Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.11198

3DTexMOR: 3D Gaussian Multi-Object Removal via Texture-Space Inpainting

Kunxin Guang·Yonghao Zhao·Jian Yang·Beibei Wang
View original source ↗Read source document ↗Export metadata ↓

Abstract

3D object removal aims to remove target objects from reconstructed scenes and complete the geometry and appearance of occluded regions. Existing NeRF- and 3DGS-based methods typically inpaint 2D images to guide 3D completion. However, complex multi-object layouts limit the surrounding context visible in each view, making 2D inpainting prone to artifacts. Inconsistent completions across views also introduce conflicting supervision and blurry reconstructions. We propose 3D Gaussian Multi-Object Removal via Texture-Space Inpainting (3DTexMOR). Our key idea is to perform inpainting in a unified texture space shared by all views. By combining complementary observations, this space provides richer context for recovering missing regions and promotes cross-view appearance consistency. We aggregate multi-view observations into texture maps, inpaint the missing regions, and reproject the completed maps into camera views to supervise Gaussian scene completion. To avoid the influence of view-dependent highlights and reflections, we decompose appearance and aggregate view-independent intrinsic attributes instead of RGB colors. We further introduce geometrically regularized Gaussian completion to constrain the geometry of the completed regions. Extensive experiments demonstrate visually plausible completions and state-of-the-art multi-object removal performance, improving PSNR by 5.8 dB and reducing LPIPS by at least 22% compared with existing methods.

Explore related subjects

cs.CV

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Kunxin Guang, Yonghao Zhao, Jian Yang, Beibei Wang. 2026-10-08. 3DTexMOR: 3D Gaussian Multi-Object Removal via Texture-Space Inpainting. https://arxiv.org/abs/2610.11198

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Accurate and Efficient Object Pose Estimation via the Aggregation of Diffusion Features

Estimating the pose of objects from images is a crucial task of 3D scene understanding, and recent approaches have shown promising results on very large benchmarks. However, these methods experience a significant performance drop when dealing with unseen objects. To address this problem, we have an in-depth analysis on the features of diffusion models, e.g. Stable Diffusion, which hold substantial potential for modeling unseen objects. Based on this analysis, we then innovatively introduce these diffusion features for object pose estimation. To verify the efficacy of diffusion features for object pose estimation, we propose three distinct architectures (vanilla, nonlinear, and context-aware weight aggregations) that capture and aggregate diffusion features for comparative analysis. To achieve an efficient feature aggregation, we propose a confidence adaptive aggregation network that automatically selects the discriminative features rather than uses all the features, achieving a better speed-and-accuracy trade-off. In particular, our confidence adaptive aggregation network achieves higher accuracy than the previous best arts on unseen objects: 97.7% vs. 93.5% on Unseen LM, 85.5% vs. 76.3% on Unseen O-LM, showing the strong generalizability of our method. On the large-scale BOP benchmark, our method also provides measurable gains, with an average recall of 58.3 compared to 57.9 previously. In addition, CAA reduces computational cost by 1.3-1.5 compared to the CWA variant while maintaining comparable accuracy. Furthermore, CAA reaches real-time performance, achieving over 68 FPS and offering a substantially improved accuracy-efficiency trade-off.

cs.CV↗
arXiv2026

A Survey on Industrial Anomaly Synthesis

This paper presents a comprehensive review of industrial anomaly synthesis (IAS). Existing surveys on industrial anomalies mainly focus on anomaly detection, while IAS is typically treated as an auxiliary component rather than as an independent topic. However, owing to its increasing importance in data augmentation, downstream model training, and controllable industrial inspection, IAS has become a research direction of growing interest. To address the lack of a dedicated review, we survey a broad range of representative methods and organize them into four paradigms: hand-crafted synthesis, distribution hypothesis-based synthesis, generative model (GM)-based synthesis, and vision-language model (VLM)-based synthesis. We further establish a dedicated taxonomy for IAS, which supports more systematic comparison across methods and offers a clearer view of the field's development. Beyond methodological categorization, we summarize the datasets, benchmarks, and evaluation metrics commonly adopted in IAS, and review recent advances in multimodal anomaly synthesis that remain underexplored in prior surveys. We also provide deployment-oriented comparisons and practical guidance by analyzing input requirements, output forms, controllability, cost, downstream tasks, and practical limitations across IAS subcategories. Overall, this survey provides a structured understanding of existing IAS methods, evaluation settings, practical trade-offs, current limitations, and promising future directions, and is intended to serve as a reference for subsequent research in this area. More resources are available at https://github.com/M-3LAB/awesome-anomaly-synthesis.

cs.CV↗
arXiv2026

Boosting the Local Invariance for Better Adversarial Transferability

Transfer-based attacks pose a significant threat to real-world applications by directly targeting victim models with adversarial examples generated on surrogate models. While numerous approaches have been proposed to enhance adversarial transferability, existing works often overlook the intrinsic relationship between adversarial perturbations and input images. In this work, we find that the adversarial perturbations often exhibit poor translation invariance for a given clean image and model, which is attributed to local invariance. Through empirical analysis, we demonstrate a positive correlation between the local invariance of adversarial perturbations w.r.t. the input image and their transferability across models. Based on this finding, we propose a general adversarial transferability boosting technique called the Local Invariance Boosting approach (LI-Boost). Extensive experiments on the standard ImageNet dataset demonstrate that LI-Boost significantly enhances five categories of transfer-based attacks, i.e., gradient-based, input transformation-based, model-related, advanced objective function, and ensemble attacks. The improvements hold not only on conventional CNNs, ViTs, and defense mechanisms, but also on real-world commercial vision API systems and vision-language models. Our approach provides a promising direction for future research on improving adversarial transferability across models. Our code is available at https://github.com/Trustworthy-AI-Group/TransferAttack.

cs.CV↗