While 3D Gaussian Splatting (3DGS) has demonstrated remarkable performance in novel view synthesis and real-time rendering, the high memory consumption due to the use of millions of Gaussians limits its practicality. To mitigate this issue, improvements have been made by pruning unnecessary Gaussians, either through a hand-crafted criterion or by using learned masks. However, these methods deterministically remove Gaussians based on a snapshot of the pruning moment, leading to sub-optimized reconstruction performance from a long-term perspective. To address this issue, we introduce MaskGaussian, which models Gaussians as probabilistic entities rather than permanently removing them, and utilize them according to their probability of existence. To achieve this, we propose a masked-rasterization technique that enables unused yet probabilistically existing Gaussians to receive gradients, allowing for dynamic assessment of their contribution to the evolving scene and adjustment of their probability of existence. Hence, the importance of Gaussians iteratively changes and the pruned Gaussians are selected diversely. Extensive experiments demonstrate the superiority of the proposed method in achieving better rendering quality with fewer Gaussians than previous pruning methods, pruning over 60% of Gaussians on average with only a 0.02 PSNR decline. Our code can be found at: https://github.com/kaikai23/MaskGaussian
SVR-GS: Spatially Variant Regularization for Probabilistic Masks in 3D Gaussian Splatting
Department of Computer Science and Software Engineering, The University of Western Australia（西澳大学计算机科学与软件工程系）；Dolby Laboratories（Dolby实验室）；Department of Electrical, Electronics and Computer Engineering, The University of Western Australia（西澳大学电气、电子与计算机工程系）；School of Information Technology, Murdoch University（穆尔德大学信息技术学院）
The Probabilistic Object Detection Challenge evaluates object detection methods using a new evaluation measure, Probability-based Detection Quality (PDQ), on a new synthetic image dataset. We present our submission to the challenge, a fine-tuned version of Mask-RCNN with some additional post-processing. Our method, submitted under username pammirato, is currently second on the leaderboard with a score of 21.432, while also achieving the highest spatial quality and average overall quality of detections. We hope this method can provide some insight into how detectors designed for mean average pr…
MaskDiff: Modeling Mask Distribution with Diffusion Probabilistic Model for Few-Shot Instance Segmentation
University of Science, VNU-HCM（胡志明市国家大学科学大学）；Vietnam National University, Ho Chi Minh City（胡志明市国家大学）；Stony Brook University（石溪大学）；University of Dayton（代顿大学）；Monash University（莫纳什大学）；University of Illinois at Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校）
Free-form inpainting is the task of adding new content to an image in the regions specified by an arbitrary binary mask. Most existing approaches train for a certain distribution of masks, which limits their generalization capabilities to unseen mask types. Furthermore, training with pixel-wise and perceptual losses often leads to simple textural extensions towards the missing areas instead of semantically meaningful generation. In this work, we propose RePaint: A Denoising Diffusion Probabilistic Model (DDPM) based inpainting approach that is applicable to even extreme masks. We employ a pret…
Dy3DGS-SLAM: Monocular 3D Gaussian Splatting SLAM for Dynamic Environments
School of Information and Communication Engineering, Dalian University of Technology（信息与通信工程学院，大连理工大学）；School of Engineering Sciences, Saarland University of Applied Sciences（应用科学大学工程科学学院，萨尔兰应用科学大学）；Fraunhofer Institute for Nondestructive Testing（非破坏性检测研究所，弗劳恩霍夫）；College of Information Science and Technology, Beijing University of Chemical Technology（信息科学与技术学院，北京化工大学）；Faculty of Science and Engineering, Laval University（科学与工程学院，拉瓦尔大学）
Instituto de Ingenieria de Aragon (I3A)（阿拉贡工程研究所）；Universidad de Zaragoza（萨拉戈萨大学）
Affordances are a fundamental concept in robotics since they relate available actions for an agent depending on its sensory-motor capabilities and the environment. We present a novel Bayesian deep network to detect affordances in images, at the same time that we quantify the distribution of the aleatoric and epistemic variance at the spatial level. We adapt the Mask-RCNN architecture to learn a probabilistic representation using Monte Carlo dropout. Our results outperform the state-of-the-art of deterministic networks. We attribute this improvement to a better probabilistic feature space repre…
Morphologically-Aware Consensus Computation via Heuristics-based IterATive Optimization (MACCHIatO)
Université Côte d’Azur（蔚蓝海岸大学）；Inria（法国国家信息与自动化研究所）；Hôpital Pitié-Salpétrière（皮蒂耶-萨尔佩特里埃医院）；Assistance Publique des Hôpitaux de Paris（巴黎公共援助医院）；Sorbonne University（索邦大学）
Unstructured neural network pruning algorithms have achieved impressive compression rates. However, the resulting - typically irregular - sparse matrices hamper efficient hardware implementations, leading to additional memory usage and complex control logic that diminishes the benefits of unstructured pruning. This has spurred structured coarse-grained pruning solutions that prune entire filters or even layers, enabling efficient implementation at the expense of reduced flexibility. Here we propose a flexible new pruning mechanism that facilitates pruning at different granularities (weights, k…
