An overview of view-based 2D-3D indexing methods

International audienceThis paper proposes a comprehensive overview of state of the art 2D/3D, view-based indexing methods. The principle of 2D/3D indexing methods consists of describing 3D models by means of a set of 2D shape descriptors, associated with a set of corresponding 2D views (under the assumption of a given projection model). Notably, such an approach makes it possible to identify 3D objects of interest from 2D images/videos. An experimental evaluation is also proposed, in order to examine the influence of the number of views and of the associated viewing angle selection strategies on the retrieval results. Experiments concern both 3D model retrieval and image recognition from a single view. Results obtained show promising performances, with recognition rates from a single view higher then 66%, which opens interesting perspectives in terms of semantic metadata extraction from still images/videos

Towards Recognizing of 3D Models Using A Single Image

As 3D data is getting more popular, techniques for retrieving a particular 3D model are necessary. We want to recognize a 3D model from a single photograph; as any user can easily get an image of a model he/she would like to find, requesting by an image is indeed simple and natural. However, a 2D intensity image is relative to viewpoint, texture and lighting condition and thus matching with a 3D geometric model is very challenging. This paper proposes a first step towards matching a 2D image to models, based on features repeatable in 2D images and in depth images (generated from 3D models); we show their independence to textures and lighting. Then, the detected features are matched to recognize 3D models by combining HOG (Histogram Of Gradients) descriptors and repeatability scores. The proposed methods reaches a recognition rate of 72% among 12 3D objects categories, and outperforms classical feature detection techniques for recognizing 3D models using a single image

Monocular 3d Object Recognition

Object recognition is one of the fundamental tasks of computer vision. Recent advances in the field enable reliable 2D detections from a single cluttered image. However, many challenges still remain. Object detection needs timely response for real world applications. Moreover, we are genuinely interested in estimating the 3D pose and shape of an object or human for the sake of robotic manipulation and human-robot interaction. In this thesis, a suite of solutions to these challenges is presented. First, Active Deformable Part Models (ADPM) is proposed for fast part-based object detection. ADPM dramatically accelerates the detection by dynamically scheduling the part evaluations and efficiently pruning the image locations. Second, we unleash the power of marrying discriminative 2D parts with an explicit 3D geometric representation. Several methods of such scheme are proposed for recovering rich 3D information of both rigid and non-rigid objects from monocular RGB images. (1) The accurate 3D pose of an object instance is recovered from cluttered images using only the CAD model. (2) A global optimal solution for simultaneous 2D part localization, 3D pose and shape estimation is obtained by optimizing a unified convex objective function. Both appearance and geometric compatibility are jointly maximized. (3) 3D human pose estimation from an image sequence is realized via an Expectation-Maximization algorithm. The 2D joint location uncertainties are marginalized out during inference and 3D pose smoothness is enforced across frames. By bridging the gap between 2D and 3D, our methods provide an end-to-end solution to 3D object recognition from images. We demonstrate a range of interesting applications using only a single image or a monocular video, including autonomous robotic grasping with a single image, 3D object image pop-up and a monocular human MoCap system. We also show empirical start-of-art results on a number of benchmarks on 2D detection and 3D pose and shape estimation

Depth-Assisted Semantic Segmentation, Image Enhancement and Parametric Modeling

This dissertation addresses the problem of employing 3D depth information on solving a number of traditional challenging computer vision/graphics problems. Humans have the abilities of perceiving the depth information in 3D world, which enable humans to reconstruct layouts, recognize objects and understand the geometric space and semantic meanings of the visual world. Therefore it is significant to explore how the 3D depth information can be utilized by computer vision systems to mimic such abilities of humans. This dissertation aims at employing 3D depth information to solve vision/graphics problems in the following aspects: scene understanding, image enhancements and 3D reconstruction and modeling. In addressing scene understanding problem, we present a framework for semantic segmentation and object recognition on urban video sequence only using dense depth maps recovered from the video. Five view-independent 3D features that vary with object class are extracted from dense depth maps and used for segmenting and recognizing different object classes in street scene images. We demonstrate a scene parsing algorithm that uses only dense 3D depth information to outperform using sparse 3D or 2D appearance features. In addressing image enhancement problem, we present a framework to overcome the imperfections of personal photographs of tourist sites using the rich information provided by large-scale internet photo collections (IPCs). By augmenting personal 2D images with 3D information reconstructed from IPCs, we address a number of traditionally challenging image enhancement techniques and achieve high-quality results using simple and robust algorithms. In addressing 3D reconstruction and modeling problem, we focus on parametric modeling of flower petals, the most distinctive part of a plant. The complex structure, severe occlusions and wide variations make the reconstruction of their 3D models a challenging task. We overcome these challenges by combining data driven modeling techniques with domain knowledge from botany. Taking a 3D point cloud of an input flower scanned from a single view, each segmented petal is fitted with a scale-invariant morphable petal shape model, which is constructed from individually scanned 3D exemplar petals. Novel constraints based on botany studies are incorporated into the fitting process for realistically reconstructing occluded regions and maintaining correct 3D spatial relations. The main contribution of the dissertation is in the intelligent usage of 3D depth information on solving traditional challenging vision/graphics problems. By developing some advanced algorithms either automatically or with minimum user interaction, the goal of this dissertation is to demonstrate that computed 3D depth behind the multiple images contains rich information of the visual world and therefore can be intelligently utilized to recognize/ understand semantic meanings of scenes, efficiently enhance and augment single 2D images, and reconstruct high-quality 3D models