A Category-Level 3-D Object Dataset: Putting the Kinect to Work

A Category-Level 3-D Object Dataset: Putting the Kinect to Work Allison Janoch, Sergey Karayev, Yangqing Jia, Jonathan T. Barron, Mario Fritz, Kate Saenko, Trevor Darrell UC Berkeley and Max-Plank-Institute for Informatics fallie, sergeyk, jiayq, barron, saenko, [email protected], [email protected] Abstract Recent proliferation of a cheap but quality depth sensor, the Microsoft Kinect, has brought the need for a chal- lenging category-level 3D object detection dataset to the fore. We review current 3D datasets and find them lack- ing in variation of scenes, categories, instances, and viewpoints. Here we present our dataset of color and depth image pairs, gathered in real domestic and office environments. It currently includes over 50 classes, with more images added continuously by a crowd-sourced collection effort. We establish baseline performance in a PASCAL VOC-style detection task, and suggest two ways that in- ferred world size of the object may be used to improve detection. The dataset and annotations can be downloaded at http://www.kinectdata.com. Figure 1. Two scenes typical of our dataset. 1. Introduction ously published datasets. The latest version of the dataset is available at http://www.kinectdata.com Recently, there has been a resurgence of interest in available 3-D sensing techniques due to advances in active depth As with existing 2-D challenge datasets, our dataset has sensing, including techniques based on LIDAR, time-of- considerable variation in pose and object size. An impor- flight (Canesta), and projected texture stereo (PR2). The tant observation our dataset enables is that the actual world Primesense sensor used on the Microsoft Kinect gaming size distribution of objects has less variance than the image- interface offers a particularly attractive set of capabilities, projected, apparent size distribution. We report the statistics and is quite likely the most common depth sensor available of these and other quantities for categories in our dataset. worldwide due to its rapid market acceptance (8 million A key question is what value does depth data offer for Kinects were sold in just the first two months). category level recognition? It is conventional wisdom that While there is a large literature on instance recogni- ideal 3-D observations provide strong shape cues for recog- tion using 3-D scans in the computer vision and robotics nition, but in practice even the cleanest 3-D scans may re- literatures, there are surprisingly few existing datasets for veal less about an object than available 2-D intensity data. category-level 3-D recognition, or for recognition in clut- Numerous schemes for defining 3-D features analogous to tered indoor scenes, despite the obvious importance of this popular 2-D features for category-level recognition have application to both communities. As reviewed below, pub- been proposed and can perform in uncluttered domains. We lished 3-D datasets have been limited to instance tasks, or evaluate the application of HOG descriptors on 3D data and to a very small numbers of categories. We have collected evaluate the benefit of such a scheme on our dataset. We and describe here the initial bulk of the Berkeley 3-D Ob- also use our observation about world size distribution to ject dataset (B3DO), an ongoing collection effort using the place a size prior on detections, and find that it improves Kinect sensor in domestic environments. The dataset al- detections as evaluated by average precision, and provides ready has an order of magnitude more variation than previ- a potential benefit for detection efficiency. 2. Related Work There have been numerous previous efforts in collect- ing datasets with aligned 2D and 3D observations for object recognition and localization. We review the most pertinent ones, and briefly highlight how our dataset is different. We also give a brief overview of previous work targeting the integration of the 2D appearance and depth modalities. Figure 2. Illustration of our depth smoothing method. 2.1. 3D Datasets for Detection examples of the “chair” category in our dataset. These qual- We present an overview of previously published datasets ities make our dataset more representative of the kind of that combine 2D and 3D observation and contrast our data that can actually be seen in people’s homes; data that a dataset from those previous efforts: domestic service robot would be required to deal with and RGBD-dataset of [21]: This dataset from Intel Research do online training on. and UW features 300 objects in 51 categories. The category count refers to nodes in a hierarchy, with, for example, 2.2. 3D and 2D/3D Recognition coffee mug having mug as parent. Each category is repre- A comprehensive review of all 3-D features proposed for sented by 4-6 instances, which are densely photographed recognition is beyond the scope of our work. Briefly, no- on a turntable. For object detection, only 8 short video clips table prominent techniques include spin images [20], 3-D are available, which lend themselves to evaluation of just 4 shape context [15], and the recent VFH model [23]–but this categories (bowl, cap, coffee mug, and soda can) and 20 in- list is not exhaustive. stances. There does not appear to be significant viewpoint A number of 2D/3D hybrid approaches have been re- variation in the detection test set. cently proposed, and our dataset should be a relevant testbed UBC Visual Robot Survey [3, 19] : This dataset from for these methods. A multi-modal object detector in which UBC provides training data for 4 categories (mug, bottle, 2D and 3D are traded off in a logistic classifier is proposed bowl, and shoe) and 30 cluttered scenes for testing. Each by [16]. Their method leverages additional handcrafted fea- scene is photographed in a controlled setting from multiple ture derived from the 3D observation such as “height above viewpoints. ground” and “surface normal”, which provide contextual in- 3D table top object dataset [24]: This dataset from formation. [24] shows how to benefit from 3D training data University of Michigan covers 3 categories (mouse, mug, in a voting based method. Fritz et al. [14] extends branch stapler) and provides 200 test images with cluttered back- & bound efficient detection to 3D and adds size and support grounds. There is no significant viewpoint variation in the surface constraints derived from the 3D observation. test set. Most prominently, a set of methods have been proposed Solutions in Perception Challenge [2]: This dataset for fusing 2D and 3D information for the task of pedestrian from Willow Garage forms the challenge which took place detection. The popular HOG detector [8] to disparity-based in conjunction with International Conference on Robotics features is extended by [18]. A late integration approach is and Automation 2011, and is instance-only. It consists of proposed by [22] for combining detectors on the appearance 35 distinct objects such as branded boxes and household as well as depth image for pedestrian detection. Instead of cleaner bottles that are presented in isolation for training directly learning on the depth map, [25] uses a depth statis- and in 27 scenes for test. tic that learns to enforce height constraints of pedestrians. Other datasets: Beyond these, other datasets have Finally, [10] explores pedestrian detection by using stereo been made available which do include simultaneous cap- and temporal information in a hough voting framework also ture of image and depth but serve more specialized purposes using scene constraints. like autonomous driving [1], pedestrian detection [10] and driver assistance [25]. Their specialized nature means that 3. Our Dataset they cannot be leveraged for the multi-object category localization task that is our goal. We have compiled a large-scale dataset of images taken In contrast to all of these datasets, our dataset contains in domestic and office settings with the commonly available both a large number of categories and many different in- Kinect sensor. The sensor provides a color and depth image stances per category, is photographed “in the wild” instead pair, and is processed by us for alignment and inpainting. of in a controlled turntable setting, and has significant vari- The data was collected by many members of our research ation in lighting and viewpoint throughout the set. For an community, as well as Amazon Mechanical Turk (AMT) illustration, consider Figure4, which presents a sample of workers, enabling us to have impressive variety in scene and chair table cup monitor book bottle pen bowl keyboard sofa mouse pillow phone power outlet speaker bookcase laptop cabinet door handle headphones light switch shoe door handle remote towel scissors plate toothbrush keys plate trash can glass magazine medicine cell phone stapler toothpaste eyeglasses pen or pencil desk lamp laptop printer backpack kleenex container calculator fork notebook table_knife soap spoon tapedispenser desk_lamp mail bike can ruler wallet purse wristwatch bike_helmet hair_brush medicine_box paper_notebook ring_binder tv_remote letter_tray back_pack computer_mouse shirt coin plastic_food_container shoe_or_sandal glasses marker sock credit_card newspaper pants bill tape_dispenser file_cabinet lighter 250 219 202 200 189 161 131 126 106 86 84 71 66 59 53 46 44 42 41 40 38 38 36 36 34 32 31 28 27 26 26 25 25 25 22 21 21 20 20 19 19 19 18 18 17 16 15 15 15 14 14 14 13 13 11 11 11 11 10 10 9 9 9 7 7 7 6 5 5 5 4 4 4 3 3 3 2 2 2 1 1 0 0 250 200 150 100 50 0 cup pen sofa keys chair plate shoe glass bowl book table plate towel pillow bottle phone mouse laptop stapler scissors remote monitor cabinet speaker trash can trash medicine keyboard bookcase magazine cell phone toothbrush eyeglasses lightswitch toothpaste door handle door handle power outlet headphones pen or pencil Figure 3.

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