Score-PA: Score-based 3D Part Assembly

Autonomous 3D part assembly is a challenging task in the areas of robotics and 3D computer vision. This task aims to assemble individual components into a complete shape without relying on predefined instructions. In this paper, we formulate this task from a novel generative perspective, introducing the Score-based 3D Part Assembly framework (Score-PA) for 3D part assembly. Knowing that score-based methods are typically time-consuming during the inference stage. To address this issue, we introduce a novel algorithm called the Fast Predictor-Corrector Sampler (FPC) that accelerates the sampling process within the framework. We employ various metrics to assess assembly quality and diversity, and our evaluation results demonstrate that our algorithm outperforms existing state-of-the-art approaches. We release our code at https://github.com/J-F-Cheng/Score-PA_Score-based-3D-Part-Assembly.Comment: BMVC 202

Robust Perception through Equivariance

Deep networks for computer vision are not reliable when they encounter adversarial examples. In this paper, we introduce a framework that uses the dense intrinsic constraints in natural images to robustify inference. By introducing constraints at inference time, we can shift the burden of robustness from training to the inference algorithm, thereby allowing the model to adjust dynamically to each individual image's unique and potentially novel characteristics at inference time. Among different constraints, we find that equivariance-based constraints are most effective, because they allow dense constraints in the feature space without overly constraining the representation at a fine-grained level. Our theoretical results validate the importance of having such dense constraints at inference time. Our empirical experiments show that restoring feature equivariance at inference time defends against worst-case adversarial perturbations. The method obtains improved adversarial robustness on four datasets (ImageNet, Cityscapes, PASCAL VOC, and MS-COCO) on image recognition, semantic segmentation, and instance segmentation tasks. Project page is available at equi4robust.cs.columbia.edu

Repeating Ultraluminous X-ray Bursts and Repeating Fast Radio Bursts: A Possible Association?

Ultraluminous X-ray bursts (hereafter ULXBs) are ultraluminous X-ray flares with a fast rise ($\sim$ one minute) and a slow decay ($\sim$ an hour), which are commonly observed in extragalactic globular clusters. Most ULXBs are observational one-off bursts, whereas five flares from the same source in NGC 5128 were discovered by Irwin et al. (2016). In this Letter, we propose a neutron star (NS)-white dwarf (WD) binary model with super-Eddington accretion rates to explain the repeating behavior of the ULXB source in NGC 5128. With an eccentric orbit, the mass transfer occurs at the periastron where the WD fills its Roche lobe. The ultraluminous X-ray flares can be produced by the accretion column around the NS magnetic poles. On the other hand, some repeating fast radio bursts (hereafter FRBs) were also found in extragalactic globular clusters. Repeating ULXBs and repeating FRBs are the most violent bursts in the X-ray and radio bands, respectively. We propose a possible association between the repeating ULXBs and the repeating FRBs. Such an association is worth further investigation by follow-up observations on nearby extragalactic globular clusters.Comment: 8 pages, 3 figures, accepted for publication in Ap

Distributed bundle adjustment with block-based sparse matrix compression for super large scale datasets

We propose a distributed bundle adjustment (DBA) method using the exact Levenberg-Marquardt (LM) algorithm for super large-scale datasets. Most of the existing methods partition the global map to small ones and conduct bundle adjustment in the submaps. In order to fit the parallel framework, they use approximate solutions instead of the LM algorithm. However, those methods often give sub-optimal results. Different from them, we utilize the exact LM algorithm to conduct global bundle adjustment where the formation of the reduced camera system (RCS) is actually parallelized and executed in a distributed way. To store the large RCS, we compress it with a block-based sparse matrix compression format (BSMC), which fully exploits its block feature. The BSMC format also enables the distributed storage and updating of the global RCS. The proposed method is extensively evaluated and compared with the state-of-the-art pipelines using both synthetic and real datasets. Preliminary results demonstrate the efficient memory usage and vast scalability of the proposed method compared with the baselines. For the first time, we conducted parallel bundle adjustment using LM algorithm on a real datasets with 1.18 million images and a synthetic dataset with 10 million images (about 500 times that of the state-of-the-art LM-based BA) on a distributed computing system.Comment: camera ready version for ICCV202