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.

ESAPD		76.46 1	83.32 1	68.47 2	78.83 2	83.07 2	77.86 1	58.75 4	86.89 3	56.64 8	89.08 2	83.11 1	60.69 11	74.28 6	74.11 7	78.06 29	92.67 3

APDe-MVS		74.59 2	80.23 3	68.01 5	76.51 4	80.20 10	77.39 2	58.18 5	85.31 4	56.84 6	84.89 3	76.08 7	60.66 12	71.85 21	71.76 16	78.47 22	91.49 6

HSP-MVS		74.54 3	81.12 2	66.86 6	71.93 13	78.65 17	72.60 11	55.44 12	89.94 1	54.35 12	92.24 1	77.08 4	69.84 1	75.48 4	75.01 5	76.99 42	79.45 90

MCST-MVS		74.06 4	77.71 8	69.79 1	78.95 1	81.99 3	76.33 4	62.16 1	75.89 15	52.96 19	64.37 25	73.30 14	65.66 2	77.49 1	77.43 2	82.67 1	93.51 1

CNVR-MVS		73.87 5	78.60 5	68.35 4	73.32 8	81.97 4	76.19 5	59.29 3	80.12 8	56.70 7	67.09 19	76.48 5	64.26 3	75.88 3	75.75 4	80.32 7	92.93 2

CSCG		72.98 6	76.86 10	68.46 3	78.23 3	81.74 5	77.26 3	60.00 2	75.61 18	59.06 1	62.72 27	77.42 3	56.63 35	74.24 7	77.18 3	79.56 12	89.13 13

HPM-MVS++		72.44 7	78.73 4	65.11 8	71.88 14	77.31 25	71.98 14	55.67 10	83.11 6	53.59 16	75.90 8	78.49 2	61.00 10	73.99 8	73.31 11	76.55 45	88.97 14

APD-MVS		71.86 8	77.91 7	64.80 10	70.39 19	75.69 36	74.02 7	56.14 8	83.59 5	52.92 20	84.67 4	73.46 13	59.30 19	69.47 33	69.66 34	76.02 52	88.84 15
Yuesong Wang, Zhaojie Zeng and etc.: Adaptive Patch Deformation for Textureless-Resilient Multi-View Stereo. CVPR2023
ACMMP_Plus		71.50 9	77.27 9	64.77 11	69.64 21	79.26 11	73.53 8	54.73 18	79.32 10	54.23 13	74.81 9	74.61 11	59.40 18	73.00 10	72.17 14	77.10 41	87.72 21

NCCC		71.36 10	75.44 12	66.60 7	72.46 11	79.18 13	74.16 6	57.83 6	76.93 13	54.19 14	63.47 26	71.08 19	61.30 9	73.56 9	73.70 9	79.69 11	90.19 8

train_agg		70.74 11	76.53 11	63.98 13	70.33 20	75.16 38	72.33 13	55.78 9	75.74 16	50.41 28	80.08 7	73.15 15	57.75 27	71.96 20	70.94 26	77.25 40	88.69 17

TSAR-MVS + MP.		70.28 12	75.09 13	64.66 12	69.34 23	64.61 117	72.60 11	56.29 7	80.73 7	58.36 3	84.56 5	75.22 9	55.37 40	69.11 39	69.45 35	75.97 54	81.97 64

DeepPCF-MVS	62.48 1	70.07 13	78.36 6	60.39 37	62.38 53	76.96 28	65.54 48	52.23 27	87.46 2	49.07 29	74.05 11	76.19 6	59.01 21	72.79 14	71.61 18	74.13 106	89.49 10

SteuartSystems-ACMMP		69.78 14	74.76 14	63.98 13	73.45 7	78.56 18	73.13 10	55.24 15	70.68 28	48.93 31	70.43 15	69.10 21	54.00 46	72.78 16	72.98 12	79.14 16	88.74 16
Skip Steuart: Steuart Systems R&D Blog.
HFP-MVS		68.75 15	72.84 16	63.98 13	68.87 27	75.09 39	71.87 15	51.22 32	73.50 22	58.17 4	68.05 18	68.67 22	57.79 26	70.49 28	69.23 37	75.98 53	84.84 40

SD-MVS		68.30 16	72.58 18	63.31 19	69.24 24	67.85 89	70.81 20	53.65 23	79.64 9	58.52 2	74.31 10	75.37 8	53.52 52	65.63 61	63.56 101	74.13 106	81.73 71

MPTG		67.78 17	72.46 19	62.33 24	66.09 33	74.21 42	70.05 22	51.54 30	77.27 11	54.61 11	60.30 34	71.51 18	56.73 33	69.19 37	68.63 45	74.96 72	86.11 31

DELS-MVS		67.36 18	70.34 32	63.89 16	69.12 25	81.55 6	70.82 19	55.02 16	53.38 64	48.83 32	56.45 39	59.35 49	60.05 17	74.93 5	74.78 6	79.51 13	91.95 4
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
MP-MVS		67.34 19	73.08 15	60.64 34	66.20 32	76.62 30	69.22 26	50.92 34	70.07 29	48.81 33	69.66 16	70.12 20	53.68 49	68.41 44	69.13 39	74.98 71	87.53 22

DeepC-MVS	60.65 2	67.33 20	71.52 26	62.44 22	59.79 68	74.84 40	68.89 27	55.56 11	73.91 21	53.50 17	55.00 42	65.63 27	60.08 16	71.99 19	71.33 22	76.85 43	87.94 20
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
HQP-MVS		67.22 21	72.08 21	61.56 29	66.76 30	73.58 49	71.41 16	52.98 25	69.92 31	43.85 51	70.58 14	58.75 51	56.76 32	72.90 12	71.88 15	77.57 34	86.94 26

CANet		67.21 22	71.83 23	61.83 25	64.51 40	79.25 12	66.72 40	48.73 48	68.49 36	50.63 27	61.40 30	66.47 25	61.44 7	69.31 36	69.90 31	78.94 19	88.00 19

CDPH-MVS		67.03 23	71.64 24	61.65 28	69.10 26	76.84 29	71.35 18	55.42 13	67.02 39	42.83 55	65.27 23	64.60 31	53.16 55	69.70 32	71.40 20	78.02 30	86.67 27

MAR-MVS		66.85 24	69.81 33	63.39 18	73.56 6	80.51 9	69.87 23	51.51 31	67.78 38	46.44 38	51.09 53	61.60 44	60.38 13	72.67 17	73.61 10	78.59 20	81.44 75
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
DeepC-MVS_fast	60.18 3	66.84 25	70.69 30	62.36 23	62.76 48	73.21 52	67.96 30	52.31 26	72.26 25	51.03 22	56.50 38	64.26 32	63.37 4	71.64 22	70.85 27	76.70 44	86.10 32
Andreas Kuhn, Christian Sormann, Mattia Rossi, Oliver Erdler, Friedrich Fraundorfer: DeepC-MVS: Deep Confidence Prediction for Multi-View Stereo Reconstruction. 3DV 2020
TSAR-MVS + GP.		66.77 26	72.21 20	60.44 36	61.23 59	70.00 68	64.26 54	47.79 58	72.98 23	56.32 9	71.35 13	72.33 16	55.68 38	65.49 62	66.66 56	77.35 36	86.62 28

MVS_0304		66.31 27	71.61 25	60.14 38	62.59 52	78.98 15	67.13 37	45.75 86	64.35 44	45.23 46	60.69 32	67.67 24	61.73 6	71.09 24	71.03 24	78.41 25	87.44 23

ACMMPR		66.20 28	71.51 27	60.00 39	65.34 37	74.04 44	69.39 25	50.92 34	71.97 26	46.04 41	66.79 20	65.68 26	53.07 56	68.93 41	69.12 40	75.21 65	84.05 47

3Dnovator	58.39 4	65.97 29	66.85 42	64.94 9	73.72 5	79.03 14	67.73 32	54.25 19	61.52 47	52.79 21	42.27 75	60.73 47	62.01 5	71.29 23	71.75 17	79.12 17	81.34 79

TSAR-MVS + ACMM		65.95 30	72.83 17	57.93 50	69.35 22	65.85 106	73.36 9	39.84 152	76.00 14	48.69 34	82.54 6	75.03 10	49.38 86	65.33 64	63.42 103	66.94 175	81.67 72

canonicalmvs		65.55 31	70.75 29	59.49 42	62.11 55	78.26 20	66.52 41	43.82 117	71.54 27	47.84 36	61.30 31	61.68 42	58.48 24	67.56 50	69.67 33	78.16 28	85.25 38

QAPM		65.47 32	67.82 37	62.72 21	72.56 9	81.17 8	67.43 35	55.38 14	56.07 60	43.29 53	43.60 72	65.38 29	59.10 20	72.20 18	70.76 28	78.56 21	85.59 36

PGM-MVS		65.35 33	70.43 31	59.43 43	65.78 35	73.75 46	69.41 24	48.18 55	68.80 35	45.37 45	65.88 22	64.04 33	52.68 61	68.94 40	68.68 44	75.18 66	82.93 53

PHI-MVS		65.17 34	72.07 22	57.11 58	63.02 47	77.35 24	67.04 38	48.14 57	68.03 37	37.56 77	66.00 21	65.39 28	53.19 54	70.68 25	70.57 30	73.72 112	86.46 30

CLD-MVS		64.69 35	67.25 38	61.69 27	68.22 29	78.33 19	63.09 56	47.59 62	69.64 32	53.98 15	54.87 43	53.94 61	57.87 25	72.79 14	71.34 21	79.40 14	69.87 158
Zhaoxin Li, Wangmeng Zuo, Zhaoqi Wang, Lei Zhang: Confidence-based Large-scale Dense Multi-view Stereo. IEEE Transaction on Image Processing, 2020
MVS_111021_HR		64.66 36	67.11 41	61.80 26	71.04 15	77.91 21	62.75 59	54.78 17	51.43 68	47.54 37	53.77 46	54.85 58	56.84 30	70.59 26	71.50 19	77.86 31	89.70 9

EPNet		64.39 37	70.93 28	56.77 60	60.58 64	75.77 33	59.28 88	50.58 37	69.93 30	40.73 69	68.59 17	61.60 44	53.72 47	68.65 42	68.07 47	75.75 58	83.87 49
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
CP-MVS		64.37 38	69.48 34	58.39 46	62.21 54	71.81 60	67.27 36	49.51 43	69.40 34	45.76 43	60.41 33	64.96 30	51.84 63	67.33 54	67.57 51	73.78 111	84.89 39

MVS_Test		63.75 39	67.24 39	59.68 41	60.01 65	76.99 27	68.13 29	45.17 91	57.45 54	43.74 52	53.07 47	56.16 57	61.33 8	70.27 29	71.11 23	79.72 10	85.63 35

X-MVS		63.53 40	68.62 35	57.60 53	64.77 39	73.06 53	65.82 45	50.53 38	65.77 41	42.02 64	58.20 36	63.42 36	47.83 105	68.25 48	68.50 46	74.61 88	83.16 52

ACMMP		63.27 41	67.85 36	57.93 50	62.64 51	72.30 58	68.23 28	48.77 47	66.50 40	43.05 54	62.07 28	57.84 54	49.98 72	66.58 57	66.46 60	74.93 76	83.17 51
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
AdaColmap		62.79 42	62.63 57	62.98 20	70.82 16	72.90 56	67.84 31	54.09 21	65.14 42	50.71 25	41.78 76	47.64 84	60.17 15	67.41 53	66.83 54	74.28 101	76.69 104

3Dnovator+	55.76 7	62.70 43	65.10 49	59.90 40	65.89 34	72.15 59	62.94 58	49.82 42	62.77 46	49.06 30	43.62 71	61.47 46	58.60 23	68.51 43	66.75 55	73.08 126	80.40 87

OpenMVS	55.62 8	62.57 44	63.76 53	61.19 31	72.13 12	78.84 16	64.42 52	50.51 39	56.44 57	45.67 44	36.88 103	56.51 55	56.66 34	68.28 47	68.96 41	77.73 33	80.44 86

PVSNet_BlendedMVS		62.53 45	66.37 44	58.05 48	58.17 73	75.70 34	61.30 64	48.67 50	58.67 51	50.93 23	55.43 40	49.39 74	53.01 57	69.46 34	66.55 57	76.24 50	89.39 11

PVSNet_Blended		62.53 45	66.37 44	58.05 48	58.17 73	75.70 34	61.30 64	48.67 50	58.67 51	50.93 23	55.43 40	49.39 74	53.01 57	69.46 34	66.55 57	76.24 50	89.39 11

MVSTER		62.51 47	67.22 40	57.02 59	55.05 97	69.23 82	63.02 57	46.88 76	61.11 49	43.95 50	59.20 35	58.86 50	56.80 31	69.13 38	70.98 25	76.41 47	82.04 58

CHOSEN 1792x2688		62.48 48	64.06 52	60.64 34	72.50 10	84.18 1	62.43 60	53.77 22	47.90 79	39.85 70	25.15 193	44.76 96	53.72 47	77.29 2	77.61 1	81.60 4	91.53 5

CostFormer		62.45 49	65.68 46	58.67 44	63.29 44	77.65 22	67.62 33	38.42 164	54.04 62	46.00 42	48.27 61	57.89 53	56.97 29	67.03 56	67.79 50	79.74 9	87.09 25

PCF-MVS	55.99 6	62.31 50	66.60 43	57.32 56	59.12 72	73.68 48	67.53 34	48.71 49	61.35 48	42.83 55	51.33 52	63.48 35	53.48 53	65.64 60	64.87 70	72.22 133	85.83 33
Andreas Kuhn, Shan Lin, Oliver Erdler: Plane Completion and Filtering for Multi-View Stereo Reconstruction. GCPR 2019
DI_MVS_plusplus_trai		61.86 51	65.26 47	57.90 52	57.93 76	74.51 41	66.30 42	46.49 81	49.96 72	41.62 67	42.69 73	61.77 41	58.74 22	70.25 30	69.32 36	76.31 48	88.30 18

MSLP-MVS++		61.81 52	62.19 62	61.37 30	68.33 28	63.08 135	70.75 21	38.89 161	63.96 45	57.51 5	48.59 59	61.66 43	53.67 50	62.04 112	59.92 148	79.03 18	76.08 107

OPM-MVS		61.59 53	62.30 61	60.76 33	66.53 31	73.35 50	71.41 16	54.18 20	40.82 101	41.57 68	45.70 67	54.84 59	54.43 45	69.92 31	69.19 38	76.45 46	82.25 56

MS-PatchMatch		61.41 54	61.88 63	60.85 32	70.57 18	75.98 32	66.29 43	46.91 75	50.56 70	48.28 35	36.30 110	51.64 64	50.95 67	72.89 13	70.65 29	82.13 3	75.17 121

DWT-MVSNet_training		61.22 55	63.52 55	58.53 45	65.00 38	76.55 31	59.50 86	48.22 54	51.79 67	42.14 63	47.85 62	50.21 70	55.46 39	66.16 58	67.92 49	80.85 5	84.14 46

diffmvs		61.00 56	65.21 48	56.08 63	55.72 94	74.18 43	64.62 51	43.25 123	56.71 55	42.71 59	50.01 56	62.20 40	54.45 43	64.56 71	64.30 95	75.75 58	84.53 44

ACMP	56.21 5	59.78 57	61.81 65	57.41 55	61.15 60	68.88 84	65.98 44	48.85 46	58.56 53	44.19 49	48.89 58	46.31 90	48.56 97	63.61 96	64.49 92	75.75 58	81.91 65
Qingshan Xu and Wenbing Tao: Planar Prior Assisted PatchMatch Multi-View Stereo. AAAI 2020
tpmp4_e23		59.70 58	61.03 67	58.14 47	63.70 41	73.33 51	65.69 46	39.53 154	52.56 65	46.23 40	41.59 77	47.46 85	57.38 28	65.01 66	65.89 62	76.31 48	81.36 78

LGP-MVS_train		59.69 59	62.59 58	56.31 62	61.94 56	68.15 88	66.90 39	48.15 56	59.75 50	38.47 73	50.38 55	48.34 82	46.87 110	65.39 63	64.93 69	75.51 63	81.21 81

Effi-MVS+		59.63 60	61.78 66	57.12 57	61.56 57	71.63 61	63.61 55	47.59 62	47.18 80	37.79 74	45.29 68	49.93 72	56.27 36	67.45 51	67.06 53	75.91 55	83.93 48

CPTT-MVS		59.54 61	64.47 51	53.79 70	54.99 99	67.63 92	65.48 49	44.59 107	64.81 43	37.74 75	51.55 50	59.90 48	49.77 77	61.83 114	61.26 133	70.18 152	84.31 45

PVSNet_Blended_VisFu		58.56 62	62.33 60	54.16 67	56.90 78	73.92 45	57.72 94	46.16 84	44.23 85	42.73 58	46.26 64	51.06 68	46.28 113	67.99 49	65.38 66	75.18 66	87.44 23

ACMM	53.73 9	57.91 63	58.27 79	57.49 54	63.10 45	66.45 100	65.65 47	49.02 45	53.69 63	42.67 60	36.41 107	46.07 92	50.38 69	64.74 69	64.63 81	74.14 105	75.91 111
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
CANet_DTU		57.87 64	63.63 54	51.15 98	52.18 114	70.20 67	58.14 93	37.32 170	56.49 56	31.06 119	57.38 37	50.05 71	53.67 50	64.98 67	65.04 68	74.57 89	81.29 80

tpm cat1		57.41 65	58.26 80	56.42 61	60.80 62	72.56 57	64.35 53	38.43 163	49.18 76	46.36 39	36.69 105	43.50 98	54.47 42	61.39 120	62.64 112	74.11 108	81.81 69

IB-MVS	53.15 10	57.33 66	59.02 72	55.37 64	60.83 61	77.11 26	54.51 117	50.10 41	43.22 88	42.82 57	40.50 82	37.61 117	44.67 125	59.27 148	69.81 32	79.29 15	85.59 36
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
tpmrst		57.23 67	59.08 71	55.06 65	59.91 66	70.65 65	60.71 67	35.38 185	47.91 78	42.58 61	39.78 86	45.45 94	54.44 44	62.19 109	62.82 108	77.37 35	84.73 41

HyFIR lowres test		57.12 68	59.11 70	54.80 66	61.55 58	77.55 23	59.02 89	45.00 93	41.84 99	33.93 98	22.44 200	49.16 77	51.02 66	68.39 45	68.71 43	78.26 27	85.70 34

MVS_111021_LR		57.06 69	60.60 68	52.93 71	56.25 84	65.14 112	55.16 115	41.21 145	52.32 66	44.89 47	53.92 45	49.27 76	52.16 62	61.46 118	60.54 142	67.92 164	81.53 74

PMMVS		55.74 70	62.68 56	47.64 129	44.34 178	65.58 110	47.22 157	37.96 166	56.43 58	34.11 95	61.51 29	47.41 86	54.55 41	65.88 59	62.49 116	67.67 167	79.48 89

Fast-Effi-MVS+		55.73 71	58.26 80	52.76 76	54.33 102	68.19 87	57.05 98	34.66 187	46.92 81	38.96 72	40.53 81	41.55 106	55.69 37	65.31 65	65.99 61	75.90 56	79.34 91

FC-MVSNet-train		55.68 72	57.00 88	54.13 68	63.37 42	66.16 102	46.77 160	52.14 28	42.36 94	37.67 76	48.50 60	41.42 108	51.28 65	61.58 117	63.22 105	73.56 114	75.76 113

FMVSNet3		55.66 73	59.68 69	50.96 100	50.59 145	66.49 99	57.57 95	46.61 77	49.30 73	28.77 128	39.61 87	51.42 65	43.85 127	68.29 46	68.80 42	78.35 26	73.86 125

OMC-MVS		55.48 74	61.85 64	48.04 128	41.55 187	60.32 163	56.80 102	31.78 210	75.67 17	42.30 62	51.52 51	54.15 60	49.91 74	60.28 137	57.59 159	65.91 178	73.42 127

tpm		54.94 75	57.86 85	51.54 95	59.48 70	67.04 95	58.34 92	34.60 189	41.93 98	34.41 91	42.40 74	47.14 87	49.07 89	61.46 118	61.67 126	73.31 121	83.39 50

GBi-Net		54.66 76	58.42 77	50.26 108	49.36 152	65.81 107	56.80 102	46.61 77	49.30 73	28.77 128	39.61 87	51.42 65	42.71 130	64.25 76	65.54 63	77.32 37	73.03 131

test1		54.66 76	58.42 77	50.26 108	49.36 152	65.81 107	56.80 102	46.61 77	49.30 73	28.77 128	39.61 87	51.42 65	42.71 130	64.25 76	65.54 63	77.32 37	73.03 131

test-LLR		54.62 78	58.66 75	49.89 114	51.68 134	65.89 104	47.88 151	46.35 82	42.51 91	29.84 125	41.41 78	48.87 78	45.20 118	62.91 104	64.43 93	78.43 23	84.62 42

TSAR-MVS + COLMAP		54.37 79	62.43 59	44.98 144	34.33 212	58.94 171	54.11 120	34.15 198	74.06 20	34.57 90	71.63 12	42.03 105	47.88 104	61.26 122	57.33 164	64.83 183	71.74 141

v1neww		54.32 80	55.60 97	52.81 73	52.11 116	69.43 79	60.57 71	44.86 96	37.13 125	35.34 86	34.95 119	37.46 123	49.53 82	63.69 89	64.59 82	74.47 92	81.99 60

v7new		54.32 80	55.60 97	52.81 73	52.11 116	69.43 79	60.57 71	44.86 96	37.13 125	35.34 86	34.95 119	37.46 123	49.53 82	63.69 89	64.59 82	74.47 92	81.99 60

v6		54.32 80	55.61 96	52.82 72	52.11 116	69.44 78	60.57 71	44.86 96	37.15 124	35.40 84	34.96 117	37.47 122	49.52 84	63.68 92	64.59 82	74.47 92	81.99 60

EPMVS		54.07 83	56.06 91	51.75 92	56.74 80	70.80 63	55.32 113	34.20 195	46.46 82	36.59 81	40.38 84	42.55 100	49.77 77	61.25 123	60.90 137	77.86 31	70.08 154

v2v482		54.00 84	55.12 101	52.69 79	51.73 132	69.42 81	60.65 69	45.09 92	34.56 142	33.73 102	35.29 113	35.36 142	49.92 73	64.05 80	65.16 67	75.00 70	81.98 63

CNLPA		54.00 84	57.08 87	50.40 107	49.83 150	61.75 152	53.47 123	37.27 171	74.55 19	44.85 48	33.58 142	45.42 95	52.94 60	58.89 150	53.66 185	64.06 185	71.68 142

v7		53.99 86	55.20 100	52.57 81	52.08 119	69.46 77	59.68 82	44.92 95	35.90 131	34.33 92	33.99 136	35.55 140	50.08 71	64.38 75	64.67 78	74.32 97	82.47 54

FMVSNet2		53.94 87	57.29 86	50.03 111	49.36 152	65.81 107	56.80 102	45.95 85	43.13 89	28.04 132	35.68 111	48.18 83	42.71 130	67.23 55	67.95 48	77.32 37	73.03 131

v1141		53.78 88	55.02 104	52.33 84	51.89 124	69.75 72	60.24 76	44.81 101	34.04 150	33.34 106	34.57 126	36.17 137	48.76 92	63.90 81	64.81 73	74.94 73	81.87 68

divwei89l23v2f112		53.78 88	55.02 104	52.33 84	51.89 124	69.74 74	60.25 75	44.82 100	34.06 148	33.33 107	34.56 129	36.18 135	48.75 93	63.90 81	64.82 71	74.94 73	81.88 66

v1		53.78 88	55.02 104	52.34 83	51.89 124	69.75 72	60.24 76	44.81 101	34.05 149	33.39 105	34.57 126	36.18 135	48.75 93	63.90 81	64.82 71	74.94 73	81.88 66

v8		53.77 91	54.82 108	52.54 82	52.12 115	66.95 98	60.56 74	43.23 124	37.17 123	35.35 85	34.96 117	37.50 121	49.51 85	63.67 94	64.59 82	74.48 91	78.91 94

GA-MVS		53.77 91	56.41 90	50.70 102	51.63 136	69.96 69	57.55 96	44.39 108	34.31 144	27.15 134	40.99 80	36.40 129	47.65 107	67.45 51	67.16 52	75.83 57	78.60 95

Effi-MVS+-dtu		53.63 93	54.85 107	52.20 87	59.32 71	61.33 156	56.42 108	40.24 150	43.84 87	34.22 94	39.49 91	46.18 91	53.00 59	58.72 153	57.49 161	69.99 154	76.91 101

v18		53.63 93	54.35 111	52.80 75	52.25 111	62.94 137	60.80 66	42.78 131	39.23 106	36.81 79	35.07 116	37.78 116	49.82 75	63.69 89	64.65 80	74.32 97	77.07 100

v16		53.50 95	54.17 112	52.73 78	52.24 112	62.90 138	60.67 68	42.67 134	38.72 109	36.70 80	34.84 122	37.59 119	49.69 79	63.72 86	64.68 76	74.39 95	76.72 102

v1144		53.47 96	54.65 109	52.10 88	51.93 121	69.81 70	59.32 87	44.77 104	33.21 158	32.52 111	33.55 143	34.34 152	49.29 87	64.58 70	64.81 73	74.74 81	82.27 55

v10		53.44 97	54.40 110	52.31 86	52.08 119	66.99 96	59.68 82	43.41 120	35.90 131	34.30 93	33.98 137	35.56 139	50.10 70	64.39 74	64.67 78	74.32 97	79.30 92

v17		53.40 98	54.09 113	52.60 80	52.22 113	62.90 138	60.64 70	42.66 135	38.24 112	36.04 82	34.85 121	37.59 119	49.63 80	63.70 88	64.68 76	74.39 95	76.70 103

PatchmatchNet		53.37 99	55.62 95	50.75 101	55.93 92	70.54 66	51.39 137	36.41 174	44.85 83	37.26 78	39.40 93	42.54 101	47.83 105	60.29 136	60.88 139	75.69 61	70.87 148
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Pablo Speciale, Marc Pollefeys: PatchmatchNet: Learned Multi-View Patchmatch Stereo.
IterMVS-LS		53.36 100	55.65 94	50.68 104	55.34 95	59.04 169	55.00 116	39.98 151	38.72 109	33.22 109	44.52 70	47.05 88	49.63 80	61.82 115	61.77 123	70.92 146	76.61 106
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
TESTMET0.1,1		53.30 101	58.66 75	47.04 131	44.94 172	65.89 104	47.88 151	35.95 179	42.51 91	29.84 125	41.41 78	48.87 78	45.20 118	62.91 104	64.43 93	78.43 23	84.62 42

MDTV_nov1_ep13		52.99 102	55.59 99	49.95 113	54.08 104	70.69 64	56.47 107	38.42 164	42.78 90	30.19 124	39.56 90	43.31 99	45.78 115	60.07 141	62.11 120	74.74 81	70.62 149

EPP-MVSNet		52.91 103	58.91 73	45.91 137	54.99 99	68.84 85	49.27 143	42.71 132	37.53 117	20.20 169	46.09 65	56.19 56	36.90 147	61.37 121	60.90 137	71.41 142	81.41 76

dps		52.84 104	52.92 128	52.74 77	59.89 67	69.49 76	54.47 118	37.38 169	42.49 93	39.53 71	35.33 112	32.71 160	51.83 64	60.45 134	61.12 134	73.33 120	68.86 162

v1192		52.69 105	53.86 116	51.31 97	51.22 138	69.76 71	57.37 97	44.39 108	32.21 162	31.39 118	32.41 149	32.44 164	49.19 88	64.25 76	64.17 96	74.31 100	81.81 69

V42		52.63 106	55.08 102	49.76 116	44.93 173	67.49 94	60.19 78	42.13 138	37.21 122	34.08 96	34.57 126	37.30 125	47.29 108	63.48 99	64.15 97	69.96 155	81.38 77

v15		52.60 107	53.17 122	51.94 89	51.92 122	62.76 140	60.04 79	42.19 137	34.35 143	33.96 97	34.37 131	36.40 129	48.73 95	63.75 85	64.59 82	74.79 77	75.97 110

MSDG		52.58 108	51.40 145	53.95 69	65.48 36	64.31 127	61.44 63	44.02 114	44.17 86	32.92 110	30.40 167	31.81 170	46.35 112	62.13 110	62.55 114	73.49 116	64.41 172

V14		52.51 109	53.06 124	51.87 90	51.91 123	62.70 142	59.93 80	42.13 138	34.16 146	33.88 99	34.22 133	36.35 132	48.62 96	63.72 86	64.59 82	74.77 78	75.69 118

Fast-Effi-MVS+-dtu		52.47 110	55.89 92	48.48 126	56.25 84	65.07 113	58.75 91	23.79 224	41.27 100	27.07 136	37.95 97	41.34 109	50.85 68	62.90 106	62.34 118	74.17 104	80.37 88

v144192		52.43 111	53.63 118	51.03 99	51.06 139	69.60 75	56.94 100	44.84 99	32.15 163	30.88 120	32.45 148	32.71 160	48.36 99	62.98 103	63.52 102	74.10 109	82.02 59

V9		52.42 112	52.94 127	51.81 91	51.89 124	62.64 144	59.81 81	42.08 140	33.97 151	33.85 100	34.05 135	36.28 133	48.49 98	63.68 92	64.59 82	74.76 79	75.41 119

thres100view900		52.33 113	53.91 115	50.48 106	56.10 86	67.79 90	56.18 110	49.18 44	35.86 134	25.22 142	34.74 123	34.10 153	42.41 133	64.45 73	62.62 113	73.81 110	77.85 96

v12		52.31 114	52.81 130	51.72 93	51.87 128	62.56 145	59.66 84	42.02 141	33.77 153	33.70 103	33.88 138	36.20 134	48.36 99	63.64 95	64.56 89	74.73 84	75.08 122

v11		52.23 115	52.83 129	51.53 96	51.73 132	62.49 146	58.82 90	41.81 143	33.53 156	33.23 108	33.73 140	35.10 145	49.07 89	64.49 72	64.71 75	74.49 90	75.75 114

v13		52.22 116	52.69 132	51.67 94	51.85 130	62.48 147	59.57 85	41.97 142	33.61 155	33.69 104	33.71 141	36.11 138	48.23 102	63.57 97	64.55 90	74.72 85	74.77 123

v1921920		51.95 117	53.19 121	50.51 105	50.82 144	69.14 83	55.45 112	44.34 112	31.53 168	30.53 122	31.96 152	31.67 171	48.31 101	63.12 101	63.28 104	73.59 113	81.60 73

v148		51.72 118	53.15 123	50.05 110	50.15 148	67.51 93	56.98 99	42.85 129	32.60 161	32.41 113	33.88 138	34.71 149	44.45 126	61.06 124	63.00 107	73.45 117	79.24 93

TAPA-MVS	47.92 11	51.66 119	57.88 84	44.40 148	36.46 206	58.42 178	53.82 122	30.83 212	69.51 33	34.97 89	46.90 63	49.67 73	46.99 109	58.00 156	54.64 182	63.33 191	68.00 165
Andrea Romanoni, Matteo Matteucci: TAPA-MVS: Textureless-Aware PAtchMatch Multi-View Stereo. ICCV 2019
conf0.002		51.63 120	53.79 117	49.12 120	56.33 82	64.84 115	53.05 128	47.38 66	35.86 134	24.83 144	37.86 98	38.15 114	41.08 135	61.04 126	62.70 109	72.05 134	76.06 108

IS_MVSNet		51.53 121	57.98 83	44.01 152	55.96 91	66.16 102	47.65 153	42.84 130	39.82 104	19.09 176	44.97 69	50.28 69	27.20 186	63.43 100	63.84 98	71.33 143	77.33 98

v1240		51.42 122	52.66 134	49.97 112	50.31 147	68.70 86	54.05 121	43.85 116	30.78 173	30.22 123	31.43 156	31.03 180	47.98 103	62.62 108	63.16 106	73.40 118	80.93 83

conf0.01		51.32 123	53.22 120	49.11 121	56.29 83	64.78 116	53.05 128	47.37 67	35.86 134	24.83 144	36.85 104	36.64 128	41.08 135	61.01 127	61.37 128	72.03 135	76.01 109

pmmvs4		51.28 124	52.50 136	49.85 115	49.54 151	63.02 136	52.83 133	43.41 120	44.65 84	35.71 83	34.38 130	32.25 165	45.14 121	60.21 140	60.03 146	72.44 132	72.98 134

Vis-MVSNet		51.13 125	58.04 82	43.06 161	47.68 160	67.71 91	49.10 146	39.09 160	37.75 115	22.57 159	51.03 54	48.78 80	32.42 166	62.12 111	61.80 122	67.49 169	77.12 99
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang: Visibility-aware Multiview Stereo Network. BMVC 2020
UGNet		51.04 126	58.79 74	42.00 167	40.59 189	65.32 111	46.65 162	39.26 158	39.90 103	27.30 133	54.12 44	52.03 63	30.93 171	59.85 144	59.62 150	67.23 171	80.70 84
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
tfpn111		51.00 127	52.68 133	49.04 123	56.10 86	64.52 122	53.05 128	47.31 70	35.86 134	24.79 147	36.35 108	34.10 153	41.08 135	60.84 129	61.37 128	71.90 138	75.70 115

tfpn200view9		50.91 128	52.45 137	49.11 121	56.10 86	64.53 120	53.06 127	47.31 70	35.86 134	25.22 142	34.74 123	34.10 153	41.08 135	60.84 129	61.37 128	71.90 138	75.70 115

conf200view11		50.87 129	52.45 137	49.04 123	56.10 86	64.52 122	53.05 128	47.31 70	35.86 134	24.79 147	34.74 123	34.10 153	41.08 135	60.84 129	61.37 128	71.90 138	75.70 115

gg-mvs-nofinetune		50.82 130	55.83 93	44.97 145	60.63 63	75.69 36	53.40 124	34.48 191	20.05 216	6.93 213	18.27 209	52.70 62	33.57 158	70.50 27	72.93 13	80.84 6	80.68 85

thres200		50.76 131	52.52 135	48.70 125	55.98 90	64.60 118	55.29 114	47.34 68	33.91 152	24.36 151	34.33 132	33.90 157	37.27 145	60.84 129	62.41 117	71.99 136	77.63 97

thres400		50.39 132	52.22 139	48.26 127	55.02 98	66.32 101	52.97 132	48.33 53	32.68 160	22.94 157	33.21 145	33.38 159	37.27 145	62.74 107	61.38 127	73.04 127	75.81 112

EG-PatchMatch MVS		50.23 133	50.89 148	49.47 117	59.54 69	70.88 62	52.46 134	44.01 115	26.22 197	31.91 114	24.97 194	31.45 173	33.48 160	64.79 68	66.51 59	75.40 64	71.39 143

IterMVS		50.23 133	53.27 119	46.68 133	47.59 162	60.58 161	53.10 126	36.62 173	36.07 130	25.89 139	39.42 92	40.05 112	43.65 128	60.22 139	61.35 132	73.23 122	75.23 120
Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys: IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo.
FMVSNet1		50.14 135	52.78 131	47.06 130	45.56 169	63.56 131	54.22 119	43.74 118	34.10 147	25.37 141	29.79 174	42.06 104	38.70 141	64.25 76	65.54 63	74.75 80	70.18 153

ACMH	47.82 13	50.10 136	49.60 157	50.69 103	63.36 43	66.99 96	56.83 101	52.13 29	31.06 171	17.74 184	28.22 180	26.24 199	45.17 120	60.88 128	63.80 99	68.91 160	70.00 156
Qingshan Xu and Wenbing Tao: Multi-Scale Geometric Consistency Guided Multi-View Stereo. CVPR 2019
EPNet_dtu		49.85 137	56.99 89	41.52 171	52.79 108	57.06 180	41.44 179	43.13 125	56.13 59	19.24 175	52.11 48	48.38 81	22.14 196	58.19 155	58.38 156	70.35 150	68.71 163
Wanjuan Su, Wenbing Tao: Efficient Edge-Preserving Multi-View Stereo Network for Depth Estimation. AAAI 2023
LS3D		49.59 138	49.75 156	49.40 119	55.88 93	59.86 167	56.31 109	45.33 89	48.57 77	28.32 131	31.54 154	36.81 127	46.27 114	57.17 162	55.88 174	64.29 184	58.42 191

UniMVSNet_NR-MVSNet		49.56 139	53.04 125	45.49 140	51.59 137	64.42 126	46.97 158	51.01 33	37.87 113	16.42 186	39.87 85	34.91 147	33.43 162	59.59 145	62.70 109	73.52 115	71.94 136

CDS-MVSNet		49.25 140	53.97 114	43.75 154	47.53 163	64.53 120	48.59 147	42.27 136	33.77 153	26.64 137	40.46 83	42.26 103	30.01 174	61.77 116	61.71 124	67.48 170	73.28 130
Khang Truong Giang, Soohwan Song, Sungho Jo: Curvature-guided dynamic scale networks for Multi-view Stereo. ICLR 2022
PLC	44.22 14	49.14 141	51.75 142	46.10 136	42.78 185	55.60 187	53.11 125	34.46 192	55.69 61	32.47 112	34.16 134	41.45 107	48.91 91	57.13 163	54.09 183	64.84 182	64.10 173
Jie Liao, Yanping Fu, Qingan Yan, Chunxia xiao: Pyramid Multi-View Stereo with Local Consistency. Pacific Graphics 2019
ACMH+	47.85 12	49.13 142	48.86 164	49.44 118	56.75 79	62.01 151	56.62 106	47.55 64	37.49 118	23.98 153	26.68 184	29.46 189	43.12 129	57.45 161	58.85 154	68.62 162	70.05 155

view600		48.85 143	50.58 152	46.82 132	54.19 103	64.94 114	50.81 138	47.53 65	31.78 166	21.59 163	32.31 150	32.63 162	34.28 155	61.06 124	57.41 162	72.54 130	73.96 124

NR-MVSNet		48.84 144	51.76 141	45.44 141	57.66 77	60.64 159	47.39 154	47.63 60	37.26 119	13.31 193	37.31 100	29.64 188	33.53 159	63.52 98	62.09 121	73.10 125	71.89 139

CR-MVSNet		48.82 145	51.85 140	45.29 142	46.74 165	55.95 183	52.06 135	34.21 193	42.17 95	31.74 115	32.92 147	42.53 102	45.00 122	58.80 151	61.11 135	61.99 196	69.47 159

thres600view7		48.44 146	50.23 153	46.35 135	54.05 105	64.60 118	50.18 141	47.34 68	31.73 167	20.74 166	32.28 151	32.62 163	33.79 157	60.84 129	56.11 172	71.99 136	73.40 128

test-mter		48.31 147	55.04 103	40.45 174	34.12 213	59.02 170	41.77 178	28.05 216	38.43 111	22.67 158	39.35 94	44.40 97	41.88 134	60.30 135	61.68 125	74.20 102	82.12 57

PatchT		48.11 148	51.27 147	44.43 147	50.13 149	61.58 153	33.59 197	32.92 204	40.38 102	31.74 115	30.60 166	36.93 126	45.00 122	58.80 151	61.11 135	73.19 123	69.47 159

TranMVSNet+NR-MVSNet		48.06 149	51.36 146	44.21 150	50.38 146	62.09 150	47.28 155	50.88 36	36.11 129	13.25 194	37.51 99	31.60 172	30.70 172	59.34 147	62.53 115	72.81 128	70.31 151

thresconf0.02		47.89 150	50.76 151	44.54 146	53.86 106	63.96 129	46.23 164	47.72 59	33.00 159	17.08 185	36.35 108	37.80 115	29.86 176	60.01 143	60.57 141	72.49 131	63.62 177

view800		47.68 151	49.78 155	45.24 143	53.39 107	63.19 134	48.13 150	46.57 80	30.98 172	20.25 168	31.52 155	31.90 169	31.52 168	59.37 146	59.61 151	71.56 141	71.89 139

TransMVSNet (Re)		47.46 152	48.94 163	45.74 139	57.96 75	64.29 128	48.26 148	48.47 52	26.33 196	19.33 173	29.45 177	31.28 178	25.31 190	63.05 102	62.70 109	75.10 69	65.47 169

DU-MVS		47.33 153	50.86 149	43.20 159	44.43 176	60.64 159	46.97 158	47.63 60	37.26 119	16.42 186	37.31 100	31.39 174	33.43 162	57.53 159	59.98 147	70.35 150	71.94 136

v7n		47.22 154	48.38 165	45.87 138	48.20 159	63.58 130	50.69 139	40.93 148	26.60 195	26.44 138	26.52 185	29.65 187	38.19 143	58.22 154	60.23 145	70.79 147	73.83 126

UA-Net		47.19 155	53.02 126	40.38 175	55.31 96	60.02 166	38.41 186	38.68 162	36.42 128	22.47 161	51.95 49	58.72 52	25.62 189	54.11 178	53.40 187	61.79 197	56.51 195

Baseline_NR-MVSNet		47.14 156	50.83 150	42.84 163	44.43 176	63.31 133	44.50 171	50.36 40	37.71 116	11.25 200	30.84 163	32.09 166	30.96 170	57.53 159	63.73 100	75.53 62	70.60 150

pmmvs5		47.02 157	50.02 154	43.51 156	43.48 183	62.65 143	47.24 156	37.78 168	30.59 175	24.80 146	35.26 114	30.43 184	34.36 154	59.05 149	60.28 144	73.40 118	71.92 138

UniMVSNet (Re)		46.89 158	51.65 144	41.34 172	45.60 168	62.71 141	44.05 172	47.10 74	37.24 121	13.55 192	36.90 102	34.54 151	26.76 187	57.56 158	59.90 149	70.98 145	72.69 135

tfpn		46.62 159	49.07 162	43.75 154	52.70 109	61.49 155	45.65 166	45.68 87	30.25 177	18.84 179	30.87 162	33.67 158	29.22 181	57.80 157	59.49 152	70.44 149	69.95 157

tfpnnormal		46.61 160	46.82 173	46.37 134	52.70 109	62.31 148	50.39 140	47.17 73	25.74 200	21.80 162	23.13 198	24.15 208	33.45 161	60.28 137	60.77 140	72.70 129	71.39 143

tfpn_ndepth		46.53 161	49.41 159	43.18 160	54.66 101	61.56 154	42.25 176	45.66 88	35.68 140	18.31 182	36.55 106	34.84 148	28.88 182	55.45 173	57.01 167	69.32 158	64.78 171

pm-mvs1		46.14 162	49.34 161	42.41 164	48.93 155	62.22 149	44.98 169	42.68 133	27.66 188	20.76 165	29.88 173	34.96 146	26.41 188	60.03 142	60.42 143	70.70 148	70.20 152

MIMVSNet		45.62 163	49.56 158	41.02 173	38.17 196	64.43 125	49.48 142	35.43 184	36.53 127	20.06 171	22.58 199	35.16 144	28.75 184	61.97 113	62.20 119	74.20 102	64.07 174

gm-plane-assit		45.41 164	48.03 167	42.34 165	56.49 81	40.48 215	24.54 220	34.15 198	14.44 225	6.59 214	17.82 210	35.32 143	49.82 75	72.93 11	74.11 7	82.47 2	81.12 82

ADS-MVSNet		45.39 165	46.42 176	44.19 151	48.74 158	57.52 179	43.91 173	31.93 208	35.89 133	27.11 135	30.12 169	32.06 167	45.30 116	53.13 187	55.19 178	68.15 163	61.07 183

conf0.05thres1000		45.26 166	46.99 171	43.24 158	51.87 128	60.52 162	45.17 168	45.24 90	27.06 192	18.60 180	26.24 186	31.23 179	28.82 183	56.88 165	58.52 155	69.71 156	68.50 164

v748		44.90 167	46.14 178	43.46 157	45.37 171	60.89 158	48.15 149	39.42 155	25.81 199	24.36 151	25.90 189	28.48 192	34.44 153	53.39 185	57.35 163	69.00 159	71.14 147

GG-mvs-BLEND		44.87 168	64.59 50	21.86 222	0.01 237	73.70 47	55.99 111	0.01 235	50.70 69	0.01 240	49.18 57	63.61 34	0.01 235	63.83 84	64.50 91	75.13 68	86.62 28

pmmvs-eth3d		44.67 169	45.27 182	43.98 153	42.56 186	55.72 186	44.97 170	40.81 149	31.96 165	29.13 127	26.09 188	25.27 205	36.69 148	55.13 175	56.62 170	69.68 157	66.12 168

MDTV_nov1_ep13_2view		44.44 170	45.75 179	42.91 162	46.13 166	63.43 132	46.53 163	34.20 195	29.08 184	19.95 172	26.23 187	27.89 194	35.88 151	53.36 186	56.43 171	74.74 81	63.86 175

v52		44.41 171	46.76 174	41.67 168	38.93 193	60.06 164	49.26 144	36.02 176	25.57 201	24.73 150	25.66 190	31.34 175	35.93 149	55.52 171	57.99 157	65.14 180	71.21 145

V4		44.41 171	46.76 174	41.67 168	38.93 193	60.05 165	49.26 144	36.02 176	25.55 202	24.75 149	25.66 190	31.33 176	35.93 149	55.52 171	57.99 157	65.14 180	71.21 145

CMPMVS	33.64 16	44.39 173	46.41 177	42.03 166	44.21 180	56.50 182	46.73 161	26.48 221	34.20 145	35.14 88	24.22 195	34.64 150	40.52 140	56.50 168	56.07 173	59.12 203	62.74 179
M. Jancosek, T. Pajdla: Multi-View Reconstruction Preserving Weakly-Supported Surfaces. CVPR 2011
Vis-MVSNet (Re-imp)		44.31 174	51.67 143	35.72 192	51.82 131	55.24 188	34.57 196	41.63 144	39.10 107	8.84 208	45.93 66	46.63 89	14.45 211	54.09 179	57.03 166	63.00 192	63.65 176

TAMVS		44.27 175	49.35 160	38.35 185	44.74 174	61.04 157	39.07 183	31.82 209	29.95 179	18.34 181	33.55 143	39.94 113	30.01 174	56.85 166	57.58 160	66.13 177	66.54 166

MVS-HIRNet		43.98 176	43.63 189	44.39 149	47.66 161	59.31 168	32.66 203	33.88 200	30.15 178	33.75 101	16.82 216	28.39 193	45.25 117	53.92 184	55.00 181	73.16 124	61.80 180

RPMNet		43.70 177	48.17 166	38.48 184	45.52 170	55.95 183	37.66 189	26.63 220	42.17 95	25.47 140	29.59 176	37.61 117	33.87 156	50.85 192	52.02 192	61.75 198	69.00 161

PatchMatch-RL		43.37 178	44.93 186	41.56 170	37.94 197	51.70 190	40.02 181	35.75 180	39.04 108	30.71 121	35.14 115	27.43 196	46.58 111	51.99 188	50.55 197	58.38 205	58.64 189

FMVSNet5		43.29 179	47.07 170	38.87 181	30.46 219	50.99 193	45.87 165	37.19 172	42.17 95	19.32 174	26.77 183	40.51 110	30.26 173	56.82 167	55.81 175	70.10 153	56.46 197

test0.0.03 1		43.07 180	46.95 172	38.54 183	51.68 134	58.77 172	35.28 191	46.35 82	32.05 164	12.44 195	28.53 179	35.52 141	14.40 212	57.12 164	56.93 168	71.11 144	59.69 184

anonymousdsp		43.03 181	47.19 169	38.18 186	36.00 209	56.92 181	38.44 185	34.56 190	24.22 205	22.53 160	29.69 175	29.92 185	35.21 152	53.96 181	58.98 153	62.32 195	76.66 105

USDC		42.80 182	45.57 180	39.58 177	34.55 211	51.13 192	42.61 175	36.21 175	39.59 105	23.65 155	33.13 146	20.87 214	37.86 144	55.35 174	57.16 165	62.61 193	61.75 181

tfpnview11		42.71 183	45.29 181	39.71 176	51.06 139	58.61 176	38.47 184	44.80 103	30.44 176	13.60 189	31.25 158	30.97 181	22.40 193	54.20 177	55.04 180	67.90 165	56.51 195

tfpn_n400		42.55 184	45.11 183	39.55 178	50.95 141	58.68 174	38.40 187	44.75 105	29.29 181	13.60 189	31.25 158	30.97 181	22.38 194	53.96 181	55.66 176	67.20 172	56.00 198

tfpnconf		42.55 184	45.11 183	39.55 178	50.95 141	58.68 174	38.40 187	44.75 105	29.29 181	13.60 189	31.25 158	30.97 181	22.38 194	53.96 181	55.66 176	67.20 172	56.00 198

CHOSEN 280x420		42.39 186	47.40 168	36.54 190	33.56 214	39.66 218	40.67 180	26.88 219	34.66 141	18.03 183	30.09 170	45.59 93	44.82 124	54.46 176	54.00 184	55.28 212	73.32 129

pmmvs6		41.90 187	44.01 188	39.43 180	44.45 175	58.77 172	41.92 177	39.22 159	21.74 208	19.08 177	17.40 213	31.33 176	24.28 192	55.94 169	56.67 169	67.60 168	66.24 167

tfpn1000		41.76 188	45.01 185	37.96 187	50.95 141	58.44 177	34.94 193	44.09 113	30.68 174	12.08 196	30.14 168	31.96 168	18.67 200	51.96 189	53.45 186	67.05 174	58.40 192

Anonymous20231206		40.63 189	43.29 190	37.53 188	48.88 157	55.81 185	34.99 192	44.98 94	28.16 185	10.16 204	17.26 214	27.50 195	18.28 201	54.00 180	55.07 179	67.85 166	65.23 170

CVMVSNet		38.91 190	44.49 187	32.40 201	34.57 210	47.20 204	34.81 194	34.20 195	31.45 169	8.95 207	38.86 95	36.38 131	24.30 191	47.77 198	46.94 211	57.59 207	62.85 178

COLMAP_ROB	34.79 15	38.65 191	40.72 193	36.23 191	36.41 207	49.22 200	45.51 167	27.60 218	37.81 114	20.54 167	23.37 197	24.25 207	28.11 185	51.02 191	48.55 201	59.22 202	50.82 211
Johannes L. SchÃ¶nberger, Enliang Zheng, Marc Pollefeys, Jan-Michael Frahm: Pixelwise View Selection for Unstructured Multi-View Stereo. ECCV 2016
PEN-MVS		38.23 192	41.72 192	34.15 194	40.56 190	50.07 196	33.17 200	44.35 111	27.64 190	5.54 220	30.84 163	26.67 197	14.99 209	45.64 201	52.38 191	66.29 176	58.83 187

LP		38.21 193	37.72 203	38.79 182	44.07 181	51.16 191	35.54 190	31.37 211	25.38 203	23.73 154	18.64 207	18.03 218	29.31 180	47.85 197	52.63 189	68.71 161	50.34 213

WR-MVS		37.61 194	42.15 191	32.31 203	43.64 182	51.85 189	29.39 209	43.35 122	27.65 189	4.40 222	29.90 172	29.80 186	10.46 219	46.73 200	51.98 193	62.60 194	57.16 193

TinyColmap		37.18 195	37.37 207	36.95 189	31.17 218	45.21 208	39.71 182	34.65 188	29.83 180	20.20 169	18.54 208	13.72 225	38.27 142	50.33 193	51.57 195	57.71 206	52.42 207

CP-MVSNet		37.09 196	40.62 194	32.99 196	37.56 199	48.25 201	32.75 201	43.05 126	27.88 187	5.93 216	31.27 157	25.82 202	15.09 207	43.37 210	48.82 199	63.54 189	58.90 185

DTE-MVSNet		36.91 197	40.44 195	32.79 199	40.74 188	47.55 203	30.71 207	44.39 108	27.03 193	4.32 223	30.88 161	25.99 200	12.73 214	45.58 202	50.80 196	63.86 186	55.23 202

PS-CasMVS		36.84 198	40.23 198	32.89 197	37.44 200	48.09 202	32.68 202	42.97 128	27.36 191	5.89 217	30.08 171	25.48 204	14.96 210	43.28 211	48.71 200	63.39 190	58.63 190

WR-MVS_H		36.29 199	40.35 197	31.55 206	37.80 198	49.94 198	30.57 208	41.11 147	26.90 194	4.14 224	30.72 165	28.85 190	10.45 220	42.47 214	47.99 205	65.24 179	55.54 200

SixPastTwentyTwo		36.11 200	37.80 202	34.13 195	37.13 203	46.72 205	34.58 195	34.96 186	21.20 212	11.66 197	29.15 178	19.88 215	29.77 177	44.93 203	48.34 202	56.67 209	54.41 204

test20.03		36.00 201	38.92 199	32.60 200	45.92 167	50.99 193	28.05 214	43.69 119	21.62 210	6.03 215	17.61 212	25.91 201	8.34 226	51.26 190	52.60 190	63.58 187	52.46 206

TDRefinement		35.76 202	38.23 200	32.88 198	19.09 230	46.04 207	43.29 174	29.49 213	33.49 157	19.04 178	22.29 201	17.82 219	29.69 179	48.60 196	47.24 209	56.65 210	52.12 209

LTVRE_ROB	32.83 17	35.10 203	37.46 204	32.35 202	43.12 184	49.99 197	28.52 212	33.23 203	12.73 229	8.18 209	27.71 182	21.34 211	32.64 165	46.92 199	48.11 203	48.41 219	55.45 201
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
PM-MVS		34.96 204	38.17 201	31.22 207	22.78 224	40.82 214	33.56 198	23.61 225	29.16 183	21.43 164	28.00 181	21.43 210	31.90 167	44.33 208	42.12 218	54.07 214	61.34 182

testgi		34.51 205	37.42 205	31.12 208	47.37 164	50.34 195	24.38 221	41.21 145	20.32 214	5.64 219	20.56 202	26.55 198	8.06 227	49.28 194	52.65 188	60.05 200	42.23 221

MDA-MVSNet-bldmvs		34.31 206	34.11 213	34.54 193	24.73 222	49.66 199	33.42 199	43.03 127	21.59 211	11.10 201	19.81 204	12.68 227	31.41 169	35.59 222	48.05 204	63.56 188	51.39 210

test2356		34.09 207	36.84 208	30.87 209	36.25 208	43.59 211	27.92 215	35.44 183	21.73 209	6.94 212	19.31 206	18.23 217	17.77 202	49.28 194	51.58 194	60.94 199	54.17 205

N_pmnet		34.09 207	35.74 210	32.17 204	37.25 202	43.17 212	32.26 205	35.57 182	26.22 197	10.60 203	20.44 203	19.38 216	20.20 198	44.59 207	47.00 210	57.13 208	49.35 215

RPSCF		33.61 209	40.43 196	25.65 216	16.00 232	32.41 226	31.73 206	13.33 232	50.13 71	23.12 156	31.56 153	40.09 111	32.73 164	41.14 220	37.05 222	36.99 227	50.63 212

EU-MVSNet		33.00 210	36.49 209	28.92 210	33.10 215	42.86 213	29.32 211	35.99 178	22.94 206	5.83 218	25.29 192	24.43 206	15.21 206	41.22 219	41.65 220	54.08 213	57.01 194

Anonymous20231211		32.12 211	32.10 218	32.15 205	44.26 179	46.14 206	29.39 209	39.72 153	14.08 228	3.70 227	7.94 229	13.30 226	16.12 205	43.07 212	47.88 206	59.72 201	52.28 208

testpf		31.84 212	34.86 212	28.32 212	48.89 156	32.91 225	26.53 216	25.77 223	21.99 207	10.05 205	23.39 196	25.55 203	14.07 213	39.23 221	42.32 217	44.58 223	58.65 188

pmmvs3		31.22 213	33.62 214	28.43 211	22.82 223	40.26 217	26.40 217	22.05 227	16.89 223	10.99 202	14.72 218	16.26 220	29.70 178	44.82 204	47.39 208	58.61 204	54.98 203

FC-MVSNet-test		30.97 214	37.38 206	23.49 221	37.42 201	33.68 224	19.43 227	39.27 157	31.37 170	1.67 235	38.56 96	28.85 190	6.06 231	41.40 217	43.80 216	37.10 226	44.03 220

new-patchmatchnet		30.47 215	32.80 216	27.75 214	36.81 205	43.98 209	24.85 219	39.29 156	20.52 213	4.06 225	15.94 217	16.05 221	9.57 221	41.32 218	42.05 219	51.94 217	49.74 214

MIMVSNet1		29.60 216	33.37 215	25.20 220	19.52 228	43.94 210	26.29 218	37.92 167	19.95 217	3.79 226	12.64 226	21.99 209	7.70 229	43.83 209	46.32 213	55.97 211	44.92 219

testus		29.45 217	32.20 217	26.23 215	37.01 204	37.90 219	17.56 228	35.70 181	18.23 219	3.39 228	17.04 215	14.78 222	11.78 216	42.48 213	49.38 198	51.92 218	45.62 218

1111		29.41 218	30.75 219	27.85 213	39.46 191	37.63 220	22.26 222	32.15 206	17.93 221	7.92 210	13.48 220	20.98 212	17.30 203	44.76 205	46.51 212	47.99 220	33.96 225

testmv		27.97 219	29.98 220	25.62 217	32.54 216	36.86 222	20.53 224	33.33 201	14.11 226	2.64 230	12.76 224	11.77 228	11.07 217	42.34 215	45.44 214	53.60 215	46.60 216

test1235678		27.96 220	29.97 221	25.62 217	32.54 216	36.83 223	20.53 224	33.33 201	14.10 227	2.64 230	12.75 225	11.76 229	11.07 217	42.34 215	45.43 215	53.60 215	46.59 217

FPMVS		26.87 221	28.19 222	25.32 219	27.09 220	29.49 228	32.28 204	17.79 229	28.09 186	11.33 198	19.38 205	14.69 223	20.88 197	35.11 223	32.82 225	42.56 224	37.75 223

PMVS	18.18 18	21.95 222	22.85 223	20.90 224	21.92 226	14.78 231	19.95 226	17.31 230	15.69 224	11.32 199	13.70 219	13.91 224	15.02 208	34.92 224	31.72 226	39.85 225	35.20 224
Y. Furukawa, J. Ponce: Accurate, dense, and robust multiview stereopsis. PAMI (2010)
no-one		21.91 223	22.52 226	21.20 223	25.97 221	30.78 227	13.29 231	32.75 205	9.08 232	1.84 232	6.18 231	7.00 234	8.03 228	25.56 228	40.16 221	45.29 222	38.83 222

test12356		20.09 224	22.80 224	16.93 226	22.59 225	24.43 229	13.32 230	25.93 222	12.67 230	1.58 237	11.53 227	9.25 231	2.29 232	33.15 226	37.05 222	35.85 228	31.54 226

.test1245		19.53 225	19.26 227	19.85 225	39.46 191	37.63 220	22.26 222	32.15 206	17.93 221	7.92 210	13.48 220	20.98 212	17.30 203	44.76 205	0.01 233	0.00 237	0.03 234

new_pmnet		19.10 226	22.71 225	14.89 228	10.93 234	24.08 230	14.22 229	13.94 231	18.68 218	2.93 229	12.84 223	11.27 230	11.94 215	30.57 227	30.58 227	35.38 229	30.93 227

Gipuma		17.16 227	17.83 228	16.36 227	18.76 231	12.15 234	11.97 232	27.78 217	17.94 220	4.86 221	2.53 235	2.73 237	8.90 224	34.32 225	36.09 224	25.92 230	19.06 229
S. Galliani, K. Lasinger, K. Schindler: Massively Parallel Multiview Stereopsis by Surface Normal Diffusion. ICCV 2015
PMMVS2		12.25 228	14.17 229	10.00 231	11.39 233	14.35 232	8.21 233	19.29 228	9.31 231	0.19 239	7.38 230	6.19 235	1.10 234	19.26 229	21.13 229	19.85 231	21.56 228

E-PMN		10.66 229	8.30 231	13.42 229	19.91 227	7.87 235	4.30 236	29.47 214	8.37 235	1.70 234	3.67 232	1.29 240	9.12 223	8.98 233	13.59 230	16.03 232	14.30 232

EMVS		10.15 230	7.67 232	13.05 230	19.22 229	7.77 236	4.48 234	29.34 215	8.65 234	1.67 235	3.55 233	1.36 239	9.15 222	8.15 234	11.79 232	14.44 233	12.43 233

MVE	10.35 19	9.76 231	11.08 230	8.22 232	4.43 235	13.04 233	3.36 237	23.57 226	5.74 236	1.76 233	3.09 234	1.75 238	6.78 230	12.78 231	23.04 228	9.44 234	18.09 230
Simon Fuhrmann, Fabian Langguth, Michael Goesele: MVE - A Multi-View Reconstruction Environment. EUROGRAPHICS Workshops on Graphics and Cultural Heritage (2014)
testmvs		0.01 232	0.01 233	0.00 234	0.00 238	0.00 239	0.00 240	0.00 236	0.01 237	0.00 241	0.02 236	0.00 241	0.00 237	0.01 235	0.01 233	0.00 237	0.03 234

test123		0.01 232	0.01 233	0.00 234	0.00 238	0.00 239	0.00 240	0.00 236	0.01 237	0.00 241	0.02 236	0.00 241	0.01 235	0.00 236	0.01 233	0.00 237	0.03 234

sosnet-low-res		0.00 234	0.00 235	0.00 234	0.00 238	0.00 239	0.00 240	0.00 236	0.00 239	0.00 241	0.00 238	0.00 241	0.00 237	0.00 236	0.00 236	0.00 237	0.00 237

sosnet		0.00 234	0.00 235	0.00 234	0.00 238	0.00 239	0.00 240	0.00 236	0.00 239	0.00 241	0.00 238	0.00 241	0.00 237	0.00 236	0.00 236	0.00 237	0.00 237

ambc			35.52 211		38.36 195	40.40 216	28.38 213		25.20 204	14.87 188	13.22 222	7.54 233	19.34 199	55.63 170	47.79 207	47.91 221	58.89 186

MTAPA										54.82 10		71.98 17

MTMP										50.64 26		68.31 23

Patchmatch-RL test							0.69 239

tmp_tt				4.41 233	2.56 236	1.81 238	2.61 238	0.27 234	20.12 215	9.81 206	17.69 211	9.04 232	1.96 233	12.88 230	12.11 231	9.23 235

XVS					62.70 49	73.06 53	61.80 61			42.02 64		63.42 36				74.68 86

X-MVStestdata					62.70 49	73.06 53	61.80 61			42.02 64		63.42 36				74.68 86

abl_6				63.79 17	70.80 17	81.22 7	65.26 50	53.25 24	77.02 12	53.02 18	65.14 24	73.74 12	60.30 14			80.13 8	90.27 7

mPP-MVS					63.08 46							62.34 39

NP-MVS									72.62 24

Patchmtry						64.49 124	52.06 135	34.21 193		31.74 115

DeepMVS_CX						5.87 237	4.32 235	1.74 233	9.04 233	1.30 238	7.97 228	3.16 236	8.56 225	9.74 232		6.30 236	14.51 231

High-resolution multi-view benchmark