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	courty.	delive.	electro	facade	kicker	meadow	office	pipes	playgr.	relief	relief.	terrace	terrai.

HSP-MVS		86.82 1	89.95 1	83.16 4	89.38 20	91.60 11	85.63 10	74.15 6	94.20 1	75.52 13	94.99 1	83.21 4	85.96 1	87.67 3	85.88 5	88.32 24	92.13 44

APDe-MVS		86.37 2	88.41 3	84.00 3	91.43 7	91.83 9	88.34 1	74.67 4	91.19 3	81.76 1	91.13 2	81.94 10	80.07 4	83.38 20	82.58 27	87.69 35	96.78 6

CNVR-MVS		85.96 3	87.58 5	84.06 2	92.58 3	92.40 5	87.62 3	77.77 2	88.44 8	75.93 11	79.49 19	81.97 9	81.65 3	87.04 5	86.58 3	88.79 14	97.18 3

MCST-MVS		85.75 4	86.99 7	84.31 1	94.07 1	92.80 2	88.15 2	79.10 1	85.66 16	70.72 23	76.50 26	80.45 12	82.17 2	88.35 1	87.49 2	91.63 2	97.65 1

HPM-MVS++		85.64 5	88.43 2	82.39 6	92.65 2	90.24 19	85.83 8	74.21 5	90.68 5	75.63 12	86.77 8	84.15 2	78.68 8	86.33 6	85.26 8	87.32 42	95.60 12

APD-MVS		84.83 6	87.00 6	82.30 7	89.61 18	89.21 27	86.51 6	73.64 10	90.98 4	77.99 6	89.89 4	80.04 15	79.18 6	82.00 36	81.37 39	86.88 51	95.49 14
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
TSAR-MVS + MP.		84.39 7	86.58 10	81.83 8	88.09 32	86.47 51	85.63 10	73.62 11	90.13 6	79.24 3	89.67 5	82.99 5	77.72 10	81.22 42	80.92 47	86.68 54	94.66 21

SD-MVS		84.31 8	86.96 8	81.22 9	88.98 24	88.68 30	85.65 9	73.85 9	89.09 7	79.63 2	87.34 7	84.84 1	73.71 26	82.66 26	81.60 36	85.48 103	94.51 22

NCCC		84.16 9	85.46 14	82.64 5	92.34 4	90.57 16	86.57 5	76.51 3	86.85 13	72.91 16	77.20 25	78.69 18	79.09 7	84.64 14	84.88 13	88.44 22	95.41 15

ACMMP_Plus		83.54 10	86.37 11	80.25 14	89.57 19	90.10 21	85.27 13	71.66 16	87.38 9	73.08 15	84.23 12	80.16 13	75.31 17	84.85 12	83.64 19	86.57 55	94.21 27

train_agg		83.35 11	86.93 9	79.17 20	89.70 16	88.41 33	85.60 12	72.89 14	86.31 14	66.58 34	90.48 3	82.24 8	73.06 30	83.10 22	82.64 26	87.21 47	95.30 17

DeepPCF-MVS	76.94 1	83.08 12	87.77 4	77.60 28	90.11 12	90.96 13	78.48 47	72.63 15	93.10 2	65.84 35	80.67 17	81.55 11	74.80 21	85.94 8	85.39 7	83.75 146	96.77 7

CSCG		82.90 13	84.52 16	81.02 11	91.85 5	93.43 1	87.14 4	74.01 8	81.96 26	76.14 9	70.84 30	82.49 6	69.71 48	82.32 32	85.18 10	87.26 44	95.40 16

SteuartSystems-ACMMP		82.51 14	85.35 15	79.20 19	90.25 10	89.39 26	84.79 14	70.95 18	82.86 22	68.32 31	86.44 9	77.19 19	73.07 29	83.63 19	83.64 19	87.82 32	94.34 24
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		82.48 15	84.12 17	80.56 12	90.15 11	87.55 43	84.28 16	69.67 27	85.22 17	77.95 7	84.69 11	75.94 22	75.04 19	81.85 37	81.17 42	86.30 60	92.40 42

TSAR-MVS + GP.		82.27 16	85.98 12	77.94 26	80.72 64	88.25 36	81.12 36	67.71 38	87.10 10	73.31 14	85.23 10	83.68 3	76.64 13	80.43 49	81.47 38	88.15 30	95.66 11

DeepC-MVS_fast	75.41 2	81.69 17	82.10 26	81.20 10	91.04 9	87.81 41	83.42 20	74.04 7	83.77 20	71.09 21	66.88 38	72.44 30	79.48 5	85.08 10	84.97 12	88.12 31	93.78 30
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
MPTG		81.65 18	83.10 20	79.97 16	88.14 31	87.62 42	83.96 19	69.90 24	86.92 11	77.67 8	72.47 29	78.74 17	74.13 25	81.59 40	81.15 43	86.01 71	93.19 35

TSAR-MVS + ACMM		81.59 19	85.84 13	76.63 32	89.82 15	86.53 50	86.32 7	66.72 43	85.96 15	65.43 36	88.98 6	82.29 7	67.57 65	82.06 35	81.33 40	83.93 144	93.75 31

MP-MVS		80.94 20	83.49 19	77.96 25	88.48 25	88.16 37	82.82 25	69.34 29	80.79 32	69.67 27	82.35 14	77.13 20	71.60 39	80.97 46	80.96 46	85.87 84	94.06 28

CANet		80.90 21	82.93 22	78.53 24	86.83 38	92.26 6	81.19 35	66.95 41	81.60 29	69.90 26	66.93 37	74.80 24	76.79 12	84.68 13	84.77 14	89.50 8	95.50 13

ACMMPR		80.62 22	82.98 21	77.87 27	88.41 26	87.05 45	83.02 22	69.18 30	83.91 19	68.35 30	82.89 13	73.64 27	72.16 35	80.78 47	81.13 44	86.10 65	91.43 51

DeepC-MVS	74.46 3	80.30 23	81.05 29	79.42 17	87.42 34	88.50 32	83.23 21	73.27 12	82.78 23	71.01 22	62.86 46	69.93 44	74.80 21	84.30 15	84.20 16	86.79 53	94.77 19
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
DELS-MVS		79.49 24	79.84 33	79.08 21	88.26 30	92.49 3	84.12 18	70.63 20	65.27 69	69.60 29	61.29 51	66.50 51	72.75 31	88.07 2	88.03 1	89.13 11	97.22 2
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
CP-MVS		79.44 25	81.51 28	77.02 31	86.95 36	85.96 57	82.00 27	68.44 35	81.82 27	67.39 32	77.43 23	73.68 26	71.62 38	79.56 54	79.58 52	85.73 93	92.51 41

MVS_0304		79.43 26	82.20 24	76.20 35	84.22 45	91.79 10	81.82 30	63.81 62	76.83 43	61.71 46	66.37 39	75.52 23	76.38 15	85.54 9	85.03 11	89.28 10	94.32 25

PHI-MVS		79.43 26	84.06 18	74.04 48	86.15 40	91.57 12	80.85 39	68.90 33	82.22 25	51.81 76	78.10 21	74.28 25	70.39 45	84.01 18	84.00 17	86.14 64	94.24 26

PGM-MVS		79.42 28	81.84 27	76.60 33	88.38 28	86.69 48	82.97 24	65.75 49	80.39 33	64.94 37	81.95 16	72.11 35	71.41 40	80.45 48	80.55 50	86.18 62	90.76 59

CDPH-MVS		79.39 29	82.13 25	76.19 36	89.22 23	88.34 34	84.20 17	71.00 17	79.67 36	56.97 65	77.77 22	72.24 34	68.50 58	81.33 41	82.74 24	87.23 45	92.84 38

EPNet		79.28 30	82.25 23	75.83 38	88.31 29	90.14 20	79.43 45	68.07 36	81.76 28	61.26 48	77.26 24	70.08 43	70.06 46	82.43 30	82.00 31	87.82 32	92.09 45
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
MSLP-MVS++		78.57 31	77.33 42	80.02 15	88.39 27	84.79 63	84.62 15	66.17 47	75.96 45	78.40 4	61.59 49	71.47 37	73.54 28	78.43 62	78.88 57	88.97 12	90.18 65

3Dnovator	70.49 5	78.42 32	76.77 47	80.35 13	91.43 7	90.27 18	81.84 29	70.79 19	72.10 49	71.95 17	50.02 80	67.86 49	77.47 11	82.89 23	84.24 15	88.61 18	89.99 66

HQP-MVS		78.26 33	80.91 30	75.17 43	85.67 42	84.33 66	83.01 23	69.38 28	79.88 35	55.83 66	79.85 18	64.90 56	70.81 42	82.46 28	81.78 33	86.30 60	93.18 36

X-MVS		78.16 34	80.55 31	75.38 41	87.99 33	86.27 53	81.05 37	68.98 31	78.33 38	61.07 50	75.25 27	72.27 31	67.52 66	80.03 51	80.52 51	85.66 100	91.20 53

3Dnovator+	70.16 6	77.87 35	77.29 43	78.55 23	89.25 22	88.32 35	80.09 41	67.95 37	74.89 48	71.83 19	52.05 74	70.68 40	76.27 16	82.27 33	82.04 29	85.92 77	90.77 58

canonicalmvs		77.65 36	79.59 34	75.39 40	81.52 56	89.83 25	81.32 34	60.74 95	80.05 34	66.72 33	68.43 34	65.09 54	74.72 23	78.87 58	82.73 25	87.32 42	92.16 43

ACMMP		77.61 37	79.59 34	75.30 42	85.87 41	85.58 58	81.42 32	67.38 40	79.38 37	62.61 42	78.53 20	65.79 53	68.80 56	78.56 61	78.50 61	85.75 89	90.80 57
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
QAPM		77.50 38	77.43 41	77.59 29	91.52 6	92.00 8	81.41 33	70.63 20	66.22 62	58.05 62	54.70 64	71.79 36	74.49 24	82.46 28	82.04 29	89.46 9	92.79 40

MVS_111021_HR		77.42 39	78.40 38	76.28 34	86.95 36	90.68 14	77.41 54	70.56 23	66.21 63	62.48 44	66.17 40	63.98 58	72.08 36	82.87 24	83.15 22	88.24 27	95.71 10

CLD-MVS		77.36 40	77.29 43	77.45 30	82.21 52	88.11 38	81.92 28	68.96 32	77.97 40	69.62 28	62.08 47	59.44 74	73.57 27	81.75 38	81.27 41	88.41 23	90.39 62
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MAR-MVS		77.19 41	78.37 39	75.81 39	89.87 14	90.58 15	79.33 46	65.56 51	77.62 42	58.33 60	59.24 57	67.98 47	74.83 20	82.37 31	83.12 23	86.95 50	87.67 98
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
MVSTER		76.92 42	79.92 32	73.42 51	74.98 97	82.97 71	78.15 48	63.41 65	78.02 39	64.41 39	67.54 35	72.80 29	71.05 41	83.29 21	83.73 18	88.53 21	91.12 54

PVSNet_BlendedMVS		76.84 43	78.47 36	74.95 44	82.37 50	89.90 23	75.45 65	65.45 52	74.99 46	70.66 24	63.07 44	58.27 80	67.60 63	84.24 16	81.70 34	88.18 28	97.10 4

PVSNet_Blended		76.84 43	78.47 36	74.95 44	82.37 50	89.90 23	75.45 65	65.45 52	74.99 46	70.66 24	63.07 44	58.27 80	67.60 63	84.24 16	81.70 34	88.18 28	97.10 4

AdaColmap		76.23 45	73.55 59	79.35 18	89.38 20	85.00 62	79.99 43	73.04 13	76.60 44	71.17 20	55.18 62	57.99 82	77.87 9	76.82 73	76.82 71	84.67 129	86.45 106

PCF-MVS	70.85 4	75.73 46	76.55 50	74.78 47	83.67 46	88.04 40	81.47 31	70.62 22	69.24 59	57.52 63	60.59 54	69.18 45	70.65 43	77.11 70	77.65 68	84.75 127	94.01 29
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
CPTT-MVS		75.43 47	77.13 45	73.44 50	81.43 57	82.55 75	80.96 38	64.35 57	77.95 41	61.39 47	69.20 33	70.94 39	69.38 53	73.89 105	73.32 131	83.14 157	92.06 46

MVS_Test		75.22 48	76.69 48	73.51 49	79.30 69	88.82 29	80.06 42	58.74 105	69.77 56	57.50 64	59.78 56	61.35 69	75.31 17	82.07 34	83.60 21	90.13 5	91.41 52

OpenMVS	67.62 8	74.92 49	73.91 57	76.09 37	90.10 13	90.38 17	78.01 49	66.35 45	66.09 64	62.80 41	46.33 105	64.55 57	71.77 37	79.92 52	80.88 48	87.52 38	89.20 73

MVS_111021_LR		74.26 50	75.95 51	72.27 56	79.43 68	85.04 61	72.71 77	65.27 54	70.92 53	63.58 40	69.32 32	60.31 72	69.43 51	77.01 71	77.15 69	83.22 153	91.93 49

OMC-MVS		74.03 51	75.82 52	71.95 58	79.56 66	80.98 93	75.35 67	63.21 66	84.48 18	61.83 45	61.54 50	66.89 50	69.41 52	76.60 75	74.07 120	82.34 166	86.15 110

DI_MVS_plusplus_trai		73.94 52	74.85 55	72.88 53	76.57 87	86.80 46	80.41 40	61.47 85	62.35 73	59.44 59	47.91 88	68.12 46	72.24 34	82.84 25	81.50 37	87.15 48	94.42 23

diffmvs		73.50 53	75.66 53	70.97 61	74.96 99	86.71 47	77.16 56	57.42 125	71.12 52	60.43 55	57.20 59	70.40 42	68.79 57	76.11 81	76.05 79	87.10 49	92.06 46

TSAR-MVS + COLMAP		73.09 54	76.86 46	68.71 72	74.97 98	82.49 76	74.51 72	61.83 81	83.16 21	49.31 86	82.22 15	51.62 105	68.94 55	78.76 60	75.52 87	82.67 161	84.23 124

CANet_DTU		72.84 55	76.63 49	68.43 75	76.81 85	86.62 49	75.54 64	54.71 153	72.06 50	43.54 110	67.11 36	58.46 77	72.40 33	81.13 45	80.82 49	87.57 37	90.21 64

DWT-MVSNet_training		72.81 56	73.98 56	71.45 60	81.26 58	86.37 52	72.08 80	59.82 102	69.13 60	58.15 61	54.71 63	61.33 71	67.81 62	76.86 72	78.63 58	89.59 6	90.86 56

OPM-MVS		72.74 57	70.93 72	74.85 46	85.30 43	84.34 65	82.82 25	69.79 25	49.96 113	55.39 70	54.09 69	60.14 73	70.04 47	80.38 50	79.43 53	85.74 92	88.20 95

CHOSEN 1792x2688		72.55 58	71.98 64	73.22 52	86.57 39	92.41 4	75.63 61	66.77 42	62.08 74	52.32 73	30.27 196	50.74 108	66.14 68	86.22 7	85.41 6	91.90 1	96.75 8

CostFormer		72.18 59	73.90 58	70.18 65	79.47 67	86.19 56	76.94 57	48.62 186	66.07 65	60.40 56	54.14 68	65.82 52	67.98 60	75.84 84	76.41 75	87.67 36	92.83 39

ACMP	68.86 7	72.15 60	72.25 63	72.03 57	80.96 60	80.87 95	77.93 50	64.13 59	69.29 57	60.79 53	64.04 42	53.54 99	63.91 77	73.74 109	75.27 88	84.45 134	88.98 76
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
LGP-MVS_train		72.02 61	73.18 62	70.67 63	82.13 53	80.26 110	79.58 44	63.04 68	70.09 54	51.98 74	65.06 41	55.62 92	62.49 85	75.97 83	76.32 76	84.80 126	88.93 77

PVSNet_Blended_VisFu		71.76 62	73.54 60	69.69 66	79.01 70	87.16 44	72.05 81	61.80 82	56.46 91	59.66 58	53.88 70	62.48 61	59.08 125	81.17 43	78.90 56	86.53 57	94.74 20

TAPA-MVS	67.10 9	71.45 63	73.47 61	69.10 70	77.04 82	80.78 96	73.81 75	62.10 77	80.80 31	51.28 77	60.91 52	63.80 60	67.98 60	74.59 94	72.42 145	82.37 165	80.97 152
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
CNLPA		71.37 64	70.27 75	72.66 55	80.79 63	81.33 89	71.07 107	65.75 49	82.36 24	64.80 38	42.46 121	56.49 85	72.70 32	73.00 117	70.52 166	80.84 177	85.76 115

Effi-MVS+		70.42 65	71.23 70	69.47 67	78.04 74	85.24 60	75.57 63	58.88 104	59.56 80	48.47 87	52.73 73	54.94 94	69.69 49	78.34 64	77.06 70	86.18 62	90.73 60

ACMM	66.70 10	70.42 65	68.49 84	72.67 54	82.85 47	77.76 135	77.70 52	64.76 56	64.61 70	60.74 54	49.29 82	53.97 98	65.86 69	74.97 91	75.57 86	84.13 142	83.29 132
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
FMVSNet3		70.41 67	71.89 66	68.68 73	70.89 124	79.42 119	75.63 61	60.97 91	65.32 66	51.06 78	47.37 94	62.05 63	64.90 73	82.49 27	82.27 28	88.64 17	84.34 123

PMMVS		70.37 68	75.06 54	64.90 92	71.46 118	81.88 79	64.10 149	55.64 141	71.31 51	46.69 93	70.69 31	58.56 75	69.53 50	79.03 57	75.63 84	81.96 169	88.32 94

MS-PatchMatch		70.34 69	69.00 80	71.91 59	85.20 44	85.35 59	77.84 51	61.77 83	58.01 85	55.40 69	41.26 128	58.34 79	61.69 88	81.70 39	78.29 62	89.56 7	80.02 157

tpmp4_e23		69.38 70	69.47 78	69.28 69	78.20 73	82.35 77	75.92 58	49.20 184	64.15 71	59.96 57	47.93 87	55.77 90	68.06 59	73.05 116	74.53 99	84.34 136	88.50 93

GBi-Net		69.21 71	70.40 73	67.81 78	69.49 129	78.65 124	74.54 68	60.97 91	65.32 66	51.06 78	47.37 94	62.05 63	63.43 79	77.49 66	78.22 63	87.37 39	83.73 127

test1		69.21 71	70.40 73	67.81 78	69.49 129	78.65 124	74.54 68	60.97 91	65.32 66	51.06 78	47.37 94	62.05 63	63.43 79	77.49 66	78.22 63	87.37 39	83.73 127

IB-MVS	64.48 11	69.02 73	68.97 81	69.09 71	81.75 55	89.01 28	64.50 147	64.91 55	56.65 89	62.59 43	47.89 89	45.23 119	51.99 148	69.18 169	81.88 32	88.77 15	92.93 37
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
FC-MVSNet-train		68.83 74	68.29 85	69.47 67	78.35 72	79.94 111	64.72 146	66.38 44	54.96 100	54.51 71	56.75 60	47.91 114	66.91 67	75.57 88	75.75 82	85.92 77	87.12 101

PLC	64.00 12	68.54 75	66.66 95	70.74 62	80.28 65	74.88 158	72.64 78	63.70 64	69.26 58	55.71 67	47.24 97	55.31 93	70.42 44	72.05 143	70.67 164	81.66 171	77.19 166
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
HyFIR lowres test		68.39 76	68.28 86	68.52 74	80.85 61	88.11 38	71.08 106	58.09 110	54.87 102	47.80 90	27.55 201	55.80 89	64.97 72	79.11 56	79.14 55	88.31 25	93.35 32

test-LLR		68.23 77	71.61 68	64.28 110	71.37 119	81.32 90	63.98 153	61.03 88	58.62 82	42.96 120	52.74 71	61.65 67	57.74 131	75.64 86	78.09 66	88.61 18	93.21 33

FMVSNet2		68.06 78	68.57 83	67.45 81	69.49 129	78.65 124	74.54 68	60.23 101	56.29 92	49.64 85	42.13 124	57.08 84	63.43 79	81.15 44	80.99 45	87.37 39	83.73 127

Fast-Effi-MVS+		67.59 79	67.56 90	67.62 80	73.67 104	81.14 92	71.12 104	54.79 152	58.88 81	50.61 82	46.70 103	47.05 115	69.12 54	76.06 82	76.44 74	86.43 58	86.65 104

EPP-MVSNet		67.58 80	71.10 71	63.48 119	75.71 93	83.35 70	66.85 136	57.83 113	53.02 107	41.15 135	55.82 61	67.89 48	56.01 137	74.40 96	72.92 140	83.33 151	90.30 63

UGNet		67.57 81	71.69 67	62.76 130	69.88 127	82.58 74	66.43 140	58.64 106	54.71 103	51.87 75	61.74 48	62.01 66	45.46 176	74.78 93	74.99 90	84.24 138	91.02 55
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
tpm cat1		67.47 82	67.05 94	67.98 77	76.63 86	81.51 87	74.49 73	47.65 191	61.18 76	61.12 49	42.51 120	53.02 102	64.74 75	70.11 161	71.50 152	83.22 153	89.49 69

TESTMET0.1,1		67.38 83	71.61 68	62.45 134	66.05 167	81.32 90	63.98 153	55.36 145	58.62 82	42.96 120	52.74 71	61.65 67	57.74 131	75.64 86	78.09 66	88.61 18	93.21 33

IS_MVSNet		67.29 84	71.98 64	61.82 140	76.92 83	84.32 67	65.90 144	58.22 108	55.75 96	39.22 143	54.51 66	62.47 62	45.99 173	78.83 59	78.52 60	84.70 128	89.47 70

tpmrst		67.15 85	68.12 88	66.03 88	76.21 89	80.98 93	71.27 100	45.05 198	60.69 78	50.63 81	46.95 102	54.15 97	65.30 70	71.80 146	71.77 149	87.72 34	90.48 61

thres100view900		67.14 86	66.09 99	68.38 76	77.70 76	83.84 69	74.52 71	66.33 46	49.16 118	43.40 114	43.24 111	41.34 129	62.59 84	79.31 55	75.92 81	85.73 93	89.81 67

conf0.002		67.12 87	67.13 93	67.11 83	77.95 75	82.11 78	71.71 90	63.06 67	49.16 118	43.43 112	47.76 91	48.79 111	61.42 90	76.61 74	76.55 73	85.07 113	88.92 79

EPMVS		66.21 88	67.49 91	64.73 97	75.81 92	84.20 68	68.94 124	44.37 202	61.55 75	48.07 89	49.21 84	54.87 95	62.88 82	71.82 145	71.40 156	88.28 26	79.37 160

EPNet_dtu		66.17 89	70.13 76	61.54 142	81.04 59	77.39 139	68.87 125	62.50 76	69.78 55	33.51 176	63.77 43	56.22 86	37.65 193	72.20 139	72.18 147	85.69 96	79.38 159
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
IterMVS-LS		66.08 90	66.56 97	65.51 89	73.67 104	74.88 158	70.89 110	53.55 160	50.42 111	48.32 88	50.59 78	55.66 91	61.83 87	73.93 104	74.42 106	84.82 125	86.01 112
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
tfpn200view9		65.90 91	64.96 104	67.00 84	77.70 76	81.58 84	71.71 90	62.94 72	49.16 118	43.40 114	43.24 111	41.34 129	61.42 90	76.24 77	74.63 95	84.84 121	88.52 90

conf200view11		65.89 92	64.96 104	66.98 85	77.70 76	81.58 84	71.71 90	62.94 72	49.16 118	43.28 116	43.24 111	41.34 129	61.42 90	76.24 77	74.63 95	84.84 121	88.52 90

thres200		65.58 93	64.74 107	66.56 86	77.52 80	81.61 82	73.44 76	62.95 70	46.23 137	42.45 130	42.76 115	41.18 133	58.12 129	76.24 77	75.59 85	84.89 118	89.58 68

MSDG		65.57 94	61.57 143	70.24 64	82.02 54	76.47 147	74.46 74	68.73 34	56.52 90	50.33 83	38.47 155	41.10 135	62.42 86	72.12 141	72.94 139	83.47 149	73.37 182

Vis-MVSNet		65.53 95	69.83 77	60.52 146	70.80 125	84.59 64	66.37 142	55.47 144	48.40 124	40.62 139	57.67 58	58.43 78	45.37 177	77.49 66	76.24 77	84.47 133	85.99 113
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
PatchmatchNet		65.43 96	67.71 89	62.78 129	73.49 106	82.83 72	66.42 141	45.40 197	60.40 79	45.27 99	49.22 83	57.60 83	60.01 109	70.61 153	71.38 159	86.08 67	81.91 147
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
MDTV_nov1_ep13		65.21 97	67.28 92	62.79 128	70.91 123	81.72 81	69.28 123	49.50 181	58.08 84	43.94 109	50.50 79	56.02 87	58.86 126	70.72 152	73.37 129	84.24 138	80.52 153

thres400		65.18 98	64.44 109	66.04 87	76.40 88	82.63 73	71.52 98	64.27 58	44.93 145	40.69 138	41.86 125	40.79 142	58.12 129	77.67 65	74.64 94	85.26 106	88.56 89

tpm		64.85 99	66.02 100	63.48 119	74.52 101	78.38 127	70.98 108	44.99 200	51.61 109	43.28 116	47.66 92	53.18 100	60.57 100	70.58 155	71.30 161	86.54 56	89.45 71

UA-Net		64.62 100	68.23 87	60.42 147	77.53 79	81.38 88	60.08 175	57.47 121	47.01 129	44.75 105	60.68 53	71.32 38	41.84 184	73.27 111	72.25 146	80.83 178	71.68 190

Effi-MVS+-dtu		64.58 101	64.08 110	65.16 90	73.04 109	75.17 157	70.68 112	56.23 135	54.12 105	44.71 106	47.42 93	51.10 106	63.82 78	68.08 173	66.32 186	82.47 164	86.38 108

GA-MVS		64.55 102	65.76 102	63.12 125	69.68 128	81.56 86	69.59 120	58.16 109	45.23 143	35.58 167	47.01 101	41.82 128	59.41 120	79.62 53	78.54 59	86.32 59	86.56 105

LS3D		64.54 103	62.14 136	67.34 82	80.85 61	75.79 153	69.99 116	65.87 48	60.77 77	44.35 107	42.43 122	45.95 118	65.01 71	69.88 164	68.69 174	77.97 195	71.43 192

CDS-MVSNet		64.22 104	65.89 101	62.28 136	70.05 126	80.59 105	69.91 118	57.98 111	43.53 155	46.58 94	48.22 86	50.76 107	46.45 170	75.68 85	76.08 78	82.70 160	86.34 109
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
v6		64.09 105	63.40 115	64.90 92	68.28 140	80.78 96	71.85 84	57.64 117	46.73 132	45.18 101	39.40 141	40.89 139	60.54 103	72.86 122	74.40 107	85.92 77	88.72 85

v1neww		64.08 106	63.38 116	64.89 94	68.27 142	80.77 98	71.84 85	57.65 115	46.66 134	45.10 102	39.40 141	40.86 140	60.57 100	72.86 122	74.40 107	85.92 77	88.71 86

v7new		64.08 106	63.38 116	64.89 94	68.27 142	80.77 98	71.84 85	57.65 115	46.66 134	45.10 102	39.40 141	40.86 140	60.57 100	72.86 122	74.40 107	85.92 77	88.71 86

dps		64.08 106	63.22 119	65.08 91	75.27 96	79.65 115	66.68 138	46.63 196	56.94 87	55.67 68	43.96 107	43.63 125	64.00 76	69.50 168	69.82 169	82.25 167	79.02 161

test-mter		64.06 109	69.24 79	58.01 161	59.07 195	77.40 138	59.13 179	48.11 189	55.64 97	39.18 144	51.56 75	58.54 76	55.38 139	73.52 110	76.00 80	87.22 46	92.05 48

thresconf0.02		63.92 110	65.18 103	62.46 133	75.91 91	80.65 104	67.51 133	63.86 61	45.00 144	33.32 177	51.38 76	51.68 104	48.34 160	75.49 89	75.13 89	85.84 88	76.91 168

view600		63.91 111	63.27 118	64.66 99	75.57 94	81.73 80	69.71 119	63.04 68	43.97 148	39.18 144	41.09 129	40.24 150	55.38 139	76.28 76	72.04 148	85.08 112	87.52 99

thres600view7		63.77 112	63.14 120	64.51 101	75.49 95	81.61 82	69.59 120	62.95 70	43.96 149	38.90 146	41.09 129	40.24 150	55.25 141	76.24 77	71.54 151	84.89 118	87.30 100

v2v482		63.68 113	62.85 125	64.65 100	68.01 150	80.46 107	71.90 82	57.60 118	44.26 146	42.82 128	39.80 139	38.62 166	61.56 89	73.06 114	74.86 92	86.03 70	88.90 80

v7		63.61 114	63.02 122	64.29 109	67.88 154	80.32 108	71.60 96	56.63 131	45.37 141	42.84 125	38.54 153	38.91 164	61.05 95	74.39 97	74.52 100	85.75 89	89.10 75

v1		63.49 115	62.77 128	64.32 106	68.13 144	80.70 101	71.70 94	57.43 122	43.69 152	42.89 124	39.03 147	39.77 154	59.93 112	72.93 119	74.48 104	85.86 85	88.77 81

v1141		63.48 116	62.75 130	64.32 106	68.13 144	80.69 102	71.69 95	57.43 122	43.66 154	42.83 127	39.02 148	39.74 156	59.95 110	72.94 118	74.49 102	85.86 85	88.75 83

divwei89l23v2f112		63.48 116	62.76 129	64.32 106	68.13 144	80.68 103	71.71 90	57.43 122	43.69 152	42.84 125	39.01 149	39.75 155	59.94 111	72.93 119	74.49 102	85.86 85	88.75 83

FMVSNet1		63.48 116	63.07 121	63.97 112	65.31 173	76.37 149	71.77 89	57.90 112	43.32 157	45.66 97	35.06 182	49.43 110	58.57 127	77.49 66	78.22 63	84.59 131	81.60 150

v8		63.44 119	62.58 132	64.43 103	68.28 140	78.07 130	71.82 87	54.85 150	46.70 133	45.20 100	39.40 141	40.91 138	60.54 103	72.85 126	74.39 112	85.92 77	85.76 115

v18		63.31 120	62.02 138	64.81 96	68.48 136	73.38 167	72.14 79	54.28 155	48.99 123	47.21 91	39.56 140	41.20 132	60.80 97	72.89 121	74.46 105	85.96 76	83.64 130

pmmvs4		63.14 121	62.46 133	63.94 113	66.03 168	76.40 148	66.82 137	57.60 118	56.74 88	50.26 84	40.81 133	37.51 170	59.26 123	71.75 147	71.48 153	83.68 147	82.53 140

v16		63.12 122	61.78 140	64.68 98	68.45 137	73.29 168	71.86 83	54.12 156	48.36 125	47.00 92	39.30 145	41.01 136	60.67 98	72.83 127	74.40 107	86.01 71	83.24 134

Fast-Effi-MVS+-dtu		63.05 123	64.72 108	61.11 144	71.21 122	76.81 146	70.72 111	43.13 206	52.51 108	35.34 168	46.55 104	46.36 116	61.40 93	71.57 148	71.44 154	84.84 121	87.79 97

view800		63.02 124	62.69 131	63.39 121	74.79 100	80.76 100	67.83 129	61.93 80	43.16 159	37.78 155	40.43 134	39.73 157	53.16 146	75.01 90	73.32 131	84.87 120	86.43 107

v1144		63.00 125	62.39 134	63.70 115	67.72 157	80.27 109	71.23 102	56.40 132	42.51 163	40.81 137	38.12 162	37.73 168	60.42 106	74.46 95	74.55 98	85.64 101	89.12 74

v10		63.00 125	62.22 135	63.90 114	67.88 154	77.78 134	71.59 97	54.34 154	45.37 141	42.76 129	38.53 154	38.93 163	61.05 95	74.39 97	74.52 100	85.75 89	86.04 111

v17		62.99 127	61.70 141	64.51 101	68.40 138	73.28 169	71.80 88	54.11 157	47.87 126	46.14 95	39.29 146	41.01 136	60.60 99	72.81 128	74.39 112	85.99 74	83.25 133

tfpn_ndepth		62.95 128	63.75 112	62.02 138	76.89 84	79.48 118	64.09 150	60.98 90	49.48 115	38.73 147	49.92 81	44.79 120	47.37 165	71.91 144	71.66 150	84.07 143	79.00 162

V42		62.86 129	62.97 123	62.74 131	60.84 188	78.99 122	71.46 99	57.13 129	46.85 130	44.28 108	38.87 150	40.73 144	57.63 133	72.60 134	74.14 117	85.09 111	88.63 88

tfpn		62.54 130	62.79 127	62.25 137	74.16 102	79.86 113	66.07 143	60.97 91	42.43 164	36.41 159	39.88 138	43.76 124	51.25 153	73.85 106	74.17 116	84.67 129	85.57 118

gg-mvs-nofinetune		62.34 131	66.19 98	57.86 165	76.15 90	88.61 31	71.18 103	41.24 215	25.74 216	13.16 219	22.91 211	63.97 59	54.52 143	85.06 11	85.25 9	90.92 3	91.78 50

CR-MVSNet		62.31 132	64.75 106	59.47 153	68.63 135	71.29 184	67.53 131	43.18 204	55.83 94	41.40 132	41.04 131	55.85 88	57.29 134	72.76 129	73.27 134	78.77 192	83.23 135

UniMVSNet_NR-MVSNet		62.30 133	63.51 114	60.89 145	69.48 132	77.83 133	64.07 151	63.94 60	50.03 112	31.17 182	44.82 106	41.12 134	51.37 150	71.02 150	74.81 93	85.30 105	84.95 119

v1192		62.25 134	61.64 142	62.96 126	66.88 162	79.72 114	69.96 117	55.77 139	41.58 170	39.42 141	37.05 168	35.96 181	60.50 105	74.30 102	74.09 119	85.24 107	88.76 82

Vis-MVSNet (Re-imp)		62.25 134	68.74 82	54.68 180	73.70 103	78.74 123	56.51 186	57.49 120	55.22 98	26.86 194	54.56 65	61.35 69	31.06 196	73.10 113	74.90 91	82.49 163	83.31 131

CHOSEN 280x420		62.23 136	66.57 96	57.17 170	59.88 192	68.92 189	61.20 169	42.28 208	54.17 104	39.57 140	47.78 90	64.97 55	62.68 83	73.85 106	69.52 171	77.43 196	86.75 103

PatchMatch-RL		62.22 137	60.69 150	64.01 111	68.74 134	75.75 154	59.27 178	60.35 97	56.09 93	53.80 72	47.06 100	36.45 176	64.80 74	68.22 172	67.22 181	77.10 197	74.02 177

v15		62.07 138	60.70 149	63.67 116	68.09 147	73.00 170	71.27 100	53.41 161	43.70 151	43.43 112	38.77 151	39.83 152	59.87 113	72.74 131	74.25 114	85.98 75	82.61 139

v144192		62.05 139	61.46 144	62.73 132	66.59 165	79.87 112	69.30 122	55.88 137	41.50 171	39.41 142	37.23 166	36.45 176	59.62 116	72.69 132	73.51 126	85.61 102	88.93 77

v148		62.00 140	61.19 146	62.96 126	67.46 160	79.49 117	67.87 128	57.66 114	42.30 165	45.02 104	38.20 160	38.89 165	54.77 142	69.83 165	72.60 144	84.96 114	87.01 102

V14		61.96 141	60.56 151	63.59 117	68.06 148	72.93 173	71.10 105	53.33 163	43.47 156	43.28 116	38.59 152	39.78 153	59.76 115	72.65 133	74.19 115	86.01 71	82.32 144

IterMVS		61.87 142	63.55 113	59.90 149	67.29 161	72.20 179	67.34 134	48.56 187	47.48 128	37.86 154	47.07 99	48.27 112	54.08 144	72.12 141	73.71 124	84.30 137	83.99 125
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
V9		61.85 143	60.42 154	63.51 118	68.02 149	72.85 174	70.91 109	53.24 164	43.25 158	43.27 119	38.41 156	39.73 157	59.60 117	72.55 135	74.13 118	86.04 69	82.04 146

v11		61.74 144	60.47 153	63.22 124	67.83 156	72.72 176	70.31 115	52.95 170	42.75 162	41.89 131	38.16 161	38.49 167	60.40 107	74.35 99	74.40 107	85.92 77	82.39 143

v12		61.70 145	60.27 156	63.38 122	68.00 151	72.76 175	70.63 113	53.14 166	43.01 160	42.95 123	38.25 158	39.64 159	59.48 119	72.47 137	74.05 121	86.06 68	81.71 149

v1921920		61.66 146	61.10 147	62.31 135	66.32 166	79.57 116	68.41 127	55.49 143	41.03 172	38.69 148	36.64 174	35.27 187	59.60 117	73.23 112	73.41 128	85.37 104	88.51 92

v13		61.60 147	60.13 159	63.31 123	67.95 153	72.67 177	70.51 114	53.05 167	42.80 161	42.96 120	38.10 163	39.57 160	59.31 122	72.36 138	73.98 123	86.10 65	81.40 151

ACMH	59.42 14	61.59 148	59.22 169	64.36 105	78.92 71	78.26 128	67.65 130	67.48 39	39.81 176	30.98 184	38.25 158	34.59 189	61.37 94	70.55 156	73.47 127	79.74 186	79.59 158
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
ACMH+	60.36 13	61.16 149	58.38 171	64.42 104	77.37 81	74.35 163	68.45 126	62.81 75	45.86 139	38.48 149	35.71 177	37.35 171	59.81 114	67.24 175	69.80 170	79.58 187	78.32 164

v1240		61.09 150	60.55 152	61.72 141	65.92 170	79.28 120	67.16 135	54.91 149	39.79 177	38.10 151	36.08 176	34.64 188	59.15 124	72.86 122	73.36 130	85.10 109	87.84 96

NR-MVSNet		61.08 151	62.09 137	59.90 149	71.96 117	75.87 151	63.60 157	61.96 78	49.31 116	27.95 191	42.76 115	33.85 193	48.82 159	74.35 99	74.05 121	85.13 108	84.45 121

DU-MVS		60.87 152	61.82 139	59.76 151	66.69 163	75.87 151	64.07 151	61.96 78	49.31 116	31.17 182	42.76 115	36.95 173	51.37 150	69.67 166	73.20 137	83.30 152	84.95 119

UniMVSNet (Re)		60.62 153	62.93 124	57.92 162	67.64 158	77.90 132	61.75 166	61.24 87	49.83 114	29.80 186	42.57 118	40.62 148	43.36 180	70.49 158	73.27 134	83.76 145	85.81 114

PatchT		60.46 154	63.85 111	56.51 172	65.95 169	75.68 155	47.34 199	41.39 211	53.89 106	41.40 132	37.84 164	50.30 109	57.29 134	72.76 129	73.27 134	85.67 97	83.23 135

TranMVSNet+NR-MVSNet		60.38 155	61.30 145	59.30 154	68.34 139	75.57 156	63.38 160	63.78 63	46.74 131	27.73 192	42.56 119	36.84 174	47.66 163	70.36 159	74.59 97	84.91 117	82.46 141

conf0.05thres1000		60.33 156	59.42 166	61.40 143	73.15 108	78.25 129	65.29 145	60.30 98	36.61 188	35.75 165	33.25 184	39.23 161	50.35 156	72.18 140	72.67 143	83.57 148	83.74 126

pmmvs5		59.72 157	60.24 157	59.11 156	62.77 182	77.33 140	63.17 161	54.00 158	40.21 175	37.23 156	40.41 135	35.99 180	51.75 149	72.55 135	72.74 142	85.72 95	82.45 142

USDC		59.69 158	60.03 160	59.28 155	64.04 177	71.84 182	63.15 162	55.36 145	54.90 101	35.02 171	48.34 85	29.79 204	58.16 128	70.60 154	71.33 160	79.99 184	73.42 181

Baseline_NR-MVSNet		59.47 159	60.28 155	58.54 159	66.69 163	73.90 164	61.63 167	62.90 74	49.15 122	26.87 193	35.18 181	37.62 169	48.20 161	69.67 166	73.61 125	84.92 115	82.82 138

pm-mvs1		59.21 160	59.58 165	58.77 158	67.97 152	77.07 145	64.12 148	57.20 127	34.73 196	36.86 157	35.34 179	40.54 149	43.34 181	74.32 101	73.30 133	83.13 158	81.77 148

tfpnnormal		58.97 161	56.48 179	61.89 139	71.27 121	76.21 150	66.65 139	61.76 84	32.90 202	36.41 159	27.83 200	29.14 205	50.64 155	73.06 114	73.05 138	84.58 132	83.15 137

tfpnview11		58.92 162	59.60 164	58.13 160	72.99 110	77.11 143	60.48 170	60.37 96	42.10 167	29.10 188	43.45 108	40.72 145	41.67 185	70.53 157	70.43 167	84.17 141	72.85 184

FMVSNet5		58.86 163	60.24 157	57.25 169	52.66 212	66.25 195	63.77 156	52.86 171	57.85 86	37.92 153	36.12 175	52.22 103	51.37 150	70.88 151	71.43 155	84.92 115	66.91 200

TAMVS		58.86 163	60.91 148	56.47 173	62.38 184	77.57 136	58.97 180	52.98 168	38.76 182	36.17 162	42.26 123	47.94 113	46.45 170	70.23 160	70.79 163	81.86 170	78.82 163

EG-PatchMatch MVS		58.73 165	58.03 174	59.55 152	72.32 114	80.49 106	63.44 159	55.55 142	32.49 203	38.31 150	28.87 198	37.22 172	42.84 182	74.30 102	75.70 83	84.84 121	77.14 167

tfpn_n400		58.64 166	59.27 167	57.89 163	72.83 111	77.26 141	60.35 171	60.29 99	39.77 178	29.10 188	43.45 108	40.72 145	41.61 186	70.06 162	71.39 157	83.17 155	72.26 187

tfpnconf		58.64 166	59.27 167	57.89 163	72.83 111	77.26 141	60.35 171	60.29 99	39.77 178	29.10 188	43.45 108	40.72 145	41.61 186	70.06 162	71.39 157	83.17 155	72.26 187

RPMNet		58.63 168	62.80 126	53.76 186	67.59 159	71.29 184	54.60 188	38.13 219	55.83 94	35.70 166	41.58 127	53.04 101	47.89 162	66.10 177	67.38 179	78.65 194	84.40 122

ADS-MVSNet		58.40 169	59.16 170	57.52 167	65.80 171	74.57 162	60.26 173	40.17 216	50.51 110	38.01 152	40.11 137	44.72 121	59.36 121	64.91 181	66.55 184	81.53 172	72.72 186

tfpn1000		58.35 170	59.96 161	56.47 173	72.78 113	77.51 137	56.66 185	59.16 103	43.74 150	29.76 187	42.79 114	42.49 126	37.04 194	68.92 170	68.98 172	83.45 150	75.25 172

TransMVSNet (Re)		57.83 171	56.90 177	58.91 157	72.26 115	74.69 161	63.57 158	61.42 86	32.30 204	32.65 179	33.97 183	35.96 181	39.17 191	73.84 108	72.84 141	84.37 135	74.69 175

MIMVSNet		57.78 172	59.71 163	55.53 177	54.79 203	77.10 144	63.89 155	45.02 199	46.59 136	36.79 158	28.36 199	40.77 143	45.84 174	74.97 91	76.58 72	86.87 52	73.60 180

CMPMVS	43.63 17	57.67 173	55.43 180	60.28 148	72.01 116	79.00 121	62.77 163	53.23 165	41.77 169	45.42 98	30.74 195	39.03 162	53.01 147	64.81 183	64.65 192	75.26 203	68.03 198
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
test0.0.03 1		57.35 174	59.89 162	54.38 182	71.37 119	73.45 166	52.71 190	61.03 88	46.11 138	26.33 195	41.73 126	44.08 122	29.72 199	71.43 149	70.90 162	85.10 109	71.56 191

v7n		57.04 175	56.64 178	57.52 167	62.85 181	74.75 160	61.76 165	51.80 174	35.58 195	36.02 164	32.33 188	33.61 194	50.16 157	67.73 174	70.34 168	82.51 162	82.12 145

pmmvs-eth3d		55.20 176	53.95 187	56.65 171	57.34 201	67.77 191	57.54 183	53.74 159	40.93 173	41.09 136	31.19 194	29.10 206	49.07 158	65.54 178	67.28 180	81.14 175	75.81 169

v748		55.19 177	54.63 183	55.85 175	61.44 187	72.97 172	58.72 181	51.62 175	34.48 198	36.39 161	32.09 189	33.05 195	45.48 175	61.85 193	67.87 177	81.45 173	80.08 156

COLMAP_ROB	51.17 15	55.13 178	52.90 191	57.73 166	73.47 107	67.21 193	62.13 164	55.82 138	47.83 127	34.39 172	31.60 193	34.24 190	44.90 178	63.88 190	62.52 200	75.67 201	63.02 209
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
RPSCF		55.07 179	58.06 173	51.57 188	48.87 219	58.95 209	53.68 189	41.26 214	62.42 72	45.88 96	54.38 67	54.26 96	53.75 145	57.15 202	53.53 218	66.01 217	65.75 203

gm-plane-assit		54.99 180	57.99 175	51.49 190	69.27 133	54.42 215	32.32 219	42.59 207	21.18 222	13.71 217	23.61 207	43.84 123	60.21 108	87.09 4	86.55 4	90.81 4	89.28 72

anonymousdsp		54.99 180	57.24 176	52.36 187	53.82 209	71.75 183	51.49 191	48.14 188	33.74 200	33.66 175	38.34 157	36.13 179	47.54 164	64.53 185	70.60 165	79.53 188	85.59 117

CVMVSNet		54.92 182	58.16 172	51.13 191	62.61 183	68.44 190	55.45 187	52.38 172	42.28 166	21.45 202	47.10 98	46.10 117	37.96 192	64.42 186	63.81 194	76.92 199	75.01 174

v52		54.79 183	55.15 181	54.36 184	54.07 207	72.13 180	59.84 176	49.39 182	34.50 197	35.08 170	31.63 192	35.74 183	47.21 168	63.90 188	67.92 175	80.59 180	80.23 154

V4		54.78 184	55.14 182	54.37 183	54.07 207	72.13 180	59.83 177	49.39 182	34.46 199	35.11 169	31.64 191	35.72 184	47.22 167	63.90 188	67.92 175	80.59 180	80.23 154

GG-mvs-BLEND		54.54 185	77.58 40	27.67 222	0.03 234	90.09 22	77.20 55	0.02 232	66.83 61	0.05 237	59.90 55	73.33 28	0.04 232	78.40 63	79.30 54	88.65 16	95.20 18

MDTV_nov1_ep13_2view		54.47 186	54.61 184	54.30 185	60.50 189	73.82 165	57.92 182	43.38 203	39.43 181	32.51 180	33.23 185	34.05 191	47.26 166	62.36 191	66.21 187	84.24 138	73.19 183

pmmvs6		54.20 187	53.54 188	54.97 178	63.22 180	72.98 171	60.17 174	52.32 173	26.77 215	34.30 173	23.29 210	36.23 178	40.33 189	68.77 171	68.76 173	79.47 189	78.00 165

MVS-HIRNet		53.86 188	53.02 189	54.85 179	60.30 191	72.36 178	44.63 207	42.20 209	39.45 180	43.47 111	21.66 214	34.00 192	55.47 138	65.42 179	67.16 182	83.02 159	71.08 193

TDRefinement		52.70 189	51.02 197	54.66 181	57.41 200	65.06 199	61.47 168	54.94 147	44.03 147	33.93 174	30.13 197	27.57 207	46.17 172	61.86 192	62.48 201	74.01 207	66.06 202

TinyColmap		52.66 190	50.09 200	55.65 176	59.72 193	64.02 203	57.15 184	52.96 169	40.28 174	32.51 180	32.42 187	20.97 219	56.65 136	63.95 187	65.15 191	74.91 204	63.87 206

Anonymous20231206		52.23 191	52.80 192	51.56 189	64.70 176	69.41 187	51.01 192	58.60 107	36.63 187	22.44 201	21.80 213	31.42 200	30.52 197	66.79 176	67.83 178	82.10 168	75.73 170

PEN-MVS		51.04 192	52.94 190	48.82 195	61.45 186	66.00 196	48.68 196	57.20 127	36.87 186	15.36 213	36.98 169	32.72 196	28.77 203	57.63 201	66.37 185	81.44 174	74.00 178

WR-MVS		51.02 193	54.56 185	46.90 201	63.84 178	69.23 188	44.78 206	56.38 133	38.19 183	14.19 215	37.38 165	36.82 175	22.39 213	60.14 196	66.20 188	79.81 185	73.95 179

CP-MVSNet		50.57 194	52.60 194	48.21 198	58.77 197	65.82 197	48.17 197	56.29 134	37.41 184	16.59 210	37.14 167	31.95 198	29.21 200	56.60 204	63.71 195	80.22 182	75.56 171

PS-CasMVS		50.17 195	52.02 195	48.02 199	58.60 198	65.54 198	48.04 198	56.19 136	36.42 190	16.42 212	35.68 178	31.33 201	28.85 202	56.42 206	63.54 196	80.01 183	75.18 173

PM-MVS		50.11 196	50.38 199	49.80 193	47.23 221	62.08 207	50.91 193	44.84 201	41.90 168	36.10 163	35.22 180	26.05 213	46.83 169	57.64 200	55.42 217	72.90 208	74.32 176

DTE-MVSNet		49.82 197	51.92 196	47.37 200	61.75 185	64.38 201	45.89 205	57.33 126	36.11 191	12.79 220	36.87 170	31.93 199	25.73 208	58.01 198	65.22 190	80.75 179	70.93 194

WR-MVS_H		49.62 198	52.63 193	46.11 204	58.80 196	67.58 192	46.14 204	54.94 147	36.51 189	13.63 218	36.75 172	35.67 185	22.10 214	56.43 205	62.76 198	81.06 176	72.73 185

LTVRE_ROB	47.26 16	49.41 199	49.91 201	48.82 195	64.76 175	69.79 186	49.05 194	47.12 193	20.36 224	16.52 211	36.65 173	26.96 208	50.76 154	60.47 195	63.16 197	64.73 218	72.00 189
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
SixPastTwentyTwo		49.11 200	49.22 202	48.99 194	58.54 199	64.14 202	47.18 200	47.75 190	31.15 206	24.42 197	41.01 132	26.55 209	44.04 179	54.76 212	58.70 207	71.99 211	68.21 196

testgi		48.51 201	50.53 198	46.16 203	64.78 174	67.15 194	41.54 210	54.81 151	29.12 210	17.03 208	32.07 190	31.98 197	20.15 217	65.26 180	67.00 183	78.67 193	61.10 214

LP		48.21 202	46.65 208	50.03 192	60.39 190	63.86 204	48.73 195	38.71 218	35.60 194	32.99 178	23.31 209	24.95 215	40.07 190	57.73 199	61.56 202	79.29 190	59.51 215

N_pmnet		47.67 203	47.00 207	48.45 197	54.72 204	62.78 205	46.95 201	51.25 176	36.01 192	26.09 196	26.59 204	25.93 214	35.50 195	55.67 208	59.01 205	76.22 200	63.04 208

FC-MVSNet-test		47.24 204	54.37 186	38.93 214	59.49 194	58.25 211	34.48 218	53.36 162	45.66 140	6.66 229	50.62 77	42.02 127	16.62 223	58.39 197	61.21 203	62.99 219	64.40 205

test20.03		47.23 205	48.69 203	45.53 205	63.28 179	64.39 200	41.01 212	56.93 130	29.16 209	15.21 214	23.90 206	30.76 203	17.51 222	64.63 184	65.26 189	79.21 191	62.71 210

test2356		46.29 206	47.37 205	45.03 206	54.38 205	57.99 212	42.03 209	50.32 178	30.78 207	16.65 209	27.40 202	23.70 216	29.86 198	61.20 194	64.31 193	76.93 198	66.22 201

EU-MVSNet		44.84 207	47.85 204	41.32 211	49.26 216	56.59 214	43.07 208	47.64 192	33.03 201	13.82 216	36.78 171	30.99 202	24.37 211	53.80 213	55.57 216	69.78 213	68.21 196

MDA-MVSNet-bldmvs		44.15 208	42.27 214	46.34 202	38.34 224	62.31 206	46.28 202	55.74 140	29.83 208	20.98 203	27.11 203	16.45 226	41.98 183	41.11 224	57.47 209	74.72 205	61.65 213

testpf		43.39 209	47.17 206	38.98 213	65.58 172	47.38 223	36.09 216	31.67 226	36.97 185	19.47 205	33.01 186	35.62 186	23.61 212	50.86 218	56.08 214	57.48 223	70.27 195

testus		42.30 210	43.69 209	40.67 212	53.21 210	53.50 216	31.81 220	49.96 179	27.06 213	11.55 222	25.67 205	19.00 222	25.20 209	55.34 209	62.59 199	72.31 210	62.69 211

new-patchmatchnet		42.21 211	42.97 211	41.33 210	53.05 211	59.89 208	39.38 213	49.61 180	28.26 212	12.10 221	22.17 212	21.54 218	19.22 218	50.96 217	56.04 215	74.61 206	61.92 212

pmmvs3		41.86 212	42.29 213	41.36 209	39.80 222	52.66 217	38.93 215	35.85 225	23.40 219	20.22 204	19.30 215	20.84 220	40.56 188	55.98 207	58.79 206	72.80 209	65.03 204

MIMVSNet1		40.84 213	43.46 210	37.79 216	32.14 226	58.92 210	39.24 214	50.83 177	27.00 214	11.29 223	16.76 224	26.53 210	17.75 221	57.14 203	61.12 204	75.46 202	56.78 218

Anonymous20231211		40.44 214	39.25 215	41.84 208	54.29 206	57.29 213	41.10 211	49.06 185	17.67 227	10.15 224	10.63 226	16.79 225	25.15 210	52.14 214	56.70 212	71.30 212	63.51 207

FPMVS		39.11 215	36.39 219	42.28 207	55.97 202	45.94 224	46.23 203	41.57 210	35.73 193	22.61 199	23.46 208	19.82 221	28.32 206	43.57 220	40.67 223	58.96 221	45.54 220

1111		38.93 216	38.98 216	38.86 215	50.10 214	50.42 218	29.52 221	38.00 220	22.67 220	17.99 206	17.40 217	26.26 211	28.72 204	54.86 210	58.20 208	68.82 216	43.08 223

testmv		37.40 217	37.95 217	36.76 217	48.97 217	49.33 221	28.65 224	46.74 194	18.34 225	7.68 227	16.80 222	14.47 227	19.18 219	51.72 215	56.93 210	69.36 214	58.09 216

test1235678		37.40 217	37.94 218	36.76 217	48.97 217	49.30 222	28.65 224	46.73 195	18.33 226	7.68 227	16.79 223	14.46 228	19.18 219	51.72 215	56.92 211	69.36 214	58.07 217

new_pmnet		33.19 219	35.52 220	30.47 220	27.55 230	45.31 225	29.29 223	30.92 227	29.00 211	9.88 226	18.77 216	17.64 224	26.77 207	44.07 219	45.98 221	58.41 222	47.87 219

PMVS	27.44 18	32.08 220	29.07 222	35.60 219	48.33 220	24.79 229	26.97 226	41.34 212	20.45 223	22.50 200	17.11 221	18.64 223	20.44 216	41.99 223	38.06 224	54.02 226	42.44 224
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
test12356		29.92 221	31.49 221	28.08 221	38.46 223	37.74 227	21.36 227	40.17 216	16.83 228	5.61 231	15.66 225	11.48 229	6.60 229	42.01 222	51.23 219	56.29 224	45.52 221

no-one		26.96 222	26.51 223	27.49 223	37.87 225	39.14 226	17.12 229	41.31 213	12.02 230	3.68 233	8.04 228	8.42 232	10.67 227	28.11 226	45.96 222	54.27 225	43.89 222

.test1245		25.86 223	24.56 225	27.39 224	50.10 214	50.42 218	29.52 221	38.00 220	22.67 220	17.99 206	17.40 217	26.26 211	28.72 204	54.86 210	0.05 230	0.01 234	0.24 232

Gipuma		24.91 224	24.61 224	25.26 225	31.47 227	21.59 230	18.06 228	37.53 222	25.43 217	10.03 225	4.18 232	4.25 234	14.85 224	43.20 221	47.03 220	39.62 228	26.55 228
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		20.45 225	22.31 226	18.27 228	20.52 231	26.73 228	14.85 231	28.43 229	13.69 229	0.79 236	10.35 227	9.10 230	3.83 231	27.64 227	32.87 225	41.17 227	35.81 225

E-PMN		15.08 226	11.65 228	19.08 226	28.73 228	12.31 233	6.95 234	36.87 224	10.71 232	3.63 234	5.13 229	2.22 237	13.81 226	11.34 230	18.50 228	24.49 230	21.32 229

EMVS		14.40 227	10.71 229	18.70 227	28.15 229	12.09 234	7.06 233	36.89 223	11.00 231	3.56 235	4.95 230	2.27 236	13.91 225	10.13 231	16.06 229	22.63 231	18.51 230

MVE	15.98 19	14.37 228	16.36 227	12.04 230	7.72 233	20.24 231	5.90 235	29.05 228	8.28 233	3.92 232	4.72 231	2.42 235	9.57 228	18.89 229	31.46 226	16.07 233	28.53 227
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.05 229	0.08 230	0.01 231	0.00 235	0.01 236	0.03 237	0.01 233	0.05 234	0.00 238	0.14 234	0.01 238	0.03 234	0.05 232	0.05 230	0.01 234	0.24 232

test123		0.05 229	0.08 230	0.01 231	0.00 235	0.01 236	0.01 238	0.00 234	0.05 234	0.00 238	0.16 233	0.00 239	0.04 232	0.02 233	0.05 230	0.00 236	0.26 231

ESAPD		0.00 231	0.00 232	0.00 233	0.00 235	0.00 238	0.00 239	0.00 234	0.00 236	0.00 238	0.00 235	0.00 239	0.00 235	0.00 234	0.00 233	0.00 236	0.00 234

sosnet-low-res		0.00 231	0.00 232	0.00 233	0.00 235	0.00 238	0.00 239	0.00 234	0.00 236	0.00 238	0.00 235	0.00 239	0.00 235	0.00 234	0.00 233	0.00 236	0.00 234

sosnet		0.00 231	0.00 232	0.00 233	0.00 235	0.00 238	0.00 239	0.00 234	0.00 236	0.00 238	0.00 235	0.00 239	0.00 235	0.00 234	0.00 233	0.00 236	0.00 234

ambc			42.30 212		50.36 213	49.51 220	35.47 217		32.04 205	23.53 198	17.36 219	8.95 231	29.06 201	64.88 182	56.26 213	61.29 220	67.12 199

MTAPA										78.32 5		79.42 16

MTMP										76.04 10		76.65 21

Patchmatch-RL test							2.17 236

tmp_tt				16.09 229	13.07 232	8.12 235	13.61 232	2.08 231	55.09 99	30.10 185	40.26 136	22.83 217	5.35 230	29.91 225	25.25 227	32.33 229

XVS					82.43 48	86.27 53	75.70 59			61.07 50		72.27 31				85.67 97

X-MVStestdata					82.43 48	86.27 53	75.70 59			61.07 50		72.27 31				85.67 97

abl_6				79.06 22	89.68 17	92.14 7	77.70 52	69.68 26	86.87 12	71.88 18	74.29 28	80.06 14	76.56 14			88.84 13	95.82 9

mPP-MVS					86.96 35							70.61 41

NP-MVS									81.60 29

Patchmtry						78.06 131	67.53 131	43.18 204		41.40 132

DeepMVS_CX						19.81 232	17.01 230	10.02 230	23.61 218	5.85 230	17.21 220	8.03 233	21.13 215	22.60 228		21.42 232	30.01 226

High-resolution multi-view benchmark