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.

MSP-MVS		90.38 4	91.87 1	85.88 79	92.83 71	64.03 180	93.06 99	94.33 47	82.19 23	93.65 3	96.15 27	85.89 1	97.19 77	91.02 23	97.75 1	96.43 25
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
DVP-MVS++		90.53 3	91.09 4	88.87 13	97.31 4	69.91 36	93.96 64	94.37 45	72.48 163	92.07 6	96.85 12	83.82 2	99.15 2	91.53 19	97.42 4	97.55 4

OPU-MVS				89.97 3	97.52 3	73.15 12	96.89 4				97.00 9	83.82 2	99.15 2	95.72 2	97.63 3	97.62 2

PC_three_1452									80.91 38	94.07 2	96.83 14	83.57 4	99.12 5	95.70 4	97.42 4	97.55 4

DPM-MVS		90.70 2	90.52 7	91.24 1	89.68 144	76.68 2	97.29 1	95.35 11	82.87 17	91.58 10	97.22 3	79.93 5	99.10 9	83.12 82	97.64 2	97.94 1

baseline2		83.68 82	83.42 73	84.48 126	87.37 204	66.00 131	90.06 215	95.93 7	79.71 49	69.08 216	90.39 154	77.92 6	96.28 113	78.91 115	81.38 156	91.16 183

GG-mvs-BLEND				86.53 63	91.91 98	69.67 44	75.02 342	94.75 27		78.67 112	90.85 146	77.91 7	94.56 184	72.25 161	93.74 42	95.36 57

gg-mvs-nofinetune		77.18 191	74.31 211	85.80 84	91.42 111	68.36 70	71.78 344	94.72 28	49.61 347	77.12 127	45.92 368	77.41 8	93.98 212	67.62 206	93.16 52	95.05 73

SED-MVS		89.94 8	90.36 9	88.70 15	96.45 12	69.38 46	96.89 4	94.44 39	71.65 192	92.11 4	97.21 4	76.79 9	99.11 6	92.34 11	95.36 13	97.62 2

test_241102_ONE					96.45 12	69.38 46		94.44 39	71.65 192	92.11 4	97.05 7	76.79 9	99.11 6

test_0728_THIRD									72.48 163	90.55 16	96.93 10	76.24 11	99.08 11	91.53 19	94.99 16	96.43 25

DPE-MVS		88.77 15	89.21 15	87.45 36	96.26 20	67.56 92	94.17 52	94.15 52	68.77 242	90.74 14	97.27 2	76.09 12	98.49 28	90.58 27	94.91 19	96.30 28
Kehua Chen, Zhenlong Yuan, Tianlu Mao, Zhaoqi Wang: Dual-Level Precision Edges Guided Multi-View Stereo with Accurate Planarization. AAAI2025
TSAR-MVS + GP.		87.96 19	88.37 19	86.70 56	93.51 56	65.32 147	95.15 35	93.84 57	78.17 75	85.93 41	94.80 61	75.80 13	98.21 33	89.38 30	88.78 100	96.59 15

DeepPCF-MVS	81.17 1	89.72 9	91.38 3	84.72 118	93.00 69	58.16 285	96.72 7	94.41 41	86.50 7	90.25 18	97.83 1	75.46 14	98.67 24	92.78 8	95.49 12	97.32 6

dcpmvs_2		87.37 27	87.55 26	86.85 49	95.04 32	68.20 77	90.36 206	90.66 187	79.37 54	81.20 79	93.67 93	74.73 15	96.55 108	90.88 24	92.00 66	95.82 43

MVSTER		82.47 99	82.05 96	83.74 144	92.68 78	69.01 56	91.90 147	93.21 83	79.83 45	72.14 181	85.71 220	74.72 16	94.72 173	75.72 133	72.49 226	87.50 230

test_241102_TWO								94.41 41	71.65 192	92.07 6	97.21 4	74.58 17	99.11 6	92.34 11	95.36 13	96.59 15

test_one_0601					96.32 18	69.74 41		94.18 50	71.42 203	90.67 15	96.85 12	74.45 18

DELS-MVS		90.05 6	90.09 10	89.94 4	93.14 66	73.88 7	97.01 3	94.40 43	88.32 2	85.71 43	94.91 58	74.11 19	98.91 17	87.26 49	95.94 8	97.03 10
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
patch_mono-2		89.71 10	90.99 5	85.85 82	96.04 24	63.70 190	95.04 39	95.19 14	86.74 6	91.53 11	95.15 52	73.86 20	97.58 55	93.38 5	92.00 66	96.28 31

DVP-MVS		89.41 12	89.73 13	88.45 21	96.40 15	69.99 32	96.64 8	94.52 35	71.92 179	90.55 16	96.93 10	73.77 21	99.08 11	91.91 17	94.90 20	96.29 29
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					96.40 15	69.99 32	96.76 6	94.33 47	71.92 179	91.89 8	97.11 6	73.77 21

ET-MVSNet_ETH3D		84.01 73	83.15 80	86.58 60	90.78 125	70.89 23	94.74 46	94.62 33	81.44 32	58.19 307	93.64 94	73.64 23	92.35 263	82.66 84	78.66 177	96.50 23

CSCG		86.87 32	86.26 38	88.72 14	95.05 31	70.79 24	93.83 75	95.33 12	68.48 246	77.63 120	94.35 76	73.04 24	98.45 29	84.92 69	93.71 44	96.92 11

tttt0517		79.50 150	78.53 150	82.41 178	87.22 206	61.43 236	89.75 225	94.76 26	69.29 234	67.91 235	88.06 190	72.92 25	95.63 141	62.91 249	73.90 216	90.16 194

MCST-MVS		91.08 1	91.46 2	89.94 4	97.66 2	73.37 8	97.13 2	95.58 9	89.33 1	85.77 42	96.26 23	72.84 26	99.38 1	92.64 9	95.93 9	97.08 9

iter_conf05		83.27 86	82.70 88	84.98 108	93.32 59	71.84 15	94.16 53	81.76 328	82.74 18	73.83 161	88.40 180	72.77 27	94.61 178	82.10 88	75.21 204	88.48 219

thisisatest0515		83.41 83	82.49 92	86.16 73	89.46 150	68.26 74	93.54 86	94.70 29	74.31 126	75.75 137	90.92 144	72.62 28	96.52 109	69.64 184	81.50 155	93.71 122

thisisatest0530		81.15 119	80.07 124	84.39 129	88.26 182	65.63 140	91.40 167	94.62 33	71.27 205	70.93 194	89.18 171	72.47 29	96.04 124	65.62 228	76.89 194	91.49 172

TSAR-MVS + MP.		88.11 18	88.64 16	86.54 62	91.73 102	68.04 80	90.36 206	93.55 71	82.89 16	91.29 12	92.89 109	72.27 30	96.03 125	87.99 40	94.77 24	95.54 51
Zhenlong Yuan, Jiakai Cao, Zhaoqi Wang, Zhaoxin Li: TSAR-MVS: Textureless-aware Segmentation and Correlative Refinement Guided Multi-View Stereo. Pattern Recognition
EPP-MVSNet		81.79 111	81.52 103	82.61 172	88.77 169	60.21 260	93.02 103	93.66 67	68.52 245	72.90 168	90.39 154	72.19 31	94.96 165	74.93 141	79.29 171	92.67 148

CostFormer		82.33 101	81.15 106	85.86 81	89.01 163	68.46 68	82.39 301	93.01 93	75.59 109	80.25 90	81.57 266	72.03 32	94.96 165	79.06 113	77.48 188	94.16 103

HPM-MVS++		89.37 13	89.95 12	87.64 29	95.10 30	68.23 76	95.24 32	94.49 37	82.43 21	88.90 25	96.35 21	71.89 33	98.63 25	88.76 37	96.40 6	96.06 35

CNVR-MVS		90.32 5	90.89 6	88.61 18	96.76 8	70.65 25	96.47 12	94.83 24	84.83 10	89.07 24	96.80 15	70.86 34	99.06 15	92.64 9	95.71 10	96.12 34

IB-MVS	77.80 4	82.18 103	80.46 122	87.35 38	89.14 160	70.28 30	95.59 25	95.17 16	78.85 66	70.19 204	85.82 218	70.66 35	97.67 48	72.19 164	66.52 265	94.09 107
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
baseline1		81.84 110	81.03 111	84.28 134	91.60 105	66.62 117	91.08 185	91.66 150	81.87 26	74.86 148	91.67 134	69.98 36	94.92 168	71.76 167	64.75 280	91.29 181

MVS_0304		90.01 7	90.50 8	88.53 19	90.14 135	70.94 22	96.47 12	95.72 8	87.33 3	89.60 21	96.26 23	68.44 37	98.74 23	95.82 1	94.72 29	95.90 41

alignmvs		87.28 28	86.97 32	88.24 23	91.30 114	71.14 21	95.61 24	93.56 70	79.30 55	87.07 33	95.25 48	68.43 38	96.93 97	87.87 41	84.33 136	96.65 13

PAPM		85.89 46	85.46 48	87.18 41	88.20 186	72.42 13	92.41 126	92.77 101	82.11 24	80.34 89	93.07 104	68.27 39	95.02 162	78.39 120	93.59 46	94.09 107

train_agg		87.21 29	87.42 28	86.60 58	94.18 41	67.28 99	94.16 53	93.51 72	71.87 184	85.52 45	95.33 41	68.19 40	97.27 75	89.09 34	94.90 20	95.25 68

test_8					94.19 40	67.19 101	94.15 56	93.42 78	71.87 184	85.38 48	95.35 40	68.19 40	96.95 94

TEST9					94.18 41	67.28 99	94.16 53	93.51 72	71.75 190	85.52 45	95.33 41	68.01 42	97.27 75

test_prior2							95.10 37		75.40 113	85.25 51	95.61 36	67.94 43		87.47 46	94.77 24

WTY-MVS		86.32 38	85.81 44	87.85 25	92.82 73	69.37 48	95.20 33	95.25 13	82.71 19	81.91 74	94.73 62	67.93 44	97.63 52	79.55 107	82.25 148	96.54 18

APDe-MVS		87.54 25	87.84 22	86.65 57	96.07 23	66.30 125	94.84 44	93.78 58	69.35 233	88.39 26	96.34 22	67.74 45	97.66 50	90.62 26	93.44 48	96.01 38

test_fmvsm_n_1920		87.69 24	88.50 17	85.27 100	87.05 210	63.55 196	93.69 79	91.08 175	84.18 12	90.17 19	97.04 8	67.58 46	97.99 38	95.72 2	90.03 91	94.26 99

tpm2		79.80 146	77.95 159	85.34 98	88.28 181	68.26 74	81.56 307	91.42 159	70.11 224	77.59 122	80.50 284	67.40 47	94.26 197	67.34 208	77.35 189	93.51 127

miper_enhance_ethall		78.86 162	77.97 158	81.54 202	88.00 191	65.17 151	91.41 165	89.15 242	75.19 116	68.79 223	83.98 238	67.17 48	92.82 241	72.73 156	65.30 271	86.62 251

SF-MVS		87.03 31	87.09 30	86.84 50	92.70 77	67.45 97	93.64 81	93.76 61	70.78 216	86.25 36	96.44 20	66.98 49	97.79 45	88.68 38	94.56 31	95.28 64

HY-MVS	76.49 5	84.28 66	83.36 76	87.02 47	92.22 87	67.74 87	84.65 281	94.50 36	79.15 59	82.23 72	87.93 191	66.88 50	96.94 95	80.53 102	82.20 149	96.39 27

EPNet		87.84 22	88.38 18	86.23 72	93.30 60	66.05 129	95.26 31	94.84 23	87.09 4	88.06 27	94.53 67	66.79 51	97.34 68	83.89 78	91.68 71	95.29 62
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
9.14			87.63 24		93.86 47		94.41 50	94.18 50	72.76 158	86.21 37	96.51 18	66.64 52	97.88 43	90.08 28	94.04 36

FIs		79.47 151	79.41 138	79.67 247	85.95 227	59.40 270	91.68 159	93.94 55	78.06 76	68.96 220	88.28 183	66.61 53	91.77 275	66.20 222	74.99 205	87.82 227

NCCC		89.07 14	89.46 14	87.91 24	96.60 10	69.05 55	96.38 14	94.64 32	84.42 11	86.74 34	96.20 25	66.56 54	98.76 22	89.03 36	94.56 31	95.92 40

SD-MVS		87.49 26	87.49 27	87.50 35	93.60 53	68.82 61	93.90 68	92.63 109	76.86 94	87.90 28	95.76 32	66.17 55	97.63 52	89.06 35	91.48 75	96.05 36
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
UniMVSNet_NR-MVSNet		78.15 177	77.55 164	79.98 238	84.46 251	60.26 258	92.25 129	93.20 85	77.50 88	68.88 221	86.61 207	66.10 56	92.13 267	66.38 219	62.55 295	87.54 229

CHOSEN 280x420		77.35 189	76.95 177	78.55 263	87.07 209	62.68 215	69.71 350	82.95 324	68.80 241	71.48 190	87.27 202	66.03 57	84.00 339	76.47 130	82.81 146	88.95 209

CANet		89.61 11	89.99 11	88.46 20	94.39 39	69.71 42	96.53 11	93.78 58	86.89 5	89.68 20	95.78 31	65.94 58	99.10 9	92.99 7	93.91 39	96.58 17

segment_acmp												65.94 58

Vis-MVSNet (Re-imp)		79.24 154	79.57 133	78.24 268	88.46 174	52.29 324	90.41 204	89.12 244	74.24 127	69.13 214	91.91 129	65.77 60	90.09 300	59.00 272	88.09 105	92.33 156

FC-MVSNet-test		77.99 179	78.08 156	77.70 271	84.89 244	55.51 310	90.27 209	93.75 64	76.87 93	66.80 253	87.59 196	65.71 61	90.23 297	62.89 250	73.94 214	87.37 234

SMA-MVS		88.14 16	88.29 20	87.67 28	93.21 63	68.72 63	93.85 71	94.03 54	74.18 128	91.74 9	96.67 16	65.61 62	98.42 32	89.24 33	96.08 7	95.88 42
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
test12				87.09 44	94.60 36	68.86 59		92.91 97		82.67 71		65.44 63	97.55 57		93.69 45	94.84 81

旧先验1					91.94 95	60.74 250		91.50 156			94.36 72	65.23 64			91.84 68	94.55 89

1112_ss		80.56 130	79.83 130	82.77 167	88.65 170	60.78 246	92.29 128	88.36 268	72.58 161	72.46 177	94.95 54	65.09 65	93.42 227	66.38 219	77.71 182	94.10 106

MVSFormer		83.75 80	82.88 84	86.37 68	89.24 158	71.18 19	89.07 239	90.69 184	65.80 264	87.13 31	94.34 77	64.99 66	92.67 249	72.83 153	91.80 69	95.27 65

lupinMVS		87.74 23	87.77 23	87.63 33	89.24 158	71.18 19	96.57 10	92.90 98	82.70 20	87.13 31	95.27 46	64.99 66	95.80 130	89.34 31	91.80 69	95.93 39

tpmrst		80.57 129	79.14 144	84.84 112	90.10 136	68.28 73	81.70 305	89.72 224	77.63 86	75.96 136	79.54 298	64.94 68	92.71 246	75.43 135	77.28 191	93.55 126

ZD-MVS					96.63 9	65.50 145		93.50 74	70.74 217	85.26 50	95.19 51	64.92 69	97.29 71	87.51 45	93.01 53

casdiffmvs_mvg		85.66 49	85.18 51	87.09 44	88.22 185	69.35 49	93.74 78	91.89 136	81.47 29	80.10 91	91.45 136	64.80 70	96.35 111	87.23 50	87.69 108	95.58 49
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
miper_ehance_all_eth		77.60 185	76.44 182	81.09 216	85.70 232	64.41 170	90.65 198	88.64 263	72.31 169	67.37 246	82.52 252	64.77 71	92.64 253	70.67 176	65.30 271	86.24 256

Test_1112_low_res		79.56 149	78.60 149	82.43 175	88.24 184	60.39 257	92.09 136	87.99 278	72.10 177	71.84 184	87.42 199	64.62 72	93.04 231	65.80 226	77.30 190	93.85 120

test2506		83.29 85	82.92 83	84.37 130	88.39 178	63.18 202	92.01 141	91.35 161	77.66 84	78.49 113	91.42 137	64.58 73	95.09 161	73.19 149	89.23 96	94.85 78

DeepC-MVS_fast	79.48 2	87.95 20	88.00 21	87.79 27	95.86 27	68.32 71	95.74 20	94.11 53	83.82 14	83.49 63	96.19 26	64.53 74	98.44 30	83.42 81	94.88 23	96.61 14
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MG-MVS		87.11 30	86.27 37	89.62 7	97.79 1	76.27 4	94.96 42	94.49 37	78.74 70	83.87 62	92.94 107	64.34 75	96.94 95	75.19 137	94.09 35	95.66 46

casdiffmvs		85.37 51	84.87 57	86.84 50	88.25 183	69.07 54	93.04 101	91.76 143	81.27 34	80.84 86	92.07 127	64.23 76	96.06 123	84.98 68	87.43 111	95.39 54
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
cl22		77.94 181	76.78 178	81.42 204	87.57 198	64.93 159	90.67 197	88.86 255	72.45 165	67.63 241	82.68 251	64.07 77	92.91 239	71.79 165	65.30 271	86.44 252

tpm		78.58 170	77.03 174	83.22 160	85.94 229	64.56 161	83.21 295	91.14 171	78.31 73	73.67 162	79.68 296	64.01 78	92.09 269	66.07 223	71.26 236	93.03 141

CDS-MVSNet		81.43 116	80.74 114	83.52 151	86.26 222	64.45 166	92.09 136	90.65 188	75.83 108	73.95 160	89.81 166	63.97 79	92.91 239	71.27 170	82.82 145	93.20 136
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
MVS_Test		84.16 71	83.20 77	87.05 46	91.56 107	69.82 38	89.99 220	92.05 127	77.77 81	82.84 67	86.57 208	63.93 80	96.09 119	74.91 142	89.18 98	95.25 68

APD-MVS		85.93 45	85.99 42	85.76 86	95.98 26	65.21 150	93.59 84	92.58 111	66.54 259	86.17 38	95.88 30	63.83 81	97.00 87	86.39 57	92.94 54	95.06 72
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
mvs_anonymous		81.36 117	79.99 127	85.46 93	90.39 131	68.40 69	86.88 271	90.61 189	74.41 123	70.31 203	84.67 229	63.79 82	92.32 264	73.13 150	85.70 126	95.67 45

PVSNet_Blended_VisFu		83.97 74	83.50 67	85.39 96	90.02 137	66.59 119	93.77 76	91.73 144	77.43 90	77.08 129	89.81 166	63.77 83	96.97 92	79.67 106	88.21 104	92.60 150

baseline		85.01 56	84.44 59	86.71 55	88.33 180	68.73 62	90.24 211	91.82 142	81.05 37	81.18 80	92.50 116	63.69 84	96.08 122	84.45 73	86.71 120	95.32 60

CDPH-MVS		85.71 48	85.46 48	86.46 64	94.75 34	67.19 101	93.89 69	92.83 100	70.90 212	83.09 66	95.28 44	63.62 85	97.36 66	80.63 101	94.18 34	94.84 81

HyFIR lowres test		81.03 124	79.56 134	85.43 94	87.81 195	68.11 79	90.18 212	90.01 213	70.65 218	72.95 167	86.06 216	63.61 86	94.50 188	75.01 140	79.75 167	93.67 123

canonicalmvs		86.85 33	86.25 39	88.66 17	91.80 101	71.92 14	93.54 86	91.71 146	80.26 42	87.55 29	95.25 48	63.59 87	96.93 97	88.18 39	84.34 135	97.11 8

c3_l		76.83 199	75.47 195	80.93 220	85.02 242	64.18 178	90.39 205	88.11 275	71.66 191	66.65 254	81.64 264	63.58 88	92.56 254	69.31 190	62.86 292	86.04 263

SteuartSystems-ACMMP		86.82 35	86.90 33	86.58 60	90.42 129	66.38 122	96.09 16	93.87 56	77.73 82	84.01 61	95.66 34	63.39 89	97.94 39	87.40 47	93.55 47	95.42 52
Skip Steuart: Steuart Systems R&D Blog.
EI-MVSNet-Vis-set		83.77 79	83.67 65	84.06 138	92.79 76	63.56 195	91.76 155	94.81 25	79.65 50	77.87 117	94.09 84	63.35 90	97.90 41	79.35 109	79.36 169	90.74 187

UniMVSNet (Re)		77.58 186	76.78 178	79.98 238	84.11 257	60.80 245	91.76 155	93.17 87	76.56 102	69.93 210	84.78 228	63.32 91	92.36 262	64.89 235	62.51 297	86.78 246

PVSNet_BlendedMVS		83.38 84	83.43 71	83.22 160	93.76 49	67.53 94	94.06 58	93.61 68	79.13 60	81.00 84	85.14 223	63.19 92	97.29 71	87.08 51	73.91 215	84.83 286

PVSNet_Blended		86.73 36	86.86 34	86.31 71	93.76 49	67.53 94	96.33 15	93.61 68	82.34 22	81.00 84	93.08 103	63.19 92	97.29 71	87.08 51	91.38 77	94.13 105

PAPM_NR		82.97 92	81.84 100	86.37 68	94.10 44	66.76 114	87.66 260	92.84 99	69.96 226	74.07 158	93.57 96	63.10 94	97.50 59	70.66 177	90.58 87	94.85 78

nrg030		80.93 125	79.86 129	84.13 137	83.69 262	68.83 60	93.23 95	91.20 166	75.55 110	75.06 147	88.22 188	63.04 95	94.74 172	81.88 90	66.88 262	88.82 213

EI-MVSNet-UG-set		83.14 89	82.96 81	83.67 149	92.28 85	63.19 201	91.38 171	94.68 30	79.22 57	76.60 132	93.75 90	62.64 96	97.76 46	78.07 122	78.01 180	90.05 196

DeepC-MVS	77.85 3	85.52 50	85.24 50	86.37 68	88.80 168	66.64 116	92.15 132	93.68 66	81.07 36	76.91 130	93.64 94	62.59 97	98.44 30	85.50 62	92.84 56	94.03 111
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
EIA-MVS		84.84 58	84.88 56	84.69 119	91.30 114	62.36 218	93.85 71	92.04 128	79.45 51	79.33 101	94.28 80	62.42 98	96.35 111	80.05 104	91.25 80	95.38 55

CS-MVS		85.80 47	86.65 36	83.27 159	92.00 94	58.92 278	95.31 30	91.86 138	79.97 44	84.82 52	95.40 39	62.26 99	95.51 151	86.11 59	92.08 65	95.37 56

MVS_111021_HR		86.19 41	85.80 45	87.37 37	93.17 65	69.79 39	93.99 63	93.76 61	79.08 62	78.88 108	93.99 87	62.25 100	98.15 35	85.93 61	91.15 81	94.15 104

PHI-MVS		86.83 34	86.85 35	86.78 54	93.47 57	65.55 143	95.39 29	95.10 17	71.77 189	85.69 44	96.52 17	62.07 101	98.77 21	86.06 60	95.60 11	96.03 37

MP-MVS		85.02 55	84.97 55	85.17 104	92.60 81	64.27 176	93.24 94	92.27 118	73.13 150	79.63 97	94.43 70	61.90 102	97.17 78	85.00 67	92.56 58	94.06 110
Rongxuan Tan, Qing Wang, et al.: MP-MVS: Multi-Scale Windows PatchMatch and Planar Prior Multi-View Stereo.
jason		86.40 37	86.17 40	87.11 43	86.16 224	70.54 27	95.71 23	92.19 124	82.00 25	84.58 54	94.34 77	61.86 103	95.53 150	87.76 42	90.89 83	95.27 65
jason: jason.
iter_conf_final		81.74 112	80.93 112	84.18 135	92.66 79	69.10 53	92.94 105	82.80 326	79.01 65	74.85 149	88.40 180	61.83 104	94.61 178	79.36 108	76.52 197	88.83 210

CS-MVS-test		86.14 42	87.01 31	83.52 151	92.63 80	59.36 273	95.49 26	91.92 133	80.09 43	85.46 47	95.53 38	61.82 105	95.77 133	86.77 55	93.37 49	95.41 53

PAPR		85.15 54	84.47 58	87.18 41	96.02 25	68.29 72	91.85 150	93.00 95	76.59 101	79.03 104	95.00 53	61.59 106	97.61 54	78.16 121	89.00 99	95.63 47

IS-MVSNet		80.14 139	79.41 138	82.33 179	87.91 192	60.08 262	91.97 145	88.27 272	72.90 156	71.44 191	91.73 133	61.44 107	93.66 222	62.47 253	86.53 122	93.24 134

cl____		76.07 206	74.67 202	80.28 229	85.15 238	61.76 230	90.12 213	88.73 259	71.16 206	65.43 258	81.57 266	61.15 108	92.95 234	66.54 216	62.17 299	86.13 261

DIV-MVS_self_test		76.07 206	74.67 202	80.28 229	85.14 239	61.75 231	90.12 213	88.73 259	71.16 206	65.42 259	81.60 265	61.15 108	92.94 238	66.54 216	62.16 301	86.14 259

EI-MVSNet		78.97 159	78.22 154	81.25 207	85.33 235	62.73 214	89.53 229	93.21 83	72.39 168	72.14 181	90.13 162	60.99 110	94.72 173	67.73 205	72.49 226	86.29 254

IterMVS-LS		76.49 202	75.18 200	80.43 226	84.49 250	62.74 213	90.64 199	88.80 256	72.40 167	65.16 261	81.72 262	60.98 111	92.27 265	67.74 204	64.65 282	86.29 254
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
ETV-MVS		86.01 44	86.11 41	85.70 88	90.21 134	67.02 108	93.43 91	91.92 133	81.21 35	84.13 60	94.07 86	60.93 112	95.63 141	89.28 32	89.81 92	94.46 97

tpm cat1		75.30 222	72.21 240	84.58 123	88.52 171	67.77 86	78.16 333	88.02 277	61.88 299	68.45 229	76.37 321	60.65 113	94.03 210	53.77 290	74.11 212	91.93 168

TAMVS		80.37 134	79.45 137	83.13 162	85.14 239	63.37 197	91.23 179	90.76 183	74.81 121	72.65 171	88.49 177	60.63 114	92.95 234	69.41 188	81.95 151	93.08 140

ZNCC-MVS		85.33 52	85.08 53	86.06 74	93.09 68	65.65 139	93.89 69	93.41 79	73.75 139	79.94 93	94.68 64	60.61 115	98.03 37	82.63 85	93.72 43	94.52 93

thres100view900		78.37 173	77.01 175	82.46 174	91.89 99	63.21 200	91.19 183	96.33 1	72.28 171	70.45 200	87.89 192	60.31 116	95.32 155	45.16 324	77.58 185	88.83 210

thres600view7		78.00 178	76.66 180	82.03 194	91.93 96	63.69 191	91.30 177	96.33 1	72.43 166	70.46 199	87.89 192	60.31 116	94.92 168	42.64 336	76.64 195	87.48 231

CHOSEN 1792x2688		84.98 57	83.45 70	89.57 10	89.94 139	75.14 5	92.07 138	92.32 116	81.87 26	75.68 139	88.27 184	60.18 118	98.60 26	80.46 103	90.27 90	94.96 76

h-mvs33		83.01 91	82.56 91	84.35 131	89.34 151	62.02 224	92.72 111	93.76 61	81.45 30	82.73 69	92.25 125	60.11 119	97.13 80	87.69 43	62.96 291	93.91 116

hse-mvs2		81.12 122	81.11 110	81.16 210	86.52 217	57.48 295	89.40 232	91.16 168	81.45 30	82.73 69	90.49 152	60.11 119	94.58 180	87.69 43	60.41 318	91.41 175

tfpn200view9		78.79 165	77.43 166	82.88 165	92.21 88	64.49 163	92.05 139	96.28 4	73.48 145	71.75 186	88.26 185	60.07 121	95.32 155	45.16 324	77.58 185	88.83 210

thres400		78.68 167	77.43 166	82.43 175	92.21 88	64.49 163	92.05 139	96.28 4	73.48 145	71.75 186	88.26 185	60.07 121	95.32 155	45.16 324	77.58 185	87.48 231

diffmvs		84.28 66	83.83 64	85.61 90	87.40 203	68.02 81	90.88 191	89.24 236	80.54 40	81.64 76	92.52 115	59.83 123	94.52 187	87.32 48	85.11 129	94.29 98
Fangjinhua Wang, Qingshan Xu, Yew-Soon Ong, Marc Pollefeys: Lightweight and Accurate Multi-View Stereo With Confidence-Aware Diffusion Model. IEEE T-PAMI 2025
MVS		84.66 60	82.86 85	90.06 2	90.93 120	74.56 6	87.91 255	95.54 10	68.55 244	72.35 180	94.71 63	59.78 124	98.90 18	81.29 98	94.69 30	96.74 12

thres200		79.66 147	78.33 151	83.66 150	92.54 82	65.82 137	93.06 99	96.31 3	74.90 120	73.30 164	88.66 175	59.67 125	95.61 143	47.84 313	78.67 176	89.56 205

Effi-MVS+		83.82 77	82.76 86	86.99 48	89.56 147	69.40 45	91.35 174	86.12 297	72.59 160	83.22 65	92.81 113	59.60 126	96.01 127	81.76 91	87.80 107	95.56 50

eth_miper_zixun_eth		75.96 213	74.40 210	80.66 222	84.66 246	63.02 204	89.28 234	88.27 272	71.88 183	65.73 256	81.65 263	59.45 127	92.81 242	68.13 199	60.53 315	86.14 259

ACMMP_NAP		86.05 43	85.80 45	86.80 53	91.58 106	67.53 94	91.79 152	93.49 75	74.93 119	84.61 53	95.30 43	59.42 128	97.92 40	86.13 58	94.92 18	94.94 77

GST-MVS		84.63 61	84.29 61	85.66 89	92.82 73	65.27 148	93.04 101	93.13 89	73.20 148	78.89 105	94.18 83	59.41 129	97.85 44	81.45 94	92.48 60	93.86 119

UA-Net		80.02 142	79.65 132	81.11 212	89.33 153	57.72 290	86.33 274	89.00 251	77.44 89	81.01 83	89.15 172	59.33 130	95.90 128	61.01 260	84.28 138	89.73 202

NR-MVSNet		76.05 209	74.59 205	80.44 225	82.96 271	62.18 222	90.83 193	91.73 144	77.12 92	60.96 293	86.35 210	59.28 131	91.80 274	60.74 261	61.34 310	87.35 236

MP-MVS-pluss		85.24 53	85.13 52	85.56 91	91.42 111	65.59 141	91.54 162	92.51 113	74.56 122	80.62 87	95.64 35	59.15 132	97.00 87	86.94 53	93.80 40	94.07 109
MP-MVS-pluss: MP-MVS-pluss. MP-MVS-pluss
HFP-MVS		84.73 59	84.40 60	85.72 87	93.75 51	65.01 156	93.50 88	93.19 86	72.19 173	79.22 102	94.93 56	59.04 133	97.67 48	81.55 92	92.21 61	94.49 96

MSLP-MVS++		86.27 39	85.91 43	87.35 38	92.01 93	68.97 58	95.04 39	92.70 103	79.04 64	81.50 77	96.50 19	58.98 134	96.78 100	83.49 80	93.93 38	96.29 29

Patchmatch-test		65.86 298	60.94 312	80.62 224	83.75 261	58.83 279	58.91 368	75.26 346	44.50 359	50.95 338	77.09 315	58.81 135	87.90 315	35.13 356	64.03 286	95.12 71

EPNet_dtu		78.80 164	79.26 142	77.43 276	88.06 188	49.71 337	91.96 146	91.95 132	77.67 83	76.56 133	91.28 141	58.51 136	90.20 298	56.37 279	80.95 159	92.39 154
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
test_fmvsmvis_n_1920		83.80 78	83.48 68	84.77 115	82.51 274	63.72 188	91.37 172	83.99 317	81.42 33	77.68 119	95.74 33	58.37 137	97.58 55	93.38 5	86.87 114	93.00 143

EC-MVSNet		84.53 62	85.04 54	83.01 163	89.34 151	61.37 237	94.42 49	91.09 173	77.91 79	83.24 64	94.20 82	58.37 137	95.40 152	85.35 63	91.41 76	92.27 162

VNet		86.20 40	85.65 47	87.84 26	93.92 46	69.99 32	95.73 22	95.94 6	78.43 72	86.00 40	93.07 104	58.22 139	97.00 87	85.22 64	84.33 136	96.52 19

TESTMET0.1,1		82.41 100	81.98 99	83.72 147	88.08 187	63.74 186	92.70 113	93.77 60	79.30 55	77.61 121	87.57 197	58.19 140	94.08 203	73.91 148	86.68 121	93.33 133

原ACMM1				84.42 127	93.21 63	64.27 176		93.40 80	65.39 267	79.51 98	92.50 116	58.11 141	96.69 102	65.27 233	93.96 37	92.32 157

sam_mvs1												57.85 142				94.68 85

CR-MVSNet		73.79 239	70.82 252	82.70 169	83.15 268	67.96 82	70.25 347	84.00 315	73.67 143	69.97 208	72.41 335	57.82 143	89.48 304	52.99 293	73.13 219	90.64 189

Patchmtry		67.53 290	63.93 297	78.34 264	82.12 279	64.38 171	68.72 351	84.00 315	48.23 352	59.24 300	72.41 335	57.82 143	89.27 305	46.10 321	56.68 330	81.36 323

patchmatchnet-post											67.62 350	57.62 145	90.25 293

PCF-MVS	73.15 9	79.29 153	77.63 163	84.29 133	86.06 225	65.96 133	87.03 267	91.10 172	69.86 228	69.79 211	90.64 147	57.54 146	96.59 104	64.37 238	82.29 147	90.32 192
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
Fast-Effi-MVS+		81.14 120	80.01 126	84.51 125	90.24 133	65.86 135	94.12 57	89.15 242	73.81 138	75.37 145	88.26 185	57.26 147	94.53 186	66.97 213	84.92 130	93.15 137

miper_lstm_enhance		73.05 244	71.73 246	77.03 282	83.80 260	58.32 284	81.76 303	88.88 253	69.80 229	61.01 292	78.23 305	57.19 148	87.51 323	65.34 232	59.53 320	85.27 282

PatchT		69.11 275	65.37 287	80.32 227	82.07 280	63.68 192	67.96 356	87.62 282	50.86 344	69.37 212	65.18 353	57.09 149	88.53 310	41.59 339	66.60 264	88.74 214

testdata				81.34 206	89.02 162	57.72 290		89.84 217	58.65 318	85.32 49	94.09 84	57.03 150	93.28 228	69.34 189	90.56 88	93.03 141

PatchmatchNet		77.46 187	74.63 204	85.96 77	89.55 148	70.35 29	79.97 324	89.55 227	72.23 172	70.94 193	76.91 317	57.03 150	92.79 244	54.27 287	81.17 157	94.74 83
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
test_yl		84.28 66	83.16 78	87.64 29	94.52 37	69.24 50	95.78 17	95.09 18	69.19 236	81.09 81	92.88 110	57.00 152	97.44 61	81.11 99	81.76 152	96.23 32

DCV-MVSNet		84.28 66	83.16 78	87.64 29	94.52 37	69.24 50	95.78 17	95.09 18	69.19 236	81.09 81	92.88 110	57.00 152	97.44 61	81.11 99	81.76 152	96.23 32

region2R		84.36 64	84.03 63	85.36 97	93.54 55	64.31 174	93.43 91	92.95 96	72.16 176	78.86 109	94.84 60	56.97 154	97.53 58	81.38 96	92.11 64	94.24 100

新几何1				84.73 117	92.32 84	64.28 175		91.46 158	59.56 314	79.77 95	92.90 108	56.95 155	96.57 106	63.40 243	92.91 55	93.34 131

WR-MVS		76.76 200	75.74 192	79.82 244	84.60 247	62.27 221	92.60 119	92.51 113	76.06 105	67.87 238	85.34 221	56.76 156	90.24 296	62.20 254	63.69 290	86.94 244

HPM-MVS		83.25 87	82.95 82	84.17 136	92.25 86	62.88 211	90.91 188	91.86 138	70.30 222	77.12 127	93.96 88	56.75 157	96.28 113	82.04 89	91.34 79	93.34 131
Chunlin Ren, Qingshan Xu, Shikun Zhang, Jiaqi Yang: Hierarchical Prior Mining for Non-local Multi-View Stereo. ICCV 2023
sss		82.71 97	82.38 94	83.73 146	89.25 155	59.58 268	92.24 130	94.89 22	77.96 77	79.86 94	92.38 121	56.70 158	97.05 82	77.26 126	80.86 160	94.55 89

ACMMPR		84.37 63	84.06 62	85.28 99	93.56 54	64.37 172	93.50 88	93.15 88	72.19 173	78.85 110	94.86 59	56.69 159	97.45 60	81.55 92	92.20 62	94.02 112

FMVSNet3		77.73 184	76.04 187	82.80 166	91.20 117	68.99 57	91.87 148	91.99 130	73.35 147	67.04 248	83.19 246	56.62 160	92.14 266	59.80 268	69.34 244	87.28 238

Patchmatch-RL test		68.17 284	64.49 294	79.19 255	71.22 349	53.93 318	70.07 349	71.54 357	69.22 235	56.79 316	62.89 357	56.58 161	88.61 307	69.53 187	52.61 340	95.03 75

test_post											23.01 378	56.49 162	92.67 249

RPMNet		70.42 266	65.68 283	84.63 122	83.15 268	67.96 82	70.25 347	90.45 190	46.83 355	69.97 208	65.10 354	56.48 163	95.30 158	35.79 355	73.13 219	90.64 189

DU-MVS		76.86 195	75.84 190	79.91 241	82.96 271	60.26 258	91.26 178	91.54 153	76.46 103	68.88 221	86.35 210	56.16 164	92.13 267	66.38 219	62.55 295	87.35 236

Baseline_NR-MVSNet		73.99 236	72.83 231	77.48 275	80.78 288	59.29 274	91.79 152	84.55 310	68.85 240	68.99 219	80.70 280	56.16 164	92.04 270	62.67 251	60.98 312	81.11 324

API-MVS		82.28 102	80.53 120	87.54 34	96.13 22	70.59 26	93.63 82	91.04 179	65.72 266	75.45 144	92.83 112	56.11 166	98.89 19	64.10 239	89.75 95	93.15 137

MTAPA		83.91 75	83.38 75	85.50 92	91.89 99	65.16 152	81.75 304	92.23 119	75.32 114	80.53 88	95.21 50	56.06 167	97.16 79	84.86 70	92.55 59	94.18 101

JIA-IIPM		66.06 297	62.45 306	76.88 286	81.42 285	54.45 317	57.49 369	88.67 261	49.36 348	63.86 274	46.86 367	56.06 167	90.25 293	49.53 303	68.83 248	85.95 266

v148		76.19 204	74.47 209	81.36 205	80.05 298	64.44 167	91.75 157	90.23 204	73.68 142	67.13 247	80.84 279	55.92 169	93.86 219	68.95 194	61.73 306	85.76 272

WR-MVS_H		70.59 264	69.94 259	72.53 316	81.03 286	51.43 328	87.35 264	92.03 129	67.38 253	60.23 296	80.70 280	55.84 170	83.45 343	46.33 320	58.58 325	82.72 310

AUN-MVS		78.37 173	77.43 166	81.17 209	86.60 216	57.45 296	89.46 231	91.16 168	74.11 129	74.40 153	90.49 152	55.52 171	94.57 182	74.73 145	60.43 317	91.48 173

XVS		83.87 76	83.47 69	85.05 105	93.22 61	63.78 184	92.92 106	92.66 106	73.99 131	78.18 114	94.31 79	55.25 172	97.41 63	79.16 111	91.58 73	93.95 114

X-MVStestdata		76.86 195	74.13 215	85.05 105	93.22 61	63.78 184	92.92 106	92.66 106	73.99 131	78.18 114	10.19 383	55.25 172	97.41 63	79.16 111	91.58 73	93.95 114

BH-w/o		80.49 132	79.30 141	84.05 139	90.83 124	64.36 173	93.60 83	89.42 231	74.35 125	69.09 215	90.15 161	55.23 174	95.61 143	64.61 236	86.43 124	92.17 165

CP-MVS		83.71 81	83.40 74	84.65 120	93.14 66	63.84 182	94.59 47	92.28 117	71.03 210	77.41 123	94.92 57	55.21 175	96.19 115	81.32 97	90.70 85	93.91 116

PGM-MVS		83.25 87	82.70 88	84.92 109	92.81 75	64.07 179	90.44 202	92.20 123	71.28 204	77.23 126	94.43 70	55.17 176	97.31 70	79.33 110	91.38 77	93.37 130

tpmvs		72.88 248	69.76 262	82.22 184	90.98 119	67.05 106	78.22 332	88.30 270	63.10 287	64.35 272	74.98 328	55.09 177	94.27 195	43.25 330	69.57 243	85.34 280

v8		75.35 221	73.26 226	81.61 200	80.67 290	66.82 111	89.54 228	89.27 235	71.65 192	63.30 280	80.30 288	54.99 178	94.06 205	67.33 209	62.33 298	83.94 292

sam_mvs												54.91 179

EPMVS		78.49 172	75.98 188	86.02 75	91.21 116	69.68 43	80.23 319	91.20 166	75.25 115	72.48 176	78.11 306	54.65 180	93.69 221	57.66 277	83.04 143	94.69 84

ab-mvs		80.18 138	78.31 152	85.80 84	88.44 175	65.49 146	83.00 298	92.67 105	71.82 187	77.36 124	85.01 224	54.50 181	96.59 104	76.35 131	75.63 202	95.32 60

KD-MVS_2432*1600		69.03 276	66.37 279	77.01 283	85.56 233	61.06 241	81.44 308	90.25 202	67.27 254	58.00 310	76.53 319	54.49 182	87.63 321	48.04 310	35.77 367	82.34 316

miper_refine_blended		69.03 276	66.37 279	77.01 283	85.56 233	61.06 241	81.44 308	90.25 202	67.27 254	58.00 310	76.53 319	54.49 182	87.63 321	48.04 310	35.77 367	82.34 316

DP-MVS Recon		82.73 95	81.65 102	85.98 76	97.31 4	67.06 105	95.15 35	91.99 130	69.08 239	76.50 134	93.89 89	54.48 184	98.20 34	70.76 175	85.66 127	92.69 147

GeoE		78.90 161	77.43 166	83.29 158	88.95 164	62.02 224	92.31 127	86.23 295	70.24 223	71.34 192	89.27 170	54.43 185	94.04 208	63.31 245	80.81 162	93.81 121

XXY-MVS		77.94 181	76.44 182	82.43 175	82.60 273	64.44 167	92.01 141	91.83 141	73.59 144	70.00 207	85.82 218	54.43 185	94.76 170	69.63 185	68.02 255	88.10 226

MDTV_nov1_ep13_2view						59.90 264	80.13 321		67.65 251	72.79 169		54.33 187		59.83 267		92.58 151

Test By Simon												54.21 188

MAR-MVS		84.18 70	83.43 71	86.44 65	96.25 21	65.93 134	94.28 51	94.27 49	74.41 123	79.16 103	95.61 36	53.99 189	98.88 20	69.62 186	93.26 51	94.50 95
Zhenyu Xu, Yiguang Liu, Xuelei Shi, Ying Wang, Yunan Zheng: MARMVS: Matching Ambiguity Reduced Multiple View Stereo for Efficient Large Scale Scene Reconstruction. CVPR 2020
test-LLR		80.10 140	79.56 134	81.72 198	86.93 213	61.17 238	92.70 113	91.54 153	71.51 201	75.62 140	86.94 205	53.83 190	92.38 260	72.21 162	84.76 133	91.60 170

test0.0.03 1		72.76 249	72.71 234	72.88 314	80.25 296	47.99 343	91.22 180	89.45 229	71.51 201	62.51 287	87.66 195	53.83 190	85.06 334	50.16 300	67.84 258	85.58 273

v2v482		77.42 188	75.65 194	82.73 168	80.38 293	67.13 104	91.85 150	90.23 204	75.09 117	69.37 212	83.39 244	53.79 192	94.44 189	71.77 166	65.00 277	86.63 250

SR-MVS		82.81 94	82.58 90	83.50 154	93.35 58	61.16 240	92.23 131	91.28 165	64.48 273	81.27 78	95.28 44	53.71 193	95.86 129	82.87 83	88.77 101	93.49 128

pcd_1.5k_mvsjas		4.46 355	5.95 358	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	53.55 194	0.00 388	0.00 386	0.00 385	0.00 384

PS-MVSNAJss		77.26 190	76.31 184	80.13 234	80.64 291	59.16 275	90.63 201	91.06 177	72.80 157	68.58 227	84.57 231	53.55 194	93.96 213	72.97 151	71.96 230	87.27 239

PS-MVSNAJ		88.14 16	87.61 25	89.71 6	92.06 90	76.72 1	95.75 19	93.26 82	83.86 13	89.55 22	96.06 28	53.55 194	97.89 42	91.10 21	93.31 50	94.54 91

mPP-MVS		82.96 93	82.44 93	84.52 124	92.83 71	62.92 209	92.76 109	91.85 140	71.52 200	75.61 142	94.24 81	53.48 197	96.99 90	78.97 114	90.73 84	93.64 125

xiu_mvs_v2_base		87.92 21	87.38 29	89.55 11	91.41 113	76.43 3	95.74 20	93.12 90	83.53 15	89.55 22	95.95 29	53.45 198	97.68 47	91.07 22	92.62 57	94.54 91

test_post1							78.95 326				20.70 381	53.05 199	91.50 285	60.43 263

MDTV_nov1_ep13			72.61 235		89.06 161	68.48 67	80.33 317	90.11 208	71.84 186	71.81 185	75.92 325	53.01 200	93.92 215	48.04 310	73.38 217

FA-MVS(test-final)		79.12 156	77.23 172	84.81 114	90.54 127	63.98 181	81.35 310	91.71 146	71.09 209	74.85 149	82.94 247	52.85 201	97.05 82	67.97 201	81.73 154	93.41 129

test222					89.77 142	61.60 233	89.55 227	89.42 231	56.83 327	77.28 125	92.43 120	52.76 202			91.14 82	93.09 139

v1144		76.73 201	74.88 201	82.27 181	80.23 297	66.60 118	91.68 159	90.21 206	73.69 141	69.06 217	81.89 259	52.73 203	94.40 190	69.21 191	65.23 274	85.80 269

v10		74.77 228	72.54 237	81.46 203	80.33 295	66.71 115	89.15 238	89.08 247	70.94 211	63.08 281	79.86 293	52.52 204	94.04 208	65.70 227	62.17 299	83.64 294

CLD-MVS		82.73 95	82.35 95	83.86 142	87.90 193	67.65 90	95.45 27	92.18 125	85.06 9	72.58 173	92.27 124	52.46 205	95.78 131	84.18 74	79.06 172	88.16 225
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
TranMVSNet+NR-MVSNet		75.86 214	74.52 208	79.89 242	82.44 275	60.64 254	91.37 172	91.37 160	76.63 100	67.65 240	86.21 214	52.37 206	91.55 280	61.84 256	60.81 313	87.48 231

VPA-MVSNet		79.03 157	78.00 157	82.11 192	85.95 227	64.48 165	93.22 96	94.66 31	75.05 118	74.04 159	84.95 225	52.17 207	93.52 224	74.90 143	67.04 261	88.32 224

APD-MVS_3200maxsize		81.64 114	81.32 105	82.59 173	92.36 83	58.74 280	91.39 169	91.01 180	63.35 282	79.72 96	94.62 66	51.82 208	96.14 117	79.71 105	87.93 106	92.89 146

dp		75.01 226	72.09 241	83.76 143	89.28 154	66.22 128	79.96 325	89.75 219	71.16 206	67.80 239	77.19 314	51.81 209	92.54 255	50.39 298	71.44 235	92.51 153

v144192		76.05 209	74.03 216	82.12 189	79.50 304	66.55 120	91.39 169	89.71 225	72.30 170	68.17 230	81.33 271	51.75 210	94.03 210	67.94 202	64.19 284	85.77 270

BH-untuned		78.68 167	77.08 173	83.48 155	89.84 140	63.74 186	92.70 113	88.59 264	71.57 198	66.83 252	88.65 176	51.75 210	95.39 153	59.03 271	84.77 132	91.32 179

HQP2-MVS												51.63 212

HQP-MVS		81.14 120	80.64 117	82.64 171	87.54 199	63.66 193	94.06 58	91.70 148	79.80 46	74.18 154	90.30 156	51.63 212	95.61 143	77.63 124	78.90 173	88.63 215

dmvs_testset		65.55 301	66.45 277	62.86 342	79.87 299	22.35 384	76.55 337	71.74 355	77.42 91	55.85 318	87.77 194	51.39 214	80.69 357	31.51 368	65.92 269	85.55 275

V42		76.46 203	74.55 207	82.19 186	79.14 310	67.82 85	90.26 210	89.42 231	73.75 139	68.63 226	81.89 259	51.31 215	94.09 202	71.69 168	64.84 278	84.66 287

SR-MVS-dyc-post		81.06 123	80.70 115	82.15 187	92.02 91	58.56 282	90.90 189	90.45 190	62.76 289	78.89 105	94.46 68	51.26 216	95.61 143	78.77 117	86.77 118	92.28 159

CL-MVSNet_self_test		69.92 269	68.09 272	75.41 294	73.25 344	55.90 308	90.05 216	89.90 215	69.96 226	61.96 290	76.54 318	51.05 217	87.64 320	49.51 304	50.59 345	82.70 312

TransMVSNet (Re)		70.07 268	67.66 273	77.31 279	80.62 292	59.13 277	91.78 154	84.94 307	65.97 263	60.08 297	80.44 285	50.78 218	91.87 272	48.84 306	45.46 353	80.94 326

HQP_MVS		80.34 135	79.75 131	82.12 189	86.94 211	62.42 216	93.13 97	91.31 162	78.81 68	72.53 174	89.14 173	50.66 219	95.55 148	76.74 127	78.53 178	88.39 222

plane_prior6					87.23 205	62.32 219						50.66 219

ACMMP		81.49 115	80.67 116	83.93 141	91.71 103	62.90 210	92.13 133	92.22 122	71.79 188	71.68 188	93.49 98	50.32 221	96.96 93	78.47 119	84.22 140	91.93 168
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
MVS_111021_LR		82.02 108	81.52 103	83.51 153	88.42 176	62.88 211	89.77 224	88.93 252	76.78 97	75.55 143	93.10 101	50.31 222	95.38 154	83.82 79	87.02 113	92.26 163

1314		80.70 128	78.95 145	85.94 78	87.77 197	67.56 92	87.91 255	92.55 112	72.17 175	67.44 242	93.09 102	50.27 223	97.04 85	71.68 169	87.64 109	93.23 135

CP-MVSNet		70.50 265	69.91 260	72.26 319	80.71 289	51.00 331	87.23 266	90.30 200	67.84 248	59.64 298	82.69 250	50.23 224	82.30 351	51.28 295	59.28 321	83.46 299

LCM-MVSNet-Re		72.93 246	71.84 244	76.18 291	88.49 172	48.02 342	80.07 322	70.17 358	73.96 134	52.25 331	80.09 292	49.98 225	88.24 313	67.35 207	84.23 139	92.28 159

Vis-MVSNet		80.92 126	79.98 128	83.74 144	88.48 173	61.80 228	93.44 90	88.26 274	73.96 134	77.73 118	91.76 131	49.94 226	94.76 170	65.84 225	90.37 89	94.65 88
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
v1192		75.98 211	73.92 218	82.15 187	79.73 300	66.24 127	91.22 180	89.75 219	72.67 159	68.49 228	81.42 269	49.86 227	94.27 195	67.08 211	65.02 276	85.95 266

test-mter		79.96 143	79.38 140	81.72 198	86.93 213	61.17 238	92.70 113	91.54 153	73.85 136	75.62 140	86.94 205	49.84 228	92.38 260	72.21 162	84.76 133	91.60 170

cdsmvs_eth3d_5k		19.86 350	26.47 349	0.00 369	0.00 392	0.00 393	0.00 380	93.45 76	0.00 387	0.00 388	95.27 46	49.56 229	0.00 388	0.00 386	0.00 385	0.00 384

3Dnovator+	73.60 7	82.10 107	80.60 119	86.60 58	90.89 122	66.80 113	95.20 33	93.44 77	74.05 130	67.42 243	92.49 118	49.46 230	97.65 51	70.80 174	91.68 71	95.33 58

MVP-Stereo		77.12 192	76.23 185	79.79 245	81.72 282	66.34 124	89.29 233	90.88 181	70.56 220	62.01 289	82.88 248	49.34 231	94.13 200	65.55 230	93.80 40	78.88 343
Qingsong Yan: MVP-Stereo: A Parallel Multi-View Patchmatch Stereo Method with Dilation Matching for Photogrammetric Application.
RE-MVS-def			80.48 121		92.02 91	58.56 282	90.90 189	90.45 190	62.76 289	78.89 105	94.46 68	49.30 232		78.77 117	86.77 118	92.28 159

OMC-MVS		78.67 169	77.91 160	80.95 219	85.76 231	57.40 297	88.49 248	88.67 261	73.85 136	72.43 178	92.10 126	49.29 233	94.55 185	72.73 156	77.89 181	90.91 186

VPNet		78.82 163	77.53 165	82.70 169	84.52 249	66.44 121	93.93 66	92.23 119	80.46 41	72.60 172	88.38 182	49.18 234	93.13 230	72.47 160	63.97 288	88.55 218

CVMVSNet		74.04 235	74.27 212	73.33 310	85.33 235	43.94 358	89.53 229	88.39 267	54.33 335	70.37 201	90.13 162	49.17 235	84.05 337	61.83 257	79.36 169	91.99 167

v1921920		75.63 219	73.49 224	82.06 193	79.38 305	66.35 123	91.07 187	89.48 228	71.98 178	67.99 231	81.22 274	49.16 236	93.90 216	66.56 215	64.56 283	85.92 268

pm-mvs1		72.89 247	71.09 250	78.26 267	79.10 311	57.62 293	90.80 194	89.30 234	67.66 250	62.91 283	81.78 261	49.11 237	92.95 234	60.29 265	58.89 323	84.22 290

pmmvs4		73.92 237	71.81 245	80.25 231	79.17 308	65.24 149	87.43 263	87.26 286	67.64 252	63.46 278	83.91 239	48.96 238	91.53 284	62.94 248	65.49 270	83.96 291

TAPA-MVS	70.22 12	74.94 227	73.53 223	79.17 256	90.40 130	52.07 325	89.19 237	89.61 226	62.69 291	70.07 205	92.67 114	48.89 239	94.32 191	38.26 350	79.97 164	91.12 184
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
3Dnovator	73.91 6	82.69 98	80.82 113	88.31 22	89.57 146	71.26 18	92.60 119	94.39 44	78.84 67	67.89 237	92.48 119	48.42 240	98.52 27	68.80 196	94.40 33	95.15 70

CPTT-MVS		79.59 148	79.16 143	80.89 221	91.54 109	59.80 265	92.10 135	88.54 266	60.42 307	72.96 166	93.28 100	48.27 241	92.80 243	78.89 116	86.50 123	90.06 195

GBi-Net		75.65 217	73.83 219	81.10 213	88.85 165	65.11 153	90.01 217	90.32 196	70.84 213	67.04 248	80.25 289	48.03 242	91.54 281	59.80 268	69.34 244	86.64 247

test1		75.65 217	73.83 219	81.10 213	88.85 165	65.11 153	90.01 217	90.32 196	70.84 213	67.04 248	80.25 289	48.03 242	91.54 281	59.80 268	69.34 244	86.64 247

FMVSNet2		76.07 206	74.01 217	82.26 183	88.85 165	67.66 89	91.33 175	91.61 151	70.84 213	65.98 255	82.25 255	48.03 242	92.00 271	58.46 273	68.73 250	87.10 241

LFMVS		84.34 65	82.73 87	89.18 12	94.76 33	73.25 9	94.99 41	91.89 136	71.90 181	82.16 73	93.49 98	47.98 245	97.05 82	82.55 86	84.82 131	97.25 7

SDMVSNet		80.26 136	78.88 146	84.40 128	89.25 155	67.63 91	85.35 277	93.02 92	76.77 98	70.84 195	87.12 203	47.95 246	96.09 119	85.04 66	74.55 206	89.48 206

QAPM		79.95 144	77.39 170	87.64 29	89.63 145	71.41 17	93.30 93	93.70 65	65.34 269	67.39 245	91.75 132	47.83 247	98.96 16	57.71 276	89.81 92	92.54 152

HPM-MVS_fast		80.25 137	79.55 136	82.33 179	91.55 108	59.95 263	91.32 176	89.16 241	65.23 270	74.71 151	93.07 104	47.81 248	95.74 134	74.87 144	88.23 103	91.31 180

CANet_DTU		84.09 72	83.52 66	85.81 83	90.30 132	66.82 111	91.87 148	89.01 250	85.27 8	86.09 39	93.74 91	47.71 249	96.98 91	77.90 123	89.78 94	93.65 124

v1240		75.21 224	72.98 229	81.88 195	79.20 307	66.00 131	90.75 196	89.11 245	71.63 196	67.41 244	81.22 274	47.36 250	93.87 217	65.46 231	64.72 281	85.77 270

PEN-MVS		69.46 273	68.56 267	72.17 321	79.27 306	49.71 337	86.90 270	89.24 236	67.24 257	59.08 303	82.51 253	47.23 251	83.54 342	48.42 308	57.12 326	83.25 302

dmvs_re		76.93 194	75.36 197	81.61 200	87.78 196	60.71 251	80.00 323	87.99 278	79.42 52	69.02 218	89.47 169	46.77 252	94.32 191	63.38 244	74.45 209	89.81 199

CNLPA		74.31 232	72.30 239	80.32 227	91.49 110	61.66 232	90.85 192	80.72 332	56.67 328	63.85 275	90.64 147	46.75 253	90.84 288	53.79 289	75.99 201	88.47 221

114514_t		79.17 155	77.67 161	83.68 148	95.32 29	65.53 144	92.85 108	91.60 152	63.49 280	67.92 234	90.63 149	46.65 254	95.72 139	67.01 212	83.54 141	89.79 200

PS-CasMVS		69.86 271	69.13 265	72.07 322	80.35 294	50.57 333	87.02 268	89.75 219	67.27 254	59.19 302	82.28 254	46.58 255	82.24 352	50.69 297	59.02 322	83.39 301

DTE-MVSNet		68.46 282	67.33 275	71.87 324	77.94 326	49.00 340	86.16 275	88.58 265	66.36 261	58.19 307	82.21 256	46.36 256	83.87 340	44.97 327	55.17 333	82.73 309

test1111		80.84 127	80.02 125	83.33 157	87.87 194	60.76 248	92.62 118	86.86 289	77.86 80	75.73 138	91.39 139	46.35 257	94.70 176	72.79 155	88.68 102	94.52 93

ECVR-MVS		81.29 118	80.38 123	84.01 140	88.39 178	61.96 226	92.56 124	86.79 290	77.66 84	76.63 131	91.42 137	46.34 258	95.24 159	74.36 146	89.23 96	94.85 78

PMMVS		81.98 109	82.04 97	81.78 196	89.76 143	56.17 305	91.13 184	90.69 184	77.96 77	80.09 92	93.57 96	46.33 259	94.99 164	81.41 95	87.46 110	94.17 102

OPM-MVS		79.00 158	78.09 155	81.73 197	83.52 265	63.83 183	91.64 161	90.30 200	76.36 104	71.97 183	89.93 165	46.30 260	95.17 160	75.10 138	77.70 183	86.19 258
Ray L. Khuboni, Hongjun Xu: Octagram Propagation Matching for Multi-Scale View Stereopsis (OPM-MVS).
BH-RMVSNet		79.46 152	77.65 162	84.89 110	91.68 104	65.66 138	93.55 85	88.09 276	72.93 155	73.37 163	91.12 143	46.20 261	96.12 118	56.28 280	85.61 128	92.91 145

mvsmamba		76.85 197	75.71 193	80.25 231	83.07 270	59.16 275	91.44 163	80.64 333	76.84 95	67.95 233	86.33 212	46.17 262	94.24 198	76.06 132	72.92 222	87.36 235

FE-MVS		75.97 212	73.02 228	84.82 113	89.78 141	65.56 142	77.44 335	91.07 176	64.55 272	72.66 170	79.85 294	46.05 263	96.69 102	54.97 284	80.82 161	92.21 164

TR-MVS		78.77 166	77.37 171	82.95 164	90.49 128	60.88 244	93.67 80	90.07 209	70.08 225	74.51 152	91.37 140	45.69 264	95.70 140	60.12 266	80.32 163	92.29 158

IterMVS-SCA-FT		71.55 260	69.97 258	76.32 289	81.48 283	60.67 253	87.64 261	85.99 298	66.17 262	59.50 299	78.88 300	45.53 265	83.65 341	62.58 252	61.93 302	84.63 289

SCA		75.82 215	72.76 232	85.01 107	86.63 215	70.08 31	81.06 312	89.19 239	71.60 197	70.01 206	77.09 315	45.53 265	90.25 293	60.43 263	73.27 218	94.68 85

IterMVS		72.65 254	70.83 251	78.09 269	82.17 278	62.96 206	87.64 261	86.28 293	71.56 199	60.44 295	78.85 301	45.42 267	86.66 327	63.30 246	61.83 303	84.65 288
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
Effi-MVS+-dtu		76.14 205	75.28 199	78.72 262	83.22 267	55.17 312	89.87 221	87.78 281	75.42 112	67.98 232	81.43 268	45.08 268	92.52 256	75.08 139	71.63 231	88.48 219

XVG-OURS-SEG-HR		74.70 229	73.08 227	79.57 250	78.25 322	57.33 298	80.49 315	87.32 284	63.22 284	68.76 224	90.12 164	44.89 269	91.59 279	70.55 178	74.09 213	89.79 200

v7n		71.31 261	68.65 266	79.28 254	76.40 334	60.77 247	86.71 272	89.45 229	64.17 275	58.77 306	78.24 304	44.59 270	93.54 223	57.76 275	61.75 305	83.52 297

pmmvs5		73.35 241	71.52 247	78.86 260	78.64 318	60.61 255	91.08 185	86.90 287	67.69 249	63.32 279	83.64 240	44.33 271	90.53 290	62.04 255	66.02 268	85.46 277

OpenMVS	70.45 11	78.54 171	75.92 189	86.41 67	85.93 230	71.68 16	92.74 110	92.51 113	66.49 260	64.56 267	91.96 128	43.88 272	98.10 36	54.61 285	90.65 86	89.44 208

AdaColmap		78.94 160	77.00 176	84.76 116	96.34 17	65.86 135	92.66 117	87.97 280	62.18 294	70.56 197	92.37 122	43.53 273	97.35 67	64.50 237	82.86 144	91.05 185

tfpnnormal		70.10 267	67.36 274	78.32 265	83.45 266	60.97 243	88.85 242	92.77 101	64.85 271	60.83 294	78.53 302	43.52 274	93.48 225	31.73 366	61.70 307	80.52 331

mvsany_test1		68.77 278	68.56 267	69.39 329	73.57 343	45.88 354	80.93 313	60.88 371	59.65 313	71.56 189	90.26 158	43.22 275	75.05 361	74.26 147	62.70 294	87.25 240

test_djsdf		73.76 240	72.56 236	77.39 277	77.00 332	53.93 318	89.07 239	90.69 184	65.80 264	63.92 273	82.03 258	43.14 276	92.67 249	72.83 153	68.53 251	85.57 274

GA-MVS		78.33 175	76.23 185	84.65 120	83.65 263	66.30 125	91.44 163	90.14 207	76.01 106	70.32 202	84.02 237	42.50 277	94.72 173	70.98 172	77.00 193	92.94 144

PLC	68.80 14	75.23 223	73.68 222	79.86 243	92.93 70	58.68 281	90.64 199	88.30 270	60.90 304	64.43 271	90.53 150	42.38 278	94.57 182	56.52 278	76.54 196	86.33 253
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
D2MVS		73.80 238	72.02 242	79.15 258	79.15 309	62.97 205	88.58 247	90.07 209	72.94 154	59.22 301	78.30 303	42.31 279	92.70 248	65.59 229	72.00 229	81.79 321

Fast-Effi-MVS+-dtu		75.04 225	73.37 225	80.07 235	80.86 287	59.52 269	91.20 182	85.38 302	71.90 181	65.20 260	84.84 227	41.46 280	92.97 233	66.50 218	72.96 221	87.73 228

sd_testset		77.08 193	75.37 196	82.20 185	89.25 155	62.11 223	82.06 302	89.09 246	76.77 98	70.84 195	87.12 203	41.43 281	95.01 163	67.23 210	74.55 206	89.48 206

MS-PatchMatch		77.90 183	76.50 181	82.12 189	85.99 226	69.95 35	91.75 157	92.70 103	73.97 133	62.58 286	84.44 233	41.11 282	95.78 131	63.76 242	92.17 63	80.62 330

our_test_3		68.29 283	64.69 291	79.11 259	78.92 312	64.85 160	88.40 250	85.06 305	60.32 309	52.68 329	76.12 323	40.81 283	89.80 303	44.25 329	55.65 331	82.67 314

XVG-OURS		74.25 233	72.46 238	79.63 248	78.45 320	57.59 294	80.33 317	87.39 283	63.86 277	68.76 224	89.62 168	40.50 284	91.72 276	69.00 193	74.25 211	89.58 203

VDD-MVS		83.06 90	81.81 101	86.81 52	90.86 123	67.70 88	95.40 28	91.50 156	75.46 111	81.78 75	92.34 123	40.09 285	97.13 80	86.85 54	82.04 150	95.60 48

DP-MVS		69.90 270	66.48 276	80.14 233	95.36 28	62.93 207	89.56 226	76.11 340	50.27 346	57.69 313	85.23 222	39.68 286	95.73 135	33.35 360	71.05 237	81.78 322

RRT_MVS		74.44 230	72.97 230	78.84 261	82.36 276	57.66 292	89.83 223	88.79 258	70.61 219	64.58 266	84.89 226	39.24 287	92.65 252	70.11 181	66.34 266	86.21 257

ppachtmachnet_test		67.72 287	63.70 298	79.77 246	78.92 312	66.04 130	88.68 245	82.90 325	60.11 311	55.45 319	75.96 324	39.19 288	90.55 289	39.53 345	52.55 341	82.71 311

ADS-MVSNet2		66.90 293	63.44 300	77.26 280	88.06 188	60.70 252	68.01 354	75.56 344	57.57 320	64.48 268	69.87 345	38.68 289	84.10 336	40.87 341	67.89 256	86.97 242

ADS-MVSNet		68.54 281	64.38 296	81.03 217	88.06 188	66.90 110	68.01 354	84.02 314	57.57 320	64.48 268	69.87 345	38.68 289	89.21 306	40.87 341	67.89 256	86.97 242

test_cas_vis1_n_1920		80.45 133	80.61 118	79.97 240	78.25 322	57.01 301	94.04 62	88.33 269	79.06 63	82.81 68	93.70 92	38.65 291	91.63 278	90.82 25	79.81 165	91.27 182

LPG-MVS_test		75.82 215	74.58 206	79.56 251	84.31 254	59.37 271	90.44 202	89.73 222	69.49 231	64.86 262	88.42 178	38.65 291	94.30 193	72.56 158	72.76 223	85.01 284

LGP-MVS_train				79.56 251	84.31 254	59.37 271		89.73 222	69.49 231	64.86 262	88.42 178	38.65 291	94.30 193	72.56 158	72.76 223	85.01 284

VDDNet		80.50 131	78.26 153	87.21 40	86.19 223	69.79 39	94.48 48	91.31 162	60.42 307	79.34 100	90.91 145	38.48 294	96.56 107	82.16 87	81.05 158	95.27 65

ACMP	71.68 10	75.58 220	74.23 213	79.62 249	84.97 243	59.64 266	90.80 194	89.07 248	70.39 221	62.95 282	87.30 201	38.28 295	93.87 217	72.89 152	71.45 234	85.36 279
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
test_vis1_n_1920		81.66 113	82.01 98	80.64 223	82.24 277	55.09 313	94.76 45	86.87 288	81.67 28	84.40 56	94.63 65	38.17 296	94.67 177	91.98 16	83.34 142	92.16 166

UGNet		79.87 145	78.68 147	83.45 156	89.96 138	61.51 234	92.13 133	90.79 182	76.83 96	78.85 110	86.33 212	38.16 297	96.17 116	67.93 203	87.17 112	92.67 148
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
anonymousdsp		71.14 262	69.37 264	76.45 288	72.95 345	54.71 315	84.19 283	88.88 253	61.92 298	62.15 288	79.77 295	38.14 298	91.44 286	68.90 195	67.45 259	83.21 303

xiu_mvs_v1_base_debu		82.16 104	81.12 107	85.26 101	86.42 218	68.72 63	92.59 121	90.44 193	73.12 151	84.20 57	94.36 72	38.04 299	95.73 135	84.12 75	86.81 115	91.33 176

xiu_mvs_v1_base		82.16 104	81.12 107	85.26 101	86.42 218	68.72 63	92.59 121	90.44 193	73.12 151	84.20 57	94.36 72	38.04 299	95.73 135	84.12 75	86.81 115	91.33 176

xiu_mvs_v1_base_debi		82.16 104	81.12 107	85.26 101	86.42 218	68.72 63	92.59 121	90.44 193	73.12 151	84.20 57	94.36 72	38.04 299	95.73 135	84.12 75	86.81 115	91.33 176

PVSNet_0	68.08 15	71.81 256	68.32 271	82.27 181	84.68 245	62.31 220	88.68 245	90.31 199	75.84 107	57.93 312	80.65 283	37.85 302	94.19 199	69.94 182	29.05 375	90.31 193

Anonymous20231206		67.53 290	65.78 281	72.79 315	74.95 338	47.59 345	88.23 251	87.32 284	61.75 301	58.07 309	77.29 312	37.79 303	87.29 325	42.91 332	63.71 289	83.48 298

ACMM	69.62 13	74.34 231	72.73 233	79.17 256	84.25 256	57.87 288	90.36 206	89.93 214	63.17 286	65.64 257	86.04 217	37.79 303	94.10 201	65.89 224	71.52 233	85.55 275
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
cascas		78.18 176	75.77 191	85.41 95	87.14 208	69.11 52	92.96 104	91.15 170	66.71 258	70.47 198	86.07 215	37.49 305	96.48 110	70.15 180	79.80 166	90.65 188

LS3D		69.17 274	66.40 278	77.50 274	91.92 97	56.12 306	85.12 278	80.37 334	46.96 353	56.50 317	87.51 198	37.25 306	93.71 220	32.52 365	79.40 168	82.68 313

MDA-MVSNet_test_wron		63.78 310	60.16 313	74.64 300	78.15 324	60.41 256	83.49 288	84.03 313	56.17 331	39.17 365	71.59 341	37.22 307	83.24 346	42.87 334	48.73 347	80.26 334

YYNet1		63.76 311	60.14 314	74.62 301	78.06 325	60.19 261	83.46 290	83.99 317	56.18 330	39.25 364	71.56 342	37.18 308	83.34 344	42.90 333	48.70 348	80.32 333

FMVSNet5		68.04 285	65.66 284	75.18 297	84.43 252	57.89 287	83.54 287	86.26 294	61.83 300	53.64 327	73.30 332	37.15 309	85.08 333	48.99 305	61.77 304	82.56 315

test20.03		63.83 309	62.65 305	67.38 337	70.58 354	39.94 364	86.57 273	84.17 312	63.29 283	51.86 332	77.30 311	37.09 310	82.47 349	38.87 349	54.13 337	79.73 337

PVSNet	73.49 8	80.05 141	78.63 148	84.31 132	90.92 121	64.97 157	92.47 125	91.05 178	79.18 58	72.43 178	90.51 151	37.05 311	94.06 205	68.06 200	86.00 125	93.90 118

EU-MVSNet		64.01 308	63.01 302	67.02 338	74.40 341	38.86 368	83.27 292	86.19 296	45.11 357	54.27 323	81.15 277	36.91 312	80.01 359	48.79 307	57.02 327	82.19 319

Anonymous20231211		73.08 242	70.39 256	81.13 211	90.62 126	63.33 198	91.40 167	90.06 211	51.84 341	64.46 270	80.67 282	36.49 313	94.07 204	63.83 241	64.17 285	85.98 265

FMVSNet1		72.71 251	69.91 260	81.10 213	83.60 264	65.11 153	90.01 217	90.32 196	63.92 276	63.56 277	80.25 289	36.35 314	91.54 281	54.46 286	66.75 263	86.64 247

Anonymous20240529		76.84 198	74.15 214	84.88 111	91.02 118	64.95 158	93.84 74	91.09 173	53.57 336	73.00 165	87.42 199	35.91 315	97.32 69	69.14 192	72.41 228	92.36 155

CMPMVS	48.56 21	66.77 294	64.41 295	73.84 307	70.65 353	50.31 334	77.79 334	85.73 301	45.54 356	44.76 356	82.14 257	35.40 316	90.14 299	63.18 247	74.54 208	81.07 325
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
pmmvs6		67.57 289	64.76 290	76.00 292	72.82 347	53.37 320	88.71 244	86.78 291	53.19 337	57.58 314	78.03 307	35.33 317	92.41 259	55.56 282	54.88 335	82.21 318

PatchMatch-RL		72.06 255	69.98 257	78.28 266	89.51 149	55.70 309	83.49 288	83.39 322	61.24 302	63.72 276	82.76 249	34.77 318	93.03 232	53.37 292	77.59 184	86.12 262

LTVRE_ROB	59.60 19	66.27 296	63.54 299	74.45 302	84.00 259	51.55 327	67.08 357	83.53 319	58.78 317	54.94 321	80.31 287	34.54 319	93.23 229	40.64 343	68.03 254	78.58 346
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
UniMVSNet_ETH3D		72.74 250	70.53 255	79.36 253	78.62 319	56.64 303	85.01 279	89.20 238	63.77 278	64.84 264	84.44 233	34.05 320	91.86 273	63.94 240	70.89 238	89.57 204

bld_raw_dy_0_64		71.59 259	69.71 263	77.22 281	77.82 328	58.12 286	87.71 259	73.66 349	68.01 247	61.90 291	84.29 235	33.68 321	88.43 311	69.91 183	70.43 239	85.11 283

F-COLMAP		70.66 263	68.44 269	77.32 278	86.37 221	55.91 307	88.00 253	86.32 292	56.94 326	57.28 315	88.07 189	33.58 322	92.49 257	51.02 296	68.37 252	83.55 295

pmmvs-eth3d		65.53 302	62.32 307	75.19 296	69.39 357	59.59 267	82.80 299	83.43 320	62.52 292	51.30 336	72.49 333	32.86 323	87.16 326	55.32 283	50.73 344	78.83 344

MDA-MVSNet-bldmvs		61.54 317	57.70 321	73.05 312	79.53 303	57.00 302	83.08 296	81.23 329	57.57 320	34.91 367	72.45 334	32.79 324	86.26 330	35.81 354	41.95 358	75.89 353

MIMVSNet		71.64 257	68.44 269	81.23 208	81.97 281	64.44 167	73.05 343	88.80 256	69.67 230	64.59 265	74.79 329	32.79 324	87.82 317	53.99 288	76.35 198	91.42 174

UnsupCasMVSNet_eth		65.79 299	63.10 301	73.88 306	70.71 352	50.29 335	81.09 311	89.88 216	72.58 161	49.25 344	74.77 330	32.57 326	87.43 324	55.96 281	41.04 360	83.90 293

N_pmnet		50.55 330	49.11 333	54.88 350	77.17 331	4.02 390	84.36 282	2.00 389	48.59 349	45.86 352	68.82 347	32.22 327	82.80 348	31.58 367	51.38 343	77.81 349

test_0402		64.54 305	61.09 311	74.92 299	84.10 258	60.75 249	87.95 254	79.71 336	52.03 339	52.41 330	77.20 313	32.21 328	91.64 277	23.14 370	61.03 311	72.36 359

DSMNet-mixed		56.78 326	54.44 329	63.79 341	63.21 365	29.44 379	64.43 360	64.10 367	42.12 363	51.32 335	71.60 340	31.76 329	75.04 362	36.23 352	65.20 275	86.87 245

MSDG		69.54 272	65.73 282	80.96 218	85.11 241	63.71 189	84.19 283	83.28 323	56.95 325	54.50 322	84.03 236	31.50 330	96.03 125	42.87 334	69.13 247	83.14 305

RPSCF		64.24 307	61.98 309	71.01 326	76.10 336	45.00 355	75.83 340	75.94 341	46.94 354	58.96 304	84.59 230	31.40 331	82.00 353	47.76 314	60.33 319	86.04 263

tt0805		73.07 243	70.73 253	80.07 235	78.37 321	57.05 300	87.78 257	92.18 125	61.23 303	67.04 248	86.49 209	31.35 332	94.58 180	65.06 234	67.12 260	88.57 217

jajsoiax		73.05 244	71.51 248	77.67 272	77.46 329	54.83 314	88.81 243	90.04 212	69.13 238	62.85 284	83.51 242	31.16 333	92.75 245	70.83 173	69.80 240	85.43 278

MVS-HIRNet		60.25 320	55.55 327	74.35 303	84.37 253	56.57 304	71.64 345	74.11 348	34.44 366	45.54 354	42.24 373	31.11 334	89.81 301	40.36 344	76.10 200	76.67 352

SixPastTwentyTwo		64.92 303	61.78 310	74.34 304	78.74 316	49.76 336	83.42 291	79.51 337	62.86 288	50.27 339	77.35 310	30.92 335	90.49 291	45.89 322	47.06 350	82.78 307

KD-MVS_self_test		60.87 318	58.60 318	67.68 335	66.13 362	39.93 365	75.63 341	84.70 308	57.32 323	49.57 342	68.45 348	29.55 336	82.87 347	48.09 309	47.94 349	80.25 335

mvs_tets		72.71 251	71.11 249	77.52 273	77.41 330	54.52 316	88.45 249	89.76 218	68.76 243	62.70 285	83.26 245	29.49 337	92.71 246	70.51 179	69.62 242	85.34 280

Anonymous202405211		77.96 180	75.33 198	85.87 80	93.73 52	64.52 162	94.85 43	85.36 303	62.52 292	76.11 135	90.18 159	29.43 338	97.29 71	68.51 198	77.24 192	95.81 44

K. test v3		63.09 312	59.61 316	73.53 309	76.26 335	49.38 339	83.27 292	77.15 339	64.35 274	47.77 348	72.32 337	28.73 339	87.79 318	49.93 302	36.69 366	83.41 300

UnsupCasMVSNet_bld		61.60 316	57.71 320	73.29 311	68.73 358	51.64 326	78.61 328	89.05 249	57.20 324	46.11 349	61.96 360	28.70 340	88.60 308	50.08 301	38.90 364	79.63 338

lessismore_v0				73.72 308	72.93 346	47.83 344		61.72 370		45.86 352	73.76 331	28.63 341	89.81 301	47.75 315	31.37 372	83.53 296

new-patchmatchnet		59.30 323	56.48 325	67.79 334	65.86 363	44.19 356	82.47 300	81.77 327	59.94 312	43.65 360	66.20 352	27.67 342	81.68 354	39.34 346	41.40 359	77.50 350

ACMH	63.93 17	68.62 279	64.81 289	80.03 237	85.22 237	63.25 199	87.72 258	84.66 309	60.83 305	51.57 334	79.43 299	27.29 343	94.96 165	41.76 337	64.84 278	81.88 320
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
OurMVSNet-221017-0		64.68 304	62.17 308	72.21 320	76.08 337	47.35 346	80.67 314	81.02 330	56.19 329	51.60 333	79.66 297	27.05 344	88.56 309	53.60 291	53.63 338	80.71 329

ACMH+	65.35 16	67.65 288	64.55 292	76.96 285	84.59 248	57.10 299	88.08 252	80.79 331	58.59 319	53.00 328	81.09 278	26.63 345	92.95 234	46.51 318	61.69 308	80.82 327

OpenMVS_ROB	61.12 18	66.39 295	62.92 303	76.80 287	76.51 333	57.77 289	89.22 235	83.41 321	55.48 332	53.86 326	77.84 308	26.28 346	93.95 214	34.90 357	68.76 249	78.68 345

test_fmvs1		74.07 234	73.69 221	75.22 295	78.91 314	47.34 347	89.06 241	74.69 347	63.68 279	79.41 99	91.59 135	24.36 347	87.77 319	85.22 64	76.26 199	90.55 191

COLMAP_ROB	57.96 20	62.98 313	59.65 315	72.98 313	81.44 284	53.00 322	83.75 286	75.53 345	48.34 351	48.81 345	81.40 270	24.14 348	90.30 292	32.95 362	60.52 316	75.65 354
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
MIMVSNet1		60.16 321	57.33 322	68.67 331	69.71 355	44.13 357	78.92 327	84.21 311	55.05 333	44.63 357	71.85 339	23.91 349	81.54 355	32.63 364	55.03 334	80.35 332

testgi		64.48 306	62.87 304	69.31 330	71.24 348	40.62 363	85.49 276	79.92 335	65.36 268	54.18 324	83.49 243	23.74 350	84.55 335	41.60 338	60.79 314	82.77 308

ITE_SJBPF				70.43 327	74.44 340	47.06 350		77.32 338	60.16 310	54.04 325	83.53 241	23.30 351	84.01 338	43.07 331	61.58 309	80.21 336

EG-PatchMatch MVS		68.55 280	65.41 286	77.96 270	78.69 317	62.93 207	89.86 222	89.17 240	60.55 306	50.27 339	77.73 309	22.60 352	94.06 205	47.18 316	72.65 225	76.88 351

tmp_tt		22.26 349	23.75 351	17.80 365	5.23 389	12.06 389	35.26 376	39.48 383	2.82 383	18.94 374	44.20 372	22.23 353	24.64 384	36.30 351	9.31 381	16.69 378

USDC		67.43 292	64.51 293	76.19 290	77.94 326	55.29 311	78.38 330	85.00 306	73.17 149	48.36 346	80.37 286	21.23 354	92.48 258	52.15 294	64.02 287	80.81 328

Anonymous20240521		62.09 314	59.08 317	71.10 325	67.19 360	48.72 341	83.91 285	85.23 304	50.38 345	47.84 347	71.22 344	20.74 355	85.51 332	46.47 319	58.75 324	79.06 342

test_vis1_n		71.63 258	70.73 253	74.31 305	69.63 356	47.29 348	86.91 269	72.11 353	63.21 285	75.18 146	90.17 160	20.40 356	85.76 331	84.59 72	74.42 210	89.87 198

XVG-ACMP-BASELINE		68.04 285	65.53 285	75.56 293	74.06 342	52.37 323	78.43 329	85.88 299	62.03 296	58.91 305	81.21 276	20.38 357	91.15 287	60.69 262	68.18 253	83.16 304

test_fmvs1_n		72.69 253	71.92 243	74.99 298	71.15 350	47.08 349	87.34 265	75.67 342	63.48 281	78.08 116	91.17 142	20.16 358	87.87 316	84.65 71	75.57 203	90.01 197

AllTest		61.66 315	58.06 319	72.46 317	79.57 301	51.42 329	80.17 320	68.61 361	51.25 342	45.88 350	81.23 272	19.86 359	86.58 328	38.98 347	57.01 328	79.39 339

TestCases				72.46 317	79.57 301	51.42 329		68.61 361	51.25 342	45.88 350	81.23 272	19.86 359	86.58 328	38.98 347	57.01 328	79.39 339

test_vis1_rt		59.09 324	57.31 323	64.43 340	68.44 359	46.02 353	83.05 297	48.63 378	51.96 340	49.57 342	63.86 356	16.30 361	80.20 358	71.21 171	62.79 293	67.07 365

pmmvs3		55.51 327	51.50 332	67.53 336	57.90 370	50.93 332	80.37 316	73.66 349	40.63 364	44.15 359	64.75 355	16.30 361	78.97 360	44.77 328	40.98 362	72.69 357

test_fmvs2		65.78 300	64.84 288	68.60 332	66.54 361	41.71 360	83.27 292	69.81 359	54.38 334	67.91 235	84.54 232	15.35 363	81.22 356	75.65 134	66.16 267	82.88 306

TDRefinement		55.28 328	51.58 331	66.39 339	59.53 369	46.15 352	76.23 338	72.80 351	44.60 358	42.49 361	76.28 322	15.29 364	82.39 350	33.20 361	43.75 355	70.62 361

new_pmnet		49.31 331	46.44 334	57.93 345	62.84 366	40.74 362	68.47 353	62.96 369	36.48 365	35.09 366	57.81 362	14.97 365	72.18 365	32.86 363	46.44 351	60.88 367

TinyColmap		60.32 319	56.42 326	72.00 323	78.78 315	53.18 321	78.36 331	75.64 343	52.30 338	41.59 363	75.82 326	14.76 366	88.35 312	35.84 353	54.71 336	74.46 355

EGC-MVSNET		42.35 335	38.09 338	55.11 349	74.57 339	46.62 351	71.63 346	55.77 372	0.04 384	0.24 385	62.70 358	14.24 367	74.91 363	17.59 374	46.06 352	43.80 370

LF4IMVS		54.01 329	52.12 330	59.69 344	62.41 367	39.91 366	68.59 352	68.28 363	42.96 362	44.55 358	75.18 327	14.09 368	68.39 368	41.36 340	51.68 342	70.78 360

PM-MVS		59.40 322	56.59 324	67.84 333	63.63 364	41.86 359	76.76 336	63.22 368	59.01 316	51.07 337	72.27 338	11.72 369	83.25 345	61.34 258	50.28 346	78.39 347

mvsany_test3		48.86 332	46.35 335	56.41 346	46.00 378	31.67 375	62.26 362	47.25 379	43.71 361	45.54 354	68.15 349	10.84 370	64.44 376	57.95 274	35.44 369	73.13 356

ambc				69.61 328	61.38 368	41.35 361	49.07 374	85.86 300		50.18 341	66.40 351	10.16 371	88.14 314	45.73 323	44.20 354	79.32 341

FPMVS		45.64 334	43.10 337	53.23 352	51.42 375	36.46 369	64.97 359	71.91 354	29.13 370	27.53 370	61.55 361	9.83 372	65.01 374	16.00 377	55.58 332	58.22 368

ANet_high		40.27 339	35.20 342	55.47 348	34.74 386	34.47 372	63.84 361	71.56 356	48.42 350	18.80 375	41.08 374	9.52 373	64.45 375	20.18 372	8.66 382	67.49 364

test_method		38.59 340	35.16 343	48.89 355	54.33 371	21.35 385	45.32 375	53.71 373	7.41 381	28.74 369	51.62 365	8.70 374	52.87 379	33.73 358	32.89 371	72.47 358

EMVS		23.76 348	23.20 352	25.46 364	41.52 384	16.90 388	60.56 365	38.79 385	14.62 379	8.99 383	20.24 382	7.35 375	45.82 382	7.25 382	9.46 380	13.64 380

test_f		46.58 333	43.45 336	55.96 347	45.18 379	32.05 374	61.18 363	49.49 377	33.39 367	42.05 362	62.48 359	7.00 376	65.56 372	47.08 317	43.21 357	70.27 362

test_fmvs3		56.82 325	54.86 328	62.69 343	53.59 372	35.47 370	75.87 339	65.64 366	43.91 360	55.10 320	71.43 343	6.91 377	74.40 364	68.64 197	52.63 339	78.20 348

E-PMN		24.61 346	24.00 350	26.45 363	43.74 381	18.44 387	60.86 364	39.66 382	15.11 378	9.53 382	22.10 379	6.52 378	46.94 381	8.31 381	10.14 379	13.98 379

DeepMVS_CX				34.71 362	51.45 374	24.73 383		28.48 388	31.46 369	17.49 378	52.75 364	5.80 379	42.60 383	18.18 373	19.42 376	36.81 375

Gipuma		34.91 342	31.44 345	45.30 357	70.99 351	39.64 367	19.85 379	72.56 352	20.10 375	16.16 379	21.47 380	5.08 380	71.16 366	13.07 378	43.70 356	25.08 377
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
APD_test1		40.50 337	37.31 340	50.09 354	51.88 373	35.27 371	59.45 367	52.59 374	21.64 373	26.12 371	57.80 363	4.56 381	66.56 370	22.64 371	39.09 363	48.43 369

LCM-MVSNet		40.54 336	35.79 341	54.76 351	36.92 385	30.81 376	51.41 372	69.02 360	22.07 372	24.63 372	45.37 369	4.56 381	65.81 371	33.67 359	34.50 370	67.67 363

PMMVS2		37.93 341	33.61 344	50.92 353	46.31 377	24.76 382	60.55 366	50.05 375	28.94 371	20.93 373	47.59 366	4.41 383	65.13 373	25.14 369	18.55 377	62.87 366

test_vis3_rt		40.46 338	37.79 339	48.47 356	44.49 380	33.35 373	66.56 358	32.84 386	32.39 368	29.65 368	39.13 376	3.91 384	68.65 367	50.17 299	40.99 361	43.40 371

testf1		32.77 343	29.47 346	42.67 359	41.89 382	30.81 376	52.07 370	43.45 380	15.45 376	18.52 376	44.82 370	2.12 385	58.38 377	16.05 375	30.87 373	38.83 372

APD_test2		32.77 343	29.47 346	42.67 359	41.89 382	30.81 376	52.07 370	43.45 380	15.45 376	18.52 376	44.82 370	2.12 385	58.38 377	16.05 375	30.87 373	38.83 372

PMVS	26.43 22	31.84 345	28.16 348	42.89 358	25.87 388	27.58 380	50.92 373	49.78 376	21.37 374	14.17 380	40.81 375	2.01 387	66.62 369	9.61 380	38.88 365	34.49 376
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
MVE	24.84 23	24.35 347	19.77 353	38.09 361	34.56 387	26.92 381	26.57 377	38.87 384	11.73 380	11.37 381	27.44 377	1.37 388	50.42 380	11.41 379	14.60 378	36.93 374
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
wuyk23d		11.30 351	10.95 354	12.33 366	48.05 376	19.89 386	25.89 378	1.92 390	3.58 382	3.12 384	1.37 384	0.64 389	15.77 385	6.23 383	7.77 383	1.35 381

test123		6.92 354	9.21 357	0.08 367	0.03 391	0.05 391	81.65 306	0.01 392	0.02 386	0.14 387	0.85 386	0.03 390	0.02 386	0.12 385	0.00 385	0.16 382

testmvs		7.23 353	9.62 356	0.06 368	0.04 390	0.02 392	84.98 280	0.02 391	0.03 385	0.18 386	1.21 385	0.01 391	0.02 386	0.14 384	0.01 384	0.13 383

test_blank		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 385	0.00 384

uanet_test		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 385	0.00 384

DCPMVS		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 385	0.00 384

sosnet-low-res		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 385	0.00 384

sosnet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 385	0.00 384

uncertanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 385	0.00 384

Regformer		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 385	0.00 384

ab-mvs-re		7.91 352	10.55 355	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	94.95 54	0.00 392	0.00 388	0.00 386	0.00 385	0.00 384

uanet		0.00 356	0.00 359	0.00 369	0.00 392	0.00 393	0.00 380	0.00 393	0.00 387	0.00 388	0.00 387	0.00 392	0.00 388	0.00 386	0.00 385	0.00 384

FOURS1					93.95 45	61.77 229	93.96 64	91.92 133	62.14 295	86.57 35

MSC_two_6792asdad				89.60 8	97.31 4	73.22 10		95.05 20					99.07 13	92.01 14	94.77 24	96.51 20

No_MVS				89.60 8	97.31 4	73.22 10		95.05 20					99.07 13	92.01 14	94.77 24	96.51 20

eth-test2					0.00 392

eth-test					0.00 392

IU-MVS					96.46 11	69.91 36		95.18 15	80.75 39	95.28 1				92.34 11	95.36 13	96.47 24

save fliter					93.84 48	67.89 84	95.05 38	92.66 106	78.19 74

test_0728_SECOND				88.70 15	96.45 12	70.43 28	96.64 8	94.37 45					99.15 2	91.91 17	94.90 20	96.51 20

GSMVS																94.68 85

test_part2					96.29 19	68.16 78				90.78 13

MTGPA								92.23 119

MTMP							93.77 76	32.52 387

gm-plane-assit					88.42 176	67.04 107			78.62 71		91.83 130		97.37 65	76.57 129

test9_res														89.41 29	94.96 17	95.29 62

agg_prior2														86.41 56	94.75 28	95.33 58

agg_prior					94.16 43	66.97 109		93.31 81		84.49 55			96.75 101

test_prior4						67.18 103	93.92 67

test_prior				86.42 66	94.71 35	67.35 98		93.10 91					96.84 99			95.05 73

旧先验2							92.00 144		59.37 315	87.54 30			93.47 226	75.39 136

新几何2							91.41 165

无先验							92.71 112	92.61 110	62.03 296				97.01 86	66.63 214		93.97 113

原ACMM2							92.01 141

testdata2													96.09 119	61.26 259

testdata1							89.21 236		77.55 87

plane_prior7					86.94 211	61.51 234

plane_prior5								91.31 162					95.55 148	76.74 127	78.53 178	88.39 222

plane_prior4											89.14 173

plane_prior3						61.95 227			79.09 61	72.53 174

plane_prior2							93.13 97		78.81 68

plane_prior1					87.15 207

plane_prior						62.42 216	93.85 71		79.38 53						78.80 175

n2								0.00 393

nn								0.00 393

door-mid								66.01 365

test11								93.01 93

door								66.57 364

HQP5-MVS						63.66 193

HQP-NCC					87.54 199		94.06 58		79.80 46	74.18 154

ACMP_Plane					87.54 199		94.06 58		79.80 46	74.18 154

BP-MVS														77.63 124

HQP4-MVS										74.18 154			95.61 143			88.63 215

HQP3-MVS								91.70 148							78.90 173

NP-MVS					87.41 202	63.04 203					90.30 156

ACMMP++_ref															71.63 231

ACMMP++															69.72 241

High-resolution multi-view benchmark