3D shape reconstruction from a single image is a highly ill-posed problem. Modern deep learning based systems try to solve this problem by learning an end-to-end mapping from image to shape via a deep network. In this paper, we aim to solve this problem via an online optimization framework inspired by traditional methods. Our framework employs a deep autoencoder to learn a set of latent codes of 3D object shapes, which are fitted by a probabilistic shape prior using Gaussian Mixture Model (GMM). At inference, the shape and pose are jointly optimized guided by both image cues and deep shape prior without relying on an initialization from any trained deep nets. Surprisingly, our method achieves comparable performance to state-of-the-art methods even without training an end-to-end network, which shows a promising step in this direction.
3D understanding of object shapes from images is considered an important step in scene perception. While mapping an environment using Structure from Motion @cite_25 @cite_22 and SLAM @cite_3 @cite_11 facilitates localization and navigation, a higher level of understanding about objects in terms of their shape and relative position with respect to the rest of the background is essential for their manipulation. Initial works, to localize objects while estimating its pose from an image, are limited to the case where a pre-scanned object structure is available @cite_38 @cite_58 @cite_21 .
@cite_38 @cite_22 @cite_21 @cite_3 @cite_58 @cite_25 @cite_11
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We present a dense reconstruction approach that overcomes the drawbacks of traditional multiview stereo by incorporating semantic information in the form of learned category-level shape priors and object detection. Given training data comprised of 3D scans and images of objects from various viewpoints, we learn a prior comprised of a mean shape and a set of weighted anchor points. The former captures the commonality of shapes across the category, while the latter encodes similarities between instances in the form of appearance and spatial consistency. We propose robust algorithms to match anchor points across instances that enable learning a mean shape for the category, even with large shape variations across instances. We model the shape of an object instance as a warped version of the category mean, along with instance-specific details. Given multiple images of an unseen instance, we collate information from 2D object detectors to align the structure from motion point cloud with the mean shape, which is subsequently warped and refined to approach the actual shape. Extensive experiments demonstrate that our model is general enough to learn semantic priors for different object categories, yet powerful enough to reconstruct individual shapes with large variations. Qualitative and quantitative evaluations show that our framework can produce more accurate reconstructions than alternative state-of-the-art multiview stereo systems. Since most current scene understanding approaches operate either on the 2D image or using a surface-based representation, they do not allow reasoning about the physical constraints within the 3D scene. Inspired by the "Blocks World" work in the 1960's, we present a qualitative physical representation of an outdoor scene where objects have volume and mass, and relationships describe 3D structure and mechanical configurations. Our representation allows us to apply powerful global geometric constraints between 3D volumes as well as the laws of statics in a qualitative manner. We also present a novel iterative "interpretation-by-synthesis" approach where, starting from an empty ground plane, we progressively "build up" a physically-plausible 3D interpretation of the image. For surface layout estimation, our method demonstrates an improvement in performance over the state-of-the-art [9]. But more importantly, our approach automatically generates 3D parse graphs which describe qualitative geometric and mechanical properties of objects and relationships between objects within an image. In this paper, we present an unsupervised framework for discovering, detecting, tracking, and reconstructing dense objects from a video sequence. The system simultaneously localizes a moving camera, and discovers a set of shape and appearance models for multiple objects, including the scene background. Each object model is represented by both a 2D and 3D level-set. This representation is used to improve detection, 2D-tracking, 3D-registration and importantly subsequent updates to the level-set itself. This single framework performs dense simultaneous localization and mapping as well as unsupervised object discovery. At each iteration portions of the scene that fail to track, such as bulk outliers on moving rigid bodies, are used to either seed models for new objects or to update models of known objects. For the latter, once an object is successfully tracked in 2D with aid from a 2D level-set segmentation, the level-set is updated and then used to aid registration and evolution of a 3D level-set that captures shape information. For a known object either learned by our system or introduced from a third-party library, our framework can detect similar appearances and geometries in the scene. The system is tested using single and multiple object data sets. Results demonstrate an improved method for discovering and reconstructing 2D and 3D object models, which aid tracking even under significant occlusion or rapid motion. We develop new representations and algorithms for three-dimensional (3D) object detection and spatial layout prediction in cluttered indoor scenes. RGB-D images are traditionally described by local geometric features of the 3D point cloud. We propose a cloud of oriented gradient (COG) descriptor that links the 2D appearance and 3D pose of object categories, and thus accurately models how perspective projection affects perceived image boundaries. We also propose a "Manhattan voxel" representation which better captures the 3D room layout geometry of common indoor environments. Effective classification rules are learned via a structured prediction framework that accounts for the intersection-over-union overlap of hypothesized 3D cuboids with human annotations, as well as orientation estimation errors. Contextual relationships among categories and layout are captured via a cascade of classifiers, leading to holistic scene hypotheses with improved accuracy. Our model is learned solely from annotated RGB-D images, without the benefit of CAD models, but nevertheless its performance substantially exceeds the state-of-the-art on the SUN RGB-D database. Avoiding CAD models allows easier learning of detectors for many object categories.