Datasets:

maeyounes
/

SparseCraft-dataset

psnr float64 10.8 30.9	average_vgg float64 0.04 0.28	lpips_alex float64 0.04 0.34	masked_lpips_vgg float64 0.02 0.29	ssim float64 0.49 0.94	masked_psnr float64 12.1 29.7	masked_ssim float64 0.58 0.97	masked_average_vgg float64 0.02 0.21	lpips_vgg float64 0.11 0.38	masked_average_alex float64 0.01 0.2	average_alex float64 0.03 0.27	masked_lpips_alex float64 0.01 0.21
15.612605	0.182912	0.250766	0.110639	0.647386	19.720907	0.844382	0.091308	0.349011	0.083099	0.164105	0.079593
23.136719	0.089039	0.237383	0.116084	0.827529	21.450104	0.906795	0.065839	0.328666	0.067094	0.079956	0.123028
26.953545	0.061328	0.147889	0.064454	0.855635	25.483364	0.906916	0.041913	0.246764	0.037565	0.051993	0.045019
13.42104	0.232605	0.281022	0.292008	0.485001	14.469901	0.577335	0.189938	0.381917	0.170937	0.209752	0.213411
20.332878	0.090468	0.114014	0.030439	0.888065	23.640903	0.956012	0.031614	0.178288	0.027801	0.078152	0.020702
15.832437	0.156163	0.171201	0.147175	0.695472	17.465902	0.76982	0.110531	0.245631	0.096954	0.138636	0.099359
17.168257	0.133136	0.162101	0.101384	0.751055	23.259663	0.831322	0.059638	0.227315	0.050964	0.119059	0.063112
16.05372	0.155693	0.164555	0.18322	0.684855	18.277168	0.755703	0.112182	0.253515	0.096716	0.134952	0.117107
14.607919	0.177269	0.224431	0.148699	0.663664	18.956442	0.763475	0.100532	0.248887	0.096491	0.171504	0.130384
17.286722	0.124715	0.146373	0.1074	0.778992	22.024189	0.84892	0.0672	0.207764	0.060776	0.111043	0.078853
10.782827	0.282848	0.34057	0.222224	0.527205	12.067881	0.673081	0.207727	0.376662	0.199453	0.273651	0.196358
26.00028	0.072439	0.182626	0.046619	0.778757	26.335566	0.931163	0.034106	0.282038	0.031497	0.062867	0.035525
20.721743	0.080817	0.100333	0.034019	0.887107	22.658798	0.941635	0.036364	0.162234	0.033724	0.068766	0.026959
19.087677	0.105109	0.119125	0.094617	0.786322	22.366161	0.847124	0.063304	0.18924	0.055618	0.090138	0.062936
18.605896	0.106482	0.158349	0.041696	0.854243	20.099861	0.929528	0.049473	0.197407	0.046761	0.098808	0.035428
21.054716	0.09939	0.140906	0.060436	0.785014	27.103441	0.915542	0.033921	0.249205	0.026554	0.082291	0.028559
25.612026	0.063554	0.148581	0.064783	0.869743	23.89645	0.935976	0.043517	0.2346	0.044266	0.054655	0.067813
27.471127	0.053152	0.103209	0.053083	0.864302	26.920633	0.9171	0.035283	0.190299	0.031339	0.043577	0.03537
16.743391	0.144037	0.158867	0.171084	0.677456	17.66081	0.731082	0.118028	0.231358	0.104542	0.12749	0.118222
25.522343	0.046451	0.05167	0.027167	0.929437	26.165056	0.960814	0.02588	0.134786	0.021705	0.034399	0.0159
17.752598	0.115163	0.115291	0.097736	0.77778	21.171738	0.83991	0.06976	0.180864	0.059167	0.099873	0.059163
22.086767	0.077259	0.093023	0.083733	0.845673	24.96306	0.879685	0.047236	0.167984	0.040218	0.064136	0.051194
18.210844	0.118349	0.127605	0.139301	0.761475	19.401304	0.808443	0.093361	0.198977	0.081856	0.102465	0.093513
17.597433	0.118843	0.154948	0.114638	0.790134	19.841152	0.841538	0.081607	0.196178	0.077975	0.110439	0.099185
23.41309	0.063362	0.076208	0.073158	0.869683	25.410969	0.899182	0.045388	0.140009	0.039713	0.052644	0.047994
16.915365	0.117634	0.109895	0.080209	0.804557	20.234909	0.86625	0.068283	0.161282	0.060208	0.105264	0.054518
29.438812	0.050774	0.110009	0.031058	0.837293	28.244579	0.943626	0.026617	0.21047	0.024296	0.041571	0.02253
25.627495	0.045361	0.051938	0.026855	0.924561	25.480103	0.956219	0.026121	0.123718	0.023517	0.034439	0.019493
22.56492	0.071248	0.073708	0.07185	0.837402	25.058378	0.878737	0.048758	0.148793	0.041458	0.057079	0.042113
25.626848	0.045479	0.056164	0.026323	0.928316	24.396992	0.956843	0.028243	0.125018	0.024645	0.034923	0.017655
26.410034	0.060401	0.095297	0.049022	0.846149	29.095221	0.932106	0.026092	0.226345	0.019701	0.045443	0.020847
29.450077	0.039386	0.086091	0.053405	0.920402	26.204363	0.955126	0.030649	0.183023	0.029983	0.030647	0.049967
30.5319	0.03656	0.078075	0.048557	0.906829	28.347792	0.935646	0.027262	0.167928	0.023551	0.028301	0.031199
18.659431	0.110866	0.122441	0.141959	0.743151	19.224331	0.782055	0.092943	0.194633	0.080352	0.09519	0.091708
25.845377	0.046457	0.047954	0.021472	0.940632	28.711506	0.972658	0.017356	0.130252	0.013222	0.033608	0.009471
21.673559	0.073949	0.07089	0.07521	0.843405	23.050653	0.876575	0.051866	0.13965	0.042564	0.059429	0.041376
24.568323	0.054584	0.060801	0.061452	0.891342	27.120859	0.911278	0.033603	0.133057	0.027695	0.042453	0.033869
21.772087	0.073561	0.073703	0.092905	0.847974	23.258148	0.882287	0.054619	0.141073	0.044463	0.059724	0.049437
20.398712	0.089413	0.114969	0.089242	0.838152	22.643755	0.880764	0.056164	0.159939	0.051098	0.080691	0.066323
25.883759	0.044234	0.050278	0.056916	0.9106	27.849375	0.929649	0.029853	0.114235	0.024807	0.033921	0.032889
21.301495	0.068154	0.065986	0.064017	0.875362	22.729145	0.89857	0.048232	0.120095	0.041211	0.05591	0.040804
30.918505	0.037406	0.070411	0.024552	0.878878	29.683823	0.961235	0.01852	0.165441	0.016265	0.02844	0.01625
25.91861	0.043211	0.045749	0.021703	0.938527	27.002739	0.967375	0.020198	0.116586	0.017476	0.031759	0.013938
25.26935	0.051299	0.052994	0.055248	0.881623	28.48703	0.914924	0.029411	0.125082	0.022747	0.038771	0.025084
26.47788	0.040547	0.044629	0.019798	0.938763	27.061953	0.96517	0.020027	0.112714	0.016492	0.029942	0.011231

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SparseCraft

[ECCV'24] SparseCraft: Few-Shot Neural Reconstruction through Stereopsis Guided Geometric Linearization

Project

DTU Dataset

We provide preprocessed DTU data and results for the tasks of novel view synthesis and surface reconstruction.

It contains the following directories:

sparsecraft_data
├── nvs # Novel View Synthesis task data and results
│   └── mvs_data
│       ├── scan103
│       ├── ...
│   └── results # Results for training using 3, 6, and 9 views
│       ├── 3v
│       │   ├── scan103
│       │   ├── ...
│       ├── 6v
│       │   ├── scan103
│       │   ├── ...
│       └── 9v
│           ├── scan103
│           ├── ...
└── reconstruction # Surface Reconstruction task data and results
    └── mvs_data # Surface reconstruction data uses a different set of scans and views than the novel view synthesis task
        ├── set0
        │   ├── scan105
        │   ├── ...
        └── set1
            ├── scan105
            ├── ...
    └── results
        ├── set0
        │   ├── scan105
        │   ├── ...
        └── set1
            ├── scan105

Note

The DTU dataset was preprocessed as follows:

The original data is from the NeuS Project. We use the same camera poses and intrinsics as the original data.
To obtain MVS data, we used the Colmap initialized with the original camera poses and intrinsics.
We provide a script that achieves this in scripts that you can run using the following command. Note that you will need to have Colmap installed on your machine:

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Size of downloaded dataset files:

24.6 kB

Size of the auto-converted Parquet files:

171 kB

Number of rows: