This paper presents a novel method to estimate sparse 3D keypoints from single-view RGB images . Our network is trained in two steps using a knowledge distillation framework. In the first step, the teacher is trained to extract 3D features from point cloud data, which are used in combination with 2D features to estimate the 3D keypoints. In the second step, the teacher teaches the student module to hallucinate the 3D features from RGB images that are similar to those extracted from the point clouds. This procedure helps the network during inference to extract 2D and 3D features directly from images, without requiring point clouds as input. Moreover, the network also predicts a confidence score for every keypoint, which is used to select the valid ones from a set of N predicted keypoints. This allows the prediction of different number of keypoints depending on the object’s geometry. We use the estimated keypoints for computing the relative pose between two views of an object. The results are compared with those of KP-Net and StarMap , which are the state-of-the-art for estimating 3D keypoints from a single-view RGB image. The average angular distance error of our approach (5.94°) is 8.46° and 55.26° lower than that of KP-Net (14.40°) and StarMap (61.20°), respectively.

Robustly estimating camera poses from a set of images is a fundamental task which remains challenging for differentiable methods, especially in the case of small and sparse camera pose graphs. To overcome this challenge, we propose Pose-refined Rotation Averaging Graph Optimization (PRAGO). From a set of objectness detections on unordered images, our method reconstructs the rotational pose, and in turn, the absolute pose, in a differentiable manner benefiting from the optimization of a sequence of geometrical tasks. We show how our objectness pose-refinement module in PRAGO is able to refine the inherent ambiguities in pairwise relative pose estimation without removing edges and avoiding making early decisions on the viability of graph edges. PRAGO then refines the absolute rotations through iterative graph construction, reweighting the graph edges to compute the final rotational pose, which can be converted into absolute poses using translation averaging. We show that PRAGO is able to outperform non-differentiable solvers on small and sparse scenes extracted from 7-Scenes achieving a relative improvement of 21% for rotations while achieving similar translation estimates.

Real-world robotic grasping can be done robustly if a complete 3D Point Cloud Data (PCD) of an object is available. However, in practice, PCDs are often incomplete when objects are viewed from few and sparse viewpoints before the grasping action, leading to the generation of wrong or inaccurate grasp poses. We propose a novel grasping strategy, named 3DSGrasp, that predicts the missing geometry from the partial PCD to produce reliable grasp poses. Our proposed PCD completion network is a Transformer-based encoder-decoder network with an Offset-Attention layer. Our network is inherently invariant to the object pose and point's permutation, which generates PCDs that are geometrically consistent and completed properly. Experiments on a wide range of partial PCD show that 3DSGrasp outperforms the best state-of-the-art method on PCD completion tasks and largely improves the grasping success rate in real-world scenarios. The code and dataset are available at: https://github.com/NunoDuarte/3DSGrasp.

The vast quantity of visual data generated by the rapid expansion of large scale distributed multicamera networks, makes automated person detection and reidentification (RE-ID) essential components of modern surveillance systems. However, the integration of automated person detection and RE-ID algorithms is not without problems, and the errors arising in this integration must be measured (e.g., detection failures that may hamper the RE-ID performance). In this paper, we present a window-based classifier based on a recently proposed architecture for the integration of pedestrian detectors and RE-ID algorithms, that takes the output of any bounding-box RE-ID classifier and exploits the temporal continuity of persons in video streams. We evaluate our contributions on a recently proposed dataset featuring 13 high-definition cameras and over 80 people, acquired during 30 min at rush hour in an office space scenario. We expect our contributions to drive research in integrated pedestrian detection and RE-ID systems, bringing them closer to practical applications.

This paper presents a method for 3D model-based tracking of colored objects using a sampling methodology. The problem is formulated in a Monte Carlo filtering approach, whereby the state of an object is represented by a set of hypotheses. The main originality of this work is an observation model consisting in the comparison of the color information in some sampling points around the target’s hypothetical edges. On the contrary to existing approaches the method does not need to explicitly compute edges in the video stream, thus dealing well with optical or motion blur. The method does not require the projection of the full 3D object on the image, but just of some selected points around the target’s boundaries. This allows a flexible and modular architecture illustrated by experiments performed with different objects (balls and boxes), camera models (perspective, catadioptric, dioptric) and tracking methodologies (particle and Kalman filtering). 1

We present a model-based 3D tracking system, using wide angle perspective catadioptric sensors. These sensors acquire 360deg views of the environment and the projection from 3D world points to the image plane is approximated by a perspective model. This is a major advantage in structured environments because straight lines on specific surfaces are not deformed by the sensor, allowing the application of standard computer vision algorithms. Objects off the surface are distorted according to a complex projection model, but can be approximated by a simple wide angle perspective mapping. This is exploited here to develop a robust tracking system for autonomous robots using a 3D shape and color-based object model. The use of particle filters allows tracking to be done with 3D realistic motion models and tackling object occlusion, overlap and ambiguities. We show that the use of the perspective model is advantageous over more standard catadioptric projection models, since it renders a very good approximation to the true model, being simpler and more efficient to use, in particular with 3D particle filtering methods.