346 research outputs found
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
DeepVoting: A Robust and Explainable Deep Network for Semantic Part Detection under Partial Occlusion
In this paper, we study the task of detecting semantic parts of an object,
e.g., a wheel of a car, under partial occlusion. We propose that all models
should be trained without seeing occlusions while being able to transfer the
learned knowledge to deal with occlusions. This setting alleviates the
difficulty in collecting an exponentially large dataset to cover occlusion
patterns and is more essential. In this scenario, the proposal-based deep
networks, like RCNN-series, often produce unsatisfactory results, because both
the proposal extraction and classification stages may be confused by the
irrelevant occluders. To address this, [25] proposed a voting mechanism that
combines multiple local visual cues to detect semantic parts. The semantic
parts can still be detected even though some visual cues are missing due to
occlusions. However, this method is manually-designed, thus is hard to be
optimized in an end-to-end manner.
In this paper, we present DeepVoting, which incorporates the robustness shown
by [25] into a deep network, so that the whole pipeline can be jointly
optimized. Specifically, it adds two layers after the intermediate features of
a deep network, e.g., the pool-4 layer of VGGNet. The first layer extracts the
evidence of local visual cues, and the second layer performs a voting mechanism
by utilizing the spatial relationship between visual cues and semantic parts.
We also propose an improved version DeepVoting+ by learning visual cues from
context outside objects. In experiments, DeepVoting achieves significantly
better performance than several baseline methods, including Faster-RCNN, for
semantic part detection under occlusion. In addition, DeepVoting enjoys
explainability as the detection results can be diagnosed via looking up the
voting cues
Lifting GIS Maps into Strong Geometric Context for Scene Understanding
Contextual information can have a substantial impact on the performance of
visual tasks such as semantic segmentation, object detection, and geometric
estimation. Data stored in Geographic Information Systems (GIS) offers a rich
source of contextual information that has been largely untapped by computer
vision. We propose to leverage such information for scene understanding by
combining GIS resources with large sets of unorganized photographs using
Structure from Motion (SfM) techniques. We present a pipeline to quickly
generate strong 3D geometric priors from 2D GIS data using SfM models aligned
with minimal user input. Given an image resectioned against this model, we
generate robust predictions of depth, surface normals, and semantic labels. We
show that the precision of the predicted geometry is substantially more
accurate other single-image depth estimation methods. We then demonstrate the
utility of these contextual constraints for re-scoring pedestrian detections,
and use these GIS contextual features alongside object detection score maps to
improve a CRF-based semantic segmentation framework, boosting accuracy over
baseline models
- …