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.

MM		80.20 7	80.28 8	79.99 2	82.19 85	60.01 49	86.19 17	83.93 55	73.19 1	77.08 40	91.21 18	57.23 33	90.73 10	83.35 1	88.12 34	89.22 7

MGCNet		78.45 18	78.28 19	78.98 26	80.73 110	57.91 85	84.68 36	81.64 116	68.35 2	75.77 46	90.38 30	53.98 66	90.26 13	81.30 3	87.68 42	88.77 13

CANet		76.46 41	75.93 45	78.06 39	81.29 100	57.53 91	82.35 75	83.31 82	67.78 3	70.09 142	86.34 127	54.92 56	88.90 25	72.68 70	84.55 69	87.76 45

UA-Net		73.13 87	72.93 86	73.76 136	83.58 67	51.66 214	78.75 126	77.66 215	67.75 4	72.61 110	89.42 52	49.82 135	83.29 159	53.61 249	83.14 83	86.32 107

CNVR-MVS		79.84 10	79.97 10	79.45 11	87.90 2	62.17 17	84.37 40	85.03 37	66.96 5	77.58 34	90.06 41	59.47 21	89.13 22	78.67 17	89.73 16	87.03 75

TranMVSNet+NR-MVSNet		70.36 147	70.10 142	71.17 225	78.64 163	42.97 339	76.53 197	81.16 137	66.95 6	68.53 173	85.42 157	51.61 111	83.07 163	52.32 257	69.70 309	87.46 57

3Dnovator+	66.72 4	75.84 51	74.57 63	79.66 9	82.40 82	59.92 51	85.83 23	86.32 16	66.92 7	67.80 198	89.24 56	42.03 237	89.38 19	64.07 142	86.50 59	89.69 3

NCCC		78.58 16	78.31 18	79.39 12	87.51 12	62.61 13	85.20 31	84.42 46	66.73 8	74.67 69	89.38 54	55.30 51	89.18 21	74.19 58	87.34 46	86.38 99

SteuartSystems-ACMMP		79.48 11	79.31 11	79.98 3	83.01 76	62.18 16	87.60 9	85.83 20	66.69 9	78.03 31	90.98 19	54.26 62	90.06 14	78.42 23	89.02 23	87.69 47
Skip Steuart: Steuart Systems R&D Blog.
EPNet		73.09 88	72.16 98	75.90 75	75.95 248	56.28 110	83.05 62	72.39 304	66.53 10	65.27 250	87.00 101	50.40 128	85.47 114	62.48 168	86.32 60	85.94 119
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
UniMVSNet_NR-MVSNet		71.11 128	71.00 122	71.44 212	79.20 143	44.13 325	76.02 212	82.60 102	66.48 11	68.20 178	84.60 175	56.82 37	82.82 176	54.62 239	70.43 289	87.36 66

MSP-MVS		81.06 3	81.40 4	80.02 1	86.21 31	62.73 9	86.09 18	86.83 8	65.51 12	83.81 10	90.51 26	63.71 12	89.23 20	81.51 2	88.44 27	88.09 34
Zhenlong Yuan, Cong Liu, Fei Shen, Zhaoxin Li, Jingguo luo, Tianlu Mao and Zhaoqi Wang: MSP-MVS: Multi-granularity Segmentation Prior Guided Multi-View Stereo. AAAI2025
HPM-MVS++		79.88 9	80.14 9	79.10 21	88.17 1	64.80 1	86.59 12	83.70 66	65.37 13	78.78 24	90.64 22	58.63 25	87.24 55	79.00 14	90.37 14	85.26 158

NR-MVSNet		69.54 172	68.85 164	71.59 206	78.05 186	43.81 330	74.20 253	80.86 144	65.18 14	62.76 294	84.52 176	52.35 97	83.59 153	50.96 272	70.78 284	87.37 64

MTAPA		76.90 35	76.42 39	78.35 35	86.08 37	63.57 2	74.92 238	80.97 142	65.13 15	75.77 46	90.88 20	48.63 152	86.66 74	77.23 30	88.17 33	84.81 174

DVP-MVS++		81.67 1	82.40 1	79.47 10	87.24 14	59.15 65	88.18 1	87.15 3	65.04 16	84.26 5	91.86 6	67.01 1	90.84 3	79.48 7	91.38 2	88.42 21

test_0728_THIRD									65.04 16	83.82 8	92.00 3	64.69 10	90.75 8	79.48 7	90.63 10	88.09 34

EI-MVSNet-Vis-set		72.42 104	71.59 106	74.91 96	78.47 167	54.02 153	77.05 183	79.33 170	65.03 18	71.68 123	79.35 300	52.75 89	84.89 127	66.46 121	74.23 227	85.83 125

casdiffmvs_mvg		76.14 47	76.30 40	75.66 83	76.46 242	51.83 212	79.67 115	85.08 34	65.02 19	75.84 45	88.58 69	59.42 22	85.08 120	72.75 69	83.93 78	90.08 1
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
test_one_0601					87.58 9	59.30 62		86.84 7	65.01 20	83.80 11	91.86 6	64.03 11

ETV-MVS		74.46 68	73.84 74	76.33 70	79.27 141	55.24 136	79.22 121	85.00 39	64.97 21	72.65 109	79.46 296	53.65 78	87.87 44	67.45 111	82.91 89	85.89 122

NormalMVS		76.26 45	75.74 48	77.83 45	82.75 80	59.89 52	84.36 41	83.21 86	64.69 22	74.21 76	87.40 90	49.48 139	86.17 92	68.04 103	87.55 43	87.42 59

SymmetryMVS		75.28 56	74.60 62	77.30 54	83.85 65	59.89 52	84.36 41	75.51 253	64.69 22	74.21 76	87.40 90	49.48 139	86.17 92	68.04 103	83.88 79	85.85 123

WR-MVS		68.47 202	68.47 175	68.44 277	80.20 121	39.84 367	73.75 265	76.07 241	64.68 24	68.11 186	83.63 198	50.39 129	79.14 261	49.78 277	69.66 310	86.34 103

XVS		77.17 32	76.56 37	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 116	90.01 45	47.95 159	88.01 40	71.55 83	86.74 55	86.37 101

X-MVStestdata		70.21 150	67.28 209	79.00 23	86.32 29	62.62 11	85.83 23	83.92 56	64.55 25	72.17 116	6.49 469	47.95 159	88.01 40	71.55 83	86.74 55	86.37 101

HQP_MVS		74.31 69	73.73 75	76.06 73	81.41 97	56.31 108	84.22 46	84.01 53	64.52 27	69.27 161	86.10 135	45.26 202	87.21 59	68.16 101	80.58 119	84.65 178

plane_prior2							84.22 46		64.52 27

EI-MVSNet-UG-set		71.92 114	71.06 121	74.52 113	77.98 189	53.56 164	76.62 194	79.16 171	64.40 29	71.18 130	78.95 305	52.19 99	84.66 134	65.47 132	73.57 240	85.32 154

DU-MVS		70.01 155	69.53 149	71.44 212	78.05 186	44.13 325	75.01 234	81.51 119	64.37 30	68.20 178	84.52 176	49.12 149	82.82 176	54.62 239	70.43 289	87.37 64

DVP-MVS		80.84 4	81.64 3	78.42 34	87.75 7	59.07 69	87.85 5	85.03 37	64.26 31	83.82 8	92.00 3	64.82 8	90.75 8	78.66 18	90.61 11	85.45 146
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 69	87.86 4	86.83 8	64.26 31	84.19 7	91.92 5	64.82 8

test_241102_ONE					87.77 4	58.90 74		86.78 10	64.20 33	85.97 1	91.34 16	66.87 3	90.78 7

SED-MVS		81.56 2	82.30 2	79.32 13	87.77 4	58.90 74	87.82 7	86.78 10	64.18 34	85.97 1	91.84 8	66.87 3	90.83 5	78.63 20	90.87 5	88.23 29

test_241102_TWO								86.73 12	64.18 34	84.26 5	91.84 8	65.19 6	90.83 5	78.63 20	90.70 7	87.65 49

LFMVS		71.78 117	71.59 106	72.32 185	83.40 71	46.38 301	79.75 113	71.08 313	64.18 34	72.80 106	88.64 68	42.58 232	83.72 149	57.41 215	84.49 72	86.86 80

IS-MVSNet		71.57 121	71.00 122	73.27 161	78.86 153	45.63 312	80.22 104	78.69 185	64.14 37	66.46 225	87.36 93	49.30 143	85.60 107	50.26 276	83.71 82	88.59 17

plane_prior3						56.09 114			63.92 38	69.27 161

MP-MVS		78.35 20	78.26 21	78.64 31	86.54 25	63.47 4	86.02 20	83.55 71	63.89 39	73.60 85	90.60 23	54.85 57	86.72 72	77.20 31	88.06 36	85.74 132
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
DELS-MVS		74.76 61	74.46 64	75.65 84	77.84 193	52.25 202	75.59 220	84.17 50	63.76 40	73.15 94	82.79 213	59.58 20	86.80 70	67.24 112	86.04 61	87.89 37
Christian Sormann, Emanuele Santellani, Mattia Rossi, Andreas Kuhn, Friedrich Fraundorfer: DELS-MVS: Deep Epipolar Line Search for Multi-View Stereo. Winter Conference on Applications of Computer Vision (WACV), 2023
OPM-MVS		74.73 62	74.25 68	76.19 72	80.81 109	59.01 72	82.60 72	83.64 68	63.74 41	72.52 111	87.49 87	47.18 175	85.88 102	69.47 94	80.78 113	83.66 219
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
UniMVSNet (Re)		70.63 140	70.20 137	71.89 192	78.55 164	45.29 315	75.94 213	82.92 96	63.68 42	68.16 181	83.59 199	53.89 69	83.49 156	53.97 245	71.12 280	86.89 79

GST-MVS		78.14 22	77.85 24	78.99 25	86.05 38	61.82 22	85.84 22	85.21 31	63.56 43	74.29 75	90.03 43	52.56 91	88.53 29	74.79 54	88.34 29	86.63 92

testing3-2		62.06 308	62.36 291	61.17 360	79.29 138	30.31 441	64.09 382	63.49 381	63.50 44	62.84 291	82.22 235	32.35 363	69.02 366	40.01 365	73.43 245	84.17 195

EC-MVSNet		75.84 51	75.87 47	75.74 81	78.86 153	52.65 192	83.73 56	86.08 18	63.47 45	72.77 107	87.25 98	53.13 83	87.93 42	71.97 78	85.57 64	86.66 90

ZNCC-MVS		78.82 13	78.67 16	79.30 14	86.43 28	62.05 18	86.62 11	86.01 19	63.32 46	75.08 56	90.47 29	53.96 68	88.68 27	76.48 36	89.63 20	87.16 72

DPE-MVS		80.56 5	80.98 5	79.29 15	87.27 13	60.56 41	85.71 27	86.42 14	63.28 47	83.27 13	91.83 10	64.96 7	90.47 11	76.41 37	89.67 18	86.84 81
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
CS-MVS		76.25 46	75.98 44	77.06 56	80.15 124	55.63 126	84.51 39	83.90 58	63.24 48	73.30 88	87.27 97	55.06 53	86.30 89	71.78 80	84.58 68	89.25 6

DeepC-MVS	69.38 2	78.56 17	78.14 22	79.83 7	83.60 66	61.62 23	84.17 48	86.85 6	63.23 49	73.84 83	90.25 36	57.68 29	89.96 15	74.62 55	89.03 22	87.89 37
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
VDD-MVS		72.50 100	72.09 99	73.75 138	81.58 93	49.69 252	77.76 159	77.63 216	63.21 50	73.21 91	89.02 58	42.14 236	83.32 158	61.72 175	82.50 95	88.25 27

plane_prior						56.31 108	83.58 59		63.19 51						80.48 122

ACMMP		76.02 49	75.33 53	78.07 38	85.20 49	61.91 20	85.49 30	84.44 45	63.04 52	69.80 152	89.74 51	45.43 198	87.16 61	72.01 76	82.87 91	85.14 160
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
PEN-MVS		66.60 247	66.45 227	67.04 291	77.11 223	36.56 400	77.03 184	80.42 152	62.95 53	62.51 302	84.03 187	46.69 183	79.07 263	44.22 327	63.08 373	85.51 141

APDe-MVS		80.16 8	80.59 6	78.86 29	86.64 21	60.02 48	88.12 3	86.42 14	62.94 54	82.40 14	92.12 2	59.64 19	89.76 16	78.70 15	88.32 31	86.79 83
Zhaojie Zeng, Yuesong Wang, Tao Guan: Matching Ambiguity-Resilient Multi-View Stereo via Adaptive Patch Deformation. Pattern Recognition
mPP-MVS		76.54 40	75.93 45	78.34 36	86.47 26	63.50 3	85.74 26	82.28 106	62.90 55	71.77 121	90.26 35	46.61 184	86.55 80	71.71 81	85.66 63	84.97 169

ACMMP_NAP		78.77 15	78.78 14	78.74 30	85.44 45	61.04 31	83.84 55	85.16 32	62.88 56	78.10 29	91.26 17	52.51 92	88.39 30	79.34 9	90.52 13	86.78 84

DeepPCF-MVS	69.58 1	79.03 12	79.00 13	79.13 19	84.92 56	60.32 46	83.03 63	85.33 29	62.86 57	80.17 17	90.03 43	61.76 14	88.95 24	74.21 57	88.67 26	88.12 33

HFP-MVS		78.01 24	77.65 26	79.10 21	86.71 19	62.81 8	86.29 14	84.32 48	62.82 58	73.96 80	90.50 27	53.20 82	88.35 31	74.02 60	87.05 47	86.13 114

ACMMPR		77.71 26	77.23 29	79.16 17	86.75 18	62.93 7	86.29 14	84.24 49	62.82 58	73.55 86	90.56 25	49.80 136	88.24 33	74.02 60	87.03 48	86.32 107

region2R		77.67 28	77.18 30	79.15 18	86.76 17	62.95 6	86.29 14	84.16 51	62.81 60	73.30 88	90.58 24	49.90 133	88.21 34	73.78 62	87.03 48	86.29 111

casdiffmvs		74.80 60	74.89 60	74.53 112	75.59 256	50.37 234	78.17 144	85.06 36	62.80 61	74.40 72	87.86 81	57.88 27	83.61 152	69.46 95	82.79 93	89.59 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
baseline		74.61 65	74.70 61	74.34 117	75.70 251	49.99 243	77.54 164	84.63 43	62.73 62	73.98 79	87.79 84	57.67 30	83.82 148	69.49 93	82.74 94	89.20 8

HPM-MVS		77.28 30	76.85 31	78.54 32	85.00 51	60.81 38	82.91 66	85.08 34	62.57 63	73.09 99	89.97 46	50.90 124	87.48 53	75.30 48	86.85 53	87.33 67
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
DTE-MVSNet		65.58 261	65.34 253	66.31 302	76.06 247	34.79 413	76.43 199	79.38 169	62.55 64	61.66 313	83.83 192	45.60 192	79.15 260	41.64 357	60.88 388	85.00 166

SMA-MVS		80.28 6	80.39 7	79.95 4	86.60 23	61.95 19	86.33 13	85.75 22	62.49 65	82.20 15	92.28 1	56.53 38	89.70 17	79.85 6	91.48 1	88.19 31
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
CP-MVSNet		66.49 250	66.41 231	66.72 293	77.67 200	36.33 403	76.83 192	79.52 166	62.45 66	62.54 300	83.47 205	46.32 186	78.37 275	45.47 322	63.43 370	85.45 146

CP-MVS		77.12 33	76.68 33	78.43 33	86.05 38	63.18 5	87.55 10	83.45 74	62.44 67	72.68 108	90.50 27	48.18 157	87.34 54	73.59 64	85.71 62	84.76 177

PS-CasMVS		66.42 251	66.32 235	66.70 295	77.60 208	36.30 405	76.94 186	79.61 164	62.36 68	62.43 305	83.66 197	45.69 190	78.37 275	45.35 324	63.26 371	85.42 149

3Dnovator	64.47 5	72.49 101	71.39 112	75.79 78	77.70 198	58.99 73	80.66 99	83.15 91	62.24 69	65.46 246	86.59 117	42.38 235	85.52 110	59.59 195	84.72 67	82.85 242

MP-MVS-pluss		78.35 20	78.46 17	78.03 40	84.96 52	59.52 58	82.93 65	85.39 28	62.15 70	76.41 44	91.51 11	52.47 94	86.78 71	80.66 4	89.64 19	87.80 43
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HQP-NCC					80.66 111		82.31 77		62.10 71	67.85 192

ACMP_Plane					80.66 111		82.31 77		62.10 71	67.85 192

HQP-MVS		73.45 79	72.80 89	75.40 88	80.66 111	54.94 139	82.31 77	83.90 58	62.10 71	67.85 192	85.54 155	45.46 196	86.93 67	67.04 115	80.35 123	84.32 188

SPE-MVS-test		75.62 54	75.31 54	76.56 67	80.63 114	55.13 137	83.88 54	85.22 30	62.05 74	71.49 128	86.03 138	53.83 70	86.36 87	67.74 106	86.91 52	88.19 31

VPNet		67.52 226	68.11 188	65.74 316	79.18 145	36.80 398	72.17 295	72.83 300	62.04 75	67.79 199	85.83 145	48.88 151	76.60 317	51.30 268	72.97 254	83.81 209

WR-MVS_H		67.02 238	66.92 219	67.33 290	77.95 190	37.75 387	77.57 162	82.11 109	62.03 76	62.65 297	82.48 228	50.57 127	79.46 250	42.91 345	64.01 363	84.79 175

DeepC-MVS_fast	68.24 3	77.25 31	76.63 34	79.12 20	86.15 34	60.86 36	84.71 35	84.85 41	61.98 77	73.06 100	88.88 62	53.72 74	89.06 23	68.27 98	88.04 37	87.42 59
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
SF-MVS		78.82 13	79.22 12	77.60 47	82.88 78	57.83 86	84.99 32	88.13 2	61.86 78	79.16 21	90.75 21	57.96 26	87.09 64	77.08 33	90.18 15	87.87 39

PGM-MVS		76.77 38	76.06 43	78.88 28	86.14 35	62.73 9	82.55 73	83.74 65	61.71 79	72.45 114	90.34 33	48.48 155	88.13 37	72.32 73	86.85 53	85.78 126

fmvsm_s_conf0.5_n_8		74.30 70	74.39 65	74.01 127	75.33 262	52.89 185	78.24 140	77.32 224	61.65 80	78.13 28	88.90 61	52.82 88	81.54 204	78.46 22	78.67 158	87.60 52

Effi-MVS+		73.31 83	72.54 93	75.62 85	77.87 191	53.64 161	79.62 117	79.61 164	61.63 81	72.02 119	82.61 218	56.44 40	85.97 100	63.99 145	79.07 148	87.25 69

MG-MVS		73.96 74	73.89 73	74.16 124	85.65 42	49.69 252	81.59 88	81.29 130	61.45 82	71.05 131	88.11 73	51.77 108	87.73 48	61.05 181	83.09 84	85.05 165

fmvsm_s_conf0.5_n_9		75.16 57	75.22 56	75.01 95	78.34 174	55.37 134	77.30 174	73.95 285	61.40 83	79.46 19	90.14 37	57.07 34	81.15 214	80.00 5	79.31 140	88.51 20

LPG-MVS_test		72.74 94	71.74 105	75.76 79	80.22 119	57.51 92	82.55 73	83.40 76	61.32 84	66.67 222	87.33 95	39.15 277	86.59 75	67.70 107	77.30 186	83.19 232

LGP-MVS_train				75.76 79	80.22 119	57.51 92		83.40 76	61.32 84	66.67 222	87.33 95	39.15 277	86.59 75	67.70 107	77.30 186	83.19 232

CLD-MVS		73.33 82	72.68 91	75.29 92	78.82 155	53.33 173	78.23 141	84.79 42	61.30 86	70.41 139	81.04 262	52.41 95	87.12 62	64.61 141	82.49 96	85.41 150
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
RRT-MVS		71.46 124	70.70 128	73.74 139	77.76 196	49.30 260	76.60 195	80.45 151	61.25 87	68.17 180	84.78 165	44.64 210	84.90 126	64.79 137	77.88 174	87.03 75

viewcassd2359sk11		73.56 77	73.41 81	74.00 128	77.13 220	50.35 235	76.86 190	83.69 67	61.23 88	73.14 95	86.38 126	56.09 46	82.96 166	67.15 113	79.01 150	88.70 15

fmvsm_s_conf0.5_n_3		73.55 78	74.39 65	71.03 230	74.09 300	51.86 211	77.77 158	75.60 249	61.18 89	78.67 25	88.98 59	55.88 48	77.73 290	78.69 16	78.68 157	83.50 224

MVS_111021_HR		74.02 73	73.46 79	75.69 82	83.01 76	60.63 40	77.29 175	78.40 203	61.18 89	70.58 137	85.97 140	54.18 64	84.00 145	67.52 110	82.98 88	82.45 254

balanced_conf03		76.58 39	76.55 38	76.68 62	81.73 91	52.90 183	80.94 94	85.70 24	61.12 91	74.90 62	87.17 99	56.46 39	88.14 36	72.87 68	88.03 38	89.00 9

FIs		70.82 137	71.43 110	68.98 270	78.33 175	38.14 383	76.96 185	83.59 70	61.02 92	67.33 206	86.73 109	55.07 52	81.64 200	54.61 241	79.22 143	87.14 73

FOURS1					86.12 36	60.82 37	88.18 1	83.61 69	60.87 93	81.50 16

FC-MVSNet-test		69.80 162	70.58 131	67.46 286	77.61 207	34.73 416	76.05 210	83.19 90	60.84 94	65.88 240	86.46 123	54.52 61	80.76 229	52.52 256	78.12 170	86.91 78

v8		70.33 148	69.28 155	73.49 153	73.15 313	50.22 237	78.62 131	80.78 145	60.79 95	66.45 226	82.11 242	49.35 142	84.98 123	63.58 155	68.71 325	85.28 156

CSCG		76.92 34	76.75 32	77.41 51	83.96 64	59.60 56	82.95 64	86.50 13	60.78 96	75.27 51	84.83 163	60.76 15	86.56 77	67.86 105	87.87 41	86.06 116

Vis-MVSNet		72.18 108	71.37 113	74.61 107	81.29 100	55.41 132	80.90 95	78.28 206	60.73 97	69.23 164	88.09 74	44.36 214	82.65 180	57.68 212	81.75 106	85.77 129
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
KinetiMVS		71.26 127	70.16 139	74.57 110	74.59 283	52.77 190	75.91 214	81.20 134	60.72 98	69.10 167	85.71 150	41.67 246	83.53 154	63.91 148	78.62 160	87.42 59

BP-MVS1		73.41 81	72.25 97	76.88 57	76.68 235	53.70 159	79.15 122	81.07 138	60.66 99	71.81 120	87.39 92	40.93 259	87.24 55	71.23 85	81.29 110	89.71 2

APD-MVS		78.02 23	78.04 23	77.98 41	86.44 27	60.81 38	85.52 28	84.36 47	60.61 100	79.05 22	90.30 34	55.54 50	88.32 32	73.48 65	87.03 48	84.83 173
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMP	63.53 6	72.30 106	71.20 118	75.59 87	80.28 117	57.54 90	82.74 69	82.84 100	60.58 101	65.24 254	86.18 132	39.25 275	86.03 98	66.95 119	76.79 194	83.22 230
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
lecture		77.75 25	77.84 25	77.50 49	82.75 80	57.62 89	85.92 21	86.20 17	60.53 102	78.99 23	91.45 12	51.51 113	87.78 47	75.65 44	87.55 43	87.10 74

testdata1							72.65 284		60.50 103

UGNet		68.81 192	67.39 204	73.06 165	78.33 175	54.47 145	79.77 112	75.40 256	60.45 104	63.22 283	84.40 180	32.71 352	80.91 225	51.71 266	80.56 121	83.81 209
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
viewmacassd2359aftdt		73.15 86	73.16 83	73.11 164	75.15 268	49.31 259	77.53 166	83.21 86	60.42 105	73.20 92	87.34 94	53.82 71	81.05 219	67.02 117	80.79 112	88.96 10

h-mvs33		72.71 95	71.49 109	76.40 68	81.99 88	59.58 57	76.92 187	76.74 234	60.40 106	74.81 64	85.95 141	45.54 194	85.76 105	70.41 90	70.61 287	83.86 208

hse-mvs2		71.04 129	69.86 143	74.60 108	79.58 133	57.12 102	73.96 257	75.25 259	60.40 106	74.81 64	81.95 244	45.54 194	82.90 169	70.41 90	66.83 342	83.77 213

EPP-MVSNet		72.16 111	71.31 115	74.71 101	78.68 159	49.70 250	82.10 81	81.65 115	60.40 106	65.94 236	85.84 144	51.74 109	86.37 86	55.93 225	79.55 135	88.07 36

UniMVSNet_ETH3D		67.60 225	67.07 218	69.18 267	77.39 213	42.29 344	74.18 254	75.59 250	60.37 109	66.77 218	86.06 137	37.64 293	78.93 270	52.16 259	73.49 242	86.32 107

test_prior2							81.75 84		60.37 109	75.01 57	89.06 57	56.22 43		72.19 74	88.96 24

SD-MVS		77.70 27	77.62 27	77.93 42	84.47 59	61.88 21	84.55 38	83.87 61	60.37 109	79.89 18	89.38 54	54.97 55	85.58 109	76.12 40	84.94 66	86.33 105
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
VNet		69.68 166	70.19 138	68.16 280	79.73 130	41.63 353	70.53 319	77.38 221	60.37 109	70.69 134	86.63 114	51.08 120	77.09 302	53.61 249	81.69 108	85.75 131

sasdasda		74.67 63	74.98 58	73.71 141	78.94 151	50.56 231	80.23 102	83.87 61	60.30 113	77.15 37	86.56 119	59.65 17	82.00 194	66.01 126	82.12 97	88.58 18

canonicalmvs		74.67 63	74.98 58	73.71 141	78.94 151	50.56 231	80.23 102	83.87 61	60.30 113	77.15 37	86.56 119	59.65 17	82.00 194	66.01 126	82.12 97	88.58 18

v7n		69.01 188	67.36 206	73.98 129	72.51 327	52.65 192	78.54 135	81.30 129	60.26 115	62.67 296	81.62 251	43.61 220	84.49 135	57.01 216	68.70 326	84.79 175

reproduce-ours		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 79	60.22 116	77.85 32	91.42 14	50.67 125	87.69 49	72.46 71	84.53 70	85.46 144

our_new_method		76.90 35	76.58 35	77.87 43	83.99 62	60.46 43	84.75 33	83.34 79	60.22 116	77.85 32	91.42 14	50.67 125	87.69 49	72.46 71	84.53 70	85.46 144

HPM-MVS_fast		74.30 70	73.46 79	76.80 59	84.45 60	59.04 71	83.65 58	81.05 139	60.15 118	70.43 138	89.84 48	41.09 258	85.59 108	67.61 109	82.90 90	85.77 129

VPA-MVSNet		69.02 187	69.47 151	67.69 284	77.42 212	41.00 360	74.04 255	79.68 162	60.06 119	69.26 163	84.81 164	51.06 121	77.58 292	54.44 242	74.43 225	84.48 185

v10		70.21 150	69.02 160	73.81 133	73.51 307	50.92 223	78.74 127	81.39 122	60.05 120	66.39 227	81.83 247	47.58 166	85.41 117	62.80 165	68.86 324	85.09 164

viewdifsd2359ckpt07		71.90 115	71.97 101	71.69 202	74.81 275	48.08 283	75.30 225	80.49 150	60.00 121	71.63 124	86.33 128	56.34 42	79.25 254	65.40 133	77.41 182	87.76 45

SR-MVS		76.13 48	75.70 49	77.40 53	85.87 40	61.20 29	85.52 28	82.19 107	59.99 122	75.10 55	90.35 32	47.66 164	86.52 81	71.64 82	82.99 86	84.47 186

SSC-MVS3.2		60.57 321	61.39 303	58.12 383	74.29 293	32.63 431	59.52 407	65.53 362	59.90 123	62.45 303	79.75 289	41.96 238	63.90 397	39.47 369	69.65 312	77.84 334

9.14			78.75 15		83.10 73		84.15 49	88.26 1	59.90 123	78.57 26	90.36 31	57.51 32	86.86 69	77.39 29	89.52 21

v2v482		70.50 143	69.45 152	73.66 144	72.62 323	50.03 242	77.58 161	80.51 149	59.90 123	69.52 154	82.14 240	47.53 168	84.88 129	65.07 136	70.17 297	86.09 115

Baseline_NR-MVSNet		67.05 237	67.56 196	65.50 320	75.65 252	37.70 389	75.42 223	74.65 272	59.90 123	68.14 182	83.15 211	49.12 149	77.20 300	52.23 258	69.78 306	81.60 267

API-MVS		72.17 109	71.41 111	74.45 115	81.95 89	57.22 95	84.03 51	80.38 153	59.89 127	68.40 175	82.33 231	49.64 137	87.83 46	51.87 263	84.16 77	78.30 325

Effi-MVS+-dtu		69.64 168	67.53 199	75.95 74	76.10 246	62.29 15	80.20 105	76.06 242	59.83 128	65.26 253	77.09 338	41.56 249	84.02 144	60.60 186	71.09 283	81.53 268

reproduce_model		76.43 42	76.08 42	77.49 50	83.47 70	60.09 47	84.60 37	82.90 97	59.65 129	77.31 35	91.43 13	49.62 138	87.24 55	71.99 77	83.75 81	85.14 160

MVSMamba_PlusPlus		75.75 53	75.44 51	76.67 63	80.84 108	53.06 180	78.62 131	85.13 33	59.65 129	71.53 127	87.47 88	56.92 35	88.17 35	72.18 75	86.63 58	88.80 12

CANet_DTU		68.18 210	67.71 195	69.59 258	74.83 274	46.24 303	78.66 130	76.85 231	59.60 131	63.45 281	82.09 243	35.25 317	77.41 295	59.88 192	78.76 155	85.14 160

EI-MVSNet		69.27 181	68.44 177	71.73 199	74.47 286	49.39 257	75.20 229	78.45 199	59.60 131	69.16 165	76.51 350	51.29 116	82.50 185	59.86 194	71.45 277	83.30 227

IterMVS-LS		69.22 183	68.48 173	71.43 214	74.44 288	49.40 256	76.23 204	77.55 217	59.60 131	65.85 241	81.59 254	51.28 117	81.58 203	59.87 193	69.90 304	83.30 227
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
MGCFI-Net		72.45 102	73.34 82	69.81 255	77.77 195	43.21 336	75.84 217	81.18 135	59.59 134	75.45 49	86.64 112	57.74 28	77.94 282	63.92 146	81.90 102	88.30 25

VDDNet		71.81 116	71.33 114	73.26 162	82.80 79	47.60 292	78.74 127	75.27 258	59.59 134	72.94 102	89.40 53	41.51 251	83.91 146	58.75 207	82.99 86	88.26 26

viewmanbaseed2359cas		72.92 91	72.89 87	73.00 166	75.16 266	49.25 262	77.25 178	83.11 94	59.52 136	72.93 103	86.63 114	54.11 65	80.98 220	66.63 120	80.67 116	88.76 14

alignmvs		73.86 75	73.99 70	73.45 155	78.20 178	50.50 233	78.57 133	82.43 104	59.40 137	76.57 42	86.71 111	56.42 41	81.23 213	65.84 129	81.79 103	88.62 16

MVS_Test		72.45 102	72.46 94	72.42 183	74.88 271	48.50 277	76.28 202	83.14 92	59.40 137	72.46 112	84.68 168	55.66 49	81.12 215	65.98 128	79.66 132	87.63 50

TSAR-MVS + MP.		78.44 19	78.28 19	78.90 27	84.96 52	61.41 26	84.03 51	83.82 64	59.34 139	79.37 20	89.76 50	59.84 16	87.62 52	76.69 34	86.74 55	87.68 48
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
MSLP-MVS++		73.77 76	73.47 78	74.66 104	83.02 75	59.29 63	82.30 80	81.88 111	59.34 139	71.59 125	86.83 105	45.94 189	83.65 151	65.09 135	85.22 65	81.06 283

PAPM_NR		72.63 98	71.80 103	75.13 93	81.72 92	53.42 171	79.91 110	83.28 84	59.14 141	66.31 229	85.90 142	51.86 105	86.06 96	57.45 214	80.62 117	85.91 121

testing91		64.46 278	63.80 269	66.47 299	78.43 169	40.06 365	67.63 347	69.59 327	59.06 142	63.18 285	78.05 318	34.05 330	76.99 307	48.30 293	75.87 207	82.37 256

myMVS_eth3d28		60.66 320	61.04 311	59.51 367	77.32 215	31.58 436	63.11 387	63.87 377	59.00 143	60.90 322	78.26 315	32.69 354	66.15 387	36.10 395	78.13 169	80.81 288

save fliter					86.17 33	61.30 28	83.98 53	79.66 163	59.00 143

v148		68.24 208	67.19 216	71.40 215	70.43 366	47.77 289	75.76 218	77.03 229	58.91 145	67.36 205	80.10 282	48.60 154	81.89 196	60.01 190	66.52 345	84.53 183

TransMVSNet (Re)		64.72 272	64.33 262	65.87 315	75.22 263	38.56 379	74.66 244	75.08 267	58.90 146	61.79 311	82.63 217	51.18 118	78.07 280	43.63 338	55.87 411	80.99 285

Anonymous202405211		66.84 242	65.99 241	69.40 262	80.19 122	42.21 346	71.11 312	71.31 312	58.80 147	67.90 190	86.39 125	29.83 380	79.65 247	49.60 283	78.78 154	86.33 105

test2506		65.33 266	64.61 260	67.50 285	79.46 136	34.19 421	74.43 250	51.92 432	58.72 148	66.75 219	88.05 76	25.99 414	80.92 224	51.94 262	84.25 74	87.39 62

ECVR-MVS		67.72 223	67.51 200	68.35 278	79.46 136	36.29 406	74.79 241	66.93 350	58.72 148	67.19 210	88.05 76	36.10 310	81.38 208	52.07 260	84.25 74	87.39 62

test1111		67.21 230	67.14 217	67.42 287	79.24 142	34.76 415	73.89 262	65.65 360	58.71 150	66.96 215	87.95 80	36.09 311	80.53 231	52.03 261	83.79 80	86.97 77

LCM-MVSNet-Re		61.88 311	61.35 304	63.46 340	74.58 284	31.48 437	61.42 397	58.14 410	58.71 150	53.02 405	79.55 294	43.07 226	76.80 311	45.69 315	77.96 172	82.11 262

testing99		64.05 282	63.29 280	66.34 301	78.17 182	39.76 369	67.33 352	68.00 341	58.60 152	63.03 288	78.10 317	32.57 359	76.94 309	48.22 294	75.58 211	82.34 257

v1144		70.42 145	69.31 154	73.76 136	73.22 311	50.64 228	77.83 156	81.43 121	58.58 153	69.40 158	81.16 259	47.53 168	85.29 119	64.01 144	70.64 285	85.34 153

TSAR-MVS + GP.		74.90 59	74.15 69	77.17 55	82.00 87	58.77 77	81.80 83	78.57 192	58.58 153	74.32 74	84.51 178	55.94 47	87.22 58	67.11 114	84.48 73	85.52 140

BH-RMVSNet		68.81 192	67.42 203	72.97 167	80.11 125	52.53 196	74.26 252	76.29 237	58.48 155	68.38 176	84.20 182	42.59 231	83.83 147	46.53 307	75.91 206	82.56 248

APD-MVS_3200maxsize		74.96 58	74.39 65	76.67 63	82.20 84	58.24 82	83.67 57	83.29 83	58.41 156	73.71 84	90.14 37	45.62 191	85.99 99	69.64 92	82.85 92	85.78 126

OMC-MVS		71.40 126	70.60 129	73.78 134	76.60 238	53.15 177	79.74 114	79.78 160	58.37 157	68.75 169	86.45 124	45.43 198	80.60 230	62.58 166	77.73 175	87.58 54

nrg030		72.96 90	73.01 85	72.84 170	75.41 260	50.24 236	80.02 106	82.89 99	58.36 158	74.44 71	86.73 109	58.90 24	80.83 226	65.84 129	74.46 223	87.44 58

K. test v3		60.47 324	57.11 343	70.56 240	73.74 304	48.22 280	75.10 233	62.55 389	58.27 159	53.62 400	76.31 354	27.81 398	81.59 202	47.42 298	39.18 448	81.88 265

FA-MVS(test-final)		69.82 160	68.48 173	73.84 132	78.44 168	50.04 241	75.58 222	78.99 177	58.16 160	67.59 202	82.14 240	42.66 230	85.63 106	56.60 218	76.19 200	85.84 124

MVS_111021_LR		69.50 175	68.78 167	71.65 204	78.38 170	59.33 61	74.82 240	70.11 321	58.08 161	67.83 197	84.68 168	41.96 238	76.34 322	65.62 131	77.54 178	79.30 316

SR-MVS-dyc-post		74.57 66	73.90 72	76.58 66	83.49 68	59.87 54	84.29 43	81.36 124	58.07 162	73.14 95	90.07 39	44.74 208	85.84 103	68.20 99	81.76 104	84.03 198

RE-MVS-def			73.71 76		83.49 68	59.87 54	84.29 43	81.36 124	58.07 162	73.14 95	90.07 39	43.06 227		68.20 99	81.76 104	84.03 198

SDMVSNet		68.03 213	68.10 189	67.84 282	77.13 220	48.72 273	65.32 369	79.10 172	58.02 164	65.08 257	82.55 224	47.83 161	73.40 336	63.92 146	73.92 231	81.41 270

sd_testset		64.46 278	64.45 261	64.51 331	77.13 220	42.25 345	62.67 390	72.11 307	58.02 164	65.08 257	82.55 224	41.22 257	69.88 362	47.32 300	73.92 231	81.41 270

GeoE		71.01 131	70.15 140	73.60 149	79.57 134	52.17 203	78.93 125	78.12 208	58.02 164	67.76 201	83.87 191	52.36 96	82.72 178	56.90 217	75.79 208	85.92 120

viewdifsd2359ckpt09		73.42 80	72.45 95	76.30 71	77.25 218	53.27 174	80.36 101	82.48 103	57.96 167	72.24 115	85.73 149	53.22 81	86.27 90	63.79 152	79.06 149	89.36 5

ZD-MVS					86.64 21	60.38 45		82.70 101	57.95 168	78.10 29	90.06 41	56.12 45	88.84 26	74.05 59	87.00 51

EIA-MVS		71.78 117	70.60 129	75.30 91	79.85 128	53.54 165	77.27 177	83.26 85	57.92 169	66.49 224	79.39 298	52.07 102	86.69 73	60.05 189	79.14 147	85.66 136

test_yl		69.69 164	69.13 157	71.36 218	78.37 172	45.74 308	74.71 242	80.20 155	57.91 170	70.01 147	83.83 192	42.44 233	82.87 172	54.97 235	79.72 130	85.48 142

DCV-MVSNet		69.69 164	69.13 157	71.36 218	78.37 172	45.74 308	74.71 242	80.20 155	57.91 170	70.01 147	83.83 192	42.44 233	82.87 172	54.97 235	79.72 130	85.48 142

MonoMVSNet		64.15 281	63.31 279	66.69 296	70.51 364	44.12 327	74.47 248	74.21 280	57.81 172	63.03 288	76.62 346	38.33 286	77.31 298	54.22 243	60.59 393	78.64 323

dcpmvs_2		74.55 67	75.23 55	72.48 179	82.34 83	53.34 172	77.87 153	81.46 120	57.80 173	75.49 48	86.81 106	62.22 13	77.75 289	71.09 86	82.02 100	86.34 103

diffmvs_AUTHOR		71.02 130	70.87 124	71.45 211	69.89 377	48.97 268	73.16 278	78.33 205	57.79 174	72.11 118	85.26 160	51.84 106	77.89 285	71.00 87	78.47 165	87.49 56

viewdifsd2359ckpt11		69.13 184	68.38 180	71.38 216	71.57 344	48.61 274	73.22 276	73.18 295	57.65 175	70.67 135	84.73 166	50.03 131	79.80 244	63.25 158	71.10 281	85.74 132

viewmsd2359difaftdt		69.13 184	68.38 180	71.38 216	71.57 344	48.61 274	73.22 276	73.18 295	57.65 175	70.67 135	84.73 166	50.03 131	79.80 244	63.25 158	71.10 281	85.74 132

fmvsm_s_conf0.5_n_6		72.59 99	72.87 88	71.73 199	75.14 269	51.96 209	76.28 202	77.12 227	57.63 177	73.85 82	86.91 103	51.54 112	77.87 286	77.18 32	80.18 127	85.37 152

Fast-Effi-MVS+-dtu		67.37 228	65.33 254	73.48 154	72.94 318	57.78 88	77.47 167	76.88 230	57.60 178	61.97 308	76.85 342	39.31 273	80.49 234	54.72 238	70.28 295	82.17 261

v1192		69.97 157	68.68 169	73.85 131	73.19 312	50.94 221	77.68 160	81.36 124	57.51 179	68.95 168	80.85 269	45.28 201	85.33 118	62.97 164	70.37 291	85.27 157

ACMH+	57.40 11	66.12 255	64.06 264	72.30 186	77.79 194	52.83 188	80.39 100	78.03 209	57.30 180	57.47 360	82.55 224	27.68 400	84.17 139	45.54 318	69.78 306	79.90 305

diffmvs		70.69 139	70.43 132	71.46 209	69.45 384	48.95 269	72.93 281	78.46 198	57.27 181	71.69 122	83.97 190	51.48 114	77.92 284	70.70 89	77.95 173	87.53 55
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
BH-untuned		68.27 206	67.29 208	71.21 222	79.74 129	53.22 175	76.06 209	77.46 220	57.19 182	66.10 233	81.61 252	45.37 200	83.50 155	45.42 323	76.68 196	76.91 350

fmvsm_s_conf0.5_n_10		74.11 72	73.98 71	74.48 114	74.61 282	52.86 187	78.10 148	77.06 228	57.14 183	78.24 27	88.79 66	52.83 87	82.26 190	77.79 28	81.30 109	88.32 24

viewdifsd2359ckpt13		72.40 105	71.79 104	74.22 122	75.63 253	51.77 213	78.67 129	83.13 93	57.08 184	71.59 125	85.36 159	53.10 84	82.64 181	63.07 162	78.51 162	88.24 28

thres100view900		63.28 291	62.41 290	65.89 313	77.31 216	38.66 378	72.65 284	69.11 334	57.07 185	62.45 303	81.03 263	37.01 305	79.17 257	31.84 416	73.25 249	79.83 308

fmvsm_s_conf0.5_n_7		69.54 172	69.67 147	69.15 269	73.47 309	51.41 216	70.35 323	73.34 291	57.05 186	68.41 174	85.83 145	49.86 134	72.84 339	71.86 79	76.83 193	83.19 232

DP-MVS Recon		72.15 112	70.73 127	76.40 68	86.57 24	57.99 84	81.15 93	82.96 95	57.03 187	66.78 217	85.56 152	44.50 212	88.11 38	51.77 265	80.23 126	83.10 237

thres600view7		63.30 290	62.27 292	66.41 300	77.18 219	38.87 376	72.35 291	69.11 334	56.98 188	62.37 306	80.96 265	37.01 305	79.00 268	31.43 423	73.05 253	81.36 273

V42		68.65 196	67.35 207	72.56 176	68.93 390	50.18 238	72.90 282	79.47 167	56.92 189	69.45 157	80.26 278	46.29 187	82.99 165	64.07 142	67.82 333	84.53 183

MCST-MVS		77.48 29	77.45 28	77.54 48	86.67 20	58.36 81	83.22 61	86.93 5	56.91 190	74.91 61	88.19 71	59.15 23	87.68 51	73.67 63	87.45 45	86.57 93

GA-MVS		65.53 262	63.70 271	71.02 231	70.87 359	48.10 282	70.48 320	74.40 274	56.69 191	64.70 266	76.77 343	33.66 338	81.10 216	55.42 234	70.32 294	83.87 207

v144192		69.71 163	68.51 172	73.33 160	73.10 314	50.13 239	77.54 164	80.64 146	56.65 192	68.57 172	80.55 272	46.87 182	84.96 125	62.98 163	69.66 310	84.89 172

fmvsm_l_conf0.5_n_3		73.23 85	73.13 84	73.55 151	74.40 289	55.13 137	78.97 124	74.96 268	56.64 193	74.76 67	88.75 67	55.02 54	78.77 272	76.33 38	78.31 168	86.74 85

tfpn200view9		63.18 293	62.18 294	66.21 305	76.85 232	39.62 370	71.96 299	69.44 330	56.63 194	62.61 298	79.83 285	37.18 299	79.17 257	31.84 416	73.25 249	79.83 308

thres400		63.31 289	62.18 294	66.72 293	76.85 232	39.62 370	71.96 299	69.44 330	56.63 194	62.61 298	79.83 285	37.18 299	79.17 257	31.84 416	73.25 249	81.36 273

GBi-Net		67.21 230	66.55 225	69.19 264	77.63 202	43.33 333	77.31 171	77.83 212	56.62 196	65.04 259	82.70 214	41.85 241	80.33 236	47.18 302	72.76 257	83.92 204

test1		67.21 230	66.55 225	69.19 264	77.63 202	43.33 333	77.31 171	77.83 212	56.62 196	65.04 259	82.70 214	41.85 241	80.33 236	47.18 302	72.76 257	83.92 204

FMVSNet2		66.93 240	66.31 236	68.79 273	77.63 202	42.98 338	76.11 207	77.47 218	56.62 196	65.22 256	82.17 238	41.85 241	80.18 242	47.05 305	72.72 260	83.20 231

fmvsm_l_conf0.5_n_9		73.27 84	73.66 77	72.09 188	73.82 301	52.72 191	77.45 168	74.28 278	56.61 199	77.10 39	88.16 72	56.17 44	77.09 302	78.27 24	81.13 111	86.48 97

DPM-MVS		75.47 55	75.00 57	76.88 57	81.38 99	59.16 64	79.94 108	85.71 23	56.59 200	72.46 112	86.76 107	56.89 36	87.86 45	66.36 122	88.91 25	83.64 221

v1921920		69.47 176	68.17 186	73.36 159	73.06 315	50.10 240	77.39 169	80.56 147	56.58 201	68.59 170	80.37 274	44.72 209	84.98 123	62.47 169	69.82 305	85.00 166

FMVSNet1		66.70 245	65.87 242	69.19 264	77.49 210	43.33 333	77.31 171	77.83 212	56.45 202	64.60 268	82.70 214	38.08 291	80.33 236	46.08 311	72.31 266	83.92 204

v1240		69.24 182	67.91 191	73.25 163	73.02 317	49.82 244	77.21 179	80.54 148	56.43 203	68.34 177	80.51 273	43.33 223	84.99 121	62.03 173	69.77 308	84.95 170

fmvsm_s_conf0.5_n_4		72.04 113	71.85 102	72.58 175	73.74 304	52.49 198	76.69 193	72.42 303	56.42 204	75.32 50	87.04 100	52.13 101	78.01 281	79.29 12	73.65 237	87.26 68

testing222		62.29 305	61.31 305	65.25 326	77.87 191	38.53 380	68.34 341	66.31 356	56.37 205	63.15 287	77.58 332	28.47 392	76.18 325	37.04 384	76.65 197	81.05 284

CDPH-MVS		76.31 43	75.67 50	78.22 37	85.35 48	59.14 67	81.31 91	84.02 52	56.32 206	74.05 78	88.98 59	53.34 80	87.92 43	69.23 96	88.42 28	87.59 53

Vis-MVSNet (Re-imp)		63.69 286	63.88 267	63.14 344	74.75 277	31.04 439	71.16 310	63.64 380	56.32 206	59.80 334	84.99 161	44.51 211	75.46 327	39.12 371	80.62 117	82.92 239

AdaColmap		69.99 156	68.66 170	73.97 130	84.94 54	57.83 86	82.63 71	78.71 184	56.28 208	64.34 269	84.14 184	41.57 248	87.06 65	46.45 308	78.88 151	77.02 346

PS-MVSNAJss		72.24 107	71.21 117	75.31 90	78.50 165	55.93 118	81.63 85	82.12 108	56.24 209	70.02 146	85.68 151	47.05 177	84.34 138	65.27 134	74.41 226	85.67 135

c3_l		68.33 205	67.56 196	70.62 239	70.87 359	46.21 304	74.47 248	78.80 182	56.22 210	66.19 230	78.53 313	51.88 104	81.40 207	62.08 170	69.04 320	84.25 191

Fast-Effi-MVS+		70.28 149	69.12 159	73.73 140	78.50 165	51.50 215	75.01 234	79.46 168	56.16 211	68.59 170	79.55 294	53.97 67	84.05 141	53.34 251	77.53 179	85.65 137

PHI-MVS		75.87 50	75.36 52	77.41 51	80.62 115	55.91 119	84.28 45	85.78 21	56.08 212	73.41 87	86.58 118	50.94 123	88.54 28	70.79 88	89.71 17	87.79 44

baseline1		63.81 285	63.87 268	63.62 339	76.29 243	36.36 401	71.78 302	67.29 346	56.05 213	64.23 274	82.95 212	47.11 176	74.41 332	47.30 301	61.85 382	80.10 302

train_agg		76.27 44	76.15 41	76.64 65	85.58 43	61.59 24	81.62 86	81.26 131	55.86 214	74.93 59	88.81 63	53.70 75	84.68 132	75.24 50	88.33 30	83.65 220

test_8					85.40 46	60.96 34	81.54 89	81.18 135	55.86 214	74.81 64	88.80 65	53.70 75	84.45 136

FMVSNet3		66.32 254	65.61 247	68.46 276	76.48 241	42.34 343	74.98 236	77.15 226	55.83 216	65.04 259	81.16 259	39.91 266	80.14 243	47.18 302	72.76 257	82.90 241

PAPR		71.72 120	70.82 125	74.41 116	81.20 104	51.17 217	79.55 119	83.33 81	55.81 217	66.93 216	84.61 172	50.95 122	86.06 96	55.79 228	79.20 144	86.00 117

eth_miper_zixun_eth		67.63 224	66.28 237	71.67 203	71.60 343	48.33 279	73.68 266	77.88 210	55.80 218	65.91 237	78.62 311	47.35 174	82.88 171	59.45 196	66.25 346	83.81 209

ACMH	55.70 15	65.20 268	63.57 273	70.07 248	78.07 185	52.01 208	79.48 120	79.69 161	55.75 219	56.59 367	80.98 264	27.12 405	80.94 222	42.90 346	71.58 275	77.25 344
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
IB-MVS	56.42 12	65.40 265	62.73 287	73.40 158	74.89 270	52.78 189	73.09 280	75.13 263	55.69 220	58.48 352	73.73 383	32.86 347	86.32 88	50.63 273	70.11 298	81.10 282
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
CL-MVSNet_self_test		61.53 314	60.94 313	63.30 342	68.95 389	36.93 397	67.60 348	72.80 301	55.67 221	59.95 331	76.63 345	45.01 207	72.22 346	39.74 368	62.09 381	80.74 290

TEST9					85.58 43	61.59 24	81.62 86	81.26 131	55.65 222	74.93 59	88.81 63	53.70 75	84.68 132

thres200		62.20 306	61.16 310	65.34 324	75.38 261	39.99 366	69.60 332	69.29 332	55.64 223	61.87 310	76.99 339	37.07 304	78.96 269	31.28 424	73.28 248	77.06 345

guyue		68.10 212	67.23 215	70.71 238	73.67 306	49.27 261	73.65 267	76.04 243	55.62 224	67.84 196	82.26 234	41.24 256	78.91 271	61.01 182	73.72 235	83.94 202

pm-mvs1		65.24 267	64.97 258	66.04 310	72.38 330	39.40 373	72.62 286	75.63 248	55.53 225	62.35 307	83.18 210	47.45 170	76.47 320	49.06 287	66.54 344	82.24 258

testing11		62.81 297	61.90 297	65.54 318	78.38 170	40.76 362	67.59 349	66.78 352	55.48 226	60.13 326	77.11 337	31.67 366	76.79 312	45.53 319	74.45 224	79.06 318

ACMM	61.98 7	70.80 138	69.73 145	74.02 126	80.59 116	58.59 79	82.68 70	82.02 110	55.46 227	67.18 211	84.39 181	38.51 283	83.17 162	60.65 185	76.10 204	80.30 297
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
AstraMVS		67.86 219	66.83 220	70.93 232	73.50 308	49.34 258	73.28 274	74.01 283	55.45 228	68.10 187	83.28 206	38.93 280	79.14 261	63.22 160	71.74 272	84.30 190

Anonymous20240529		69.91 158	69.02 160	72.56 176	80.19 122	47.65 290	77.56 163	80.99 141	55.45 228	69.88 150	86.76 107	39.24 276	82.18 192	54.04 244	77.10 190	87.85 40

tt0805		67.77 222	67.24 213	69.34 263	74.87 272	40.08 364	77.36 170	81.37 123	55.31 230	66.33 228	84.65 170	37.35 297	82.55 184	55.65 231	72.28 267	85.39 151

GDP-MVS		72.64 97	71.28 116	76.70 60	77.72 197	54.22 151	79.57 118	84.45 44	55.30 231	71.38 129	86.97 102	39.94 265	87.00 66	67.02 117	79.20 144	88.89 11

CPTT-MVS		72.78 93	72.08 100	74.87 98	84.88 57	61.41 26	84.15 49	77.86 211	55.27 232	67.51 204	88.08 75	41.93 240	81.85 197	69.04 97	80.01 128	81.35 275

XVG-OURS		68.76 195	67.37 205	72.90 169	74.32 292	57.22 95	70.09 327	78.81 181	55.24 233	67.79 199	85.81 148	36.54 308	78.28 277	62.04 172	75.74 209	83.19 232

tfpnnormal		62.47 301	61.63 300	64.99 328	74.81 275	39.01 375	71.22 308	73.72 287	55.22 234	60.21 325	80.09 283	41.26 255	76.98 308	30.02 430	68.09 331	78.97 321

cl____		67.18 233	66.26 238	69.94 250	70.20 370	45.74 308	73.30 271	76.83 232	55.10 235	65.27 250	79.57 293	47.39 172	80.53 231	59.41 198	69.22 318	83.53 223

DIV-MVS_self_test		67.18 233	66.26 238	69.94 250	70.20 370	45.74 308	73.29 273	76.83 232	55.10 235	65.27 250	79.58 292	47.38 173	80.53 231	59.43 197	69.22 318	83.54 222

PC_three_1452									55.09 237	84.46 4	89.84 48	66.68 5	89.41 18	74.24 56	91.38 2	88.42 21

EPNet_dtu		61.90 310	61.97 296	61.68 353	72.89 319	39.78 368	75.85 216	65.62 361	55.09 237	54.56 390	79.36 299	37.59 294	67.02 381	39.80 367	76.95 191	78.25 326
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
PVSNet_Blended_VisFu		71.45 125	70.39 133	74.65 105	82.01 86	58.82 76	79.93 109	80.35 154	55.09 237	65.82 242	82.16 239	49.17 146	82.64 181	60.34 187	78.62 160	82.50 253

cl22		67.47 227	66.45 227	70.54 241	69.85 379	46.49 300	73.85 263	77.35 222	55.07 240	65.51 245	77.92 322	47.64 165	81.10 216	61.58 178	69.32 314	84.01 200

miper_ehance_all_eth		68.03 213	67.24 213	70.40 243	70.54 363	46.21 304	73.98 256	78.68 186	55.07 240	66.05 234	77.80 326	52.16 100	81.31 210	61.53 180	69.32 314	83.67 217

fmvsm_s_conf0.5_n_2		69.82 160	69.27 156	71.46 209	72.00 337	51.08 218	73.30 271	67.79 342	55.06 242	75.24 52	87.51 86	44.02 217	77.00 306	75.67 43	72.86 255	86.31 110

Elysia		70.19 152	68.29 182	75.88 76	74.15 296	54.33 149	78.26 137	83.21 86	55.04 243	67.28 207	83.59 199	30.16 375	86.11 94	63.67 153	79.26 141	87.20 70

StellarMVS		70.19 152	68.29 182	75.88 76	74.15 296	54.33 149	78.26 137	83.21 86	55.04 243	67.28 207	83.59 199	30.16 375	86.11 94	63.67 153	79.26 141	87.20 70

PS-MVSNAJ		70.51 142	69.70 146	72.93 168	81.52 94	55.79 122	74.92 238	79.00 176	55.04 243	69.88 150	78.66 308	47.05 177	82.19 191	61.61 176	79.58 133	80.83 287

fmvsm_s_conf0.1_n_2		69.64 168	69.01 162	71.52 207	71.66 342	51.04 219	73.39 270	67.14 348	55.02 246	75.11 54	87.64 85	42.94 229	77.01 305	75.55 45	72.63 261	86.52 96

mmtdpeth		60.40 325	59.12 326	64.27 334	69.59 381	48.99 266	70.67 317	70.06 322	54.96 247	62.78 292	73.26 388	27.00 407	67.66 374	58.44 210	45.29 440	76.16 355

xiu_mvs_v2_base		70.52 141	69.75 144	72.84 170	81.21 103	55.63 126	75.11 231	78.92 178	54.92 248	69.96 149	79.68 291	47.00 181	82.09 193	61.60 177	79.37 136	80.81 288

MAR-MVS		71.51 122	70.15 140	75.60 86	81.84 90	59.39 60	81.38 90	82.90 97	54.90 249	68.08 188	78.70 306	47.73 162	85.51 111	51.68 267	84.17 76	81.88 265
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
reproduce_monomvs		62.56 299	61.20 309	66.62 297	70.62 362	44.30 324	70.13 326	73.13 298	54.78 250	61.13 319	76.37 353	25.63 417	75.63 326	58.75 207	60.29 394	79.93 304

XVG-OURS-SEG-HR		68.81 192	67.47 202	72.82 172	74.40 289	56.87 105	70.59 318	79.04 175	54.77 251	66.99 214	86.01 139	39.57 271	78.21 278	62.54 167	73.33 247	83.37 226

testing3		56.54 356	55.92 358	58.41 378	77.52 209	27.93 449	69.72 330	56.36 419	54.75 252	58.63 350	77.80 326	20.88 433	71.75 349	25.31 446	62.25 379	75.53 362

Anonymous20231211		69.28 180	68.47 175	71.73 199	80.28 117	47.18 296	79.98 107	82.37 105	54.61 253	67.24 209	84.01 188	39.43 272	82.41 188	55.45 233	72.83 256	85.62 138

SixPastTwentyTwo		61.65 313	58.80 330	70.20 246	75.80 249	47.22 295	75.59 220	69.68 325	54.61 253	54.11 394	79.26 301	27.07 406	82.96 166	43.27 340	49.79 433	80.41 295

test_0402		63.25 292	61.01 312	69.96 249	80.00 126	54.37 148	76.86 190	72.02 308	54.58 255	58.71 346	80.79 271	35.00 320	84.36 137	26.41 444	64.71 357	71.15 414

tttt0517		67.83 220	65.66 246	74.33 118	76.69 234	50.82 225	77.86 154	73.99 284	54.54 256	64.64 267	82.53 227	35.06 319	85.50 112	55.71 229	69.91 303	86.67 89

BH-w/o		66.85 241	65.83 243	69.90 253	79.29 138	52.46 199	74.66 244	76.65 235	54.51 257	64.85 264	78.12 316	45.59 193	82.95 168	43.26 341	75.54 212	74.27 380

AUN-MVS		68.45 204	66.41 231	74.57 110	79.53 135	57.08 103	73.93 260	75.23 260	54.44 258	66.69 220	81.85 246	37.10 303	82.89 170	62.07 171	66.84 341	83.75 214

LTVRE_ROB	55.42 16	63.15 294	61.23 308	68.92 271	76.57 239	47.80 287	59.92 406	76.39 236	54.35 259	58.67 348	82.46 229	29.44 384	81.49 205	42.12 350	71.14 279	77.46 338
Andreas Kuhn, Heiko HirschmÃ¼ller, Daniel Scharstein, Helmut Mayer: A TV Prior for High-Quality Scalable Multi-View Stereo Reconstruction. International Journal of Computer Vision 2016
test_fmvsmconf_n		73.01 89	72.59 92	74.27 120	71.28 354	55.88 120	78.21 143	75.56 251	54.31 260	74.86 63	87.80 83	54.72 58	80.23 240	78.07 26	78.48 163	86.70 86

test_fmvsmconf0.01_n		72.17 109	71.50 108	74.16 124	67.96 396	55.58 129	78.06 149	74.67 271	54.19 261	74.54 70	88.23 70	50.35 130	80.24 239	78.07 26	77.46 181	86.65 91

test_fmvsmconf0.1_n		72.81 92	72.33 96	74.24 121	69.89 377	55.81 121	78.22 142	75.40 256	54.17 262	75.00 58	88.03 79	53.82 71	80.23 240	78.08 25	78.34 167	86.69 87

ETVMVS		59.51 335	58.81 328	61.58 355	77.46 211	34.87 412	64.94 374	59.35 405	54.06 263	61.08 320	76.67 344	29.54 381	71.87 348	32.16 412	74.07 229	78.01 333

ab-mvs		66.65 246	66.42 230	67.37 288	76.17 245	41.73 350	70.41 322	76.14 240	53.99 264	65.98 235	83.51 203	49.48 139	76.24 323	48.60 290	73.46 244	84.14 196

fmvsm_s_conf0.5_n_5		72.69 96	72.80 89	72.37 184	74.11 299	53.21 176	78.12 145	73.31 292	53.98 265	76.81 41	88.05 76	53.38 79	77.37 297	76.64 35	80.78 113	86.53 95

IU-MVS					87.77 4	59.15 65		85.53 27	53.93 266	84.64 3				79.07 13	90.87 5	88.37 23

SSM_0407		70.41 146	68.96 163	74.75 100	78.65 160	53.46 167	77.28 176	80.00 158	53.88 267	68.14 182	84.61 172	43.21 224	86.26 91	58.80 205	76.11 201	84.54 180

SSM_0404		70.84 134	69.41 153	75.12 94	79.20 143	53.86 155	77.89 152	80.00 158	53.88 267	69.40 158	84.61 172	43.21 224	86.56 77	58.80 205	77.68 177	84.95 170

XVG-ACMP-BASELINE		64.36 280	62.23 293	70.74 236	72.35 331	52.45 200	70.80 316	78.45 199	53.84 269	59.87 332	81.10 261	16.24 441	79.32 253	55.64 232	71.76 271	80.47 292

mamba_0408		67.78 221	65.42 250	74.85 99	78.65 160	53.46 167	50.83 441	79.09 173	53.75 270	68.14 182	83.83 192	41.79 244	86.56 77	56.58 219	76.11 201	84.54 180

SSM_04072		64.98 271	65.42 250	63.68 338	78.65 160	53.46 167	50.83 441	79.09 173	53.75 270	68.14 182	83.83 192	41.79 244	53.03 443	56.58 219	76.11 201	84.54 180

VortexMVS		66.41 252	65.50 249	69.16 268	73.75 302	48.14 281	73.41 269	78.28 206	53.73 272	64.98 263	78.33 314	40.62 261	79.07 263	58.88 204	67.50 336	80.26 298

FE-MVS		65.91 257	63.33 278	73.63 147	77.36 214	51.95 210	72.62 286	75.81 245	53.70 273	65.31 248	78.96 304	28.81 390	86.39 85	43.93 332	73.48 243	82.55 249

thisisatest0530		67.92 217	65.78 244	74.33 118	76.29 243	51.03 220	76.89 188	74.25 279	53.67 274	65.59 244	81.76 249	35.15 318	85.50 112	55.94 224	72.47 262	86.47 98

PVSNet_BlendedMVS		68.56 201	67.72 193	71.07 229	77.03 229	50.57 229	74.50 247	81.52 117	53.66 275	64.22 275	79.72 290	49.13 147	82.87 172	55.82 226	73.92 231	79.77 311

patch_mono-2		69.85 159	71.09 120	66.16 306	79.11 148	54.80 143	71.97 298	74.31 276	53.50 276	70.90 133	84.17 183	57.63 31	63.31 399	66.17 123	82.02 100	80.38 296

EG-PatchMatch MVS		64.71 273	62.87 284	70.22 244	77.68 199	53.48 166	77.99 150	78.82 180	53.37 277	56.03 374	77.41 334	24.75 422	84.04 142	46.37 309	73.42 246	73.14 386

SD_0403		63.07 295	63.49 275	61.82 352	75.16 266	31.14 438	71.89 301	73.47 289	53.34 278	58.22 354	81.81 248	45.17 204	73.86 335	37.43 380	74.87 221	80.45 293

DP-MVS		65.68 259	63.66 272	71.75 198	84.93 55	56.87 105	80.74 98	73.16 297	53.06 279	59.09 343	82.35 230	36.79 307	85.94 101	32.82 410	69.96 302	72.45 395

TR-MVS		66.59 249	65.07 257	71.17 225	79.18 145	49.63 254	73.48 268	75.20 262	52.95 280	67.90 190	80.33 277	39.81 269	83.68 150	43.20 342	73.56 241	80.20 299

ET-MVSNet_ETH3D		67.96 216	65.72 245	74.68 103	76.67 236	55.62 128	75.11 231	74.74 269	52.91 281	60.03 329	80.12 281	33.68 337	82.64 181	61.86 174	76.34 198	85.78 126

QAPM		70.05 154	68.81 166	73.78 134	76.54 240	53.43 170	83.23 60	83.48 72	52.89 282	65.90 238	86.29 129	41.55 250	86.49 83	51.01 270	78.40 166	81.42 269

LuminaMVS		68.24 208	66.82 221	72.51 178	73.46 310	53.60 163	76.23 204	78.88 179	52.78 283	68.08 188	80.13 280	32.70 353	81.41 206	63.16 161	75.97 205	82.53 250

icg_test_0407_2		66.41 252	66.75 222	65.37 323	77.06 224	49.73 246	63.79 383	78.60 188	52.70 284	66.19 230	82.58 219	45.17 204	63.65 398	59.20 200	75.46 214	82.74 244

IMVS_0407		68.90 190	67.93 190	71.82 195	77.06 224	49.73 246	74.40 251	78.60 188	52.70 284	66.19 230	82.58 219	45.17 204	83.00 164	59.20 200	75.46 214	82.74 244

IMVS_0404		64.63 275	64.22 263	65.88 314	77.06 224	49.73 246	64.40 377	78.60 188	52.70 284	53.16 404	82.58 219	34.82 322	65.16 392	59.20 200	75.46 214	82.74 244

IMVS_0403		69.09 186	68.14 187	71.95 190	77.06 224	49.73 246	74.51 246	78.60 188	52.70 284	66.69 220	82.58 219	46.43 185	83.38 157	59.20 200	75.46 214	82.74 244

OpenMVS	61.03 9	68.85 191	67.56 196	72.70 174	74.26 294	53.99 154	81.21 92	81.34 128	52.70 284	62.75 295	85.55 154	38.86 281	84.14 140	48.41 292	83.01 85	79.97 303

pmmvs6		63.69 286	62.82 286	66.27 304	70.63 361	39.27 374	73.13 279	75.47 255	52.69 289	59.75 336	82.30 232	39.71 270	77.03 304	47.40 299	64.35 362	82.53 250

IterMVS		62.79 298	61.27 306	67.35 289	69.37 385	52.04 207	71.17 309	68.24 340	52.63 290	59.82 333	76.91 341	37.32 298	72.36 342	52.80 255	63.19 372	77.66 336
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
mvs_tets		68.18 210	66.36 233	73.63 147	75.61 255	55.35 135	80.77 97	78.56 193	52.48 291	64.27 272	84.10 186	27.45 402	81.84 198	63.45 157	70.56 288	83.69 216

jajsoiax		68.25 207	66.45 227	73.66 144	75.62 254	55.49 131	80.82 96	78.51 195	52.33 292	64.33 270	84.11 185	28.28 394	81.81 199	63.48 156	70.62 286	83.67 217

TAMVS		66.78 244	65.27 255	71.33 221	79.16 147	53.67 160	73.84 264	69.59 327	52.32 293	65.28 249	81.72 250	44.49 213	77.40 296	42.32 349	78.66 159	82.92 239

CDS-MVSNet		66.80 243	65.37 252	71.10 228	78.98 150	53.13 179	73.27 275	71.07 314	52.15 294	64.72 265	80.23 279	43.56 221	77.10 301	45.48 321	78.88 151	83.05 238
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
mvsmamba		68.47 202	66.56 224	74.21 123	79.60 132	52.95 181	74.94 237	75.48 254	52.09 295	60.10 327	83.27 207	36.54 308	84.70 131	59.32 199	77.69 176	84.99 168

viewmambaseed2359dif		68.91 189	68.18 185	71.11 227	70.21 369	48.05 286	72.28 293	75.90 244	51.96 296	70.93 132	84.47 179	51.37 115	78.59 273	61.55 179	74.97 219	86.68 88

PVSNet_Blended		68.59 197	67.72 193	71.19 223	77.03 229	50.57 229	72.51 289	81.52 117	51.91 297	64.22 275	77.77 329	49.13 147	82.87 172	55.82 226	79.58 133	80.14 301

mvs_anonymous		68.03 213	67.51 200	69.59 258	72.08 335	44.57 322	71.99 297	75.23 260	51.67 298	67.06 213	82.57 223	54.68 59	77.94 282	56.56 221	75.71 210	86.26 112

xiu_mvs_v1_base_debu		68.58 198	67.28 209	72.48 179	78.19 179	57.19 97	75.28 226	75.09 264	51.61 299	70.04 143	81.41 256	32.79 348	79.02 265	63.81 149	77.31 183	81.22 278

xiu_mvs_v1_base		68.58 198	67.28 209	72.48 179	78.19 179	57.19 97	75.28 226	75.09 264	51.61 299	70.04 143	81.41 256	32.79 348	79.02 265	63.81 149	77.31 183	81.22 278

xiu_mvs_v1_base_debi		68.58 198	67.28 209	72.48 179	78.19 179	57.19 97	75.28 226	75.09 264	51.61 299	70.04 143	81.41 256	32.79 348	79.02 265	63.81 149	77.31 183	81.22 278

MVSTER		67.16 235	65.58 248	71.88 193	70.37 368	49.70 250	70.25 325	78.45 199	51.52 302	69.16 165	80.37 274	38.45 284	82.50 185	60.19 188	71.46 276	83.44 225

CNLPA		65.43 263	64.02 265	69.68 256	78.73 158	58.07 83	77.82 157	70.71 317	51.49 303	61.57 315	83.58 202	38.23 289	70.82 354	43.90 333	70.10 299	80.16 300

原ACMM1				74.69 102	85.39 47	59.40 59		83.42 75	51.47 304	70.27 141	86.61 116	48.61 153	86.51 82	53.85 247	87.96 39	78.16 327

miper_enhance_ethall		67.11 236	66.09 240	70.17 247	69.21 387	45.98 306	72.85 283	78.41 202	51.38 305	65.65 243	75.98 360	51.17 119	81.25 211	60.82 184	69.32 314	83.29 229

MSDG		61.81 312	59.23 324	69.55 261	72.64 322	52.63 194	70.45 321	75.81 245	51.38 305	53.70 397	76.11 355	29.52 382	81.08 218	37.70 378	65.79 350	74.93 371

test20.03		53.87 378	54.02 375	53.41 410	61.47 432	28.11 448	61.30 398	59.21 406	51.34 307	52.09 408	77.43 333	33.29 342	58.55 420	29.76 431	60.27 395	73.58 385

MVSFormer		71.50 123	70.38 134	74.88 97	78.76 156	57.15 100	82.79 67	78.48 196	51.26 308	69.49 155	83.22 208	43.99 218	83.24 160	66.06 124	79.37 136	84.23 192

test_djsdf		69.45 177	67.74 192	74.58 109	74.57 285	54.92 141	82.79 67	78.48 196	51.26 308	65.41 247	83.49 204	38.37 285	83.24 160	66.06 124	69.25 317	85.56 139

dmvs_testset		50.16 396	51.90 386	44.94 431	66.49 407	11.78 471	61.01 403	51.50 433	51.17 310	50.30 420	67.44 425	39.28 274	60.29 410	22.38 450	57.49 404	62.76 436

PAPM		67.92 217	66.69 223	71.63 205	78.09 184	49.02 265	77.09 182	81.24 133	51.04 311	60.91 321	83.98 189	47.71 163	84.99 121	40.81 359	79.32 139	80.90 286

Syy-MVS		56.00 363	56.23 356	55.32 396	74.69 279	26.44 455	65.52 364	57.49 414	50.97 312	56.52 368	72.18 392	39.89 267	68.09 370	24.20 447	64.59 360	71.44 410

myMVS_eth3d		54.86 374	54.61 367	55.61 395	74.69 279	27.31 452	65.52 364	57.49 414	50.97 312	56.52 368	72.18 392	21.87 431	68.09 370	27.70 438	64.59 360	71.44 410

miper_lstm_enhance		62.03 309	60.88 314	65.49 321	66.71 405	46.25 302	56.29 425	75.70 247	50.68 314	61.27 317	75.48 367	40.21 264	68.03 372	56.31 223	65.25 353	82.18 259

gg-mvs-nofinetune		57.86 347	56.43 353	62.18 350	72.62 323	35.35 411	66.57 354	56.33 420	50.65 315	57.64 359	57.10 447	30.65 369	76.36 321	37.38 381	78.88 151	74.82 373

TAPA-MVS	59.36 10	66.60 247	65.20 256	70.81 234	76.63 237	48.75 271	76.52 198	80.04 157	50.64 316	65.24 254	84.93 162	39.15 277	78.54 274	36.77 386	76.88 192	85.14 160
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
dmvs_re		56.77 355	56.83 348	56.61 390	69.23 386	41.02 357	58.37 412	64.18 373	50.59 317	57.45 361	71.42 400	35.54 315	58.94 418	37.23 382	67.45 337	69.87 423

MVP-Stereo		65.41 264	63.80 269	70.22 244	77.62 206	55.53 130	76.30 201	78.53 194	50.59 317	56.47 370	78.65 309	39.84 268	82.68 179	44.10 331	72.12 269	72.44 396
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
PCF-MVS	61.88 8	70.95 133	69.49 150	75.35 89	77.63 202	55.71 123	76.04 211	81.81 113	50.30 319	69.66 153	85.40 158	52.51 92	84.89 127	51.82 264	80.24 125	85.45 146
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
mvs5depth		55.64 366	53.81 377	61.11 361	59.39 442	40.98 361	65.89 359	68.28 339	50.21 320	58.11 356	75.42 368	17.03 437	67.63 376	43.79 335	46.21 437	74.73 375

baseline2		63.42 288	61.26 307	69.89 254	72.55 325	47.62 291	71.54 303	68.38 338	50.11 321	54.82 386	75.55 365	43.06 227	80.96 221	48.13 295	67.16 340	81.11 281

test-LLR		58.15 345	58.13 338	58.22 380	68.57 391	44.80 318	65.46 366	57.92 411	50.08 322	55.44 378	69.82 413	32.62 356	57.44 425	49.66 281	73.62 238	72.41 397

test0.0.03 1		53.32 383	53.59 380	52.50 416	62.81 427	29.45 443	59.51 408	54.11 428	50.08 322	54.40 392	74.31 377	32.62 356	55.92 434	30.50 427	63.95 365	72.15 402

fmvsm_s_conf0.5_n		69.58 170	68.84 165	71.79 197	72.31 333	52.90 183	77.90 151	62.43 392	49.97 324	72.85 105	85.90 142	52.21 98	76.49 318	75.75 42	70.26 296	85.97 118

COLMAP_ROB	52.97 17	61.27 318	58.81 328	68.64 274	74.63 281	52.51 197	78.42 136	73.30 293	49.92 325	50.96 412	81.51 255	23.06 425	79.40 251	31.63 420	65.85 348	74.01 383
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
fmvsm_s_conf0.5_n_a		69.54 172	68.74 168	71.93 191	72.47 328	53.82 157	78.25 139	62.26 394	49.78 326	73.12 98	86.21 131	52.66 90	76.79 312	75.02 51	68.88 322	85.18 159

WBMVS		60.54 322	60.61 316	60.34 364	78.00 188	35.95 408	64.55 376	64.89 366	49.63 327	63.39 282	78.70 306	33.85 335	67.65 375	42.10 351	70.35 293	77.43 339

tpmvs		58.47 340	56.95 346	63.03 346	70.20 370	41.21 356	67.90 346	67.23 347	49.62 328	54.73 388	70.84 404	34.14 329	76.24 323	36.64 390	61.29 386	71.64 406

fmvsm_s_conf0.1_n		69.41 178	68.60 171	71.83 194	71.07 356	52.88 186	77.85 155	62.44 391	49.58 329	72.97 101	86.22 130	51.68 110	76.48 319	75.53 46	70.10 299	86.14 113

UBG		59.62 334	59.53 322	59.89 365	78.12 183	35.92 409	64.11 381	60.81 402	49.45 330	61.34 316	75.55 365	33.05 343	67.39 379	38.68 373	74.62 222	76.35 354

thisisatest0515		65.83 258	63.50 274	72.82 172	73.75 302	49.50 255	71.32 306	73.12 299	49.39 331	63.82 277	76.50 352	34.95 321	84.84 130	53.20 253	75.49 213	84.13 197

fmvsm_s_conf0.1_n_a		69.32 179	68.44 177	71.96 189	70.91 358	53.78 158	78.12 145	62.30 393	49.35 332	73.20 92	86.55 121	51.99 103	76.79 312	74.83 53	68.68 327	85.32 154

HY-MVS	56.14 13	64.55 277	63.89 266	66.55 298	74.73 278	41.02 357	69.96 328	74.43 273	49.29 333	61.66 313	80.92 266	47.43 171	76.68 316	44.91 326	71.69 273	81.94 263

MIMVSNet1		55.17 371	54.31 372	57.77 386	70.03 374	32.01 434	65.68 362	64.81 367	49.19 334	46.75 431	76.00 357	25.53 418	64.04 395	28.65 435	62.13 380	77.26 343

SCA		60.49 323	58.38 334	66.80 292	74.14 298	48.06 284	63.35 386	63.23 384	49.13 335	59.33 342	72.10 394	37.45 295	74.27 333	44.17 328	62.57 376	78.05 329

test_fmvsmvis_n_1920		70.84 134	70.38 134	72.22 187	71.16 355	55.39 133	75.86 215	72.21 306	49.03 336	73.28 90	86.17 133	51.83 107	77.29 299	75.80 41	78.05 171	83.98 201

testgi		51.90 388	52.37 384	50.51 423	60.39 440	23.55 462	58.42 411	58.15 409	49.03 336	51.83 409	79.21 302	22.39 426	55.59 435	29.24 434	62.64 375	72.40 399

sc_t1		59.76 330	57.84 341	65.54 318	74.87 272	42.95 340	69.61 331	64.16 375	48.90 338	58.68 347	77.12 336	28.19 395	72.35 343	43.75 337	55.28 413	81.31 276

MIMVSNet		57.35 349	57.07 344	58.22 380	74.21 295	37.18 392	62.46 391	60.88 401	48.88 339	55.29 381	75.99 359	31.68 365	62.04 404	31.87 415	72.35 264	75.43 364

gm-plane-assit					71.40 351	41.72 352			48.85 340		73.31 386		82.48 187	48.90 288

fmvsm_l_conf0.5_n		70.99 132	70.82 125	71.48 208	71.45 347	54.40 147	77.18 180	70.46 319	48.67 341	75.17 53	86.86 104	53.77 73	76.86 310	76.33 38	77.51 180	83.17 236

UWE-MVS		60.18 326	59.78 320	61.39 358	77.67 200	33.92 424	69.04 338	63.82 378	48.56 342	64.27 272	77.64 331	27.20 404	70.40 359	33.56 407	76.24 199	79.83 308

cascas		65.98 256	63.42 276	73.64 146	77.26 217	52.58 195	72.26 294	77.21 225	48.56 342	61.21 318	74.60 375	32.57 359	85.82 104	50.38 275	76.75 195	82.52 252

PLC	56.13 14	65.09 269	63.21 281	70.72 237	81.04 106	54.87 142	78.57 133	77.47 218	48.51 344	55.71 375	81.89 245	33.71 336	79.71 246	41.66 355	70.37 291	77.58 337
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
LS3D		64.71 273	62.50 289	71.34 220	79.72 131	55.71 123	79.82 111	74.72 270	48.50 345	56.62 366	84.62 171	33.59 339	82.34 189	29.65 432	75.23 218	75.97 356

anonymousdsp		67.00 239	64.82 259	73.57 150	70.09 373	56.13 113	76.35 200	77.35 222	48.43 346	64.99 262	80.84 270	33.01 345	80.34 235	64.66 139	67.64 335	84.23 192

无先验							79.66 116	74.30 277	48.40 347				80.78 228	53.62 248		79.03 320

FE-MVSNET		55.16 372	53.75 378	59.41 368	65.29 415	33.20 428	67.21 353	66.21 357	48.39 348	49.56 422	73.53 385	29.03 386	72.51 341	30.38 428	54.10 419	72.52 393

114514_t		70.83 136	69.56 148	74.64 106	86.21 31	54.63 144	82.34 76	81.81 113	48.22 349	63.01 290	85.83 145	40.92 260	87.10 63	57.91 211	79.79 129	82.18 259

tpm		57.34 350	58.16 336	54.86 399	71.80 341	34.77 414	67.47 351	56.04 423	48.20 350	60.10 327	76.92 340	37.17 301	53.41 442	40.76 360	65.01 354	76.40 353

test_fmvsm_n_1920		71.73 119	71.14 119	73.50 152	72.52 326	56.53 107	75.60 219	76.16 238	48.11 351	77.22 36	85.56 152	53.10 84	77.43 294	74.86 52	77.14 188	86.55 94

MDA-MVSNet-bldmvs		53.87 378	50.81 391	63.05 345	66.25 409	48.58 276	56.93 423	63.82 378	48.09 352	41.22 443	70.48 409	30.34 372	68.00 373	34.24 402	45.92 439	72.57 392

XXY-MVS		60.68 319	61.67 299	57.70 387	70.43 366	38.45 381	64.19 379	66.47 353	48.05 353	63.22 283	80.86 268	49.28 144	60.47 408	45.25 325	67.28 339	74.19 381

F-COLMAP		63.05 296	60.87 315	69.58 260	76.99 231	53.63 162	78.12 145	76.16 238	47.97 354	52.41 407	81.61 252	27.87 397	78.11 279	40.07 362	66.66 343	77.00 347

tt0320-xc		58.33 342	56.41 354	64.08 335	75.79 250	41.34 354	68.30 342	62.72 388	47.90 355	56.29 371	74.16 380	28.53 391	71.04 353	41.50 358	52.50 425	79.88 306

fmvsm_l_conf0.5_n_a		70.50 143	70.27 136	71.18 224	71.30 353	54.09 152	76.89 188	69.87 323	47.90 355	74.37 73	86.49 122	53.07 86	76.69 315	75.41 47	77.11 189	82.76 243

Patchmatch-RL test		58.16 344	55.49 361	66.15 307	67.92 397	48.89 270	60.66 404	51.07 436	47.86 357	59.36 339	62.71 441	34.02 332	72.27 345	56.41 222	59.40 397	77.30 341

D2MVS		62.30 304	60.29 318	68.34 279	66.46 408	48.42 278	65.70 361	73.42 290	47.71 358	58.16 355	75.02 371	30.51 370	77.71 291	53.96 246	71.68 274	78.90 322

ANet_high		41.38 415	37.47 422	53.11 412	39.73 468	24.45 460	56.94 422	69.69 324	47.65 359	26.04 460	52.32 450	12.44 449	62.38 403	21.80 451	10.61 469	72.49 394

CostFormer		64.04 283	62.51 288	68.61 275	71.88 339	45.77 307	71.30 307	70.60 318	47.55 360	64.31 271	76.61 348	41.63 247	79.62 249	49.74 279	69.00 321	80.42 294

PatchmatchNet		59.84 329	58.24 335	64.65 330	73.05 316	46.70 299	69.42 334	62.18 395	47.55 360	58.88 345	71.96 396	34.49 326	69.16 364	42.99 344	63.60 367	78.07 328
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
KD-MVS_self_test		55.22 370	53.89 376	59.21 372	57.80 446	27.47 451	57.75 418	74.32 275	47.38 362	50.90 413	70.00 412	28.45 393	70.30 360	40.44 361	57.92 402	79.87 307

ITE_SJBPF				62.09 351	66.16 410	44.55 323		64.32 371	47.36 363	55.31 380	80.34 276	19.27 434	62.68 402	36.29 394	62.39 378	79.04 319

KD-MVS_2432*1600		53.45 380	51.50 389	59.30 369	62.82 425	37.14 393	55.33 426	71.79 310	47.34 364	55.09 383	70.52 407	21.91 429	70.45 357	35.72 397	42.97 443	70.31 419

miper_refine_blended		53.45 380	51.50 389	59.30 369	62.82 425	37.14 393	55.33 426	71.79 310	47.34 364	55.09 383	70.52 407	21.91 429	70.45 357	35.72 397	42.97 443	70.31 419

OurMVSNet-221017-0		61.37 317	58.63 332	69.61 257	72.05 336	48.06 284	73.93 260	72.51 302	47.23 366	54.74 387	80.92 266	21.49 432	81.24 212	48.57 291	56.22 410	79.53 313

tpmrst		58.24 343	58.70 331	56.84 389	66.97 402	34.32 419	69.57 333	61.14 400	47.17 367	58.58 351	71.60 399	41.28 254	60.41 409	49.20 285	62.84 374	75.78 359

tt0320		58.59 339	56.81 349	63.92 337	75.46 258	41.32 355	68.63 340	64.06 376	47.05 368	56.19 372	74.19 378	30.34 372	71.36 350	39.92 366	55.45 412	79.09 317

PVSNet	50.76 19	58.40 341	57.39 342	61.42 356	75.53 257	44.04 328	61.43 396	63.45 382	47.04 369	56.91 364	73.61 384	27.00 407	64.76 393	39.12 371	72.40 263	75.47 363

WB-MVSnew		59.66 332	59.69 321	59.56 366	75.19 265	35.78 410	69.34 335	64.28 372	46.88 370	61.76 312	75.79 361	40.61 262	65.20 391	32.16 412	71.21 278	77.70 335

UWE-MVS-28		52.25 387	52.35 385	51.93 420	66.99 401	22.79 463	63.48 385	48.31 444	46.78 371	52.73 406	76.11 355	27.78 399	57.82 424	20.58 453	68.41 329	75.17 365

FMVSNet5		55.86 364	54.93 364	58.66 377	71.05 357	36.35 402	64.18 380	62.48 390	46.76 372	50.66 417	74.73 374	25.80 415	64.04 395	33.11 408	65.57 351	75.59 361

jason		69.65 167	68.39 179	73.43 157	78.27 177	56.88 104	77.12 181	73.71 288	46.53 373	69.34 160	83.22 208	43.37 222	79.18 256	64.77 138	79.20 144	84.23 192
jason: jason.
MS-PatchMatch		62.42 302	61.46 302	65.31 325	75.21 264	52.10 204	72.05 296	74.05 282	46.41 374	57.42 362	74.36 376	34.35 328	77.57 293	45.62 317	73.67 236	66.26 433

1112_ss		64.00 284	63.36 277	65.93 312	79.28 140	42.58 342	71.35 305	72.36 305	46.41 374	60.55 324	77.89 324	46.27 188	73.28 337	46.18 310	69.97 301	81.92 264

lupinMVS		69.57 171	68.28 184	73.44 156	78.76 156	57.15 100	76.57 196	73.29 294	46.19 376	69.49 155	82.18 236	43.99 218	79.23 255	64.66 139	79.37 136	83.93 203

testdata				64.66 329	81.52 94	52.93 182		65.29 364	46.09 377	73.88 81	87.46 89	38.08 291	66.26 386	53.31 252	78.48 163	74.78 374

UnsupCasMVSNet_eth		53.16 385	52.47 383	55.23 397	59.45 441	33.39 427	59.43 409	69.13 333	45.98 378	50.35 419	72.32 391	29.30 385	58.26 422	42.02 353	44.30 441	74.05 382

AllTest		57.08 352	54.65 366	64.39 332	71.44 348	49.03 263	69.92 329	67.30 344	45.97 379	47.16 428	79.77 287	17.47 435	67.56 377	33.65 404	59.16 398	76.57 351

TestCases				64.39 332	71.44 348	49.03 263		67.30 344	45.97 379	47.16 428	79.77 287	17.47 435	67.56 377	33.65 404	59.16 398	76.57 351

WTY-MVS		59.75 331	60.39 317	57.85 385	72.32 332	37.83 386	61.05 402	64.18 373	45.95 381	61.91 309	79.11 303	47.01 180	60.88 407	42.50 348	69.49 313	74.83 372

IterMVS-SCA-FT		62.49 300	61.52 301	65.40 322	71.99 338	50.80 226	71.15 311	69.63 326	45.71 382	60.61 323	77.93 321	37.45 295	65.99 388	55.67 230	63.50 369	79.42 314

WB-MVS		43.26 409	43.41 409	42.83 435	63.32 424	10.32 473	58.17 414	45.20 451	45.42 383	40.44 446	67.26 428	34.01 333	58.98 417	11.96 464	24.88 458	59.20 439

旧先验2							76.08 208		45.32 384	76.55 43			65.56 390	58.75 207

OpenMVS_ROB	52.78 18	60.03 327	58.14 337	65.69 317	70.47 365	44.82 317	75.33 224	70.86 316	45.04 385	56.06 373	76.00 357	26.89 409	79.65 247	35.36 399	67.29 338	72.60 391

TinyColmap		54.14 375	51.72 387	61.40 357	66.84 404	41.97 347	66.52 355	68.51 337	44.81 386	42.69 442	75.77 362	11.66 451	72.94 338	31.96 414	56.77 408	69.27 427

MDTV_nov1_ep13			57.00 345		72.73 321	38.26 382	65.02 373	64.73 369	44.74 387	55.46 377	72.48 390	32.61 358	70.47 356	37.47 379	67.75 334

新几何1				70.76 235	85.66 41	61.13 30		66.43 354	44.68 388	70.29 140	86.64 112	41.29 253	75.23 328	49.72 280	81.75 106	75.93 357

Patchmtry		57.16 351	56.47 352	59.23 371	69.17 388	34.58 417	62.98 388	63.15 385	44.53 389	56.83 365	74.84 372	35.83 313	68.71 367	40.03 363	60.91 387	74.39 379

ppachtmachnet_test		58.06 346	55.38 362	66.10 309	69.51 382	48.99 266	68.01 345	66.13 358	44.50 390	54.05 395	70.74 405	32.09 364	72.34 344	36.68 389	56.71 409	76.99 349

PatchT		53.17 384	53.44 381	52.33 417	68.29 395	25.34 459	58.21 413	54.41 427	44.46 391	54.56 390	69.05 419	33.32 341	60.94 406	36.93 385	61.76 384	70.73 417

EPMVS		53.96 376	53.69 379	54.79 400	66.12 411	31.96 435	62.34 393	49.05 440	44.42 392	55.54 376	71.33 402	30.22 374	56.70 428	41.65 356	62.54 377	75.71 360

pmmvs4		61.48 316	59.39 323	67.76 283	71.57 344	53.86 155	71.42 304	65.34 363	44.20 393	59.46 338	77.92 322	35.90 312	74.71 330	43.87 334	64.87 356	74.71 376

dp		51.89 389	51.60 388	52.77 414	68.44 394	32.45 433	62.36 392	54.57 426	44.16 394	49.31 423	67.91 421	28.87 389	56.61 430	33.89 403	54.89 415	69.24 428

PatchMatch-RL		56.25 361	54.55 368	61.32 359	77.06 224	56.07 115	65.57 363	54.10 429	44.13 395	53.49 403	71.27 403	25.20 419	66.78 382	36.52 392	63.66 366	61.12 437

our_test_3		56.49 357	54.42 369	62.68 348	69.51 382	45.48 313	66.08 358	61.49 398	44.11 396	50.73 416	69.60 416	33.05 343	68.15 369	38.38 375	56.86 406	74.40 378

USDC		56.35 360	54.24 373	62.69 347	64.74 417	40.31 363	65.05 372	73.83 286	43.93 397	47.58 426	77.71 330	15.36 444	75.05 329	38.19 377	61.81 383	72.70 390

PM-MVS		52.33 386	50.19 395	58.75 376	62.10 430	45.14 316	65.75 360	40.38 458	43.60 398	53.52 401	72.65 389	9.16 459	65.87 389	50.41 274	54.18 418	65.24 435

pmmvs-eth3d		58.81 338	56.31 355	66.30 303	67.61 398	52.42 201	72.30 292	64.76 368	43.55 399	54.94 385	74.19 378	28.95 387	72.60 340	43.31 339	57.21 405	73.88 384

SSC-MVS		41.96 414	41.99 413	41.90 436	62.46 429	9.28 475	57.41 421	44.32 454	43.38 400	38.30 452	66.45 431	32.67 355	58.42 421	10.98 465	21.91 461	57.99 443

new-patchmatchnet		47.56 403	47.73 403	47.06 426	58.81 444	9.37 474	48.78 445	59.21 406	43.28 401	44.22 438	68.66 420	25.67 416	57.20 427	31.57 422	49.35 434	74.62 377

Test_1112_low_res		62.32 303	61.77 298	64.00 336	79.08 149	39.53 372	68.17 343	70.17 320	43.25 402	59.03 344	79.90 284	44.08 215	71.24 352	43.79 335	68.42 328	81.25 277

RPMNet		61.53 314	58.42 333	70.86 233	69.96 375	52.07 205	65.31 370	81.36 124	43.20 403	59.36 339	70.15 411	35.37 316	85.47 114	36.42 393	64.65 358	75.06 367

tpm2		62.07 307	60.10 319	67.99 281	72.79 320	43.86 329	71.05 314	66.85 351	43.14 404	62.77 293	75.39 369	38.32 287	80.80 227	41.69 354	68.88 322	79.32 315

JIA-IIPM		51.56 390	47.68 404	63.21 343	64.61 418	50.73 227	47.71 447	58.77 408	42.90 405	48.46 425	51.72 451	24.97 420	70.24 361	36.06 396	53.89 420	68.64 429

1314		64.61 276	63.21 281	68.80 272	71.87 340	47.46 293	73.95 258	78.39 204	42.88 406	59.97 330	76.60 349	38.11 290	79.39 252	54.84 237	72.32 265	79.55 312

HyFIR lowres test		65.67 260	63.01 283	73.67 143	79.97 127	55.65 125	69.07 337	75.52 252	42.68 407	63.53 280	77.95 320	40.43 263	81.64 200	46.01 312	71.91 270	83.73 215

CR-MVSNet		59.91 328	57.90 340	65.96 311	69.96 375	52.07 205	65.31 370	63.15 385	42.48 408	59.36 339	74.84 372	35.83 313	70.75 355	45.50 320	64.65 358	75.06 367

test222					83.14 72	58.68 78	72.57 288	63.45 382	41.78 409	67.56 203	86.12 134	37.13 302			78.73 156	74.98 370

TDRefinement		53.44 382	50.72 392	61.60 354	64.31 420	46.96 297	70.89 315	65.27 365	41.78 409	44.61 437	77.98 319	11.52 453	66.36 385	28.57 436	51.59 427	71.49 409

sss		56.17 362	56.57 351	54.96 398	66.93 403	36.32 404	57.94 415	61.69 397	41.67 411	58.64 349	75.32 370	38.72 282	56.25 432	42.04 352	66.19 347	72.31 400

PVSNet_0	43.31 20	47.46 404	45.64 407	52.92 413	67.60 399	44.65 320	54.06 431	54.64 425	41.59 412	46.15 433	58.75 444	30.99 368	58.66 419	32.18 411	24.81 459	55.46 447

MVS		67.37 228	66.33 234	70.51 242	75.46 258	50.94 221	73.95 258	81.85 112	41.57 413	62.54 300	78.57 312	47.98 158	85.47 114	52.97 254	82.05 99	75.14 366

Anonymous20240521		55.30 368	54.41 370	57.96 384	60.92 439	41.73 350	71.09 313	71.06 315	41.18 414	48.65 424	73.31 386	16.93 438	59.25 415	42.54 347	64.01 363	72.90 388

Anonymous20231206		55.10 373	55.30 363	54.48 401	69.81 380	33.94 423	62.91 389	62.13 396	41.08 415	55.18 382	75.65 363	32.75 351	56.59 431	30.32 429	67.86 332	72.91 387

MDA-MVSNet_test_wron		50.71 395	48.95 397	56.00 394	61.17 434	41.84 348	51.90 437	56.45 417	40.96 416	44.79 436	67.84 422	30.04 378	55.07 439	36.71 388	50.69 430	71.11 415

YYNet1		50.73 394	48.96 396	56.03 393	61.10 435	41.78 349	51.94 436	56.44 418	40.94 417	44.84 435	67.80 423	30.08 377	55.08 438	36.77 386	50.71 429	71.22 412

dongtai		34.52 424	34.94 424	33.26 445	61.06 436	16.00 470	52.79 435	23.78 471	40.71 418	39.33 450	48.65 459	16.91 439	48.34 451	12.18 463	19.05 463	35.44 462

CHOSEN 1792x2688		65.08 270	62.84 285	71.82 195	81.49 96	56.26 111	66.32 357	74.20 281	40.53 419	63.16 286	78.65 309	41.30 252	77.80 288	45.80 314	74.09 228	81.40 272

pmmvs5		56.47 358	55.68 360	58.86 375	61.41 433	36.71 399	66.37 356	62.75 387	40.38 420	53.70 397	76.62 346	34.56 324	67.05 380	40.02 364	65.27 352	72.83 389

test_vis1_n_1920		58.86 337	59.06 327	58.25 379	63.76 421	43.14 337	67.49 350	66.36 355	40.22 421	65.89 239	71.95 397	31.04 367	59.75 413	59.94 191	64.90 355	71.85 404

MDTV_nov1_ep13_2view						25.89 457	61.22 399		40.10 422	51.10 411		32.97 346		38.49 374		78.61 324

tpm cat1		59.25 336	56.95 346	66.15 307	72.19 334	46.96 297	68.09 344	65.76 359	40.03 423	57.81 358	70.56 406	38.32 287	74.51 331	38.26 376	61.50 385	77.00 347

test-mter		56.42 359	55.82 359	58.22 380	68.57 391	44.80 318	65.46 366	57.92 411	39.94 424	55.44 378	69.82 413	21.92 428	57.44 425	49.66 281	73.62 238	72.41 397

UnsupCasMVSNet_bld		50.07 397	48.87 398	53.66 406	60.97 438	33.67 425	57.62 419	64.56 370	39.47 425	47.38 427	64.02 439	27.47 401	59.32 414	34.69 401	43.68 442	67.98 431

TESTMET0.1,1		55.28 369	54.90 365	56.42 391	66.56 406	43.67 331	65.46 366	56.27 421	39.18 426	53.83 396	67.44 425	24.21 423	55.46 436	48.04 296	73.11 252	70.13 421

mamv4		56.85 354	58.00 339	53.43 409	72.46 329	54.47 145	57.56 420	54.74 424	38.81 427	57.42 362	79.45 297	47.57 167	38.70 462	60.88 183	53.07 422	67.11 432

ADS-MVSNet2		51.33 392	48.76 399	59.07 374	66.02 412	44.60 321	50.90 439	59.76 404	36.90 428	50.74 414	66.18 433	26.38 410	63.11 400	27.17 440	54.76 416	69.50 425

ADS-MVSNet		48.48 401	47.77 402	50.63 422	66.02 412	29.92 442	50.90 439	50.87 438	36.90 428	50.74 414	66.18 433	26.38 410	52.47 445	27.17 440	54.76 416	69.50 425

RPSCF		55.80 365	54.22 374	60.53 363	65.13 416	42.91 341	64.30 378	57.62 413	36.84 430	58.05 357	82.28 233	28.01 396	56.24 433	37.14 383	58.61 400	82.44 255

test_cas_vis1_n_1920		56.91 353	56.71 350	57.51 388	59.13 443	45.40 314	63.58 384	61.29 399	36.24 431	67.14 212	71.85 398	29.89 379	56.69 429	57.65 213	63.58 368	70.46 418

Patchmatch-test		49.08 399	48.28 401	51.50 421	64.40 419	30.85 440	45.68 451	48.46 443	35.60 432	46.10 434	72.10 394	34.47 327	46.37 454	27.08 442	60.65 391	77.27 342

CHOSEN 280x420		47.83 402	46.36 406	52.24 419	67.37 400	49.78 245	38.91 459	43.11 456	35.00 433	43.27 441	63.30 440	28.95 387	49.19 450	36.53 391	60.80 389	57.76 444

N_pmnet		39.35 419	40.28 416	36.54 442	63.76 421	1.62 479	49.37 444	0.76 478	34.62 434	43.61 440	66.38 432	26.25 412	42.57 458	26.02 445	51.77 426	65.44 434

kuosan		29.62 431	30.82 430	26.02 450	52.99 449	16.22 469	51.09 438	22.71 472	33.91 435	33.99 454	40.85 460	15.89 442	33.11 467	7.59 471	18.37 464	28.72 464

PMMVS		53.96 376	53.26 382	56.04 392	62.60 428	50.92 223	61.17 400	56.09 422	32.81 436	53.51 402	66.84 430	34.04 331	59.93 412	44.14 330	68.18 330	57.27 445

CMPMVS	42.80 21	57.81 348	55.97 357	63.32 341	60.98 437	47.38 294	64.66 375	69.50 329	32.06 437	46.83 430	77.80 326	29.50 383	71.36 350	48.68 289	73.75 234	71.21 413
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
ttmdpeth		45.56 405	42.95 410	53.39 411	52.33 453	29.15 444	57.77 416	48.20 445	31.81 438	49.86 421	77.21 335	8.69 460	59.16 416	27.31 439	33.40 455	71.84 405

CVMVSNet		59.63 333	59.14 325	61.08 362	74.47 286	38.84 377	75.20 229	68.74 336	31.15 439	58.24 353	76.51 350	32.39 361	68.58 368	49.77 278	65.84 349	75.81 358

FPMVS		42.18 413	41.11 415	45.39 428	58.03 445	41.01 359	49.50 443	53.81 430	30.07 440	33.71 455	64.03 437	11.69 450	52.08 448	14.01 459	55.11 414	43.09 456

EU-MVSNet		55.61 367	54.41 370	59.19 373	65.41 414	33.42 426	72.44 290	71.91 309	28.81 441	51.27 410	73.87 382	24.76 421	69.08 365	43.04 343	58.20 401	75.06 367

test_vis1_n		49.89 398	48.69 400	53.50 408	53.97 447	37.38 391	61.53 395	47.33 448	28.54 442	59.62 337	67.10 429	13.52 446	52.27 446	49.07 286	57.52 403	70.84 416

test_fmvs1_n		51.37 391	50.35 394	54.42 403	52.85 450	37.71 388	61.16 401	51.93 431	28.15 443	63.81 278	69.73 415	13.72 445	53.95 440	51.16 269	60.65 391	71.59 407

LF4IMVS		42.95 410	42.26 412	45.04 429	48.30 458	32.50 432	54.80 428	48.49 442	28.03 444	40.51 445	70.16 410	9.24 458	43.89 457	31.63 420	49.18 435	58.72 441

test_fmvs1		51.32 393	50.48 393	53.81 405	53.57 448	37.51 390	60.63 405	51.16 434	28.02 445	63.62 279	69.23 418	16.41 440	53.93 441	51.01 270	60.70 390	69.99 422

MVS-HIRNet		45.52 406	44.48 408	48.65 425	68.49 393	34.05 422	59.41 410	44.50 453	27.03 446	37.96 453	50.47 455	26.16 413	64.10 394	26.74 443	59.52 396	47.82 454

PMVS	28.69 22	36.22 422	33.29 427	45.02 430	36.82 470	35.98 407	54.68 429	48.74 441	26.31 447	21.02 463	51.61 452	2.88 472	60.10 411	9.99 468	47.58 436	38.99 461
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
pmmvs3		44.92 407	41.95 414	53.86 404	52.58 452	43.55 332	62.11 394	46.90 450	26.05 448	40.63 444	60.19 443	11.08 456	57.91 423	31.83 419	46.15 438	60.11 438

test_fmvs2		48.69 400	47.49 405	52.29 418	48.63 457	33.06 430	57.76 417	48.05 446	25.71 449	59.76 335	69.60 416	11.57 452	52.23 447	49.45 284	56.86 406	71.58 408

PMMVS2		27.40 432	25.91 435	31.87 447	39.46 469	6.57 476	31.17 462	28.52 467	23.96 450	20.45 464	48.94 458	4.20 468	37.94 463	16.51 456	19.97 462	51.09 449

MVStest1		42.65 411	39.29 418	52.71 415	47.26 460	34.58 417	54.41 430	50.84 439	23.35 451	39.31 451	74.08 381	12.57 448	55.09 437	23.32 448	28.47 457	68.47 430

Gipuma		34.77 423	31.91 428	43.33 433	62.05 431	37.87 384	20.39 464	67.03 349	23.23 452	18.41 465	25.84 465	4.24 466	62.73 401	14.71 458	51.32 428	29.38 463
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
test_vis1_rt		41.35 416	39.45 417	47.03 427	46.65 461	37.86 385	47.76 446	38.65 459	23.10 453	44.21 439	51.22 453	11.20 455	44.08 456	39.27 370	53.02 423	59.14 440

new_pmnet		34.13 425	34.29 426	33.64 444	52.63 451	18.23 468	44.43 454	33.90 464	22.81 454	30.89 457	53.18 449	10.48 457	35.72 466	20.77 452	39.51 447	46.98 455

mvsany_test1		39.38 418	38.16 421	43.02 434	49.05 455	34.28 420	44.16 455	25.94 469	22.74 455	46.57 432	62.21 442	23.85 424	41.16 461	33.01 409	35.91 451	53.63 448

LCM-MVSNet		40.30 417	35.88 423	53.57 407	42.24 463	29.15 444	45.21 453	60.53 403	22.23 456	28.02 458	50.98 454	3.72 469	61.78 405	31.22 425	38.76 449	69.78 424

test_fmvs3		44.30 408	42.55 411	49.55 424	42.83 462	27.15 454	53.03 433	44.93 452	22.03 457	53.69 399	64.94 436	4.21 467	49.63 449	47.47 297	49.82 432	71.88 403

APD_test1		37.39 421	34.94 424	44.72 432	48.88 456	33.19 429	52.95 434	44.00 455	19.49 458	27.28 459	58.59 445	3.18 471	52.84 444	18.92 454	41.17 446	48.14 453

mvsany_test3		32.62 426	30.57 431	38.77 440	36.16 471	24.20 461	38.10 460	20.63 473	19.14 459	40.36 447	57.43 446	5.06 464	36.63 465	29.59 433	28.66 456	55.49 446

E-PMN		23.77 433	22.73 437	26.90 448	42.02 464	20.67 465	42.66 456	35.70 462	17.43 460	10.28 470	25.05 466	6.42 462	42.39 459	10.28 467	14.71 466	17.63 465

EMVS		22.97 434	21.84 438	26.36 449	40.20 467	19.53 467	41.95 457	34.64 463	17.09 461	9.73 471	22.83 467	7.29 461	42.22 460	9.18 469	13.66 467	17.32 466

test_vis3_rt		32.09 427	30.20 432	37.76 441	35.36 472	27.48 450	40.60 458	28.29 468	16.69 462	32.52 456	40.53 461	1.96 473	37.40 464	33.64 406	42.21 445	48.39 451

test_f		31.86 428	31.05 429	34.28 443	32.33 474	21.86 464	32.34 461	30.46 466	16.02 463	39.78 449	55.45 448	4.80 465	32.36 468	30.61 426	37.66 450	48.64 450

DSMNet-mixed		39.30 420	38.72 419	41.03 437	51.22 454	19.66 466	45.53 452	31.35 465	15.83 464	39.80 448	67.42 427	22.19 427	45.13 455	22.43 449	52.69 424	58.31 442

testf1		31.46 429	28.89 433	39.16 438	41.99 465	28.78 446	46.45 449	37.56 460	14.28 465	21.10 461	48.96 456	1.48 475	47.11 452	13.63 460	34.56 452	41.60 457

APD_test2		31.46 429	28.89 433	39.16 438	41.99 465	28.78 446	46.45 449	37.56 460	14.28 465	21.10 461	48.96 456	1.48 475	47.11 452	13.63 460	34.56 452	41.60 457

MVE	17.77 23	21.41 435	17.77 440	32.34 446	34.34 473	25.44 458	16.11 465	24.11 470	11.19 467	13.22 467	31.92 463	1.58 474	30.95 469	10.47 466	17.03 465	40.62 460
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
DeepMVS_CX				12.03 453	17.97 475	10.91 472		10.60 476	7.46 468	11.07 469	28.36 464	3.28 470	11.29 472	8.01 470	9.74 471	13.89 467

wuyk23d		13.32 438	12.52 441	15.71 452	47.54 459	26.27 456	31.06 463	1.98 477	4.93 469	5.18 472	1.94 472	0.45 477	18.54 471	6.81 472	12.83 468	2.33 469

test_method		19.68 436	18.10 439	24.41 451	13.68 476	3.11 478	12.06 467	42.37 457	2.00 470	11.97 468	36.38 462	5.77 463	29.35 470	15.06 457	23.65 460	40.76 459

tmp_tt		9.43 439	11.14 442	4.30 454	2.38 477	4.40 477	13.62 466	16.08 475	0.39 471	15.89 466	13.06 468	15.80 443	5.54 473	12.63 462	10.46 470	2.95 468

EGC-MVSNET		42.47 412	38.48 420	54.46 402	74.33 291	48.73 272	70.33 324	51.10 435	0.03 472	0.18 473	67.78 424	13.28 447	66.49 384	18.91 455	50.36 431	48.15 452

testmvs		4.52 442	6.03 445	0.01 456	0.01 478	0.00 481	53.86 432	0.00 479	0.01 473	0.04 474	0.27 473	0.00 479	0.00 474	0.04 473	0.00 472	0.03 471

test123		4.73 441	6.30 444	0.02 455	0.01 478	0.01 480	56.36 424	0.00 479	0.01 473	0.04 474	0.21 474	0.01 478	0.00 474	0.03 474	0.00 472	0.04 470

mmdepth		0.00 444	0.00 447	0.00 457	0.00 480	0.00 481	0.00 468	0.00 479	0.00 475	0.00 476	0.00 475	0.00 479	0.00 474	0.00 475	0.00 472	0.00 472

monomultidepth		0.00 444	0.00 447	0.00 457	0.00 480	0.00 481	0.00 468	0.00 479	0.00 475	0.00 476	0.00 475	0.00 479	0.00 474	0.00 475	0.00 472	0.00 472

test_blank		0.00 444	0.00 447	0.00 457	0.00 480	0.00 481	0.00 468	0.00 479	0.00 475	0.00 476	0.00 475	0.00 479	0.00 474	0.00 475	0.00 472	0.00 472

uanet_test		0.00 444	0.00 447	0.00 457	0.00 480	0.00 481	0.00 468	0.00 479	0.00 475	0.00 476	0.00 475	0.00 479	0.00 474	0.00 475	0.00 472	0.00 472

DCPMVS		0.00 444	0.00 447	0.00 457	0.00 480	0.00 481	0.00 468	0.00 479	0.00 475	0.00 476	0.00 475	0.00 479	0.00 474	0.00 475	0.00 472	0.00 472

cdsmvs_eth3d_5k		17.50 437	23.34 436	0.00 457	0.00 480	0.00 481	0.00 468	78.63 187	0.00 475	0.00 476	82.18 236	49.25 145	0.00 474	0.00 475	0.00 472	0.00 472

pcd_1.5k_mvsjas		3.92 443	5.23 446	0.00 457	0.00 480	0.00 481	0.00 468	0.00 479	0.00 475	0.00 476	0.00 475	47.05 177	0.00 474	0.00 475	0.00 472	0.00 472

sosnet-low-res		0.00 444	0.00 447	0.00 457	0.00 480	0.00 481	0.00 468	0.00 479	0.00 475	0.00 476	0.00 475	0.00 479	0.00 474	0.00 475	0.00 472	0.00 472

sosnet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 481	0.00 468	0.00 479	0.00 475	0.00 476	0.00 475	0.00 479	0.00 474	0.00 475	0.00 472	0.00 472

uncertanet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 481	0.00 468	0.00 479	0.00 475	0.00 476	0.00 475	0.00 479	0.00 474	0.00 475	0.00 472	0.00 472

Regformer		0.00 444	0.00 447	0.00 457	0.00 480	0.00 481	0.00 468	0.00 479	0.00 475	0.00 476	0.00 475	0.00 479	0.00 474	0.00 475	0.00 472	0.00 472

ab-mvs-re		6.49 440	8.65 443	0.00 457	0.00 480	0.00 481	0.00 468	0.00 479	0.00 475	0.00 476	77.89 324	0.00 479	0.00 474	0.00 475	0.00 472	0.00 472

uanet		0.00 444	0.00 447	0.00 457	0.00 480	0.00 481	0.00 468	0.00 479	0.00 475	0.00 476	0.00 475	0.00 479	0.00 474	0.00 475	0.00 472	0.00 472

WAC-MVS						27.31 452								27.77 437

MSC_two_6792asdad				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 40

No_MVS				79.95 4	87.24 14	61.04 31		85.62 25					90.96 1	79.31 10	90.65 8	87.85 40

eth-test2					0.00 480

eth-test					0.00 480

OPU-MVS				79.83 7	87.54 11	60.93 35	87.82 7				89.89 47	67.01 1	90.33 12	73.16 66	91.15 4	88.23 29

test_0728_SECOND				79.19 16	87.82 3	59.11 68	87.85 5	87.15 3					90.84 3	78.66 18	90.61 11	87.62 51

GSMVS																78.05 329

test_part2					87.58 9	60.47 42				83.42 12

sam_mvs1												34.74 323				78.05 329

sam_mvs												33.43 340

ambc				65.13 327	63.72 423	37.07 395	47.66 448	78.78 183		54.37 393	71.42 400	11.24 454	80.94 222	45.64 316	53.85 421	77.38 340

MTGPA								80.97 142

test_post1							68.67 339				3.64 470	32.39 361	69.49 363	44.17 328

test_post											3.55 471	33.90 334	66.52 383

patchmatchnet-post											64.03 437	34.50 325	74.27 333

GG-mvs-BLEND				62.34 349	71.36 352	37.04 396	69.20 336	57.33 416		54.73 388	65.48 435	30.37 371	77.82 287	34.82 400	74.93 220	72.17 401

MTMP							86.03 19	17.08 474

test9_res														75.28 49	88.31 32	83.81 209

agg_prior2														73.09 67	87.93 40	84.33 187

agg_prior					85.04 50	59.96 50		81.04 140		74.68 68			84.04 142

test_prior4						62.51 14	82.08 82

test_prior				76.69 61	84.20 61	57.27 94		84.88 40					86.43 84			86.38 99

新几何2							76.12 206

旧先验1					83.04 74	53.15 177		67.52 343			87.85 82	44.08 215			80.76 115	78.03 332

原ACMM2							79.02 123

testdata2													72.18 347	46.95 306

segment_acmp												54.23 63

test12				77.76 46	84.52 58	58.41 80		83.36 78		72.93 103		54.61 60	88.05 39		88.12 34	86.81 82

plane_prior7					81.41 97	55.96 117

plane_prior6					81.20 104	56.24 112						45.26 202

plane_prior5								84.01 53					87.21 59	68.16 101	80.58 119	84.65 178

plane_prior4											86.10 135

plane_prior1					81.27 102

n2								0.00 479

nn								0.00 479

door-mid								47.19 449

lessismore_v0				69.91 252	71.42 350	47.80 287		50.90 437		50.39 418	75.56 364	27.43 403	81.33 209	45.91 313	34.10 454	80.59 291

test11								83.47 73

door								47.60 447

HQP5-MVS						54.94 139

BP-MVS														67.04 115

HQP4-MVS										67.85 192			86.93 67			84.32 188

HQP3-MVS								83.90 58							80.35 123

HQP2-MVS												45.46 196

NP-MVS					80.98 107	56.05 116					85.54 155

ACMMP++_ref															74.07 229

ACMMP++															72.16 268

Test By Simon												48.33 156

High-resolution multi-view benchmark