120

CostFormer		75.24 253	73.90 253	79.27 255	82.65 306	58.27 289	80.80 293	82.73 301	61.57 320	75.33 233	83.13 306	55.52 215	91.07 249	64.98 234	78.34 262	88.45 261

Test_1112_low_res		76.40 236	75.44 231	79.27 255	89.28 134	58.09 290	81.69 282	87.07 233	59.53 336	72.48 272	86.67 231	61.30 169	89.33 276	60.81 272	80.15 240	90.41 188

tfpnnormal		74.39 257	73.16 261	78.08 276	86.10 234	58.05 291	84.65 234	87.53 223	70.32 196	71.22 285	85.63 258	54.97 218	89.86 266	43.03 373	75.02 310	86.32 303

test-LLR		72.94 277	72.43 267	74.48 314	81.35 326	58.04 292	78.38 328	77.46 346	66.66 259	69.95 299	79.00 349	48.06 296	79.24 353	66.13 222	84.83 170	86.15 307

test-mter		71.41 288	70.39 291	74.48 314	81.35 326	58.04 292	78.38 328	77.46 346	60.32 328	69.95 299	79.00 349	36.08 367	79.24 353	66.13 222	84.83 170	86.15 307

mvs_anonymous		79.42 167	79.11 155	80.34 234	84.45 264	57.97 294	82.59 273	87.62 221	67.40 254	76.17 212	88.56 178	68.47 86	89.59 272	70.65 181	86.05 158	93.47 79

tpm cat1		70.57 297	68.31 303	77.35 288	82.41 311	57.95 295	78.08 332	80.22 328	52.04 370	68.54 314	77.66 360	52.00 253	87.84 299	51.77 329	72.07 336	86.25 304

SixPastTwentyTwo		73.37 269	71.26 281	79.70 247	85.08 252	57.89 296	85.57 211	83.56 283	71.03 181	65.66 341	85.88 251	42.10 342	92.57 189	59.11 284	63.34 367	88.65 258

thres200		75.55 247	74.47 246	78.82 262	87.78 194	57.85 297	83.07 269	83.51 284	72.44 155	75.84 216	84.42 279	52.08 251	91.75 220	47.41 357	83.64 195	86.86 295

XXY-MVS		75.41 251	75.56 229	74.96 309	83.59 280	57.82 298	80.59 300	83.87 279	66.54 265	74.93 245	88.31 185	63.24 135	80.09 351	62.16 258	76.85 276	86.97 293

K. test v3		71.19 289	68.51 301	79.21 257	83.04 295	57.78 299	84.35 245	76.91 352	72.90 152	62.99 358	82.86 311	39.27 354	91.09 248	61.65 264	52.66 385	88.75 255

tfpn200view9		76.42 235	75.37 235	79.55 253	89.13 140	57.65 300	85.17 220	83.60 281	73.41 140	76.45 201	86.39 242	52.12 248	91.95 212	48.33 350	83.75 190	89.07 235

thres400		76.50 232	75.37 235	79.86 243	89.13 140	57.65 300	85.17 220	83.60 281	73.41 140	76.45 201	86.39 242	52.12 248	91.95 212	48.33 350	83.75 190	90.00 209

CMPMVS	51.72 21	70.19 302	68.16 305	76.28 296	73.15 381	57.55 302	79.47 314	83.92 277	48.02 378	56.48 379	84.81 275	43.13 334	86.42 309	62.67 252	81.81 221	84.89 328
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		74.69 256	73.39 258	78.61 265	81.38 325	57.48 303	86.64 184	87.95 213	64.99 283	70.18 293	86.61 233	50.43 272	89.52 273	62.12 259	70.18 345	88.83 251

test_vis1_n_1920		75.52 248	75.78 224	74.75 313	79.84 344	57.44 304	83.26 263	85.52 256	62.83 308	79.34 138	86.17 247	45.10 321	79.71 352	78.75 102	81.21 226	87.10 292

PVSNet_0	57.27 20	61.67 345	59.27 348	68.85 353	79.61 349	57.44 304	68.01 378	73.44 368	55.93 361	58.54 372	70.41 382	44.58 323	77.55 362	47.01 358	35.91 394	71.55 382

thres600view7		76.50 232	75.44 231	79.68 248	89.40 126	57.16 306	85.53 217	83.23 289	73.79 129	76.26 207	87.09 219	51.89 256	91.89 215	48.05 355	83.72 193	90.00 209

lessismore_v0				78.97 260	81.01 331	57.15 307		65.99 385		61.16 363	82.82 312	39.12 355	91.34 239	59.67 278	46.92 391	88.43 262

TransMVSNet (Re)		75.39 252	74.56 244	77.86 278	85.50 242	57.10 308	86.78 180	86.09 250	72.17 159	71.53 282	87.34 209	63.01 142	89.31 277	56.84 307	61.83 369	87.17 286

thres100view900		76.50 232	75.55 230	79.33 254	89.52 119	56.99 309	85.83 209	83.23 289	73.94 124	76.32 206	87.12 218	51.89 256	91.95 212	48.33 350	83.75 190	89.07 235

TESTMET0.1,1		69.89 305	69.00 299	72.55 331	79.27 354	56.85 310	78.38 328	74.71 364	57.64 351	68.09 316	77.19 362	37.75 362	76.70 366	63.92 241	84.09 185	84.10 338

WTY-MVS		75.65 246	75.68 226	75.57 303	86.40 229	56.82 311	77.92 335	82.40 303	65.10 279	76.18 210	87.72 198	63.13 141	80.90 348	60.31 274	81.96 218	89.00 244

MDA-MVSNet_test_wron		65.03 335	62.92 339	71.37 338	75.93 364	56.73 312	69.09 377	74.73 363	57.28 355	54.03 383	77.89 357	45.88 313	74.39 383	49.89 343	61.55 370	82.99 351

pmmvs3		57.79 348	54.26 353	68.37 356	64.02 394	56.72 313	75.12 353	65.17 387	40.20 386	52.93 384	69.86 383	20.36 390	75.48 377	45.45 368	55.25 383	72.90 381

tpm2		73.26 272	71.46 276	78.63 264	83.34 285	56.71 314	80.65 299	80.40 325	56.63 358	73.55 259	82.02 323	51.80 258	91.24 241	56.35 311	78.42 260	87.95 267

TinyColmap		67.30 324	64.81 329	74.76 312	81.92 317	56.68 315	80.29 306	81.49 312	60.33 327	56.27 380	83.22 303	24.77 385	87.66 302	45.52 367	69.47 347	79.95 368

YYNet1		65.03 335	62.91 340	71.38 337	75.85 366	56.60 316	69.12 376	74.66 365	57.28 355	54.12 382	77.87 358	45.85 314	74.48 382	49.95 342	61.52 371	83.05 349

PM-MVS		66.41 330	64.14 332	73.20 326	73.92 374	56.45 317	78.97 322	64.96 389	63.88 298	64.72 348	80.24 337	19.84 391	83.44 334	66.24 221	64.52 365	79.71 369

PVSNet	64.34 18	72.08 285	70.87 285	75.69 301	86.21 231	56.44 318	74.37 356	80.73 318	62.06 318	70.17 294	82.23 320	42.86 336	83.31 335	54.77 316	84.45 179	87.32 283

pmmvs5		71.55 287	70.20 293	75.61 302	77.83 358	56.39 319	81.74 281	80.89 315	57.76 350	67.46 322	84.49 278	49.26 287	85.32 321	57.08 304	75.29 306	85.11 326

testing11		75.14 254	74.01 250	78.53 269	88.16 175	56.38 320	80.74 297	80.42 324	70.67 187	72.69 270	83.72 297	43.61 330	89.86 266	62.29 256	83.76 189	89.36 231

WR-MVS_H		78.51 190	78.49 166	78.56 267	88.02 183	56.38 320	88.43 126	92.67 61	77.14 54	73.89 256	87.55 205	66.25 108	89.24 278	58.92 286	73.55 324	90.06 207

MIMVSNet		70.69 296	69.30 295	74.88 310	84.52 262	56.35 322	75.87 346	79.42 334	64.59 285	67.76 317	82.41 316	41.10 346	81.54 344	46.64 361	81.34 223	86.75 298

USDC		70.33 300	68.37 302	76.21 297	80.60 334	56.23 323	79.19 319	86.49 242	60.89 324	61.29 362	85.47 262	31.78 375	89.47 275	53.37 323	76.21 289	82.94 352

Baseline_NR-MVSNet		78.15 199	78.33 172	77.61 284	85.79 236	56.21 324	86.78 180	85.76 254	73.60 134	77.93 169	87.57 203	65.02 121	88.99 282	67.14 217	75.33 305	87.63 274

tpmvs		71.09 291	69.29 296	76.49 295	82.04 314	56.04 325	78.92 323	81.37 314	64.05 294	67.18 326	78.28 355	49.74 280	89.77 268	49.67 344	72.37 332	83.67 342

FC-MVSNet-test		81.52 117	82.02 100	80.03 240	88.42 168	55.97 326	87.95 145	93.42 29	77.10 56	77.38 178	90.98 122	69.96 68	91.79 218	68.46 205	84.50 175	92.33 121

testing91		76.54 230	75.66 228	79.18 258	88.43 167	55.89 327	81.08 290	83.00 295	73.76 130	75.34 229	84.29 284	46.20 311	90.07 263	64.33 238	84.50 175	91.58 144

GG-mvs-BLEND				75.38 306	81.59 321	55.80 328	79.32 316	69.63 377		67.19 325	73.67 375	43.24 332	88.90 287	50.41 336	84.50 175	81.45 361

VPNet		78.69 186	78.66 163	78.76 263	88.31 171	55.72 329	84.45 241	86.63 241	76.79 64	78.26 159	90.55 128	59.30 191	89.70 271	66.63 220	77.05 272	90.88 169

baseline1		76.98 225	76.75 213	77.66 282	88.13 177	55.66 330	85.12 223	81.89 307	73.04 149	76.79 192	88.90 166	62.43 149	87.78 300	63.30 246	71.18 341	89.55 227

test_vis1_rt		60.28 346	58.42 349	65.84 361	67.25 391	55.60 331	70.44 370	60.94 394	44.33 382	59.00 370	66.64 384	24.91 384	68.67 392	62.80 248	69.48 346	73.25 380

testing99		76.09 241	75.12 239	79.00 259	88.16 175	55.50 332	80.79 294	81.40 313	73.30 143	75.17 237	84.27 286	44.48 324	90.02 264	64.28 239	84.22 184	91.48 150

testing222		74.04 262	72.66 265	78.19 274	87.89 186	55.36 333	81.06 291	79.20 337	71.30 174	74.65 249	83.57 300	39.11 356	88.67 289	51.43 333	85.75 164	90.53 183

FMVSNet5		69.50 307	67.96 308	74.15 318	82.97 299	55.35 334	80.01 309	82.12 306	62.56 312	63.02 356	81.53 325	36.92 364	81.92 342	48.42 349	74.06 318	85.17 325

test_fmvs1_n		70.86 294	70.24 292	72.73 330	72.51 385	55.28 335	81.27 289	79.71 332	51.49 374	78.73 145	84.87 274	27.54 382	77.02 364	76.06 130	79.97 243	85.88 314

test_vis1_n		69.85 306	69.21 297	71.77 335	72.66 384	55.27 336	81.48 285	76.21 356	52.03 371	75.30 234	83.20 305	28.97 380	76.22 372	74.60 143	78.41 261	83.81 341

test_fmvs1		70.93 293	70.52 287	72.16 333	73.71 375	55.05 337	80.82 292	78.77 339	51.21 375	78.58 151	84.41 280	31.20 377	76.94 365	75.88 133	80.12 242	84.47 333

sss		73.60 267	73.64 257	73.51 323	82.80 301	55.01 338	76.12 342	81.69 310	62.47 313	74.68 248	85.85 253	57.32 207	78.11 359	60.86 271	80.93 228	87.39 280

mvsany_test1		62.30 343	61.26 347	65.41 362	69.52 387	54.86 339	66.86 381	49.78 402	46.65 379	68.50 315	83.21 304	49.15 288	66.28 394	56.93 306	60.77 372	75.11 378

ECVR-MVS		79.61 159	79.26 150	80.67 228	90.08 103	54.69 340	87.89 149	77.44 348	74.88 104	80.27 126	92.79 79	48.96 293	92.45 193	68.55 203	92.50 73	94.86 17

EPNet_dtu		75.46 249	74.86 240	77.23 290	82.57 307	54.60 341	86.89 175	83.09 292	71.64 163	66.25 339	85.86 252	55.99 214	88.04 297	54.92 315	86.55 149	89.05 240
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVSNet		78.22 195	78.34 171	77.84 279	87.83 190	54.54 342	87.94 146	91.17 116	77.65 38	73.48 260	88.49 180	62.24 153	88.43 292	62.19 257	74.07 317	90.55 182

gg-mvs-nofinetune		69.95 304	67.96 308	75.94 298	83.07 293	54.51 343	77.23 339	70.29 375	63.11 302	70.32 291	62.33 386	43.62 329	88.69 288	53.88 320	87.76 132	84.62 332

PS-CasMVS		78.01 204	78.09 176	77.77 281	87.71 196	54.39 344	88.02 142	91.22 113	77.50 46	73.26 262	88.64 174	60.73 178	88.41 293	61.88 261	73.88 321	90.53 183

Anonymous20240521		68.80 312	67.22 321	73.55 322	74.33 372	54.11 345	83.18 264	85.61 255	58.15 347	61.68 361	80.94 331	30.71 378	81.27 346	57.00 305	73.34 328	85.28 321

Patchmtry		70.74 295	69.16 298	75.49 305	80.72 332	54.07 346	74.94 355	80.30 326	58.34 345	70.01 296	81.19 326	52.50 242	86.54 307	53.37 323	71.09 342	85.87 315

PEN-MVS		77.73 210	77.69 191	77.84 279	87.07 218	53.91 347	87.91 148	91.18 115	77.56 43	73.14 264	88.82 169	61.23 171	89.17 279	59.95 276	72.37 332	90.43 187

gm-plane-assit					81.40 324	53.83 348			62.72 311		80.94 331		92.39 196	63.40 245

CL-MVSNet_self_test		72.37 281	71.46 276	75.09 308	79.49 351	53.53 349	80.76 296	85.01 263	69.12 227	70.51 288	82.05 322	57.92 200	84.13 328	52.27 328	66.00 361	87.60 275

MDTV_nov1_ep13			69.97 294		83.18 290	53.48 350	77.10 340	80.18 329	60.45 326	69.33 307	80.44 335	48.89 294	86.90 305	51.60 331	78.51 258

KD-MVS_2432*1600		66.22 332	63.89 334	73.21 324	75.47 370	53.42 351	70.76 368	84.35 270	64.10 292	66.52 335	78.52 353	34.55 370	84.98 322	50.40 337	50.33 388	81.23 362

miper_refine_blended		66.22 332	63.89 334	73.21 324	75.47 370	53.42 351	70.76 368	84.35 270	64.10 292	66.52 335	78.52 353	34.55 370	84.98 322	50.40 337	50.33 388	81.23 362

test1111		79.43 166	79.18 154	80.15 238	89.99 108	53.31 353	87.33 163	77.05 351	75.04 101	80.23 128	92.77 81	48.97 292	92.33 201	68.87 200	92.40 75	94.81 20

LF4IMVS		64.02 339	62.19 343	69.50 349	70.90 386	53.29 354	76.13 341	77.18 350	52.65 369	58.59 371	80.98 330	23.55 387	76.52 368	53.06 325	66.66 357	78.68 371

DTE-MVSNet		76.99 224	76.80 209	77.54 286	86.24 230	53.06 355	87.52 157	90.66 129	77.08 57	72.50 271	88.67 173	60.48 185	89.52 273	57.33 302	70.74 343	90.05 208

test2506		77.30 221	76.49 217	79.74 246	90.08 103	52.02 356	87.86 151	63.10 391	74.88 104	80.16 129	92.79 79	38.29 360	92.35 199	68.74 202	92.50 73	94.86 17

tpm		72.37 281	71.71 273	74.35 316	82.19 313	52.00 357	79.22 318	77.29 349	64.56 286	72.95 266	83.68 299	51.35 261	83.26 336	58.33 293	75.80 292	87.81 271

test_fmvs2		68.35 318	67.48 319	70.98 344	69.50 388	51.95 358	80.05 308	76.38 355	49.33 377	74.65 249	84.38 281	23.30 388	75.40 379	74.51 144	75.17 309	85.60 317

ETVMVS		72.25 283	71.05 282	75.84 299	87.77 195	51.91 359	79.39 315	74.98 360	69.26 221	73.71 257	82.95 308	40.82 349	86.14 311	46.17 363	84.43 180	89.47 228

WB-MVSnew		71.96 286	71.65 274	72.89 328	84.67 261	51.88 360	82.29 276	77.57 345	62.31 314	73.67 258	83.00 307	53.49 237	81.10 347	45.75 366	82.13 216	85.70 316

MIMVSNet1		68.58 314	66.78 324	73.98 320	80.07 341	51.82 361	80.77 295	84.37 269	64.40 288	59.75 369	82.16 321	36.47 365	83.63 332	42.73 374	70.33 344	86.48 302

Vis-MVSNet (Re-imp)		78.36 193	78.45 167	78.07 277	88.64 159	51.78 362	86.70 183	79.63 333	74.14 121	75.11 240	90.83 123	61.29 170	89.75 269	58.10 295	91.60 83	92.69 109

LCM-MVSNet-Re		77.05 223	76.94 206	77.36 287	87.20 215	51.60 363	80.06 307	80.46 323	75.20 97	67.69 319	86.72 226	62.48 147	88.98 283	63.44 244	89.25 115	91.51 146

Gipuma		45.18 364	41.86 367	55.16 377	77.03 363	51.52 364	32.50 399	80.52 321	32.46 395	27.12 398	35.02 399	9.52 402	75.50 376	22.31 398	60.21 375	38.45 398
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
UnsupCasMVSNet_eth		67.33 323	65.99 327	71.37 338	73.48 378	51.47 365	75.16 351	85.19 260	65.20 278	60.78 364	80.93 333	42.35 338	77.20 363	57.12 303	53.69 384	85.44 319

UnsupCasMVSNet_bld		63.70 340	61.53 346	70.21 347	73.69 376	51.39 366	72.82 360	81.89 307	55.63 362	57.81 375	71.80 379	38.67 357	78.61 356	49.26 346	52.21 386	80.63 365

FPMVS		53.68 354	51.64 356	59.81 369	65.08 393	51.03 367	69.48 373	69.58 378	41.46 385	40.67 391	72.32 378	16.46 395	70.00 391	24.24 397	65.42 362	58.40 393

CVMVSNet		72.99 276	72.58 266	74.25 317	84.28 265	50.85 368	86.41 190	83.45 286	44.56 381	73.23 263	87.54 206	49.38 284	85.70 314	65.90 226	78.44 259	86.19 306

Anonymous20231206		68.60 313	67.80 313	71.02 343	80.23 339	50.75 369	78.30 331	80.47 322	56.79 357	66.11 340	82.63 315	46.35 308	78.95 355	43.62 372	75.70 293	83.36 345

ambc				75.24 307	73.16 380	50.51 370	63.05 391	87.47 225		64.28 350	77.81 359	17.80 393	89.73 270	57.88 297	60.64 373	85.49 318

APD_test1		53.31 355	49.93 360	63.42 365	65.68 392	50.13 371	71.59 364	66.90 384	34.43 393	40.58 392	71.56 380	8.65 404	76.27 371	34.64 387	55.36 382	63.86 389

tpmrst		72.39 279	72.13 270	73.18 327	80.54 335	49.91 372	79.91 311	79.08 338	63.11 302	71.69 281	79.95 340	55.32 216	82.77 338	65.66 229	73.89 320	86.87 294

Patchmatch-test		64.82 337	63.24 338	69.57 348	79.42 352	49.82 373	63.49 390	69.05 380	51.98 372	59.95 368	80.13 338	50.91 265	70.98 388	40.66 378	73.57 323	87.90 269

EPMVS		69.02 310	68.16 305	71.59 336	79.61 349	49.80 374	77.40 337	66.93 383	62.82 309	70.01 296	79.05 347	45.79 315	77.86 361	56.58 309	75.26 307	87.13 289

dp		66.80 326	65.43 328	70.90 345	79.74 348	48.82 375	75.12 353	74.77 362	59.61 334	64.08 352	77.23 361	42.89 335	80.72 349	48.86 348	66.58 358	83.16 347

UWE-MVS		72.13 284	71.49 275	74.03 319	86.66 226	47.70 376	81.40 288	76.89 353	63.60 299	75.59 219	84.22 287	39.94 352	85.62 316	48.98 347	86.13 157	88.77 254

test0.0.03 1		68.00 320	67.69 315	68.90 352	77.55 359	47.43 377	75.70 347	72.95 371	66.66 259	66.56 333	82.29 319	48.06 296	75.87 374	44.97 370	74.51 315	83.41 344

ADS-MVSNet		64.36 338	62.88 341	68.78 354	79.92 342	47.17 378	67.55 379	71.18 373	53.37 367	65.25 345	75.86 368	42.32 339	73.99 384	41.57 376	68.91 350	85.18 323

EU-MVSNet		68.53 316	67.61 317	71.31 341	78.51 357	47.01 379	84.47 238	84.27 273	42.27 384	66.44 338	84.79 276	40.44 350	83.76 330	58.76 289	68.54 353	83.17 346

test_fmvs3		63.36 341	61.82 344	67.98 357	62.51 395	46.96 380	77.37 338	74.03 366	45.24 380	67.50 321	78.79 352	12.16 399	72.98 387	72.77 164	66.02 360	83.99 339

KD-MVS_self_test		68.81 311	67.59 318	72.46 332	74.29 373	45.45 381	77.93 334	87.00 234	63.12 301	63.99 353	78.99 351	42.32 339	84.77 325	56.55 310	64.09 366	87.16 288

testf1		45.72 362	41.96 365	57.00 371	56.90 397	45.32 382	66.14 384	59.26 396	26.19 397	30.89 396	60.96 390	4.14 407	70.64 389	26.39 395	46.73 392	55.04 394

APD_test2		45.72 362	41.96 365	57.00 371	56.90 397	45.32 382	66.14 384	59.26 396	26.19 397	30.89 396	60.96 390	4.14 407	70.64 389	26.39 395	46.73 392	55.04 394

LCM-MVSNet		54.25 351	49.68 361	67.97 358	53.73 403	45.28 384	66.85 382	80.78 317	35.96 392	39.45 393	62.23 388	8.70 403	78.06 360	48.24 353	51.20 387	80.57 366

test_vis3_rt		49.26 361	47.02 363	56.00 373	54.30 400	45.27 385	66.76 383	48.08 403	36.83 390	44.38 390	53.20 395	7.17 406	64.07 396	56.77 308	55.66 380	58.65 392

test20.03		67.45 322	66.95 323	68.94 351	75.48 369	44.84 386	77.50 336	77.67 344	66.66 259	63.01 357	83.80 294	47.02 302	78.40 357	42.53 375	68.86 352	83.58 343

mvsany_test3		53.99 352	51.45 357	61.61 367	55.51 399	44.74 387	63.52 389	45.41 406	43.69 383	58.11 374	76.45 365	17.99 392	63.76 397	54.77 316	47.59 390	76.34 376

PatchT		68.46 317	67.85 310	70.29 346	80.70 333	43.93 388	72.47 361	74.88 361	60.15 330	70.55 287	76.57 364	49.94 277	81.59 343	50.58 335	74.83 312	85.34 320

MVS-HIRNet		59.14 347	57.67 350	63.57 364	81.65 319	43.50 389	71.73 363	65.06 388	39.59 388	51.43 385	57.73 392	38.34 359	82.58 339	39.53 379	73.95 319	64.62 388

testing3		68.56 315	67.67 316	71.22 342	87.33 212	42.87 390	83.06 270	71.54 372	70.36 194	69.08 309	84.38 281	30.33 379	85.69 315	37.50 384	75.45 301	85.09 327

WAC-MVS						42.58 391								39.46 380

myMVS_eth3d		67.02 325	66.29 326	69.21 350	84.68 258	42.58 391	78.62 326	73.08 369	66.65 262	66.74 331	79.46 344	31.53 376	82.30 340	39.43 381	76.38 286	82.75 353

PMVS	37.38 22	44.16 365	40.28 368	55.82 375	40.82 408	42.54 393	65.12 387	63.99 390	34.43 393	24.48 399	57.12 394	3.92 409	76.17 373	17.10 401	55.52 381	48.75 396
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test_f		52.09 357	50.82 358	55.90 374	53.82 402	42.31 394	59.42 392	58.31 398	36.45 391	56.12 381	70.96 381	12.18 398	57.79 399	53.51 322	56.57 379	67.60 385

testgi		66.67 328	66.53 325	67.08 360	75.62 368	41.69 395	75.93 343	76.50 354	66.11 268	65.20 347	86.59 234	35.72 368	74.71 381	43.71 371	73.38 327	84.84 329

Syy-MVS		68.05 319	67.85 310	68.67 355	84.68 258	40.97 396	78.62 326	73.08 369	66.65 262	66.74 331	79.46 344	52.11 250	82.30 340	32.89 388	76.38 286	82.75 353

ANet_high		50.57 360	46.10 364	63.99 363	48.67 406	39.13 397	70.99 367	80.85 316	61.39 322	31.18 395	57.70 393	17.02 394	73.65 386	31.22 390	15.89 403	79.18 370

MDTV_nov1_ep13_2view						37.79 398	75.16 351		55.10 363	66.53 334		49.34 285		53.98 319		87.94 268

DSMNet-mixed		57.77 349	56.90 351	60.38 368	67.70 390	35.61 399	69.18 374	53.97 400	32.30 396	57.49 376	79.88 341	40.39 351	68.57 393	38.78 382	72.37 332	76.97 374

MVE	26.22 23	30.37 370	25.89 374	43.81 382	44.55 407	35.46 400	28.87 400	39.07 407	18.20 401	18.58 403	40.18 398	2.68 410	47.37 404	17.07 402	23.78 400	48.60 397
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
new_pmnet		50.91 359	50.29 359	52.78 379	68.58 389	34.94 401	63.71 388	56.63 399	39.73 387	44.95 389	65.47 385	21.93 389	58.48 398	34.98 386	56.62 378	64.92 387

wuyk23d		16.82 373	15.94 376	19.46 387	58.74 396	31.45 402	39.22 397	3.74 412	6.84 403	6.04 406	2.70 406	1.27 411	24.29 406	10.54 406	14.40 405	2.63 403

E-PMN		31.77 367	30.64 370	35.15 384	52.87 404	27.67 403	57.09 394	47.86 404	24.64 399	16.40 404	33.05 400	11.23 400	54.90 401	14.46 404	18.15 401	22.87 400

DeepMVS_CX				27.40 386	40.17 409	26.90 404		24.59 410	17.44 402	23.95 400	48.61 397	9.77 401	26.48 405	18.06 399	24.47 399	28.83 399

EMVS		30.81 369	29.65 371	34.27 385	50.96 405	25.95 405	56.58 395	46.80 405	24.01 400	15.53 405	30.68 401	12.47 397	54.43 402	12.81 405	17.05 402	22.43 401

dmvs_testset		62.63 342	64.11 333	58.19 370	78.55 356	24.76 406	75.28 349	65.94 386	67.91 248	60.34 365	76.01 367	53.56 235	73.94 385	31.79 389	67.65 354	75.88 377

new-patchmatchnet		61.73 344	61.73 345	61.70 366	72.74 383	24.50 407	69.16 375	78.03 342	61.40 321	56.72 378	75.53 371	38.42 358	76.48 369	45.95 365	57.67 376	84.13 337

WB-MVS		54.94 350	54.72 352	55.60 376	73.50 377	20.90 408	74.27 357	61.19 393	59.16 339	50.61 386	74.15 373	47.19 301	75.78 375	17.31 400	35.07 395	70.12 383

SSC-MVS		53.88 353	53.59 354	54.75 378	72.87 382	19.59 409	73.84 359	60.53 395	57.58 353	49.18 388	73.45 376	46.34 309	75.47 378	16.20 403	32.28 397	69.20 384

PMMVS2		40.82 366	38.86 369	46.69 381	53.84 401	16.45 410	48.61 396	49.92 401	37.49 389	31.67 394	60.97 389	8.14 405	56.42 400	28.42 392	30.72 398	67.19 386

tmp_tt		18.61 372	21.40 375	10.23 388	4.82 411	10.11 411	34.70 398	30.74 409	1.48 405	23.91 401	26.07 402	28.42 381	13.41 407	27.12 393	15.35 404	7.17 402

N_pmnet		52.79 356	53.26 355	51.40 380	78.99 355	7.68 412	69.52 372	3.89 411	51.63 373	57.01 377	74.98 372	40.83 348	65.96 395	37.78 383	64.67 364	80.56 367

test_method		31.52 368	29.28 372	38.23 383	27.03 410	6.50 413	20.94 401	62.21 392	4.05 404	22.35 402	52.50 396	13.33 396	47.58 403	27.04 394	34.04 396	60.62 390

test123		6.12 375	8.11 378	0.14 389	0.06 413	0.09 414	71.05 366	0.03 414	0.04 408	0.25 409	1.30 408	0.05 412	0.03 409	0.21 408	0.01 407	0.29 404

testmvs		6.04 376	8.02 379	0.10 390	0.08 412	0.03 415	69.74 371	0.04 413	0.05 407	0.31 408	1.68 407	0.02 413	0.04 408	0.24 407	0.02 406	0.25 405

test_blank		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet_test		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

DCPMVS		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

cdsmvs_eth3d_5k		19.96 371	26.61 373	0.00 391	0.00 414	0.00 416	0.00 402	89.26 173	0.00 409	0.00 410	88.61 175	61.62 161	0.00 410	0.00 409	0.00 408	0.00 406

pcd_1.5k_mvsjas		5.26 377	7.02 380	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	63.15 138	0.00 410	0.00 409	0.00 408	0.00 406

sosnet-low-res		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

sosnet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uncertanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

Regformer		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

ab-mvs-re		7.23 374	9.64 377	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	86.72 226	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

uanet		0.00 378	0.00 381	0.00 391	0.00 414	0.00 416	0.00 402	0.00 415	0.00 409	0.00 410	0.00 409	0.00 414	0.00 410	0.00 409	0.00 408	0.00 406

PC_three_1452									68.21 245	92.02 12	94.00 46	82.09 5	95.98 51	84.58 48	96.68 2	94.95 10

eth-test2					0.00 414

eth-test					0.00 414

test_241102_TWO								94.06 10	77.24 50	92.78 4	95.72 8	81.26 8	97.44 6	89.07 14	96.58 6	94.26 41

9.14			88.26 15		92.84 60		91.52 46	94.75 1	73.93 125	88.57 22	94.67 19	75.57 22	95.79 54	86.77 35	95.76 23

test_0728_THIRD									78.38 33	92.12 9	95.78 4	81.46 7	97.40 8	89.42 9	96.57 7	94.67 24

GSMVS																88.96 246

sam_mvs1												51.32 262				88.96 246

sam_mvs												50.01 275

MTGPA								92.02 85

test_post1							78.90 324				5.43 405	48.81 295	85.44 320	59.25 282

test_post											5.46 404	50.36 273	84.24 327

patchmatchnet-post											74.00 374	51.12 264	88.60 290

MTMP							92.18 35	32.83 408

test9_res														84.90 42	95.70 26	92.87 104

agg_prior2														82.91 66	95.45 30	92.70 107

test_prior2							88.85 111		75.41 93	84.91 59	93.54 56	74.28 29		83.31 61	95.86 20

旧先验2							86.56 187		58.10 348	87.04 39			88.98 283	74.07 149

新几何2							86.29 195

无先验							87.48 158	88.98 186	60.00 331				94.12 122	67.28 214		88.97 245

原ACMM2							86.86 176

testdata2													91.01 250	62.37 255

segment_acmp												73.08 37

testdata1							84.14 249		75.71 87

plane_prior5								92.44 69					95.38 71	78.71 103	86.32 152	91.33 153

plane_prior4											91.00 120

plane_prior2							91.25 50		79.12 23

plane_prior1					89.90 111

n2								0.00 415

nn								0.00 415

door-mid								69.98 376

test11								92.23 79

door								69.44 379

HQP-NCC					89.33 129		89.17 98		76.41 72	77.23 183

ACMP_Plane					89.33 129		89.17 98		76.41 72	77.23 183

BP-MVS														77.47 115

HQP4-MVS										77.24 182			95.11 82			91.03 164

HQP3-MVS								92.19 82							85.99 160

HQP2-MVS												60.17 189

ACMMP++_ref															81.95 219

ACMMP++															81.25 224

Test By Simon												64.33 125

High-resolution multi-view benchmark