Detecting and Reconstructing 3D Mirror Symmetric Objects

Sinha, Sudipta N.; Ramnath, Krishnan; Szeliski, Richard

doi:10.1007/978-3-642-33709-3_42

Cited by 31 publications

(22 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Cars and trucks contain a variety of structural features such as headlights and taillights, grills, and logos, which could all potentially be used for detection and pose estimation. They also have bilateral symmetry [27], which for frontal or near-frontal views, can provide one degree of freedom -the rotation of the car around its vertical axis.…”

Section: Ellipse-based Pose Estimationmentioning

confidence: 99%

Car make and model recognition using 3D curve alignment

Ramnath¹,

Sinha²,

Szeliski³

et al. 2014

IEEE Winter Conference on Applications of Computer Vision

Self Cite

View full text Add to dashboard Cite

We present a new approach for recognizing the make and model of a car from a single image. While most previous methods are restricted to fixed or limited viewpoints, our system is able to verify a car's make and model from an arbitrary view. Our model consists of 3D space curves obtained by backprojecting image curves onto silhouettebased visual hulls and then refining them using three-view curve matching. These 3D curves are then matched to 2D image curves using a 3D view-based alignment technique. We present two different methods for estimating the pose of a car, which we then use to initialize the 3D curve matching. Our approach is able to verify the exact make and model of a car over a wide range of viewpoints in cluttered scenes.

show abstract

Section: Ellipse-based Pose Estimationmentioning

confidence: 99%

Car make and model recognition using 3D curve alignment

Ramnath¹,

Sinha²,

Szeliski³

et al. 2014

IEEE Winter Conference on Applications of Computer Vision

Self Cite

View full text Add to dashboard Cite

show abstract

“…Solving for symmetry correspondence has been tried for surfaces of revolution, which are characterized by rotational symmetry [21][22][23] as well as for mirror-symmetrical polyhedral objects, where edge features are compared with respect to 2-D affine similarities (Refs. [24][25][26]. The inherent difficulty of the 3-D symmetry correspondence problem in 2-D images has resulted in incremental successes, where the proposed methods work only for special cases such as nearly degenerate views (e.g., Ref.…”

Section: Related Researchmentioning

confidence: 99%

Figure-ground organization based on three-dimensional symmetry

Michaux

Jayadevan

Delp

et al. 2016

J. Electron. Imaging

View full text Add to dashboard Cite

Abstract. We present an approach to figure/ground organization using mirror symmetry as a general purpose and biologically motivated prior. Psychophysical evidence suggests that the human visual system makes use of symmetry in producing three-dimensional (3-D) percepts of objects. 3-D symmetry aids in scene organization because (i) almost all objects exhibit symmetry, and (ii) configurations of objects are not likely to be symmetric unless they share some additional relationship. No general purpose approach is known for solving 3-D symmetry correspondence in two-dimensional (2-D) camera images, because few invariants exist. Therefore, we present a general purpose method for finding 3-D symmetry correspondence by pairing the problem with the two-view geometry of the binocular correspondence problem. Mirror symmetry is a spatially global property that is not likely to be lost in the spatially local noise of binocular depth maps. We tested our approach on a corpus of 180 images collected indoors with a stereo camera system. K -means clustering was used as a baseline for comparison. The informative nature of the symmetry prior makes it possible to cluster data without a priori knowledge of which objects may appear in the scene, and without knowing how many objects there are in the scene. IntroductionAccording to most studies of human vision, the first step in visual perception is determining whether there are objects in front of the observer: where they are and how many there are. This step (visual function) is called figure-ground organization (FGO). 1 The computer vision community refers to this problem as object discovery. As with all natural visual functions of human observers, FGO operates in three-dimensional (3-D) space, as opposed to the two-dimensional (2-D) retinal image. It follows that it is natural to think about visual mechanisms underlying FGO as based on 3-D operations. However, the fact that the input to the visual system is one or more 2-D retinal images encouraged previous researchers to look for a theory of FGO based on 2-D operations. This is how the human vision community studied FGO. Consider the prototypical example of Edgar Rubin's vase-faces stimulus.2 In this 2-D stimulus, there are two possible interpretations depending on which region is perceived as a "figure" as opposed to the "ground." Similar bistable stimuli have been used during the last several dozen years of FGO research in human vision.3,4 This research provided a large body of results, but few theories and computational models. Furthermore, the proposed models are usually not suitable for real retinal or camera images representing 3-D scenes. This paper breaks with this tradition and looks for 3-D operations that can establish the correct 3-D FGO.

show abstract

“…Duplicate structure has been of recent interest in the research community and has motivated a variety of applications [3,8,14,22,23,25,28]. Generally, there are different types of duplicate scene structures, ranging from duplicate instances caused by 3D rotational symmetries, separate identical surfaces, or repetitive or mirrored structures often found on facades (a survey of symmetry is provided in Fig.…”

Section: Related Workmentioning

confidence: 99%

“…Methods have leveraged these patterns for urban geolocalization [3,22,25] and reconstruction of a scene from only a single image [14,23,28]. Furthermore, there has been recent work on utilizing symmetry as a constraint in bundle adjustment to improve the accuracy of an SfM result [8].…”

Section: Related Workmentioning

confidence: 99%

Correcting for Duplicate Scene Structure in Sparse 3D Reconstruction

Heinly

Dunn

Frahm

2014

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Abstract. Structure from motion (SfM) is a common technique to recover 3D geometry and camera poses from sets of images of a common scene. In many urban environments, however, there are symmetric, repetitive, or duplicate structures that pose challenges for SfM pipelines. The result of these ambiguous structures is incorrectly placed cameras and points within the reconstruction. In this paper, we present a postprocessing method that can not only detect these errors, but successfully resolve them. Our novel approach proposes the strong and informative measure of conflicting observations, and we demonstrate that it is robust to a large variety of scenes.

show abstract

Detecting and Reconstructing 3D Mirror Symmetric Objects

Cited by 31 publications

References 26 publications

Car make and model recognition using 3D curve alignment

Car make and model recognition using 3D curve alignment

Figure-ground organization based on three-dimensional symmetry

Correcting for Duplicate Scene Structure in Sparse 3D Reconstruction

Contact Info

Product

Resources

About