Ravi Krishna Bala Venkata Sai Kolluri

Bio: Ravi Krishna Bala Venkata Sai Kolluri is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Dual polyhedron & Laplacian matrix. The author has an hindex of 2, co-authored 2 publications receiving 848 citations.

Recognizing Objects in Range Data Using Regional Point Descriptors

Abstract: Recognition of three dimensional (3D) objects in noisy and cluttered scenes is a challenging problem in 3D computer vision. One approach that has been successful in past research is the regional shape descriptor. In this paper, we introduce two new regional shape descriptors: 3D shape contexts and harmonic shape contexts. We evaluate the performance of these descriptors on the task of recognizing vehicles in range scans of scenes using a database of 56 cars. We compare the two novel descriptors to an existing descriptor, the spin image, showing that the shape context based descriptors have a higher recognition rate on noisy scenes and that 3D shape contexts outperform the others on cluttered scenes.

Spectral watertight surface reconstruction

Abstract: To reconstruct a surface from an unorganized point set S, we create a point set S+ that adds the vertices of a cubical bounding box, then compute the Delaunay triangulation T and Voronoi diagram Q of S+. We form a graph G whose nodes are vertices of Q, and use a spectral graph partitioning algorithm to cut this graph into two pieces, the inside and outside subgraphs. Because every Voronoi vertex in Q represents a tetrahedron in T , these labels are affixed to the tetrahedra too. If the points in S are sampled densely enough from a simple closed surface, then the surface is approximated reasonably well by the faces of T that separate the inside tetrahedra from the outside tetrahedra. Our algorithm first identifies the set V of Voronoi vertices called poles [Amenta et al. 2001], which are likely to lie near the medial axis of the surface being recovered. The algorithm then constructs a sparse pole graph G = (V,E). The set E of edges is defined as follows. For each sample point s with poles u and v, (u,v) is an edge in E. For each edge (s,s′), of the Delaunay tetrahedralization T , the edges (u,u′), (u,v′), (v,u′), and (v,v′) are all edges of E, where u and v are the poles of s, and u′ and v′ are the poles of s′. The edge weights are based on observations of Amenta et al. [2001]. If a sample s has a long, thin Voronoi cell, the likelihood is high that its poles u and v are on opposite sides of the surface. We assign a negative weight to edge (u,v). Let tu and tv be the tetrahedra in T whose duals are u and v. The circumscribing spheres of tu and tv intersect at an angle φ . We assign (u,v) a weight of wu,v = −e4+4cosφ . Next, let (u,v) be an edge of E that is not assigned a negative weight. We assign (u,v) a weight of wu,v = e4−4cosφ . If φ is close to 180◦, u and v are likely to lie on the same side of the surface, so we use a large, positive edge weight. We know a priori that tetrahedra with vertices on the bounding box must be labeled outside. So, we fix their labels prior to the partitioning step by collapsing the poles dual to such tetrahedra into a single supernode z, yielding a modified graph G′. From the modified pole graph G′, we construct a pole matrix L. (L is often called the Laplacian matrix.) L is sparse and symmetric and has one row and one column for each node of the graph G′. For each edge (u,v) of G′ with weight wu,v, the pole matrix L has the entries Li j = −wu,v and L ji = −wu,v. The diagonal entries of L are the row sums Lii = ∑ j 6=i |Li j|. We partition G′ by finding the eigenvector x associated with the smallest eigenvalue λ of the generalized eigensystem Lx = λDx, where D is a diagonal matrix whose diagonal is identical to that of L. Because L is a sparse matrix, we compute the eigenvector x using TRLAN, an implementation of the Lanczos algorithm [Pothen et al. 1990]. When this method is applied to smooth, well-sampled surfaces, we find that the eigenvector x is relatively polarized: most of its entries are clearly negative or clearly positive, with few en0.5 1 1.5 2 2.5 3

VoxNet: A 3D Convolutional Neural Network for real-time object recognition

Abstract: Robust object recognition is a crucial skill for robots operating autonomously in real world environments. Range sensors such as LiDAR and RGBD cameras are increasingly found in modern robotic systems, providing a rich source of 3D information that can aid in this task. However, many current systems do not fully utilize this information and have trouble efficiently dealing with large amounts of point cloud data. In this paper, we propose VoxNet, an architecture to tackle this problem by integrating a volumetric Occupancy Grid representation with a supervised 3D Convolutional Neural Network (3D CNN). We evaluate our approach on publicly available benchmarks using LiDAR, RGBD, and CAD data. VoxNet achieves accuracy beyond the state of the art while labeling hundreds of instances per second.

Behavior recognition via sparse spatio-temporal features

Abstract: A common trend in object recognition is to detect and leverage the use of sparse, informative feature points. The use of such features makes the problem more manageable while providing increased robustness to noise and pose variation. In this work we develop an extension of these ideas to the spatio-temporal case. For this purpose, we show that the direct 3D counterparts to commonly used 2D interest point detectors are inadequate, and we propose an alternative. Anchoring off of these interest points, we devise a recognition algorithm based on spatio-temporally windowed data. We present recognition results on a variety of datasets including both human and rodent behavior.

FAB-MAP: Probabilistic Localization and Mapping in the Space of Appearance

Abstract: This paper describes a probabilistic approach to the problem of recognizing places based on their appearance. The system we present is not limited to localization, but can determine that a new observation comes from a previously unseen place, and so augment its map. Effectively this is a SLAM system in the space of appearance. Our probabilistic approach allows us to explicitly account for perceptual aliasing in the environment—identical but indistinctive observations receive a low probability of having come from the same place. We achieve this by learning a generative model of place appearance. By partitioning the learning problem into two parts, new place models can be learned online from only a single observation of a place. The algorithm complexity is linear in the number of places in the map, and is particularly suitable for online loop closure detection in mobile robotics.

Unique signatures of histograms for local surface description

Abstract: This paper deals with local 3D descriptors for surface matching. First, we categorize existing methods into two classes: Signatures and Histograms. Then, by discussion and experiments alike, we point out the key issues of uniqueness and repeatability of the local reference frame. Based on these observations, we formulate a novel comprehensive proposal for surface representation, which encompasses a new unique and repeatable local reference frame as well as a new 3D descriptor. The latter lays at the intersection between Signatures and Histograms, so as to possibly achieve a better balance between descriptiveness and robustness. Experiments on publicly available datasets as well as on range scans obtained with Spacetime Stereo provide a thorough validation of our proposal.