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++		79.88 8	80.14 8	79.10 20	88.17 1	64.80 1	86.59 12	83.70 66	65.37 15	78.78 25	90.64 19	58.63 23	87.24 53	79.00 10	90.37 14	85.26 123

zzz-MVS		77.61 32	77.36 32	78.35 37	86.08 41	63.57 2	83.37 59	80.97 132	65.13 18	75.77 38	90.88 15	48.63 120	86.66 72	77.23 19	88.17 38	84.81 135

MTAPA		76.90 39	76.42 41	78.35 37	86.08 41	63.57 2	74.92 205	80.97 132	65.13 18	75.77 38	90.88 15	48.63 120	86.66 72	77.23 19	88.17 38	84.81 135

mPP-MVS		76.54 42	75.93 46	78.34 39	86.47 28	63.50 4	85.74 25	82.28 96	62.90 56	71.77 97	90.26 34	46.61 151	86.55 78	71.71 59	85.66 65	84.97 131

MP-MVS		78.35 22	78.26 22	78.64 31	86.54 27	63.47 5	86.02 20	83.55 70	63.89 40	73.60 70	90.60 20	54.85 54	86.72 70	77.20 21	88.06 41	85.74 103
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
mvs-test1		70.44 119	68.19 146	77.18 55	76.10 219	63.22 6	80.59 103	76.06 216	59.83 117	66.32 184	79.87 233	41.56 204	85.53 104	60.60 147	72.77 203	82.80 199

CP-MVS		77.12 37	76.68 38	78.43 35	86.05 43	63.18 7	87.55 10	83.45 73	62.44 68	72.68 87	90.50 24	48.18 126	87.34 52	73.59 46	85.71 64	84.76 139

region2R		77.67 31	77.18 34	79.15 17	86.76 18	62.95 8	86.29 14	84.16 53	62.81 61	73.30 74	90.58 21	49.90 105	88.21 35	73.78 43	87.03 52	86.29 84

ACMMPR		77.71 29	77.23 33	79.16 16	86.75 19	62.93 9	86.29 14	84.24 51	62.82 59	73.55 71	90.56 22	49.80 107	88.24 34	74.02 41	87.03 52	86.32 81

HFP-MVS		78.01 27	77.65 28	79.10 20	86.71 20	62.81 10	86.29 14	84.32 49	62.82 59	73.96 62	90.50 24	53.20 73	88.35 31	74.02 41	87.05 50	86.13 86

#test#		77.83 28	77.41 31	79.10 20	86.71 20	62.81 10	85.69 27	84.32 49	61.61 82	73.96 62	90.50 24	53.20 73	88.35 31	73.68 44	87.05 50	86.13 86

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 34	62.73 12	86.09 17	86.83 12	65.51 14	83.81 10	90.51 23	63.71 12	89.23 20	81.51 1	88.44 32	88.09 19
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		76.77 41	76.06 43	78.88 27	86.14 39	62.73 12	82.55 75	83.74 65	61.71 80	72.45 93	90.34 31	48.48 124	88.13 36	72.32 52	86.85 56	85.78 97

XVS		77.17 36	76.56 40	79.00 23	86.32 32	62.62 14	85.83 22	83.92 58	64.55 26	72.17 94	90.01 45	47.95 128	88.01 40	71.55 61	86.74 58	86.37 76

X-MVStestdata		70.21 124	67.28 167	79.00 23	86.32 32	62.62 14	85.83 22	83.92 58	64.55 26	72.17 94	6.49 372	47.95 128	88.01 40	71.55 61	86.74 58	86.37 76

NCCC		78.58 16	78.31 20	79.39 11	87.51 12	62.61 16	85.20 31	84.42 47	66.73 9	74.67 54	89.38 56	55.30 47	89.18 21	74.19 39	87.34 48	86.38 73

test_prior4						62.51 17	82.08 84

Effi-MVS+-dtu		69.64 138	67.53 157	75.95 75	76.10 219	62.29 18	80.20 108	76.06 216	59.83 117	65.26 205	77.09 273	41.56 204	84.02 139	60.60 147	71.09 224	81.53 216

SteuartSystems-ACMMP		79.48 10	79.31 11	79.98 2	83.01 82	62.18 19	87.60 9	85.83 24	66.69 10	78.03 31	90.98 14	54.26 58	90.06 12	78.42 17	89.02 27	87.69 34
Skip Steuart: Steuart Systems R&D Blog.
CNVR-MVS		79.84 9	79.97 9	79.45 10	87.90 2	62.17 20	84.37 37	85.03 38	66.96 5	77.58 32	90.06 40	59.47 20	89.13 22	78.67 12	89.73 20	87.03 58

ZNCC-MVS		78.82 13	78.67 17	79.30 13	86.43 30	62.05 21	86.62 11	86.01 23	63.32 47	75.08 43	90.47 27	53.96 62	88.68 27	76.48 26	89.63 24	87.16 55

SMA-MVS		80.28 6	80.39 7	79.95 3	86.60 25	61.95 22	86.33 13	85.75 26	62.49 66	82.20 15	92.28 1	56.53 35	89.70 15	79.85 3	91.48 1	88.19 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
ACMMP		76.02 49	75.33 53	78.07 41	85.20 56	61.91 23	85.49 30	84.44 46	63.04 53	69.80 121	89.74 52	45.43 164	87.16 57	72.01 55	82.87 89	85.14 124
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
SD-MVS		77.70 30	77.62 29	77.93 45	84.47 67	61.88 24	84.55 35	83.87 62	60.37 101	79.89 18	89.38 56	54.97 51	85.58 103	76.12 29	84.94 68	86.33 79
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
GST-MVS		78.14 24	77.85 27	78.99 25	86.05 43	61.82 25	85.84 21	85.21 34	63.56 44	74.29 59	90.03 43	52.56 77	88.53 29	74.79 34	88.34 34	86.63 70

DeepC-MVS	69.38 2	78.56 18	78.14 24	79.83 6	83.60 72	61.62 26	84.17 43	86.85 10	63.23 50	73.84 67	90.25 35	57.68 29	89.96 13	74.62 35	89.03 26	87.89 23
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TEST9					85.58 49	61.59 27	81.62 89	81.26 123	55.65 196	74.93 46	88.81 65	53.70 68	84.68 124

train_agg		76.27 45	76.15 42	76.64 66	85.58 49	61.59 27	81.62 89	81.26 123	55.86 188	74.93 46	88.81 65	53.70 68	84.68 124	75.24 32	88.33 35	83.65 177

TSAR-MVS + MP.		78.44 20	78.28 21	78.90 26	84.96 60	61.41 29	84.03 47	83.82 64	59.34 126	79.37 19	89.76 51	59.84 16	87.62 50	76.69 25	86.74 58	87.68 35
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
CPTT-MVS		72.78 83	72.08 87	74.87 98	84.88 65	61.41 29	84.15 44	77.86 189	55.27 201	67.51 163	88.08 72	41.93 197	81.85 185	69.04 77	80.01 121	81.35 221

xxxxxxxxxxxxxcwj		78.37 21	78.25 23	78.76 29	86.17 36	61.30 31	83.98 49	79.95 147	59.00 129	79.16 20	90.75 17	57.96 25	87.09 60	77.08 23	90.18 15	87.87 25

save fliter					86.17 36	61.30 31	83.98 49	79.66 152	59.00 129

SR-MVS		76.13 47	75.70 50	77.40 54	85.87 45	61.20 33	85.52 28	82.19 97	59.99 112	75.10 42	90.35 29	47.66 132	86.52 79	71.64 60	82.99 84	84.47 145

1121		68.53 162	67.16 173	72.63 156	85.64 48	61.14 34	73.95 222	66.46 300	44.61 312	70.28 109	86.68 93	41.42 208	80.78 212	53.62 193	81.79 99	75.97 287

新几何1				70.76 195	85.66 46	61.13 35		66.43 301	44.68 311	70.29 108	86.64 94	41.29 210	75.23 279	49.72 224	81.75 103	75.93 289

MSC_two_6792asdad				79.95 3	87.24 14	61.04 36		85.62 28					90.96 1	79.31 7	90.65 8	87.85 27

No_MVS				79.95 3	87.24 14	61.04 36		85.62 28					90.96 1	79.31 7	90.65 8	87.85 27

ACMMP_NAP		78.77 15	78.78 15	78.74 30	85.44 52	61.04 36	83.84 53	85.16 35	62.88 57	78.10 28	91.26 13	52.51 78	88.39 30	79.34 6	90.52 13	86.78 67

test_8					85.40 53	60.96 39	81.54 92	81.18 126	55.86 188	74.81 49	88.80 67	53.70 68	84.45 129

OPU-MVS				79.83 6	87.54 11	60.93 40	87.82 7				89.89 47	67.01 1	90.33 11	73.16 49	91.15 4	88.23 14

DeepC-MVS_fast	68.24 3	77.25 35	76.63 39	79.12 19	86.15 38	60.86 41	84.71 33	84.85 43	61.98 78	73.06 82	88.88 64	53.72 67	89.06 23	68.27 78	88.04 42	87.42 46
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
FOURS1					86.12 40	60.82 42	88.18 1	83.61 68	60.87 89	81.50 16

HPM-MVS		77.28 34	76.85 36	78.54 34	85.00 59	60.81 43	82.91 67	85.08 36	62.57 64	73.09 81	89.97 46	50.90 101	87.48 51	75.30 30	86.85 56	87.33 52
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
APD-MVS		78.02 25	78.04 26	77.98 44	86.44 29	60.81 43	85.52 28	84.36 48	60.61 94	79.05 23	90.30 32	55.54 46	88.32 33	73.48 48	87.03 52	84.83 134
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
test1172		75.36 57	74.81 59	77.02 58	85.47 51	60.79 45	83.94 52	81.63 109	59.52 123	74.66 55	90.18 36	44.74 171	85.84 97	70.63 67	82.52 93	84.42 146

testtj		78.47 19	78.43 19	78.61 32	86.82 17	60.67 46	86.07 18	85.38 32	62.12 72	78.65 26	90.29 33	55.76 43	89.31 19	73.55 47	87.22 49	85.84 95

MVS_111021_HR		74.02 72	73.46 76	75.69 84	83.01 82	60.63 47	77.29 157	78.40 183	61.18 87	70.58 105	85.97 112	54.18 60	84.00 140	67.52 88	82.98 86	82.45 204

DPE-MVS		80.56 5	80.98 5	79.29 14	87.27 13	60.56 48	85.71 26	86.42 18	63.28 48	83.27 13	91.83 10	64.96 7	90.47 10	76.41 27	89.67 22	86.84 63
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
test_part2					87.58 9	60.47 49				83.42 12

ZD-MVS					86.64 23	60.38 50		82.70 92	57.95 152	78.10 28	90.06 40	56.12 41	88.84 26	74.05 40	87.00 55

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 18	84.92 64	60.32 51	83.03 63	85.33 33	62.86 58	80.17 17	90.03 43	61.76 14	88.95 24	74.21 38	88.67 31	88.12 18

abl_6		74.34 68	73.50 73	76.86 60	82.43 86	60.16 52	83.48 58	81.86 103	58.81 133	73.95 64	89.86 48	41.87 198	86.62 74	67.98 83	81.23 106	83.80 169

APDe-MVS		80.16 7	80.59 6	78.86 28	86.64 23	60.02 53	88.12 3	86.42 18	62.94 55	82.40 14	92.12 2	59.64 18	89.76 14	78.70 11	88.32 36	86.79 66

agg_prior1		75.94 50	76.01 44	75.72 81	85.04 57	59.96 54	81.44 93	81.04 129	56.14 184	74.68 52	88.90 63	53.91 64	84.04 136	75.01 33	87.92 45	83.16 192

agg_prior					85.04 57	59.96 54		81.04 129		74.68 52			84.04 136

3Dnovator+	66.72 4	75.84 52	74.57 61	79.66 8	82.40 87	59.92 56	85.83 22	86.32 20	66.92 8	67.80 158	89.24 58	42.03 195	89.38 18	64.07 116	86.50 61	89.69 1

SR-MVS-dyc-post		74.57 66	73.90 68	76.58 68	83.49 74	59.87 57	84.29 38	81.36 116	58.07 149	73.14 78	90.07 38	44.74 171	85.84 97	68.20 79	81.76 101	84.03 156

RE-MVS-def			73.71 72		83.49 74	59.87 57	84.29 38	81.36 116	58.07 149	73.14 78	90.07 38	43.06 187		68.20 79	81.76 101	84.03 156

CSCG		76.92 38	76.75 37	77.41 52	83.96 71	59.60 59	82.95 64	86.50 17	60.78 92	75.27 41	84.83 128	60.76 15	86.56 77	67.86 84	87.87 46	86.06 89

h-mvs33		72.71 85	71.49 92	76.40 70	81.99 92	59.58 60	76.92 166	76.74 209	60.40 98	74.81 49	85.95 113	45.54 160	85.76 100	70.41 68	70.61 228	83.86 164

MP-MVS-pluss		78.35 22	78.46 18	78.03 43	84.96 60	59.52 61	82.93 66	85.39 31	62.15 71	76.41 36	91.51 11	52.47 80	86.78 69	80.66 2	89.64 23	87.80 30
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
原ACMM1				74.69 100	85.39 54	59.40 62		83.42 74	51.47 248	70.27 110	86.61 96	48.61 122	86.51 80	53.85 192	87.96 43	78.16 264

MAR-MVS		71.51 103	70.15 115	75.60 87	81.84 94	59.39 63	81.38 94	82.90 89	54.90 213	68.08 150	78.70 252	47.73 130	85.51 106	51.68 213	84.17 77	81.88 213
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
MVS_111021_LR		69.50 141	68.78 136	71.65 173	78.38 167	59.33 64	74.82 207	70.11 274	58.08 148	67.83 157	84.68 130	41.96 196	76.34 275	65.62 105	77.54 152	79.30 256

test_one_0601					87.58 9	59.30 65		86.84 11	65.01 23	83.80 11	91.86 6	64.03 11

MSLP-MVS++		73.77 76	73.47 75	74.66 102	83.02 81	59.29 66	82.30 82	81.88 102	59.34 126	71.59 100	86.83 87	45.94 155	83.65 146	65.09 110	85.22 67	81.06 228

DPM-MVS		75.47 55	75.00 54	76.88 59	81.38 103	59.16 67	79.94 111	85.71 27	56.59 173	72.46 91	86.76 88	56.89 33	87.86 45	66.36 96	88.91 30	83.64 178

DVP-MVS++		81.67 1	82.40 1	79.47 9	87.24 14	59.15 68	88.18 1	87.15 6	65.04 20	84.26 5	91.86 6	67.01 1	90.84 3	79.48 4	91.38 2	88.42 9

IU-MVS					87.77 4	59.15 68		85.53 30	53.93 223	84.64 3				79.07 9	90.87 5	88.37 11

CDPH-MVS		76.31 44	75.67 51	78.22 40	85.35 55	59.14 70	81.31 95	84.02 54	56.32 178	74.05 60	88.98 62	53.34 72	87.92 43	69.23 75	88.42 33	87.59 39

test_0728_SECOND				79.19 15	87.82 3	59.11 71	87.85 5	87.15 6					90.84 3	78.66 13	90.61 11	87.62 38

DVP-MVS		80.84 4	81.64 3	78.42 36	87.75 7	59.07 72	87.85 5	85.03 38	64.26 32	83.82 8	92.00 3	64.82 8	90.75 8	78.66 13	90.61 11	85.45 114
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					87.75 7	59.07 72	87.86 4	86.83 12	64.26 32	84.19 7	91.92 5	64.82 8

HPM-MVS_fast		74.30 70	73.46 76	76.80 61	84.45 68	59.04 74	83.65 56	81.05 128	60.15 109	70.43 106	89.84 49	41.09 213	85.59 102	67.61 87	82.90 88	85.77 100

OPM-MVS		74.73 63	74.25 65	76.19 73	80.81 112	59.01 75	82.60 74	83.64 67	63.74 42	72.52 90	87.49 79	47.18 142	85.88 96	69.47 73	80.78 107	83.66 176
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
3Dnovator	64.47 5	72.49 88	71.39 95	75.79 77	77.70 186	58.99 76	80.66 102	83.15 84	62.24 70	65.46 199	86.59 97	42.38 193	85.52 105	59.59 156	84.72 69	82.85 198

SED-MVS		81.56 2	82.30 2	79.32 12	87.77 4	58.90 77	87.82 7	86.78 14	64.18 35	85.97 1	91.84 8	66.87 3	90.83 5	78.63 15	90.87 5	88.23 14

test_241102_ONE					87.77 4	58.90 77		86.78 14	64.20 34	85.97 1	91.34 12	66.87 3	90.78 7

PVSNet_Blended_VisFu		71.45 105	70.39 111	74.65 103	82.01 90	58.82 79	79.93 112	80.35 143	55.09 206	65.82 195	82.16 185	49.17 114	82.64 173	60.34 149	78.62 145	82.50 203

TSAR-MVS + GP.		74.90 59	74.15 66	77.17 56	82.00 91	58.77 80	81.80 85	78.57 172	58.58 139	74.32 58	84.51 137	55.94 42	87.22 54	67.11 91	84.48 74	85.52 110

test222					83.14 77	58.68 81	72.57 244	63.45 318	41.78 332	67.56 162	86.12 107	37.13 248			78.73 143	74.98 300

ETH3D cwj APD-0.16		78.02 25	78.13 25	77.71 47	82.10 89	58.65 82	82.72 71	87.55 5	58.33 146	78.05 30	90.06 40	58.35 24	87.65 49	76.15 28	89.86 19	86.82 64

ACMM	61.98 7	70.80 113	69.73 120	74.02 117	80.59 117	58.59 83	82.68 72	82.02 101	55.46 199	67.18 168	84.39 140	38.51 231	83.17 155	60.65 146	76.10 168	80.30 239
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
RRT_MVS		68.77 154	66.71 178	74.95 95	75.93 223	58.55 84	80.50 104	75.84 218	56.09 185	68.17 146	83.74 154	28.50 319	82.98 158	65.67 104	65.91 284	83.33 183

test12				77.76 46	84.52 66	58.41 85		83.36 77		72.93 84		54.61 56	88.05 39		88.12 40	86.81 65

MCST-MVS		77.48 33	77.45 30	77.54 50	86.67 22	58.36 86	83.22 61	86.93 9	56.91 165	74.91 48	88.19 69	59.15 21	87.68 48	73.67 45	87.45 47	86.57 71

Regformer-2		75.63 54	74.99 55	77.54 50	80.43 118	58.32 87	79.50 121	82.92 87	67.84 1	75.94 37	80.75 217	55.73 44	86.80 67	71.44 63	80.38 115	87.50 42

APD-MVS_3200maxsize		74.96 58	74.39 64	76.67 65	82.20 88	58.24 88	83.67 55	83.29 80	58.41 143	73.71 68	90.14 37	45.62 157	85.99 92	69.64 71	82.85 90	85.78 97

ETH3 D test6400		79.14 11	79.32 10	78.61 32	86.34 31	58.11 89	84.65 34	87.66 4	58.56 141	78.87 24	89.54 53	63.67 13	89.57 16	74.60 36	89.98 17	88.14 17

CNLPA		65.43 216	64.02 215	69.68 214	78.73 160	58.07 90	77.82 144	70.71 270	51.49 247	61.57 253	83.58 158	38.23 236	70.82 296	43.90 270	70.10 238	80.16 241

DP-MVS Recon		72.15 96	70.73 106	76.40 70	86.57 26	57.99 91	81.15 97	82.96 86	57.03 162	66.78 173	85.56 120	44.50 175	88.11 37	51.77 211	80.23 120	83.10 193

ETH3D-3000-0.1		78.58 16	78.91 14	77.61 48	83.06 79	57.86 92	84.14 46	88.31 1	60.37 101	79.14 22	90.35 29	57.76 28	87.00 63	77.16 22	89.90 18	87.97 22

SF-MVS		78.82 13	79.22 12	77.60 49	82.88 84	57.83 93	84.99 32	88.13 3	61.86 79	79.16 20	90.75 17	57.96 25	87.09 60	77.08 23	90.18 15	87.87 25

AdaColmap		69.99 128	68.66 138	73.97 119	84.94 62	57.83 93	82.63 73	78.71 168	56.28 180	64.34 219	84.14 143	41.57 203	87.06 62	46.45 247	78.88 138	77.02 279

Fast-Effi-MVS+-dtu		67.37 183	65.33 206	73.48 137	72.94 265	57.78 95	77.47 152	76.88 205	57.60 156	61.97 247	76.85 277	39.31 223	80.49 219	54.72 184	70.28 235	82.17 209

Regformer-1		75.47 55	74.93 57	77.09 57	80.43 118	57.70 96	79.50 121	82.13 98	67.84 1	75.73 40	80.75 217	56.50 36	86.07 88	71.07 64	80.38 115	87.50 42

ACMP	63.53 6	72.30 91	71.20 100	75.59 88	80.28 121	57.54 97	82.74 70	82.84 91	60.58 95	65.24 206	86.18 106	39.25 224	86.03 91	66.95 94	76.79 164	83.22 187
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
CANet		76.46 43	75.93 46	78.06 42	81.29 104	57.53 98	82.35 77	83.31 79	67.78 3	70.09 111	86.34 103	54.92 52	88.90 25	72.68 51	84.55 72	87.76 33

LPG-MVS_test		72.74 84	71.74 89	75.76 78	80.22 123	57.51 99	82.55 75	83.40 75	61.32 84	66.67 177	87.33 82	39.15 226	86.59 75	67.70 85	77.30 157	83.19 189

LGP-MVS_train				75.76 78	80.22 123	57.51 99		83.40 75	61.32 84	66.67 177	87.33 82	39.15 226	86.59 75	67.70 85	77.30 157	83.19 189

test_prior3		76.89 40	76.96 35	76.69 63	84.20 69	57.27 101	81.75 86	84.88 41	60.37 101	75.01 44	89.06 59	56.22 39	86.43 83	72.19 53	88.96 28	86.38 73

test_prior				76.69 63	84.20 69	57.27 101		84.88 41					86.43 83			86.38 73

XVG-OURS		68.76 155	67.37 163	72.90 150	74.32 251	57.22 103	70.09 280	78.81 165	55.24 202	67.79 159	85.81 118	36.54 254	78.28 251	62.04 136	75.74 171	83.19 189

API-MVS		72.17 94	71.41 94	74.45 111	81.95 93	57.22 103	84.03 47	80.38 142	59.89 116	68.40 141	82.33 179	49.64 108	87.83 46	51.87 209	84.16 78	78.30 262

xiu_mvs_v1_base_debu		68.58 158	67.28 167	72.48 159	78.19 174	57.19 105	75.28 194	75.09 232	51.61 243	70.04 112	81.41 200	32.79 287	79.02 242	63.81 120	77.31 154	81.22 223

xiu_mvs_v1_base		68.58 158	67.28 167	72.48 159	78.19 174	57.19 105	75.28 194	75.09 232	51.61 243	70.04 112	81.41 200	32.79 287	79.02 242	63.81 120	77.31 154	81.22 223

xiu_mvs_v1_base_debi		68.58 158	67.28 167	72.48 159	78.19 174	57.19 105	75.28 194	75.09 232	51.61 243	70.04 112	81.41 200	32.79 287	79.02 242	63.81 120	77.31 154	81.22 223

MVSFormer		71.50 104	70.38 112	74.88 97	78.76 158	57.15 108	82.79 68	78.48 176	51.26 252	69.49 124	83.22 162	43.99 180	83.24 153	66.06 98	79.37 129	84.23 151

lupinMVS		69.57 139	68.28 145	73.44 139	78.76 158	57.15 108	76.57 171	73.29 253	46.19 300	69.49 124	82.18 182	43.99 180	79.23 234	64.66 113	79.37 129	83.93 159

hse-mvs2		71.04 108	69.86 118	74.60 106	79.58 138	57.12 110	73.96 221	75.25 227	60.40 98	74.81 49	81.95 189	45.54 160	82.90 161	70.41 68	66.83 278	83.77 170

AUN-MVS		68.45 164	66.41 186	74.57 108	79.53 140	57.08 111	73.93 225	75.23 228	54.44 220	66.69 176	81.85 191	37.10 249	82.89 162	62.07 135	66.84 277	83.75 171

jason		69.65 137	68.39 144	73.43 140	78.27 172	56.88 112	77.12 160	73.71 250	46.53 297	69.34 128	83.22 162	43.37 184	79.18 235	64.77 112	79.20 134	84.23 151
jason: jason.
XVG-OURS-SEG-HR		68.81 151	67.47 160	72.82 153	74.40 249	56.87 113	70.59 273	79.04 161	54.77 214	66.99 170	86.01 111	39.57 221	78.21 252	62.54 131	73.33 193	83.37 182

DP-MVS		65.68 211	63.66 222	71.75 169	84.93 63	56.87 113	80.74 101	73.16 254	53.06 230	59.09 273	82.35 178	36.79 253	85.94 95	32.82 332	69.96 240	72.45 324

Regformer-4		74.25 71	73.48 74	76.57 69	79.75 133	56.54 115	78.54 134	81.49 113	66.93 7	73.90 65	80.30 225	53.84 66	85.98 93	69.76 70	76.84 162	87.17 54

HQP_MVS		74.31 69	73.73 71	76.06 74	81.41 101	56.31 116	84.22 41	84.01 55	64.52 28	69.27 129	86.10 108	45.26 168	87.21 55	68.16 81	80.58 111	84.65 140

plane_prior						56.31 116	83.58 57		63.19 52						80.48 114

EPNet		73.09 81	72.16 85	75.90 76	75.95 222	56.28 118	83.05 62	72.39 259	66.53 12	65.27 202	87.00 86	50.40 103	85.47 109	62.48 132	86.32 62	85.94 91
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CHOSEN 1792x2688		65.08 223	62.84 231	71.82 168	81.49 100	56.26 119	66.32 299	74.20 245	40.53 341	63.16 231	78.65 254	41.30 209	77.80 258	45.80 253	74.09 181	81.40 218

plane_prior6					81.20 108	56.24 120						45.26 168

anonymousdsp		67.00 194	64.82 211	73.57 135	70.09 305	56.13 121	76.35 176	77.35 200	48.43 277	64.99 213	80.84 215	33.01 284	80.34 220	64.66 113	67.64 273	84.23 151

plane_prior3						56.09 122			63.92 39	69.27 129

PatchMatch-RL		56.25 289	54.55 294	61.32 298	77.06 203	56.07 123	65.57 304	54.10 353	44.13 319	53.49 323	71.27 323	25.20 341	66.78 316	36.52 318	63.66 300	61.12 352

NP-MVS					80.98 111	56.05 124					85.54 122

plane_prior7					81.41 101	55.96 125

PS-MVSNAJss		72.24 92	71.21 99	75.31 91	78.50 164	55.93 126	81.63 88	82.12 99	56.24 181	70.02 115	85.68 119	47.05 144	84.34 131	65.27 108	74.41 179	85.67 104

PHI-MVS		75.87 51	75.36 52	77.41 52	80.62 116	55.91 127	84.28 40	85.78 25	56.08 186	73.41 72	86.58 98	50.94 100	88.54 28	70.79 65	89.71 21	87.79 31

PS-MVSNAJ		70.51 117	69.70 121	72.93 149	81.52 98	55.79 128	74.92 205	79.00 162	55.04 211	69.88 119	78.66 253	47.05 144	82.19 180	61.61 140	79.58 126	80.83 231

PCF-MVS	61.88 8	70.95 110	69.49 124	75.35 90	77.63 189	55.71 129	76.04 185	81.81 105	50.30 260	69.66 122	85.40 125	52.51 78	84.89 120	51.82 210	80.24 119	85.45 114
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
LS3D		64.71 225	62.50 235	71.34 183	79.72 137	55.71 129	79.82 114	74.72 237	48.50 276	56.62 291	84.62 132	33.59 279	82.34 179	29.65 350	75.23 175	75.97 287

HyFIR lowres test		65.67 212	63.01 229	73.67 129	79.97 131	55.65 131	69.07 288	75.52 223	42.68 330	63.53 227	77.95 261	40.43 215	81.64 189	46.01 251	71.91 215	83.73 172

xiu_mvs_v2_base		70.52 116	69.75 119	72.84 151	81.21 107	55.63 132	75.11 199	78.92 163	54.92 212	69.96 118	79.68 239	47.00 148	82.09 182	61.60 141	79.37 129	80.81 232

ET-MVSNet_ETH3D		67.96 174	65.72 200	74.68 101	76.67 210	55.62 133	75.11 199	74.74 236	52.91 232	60.03 261	80.12 229	33.68 277	82.64 173	61.86 138	76.34 167	85.78 97

MVP-Stereo		65.41 217	63.80 219	70.22 203	77.62 193	55.53 134	76.30 177	78.53 174	50.59 259	56.47 293	78.65 254	39.84 218	82.68 171	44.10 269	72.12 214	72.44 325
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
jajsoiax		68.25 167	66.45 182	73.66 130	75.62 228	55.49 135	80.82 99	78.51 175	52.33 238	64.33 220	84.11 144	28.28 321	81.81 187	63.48 125	70.62 227	83.67 174

Regformer-3		73.89 74	73.28 78	75.71 82	79.75 133	55.48 136	78.54 134	79.93 148	66.58 11	73.62 69	80.30 225	54.87 53	84.54 127	69.09 76	76.84 162	87.10 57

Vis-MVSNet		72.18 93	71.37 96	74.61 105	81.29 104	55.41 137	80.90 98	78.28 185	60.73 93	69.23 132	88.09 71	44.36 177	82.65 172	57.68 163	81.75 103	85.77 100
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
mvs_tets		68.18 169	66.36 188	73.63 133	75.61 229	55.35 138	80.77 100	78.56 173	52.48 237	64.27 222	84.10 145	27.45 327	81.84 186	63.45 126	70.56 229	83.69 173

ETV-MVS		74.46 67	73.84 70	76.33 72	79.27 145	55.24 139	79.22 124	85.00 40	64.97 24	72.65 88	79.46 244	53.65 71	87.87 44	67.45 89	82.91 87	85.89 94

HQP5-MVS						54.94 140

HQP-MVS		73.45 77	72.80 81	75.40 89	80.66 113	54.94 140	82.31 79	83.90 60	62.10 73	67.85 153	85.54 122	45.46 162	86.93 64	67.04 92	80.35 117	84.32 148

test_djsdf		69.45 143	67.74 150	74.58 107	74.57 245	54.92 142	82.79 68	78.48 176	51.26 252	65.41 200	83.49 160	38.37 233	83.24 153	66.06 98	69.25 254	85.56 109

PLC	56.13 14	65.09 222	63.21 227	70.72 197	81.04 110	54.87 143	78.57 132	77.47 196	48.51 275	55.71 296	81.89 190	33.71 276	79.71 226	41.66 290	70.37 232	77.58 271
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
patch_mono-2		69.85 131	71.09 101	66.16 257	79.11 151	54.80 144	71.97 253	74.31 242	53.50 227	70.90 103	84.17 142	57.63 31	63.31 328	66.17 97	82.02 98	80.38 238

114514_t		70.83 111	69.56 122	74.64 104	86.21 34	54.63 145	82.34 78	81.81 105	48.22 279	63.01 232	85.83 116	40.92 214	87.10 59	57.91 162	79.79 122	82.18 207

UGNet		68.81 151	67.39 162	73.06 147	78.33 170	54.47 146	79.77 115	75.40 225	60.45 97	63.22 229	84.40 139	32.71 291	80.91 209	51.71 212	80.56 113	83.81 165
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
CS-MVS-test		76.07 48	75.85 49	76.73 62	80.33 120	54.44 147	82.95 64	86.18 21	63.54 45	73.32 73	84.50 138	55.21 48	86.44 82	71.92 57	84.65 70	89.32 3

test_0402		63.25 239	61.01 252	69.96 208	80.00 130	54.37 148	76.86 168	72.02 261	54.58 217	58.71 276	80.79 216	35.00 264	84.36 130	26.41 358	64.71 293	71.15 336

CS-MVS		76.16 46	76.00 45	76.62 67	80.18 128	54.35 149	84.52 36	83.98 57	63.24 49	73.29 75	87.26 84	55.11 49	86.25 87	71.80 58	84.65 70	89.26 4

EI-MVSNet-Vis-set		72.42 90	71.59 90	74.91 96	78.47 166	54.02 150	77.05 162	79.33 159	65.03 22	71.68 99	79.35 247	52.75 76	84.89 120	66.46 95	74.23 180	85.83 96

OpenMVS	61.03 9	68.85 150	67.56 154	72.70 155	74.26 252	53.99 151	81.21 96	81.34 120	52.70 234	62.75 235	85.55 121	38.86 229	84.14 134	48.41 235	83.01 83	79.97 245

pmmvs4		61.48 259	59.39 260	67.76 238	71.57 286	53.86 152	71.42 258	65.34 307	44.20 317	59.46 268	77.92 263	35.90 257	74.71 281	43.87 271	64.87 292	74.71 305

TAMVS		66.78 199	65.27 207	71.33 184	79.16 150	53.67 153	73.84 229	69.59 279	52.32 239	65.28 201	81.72 194	44.49 176	77.40 264	42.32 284	78.66 144	82.92 195

Effi-MVS+		73.31 79	72.54 83	75.62 86	77.87 183	53.64 154	79.62 119	79.61 153	61.63 81	72.02 96	82.61 172	56.44 37	85.97 94	63.99 119	79.07 137	87.25 53

F-COLMAP		63.05 242	60.87 255	69.58 218	76.99 206	53.63 155	78.12 140	76.16 213	47.97 283	52.41 326	81.61 196	27.87 323	78.11 253	40.07 296	66.66 279	77.00 280

EI-MVSNet-UG-set		71.92 97	71.06 102	74.52 110	77.98 181	53.56 156	76.62 170	79.16 160	64.40 30	71.18 101	78.95 251	52.19 84	84.66 126	65.47 106	73.57 188	85.32 120

EIA-MVS		71.78 99	70.60 107	75.30 92	79.85 132	53.54 157	77.27 158	83.26 82	57.92 153	66.49 180	79.39 245	52.07 86	86.69 71	60.05 151	79.14 136	85.66 105

EG-PatchMatch MVS		64.71 225	62.87 230	70.22 203	77.68 187	53.48 158	77.99 141	78.82 164	53.37 228	56.03 295	77.41 272	24.75 344	84.04 136	46.37 248	73.42 192	73.14 316

QAPM		70.05 126	68.81 135	73.78 122	76.54 214	53.43 159	83.23 60	83.48 71	52.89 233	65.90 192	86.29 104	41.55 206	86.49 81	51.01 215	78.40 147	81.42 217

PAPM_NR		72.63 86	71.80 88	75.13 94	81.72 95	53.42 160	79.91 113	83.28 81	59.14 128	66.31 185	85.90 114	51.86 88	86.06 89	57.45 164	80.62 109	85.91 93

CLD-MVS		73.33 78	72.68 82	75.29 93	78.82 157	53.33 161	78.23 138	84.79 44	61.30 86	70.41 107	81.04 207	52.41 81	87.12 58	64.61 115	82.49 95	85.41 118
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
BH-untuned		68.27 166	67.29 166	71.21 185	79.74 135	53.22 162	76.06 183	77.46 198	57.19 160	66.10 187	81.61 196	45.37 166	83.50 149	45.42 261	76.68 166	76.91 283

旧先验1					83.04 80	53.15 163		67.52 291			87.85 77	44.08 178			80.76 108	78.03 269

OMC-MVS		71.40 106	70.60 107	73.78 122	76.60 212	53.15 163	79.74 117	79.78 149	58.37 144	68.75 136	86.45 101	45.43 164	80.60 215	62.58 130	77.73 151	87.58 40

CDS-MVSNet		66.80 198	65.37 204	71.10 190	78.98 153	53.13 165	73.27 235	71.07 267	52.15 240	64.72 215	80.23 228	43.56 183	77.10 266	45.48 259	78.88 138	83.05 194
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
testdata				64.66 275	81.52 98	52.93 166		65.29 308	46.09 301	73.88 66	87.46 80	38.08 238	66.26 320	53.31 198	78.48 146	74.78 304

ACMH+	57.40 11	66.12 208	64.06 214	72.30 164	77.79 185	52.83 167	80.39 105	78.03 187	57.30 158	57.47 287	82.55 174	27.68 325	84.17 133	45.54 257	69.78 244	79.90 246

IB-MVS	56.42 12	65.40 218	62.73 233	73.40 141	74.89 237	52.78 168	73.09 237	75.13 231	55.69 194	58.48 281	73.73 310	32.86 286	86.32 86	50.63 217	70.11 237	81.10 227
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
v7n		69.01 149	67.36 164	73.98 118	72.51 273	52.65 169	78.54 134	81.30 121	60.26 108	62.67 236	81.62 195	43.61 182	84.49 128	57.01 166	68.70 263	84.79 137

DROMVSNet		75.84 52	75.87 48	75.74 80	78.86 155	52.65 169	83.73 54	86.08 22	63.47 46	72.77 86	87.25 85	53.13 75	87.93 42	71.97 56	85.57 66	86.66 69

MSDG		61.81 255	59.23 261	69.55 219	72.64 269	52.63 171	70.45 276	75.81 219	51.38 249	53.70 318	76.11 288	29.52 312	81.08 204	37.70 307	65.79 287	74.93 301

cascas		65.98 209	63.42 225	73.64 132	77.26 200	52.58 172	72.26 249	77.21 202	48.56 274	61.21 255	74.60 304	32.57 295	85.82 99	50.38 219	76.75 165	82.52 202

BH-RMVSNet		68.81 151	67.42 161	72.97 148	80.11 129	52.53 173	74.26 216	76.29 212	58.48 142	68.38 142	84.20 141	42.59 189	83.83 142	46.53 246	75.91 169	82.56 200

COLMAP_ROB	52.97 17	61.27 261	58.81 263	68.64 229	74.63 243	52.51 174	78.42 137	73.30 252	49.92 265	50.96 331	81.51 199	23.06 346	79.40 231	31.63 340	65.85 285	74.01 312
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
BH-w/o		66.85 196	65.83 198	69.90 212	79.29 143	52.46 175	74.66 211	76.65 210	54.51 219	64.85 214	78.12 259	45.59 159	82.95 160	43.26 276	75.54 173	74.27 309

XVG-ACMP-BASELINE		64.36 229	62.23 238	70.74 196	72.35 275	52.45 176	70.80 272	78.45 179	53.84 224	59.87 264	81.10 206	16.24 358	79.32 233	55.64 178	71.76 216	80.47 235

pmmvs-eth3d		58.81 271	56.31 284	66.30 254	67.61 326	52.42 177	72.30 248	64.76 311	43.55 323	54.94 306	74.19 307	28.95 316	72.60 288	43.31 274	57.21 331	73.88 313

DELS-MVS		74.76 61	74.46 62	75.65 85	77.84 184	52.25 178	75.59 190	84.17 52	63.76 41	73.15 77	82.79 167	59.58 19	86.80 67	67.24 90	86.04 63	87.89 23
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
GeoE		71.01 109	70.15 115	73.60 134	79.57 139	52.17 179	78.93 126	78.12 186	58.02 151	67.76 161	83.87 150	52.36 82	82.72 170	56.90 167	75.79 170	85.92 92

MS-PatchMatch		62.42 246	61.46 246	65.31 272	75.21 236	52.10 180	72.05 251	74.05 246	46.41 298	57.42 288	74.36 305	34.35 271	77.57 262	45.62 256	73.67 185	66.26 349

CR-MVSNet		59.91 266	57.90 273	65.96 262	69.96 308	52.07 181	65.31 308	63.15 321	42.48 331	59.36 269	74.84 301	35.83 258	70.75 297	45.50 258	64.65 294	75.06 297

RPMNet		61.53 257	58.42 267	70.86 193	69.96 308	52.07 181	65.31 308	81.36 116	43.20 326	59.36 269	70.15 332	35.37 260	85.47 109	36.42 319	64.65 294	75.06 297

IterMVS		62.79 243	61.27 248	67.35 244	69.37 315	52.04 183	71.17 264	68.24 290	52.63 236	59.82 265	76.91 276	37.32 244	72.36 289	52.80 201	63.19 305	77.66 270
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ACMH	55.70 15	65.20 221	63.57 223	70.07 207	78.07 178	52.01 184	79.48 123	79.69 150	55.75 193	56.59 292	80.98 209	27.12 329	80.94 206	42.90 281	71.58 219	77.25 277
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
bset_n11_16_dypcd		65.57 214	63.69 221	71.19 186	70.84 295	51.79 185	71.37 259	70.48 272	53.33 229	65.19 209	76.41 286	31.46 301	81.76 188	65.12 109	69.04 257	80.01 244

UA-Net		73.13 80	72.93 80	73.76 124	83.58 73	51.66 186	78.75 127	77.66 193	67.75 4	72.61 89	89.42 54	49.82 106	83.29 152	53.61 195	83.14 81	86.32 81

Fast-Effi-MVS+		70.28 123	69.12 131	73.73 127	78.50 164	51.50 187	75.01 202	79.46 157	56.16 183	68.59 137	79.55 242	53.97 61	84.05 135	53.34 197	77.53 153	85.65 106

test_part1		74.74 62	74.42 63	75.70 83	81.69 96	51.26 188	83.98 49	87.05 8	65.31 16	73.10 80	86.20 105	53.94 63	88.06 38	65.32 107	73.17 198	87.77 32

PAPR		71.72 101	70.82 105	74.41 112	81.20 108	51.17 189	79.55 120	83.33 78	55.81 191	66.93 172	84.61 133	50.95 99	86.06 89	55.79 175	79.20 134	86.00 90

thisisatest0530		67.92 175	65.78 199	74.33 114	76.29 216	51.03 190	76.89 167	74.25 244	53.67 225	65.59 197	81.76 193	35.15 262	85.50 107	55.94 171	72.47 208	86.47 72

v1192		69.97 129	68.68 137	73.85 120	73.19 259	50.94 191	77.68 146	81.36 116	57.51 157	68.95 135	80.85 214	45.28 167	85.33 113	62.97 128	70.37 232	85.27 122

MVS		67.37 183	66.33 189	70.51 201	75.46 232	50.94 191	73.95 222	81.85 104	41.57 336	62.54 240	78.57 257	47.98 127	85.47 109	52.97 200	82.05 97	75.14 296

v10		70.21 124	69.02 132	73.81 121	73.51 257	50.92 193	78.74 128	81.39 115	60.05 111	66.39 183	81.83 192	47.58 134	85.41 112	62.80 129	68.86 261	85.09 127

PMMVS		53.96 300	53.26 306	56.04 319	62.60 351	50.92 193	61.17 327	56.09 347	32.81 354	53.51 322	66.84 345	34.04 273	59.93 340	44.14 268	68.18 266	57.27 357

tttt0517		67.83 177	65.66 201	74.33 114	76.69 209	50.82 195	77.86 142	73.99 247	54.54 218	64.64 217	82.53 175	35.06 263	85.50 107	55.71 176	69.91 241	86.67 68

IterMVS-SCA-FT		62.49 244	61.52 245	65.40 270	71.99 280	50.80 196	71.15 266	69.63 278	45.71 306	60.61 257	77.93 262	37.45 242	65.99 321	55.67 177	63.50 302	79.42 254

JIA-IIPM		51.56 313	47.68 323	63.21 284	64.61 343	50.73 197	47.71 356	58.77 336	42.90 328	48.46 341	51.72 360	24.97 342	70.24 302	36.06 321	53.89 342	68.64 347

v1144		70.42 120	69.31 127	73.76 124	73.22 258	50.64 198	77.83 143	81.43 114	58.58 139	69.40 127	81.16 204	47.53 135	85.29 114	64.01 118	70.64 226	85.34 119

PVSNet_BlendedMVS		68.56 161	67.72 151	71.07 191	77.03 204	50.57 199	74.50 213	81.52 110	53.66 226	64.22 224	79.72 238	49.13 115	82.87 164	55.82 173	73.92 183	79.77 251

PVSNet_Blended		68.59 157	67.72 151	71.19 186	77.03 204	50.57 199	72.51 245	81.52 110	51.91 241	64.22 224	77.77 269	49.13 115	82.87 164	55.82 173	79.58 126	80.14 242

canonicalmvs		74.67 64	74.98 56	73.71 128	78.94 154	50.56 201	80.23 106	83.87 62	60.30 107	77.15 33	86.56 99	59.65 17	82.00 183	66.01 100	82.12 96	88.58 8

alignmvs		73.86 75	73.99 67	73.45 138	78.20 173	50.50 202	78.57 132	82.43 94	59.40 124	76.57 34	86.71 92	56.42 38	81.23 200	65.84 102	81.79 99	88.62 6

casdiffmvs		74.80 60	74.89 58	74.53 109	75.59 230	50.37 203	78.17 139	85.06 37	62.80 62	74.40 57	87.86 76	57.88 27	83.61 147	69.46 74	82.79 91	89.59 2

nrg030		72.96 82	73.01 79	72.84 151	75.41 233	50.24 204	80.02 109	82.89 90	58.36 145	74.44 56	86.73 90	58.90 22	80.83 210	65.84 102	74.46 177	87.44 45

v8		70.33 122	69.28 128	73.49 136	73.15 260	50.22 205	78.62 131	80.78 136	60.79 91	66.45 182	82.11 187	49.35 110	84.98 117	63.58 124	68.71 262	85.28 121

V42		68.65 156	67.35 165	72.56 157	68.93 319	50.18 206	72.90 239	79.47 156	56.92 164	69.45 126	80.26 227	46.29 153	82.99 157	64.07 116	67.82 271	84.53 142

v144192		69.71 133	68.51 139	73.33 143	73.10 261	50.13 207	77.54 150	80.64 137	56.65 167	68.57 139	80.55 219	46.87 149	84.96 119	62.98 127	69.66 248	84.89 133

v1921920		69.47 142	68.17 147	73.36 142	73.06 262	50.10 208	77.39 153	80.56 138	56.58 174	68.59 137	80.37 221	44.72 173	84.98 117	62.47 133	69.82 243	85.00 129

v2v482		70.50 118	69.45 126	73.66 130	72.62 270	50.03 209	77.58 147	80.51 140	59.90 113	69.52 123	82.14 186	47.53 135	84.88 122	65.07 111	70.17 236	86.09 88

baseline		74.61 65	74.70 60	74.34 113	75.70 226	49.99 210	77.54 150	84.63 45	62.73 63	73.98 61	87.79 78	57.67 30	83.82 143	69.49 72	82.74 92	89.20 5

v1240		69.24 146	67.91 149	73.25 146	73.02 264	49.82 211	77.21 159	80.54 139	56.43 176	68.34 143	80.51 220	43.33 185	84.99 115	62.03 137	69.77 246	84.95 132

CHOSEN 280x420		47.83 320	46.36 324	52.24 337	67.37 328	49.78 212	38.91 364	43.11 368	35.00 352	43.27 355	63.30 353	28.95 316	49.19 363	36.53 317	60.80 321	57.76 356

MVSTER		67.16 190	65.58 203	71.88 167	70.37 301	49.70 213	70.25 279	78.45 179	51.52 246	69.16 133	80.37 221	38.45 232	82.50 175	60.19 150	71.46 220	83.44 181

EPP-MVSNet		72.16 95	71.31 98	74.71 99	78.68 161	49.70 213	82.10 83	81.65 108	60.40 98	65.94 190	85.84 115	51.74 90	86.37 85	55.93 172	79.55 128	88.07 21

VDD-MVS		72.50 87	72.09 86	73.75 126	81.58 97	49.69 215	77.76 145	77.63 194	63.21 51	73.21 76	89.02 61	42.14 194	83.32 151	61.72 139	82.50 94	88.25 13

MG-MVS		73.96 73	73.89 69	74.16 116	85.65 47	49.69 215	81.59 91	81.29 122	61.45 83	71.05 102	88.11 70	51.77 89	87.73 47	61.05 144	83.09 82	85.05 128

TR-MVS		66.59 204	65.07 209	71.17 188	79.18 148	49.63 217	73.48 232	75.20 230	52.95 231	67.90 151	80.33 224	39.81 219	83.68 145	43.20 277	73.56 189	80.20 240

thisisatest0515		65.83 210	63.50 224	72.82 153	73.75 255	49.50 218	71.32 261	73.12 255	49.39 267	63.82 226	76.50 285	34.95 265	84.84 123	53.20 199	75.49 174	84.13 155

IterMVS-LS		69.22 147	68.48 140	71.43 179	74.44 248	49.40 219	76.23 179	77.55 195	59.60 119	65.85 194	81.59 198	51.28 94	81.58 192	59.87 154	69.90 242	83.30 184
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
EI-MVSNet		69.27 145	68.44 143	71.73 170	74.47 246	49.39 220	75.20 197	78.45 179	59.60 119	69.16 133	76.51 283	51.29 93	82.50 175	59.86 155	71.45 221	83.30 184

AllTest		57.08 284	54.65 293	64.39 277	71.44 287	49.03 221	69.92 282	67.30 292	45.97 303	47.16 344	79.77 236	17.47 355	67.56 312	33.65 329	59.16 326	76.57 284

TestCases				64.39 277	71.44 287	49.03 221		67.30 292	45.97 303	47.16 344	79.77 236	17.47 355	67.56 312	33.65 329	59.16 326	76.57 284

PAPM		67.92 175	66.69 179	71.63 174	78.09 177	49.02 223	77.09 161	81.24 125	51.04 254	60.91 256	83.98 148	47.71 131	84.99 115	40.81 293	79.32 132	80.90 230

ppachtmachnet_test		58.06 278	55.38 289	66.10 260	69.51 312	48.99 224	68.01 292	66.13 302	44.50 314	54.05 316	70.74 325	32.09 298	72.34 290	36.68 315	56.71 334	76.99 282

diffmvs		70.69 114	70.43 110	71.46 176	69.45 314	48.95 225	72.93 238	78.46 178	57.27 159	71.69 98	83.97 149	51.48 92	77.92 256	70.70 66	77.95 150	87.53 41

Patchmatch-RL test		58.16 276	55.49 288	66.15 258	67.92 325	48.89 226	60.66 329	51.07 357	47.86 284	59.36 269	62.71 354	34.02 274	72.27 291	56.41 169	59.40 325	77.30 274

TAPA-MVS	59.36 10	66.60 202	65.20 208	70.81 194	76.63 211	48.75 227	76.52 173	80.04 146	50.64 258	65.24 206	84.93 127	39.15 226	78.54 248	36.77 312	76.88 161	85.14 124
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
EGC-MVSNET		42.47 326	38.48 332	54.46 327	74.33 250	48.73 228	70.33 278	51.10 356	0.03 375	0.18 376	67.78 342	13.28 361	66.49 318	18.91 363	50.36 350	48.15 360

MDA-MVSNet-bldmvs		53.87 302	50.81 313	63.05 286	66.25 335	48.58 229	56.93 340	63.82 316	48.09 281	41.22 357	70.48 330	30.34 306	68.00 311	34.24 327	45.92 356	72.57 322

MVS_Test		72.45 89	72.46 84	72.42 162	74.88 238	48.50 230	76.28 178	83.14 85	59.40 124	72.46 91	84.68 130	55.66 45	81.12 201	65.98 101	79.66 125	87.63 37

D2MVS		62.30 248	60.29 257	68.34 234	66.46 334	48.42 231	65.70 302	73.42 251	47.71 285	58.16 283	75.02 300	30.51 304	77.71 259	53.96 191	71.68 218	78.90 260

eth_miper_zixun_eth		67.63 179	66.28 192	71.67 172	71.60 285	48.33 232	73.68 231	77.88 188	55.80 192	65.91 191	78.62 256	47.35 141	82.88 163	59.45 157	66.25 282	83.81 165

K. test v3		60.47 264	57.11 276	70.56 199	73.74 256	48.22 233	75.10 201	62.55 324	58.27 147	53.62 320	76.31 287	27.81 324	81.59 191	47.42 238	39.18 362	81.88 213

GA-MVS		65.53 215	63.70 220	71.02 192	70.87 293	48.10 234	70.48 275	74.40 240	56.69 166	64.70 216	76.77 278	33.66 278	81.10 202	55.42 180	70.32 234	83.87 163

SCA		60.49 263	58.38 268	66.80 247	74.14 254	48.06 235	63.35 316	63.23 320	49.13 270	59.33 272	72.10 317	37.45 242	74.27 284	44.17 266	62.57 309	78.05 266

OurMVSNet-221017-0		61.37 260	58.63 266	69.61 215	72.05 279	48.06 235	73.93 225	72.51 258	47.23 293	54.74 308	80.92 211	21.49 353	81.24 199	48.57 234	56.22 335	79.53 253

lessismore_v0				69.91 211	71.42 289	47.80 237		50.90 358		50.39 337	75.56 295	27.43 328	81.33 196	45.91 252	34.10 364	80.59 234

LTVRE_ROB	55.42 16	63.15 241	61.23 250	68.92 226	76.57 213	47.80 237	59.92 330	76.39 211	54.35 221	58.67 277	82.46 177	29.44 314	81.49 193	42.12 286	71.14 222	77.46 272
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
v148		68.24 168	67.19 172	71.40 180	70.43 299	47.77 239	75.76 189	77.03 204	58.91 131	67.36 164	80.10 230	48.60 123	81.89 184	60.01 152	66.52 281	84.53 142

Anonymous20240529		69.91 130	69.02 132	72.56 157	80.19 126	47.65 240	77.56 149	80.99 131	55.45 200	69.88 119	86.76 88	39.24 225	82.18 181	54.04 189	77.10 159	87.85 27

baseline2		63.42 235	61.26 249	69.89 213	72.55 272	47.62 241	71.54 257	68.38 289	50.11 261	54.82 307	75.55 296	43.06 187	80.96 205	48.13 236	67.16 276	81.11 226

VDDNet		71.81 98	71.33 97	73.26 145	82.80 85	47.60 242	78.74 128	75.27 226	59.59 122	72.94 83	89.40 55	41.51 207	83.91 141	58.75 160	82.99 84	88.26 12

1314		64.61 227	63.21 227	68.80 227	71.87 283	47.46 243	73.95 222	78.39 184	42.88 329	59.97 262	76.60 282	38.11 237	79.39 232	54.84 183	72.32 211	79.55 252

CMPMVS	42.80 21	57.81 280	55.97 285	63.32 282	60.98 358	47.38 244	64.66 312	69.50 280	32.06 355	46.83 346	77.80 267	29.50 313	71.36 294	48.68 232	73.75 184	71.21 335
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
SixPastTwentyTwo		61.65 256	58.80 264	70.20 205	75.80 225	47.22 245	75.59 190	69.68 277	54.61 215	54.11 315	79.26 248	27.07 330	82.96 159	43.27 275	49.79 351	80.41 237

Anonymous20231211		69.28 144	68.47 141	71.73 170	80.28 121	47.18 246	79.98 110	82.37 95	54.61 215	67.24 166	84.01 147	39.43 222	82.41 178	55.45 179	72.83 202	85.62 108

tpm cat1		59.25 270	56.95 279	66.15 258	72.19 277	46.96 247	68.09 291	65.76 303	40.03 344	57.81 285	70.56 327	38.32 234	74.51 282	38.26 305	61.50 317	77.00 280

TDRefinement		53.44 306	50.72 314	61.60 295	64.31 345	46.96 247	70.89 271	65.27 309	41.78 332	44.61 352	77.98 260	11.52 365	66.36 319	28.57 353	51.59 346	71.49 333

PatchmatchNet		59.84 267	58.24 269	64.65 276	73.05 263	46.70 249	69.42 285	62.18 326	47.55 287	58.88 275	71.96 319	34.49 269	69.16 305	42.99 279	63.60 301	78.07 265
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
cl22		67.47 182	66.45 182	70.54 200	69.85 310	46.49 250	73.85 228	77.35 200	55.07 209	65.51 198	77.92 263	47.64 133	81.10 202	61.58 142	69.32 251	84.01 158

LFMVS		71.78 99	71.59 90	72.32 163	83.40 76	46.38 251	79.75 116	71.08 266	64.18 35	72.80 85	88.64 68	42.58 190	83.72 144	57.41 165	84.49 73	86.86 62

RRT_test8_iter05		68.17 171	66.86 177	72.07 165	75.81 224	46.33 252	76.41 175	81.81 105	56.43 176	66.52 179	81.30 203	31.90 299	84.25 132	63.77 123	67.83 270	85.64 107

miper_lstm_enhance		62.03 251	60.88 254	65.49 269	66.71 332	46.25 253	56.29 342	75.70 220	50.68 256	61.27 254	75.48 297	40.21 216	68.03 310	56.31 170	65.25 290	82.18 207

CANet_DTU		68.18 169	67.71 153	69.59 216	74.83 239	46.24 254	78.66 130	76.85 206	59.60 119	63.45 228	82.09 188	35.25 261	77.41 263	59.88 153	78.76 142	85.14 124

miper_ehance_all_eth		68.03 172	67.24 171	70.40 202	70.54 297	46.21 255	73.98 220	78.68 170	55.07 209	66.05 188	77.80 267	52.16 85	81.31 197	61.53 143	69.32 251	83.67 174

c3_l		68.33 165	67.56 154	70.62 198	70.87 293	46.21 255	74.47 214	78.80 166	56.22 182	66.19 186	78.53 258	51.88 87	81.40 194	62.08 134	69.04 257	84.25 150

DWT-MVSNet_test		61.90 252	59.93 259	67.83 237	71.98 281	46.09 257	71.03 270	69.71 275	50.09 262	58.51 280	70.62 326	30.21 308	77.63 260	49.28 228	67.91 268	79.78 250

miper_enhance_ethall		67.11 191	66.09 195	70.17 206	69.21 316	45.98 258	72.85 240	78.41 182	51.38 249	65.65 196	75.98 292	51.17 96	81.25 198	60.82 145	69.32 251	83.29 186

CostFormer		64.04 230	62.51 234	68.61 230	71.88 282	45.77 259	71.30 262	70.60 271	47.55 287	64.31 221	76.61 281	41.63 202	79.62 229	49.74 223	69.00 259	80.42 236

cl____		67.18 188	66.26 193	69.94 209	70.20 302	45.74 260	73.30 233	76.83 207	55.10 204	65.27 202	79.57 241	47.39 139	80.53 216	59.41 159	69.22 255	83.53 180

DIV-MVS_self_test		67.18 188	66.26 193	69.94 209	70.20 302	45.74 260	73.29 234	76.83 207	55.10 204	65.27 202	79.58 240	47.38 140	80.53 216	59.43 158	69.22 255	83.54 179

test_yl		69.69 134	69.13 129	71.36 181	78.37 168	45.74 260	74.71 209	80.20 144	57.91 154	70.01 116	83.83 151	42.44 191	82.87 164	54.97 181	79.72 123	85.48 112

DCV-MVSNet		69.69 134	69.13 129	71.36 181	78.37 168	45.74 260	74.71 209	80.20 144	57.91 154	70.01 116	83.83 151	42.44 191	82.87 164	54.97 181	79.72 123	85.48 112

IS-MVSNet		71.57 102	71.00 103	73.27 144	78.86 155	45.63 264	80.22 107	78.69 169	64.14 38	66.46 181	87.36 81	49.30 111	85.60 101	50.26 220	83.71 80	88.59 7

our_test_3		56.49 285	54.42 295	62.68 289	69.51 312	45.48 265	66.08 300	61.49 329	44.11 320	50.73 335	69.60 336	33.05 283	68.15 309	38.38 304	56.86 332	74.40 307

UniMVSNet (Re)		70.63 115	70.20 113	71.89 166	78.55 163	45.29 266	75.94 187	82.92 87	63.68 43	68.16 147	83.59 157	53.89 65	83.49 150	53.97 190	71.12 223	86.89 61

PM-MVS		52.33 310	50.19 315	58.75 308	62.10 352	45.14 267	65.75 301	40.38 370	43.60 322	53.52 321	72.65 314	9.16 370	65.87 322	50.41 218	54.18 341	65.24 351

OpenMVS_ROB	52.78 18	60.03 265	58.14 271	65.69 267	70.47 298	44.82 268	75.33 193	70.86 269	45.04 308	56.06 294	76.00 289	26.89 332	79.65 227	35.36 324	67.29 274	72.60 321

test-LLR		58.15 277	58.13 272	58.22 310	68.57 320	44.80 269	65.46 305	57.92 339	50.08 263	55.44 299	69.82 334	32.62 292	57.44 347	49.66 225	73.62 186	72.41 326

test-mter		56.42 287	55.82 286	58.22 310	68.57 320	44.80 269	65.46 305	57.92 339	39.94 345	55.44 299	69.82 334	21.92 350	57.44 347	49.66 225	73.62 186	72.41 326

PVSNet_0	43.31 20	47.46 322	45.64 325	52.92 333	67.60 327	44.65 271	54.06 347	54.64 349	41.59 335	46.15 348	58.75 356	30.99 302	58.66 343	32.18 333	24.81 365	55.46 358

ADS-MVSNet2		51.33 314	48.76 319	59.07 306	66.02 338	44.60 272	50.90 351	59.76 333	36.90 348	50.74 333	66.18 347	26.38 333	63.11 329	27.17 354	54.76 339	69.50 343

mvs_anonymous		68.03 172	67.51 158	69.59 216	72.08 278	44.57 273	71.99 252	75.23 228	51.67 242	67.06 169	82.57 173	54.68 55	77.94 255	56.56 168	75.71 172	86.26 85

ITE_SJBPF				62.09 292	66.16 336	44.55 274		64.32 314	47.36 290	55.31 301	80.34 223	19.27 354	62.68 331	36.29 320	62.39 311	79.04 257

UniMVSNet_NR-MVSNet		71.11 107	71.00 103	71.44 177	79.20 147	44.13 275	76.02 186	82.60 93	66.48 13	68.20 144	84.60 134	56.82 34	82.82 168	54.62 185	70.43 230	87.36 51

DU-MVS		70.01 127	69.53 123	71.44 177	78.05 179	44.13 275	75.01 202	81.51 112	64.37 31	68.20 144	84.52 135	49.12 117	82.82 168	54.62 185	70.43 230	87.37 49

PVSNet	50.76 19	58.40 274	57.39 274	61.42 296	75.53 231	44.04 277	61.43 323	63.45 318	47.04 295	56.91 289	73.61 311	27.00 331	64.76 324	39.12 301	72.40 209	75.47 294

tpm2		62.07 250	60.10 258	67.99 236	72.79 267	43.86 278	71.05 269	66.85 298	43.14 327	62.77 233	75.39 298	38.32 234	80.80 211	41.69 289	68.88 260	79.32 255

NR-MVSNet		69.54 140	68.85 134	71.59 175	78.05 179	43.81 279	74.20 217	80.86 135	65.18 17	62.76 234	84.52 135	52.35 83	83.59 148	50.96 216	70.78 225	87.37 49

TESTMET0.1,1		55.28 295	54.90 292	56.42 318	66.56 333	43.67 280	65.46 305	56.27 346	39.18 347	53.83 317	67.44 343	24.21 345	55.46 357	48.04 237	73.11 199	70.13 341

pmmvs3		44.92 324	41.95 328	53.86 328	52.58 367	43.55 281	62.11 322	46.90 366	26.05 362	40.63 358	60.19 355	11.08 367	57.91 346	31.83 339	46.15 355	60.11 353

GBi-Net		67.21 185	66.55 180	69.19 221	77.63 189	43.33 282	77.31 154	77.83 190	56.62 170	65.04 210	82.70 168	41.85 199	80.33 221	47.18 241	72.76 204	83.92 160

test1		67.21 185	66.55 180	69.19 221	77.63 189	43.33 282	77.31 154	77.83 190	56.62 170	65.04 210	82.70 168	41.85 199	80.33 221	47.18 241	72.76 204	83.92 160

FMVSNet1		66.70 200	65.87 197	69.19 221	77.49 196	43.33 282	77.31 154	77.83 190	56.45 175	64.60 218	82.70 168	38.08 238	80.33 221	46.08 250	72.31 212	83.92 160

FMVSNet2		66.93 195	66.31 191	68.79 228	77.63 189	42.98 285	76.11 181	77.47 196	56.62 170	65.22 208	82.17 184	41.85 199	80.18 224	47.05 244	72.72 207	83.20 188

TranMVSNet+NR-MVSNet		70.36 121	70.10 117	71.17 188	78.64 162	42.97 286	76.53 172	81.16 127	66.95 6	68.53 140	85.42 124	51.61 91	83.07 156	52.32 203	69.70 247	87.46 44

RPSCF		55.80 292	54.22 300	60.53 300	65.13 341	42.91 287	64.30 313	57.62 341	36.84 350	58.05 284	82.28 181	28.01 322	56.24 354	37.14 310	58.61 328	82.44 205

1112_ss		64.00 231	63.36 226	65.93 263	79.28 144	42.58 288	71.35 260	72.36 260	46.41 298	60.55 258	77.89 265	46.27 154	73.28 286	46.18 249	69.97 239	81.92 212

FMVSNet3		66.32 207	65.61 202	68.46 231	76.48 215	42.34 289	74.98 204	77.15 203	55.83 190	65.04 210	81.16 204	39.91 217	80.14 225	47.18 241	72.76 204	82.90 197

UniMVSNet_ETH3D		67.60 180	67.07 175	69.18 224	77.39 198	42.29 290	74.18 218	75.59 222	60.37 101	66.77 174	86.06 110	37.64 240	78.93 247	52.16 205	73.49 190	86.32 81

Anonymous202405211		66.84 197	65.99 196	69.40 220	80.19 126	42.21 291	71.11 267	71.31 265	58.80 134	67.90 151	86.39 102	29.83 311	79.65 227	49.60 227	78.78 141	86.33 79

TinyColmap		54.14 299	51.72 309	61.40 297	66.84 331	41.97 292	66.52 297	68.51 288	44.81 309	42.69 356	75.77 293	11.66 364	72.94 287	31.96 334	56.77 333	69.27 345

MDA-MVSNet_test_wron		50.71 316	48.95 317	56.00 321	61.17 356	41.84 293	51.90 350	56.45 343	40.96 339	44.79 351	67.84 340	30.04 310	55.07 359	36.71 314	50.69 349	71.11 337

MVS_0304		58.51 272	57.36 275	61.96 293	70.04 306	41.83 294	69.40 286	65.46 306	50.73 255	53.30 324	74.06 308	22.65 347	70.18 303	42.16 285	68.44 264	73.86 314

YYNet1		50.73 315	48.96 316	56.03 320	61.10 357	41.78 295	51.94 349	56.44 344	40.94 340	44.84 350	67.80 341	30.08 309	55.08 358	36.77 312	50.71 348	71.22 334

Anonymous20240521		55.30 294	54.41 296	57.96 313	60.92 360	41.73 296	71.09 268	71.06 268	41.18 337	48.65 340	73.31 312	16.93 357	59.25 342	42.54 282	64.01 297	72.90 318

ab-mvs		66.65 201	66.42 185	67.37 243	76.17 218	41.73 296	70.41 277	76.14 214	53.99 222	65.98 189	83.51 159	49.48 109	76.24 276	48.60 233	73.46 191	84.14 154

gm-plane-assit					71.40 290	41.72 298			48.85 273		73.31 312		82.48 177	48.90 231

VNet		69.68 136	70.19 114	68.16 235	79.73 136	41.63 299	70.53 274	77.38 199	60.37 101	70.69 104	86.63 95	51.08 97	77.09 267	53.61 195	81.69 105	85.75 102

tpmvs		58.47 273	56.95 279	63.03 287	70.20 302	41.21 300	67.90 293	67.23 295	49.62 266	54.73 309	70.84 324	34.14 272	76.24 276	36.64 316	61.29 318	71.64 332

HY-MVS	56.14 13	64.55 228	63.89 216	66.55 251	74.73 242	41.02 301	69.96 281	74.43 239	49.29 268	61.66 251	80.92 211	47.43 138	76.68 271	44.91 264	71.69 217	81.94 211

FPMVS		42.18 327	41.11 329	45.39 343	58.03 364	41.01 302	49.50 353	53.81 354	30.07 357	33.71 362	64.03 350	11.69 363	52.08 362	14.01 367	55.11 337	43.09 363

VPA-MVSNet		69.02 148	69.47 125	67.69 239	77.42 197	41.00 303	74.04 219	79.68 151	60.06 110	69.26 131	84.81 129	51.06 98	77.58 261	54.44 188	74.43 178	84.48 144

USDC		56.35 288	54.24 299	62.69 288	64.74 342	40.31 304	65.05 310	73.83 248	43.93 321	47.58 342	77.71 270	15.36 360	75.05 280	38.19 306	61.81 315	72.70 320

thres200		62.20 249	61.16 251	65.34 271	75.38 234	39.99 305	69.60 283	69.29 283	55.64 197	61.87 249	76.99 274	37.07 250	78.96 246	31.28 344	73.28 194	77.06 278

WR-MVS		68.47 163	68.47 141	68.44 232	80.20 125	39.84 306	73.75 230	76.07 215	64.68 25	68.11 149	83.63 156	50.39 104	79.14 240	49.78 221	69.66 248	86.34 78

EPNet_dtu		61.90 252	61.97 241	61.68 294	72.89 266	39.78 307	75.85 188	65.62 305	55.09 206	54.56 311	79.36 246	37.59 241	67.02 315	39.80 299	76.95 160	78.25 263
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
tfpn200view9		63.18 240	62.18 239	66.21 256	76.85 207	39.62 308	71.96 254	69.44 281	56.63 168	62.61 238	79.83 234	37.18 245	79.17 236	31.84 336	73.25 195	79.83 248

thres400		63.31 236	62.18 239	66.72 248	76.85 207	39.62 308	71.96 254	69.44 281	56.63 168	62.61 238	79.83 234	37.18 245	79.17 236	31.84 336	73.25 195	81.36 219

Test_1112_low_res		62.32 247	61.77 242	64.00 279	79.08 152	39.53 310	68.17 290	70.17 273	43.25 325	59.03 274	79.90 232	44.08 178	71.24 295	43.79 272	68.42 265	81.25 222

pm-mvs1		65.24 220	64.97 210	66.04 261	72.38 274	39.40 311	72.62 243	75.63 221	55.53 198	62.35 246	83.18 164	47.45 137	76.47 273	49.06 230	66.54 280	82.24 206

pmmvs6		63.69 233	62.82 232	66.27 255	70.63 296	39.27 312	73.13 236	75.47 224	52.69 235	59.75 267	82.30 180	39.71 220	77.03 268	47.40 239	64.35 296	82.53 201

tfpnnormal		62.47 245	61.63 244	64.99 274	74.81 240	39.01 313	71.22 263	73.72 249	55.22 203	60.21 259	80.09 231	41.26 212	76.98 269	30.02 348	68.09 267	78.97 259

thres600view7		63.30 237	62.27 237	66.41 252	77.18 201	38.87 314	72.35 247	69.11 285	56.98 163	62.37 245	80.96 210	37.01 251	79.00 245	31.43 343	73.05 200	81.36 219

CVMVSNet		59.63 269	59.14 262	61.08 299	74.47 246	38.84 315	75.20 197	68.74 287	31.15 356	58.24 282	76.51 283	32.39 296	68.58 308	49.77 222	65.84 286	75.81 290

thres100view900		63.28 238	62.41 236	65.89 264	77.31 199	38.66 316	72.65 241	69.11 285	57.07 161	62.45 243	81.03 208	37.01 251	79.17 236	31.84 336	73.25 195	79.83 248

TransMVSNet (Re)		64.72 224	64.33 213	65.87 265	75.22 235	38.56 317	74.66 211	75.08 235	58.90 132	61.79 250	82.63 171	51.18 95	78.07 254	43.63 273	55.87 336	80.99 229

XXY-MVS		60.68 262	61.67 243	57.70 316	70.43 299	38.45 318	64.19 314	66.47 299	48.05 282	63.22 229	80.86 213	49.28 112	60.47 337	45.25 263	67.28 275	74.19 310

MDTV_nov1_ep13			57.00 278		72.73 268	38.26 319	65.02 311	64.73 312	44.74 310	55.46 298	72.48 315	32.61 294	70.47 298	37.47 308	67.75 272

FIs		70.82 112	71.43 93	68.98 225	78.33 170	38.14 320	76.96 164	83.59 69	61.02 88	67.33 165	86.73 90	55.07 50	81.64 189	54.61 187	79.22 133	87.14 56

Gipuma		34.77 333	31.91 337	43.33 346	62.05 353	37.87 321	20.39 367	67.03 296	23.23 364	18.41 368	25.84 368	4.24 374	62.73 330	14.71 366	51.32 347	29.38 367
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
WTY-MVS		59.75 268	60.39 256	57.85 314	72.32 276	37.83 322	61.05 328	64.18 315	45.95 305	61.91 248	79.11 250	47.01 147	60.88 336	42.50 283	69.49 250	74.83 302

WR-MVS_H		67.02 193	66.92 176	67.33 245	77.95 182	37.75 323	77.57 148	82.11 100	62.03 77	62.65 237	82.48 176	50.57 102	79.46 230	42.91 280	64.01 297	84.79 137

Baseline_NR-MVSNet		67.05 192	67.56 154	65.50 268	75.65 227	37.70 324	75.42 192	74.65 238	59.90 113	68.14 148	83.15 165	49.12 117	77.20 265	52.23 204	69.78 244	81.60 215

MIMVSNet		57.35 281	57.07 277	58.22 310	74.21 253	37.18 325	62.46 319	60.88 331	48.88 272	55.29 302	75.99 291	31.68 300	62.04 333	31.87 335	72.35 210	75.43 295

KD-MVS_2432*1600		53.45 304	51.50 311	59.30 301	62.82 348	37.14 326	55.33 343	71.79 263	47.34 291	55.09 304	70.52 328	21.91 351	70.45 299	35.72 322	42.97 359	70.31 339

miper_refine_blended		53.45 304	51.50 311	59.30 301	62.82 348	37.14 326	55.33 343	71.79 263	47.34 291	55.09 304	70.52 328	21.91 351	70.45 299	35.72 322	42.97 359	70.31 339

ambc				65.13 273	63.72 347	37.07 328	47.66 357	78.78 167		54.37 314	71.42 321	11.24 366	80.94 206	45.64 255	53.85 343	77.38 273

GG-mvs-BLEND				62.34 290	71.36 291	37.04 329	69.20 287	57.33 342		54.73 309	65.48 349	30.37 305	77.82 257	34.82 325	74.93 176	72.17 330

CL-MVSNet_self_test		61.53 257	60.94 253	63.30 283	68.95 318	36.93 330	67.60 294	72.80 257	55.67 195	59.95 263	76.63 279	45.01 170	72.22 292	39.74 300	62.09 313	80.74 233

VPNet		67.52 181	68.11 148	65.74 266	79.18 148	36.80 331	72.17 250	72.83 256	62.04 76	67.79 159	85.83 116	48.88 119	76.60 272	51.30 214	72.97 201	83.81 165

pmmvs5		56.47 286	55.68 287	58.86 307	61.41 355	36.71 332	66.37 298	62.75 323	40.38 342	53.70 318	76.62 280	34.56 267	67.05 314	40.02 298	65.27 289	72.83 319

PEN-MVS		66.60 202	66.45 182	67.04 246	77.11 202	36.56 333	77.03 163	80.42 141	62.95 54	62.51 242	84.03 146	46.69 150	79.07 241	44.22 265	63.08 306	85.51 111

baseline1		63.81 232	63.87 218	63.62 280	76.29 216	36.36 334	71.78 256	67.29 294	56.05 187	64.23 223	82.95 166	47.11 143	74.41 283	47.30 240	61.85 314	80.10 243

FMVSNet5		55.86 291	54.93 291	58.66 309	71.05 292	36.35 335	64.18 315	62.48 325	46.76 296	50.66 336	74.73 303	25.80 338	64.04 326	33.11 331	65.57 288	75.59 293

CP-MVSNet		66.49 205	66.41 186	66.72 248	77.67 188	36.33 336	76.83 169	79.52 155	62.45 67	62.54 240	83.47 161	46.32 152	78.37 249	45.47 260	63.43 303	85.45 114

sss		56.17 290	56.57 281	54.96 323	66.93 330	36.32 337	57.94 336	61.69 328	41.67 334	58.64 278	75.32 299	38.72 230	56.25 353	42.04 287	66.19 283	72.31 329

PS-CasMVS		66.42 206	66.32 190	66.70 250	77.60 195	36.30 338	76.94 165	79.61 153	62.36 69	62.43 244	83.66 155	45.69 156	78.37 249	45.35 262	63.26 304	85.42 117

ECVR-MVS		67.72 178	67.51 158	68.35 233	79.46 141	36.29 339	74.79 208	66.93 297	58.72 135	67.19 167	88.05 73	36.10 255	81.38 195	52.07 206	84.25 75	87.39 47

PMVS	28.69 22	36.22 332	33.29 336	45.02 345	36.82 376	35.98 340	54.68 346	48.74 361	26.31 361	21.02 366	51.61 361	2.88 378	60.10 339	9.99 371	47.58 354	38.99 366
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
gg-mvs-nofinetune		57.86 279	56.43 283	62.18 291	72.62 270	35.35 341	66.57 296	56.33 345	50.65 257	57.64 286	57.10 357	30.65 303	76.36 274	37.38 309	78.88 138	74.82 303

DTE-MVSNet		65.58 213	65.34 205	66.31 253	76.06 221	34.79 342	76.43 174	79.38 158	62.55 65	61.66 251	83.83 151	45.60 158	79.15 239	41.64 292	60.88 320	85.00 129

tpm		57.34 282	58.16 270	54.86 324	71.80 284	34.77 343	67.47 295	56.04 348	48.20 280	60.10 260	76.92 275	37.17 247	53.41 360	40.76 294	65.01 291	76.40 286

test1111		67.21 185	67.14 174	67.42 242	79.24 146	34.76 344	73.89 227	65.65 304	58.71 137	66.96 171	87.95 75	36.09 256	80.53 216	52.03 207	83.79 79	86.97 59

FC-MVSNet-test		69.80 132	70.58 109	67.46 241	77.61 194	34.73 345	76.05 184	83.19 83	60.84 90	65.88 193	86.46 100	54.52 57	80.76 214	52.52 202	78.12 148	86.91 60

Patchmtry		57.16 283	56.47 282	59.23 303	69.17 317	34.58 346	62.98 317	63.15 321	44.53 313	56.83 290	74.84 301	35.83 258	68.71 307	40.03 297	60.91 319	74.39 308

tpmrst		58.24 275	58.70 265	56.84 317	66.97 329	34.32 347	69.57 284	61.14 330	47.17 294	58.58 279	71.60 320	41.28 211	60.41 338	49.20 229	62.84 307	75.78 291

test2506		65.33 219	64.61 212	67.50 240	79.46 141	34.19 348	74.43 215	51.92 355	58.72 135	66.75 175	88.05 73	25.99 337	80.92 208	51.94 208	84.25 75	87.39 47

MVS-HIRNet		45.52 323	44.48 326	48.65 341	68.49 322	34.05 349	59.41 333	44.50 367	27.03 360	37.96 361	50.47 363	26.16 336	64.10 325	26.74 357	59.52 324	47.82 361

Anonymous20231206		55.10 298	55.30 290	54.48 326	69.81 311	33.94 350	62.91 318	62.13 327	41.08 338	55.18 303	75.65 294	32.75 290	56.59 352	30.32 347	67.86 269	72.91 317

UnsupCasMVSNet_bld		50.07 317	48.87 318	53.66 329	60.97 359	33.67 351	57.62 338	64.56 313	39.47 346	47.38 343	64.02 352	27.47 326	59.32 341	34.69 326	43.68 358	67.98 348

EU-MVSNet		55.61 293	54.41 296	59.19 305	65.41 340	33.42 352	72.44 246	71.91 262	28.81 358	51.27 329	73.87 309	24.76 343	69.08 306	43.04 278	58.20 329	75.06 297

UnsupCasMVSNet_eth		53.16 309	52.47 307	55.23 322	59.45 362	33.39 353	59.43 332	69.13 284	45.98 302	50.35 338	72.32 316	29.30 315	58.26 345	42.02 288	44.30 357	74.05 311

LF4IMVS		42.95 325	42.26 327	45.04 344	48.30 369	32.50 354	54.80 345	48.49 362	28.03 359	40.51 359	70.16 331	9.24 369	43.89 366	31.63 340	49.18 353	58.72 354

dp		51.89 312	51.60 310	52.77 334	68.44 323	32.45 355	62.36 320	54.57 350	44.16 318	49.31 339	67.91 339	28.87 318	56.61 351	33.89 328	54.89 338	69.24 346

MIMVSNet1		55.17 297	54.31 298	57.77 315	70.03 307	32.01 356	65.68 303	64.81 310	49.19 269	46.75 347	76.00 289	25.53 340	64.04 326	28.65 352	62.13 312	77.26 276

EPMVS		53.96 300	53.69 303	54.79 325	66.12 337	31.96 357	62.34 321	49.05 360	44.42 316	55.54 297	71.33 322	30.22 307	56.70 350	41.65 291	62.54 310	75.71 292

LCM-MVSNet-Re		61.88 254	61.35 247	63.46 281	74.58 244	31.48 358	61.42 324	58.14 338	58.71 137	53.02 325	79.55 242	43.07 186	76.80 270	45.69 254	77.96 149	82.11 210

Vis-MVSNet (Re-imp)		63.69 233	63.88 217	63.14 285	74.75 241	31.04 359	71.16 265	63.64 317	56.32 178	59.80 266	84.99 126	44.51 174	75.46 278	39.12 301	80.62 109	82.92 195

Patchmatch-test		49.08 318	48.28 320	51.50 338	64.40 344	30.85 360	45.68 358	48.46 363	35.60 351	46.10 349	72.10 317	34.47 270	46.37 364	27.08 356	60.65 322	77.27 275

ADS-MVSNet		48.48 319	47.77 321	50.63 339	66.02 338	29.92 361	50.90 351	50.87 359	36.90 348	50.74 333	66.18 347	26.38 333	52.47 361	27.17 354	54.76 339	69.50 343

test0.0.03 1		53.32 307	53.59 304	52.50 335	62.81 350	29.45 362	59.51 331	54.11 352	50.08 263	54.40 313	74.31 306	32.62 292	55.92 355	30.50 346	63.95 299	72.15 331

LCM-MVSNet		40.30 329	35.88 334	53.57 330	42.24 371	29.15 363	45.21 360	60.53 332	22.23 366	28.02 364	50.98 362	3.72 376	61.78 334	31.22 345	38.76 363	69.78 342

test20.03		53.87 302	54.02 301	53.41 331	61.47 354	28.11 364	61.30 325	59.21 334	51.34 251	52.09 327	77.43 271	33.29 282	58.55 344	29.76 349	60.27 323	73.58 315

KD-MVS_self_test		55.22 296	53.89 302	59.21 304	57.80 365	27.47 365	57.75 337	74.32 241	47.38 289	50.90 332	70.00 333	28.45 320	70.30 301	40.44 295	57.92 330	79.87 247

wuyk23d		13.32 341	12.52 344	15.71 355	47.54 370	26.27 366	31.06 366	1.98 380	4.93 372	5.18 375	1.94 375	0.45 380	18.54 374	6.81 374	12.83 371	2.33 372

MDTV_nov1_ep13_2view						25.89 367	61.22 326		40.10 343	51.10 330		32.97 285		38.49 303		78.61 261

MVE	17.77 23	21.41 338	17.77 343	32.34 350	34.34 377	25.44 368	16.11 368	24.11 376	11.19 370	13.22 370	31.92 366	1.58 379	30.95 372	10.47 369	17.03 368	40.62 365
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
PatchT		53.17 308	53.44 305	52.33 336	68.29 324	25.34 369	58.21 335	54.41 351	44.46 315	54.56 311	69.05 337	33.32 281	60.94 335	36.93 311	61.76 316	70.73 338

ANet_high		41.38 328	37.47 333	53.11 332	39.73 374	24.45 370	56.94 339	69.69 276	47.65 286	26.04 365	52.32 359	12.44 362	62.38 332	21.80 361	10.61 372	72.49 323

testgi		51.90 311	52.37 308	50.51 340	60.39 361	23.55 371	58.42 334	58.15 337	49.03 271	51.83 328	79.21 249	22.39 348	55.59 356	29.24 351	62.64 308	72.40 328

E-PMN		23.77 336	22.73 340	26.90 352	42.02 372	20.67 372	42.66 362	35.70 371	17.43 367	10.28 373	25.05 369	6.42 372	42.39 368	10.28 370	14.71 369	17.63 368

DSMNet-mixed		39.30 331	38.72 331	41.03 347	51.22 368	19.66 373	45.53 359	31.35 374	15.83 369	39.80 360	67.42 344	22.19 349	45.13 365	22.43 360	52.69 344	58.31 355

EMVS		22.97 337	21.84 341	26.36 353	40.20 373	19.53 374	41.95 363	34.64 372	17.09 368	9.73 374	22.83 370	7.29 371	42.22 369	9.18 372	13.66 370	17.32 369

new_pmnet		34.13 334	34.29 335	33.64 349	52.63 366	18.23 375	44.43 361	33.90 373	22.81 365	30.89 363	53.18 358	10.48 368	35.72 371	20.77 362	39.51 361	46.98 362

DeepMVS_CX				12.03 356	17.97 378	10.91 376		10.60 379	7.46 371	11.07 372	28.36 367	3.28 377	11.29 375	8.01 373	9.74 374	13.89 370

new-patchmatchnet		47.56 321	47.73 322	47.06 342	58.81 363	9.37 377	48.78 355	59.21 334	43.28 324	44.22 353	68.66 338	25.67 339	57.20 349	31.57 342	49.35 352	74.62 306

PMMVS2		27.40 335	25.91 338	31.87 351	39.46 375	6.57 378	31.17 365	28.52 375	23.96 363	20.45 367	48.94 364	4.20 375	37.94 370	16.51 364	19.97 367	51.09 359

tmp_tt		9.43 342	11.14 345	4.30 357	2.38 380	4.40 379	13.62 369	16.08 378	0.39 374	15.89 369	13.06 371	15.80 359	5.54 376	12.63 368	10.46 373	2.95 371

test_method		19.68 339	18.10 342	24.41 354	13.68 379	3.11 380	12.06 370	42.37 369	2.00 373	11.97 371	36.38 365	5.77 373	29.35 373	15.06 365	23.65 366	40.76 364

N_pmnet		39.35 330	40.28 330	36.54 348	63.76 346	1.62 381	49.37 354	0.76 381	34.62 353	43.61 354	66.38 346	26.25 335	42.57 367	26.02 359	51.77 345	65.44 350

test123		4.73 344	6.30 347	0.02 358	0.01 381	0.01 382	56.36 341	0.00 382	0.01 376	0.04 377	0.21 377	0.01 381	0.00 377	0.03 376	0.00 375	0.04 373

test_blank		0.00 347	0.00 350	0.00 360	0.00 383	0.00 383	0.00 371	0.00 382	0.00 378	0.00 379	0.00 378	0.00 382	0.00 377	0.00 377	0.00 375	0.00 375

uanet_test		0.00 347	0.00 350	0.00 360	0.00 383	0.00 383	0.00 371	0.00 382	0.00 378	0.00 379	0.00 378	0.00 382	0.00 377	0.00 377	0.00 375	0.00 375

cdsmvs_eth3d_5k		17.50 340	23.34 339	0.00 360	0.00 383	0.00 383	0.00 371	78.63 171	0.00 378	0.00 379	82.18 182	49.25 113	0.00 377	0.00 377	0.00 375	0.00 375

pcd_1.5k_mvsjas		3.92 346	5.23 349	0.00 360	0.00 383	0.00 383	0.00 371	0.00 382	0.00 378	0.00 379	0.00 378	47.05 144	0.00 377	0.00 377	0.00 375	0.00 375

sosnet-low-res		0.00 347	0.00 350	0.00 360	0.00 383	0.00 383	0.00 371	0.00 382	0.00 378	0.00 379	0.00 378	0.00 382	0.00 377	0.00 377	0.00 375	0.00 375

sosnet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 383	0.00 371	0.00 382	0.00 378	0.00 379	0.00 378	0.00 382	0.00 377	0.00 377	0.00 375	0.00 375

uncertanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 383	0.00 371	0.00 382	0.00 378	0.00 379	0.00 378	0.00 382	0.00 377	0.00 377	0.00 375	0.00 375

Regformer		0.00 347	0.00 350	0.00 360	0.00 383	0.00 383	0.00 371	0.00 382	0.00 378	0.00 379	0.00 378	0.00 382	0.00 377	0.00 377	0.00 375	0.00 375

testmvs		4.52 345	6.03 348	0.01 359	0.01 381	0.00 383	53.86 348	0.00 382	0.01 376	0.04 377	0.27 376	0.00 382	0.00 377	0.04 375	0.00 375	0.03 374

ab-mvs-re		6.49 343	8.65 346	0.00 360	0.00 383	0.00 383	0.00 371	0.00 382	0.00 378	0.00 379	77.89 265	0.00 382	0.00 377	0.00 377	0.00 375	0.00 375

uanet		0.00 347	0.00 350	0.00 360	0.00 383	0.00 383	0.00 371	0.00 382	0.00 378	0.00 379	0.00 378	0.00 382	0.00 377	0.00 377	0.00 375	0.00 375

PC_three_1452									55.09 206	84.46 4	89.84 49	66.68 5	89.41 17	74.24 37	91.38 2	88.42 9

eth-test2					0.00 383

eth-test					0.00 383

test_241102_TWO								86.73 16	64.18 35	84.26 5	91.84 8	65.19 6	90.83 5	78.63 15	90.70 7	87.65 36

9.14			78.75 16		83.10 78		84.15 44	88.26 2	59.90 113	78.57 27	90.36 28	57.51 32	86.86 66	77.39 18	89.52 25

test_0728_THIRD									65.04 20	83.82 8	92.00 3	64.69 10	90.75 8	79.48 4	90.63 10	88.09 19

GSMVS																78.05 266

sam_mvs1												34.74 266				78.05 266

sam_mvs												33.43 280

MTGPA								80.97 132

test_post1							68.67 289				3.64 373	32.39 296	69.49 304	44.17 266

test_post											3.55 374	33.90 275	66.52 317

patchmatchnet-post											64.03 350	34.50 268	74.27 284

MTMP							86.03 19	17.08 377

test9_res														75.28 31	88.31 37	83.81 165

agg_prior2														73.09 50	87.93 44	84.33 147

test_prior2							81.75 86		60.37 101	75.01 44	89.06 59	56.22 39		72.19 53	88.96 28

旧先验2							76.08 182		45.32 307	76.55 35			65.56 323	58.75 160

新几何2							76.12 180

无先验							79.66 118	74.30 243	48.40 278				80.78 212	53.62 193		79.03 258

原ACMM2							79.02 125

testdata2													72.18 293	46.95 245

segment_acmp												54.23 59

testdata1							72.65 241		60.50 96

plane_prior5								84.01 55					87.21 55	68.16 81	80.58 111	84.65 140

plane_prior4											86.10 108

plane_prior2							84.22 41		64.52 28

plane_prior1					81.27 106

n2								0.00 382

nn								0.00 382

door-mid								47.19 365

test11								83.47 72

door								47.60 364

HQP-NCC					80.66 113		82.31 79		62.10 73	67.85 153

ACMP_Plane					80.66 113		82.31 79		62.10 73	67.85 153

BP-MVS														67.04 92

HQP4-MVS										67.85 153			86.93 64			84.32 148

HQP3-MVS								83.90 60							80.35 117

HQP2-MVS												45.46 162

ACMMP++_ref															74.07 182

ACMMP++															72.16 213

Test By Simon												48.33 125

High-resolution multi-view benchmark