Collecting 3D object datasets involves a large amount of manual work and is time consuming. Getting complete models of objects either requires a 3D scanner that covers all the surfaces of an object or one needs to rotate it to completely observe it. We present a system that incrementally builds a database of objects as a mobile agent traverses a scene. Our approach requires no prior knowledge of the shapes present in the scene. Object-like segments are extracted from a global segmentation map, which is built online using the input of segmented RGB-D images. These segments are stored in a database, matched among each other, and merged with other previously observed instances. This allows us to create and improve object models on the fly and to use these merged models to reconstruct also unobserved parts of the scene. The database contains each (potentially merged) object model only once, together with a set of poses where it was observed. We evaluate our pipeline with one public dataset, and on a newly created Google Tango dataset containing four indoor scenes with some of the objects appearing multiple times, both within and across scenes.
This work proposes a real-time segmentation method for 3D point clouds obtained via Simultaneous Localization And Mapping (SLAM). The proposed method incrementally merges segments obtained from each input depth image in a unified global model using a SLAM framework. Differently from all other approaches, our method is able to yield segmentation of scenes reconstructed from multiple views in real-time, with a complexity that does not depend on the size of the global model. At the same time, it is also general, as it can be deployed with any frame-wise segmentation approach as well as any SLAM algorithm. We validate our proposal by a comparison with the state of the art in terms of computational efficiency and accuracy on a benchmark dataset, as well as by showing how our method can enable real-time segmentation from reconstructions of diverse real indoor environments. We present a real-time algorithm that segments unstructured and highly cluttered scenes. The algorithm robustly separates objects of unknown shape in congested scenes of stacked and occluded objects. The model-free approach finds smooth surface patches, using a depth image from a Kinect camera, which are subsequently combined to form highly probable object hypotheses. Coplanarity and curvature matching is used to recombine surfaces separated by occlusion. The real-time capabilities are proven and the quality of the algorithm is evaluated on a benchmark database. Advantages compared to existing approaches as well as weaknesses are discussed.  Localization in 3-D point clouds is a highly challenging task due to the complexity associated with extracting information from 3-D data. This letter proposes an incremental approach addressing this problem efficiently. The presented method first accumulates the measurements in a dynamic voxel grid and selectively updates the point normals affected by the insertion. An incremental segmentation algorithm, based on region growing, tracks the evolution of single segments, which enables an efficient recognition strategy using partitioning and caching of geometric consistencies. We show that the incremental method can perform global localization at 10 Hz in an urban driving environment, a speedup of @math 7.1 over the compared batch solution. The efficiency of the method makes it suitable for applications where real-time localization is required and enables its usage on cheaper low-energy systems. Our implementation is available open source along with instructions for running the system. (The implementation is available at https: github.com ethz-asl segmatch and a video demonstration is available at https: youtu.be cHfs3HLzc2Y .) Robots that are operating for extended periods of time need to be able to deal with changes in their environment and represent them adequately in their maps. In this paper, we present a novel 3D reconstruction algorithm based on an extended Truncated Signed Distance Function (TSDF) that enables to continuously refine the static map while simultaneously obtaining 3D reconstructions of dynamic objects in the scene. This is a challenging problem because map updates happen incrementally and are often incomplete. Previous work typically performs change detection on point clouds, surfels or maps, which are not able to distinguish between unexplored and empty space. In contrast, our TSDF-based representation naturally contains this information and thus allows us to more robustly solve the scene differencing problem. We demonstrate the algorithms performance as part of a system for unsupervised object discovery and class recognition. We evaluated our algorithm on challenging datasets that we recorded over several days with RGB-D enabled tablets. To stimulate further research in this area, all of our datasets are publicly available3. This paper addresses the problem of segmenting an image into regions. We define a predicate for measuring the evidence for a boundary between two regions using a graph-based representation of the image. We then develop an efficient segmentation algorithm based on this predicate, and show that although this algorithm makes greedy decisions it produces segmentations that satisfy global properties. We apply the algorithm to image segmentation using two different kinds of local neighborhoods in constructing the graph, and illustrate the results with both real and synthetic images. The algorithm runs in time nearly linear in the number of graph edges and is also fast in practice. An important characteristic of the method is its ability to preserve detail in low-variability image regions while ignoring detail in high-variability regions.  We present a method for discovering object models from 3D meshes of indoor environments. Our algorithm first decomposes the scene into a set of candidate mesh segments and then ranks each segment according to its “objectness” - a quality that distinguishes objects from clutter. To do so, we propose five intrinsic shape measures: compactness, symmetry, smoothness, and local and global convexity. We additionally propose a recurrence measure, codifying the intuition that frequently occurring geometries are more likely to correspond to complete objects. We evaluate our method in both supervised and unsupervised regimes on a dataset of 58 indoor scenes collected using an Open Source implementation of Kinect Fusion [1]. We show that our approach can reliably and efficiently distinguish objects from clutter, with Average Precision score of .92. We make our dataset available to the public.    Recent years have seen tremendous progress in still-image segmentation; however the naive application of these state-of-the-art algorithms to every video frame requires considerable computation and ignores the temporal continuity inherent in video. We propose a video recognition framework that relies on two key observations: (1) while pixels may change rapidly from frame to frame, the semantic content of a scene evolves more slowly, and (2) execution can be viewed as an aspect of architecture, yielding purpose-fit computation schedules for networks. We define a novel family of “clockwork” convnets driven by fixed or adaptive clock signals that schedule the processing of different layers at different update rates according to their semantic stability. We design a pipeline schedule to reduce latency for real-time recognition and a fixed-rate schedule to reduce overall computation. Finally, we extend clockwork scheduling to adaptive video processing by incorporating data-driven clocks that can be tuned on unlabeled video. The accuracy and efficiency of clockwork convnets are evaluated on the Youtube-Objects, NYUD, and Cityscapes video datasets. To perform successful manipulation, robots depend on information about objects in their environment. In unstructured environments, such information cannot be given to the robot a priori. It is thus critical for the robot to be able to continuously acquire task-specific information about objects. Towards this goal, we present a robust perceptual skill for identifying, tracking, and segmenting objects in a cluttered environment. We increase the robot's perceptual capabilities by closely coupling them with the robot's manipulation skills. The robot's interaction with objects in the environment creates a perceptual signal, i.e. motion, that renders segmentation and tracking robust and reliable. In addition, the resulting perceptual signal reveals the type of segmentation most relevant to manipulation, namely a segmentation of rigidly connected physical bodies. We demonstrate our approach with experiments on a real world mobile manipulation platform with multiple objects in a cluttered scene. 