An official website of the United States government
Integral imaging (InIm) is useful for passive ranging and 3D visualization of partially-occluded objects. We consider 3D object localization within a scene and in occlusions. 2D localization can be achieved using machine learning and non-machine learning-based techniques. These techniques aim to provide a 2D bounding box around each one of the objects of interest. A recent study uses InIm for the 3D reconstruction of the scene with occlusions and utilizes mutual information (MI) between the bounding box in this 3D reconstructed scene and the corresponding bounding box in the central elemental image to achieve passive depth estimation of partially occluded objects. Here, we improve upon this InIm method by using Bayesian optimization to minimize the number of required 3D scene reconstructions. We evaluate the performance of the proposed approach by analyzing different kernel functions, acquisition functions, and parameter estimation algorithms for Bayesian optimization-based inference for simultaneous depth estimation of objects and occlusion. In our optical experiments, mutual-information-based depth estimation with Bayesian optimization achieves depth estimation with a handful of 3D reconstructions. To the best of our knowledge, this is the first report to use Bayesian optimization for mutual information-based InIm depth estimation.
more »
« less
This content will become publicly available on December 15, 2025
DiffuBox: Refining 3D Object Detection with Point Diffusion
Ensuring robust 3D object detection and localization is crucial for many applications in robotics and autonomous driving. Recent models, however, face difficulties in maintaining high performance when applied to domains with differing sensor setups or geographic locations, often resulting in poor localization accuracy due to domain shift. To overcome this challenge, we introduce a novel diffusion-based box refinement approach. This method employs a domain-agnostic diffusion model, conditioned on the LiDAR points surrounding a coarse bounding box, to simultaneously refine the box’s location, size, and orientation. We evaluate this approach under various domain adaptation settings, and our results reveal significant improvements across different datasets, object classes and detectors. Our PyTorch implementation is available at https://github.com/cxy1997/DiffuBox.
more »
« less
- Award ID(s):
- 2107077
- PAR ID:
- 10639190
- Publisher / Repository:
- Advances in Neural Information Processing Systems 37 (NeurIPS 2024)
- Date Published:
- Format(s):
- Medium: X
- Location:
- The Thirty-Eighth Annual Conference on Neural Information Processing Systems, Vancouver, Canada
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Commercial image search applications like eBay and Pinterest allow users to select the focused area as bounding box over the query images, which improves the retrieval accuracy. The focused area image retrieval strategy motivated our research, but our system has three main advantages over the existing works. (1) Given a query focus area, our approach localizes the most similar region in the database image and only this region is used for computing image similarity. This is done in a unified network whose weights are adjusted both for localization and similarity learning in an end-to-end manner. (2) This is achieved using fewer than five proposals extracted from a saliency map, which speedups the pairwise similarity computation. Usually hundreds or even thousands of proposals are used for localization. (3) For users, our system explains the relevance of the retrieved results by locating the regions in database images most similar to query object. Our method achieves significantly better retrieval performance than the off-the-shelf object localization-based retrieval methods and end-to-end trained triplet method with a region proposal network. Our experimental results demonstrate 86% retrieval rate as compared to 73% achieved by the existing methods on PASCAL VOC07 and VOC12 datasets. Extensive experiments are also conducted on the instance retrieval databases Oxford5k and INSTRE, wherewe exhibit competitive performance. Finally, we provide both quantitative and qualitative results of our retrieval method demonstrating its superiority over commercial image search systems.more » « less
-
Collaborative object localization aims to collaboratively estimate locations of objects observed from multiple views or perspectives, which is a critical ability for multi-agent systems such as connected vehicles. To enable collaborative localization, several model-based state estimation and learning-based localization methods have been developed. Given their encouraging performance, model-based state estimation often lacks the ability to model the complex relationships among multiple objects, while learning-based methods are typically not able to fuse the observations from an arbitrary number of views and cannot well model uncertainty. In this paper, we introduce a novel spatiotemporal graph filter approach that integrates graph learning and model-based estimation to perform multi-view sensor fusion for collaborative object localization. Our approach models complex object relationships using a new spatiotemporal graph representation and fuses multi-view observations in a Bayesian fashion to improve location estimation under uncertainty. We evaluate our approach in the applications of connected autonomous driving and multiple pedestrian localization. Experimental results show that our approach outperforms previous techniques and achieves the state-of-the-art performance on collaborative localization.more » « less
-
Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations from multiple perspectives with reliant cooperation. To enable collaborative localization, four key challenges must be addressed, including modeling complex relationships between observed objects, fusing observations from an arbitrary number of collaborating robots, quantifying localization uncertainty, and addressing latency of robot communications. In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph learning and model-based state estimation for a team of robots to collaboratively localize objects. Specifically, we introduce a new uncertainty-aware graph learning model that learns spatiotemporal graphs to represent historical motions of the objects observed by each robot over time and provides uncertainties in object localization. Moreover, we propose a novel method for integrated learning and model-based state estimation, which fuses asynchronous observations obtained from an arbitrary number of robots for collaborative localization. We evaluate our approach in two collaborative object localization scenarios in simulations and on real robots. Experimental results show that our approach outperforms previous methods and achieves state-of-the-art performance on asynchronous collaborative localization.more » « less
-
Recently, adversarial examples against object detection have been widely studied. However, it is difficult for these attacks to have an impact on visual perception in autonomous driving because the complete visual pipeline of real-world autonomous driving systems includes not only object detection but also object tracking. In this paper, we present a novel tracker hijacking attack against the multi-target tracking algorithm employed by real-world autonomous driving systems, which controls the bounding box of object detection to spoof the multiple object tracking process. Our approach exploits the detection box generation process of the anchor-based object detection algorithm and designs new optimization methods to generate adversarial patches that can successfully perform tracker hijacking attacks, causing security risks. The evaluation results show that our approach has 85% attack success rate on two detection models employed by real-world autonomous driving systems. We discuss our potential next step for this work.more » « less
