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BOP是6D位姿估计基准#xff0c;汇总整理了多个数据集#xff0c;还举行挑战赛#xff0c;相关报告被CVPR2024接受和认可。
它提供3D物体模型和RGB-D图像#xff0c;其中标注信息包括6D位姿、2D边界框和2D蒙版等。
包含数据集#xff1a;LM 、LM-O 、T-LESS 、IT…前言
BOP是6D位姿估计基准汇总整理了多个数据集还举行挑战赛相关报告被CVPR2024接受和认可。
它提供3D物体模型和RGB-D图像其中标注信息包括6D位姿、2D边界框和2D蒙版等。
包含数据集LM 、LM-O 、T-LESS 、ITODD 、HB 、HOPE 、YCB-V 、RU-APC 、IC-BIN 、IC-MI 、TUD-L、TYO-L 数据集汇总地址https://bop.felk.cvut.cz/datasets/
BOP细分数据集标注内容特点数据集LM15 件无纹理的家居用品具有不同的颜色、形状和大小该实例具有明显的杂波但只有轻微的遮挡LM-O 15 件无纹理的家居用品具有不同的颜色、形状和大小在LM基础上引入了不同遮挡级别的干扰T-LESS30 个与工业相关的物体没有明显的纹理或可辨别的颜色ITODD28 个真实的工业环境中物体使用高质量的 Gray-D 传感器拍摄HB33 个物体包括17 个玩具物体、8 个家居物体和 8 个行业相关物体在 13 个场景中捕获复杂程度各不相同HOPE28 个玩具杂货物品10 个家庭/办公环境中的 50 个场景中捕获杂乱场景有不同程度的遮挡YCB-V21个日常生活物品具有有不同形状、大小、纹理、重量和刚度在92个视频中捕获RU-APC14 个纹理物品杂乱仓库货架场景杂乱场景IC-BIN2个物体来自IC-MI的箱子拾取场景受到严重遮挡IC-MI2个无纹理和4个有纹理的家居用品具有杂乱和轻微遮挡TUD-L3个移动物体8种照明变化场景8种照明条件下的移动物体TYO-L21 个物体每个物体在桌面设置中以多种姿势捕捉有4个不同的 桌布和五种不同的照明条件 一、数据集下载
BOP提供了整理汇总后的数据集
下载地址https://huggingface.co/datasets/bop-benchmark/datasets/tree/main 比如下载YCB-V数据集点进去能看到然后点击下载即可 或者下载LMLinemod数据集 二、数据格式
默认采用 BOP-webdataset 格式数据集具有以下结构
DATASET_NAME
├─ camera[_TYPE].json # 相机参数仅用于模拟传感器
├─ dataset_info.json # 特定于数据集的信息
├─ test_targets_bop19.json # 用于评估的测试目标列表BOP挑战赛 2019/2020/2022等。
├─ models[_MODELTYPE][_eval] # 3D物体模型
│ ├─ models_info.json
│ ├─ obj_OBJ_ID.ply
├─ train|val|test[_TYPE] # 对应训练集、验证集、测试集
│ ├─ SCENE_ID|OBJ_ID
│ │ ├─ scene_camera.json # 相机参数真实数据的相机
│ │ ├─ scene_gt.json
│ │ ├─ scene_gt_info.json
│ │ ├─ depth # 深度图
│ │ ├─ mask # 物体完整mask掩码图
│ │ ├─ mask_visib # 物体实际可见部分的mask掩码图
│ │ ├─ rgb|gray # 彩色图/灰度图
其中相应图像具有相同的 ID例如 rgb/000000.png 和 depth/000000.png 是颜色和深度图像 相同的RGB-D帧。掩码的命名约定是IMID_GTID.png 其中 IMID 是影像 IDGTID 是真值注释的索引 存储在scene_gt.json中。
详细介绍参考官方文档https://github.com/thodan/bop_toolkit/blob/master/docs/bop_datasets_format.md 2.1 相机参数 scene_camera.json
通常每个物体有一组图像数据数据集有多个物体形成多组数据。
每组图像都有文件scene_camera.json表示真实相机的参数其中包含每个图像的以下信息
cam_K - 3x3 固有相机矩阵 K。depth_scale - 将深度图像乘以此系数得到以毫米为单位的深度。cam_R_w2c可选- 3x3 旋转矩阵R_w2c。cam_t_w2c可选- 3x1 平移向量t_w2c。view_level可选- 视点细分级别。
注意每个图像的矩阵 K 可能不同。camera.json仅表示用于在渲染训练图像时模拟使用的传感器。 2.2 真实姿势信息 scene_gt_info.json
文件scene_gt_info.json中提供了以下有关地面真实姿势的信息
bbox_obj - 对象轮廓的 2D 边界框由 xy widthheight其中x, y是边界框的左上角。bbox_visib - 对象剪影可见部分的 2D 边界框。px_count_all - 对象侧面像中的像素数。px_count_valid - 对象侧面图像中具有有效 深度测量即深度图像中的非零值。px_count_visib - 对象可见部分的像素数 剪影。visib_fract - 对象轮廓的可见部分 px_count_visib/px_count _all。 2.3 3D物体模型
3D 对象模型以PLY格式提供包括顶点法线。
大多数模型还包括顶点颜色或顶点纹理与保存为单独图像的纹理相协调。顶点法线是使用 MeshLab 作为面的角度加权和计算的 入射到顶点的法线每个包含对象模型的文件夹都包含文件models_info.json其中包括每个对象模型的 3D 边界框和直径。直径为计算为任意一对模型顶点之间的最大距离。 2.4 坐标系
所有坐标系模型、相机、世界都是右手坐标系。
在模型坐标系中Z 轴指向上方当对象 站立“自然直立”原点与中心重合 对象模型的 3D 边界框。相机坐标系与 OpenCV 中一样相机沿 Z 轴查看。
单位信息
3D 物体模型1 毫米平移矢量 1 mm 2.5 数据格式示例
以YCB-V数据集为例看下其中一个物体的示例数据 depth文件夹存放深度图 mask文件夹中存放物体完整mask掩码图 mask_visib文件夹中存放物体实际可见部分的mask掩码图 rgb文件夹存放彩色图片 scene_camera.json文件
{1: {cam_K: [1066.778, 0.0, 312.9869, 0.0, 1067.487, 241.3109, 0.0, 0.0, 1.0], cam_R_w2c: [0.775038, 0.630563, -0.0413049, 0.1427, -0.238322, -0.960645, -0.615591, 0.738643, -0.27469], cam_t_w2c: [22.278120142899976, 67.27103635299997, 833.583980809], depth_scale: 0.1},36: {cam_K: [1066.778, 0.0, 312.9869, 0.0, 1067.487, 241.3109, 0.0, 0.0, 1.0], cam_R_w2c: [0.780182, 0.625096, -0.0238987, 0.151021, -0.225288, -0.962516, -0.607049, 0.747329, -0.270168], cam_t_w2c: [5.508214978099937, 64.68344464100001, 825.7533207070001], depth_scale: 0.1},47: {cam_K: [1066.778, 0.0, 312.9869, 0.0, 1067.487, 241.3109, 0.0, 0.0, 1.0], cam_R_w2c: [0.767769, 0.640207, -0.0257968, 0.152583, -0.221792, -0.963082, -0.622293, 0.735488, -0.26797], cam_t_w2c: [27.66960968699998, 63.071926080000004, 832.8279904959999], depth_scale: 0.1},83: {cam_K: [1066.778, 0.0, 312.9869, 0.0, 1067.487, 241.3109, 0.0, 0.0, 1.0], cam_R_w2c: [0.747494, 0.663726, -0.0268341, 0.151009, -0.209129, -0.966158, -0.646876, 0.718145, -0.256551], cam_t_w2c: [47.37378315260004, 70.23191834300002, 839.83011703], depth_scale: 0.1},112: {cam_K: [1066.778, 0.0, 312.9869, 0.0, 1067.487, 241.3109, 0.0, 0.0, 1.0], cam_R_w2c: [0.732966, 0.679258, -0.0369964, 0.154704, -0.219403, -0.963291, -0.66244, 0.700336, -0.265899], cam_t_w2c: [57.30521621640001, 60.03261259300005, 845.084446656], depth_scale: 0.1},1024: {cam_K: [1066.778, 0.0, 312.9869, 0.0, 1067.487, 241.3109, 0.0, 0.0, 1.0], cam_R_w2c: [0.221497, 0.974633, -0.0320828, 0.293663, -0.0980389, -0.950868, -0.929893, 0.201193, -0.307929], cam_t_w2c: [84.5553717548, 78.09197834190002, 966.855761809], depth_scale: 0.1},1027: {cam_K: [1066.778, 0.0, 312.9869, 0.0, 1067.487, 241.3109, 0.0, 0.0, 1.0], cam_R_w2c: [0.208285, 0.977538, -0.0322094, 0.291752, -0.0935287, -0.95191, -0.933541, 0.188871, -0.304679], cam_t_w2c: [95.29885425239999, 83.36950151319999, 964.165435848], depth_scale: 0.1},1059: {cam_K: [1066.778, 0.0, 312.9869, 0.0, 1067.487, 241.3109, 0.0, 0.0, 1.0], cam_R_w2c: [0.184825, 0.982726, -0.00947274, 0.310088, -0.0674605, -0.948311, -0.932569, 0.172334, -0.3172], cam_t_w2c: [100.85003064122, 67.49651895950004, 969.0728422079999], depth_scale: 0.1},
...
}
scene_gt.json文件
{1: [{cam_R_m2c: [0.6155426282490462, -0.7872002747152219, -0.03771988906432555, -0.19894986552077276, -0.10889926681888931, -0.9739401059834828, 0.7625789371251613, 0.6070060649389416, -0.22364550906920733], cam_t_m2c: [-31.677025422232273, -17.368816807616497, 865.056765056294], obj_id: 1}, {cam_R_m2c: [-0.888800154374123, 0.45524946687574985, -0.05275501966907288, 0.15161264602174085, 0.18344755196788856, -0.9712669209190921, -0.4324911950904046, -0.8712604167821704, -0.2320697374100931], cam_t_m2c: [19.826959191155, 56.52491050704691, 810.7227026810766], obj_id: 6}, {cam_R_m2c: [0.800270328363595, -0.5984833728683278, -0.03721742358490441, -0.16673638817023093, -0.16247691893389601, -0.97252257766676, 0.5759917033378716, 0.7844862673094515, -0.22981459219802255], cam_t_m2c: [-23.310093543898354, -112.62940453072957, 848.1679482820954], obj_id: 14}, {cam_R_m2c: [-0.09489741249029623, 0.3561386300696222, -0.9296019480411393, -0.34937416104044705, 0.8625018988232865, 0.36609704814610067, 0.932165006412512, 0.3595205652274382, 0.04257586846391211], cam_t_m2c: [52.33599371521044, -13.031296641365195, 861.5899289137005], obj_id: 19}, {cam_R_m2c: [-0.9984789779904625, -0.046702003100451896, 0.029310392655164948, 0.04118009201782687, -0.27814158992816, 0.9596564448294642, -0.036666017780062427, 0.9594042011059428, 0.27964165456635065], cam_t_m2c: [-24.98442834314123, 91.09670661036117, 685.170819763342], obj_id: 20}],36: [{cam_R_m2c: [0.609160304011732, -0.7927779371553533, -0.020665842847375732, -0.18625494062652728, -0.11768910284587072, -0.9754267744096374, 0.7708650760967027, 0.5980404962914244, -0.2193500045932242], cam_t_m2c: [-47.54392055014297, -20.624979740453274, 857.3694396811588], obj_id: 1}, {cam_R_m2c: [-0.885654032823929, 0.46297766711836735, -0.03563597970836668, 0.13642613789028796, 0.18608279820967355, -0.9730156842215326, -0.4438532397201184, -0.8666167711437901, -0.22796686931874305], cam_t_m2c: [3.517734961917874, 53.76740226064, 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-0.11905916022552619, -0.975974147637646, 0.7593548693744658, 0.6134888758861301, -0.21681937469597998], cam_t_m2c: [-24.395090683468215, -21.783960857519098, 865.0794295130081], obj_id: 1}, {cam_R_m2c: [-0.894520458378818, 0.4455202651911757, -0.03668433387925216, 0.13243670002639793, 0.18573890794853912, -0.9736329537290823, -0.42695875163671215, -0.8757921280786526, -0.22515068766577487], cam_t_m2c: [25.357235475837868, 52.66179328072182, 809.873745451658], obj_id: 6}, {cam_R_m2c: [0.807320112848335, -0.5897361063376075, -0.02111051938004192, -0.1481874231217827, -0.16797481282866994, -0.9745899708885118, 0.5712038666214954, 0.7899336536550774, -0.22300135488429054], cam_t_m2c: [-14.412173387618193, -116.98979989756661, 848.76946639409], obj_id: 14}, {cam_R_m2c: [-0.08176790305726558, 0.34291594675714004, -0.9358005665368483, -0.34465464710178356, 0.8712861049416898, 0.34938989394733905, 0.9351606006571631, 0.3510967987887798, 0.046944407423005166], cam_t_m2c: [59.49748765951539, -15.940989444141788, 860.9665492937945], obj_id: 19}, {cam_R_m2c: [-0.9993032187000758, -0.03468586763469483, 0.013799769394337228, 0.02273746658654025, -0.27237133848523803, 0.9619233928797483, -0.02960648331060263, 0.9615662504172701, 0.2729703664836574], cam_t_m2c: [-20.983454465364908, 85.55142809136696, 684.4252367092936], obj_id: 20}],83: [{cam_R_m2c: [0.6483560298162269, -0.7610510488761957, -0.02086610662784509, -0.1692011189126235, -0.11731610680604301, -0.9785743648106761, 0.7422968557005021, 0.6379953191679695, -0.2048329708565904], cam_t_m2c: [-3.182798020397842, -14.00835493497242, 873.8116262427217], obj_id: 1}, {cam_R_m2c: [-0.9078584178495492, 0.4177062056136942, -0.03626756862269121, 0.12116889278699025, 0.17857338188688224, -0.9764368511592453, -0.4013865966547271, -0.8908605421313989, -0.21273198641370872], cam_t_m2c: [44.345559041277, 60.13133053644625, 816.2833963297118], obj_id: 6}, {cam_R_m2c: [0.8251367861465224, -0.5645561164478825, -0.020629748503466508, -0.13583743253946046, -0.16282504159094316, -0.9772597366958712, 0.5483580610786056, 0.8091749254401457, -0.2110406512057419], cam_t_m2c: [7.00537338156821, -109.3266555886105, 858.3053675688028], obj_id: 14}, {cam_R_m2c: [-0.051182081267789885, 0.34687788183738805, -0.9365126806882695, -0.33423610323086433, 0.8777158286503173, 0.3433663873103977, 0.9410979073728976, 0.3305899343507701, 0.07101581339481473], cam_t_m2c: [80.50375242502996, -7.627733393267646, 867.1132222035583], obj_id: 19}, {cam_R_m2c: [-0.9998814983012471, -0.003861774590082771, 0.014912517026945876, 0.015403543280134506, -0.26132102760791526, 0.9651287621047637, 0.0001699907030837675, 0.9652438747296362, 0.2613491515087478], cam_t_m2c: [-5.972908641135359, 91.22401864073694, 691.9137142738838], obj_id: 20}],112: [{cam_R_m2c: [0.6647649468317126, -0.7464489096042396, -0.03001657167948718, -0.17934682759164805, -0.1204579130201553, -0.9763836821566041, 0.725204605642379, 0.6544489331842311, -0.21394953128846064], 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...
}
scene_gt_info.json文件
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...
}三、BOP Challenge 2023数据集CVPR2024
这些数据集包括超过 2M 张图像显示超过 50K 个不同的对象。
这些图像最初是使用 BlenderProc 为 MegaPose 合成的。这些对象来自 Google Scanned Objects 和 ShapeNetCore 数据集其 3D 模型可从各自的网站下载。 3.1 MegaPose-GSO数据集 3D物体模型可以从Google扫描物体下载。为了使模型与 GT 姿势兼容需要将对象居中并重新缩放它们使其适合单位球体以及将归一化模型缩放 0.1。有关伪代码参阅此注释。从 BOP 中使用的obj_id映射到原始对象标识符从映像键映射到存储该键的分片索引数据集采用BOP-webdataset格式分为1040个分片每个分片包含~1000张图片以及对象注解和相机参数。 使用以下 URL 模板下载分片 is from to 。SHARD-ID000000至001039 https://huggingface.co/datasets/bop-benchmark/datasets/resolve/main/MegaPose-GSO/shard-SHARD-ID.tar 比如 https://bop.felk.cvut.cz/media/data/bop_datasets/bop23_datasets/megapose-gso/train_pbr_web/shard-000000.tar https://bop.felk.cvut.cz/media/data/bop_datasets/bop23_datasets/megapose-gso/train_pbr_web/shard-000001.tar ...... https://bop.felk.cvut.cz/media/data/bop_datasets/bop23_datasets/megapose-gso/train_pbr_web/shard-001039.tar 3.2 MegaPose-ShapeNetCore 数据集
可以从 ShapeNet 下载 3D 对象模型将模型缩放 0.1 以与 GT 姿势兼容从 BOP 中使用的obj_id映射到原始对象标识符从映像键映射到存储该键的分片索引数据集采用BOP-webdataset格式分为1040个分片每个分片包含~1000张图片以及对象注解和相机参数。
使用以下 URL 模板下载分片 is from to 。SHARD-ID000000至001039
https://huggingface.co/datasets/bop-benchmark/datasets/resolve/main/MegaPose-ShapeNetCore/shard-SHARD-ID.tar 四、BOP挑战赛 6D位姿估计
BOP Challenge 2023 报告已被 CVPR 2024接收和认可下面6D位姿估计的排行榜。
主要在LM-O, T-LESS, TUD-L, IC-BIN, ITODD, HB, YCB-V数据集进行训练和测试的。
对应可见物体的测试排行榜https://bop.felk.cvut.cz/leaderboards/ 对应不可见物体的测试排行榜https://bop.felk.cvut.cz/leaderboards/pose-estimation-unseen-bop23/core-datasets/ 推荐一下Top2的方法
Top1——FoundationPose CVPR2024 https://github.com/NVlabs/FoundationPose
Top2——SAM-6D CVPR2024 https://github.com/JiehongLin/SAM-6D/ 分享完成~
本文先介绍到这里后面会分享“6D位姿估计”的其它数据集、算法、代码、具体应用示例。
