BSGS: Bi-stage 3D Gaussian Splatting for Camera Motion Deblurring

3D Gaussian Splatting has exhibited remarkable capabilities in 3D scene reconstruction.However, reconstructing high-quality 3D scenes from motion-blurred images caused by camera motion poses a significant challenge.The performance of existing 3DGS-based deblurring methods are limited due to their inherent mechanisms, such as extreme dependence on the accuracy of camera poses and inability to effectively control erroneous Gaussian primitives densification caused by motion blur.To solve these problems, we introduce a novel framework, Bi-Stage 3D Gaussian Splatting, to accurately reconstruct 3D scenes from motion-blurred images.BSGS contains two stages. First, Camera Pose Refinement roughly optimizes camera poses to reduce motion-induced distortions. Second, with fixed rough camera poses, Global RigidTransformation further corrects motion-induced blur distortions.To alleviate multi-subframe gradient conflicts, we propose a subframe gradient aggregation strategy to optimize both stages.Furthermore, a space-time bi-stage optimization strategy is introduced to dynamically adjust primitive densification thresholds and prevent premature noisy Gaussian generation in blurred regions. Comprehensive experiments verify the effectiveness of our proposed deblurring method and show its superiority over the state of the arts.

PartSAM: A Scalable Promptable Part Segmentation Model Trained on Native 3D Data

Segmenting 3D objects into parts is a long-standing challenge in computer vision. To overcome taxonomy constraints and generalize to unseen 3D objects, recent works turn to open-world part segmentation. These approaches typically transfer supervision from 2D foundation models, such as SAM, by lifting multi-view masks into 3D. However, this indirect paradigm fails to capture intrinsic geometry, leading to surface-only understanding, uncontrolled decomposition, and limited generalization. We present PartSAM, the first promptable part segmentation model trained natively on large-scale 3D data. Following the design philosophy of SAM, PartSAM employs an encoder-decoder architecture in which a triplane-based dual-branch encoder produces spatially structured tokens for scalable part-aware representation learning. To enable large-scale supervision, we further introduce a model-in-the-loop annotation pipeline that curates over five million 3D shape-part pairs from online assets, providing diverse and fine-grained labels. This combination of scalable architecture and diverse 3D data yields emergent open-world capabilities: with a single prompt, PartSAM achieves highly accurate part identification, and in a Segment-Every-Part mode, it automatically decomposes shapes into both surface and internal structures. Extensive experiments show that PartSAM outperforms state-of-the-art methods by large margins across multiple benchmarks, marking a decisive step toward foundation models for 3D part understanding. Our code and model will be released soon.

MonoRelief V2: Leveraging Real Data for High-Fidelity Monocular Relief Recovery

This paper presents MonoRelief V2, an end-to-end model designed for directly recovering 2.5D reliefs from single images under complex material and illumination variations. In contrast to its predecessor, MonoRelief V1 [1], which was solely trained on synthetic data, MonoRelief V2 incorporates real data to achieve improved robustness, accuracy and efficiency. To overcome the challenge of acquiring large-scale real-world dataset, we generate approximately 15,000 pseudo real images using a text-to-image generative model, and derive corresponding depth pseudo-labels through fusion of depth and normal predictions. Furthermore, we construct a small-scale real-world dataset (800 samples) via multi-view reconstruction and detail refinement. MonoRelief V2 is then progressively trained on the pseudo-real and real-world datasets. Comprehensive experiments demonstrate its state-of-the-art performance both in depth and normal predictions, highlighting its strong potential for a range of downstream applications. Code is at: https://github.com/glp1001/MonoreliefV2.

ColorGS: High-fidelity Surgical Scene Reconstruction with Colored Gaussian Splatting

High-fidelity reconstruction of deformable tissues from endoscopic videos remains challenging due to the limitations of existing methods in capturing subtle color variations and modeling global deformations. While 3D Gaussian Splatting (3DGS) enables efficient dynamic reconstruction, its fixed per-Gaussian color assignment struggles with intricate textures, and linear deformation modeling fails to model consistent global deformation. To address these issues, we propose ColorGS, a novel framework that integrates spatially adaptive color encoding and enhanced deformation modeling for surgical scene reconstruction. First, we introduce Colored Gaussian Primitives, which employ dynamic anchors with learnable color parameters to adaptively encode spatially varying textures, significantly improving color expressiveness under complex lighting and tissue similarity. Second, we design an Enhanced Deformation Model (EDM) that combines time-aware Gaussian basis functions with learnable time-independent deformations, enabling precise capture of both localized tissue deformations and global motion consistency caused by surgical interactions. Extensive experiments on DaVinci robotic surgery videos and benchmark datasets (EndoNeRF, StereoMIS) demonstrate that ColorGS achieves state-of-the-art performance, attaining a PSNR of 39.85 (1.5 higher than prior 3DGS-based methods) and superior SSIM (97.25\%) while maintaining real-time rendering efficiency. Our work advances surgical scene reconstruction by balancing high fidelity with computational practicality, critical for intraoperative guidance and AR/VR applications.

Wavelet-guided Misalignment-aware Network for Visible-Infrared Object Detection

Visible-infrared object detection aims to enhance the detection robustness by exploiting the complementary information of visible and infrared image pairs. However, its performance is often limited by frequent misalignments caused by resolution disparities, spatial displacements, and modality inconsistencies. To address this issue, we propose the Wavelet-guided Misalignment-aware Network (WMNet), a unified framework designed to adaptively address different cross-modal misalignment patterns. WMNet incorporates wavelet-based multi-frequency analysis and modality-aware fusion mechanisms to improve the alignment and integration of cross-modal features. By jointly exploiting low and high-frequency information and introducing adaptive guidance across modalities, WMNet alleviates the adverse effects of noise, illumination variation, and spatial misalignment. Furthermore, it enhances the representation of salient target features while suppressing spurious or misleading information, thereby promoting more accurate and robust detection. Extensive evaluations on the DVTOD, DroneVehicle, and M3FD datasets demonstrate that WMNet achieves state-of-the-art performance on misaligned cross-modal object detection tasks, confirming its effectiveness and practical applicability.

Unified Representation Space for 3D Visual Grounding

3D visual grounding (3DVG) is a critical task in scene understanding that aims to identify objects in 3D scenes based on text descriptions. However, existing methods rely on separately pre-trained vision and text encoders, resulting in a significant gap between the two modalities in terms of spatial geometry and semantic categories. This discrepancy often causes errors in object positioning and classification. The paper proposes UniSpace-3D, which innovatively introduces a unified representation space for 3DVG, effectively bridging the gap between visual and textual features. Specifically, UniSpace-3D incorporates three innovative designs: i) a unified representation encoder that leverages the pre-trained CLIP model to map visual and textual features into a unified representation space, effectively bridging the gap between the two modalities; ii) a multi-modal contrastive learning module that further reduces the modality gap; iii) a language-guided query selection module that utilizes the positional and semantic information to identify object candidate points aligned with textual descriptions. Extensive experiments demonstrate that UniSpace-3D outperforms baseline models by at least 2.24% on the ScanRefer and Nr3D/Sr3D datasets. The code will be made available upon acceptance of the paper.

I Speak and You Find: Robust 3D Visual Grounding with Noisy and Ambiguous Speech Inputs

Existing 3D visual grounding methods rely on precise text prompts to locate objects within 3D scenes. Speech, as a natural and intuitive modality, offers a promising alternative. Real-world speech inputs, however, often suffer from transcription errors due to accents, background noise, and varying speech rates, limiting the applicability of existing 3DVG methods. To address these challenges, we propose \textbf{SpeechRefer}, a novel 3DVG framework designed to enhance performance in the presence of noisy and ambiguous speech-to-text transcriptions. SpeechRefer integrates seamlessly with xisting 3DVG models and introduces two key innovations. First, the Speech Complementary Module captures acoustic similarities between phonetically related words and highlights subtle distinctions, generating complementary proposal scores from the speech signal. This reduces dependence on potentially erroneous transcriptions. Second, the Contrastive Complementary Module employs contrastive learning to align erroneous text features with corresponding speech features, ensuring robust performance even when transcription errors dominate. Extensive experiments on the SpeechRefer and peechNr3D datasets demonstrate that SpeechRefer improves the performance of existing 3DVG methods by a large margin, which highlights SpeechRefer's potential to bridge the gap between noisy speech inputs and reliable 3DVG, enabling more intuitive and practical multimodal systems.

Hierarchical Error Assessment of CAD Models for Aircraft Manufacturing-and-Measurement

The most essential feature of aviation equipment is high quality, including high performance, high stability and high reliability. In this paper, we propose a novel hierarchical error assessment framework for aircraft CAD models within a manufacturing-and-measurement platform, termed HEA-MM. HEA-MM employs structured light scanners to obtain comprehensive 3D measurements of manufactured workpieces. The measured point cloud is registered with the reference CAD model, followed by an error analysis conducted at three hierarchical levels: global, part, and feature. At the global level, the error analysis evaluates the overall deviation of the scanned point cloud from the reference CAD model. At the part level, error analysis is performed on these patches underlying the point clouds. We propose a novel optimization-based primitive refinement method to obtain a set of meaningful patches of point clouds. Two basic operations, splitting and merging, are introduced to refine the coarse primitives. At the feature level, error analysis is performed on circular holes, which are commonly found in CAD models. To facilitate it, a two-stage algorithm is introduced for the detection of circular holes. First, edge points are identified using a tensor-voting algorithm. Then, multiple circles are fitted through a hypothesize-and-clusterize framework, ensuring accurate detection and analysis of the circular features. Experimental results on various aircraft CAD models demonstrate the effectiveness of our proposed method.

J-DDL: Surface Damage Detection and Localization System for Fighter Aircraft

Ensuring the safety and extended operational life of fighter aircraft necessitates frequent and exhaustive inspections. While surface defect detection is feasible for human inspectors, manual methods face critical limitations in scalability, efficiency, and consistency due to the vast surface area, structural complexity, and operational demands of aircraft maintenance. We propose a smart surface damage detection and localization system for fighter aircraft, termed J-DDL. J-DDL integrates 2D images and 3D point clouds of the entire aircraft surface, captured using a combined system of laser scanners and cameras, to achieve precise damage detection and localization. Central to our system is a novel damage detection network built on the YOLO architecture, specifically optimized for identifying surface defects in 2D aircraft images. Key innovations include lightweight Fasternet blocks for efficient feature extraction, an optimized neck architecture incorporating Efficient Multiscale Attention (EMA) modules for superior feature aggregation, and the introduction of a novel loss function, Inner-CIOU, to enhance detection accuracy. After detecting damage in 2D images, the system maps the identified anomalies onto corresponding 3D point clouds, enabling accurate 3D localization of defects across the aircraft surface. Our J-DDL not only streamlines the inspection process but also ensures more comprehensive and detailed coverage of large and complex aircraft exteriors. To facilitate further advancements in this domain, we have developed the first publicly available dataset specifically focused on aircraft damage. Experimental evaluations validate the effectiveness of our framework, underscoring its potential to significantly advance automated aircraft inspection technologies.

SceneSplat++: A Large Dataset and Comprehensive Benchmark for Language Gaussian Splatting

3D Gaussian Splatting (3DGS) serves as a highly performant and efficient encoding of scene geometry, appearance, and semantics. Moreover, grounding language in 3D scenes has proven to be an effective strategy for 3D scene understanding. Current Language Gaussian Splatting line of work fall into three main groups: (i) per-scene optimization-based, (ii) per-scene optimization-free, and (iii) generalizable approach. However, most of them are evaluated only on rendered 2D views of a handful of scenes and viewpoints close to the training views, limiting ability and insight into holistic 3D understanding. To address this gap, we propose the first large-scale benchmark that systematically assesses these three groups of methods directly in 3D space, evaluating on 1060 scenes across three indoor datasets and one outdoor dataset. Benchmark results demonstrate a clear advantage of the generalizable paradigm, particularly in relaxing the scene-specific limitation, enabling fast feed-forward inference on novel scenes, and achieving superior segmentation performance. We further introduce GaussianWorld-49K a carefully curated 3DGS dataset comprising around 49K diverse indoor and outdoor scenes obtained from multiple sources, with which we demonstrate the generalizable approach could harness strong data priors. Our codes, benchmark, and datasets will be made public to accelerate research in generalizable 3DGS scene understanding.