SLAM > Datasets
Training datasets
Test datasets
Calibration datasets
Synthetic datasets
We provide a Python script to enable downloading the desired part(s) of the dataset comfortably (except for the synthetic datasets, which are not a part of the benchmark): download_eth3d_slam_datasets.py. Simply call this interactive script from the directory that you would like to download the files into. The script should work both with Python 2 and 3. All download links are additionally also listed below.
Please note that the images have been recorded while trying to avoid overexposure (which leaves no information in the overexposed areas). As a result, often many image areas are relatively dark (while however still containing information). In addition, we did not apply white-balancing to the images in order not to disturb the photometric consistency. While both of these decisions might be beneficial for SLAM systems, they are not well-suited for visualization to humans, and the images may appear yellowish and dark. Therefore, for visualization of the images or of 3D reconstruction results, we recommend to brighten up the colors and apply white balancing as needed.
Training datasets
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cables_1 - 1180 frames The camera views some cable clutter on a table. This arrangement may make it hard to detect reliable features in the RGB images. |
cables_1_mono.zip cables_1_stereo.zip cables_1_rgbd.zip cables_1_imu.zip cables_1_raw.zip |
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cables_2 - 98 frames The camera views some cable clutter on a table while moving quickly. This arrangement may make it hard to detect reliable features in the RGB images. |
cables_2_mono.zip cables_2_stereo.zip cables_2_rgbd.zip cables_2_imu.zip cables_2_raw.zip |
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cables_3 - 313 frames The camera views some cable clutter on a table while moving quickly. This arrangement may make it hard to detect reliable features in the RGB images. |
cables_3_mono.zip cables_3_stereo.zip cables_3_rgbd.zip cables_3_imu.zip cables_3_raw.zip |
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camera_shake_1 - 318 frames The camera is shaken quickly. Using IMU data is probably very helpful here. |
camera_shake_1_mono.zip camera_shake_1_stereo.zip camera_shake_1_rgbd.zip camera_shake_1_imu.zip camera_shake_1_raw.zip |
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camera_shake_2 - 871 frames The camera is shaken quickly. Using IMU data is probably very helpful here. |
camera_shake_2_mono.zip camera_shake_2_stereo.zip camera_shake_2_rgbd.zip camera_shake_2_imu.zip camera_shake_2_raw.zip |
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camera_shake_3 - 285 frames The camera is shaken quickly. Using IMU data is probably very helpful here. |
camera_shake_3_mono.zip camera_shake_3_stereo.zip camera_shake_3_rgbd.zip camera_shake_3_imu.zip camera_shake_3_raw.zip |
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ceiling_1 - 1595 frames The camera moves around while viewing the ceiling of the Vicon area. The lights potentially create difficult illumination conditions, and there may be little to constrain the camera pose sometimes. |
ceiling_1_mono.zip ceiling_1_stereo.zip ceiling_1_rgbd.zip ceiling_1_imu.zip ceiling_1_raw.zip |
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ceiling_2 - 1535 frames The camera moves around while viewing the ceiling of the Vicon area. The lights potentially create difficult illumination conditions, and there may be little to constrain the camera pose sometimes. |
ceiling_2_mono.zip ceiling_2_stereo.zip ceiling_2_rgbd.zip ceiling_2_imu.zip ceiling_2_raw.zip |
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desk_3 - 2061 frames The camera moves through a scene with clutter on several tables. |
desk_3_mono.zip desk_3_stereo.zip desk_3_rgbd.zip desk_3_imu.zip desk_3_raw.zip |
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desk_changing_1 - 3931 frames The camera moves through a scene while objects in the scene are being moved when they are not visible. This can confuse loop closure systems and SLAM methods with implicit map re-use. |
desk_changing_1_mono.zip desk_changing_1_stereo.zip desk_changing_1_rgbd.zip desk_changing_1_imu.zip desk_changing_1_raw.zip |
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einstein_1 - 487 frames The camera moves around in a scene with several objects, including a laptop which shows a video. The moving video on the static surface might confuse SLAM systems. |
einstein_1_mono.zip einstein_1_stereo.zip einstein_1_rgbd.zip einstein_1_imu.zip einstein_1_raw.zip |
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einstein_2 - 1530 frames The camera moves around in a scene with several objects, including a laptop which shows a video. The moving video on the static surface might confuse SLAM systems. |
einstein_2_mono.zip einstein_2_stereo.zip einstein_2_rgbd.zip einstein_2_imu.zip einstein_2_raw.zip |
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einstein_dark - 2035 frames The camera moves around in a scene with several objects, including a laptop which shows a video. The moving video on the static surface might confuse SLAM systems. Since the ambient lights are off, the RGB cameras do not provide information (apart from the laptop screen), so SLAM methods have to rely on other data. |
einstein_dark_mono.zip einstein_dark_stereo.zip einstein_dark_rgbd.zip einstein_dark_imu.zip einstein_dark_raw.zip |
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einstein_flashlight - 1851 frames The camera moves around in a scene with several objects. The scene is mostly only illuminated by a hand-held flashlight, which may be a very challenging illumination condition for SLAM systems. |
einstein_flashlight_mono.zip einstein_flashlight_stereo.zip einstein_flashlight_rgbd.zip einstein_flashlight_imu.zip einstein_flashlight_raw.zip |
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einstein_global_light_changes_1 - 872 frames While the camera moves through a scene with several objects, the room lights are turned on and off. |
einstein_global_light_changes_1_mono.zip einstein_global_light_changes_1_stereo.zip einstein_global_light_changes_1_rgbd.zip einstein_global_light_changes_1_imu.zip einstein_global_light_changes_1_raw.zip |
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einstein_global_light_changes_2 - 471 frames While the camera moves through a scene with several objects, the room lights are turned on and off. |
einstein_global_light_changes_2_mono.zip einstein_global_light_changes_2_stereo.zip einstein_global_light_changes_2_rgbd.zip einstein_global_light_changes_2_imu.zip einstein_global_light_changes_2_raw.zip |
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einstein_global_light_changes_3 - 2481 frames While the camera moves through a scene with several objects, the room lights are turned on and off. |
einstein_global_light_changes_3_mono.zip einstein_global_light_changes_3_stereo.zip einstein_global_light_changes_3_rgbd.zip einstein_global_light_changes_3_imu.zip einstein_global_light_changes_3_raw.zip |
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kidnap_1 - 884 frames The camera gets 'kidnapped', i.e., moved while its view is blocked, then moved back to the original view. SLAM methods will have to relocalize against the initial scene after the kidnap. |
kidnap_1_mono.zip kidnap_1_stereo.zip kidnap_1_rgbd.zip kidnap_1_imu.zip kidnap_1_raw.zip |
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kidnap_dark - 526 frames The camera gets 'kidnapped', i.e., moved while its view is blocked, then moved back to the original view. SLAM methods will have to relocalize against the initial scene after the kidnap. The RGB cameras do not provide information, so SLAM methods have to rely on other data. |
kidnap_dark_mono.zip kidnap_dark_stereo.zip kidnap_dark_rgbd.zip kidnap_dark_imu.zip kidnap_dark_raw.zip |
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large_loop_1 - 1511 frames The camera moves around in a large loop. Detecting this loop is likely important to achieve a good result. |
large_loop_1_mono.zip large_loop_1_stereo.zip large_loop_1_rgbd.zip large_loop_1_imu.zip large_loop_1_raw.zip |
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mannequin_1 - 643 frames The camera moves around a mannequin to create a 3D reconstruction of it. |
mannequin_1_mono.zip mannequin_1_stereo.zip mannequin_1_rgbd.zip mannequin_1_imu.zip mannequin_1_raw.zip |
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mannequin_3 - 649 frames The camera moves around a mannequin to create a 3D reconstruction of it. |
mannequin_3_mono.zip mannequin_3_stereo.zip mannequin_3_rgbd.zip mannequin_3_imu.zip mannequin_3_raw.zip |
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mannequin_4 - 551 frames The mannequin enters and leaves the field of view multiple times. Associating all observations correctly is important to obtain a good result. |
mannequin_4_mono.zip mannequin_4_stereo.zip mannequin_4_rgbd.zip mannequin_4_imu.zip mannequin_4_raw.zip |
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mannequin_5 - 921 frames The mannequin enters and leaves the field of view multiple times. Associating all observations correctly is important to obtain a good result. |
mannequin_5_mono.zip mannequin_5_stereo.zip mannequin_5_rgbd.zip mannequin_5_imu.zip mannequin_5_raw.zip |
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mannequin_7 - 634 frames The camera moves around a mannequin to create a 3D reconstruction of it, but loses view of it multiple times. |
mannequin_7_mono.zip mannequin_7_stereo.zip mannequin_7_rgbd.zip mannequin_7_imu.zip mannequin_7_raw.zip |
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mannequin_face_1 - 421 frames The camera scans a mannequin's face and front. |
mannequin_face_1_mono.zip mannequin_face_1_stereo.zip mannequin_face_1_rgbd.zip mannequin_face_1_imu.zip mannequin_face_1_raw.zip |
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mannequin_face_2 - 444 frames The camera slowly scans a mannequin's face. |
mannequin_face_2_mono.zip mannequin_face_2_stereo.zip mannequin_face_2_rgbd.zip mannequin_face_2_imu.zip mannequin_face_2_raw.zip |
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mannequin_face_3 - 637 frames The camera sloppily scans a mannequin's face, losing view of it multiple times. Associating all observations correctly is important to obtain a good result. |
mannequin_face_3_mono.zip mannequin_face_3_stereo.zip mannequin_face_3_rgbd.zip mannequin_face_3_imu.zip mannequin_face_3_raw.zip |
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mannequin_head - 664 frames The camera scans a mannequin's head, but also shakes strongly. |
mannequin_head_mono.zip mannequin_head_stereo.zip mannequin_head_rgbd.zip mannequin_head_imu.zip mannequin_head_raw.zip |
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motion_1 - 2366 frames The camera films a person who moves around in a scene and moves several objects. |
motion_1_mono.zip motion_1_stereo.zip motion_1_rgbd.zip motion_1_imu.zip motion_1_raw.zip |
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planar_2 - 630 frames The camera views a textured plane. It is important to use photometric information to track the camera pose here, since the geometry does likely not sufficiently constrain it. |
planar_2_mono.zip planar_2_stereo.zip planar_2_rgbd.zip planar_2_imu.zip planar_2_raw.zip |
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planar_3 - 901 frames The camera views a textured plane. It is important to use photometric information to track the camera pose here, since the geometry does likely not sufficiently constrain it. |
planar_3_mono.zip planar_3_stereo.zip planar_3_rgbd.zip planar_3_imu.zip planar_3_raw.zip |
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plant_1 - 70 frames The camera shortly views a plant. |
plant_1_mono.zip plant_1_stereo.zip plant_1_rgbd.zip plant_1_imu.zip plant_1_raw.zip |
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plant_2 - 124 frames The camera shortly views a plant. |
plant_2_mono.zip plant_2_stereo.zip plant_2_rgbd.zip plant_2_imu.zip plant_2_raw.zip |
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plant_3 - 187 frames The camera shortly views a plant. |
plant_3_mono.zip plant_3_stereo.zip plant_3_rgbd.zip plant_3_imu.zip plant_3_raw.zip |
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plant_4 - 86 frames The camera shortly views a plant. |
plant_4_mono.zip plant_4_stereo.zip plant_4_rgbd.zip plant_4_imu.zip plant_4_raw.zip |
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plant_5 - 169 frames The camera shortly views a plant. |
plant_5_mono.zip plant_5_stereo.zip plant_5_rgbd.zip plant_5_imu.zip plant_5_raw.zip |
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plant_dark - 1140 frames The camera moves around a plant in the dark. The RGB cameras do not provide information, so SLAM methods have to rely on other data. |
plant_dark_mono.zip plant_dark_stereo.zip plant_dark_rgbd.zip plant_dark_imu.zip plant_dark_raw.zip |
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plant_scene_1 - 740 frames The camera slowly moves around in a scene with a plant, a sofa, a table, and a large checkerboard. |
plant_scene_1_mono.zip plant_scene_1_stereo.zip plant_scene_1_rgbd.zip plant_scene_1_imu.zip plant_scene_1_raw.zip |
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plant_scene_2 - 722 frames The camera slowly moves around in a scene with a plant, a sofa, a table, and a large checkerboard. |
plant_scene_2_mono.zip plant_scene_2_stereo.zip plant_scene_2_rgbd.zip plant_scene_2_imu.zip plant_scene_2_raw.zip |
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plant_scene_3 - 618 frames The camera slowly moves around in a scene with a plant, a sofa, a table, and a large checkerboard. The plant occludes the other objects several times, which may interrupt feature tracks. |
plant_scene_3_mono.zip plant_scene_3_stereo.zip plant_scene_3_rgbd.zip plant_scene_3_imu.zip plant_scene_3_raw.zip |
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reflective_1 - 4601 frames The camera observes some reflective metal parts which make it very hard to correctly estimate depth or track features. |
reflective_1_mono.zip reflective_1_stereo.zip reflective_1_rgbd.zip reflective_1_imu.zip reflective_1_raw.zip |
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repetitive - 1966 frames The camera slowly moves around a table with some objects on it and next to it. Some of the objects look very similar, which may confuse loop closure detectors. |
repetitive_mono.zip repetitive_stereo.zip repetitive_rgbd.zip repetitive_imu.zip repetitive_raw.zip |
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sfm_bench - 660 frames The camera moves around a bench to create a 3D reconstruction of it. This dataset was filmed outside of the Vicon system, thus ground-truth is determined with Structure-from-Motion. |
sfm_bench_mono.zip sfm_bench_stereo.zip sfm_bench_rgbd.zip sfm_bench_imu.zip sfm_bench_raw.zip |
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sfm_garden - 674 frames The camera moves in a loop in a garden, mostly filming the meadow. This dataset was filmed outside of the Vicon system, thus ground-truth is determined with Structure-from-Motion. |
sfm_garden_mono.zip sfm_garden_stereo.zip sfm_garden_rgbd.zip sfm_garden_imu.zip sfm_garden_raw.zip |
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sfm_house_loop - 556 frames The camera moves in a large loop around a house. This dataset was filmed outside of the Vicon system, thus ground-truth is determined with Structure-from-Motion. |
sfm_house_loop_mono.zip sfm_house_loop_stereo.zip sfm_house_loop_rgbd.zip sfm_house_loop_imu.zip sfm_house_loop_raw.zip |
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sfm_lab_room_1 - 263 frames The camera moves through a cluttered lab room. This dataset was filmed outside of the Vicon system, thus ground-truth is determined with Structure-from-Motion. |
sfm_lab_room_1_mono.zip sfm_lab_room_1_stereo.zip sfm_lab_room_1_rgbd.zip sfm_lab_room_1_imu.zip sfm_lab_room_1_raw.zip |
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sfm_lab_room_2 - 125 frames The camera moves through a cluttered lab room. This dataset was filmed outside of the Vicon system, thus ground-truth is determined with Structure-from-Motion. |
sfm_lab_room_2_mono.zip sfm_lab_room_2_stereo.zip sfm_lab_room_2_rgbd.zip sfm_lab_room_2_imu.zip sfm_lab_room_2_raw.zip |
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sofa_1 - 976 frames The camera slowly moves around a sofa. |
sofa_1_mono.zip sofa_1_stereo.zip sofa_1_rgbd.zip sofa_1_imu.zip sofa_1_raw.zip |
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sofa_2 - 676 frames The camera slowly moves around a sofa. |
sofa_2_mono.zip sofa_2_stereo.zip sofa_2_rgbd.zip sofa_2_imu.zip sofa_2_raw.zip |
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sofa_3 - 533 frames The camera moves in front of a sofa. |
sofa_3_mono.zip sofa_3_stereo.zip sofa_3_rgbd.zip sofa_3_imu.zip sofa_3_raw.zip |
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sofa_4 - 788 frames The camera moves in front of a sofa. |
sofa_4_mono.zip sofa_4_stereo.zip sofa_4_rgbd.zip sofa_4_imu.zip sofa_4_raw.zip |
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sofa_dark_1 - 1605 frames The camera moves around a sofa in the dark. The RGB cameras do not provide information, so SLAM methods have to rely on other data. |
sofa_dark_1_mono.zip sofa_dark_1_stereo.zip sofa_dark_1_rgbd.zip sofa_dark_1_imu.zip sofa_dark_1_raw.zip |
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sofa_dark_2 - 281 frames The camera moves in front of a sofa in the dark. The RGB cameras do not provide information, so SLAM methods have to rely on other data. |
sofa_dark_2_mono.zip sofa_dark_2_stereo.zip sofa_dark_2_rgbd.zip sofa_dark_2_imu.zip sofa_dark_2_raw.zip |
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sofa_dark_3 - 1187 frames The camera moves in front of a sofa in the dark. The RGB cameras do not provide information, so SLAM methods have to rely on other data. |
sofa_dark_3_mono.zip sofa_dark_3_stereo.zip sofa_dark_3_rgbd.zip sofa_dark_3_imu.zip sofa_dark_3_raw.zip |
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sofa_shake - 500 frames The camera rotates in front of a sofa. |
sofa_shake_mono.zip sofa_shake_stereo.zip sofa_shake_rgbd.zip sofa_shake_imu.zip sofa_shake_raw.zip |
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table_3 - 1180 frames The camera slowly moves around a table with some objects on it and some clutter next to it. |
table_3_mono.zip table_3_stereo.zip table_3_rgbd.zip table_3_imu.zip table_3_raw.zip |
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table_4 - 1016 frames The camera slowly moves around a table with some objects on it and some clutter next to it. |
table_4_mono.zip table_4_stereo.zip table_4_rgbd.zip table_4_imu.zip table_4_raw.zip |
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table_7 - 1675 frames The camera slowly moves around a table with some objects on it and some clutter next to it. |
table_7_mono.zip table_7_stereo.zip table_7_rgbd.zip table_7_imu.zip table_7_raw.zip |
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vicon_light_1 - 813 frames The camera moves around one of the infrared lights of the Vicon system. |
vicon_light_1_mono.zip vicon_light_1_stereo.zip vicon_light_1_rgbd.zip vicon_light_1_imu.zip vicon_light_1_raw.zip |
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vicon_light_2 - 507 frames The camera moves around one of the infrared lights of the Vicon system. |
vicon_light_2_mono.zip vicon_light_2_stereo.zip vicon_light_2_rgbd.zip vicon_light_2_imu.zip vicon_light_2_raw.zip |
Test datasets
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boxes - 1554 frames The camera slowly moves around some presents on a table to create a 3D reconstruction of them. |
boxes_mono.zip boxes_stereo.zip boxes_rgbd.zip boxes_imu.zip boxes_raw.zip |
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boxes_dark - 816 frames The camera moves around some stacked boxes. The RGB cameras do not provide information, so SLAM methods have to rely on other data. |
boxes_dark_mono.zip boxes_dark_stereo.zip boxes_dark_rgbd.zip boxes_dark_imu.zip boxes_dark_raw.zip |
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buddha - 2048 frames The camera slowly moves around a figure on a table to create a 3D reconstruction of it. |
buddha_mono.zip buddha_stereo.zip buddha_rgbd.zip buddha_imu.zip buddha_raw.zip |
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cables_4 - 681 frames The camera views some cable clutter on a table. This arrangement may make it hard to detect reliable features in the RGB images. |
cables_4_mono.zip cables_4_stereo.zip cables_4_rgbd.zip cables_4_imu.zip cables_4_raw.zip |
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cables_5 - 386 frames The camera views some cable clutter on a table while moving quickly. This arrangement may make it hard to detect reliable features in the RGB images. |
cables_5_mono.zip cables_5_stereo.zip cables_5_rgbd.zip cables_5_imu.zip cables_5_raw.zip |
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desk_1 - 771 frames The camera moves around a scene with several objects. |
desk_1_mono.zip desk_1_stereo.zip desk_1_rgbd.zip desk_1_imu.zip desk_1_raw.zip |
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desk_2 - 1324 frames The camera moves quickly through a scene with clutter on several tables. |
desk_2_mono.zip desk_2_stereo.zip desk_2_rgbd.zip desk_2_imu.zip desk_2_raw.zip |
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desk_changing_2 - 3591 frames The camera moves through a scene while objects in the scene are being moved when they are not visible. This can confuse loop closure systems and SLAM methods with implicit map re-use. |
desk_changing_2_mono.zip desk_changing_2_stereo.zip desk_changing_2_rgbd.zip desk_changing_2_imu.zip desk_changing_2_raw.zip |
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desk_dark_1 - 1059 frames The camera moves around in a scene with several objects in the dark. The RGB cameras do not provide information, so SLAM methods have to rely on other data. |
desk_dark_1_mono.zip desk_dark_1_stereo.zip desk_dark_1_rgbd.zip desk_dark_1_imu.zip desk_dark_1_raw.zip |
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desk_dark_2 - 1424 frames The camera moves around in a scene with several objects in the dark. The RGB cameras do not provide information, so SLAM methods have to rely on other data. |
desk_dark_2_mono.zip desk_dark_2_stereo.zip desk_dark_2_rgbd.zip desk_dark_2_imu.zip desk_dark_2_raw.zip |
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desk_global_light_changes - 1517 frames The camera moves through a scene with clutter on several tables while the room lights are being turned on and off. |
desk_global_light_changes_mono.zip desk_global_light_changes_stereo.zip desk_global_light_changes_rgbd.zip desk_global_light_changes_imu.zip desk_global_light_changes_raw.zip |
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desk_ir_light - 359 frames The camera views a desk with objects on it that is also illuminated by an external, hand-held infrared light. |
desk_ir_light_mono.zip desk_ir_light_stereo.zip desk_ir_light_rgbd.zip desk_ir_light_imu.zip desk_ir_light_raw.zip |
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dino - 2074 frames The camera slowly moves around a toy dinosaur on a table to create a 3D reconstruction of it. |
dino_mono.zip dino_stereo.zip dino_rgbd.zip dino_imu.zip dino_raw.zip |
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drone - 1412 frames The camera slowly moves around a drone on a table to create a 3D reconstruction of it. |
drone_mono.zip drone_stereo.zip drone_rgbd.zip drone_imu.zip drone_raw.zip |
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foreground_occlusion - 451 frames The camera views a scene, while its view is often temporarily obscured by a foreground object, which may interrupt feature tracks. |
foreground_occlusion_mono.zip foreground_occlusion_stereo.zip foreground_occlusion_rgbd.zip foreground_occlusion_imu.zip foreground_occlusion_raw.zip |
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helmet - 1704 frames The camera slowly moves around a bicycle helmet on a table to create a 3D reconstruction of it. |
helmet_mono.zip helmet_stereo.zip helmet_rgbd.zip helmet_imu.zip helmet_raw.zip |
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kidnap_2 - 878 frames The camera gets 'kidnapped', i.e., moved while its view is blocked, then moved back to the original view. SLAM methods will have to relocalize against the initial scene after the kidnap. |
kidnap_2_mono.zip kidnap_2_stereo.zip kidnap_2_rgbd.zip kidnap_2_imu.zip kidnap_2_raw.zip |
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lamp - 984 frames The camera scans a large studio lamp. |
lamp_mono.zip lamp_stereo.zip lamp_rgbd.zip lamp_imu.zip lamp_raw.zip |
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large_loop_2 - 1947 frames The camera moves around in a large loop in the Vicon area. |
large_loop_2_mono.zip large_loop_2_stereo.zip large_loop_2_rgbd.zip large_loop_2_imu.zip large_loop_2_raw.zip |
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large_loop_3 - 1751 frames The camera performs loopy movement within the Vicon area. |
large_loop_3_mono.zip large_loop_3_stereo.zip large_loop_3_rgbd.zip large_loop_3_imu.zip large_loop_3_raw.zip |
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large_non_loop - 1727 frames The camera moves along a long trajectory without closing a loop. |
large_non_loop_mono.zip large_non_loop_stereo.zip large_non_loop_rgbd.zip large_non_loop_imu.zip large_non_loop_raw.zip |
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motion_2 - 401 frames The camera films a person who moves several objects in a scene with two tables. |
motion_2_mono.zip motion_2_stereo.zip motion_2_rgbd.zip motion_2_imu.zip motion_2_raw.zip |
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motion_3 - 1119 frames The camera films a person who moves several objects in a scene with two tables. |
motion_3_mono.zip motion_3_stereo.zip motion_3_rgbd.zip motion_3_imu.zip motion_3_raw.zip |
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motion_4 - 2833 frames The camera films a person who moves several objects in a scene with two tables. |
motion_4_mono.zip motion_4_stereo.zip motion_4_rgbd.zip motion_4_imu.zip motion_4_raw.zip |
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planar_1 - 425 frames The camera views a painting which is mostly planar. It is important to use photometric information to track the camera pose here, since the geometry does likely not sufficiently constrain it. |
planar_1_mono.zip planar_1_stereo.zip planar_1_rgbd.zip planar_1_imu.zip planar_1_raw.zip |
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reflective_2 - 1128 frames The camera views a reflective (and very dark) TV screen. This makes it very hard to correctly estimate depth or track features. |
reflective_2_mono.zip reflective_2_stereo.zip reflective_2_rgbd.zip reflective_2_imu.zip reflective_2_raw.zip |
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scale_change - 406 frames The camera starts close to an object, moves far away, and then returns. Correctly associating the initial and final views is likely important for high accuracy. |
scale_change_mono.zip scale_change_stereo.zip scale_change_rgbd.zip scale_change_imu.zip scale_change_raw.zip |
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table_1 - 858 frames The camera slowly moves around a blank table with two chairs next to it. |
table_1_mono.zip table_1_stereo.zip table_1_rgbd.zip table_1_imu.zip table_1_raw.zip |
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table_2 - 1136 frames The camera slowly moves around a blank table to create a 3D reconstruction of it. |
table_2_mono.zip table_2_stereo.zip table_2_rgbd.zip table_2_imu.zip table_2_raw.zip |
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table_5 - 934 frames The camera slowly moves around a table with some objects on it and some clutter next to it. |
table_5_mono.zip table_5_stereo.zip table_5_rgbd.zip table_5_imu.zip table_5_raw.zip |
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table_6 - 1409 frames The camera slowly moves around a table with some objects on it and some clutter next to it. |
table_6_mono.zip table_6_stereo.zip table_6_rgbd.zip table_6_imu.zip table_6_raw.zip |
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table_global_light_changes - 2556 frames While the camera moves through a scene with several objects on two tables, the room lights are turned on and off. |
table_global_light_changes_mono.zip table_global_light_changes_stereo.zip table_global_light_changes_rgbd.zip table_global_light_changes_imu.zip table_global_light_changes_raw.zip |
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table_local_light_changes - 1931 frames The camera moves through a scene with several objects on two tables. The scene is only illuminated by a hand-held flashlight, which may be a very challenging illumination condition for SLAM systems. |
table_local_light_changes_mono.zip table_local_light_changes_stereo.zip table_local_light_changes_rgbd.zip table_local_light_changes_imu.zip table_local_light_changes_raw.zip |
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table_scene - 1116 frames The camera moves around in a scene with objects on two tables and some clutter. |
table_scene_mono.zip table_scene_stereo.zip table_scene_rgbd.zip table_scene_imu.zip table_scene_raw.zip |
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trashbin - 573 frames The camera slowly moves around a trashbin to create a 3D reconstruction of it. There is a translucent foil on the trashbin which may cause issues with depth sensing. |
trashbin_mono.zip trashbin_stereo.zip trashbin_rgbd.zip trashbin_imu.zip trashbin_raw.zip |
Calibration datasets
These raw datasets in rosbag format were used to calibrate the camera system. They are provided here to make it possible to improve upon our calibration. If using the already-processed, non-raw datasets, these calibration datasets do not need to be used.
Note that the images of the RGB cameras are set to 'mono8' encoding, however they actually use a Bayer pattern which would correspond to the 'bayer_rggb8' encoding. This means that the pattern consists of the following repeating block:
R G
G B
A list of dead pixels or pixels with broken color filter that we compiled is provided here: dead_pixels.txt.
Please note that in some of the raw datasets, the images of some cameras may be shifted to the left/right by two pixels, which is for example noticeable from the location of the dead pixels, and potentially from missing data on the left image border. This is due to a bug in the camera system that we used, and needs to be detected and corrected on a per-dataset basis.
The images also have artifacts at the borders that should be cut off. We cut the following numbers of pixels from different sides: left 4, bottom 10, right 2, top 2.
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Raw calibration dataset for videos recorded in the motion capturing system (with 4 cameras), using a large checkerboard pattern. |
2018-08-13-15-44-25-camera-calibration.bag large_checkerboard_target.yaml (ideal geometry of the checkerboard; compatible with Kalibr) |
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Raw calibration dataset for videos recorded outside of the motion capturing system (with 8 cameras), using several small checkerboard patterns tagged with AprilTags. |
2018-10-18-15-30-00-8-camera-calibration.bag small_checkerboard_pattern.txt (ideal geometry of a single checkerboard; one 3D corner point per line) |
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Several raw calibration datasets for fixed-pattern noise calibration. Consists of black recordings, in which images are supposed to show pure black, and homogeneous recordings, in which images are supposed to show approximately homogeneous color. |
fpn_calibration_datasets.zip |
| Per-dataset calibration sequences: We recorded data for camera rig extrinsics refinement and for camera-mocap calibration for each dataset. However, one such calibration set may correspond to multiple datasets. In a raw dataset archive, the text file calibration_dataset.txt contains the name of the corresponding calibration dataset. The calibration dataset can then be downloaded from: https://www.eth3d.net/data/slam/calibration/dataset-name.zip. The Python download script can download these automatically. | ||
Synthetic datasets
These datasets were used to evaluate the effect of rolling shutter and asynchronous frames in our paper on BAD SLAM. They are not part of the ETH3D SLAM benchmark, but provided here for the case that they may be useful.
The datasets with suffix clean are perfect renderings without any distortions.
The datasets with suffix async have asynchronous color and depth images.
The datasets with suffix rs use simulated rolling shutter cameras.
The datasets with suffix async_rs both have asynchronous frames and rolling shutter cameras.
The datasets were created by first making 3D reconstructions of TUM RGB-D benchmark datasets with SurfelMeshing and then rendering those. The 3D reconstructions may serve as ground truth geometry and can be downloaded here: synthetic_groundtruth_meshes.zip.
The original TUM RGB-D datasets are licensed under a CC BY 4.0 license and were authored by J. Sturm, N. Engelhard, F. Endres, W. Burgard, and D. Cremers.
Downloads:
rgbd_dataset_freiburg1_360 -
clean
async
rs
async_rs
rgbd_dataset_freiburg1_desk2 -
clean
async
rs
async_rs
rgbd_dataset_freiburg1_desk -
clean
async
rs
async_rs
rgbd_dataset_freiburg1_rpy -
clean
async
rs
async_rs
rgbd_dataset_freiburg1_xyz -
clean
async
rs
async_rs
rgbd_dataset_freiburg3_long_office_household -
clean
async
rs
async_rs
rgbd_dataset_freiburg3_nostructure_texture_near_withloop -
clean
async
rs
async_rs