因果发现

In this paper, we present a new generic Structure-from-Motion pipeline, GenSfM, that uses a non-parametric camera projection model. The model is self-calibrated during the reconstruction process and can fit a wide variety of cameras, ranging from simple low-distortion pinhole cameras to more extreme optical systems such as fisheye or catadioptric cameras. The key component in our framework is an adaptive calibration procedure that can estimate partial calibrations, only modeling regions of the image where sufficient constraints are available. In experiments, we show that our method achieves comparable accuracy to traditional Structure-from-Motion pipelines in easy scenarios, and outperforms them in cases where they are unable to self-calibrate their parametric models. Code is at https://github.com/Ivonne320/GenSfM.git

Recent advancements in neural 3D representations, such as neural radiance fields (NeRF) and 3D Gaussian splatting (3DGS), have made accurate estimation of the 3D structure from multiview images possible. However, this capability is limited to estimating the visible external structure, and it is still difficult to identify the invisible internal structure hidden behind the surface. To overcome this limitation, we address a new task called structure from collision (SfC), which aims to estimate the structure (including the invisible internal one) of an object from the appearance changes at collision. To solve this task, we propose a novel model called SfC-NeRF, which optimizes the invisible internal structure of the object through a video sequence under physical, appearance (i.e., visible external structure)-preserving, and keyframe constraints. In particular, to avoid falling into undesirable local optima owing to its ill-posed nature, we propose volume annealing, i.e., searching for the global optima by repeatedly reducing and expanding the volume. Extensive experiments on 115 objects involving diverse structures (i.e., various cavity shapes, locations, and sizes) and various material properties reveal the properties of SfC and demonstrate the effectiveness of the proposed SfC-NeRF.

Articulated 3D object generation is fundamental for creating realistic, functional, and interactable virtual assets which are not simply static. We introduce MeshArt, a hierarchical transformer-based approach to generate articulated 3D meshes with clean, compact geometry, reminiscent of human-crafted 3D models. We approach articulated mesh generation in a part-by-part fashion across two stages.First, we generate a high-level articulation-aware object structure; then, based on this structural information, we synthesize each part's mesh faces. Key to our approach is modeling both articulation structures and part meshes as sequences of quantized triangle embeddings, leading to a unified hierarchical framework with transformers for autoregressive generation.Object part structures are first generated as their bounding primitives and articulation modes; a second transformer, guided by these articulation structures, then generates each part's mesh triangles.To ensure coherency among generated parts, we introduce structure-guided conditioning that also incorporates local part mesh connectivity.MeshArt shows significant improvements over state of the art, with 57.1% improvement in structure coverage and a 209-point improvement in mesh generation FID.

Generalized Category Discovery (GCD) typically relies on the pre-trained Vision Transformer (ViT) to extract features from a global receptive field, followed by contrastive learning to simultaneously classify unlabeled known classes and unknown classes without priors. Owing to the deficiency in the modeling capacity for inner-patch local information within ViT, current methods primarily focus on discriminative features at global level. This results in a model with more yet scattered attention, where neither excessive nor insufficient focus can grasp subtle differences to classify fine-grained unknown and known categories. To address this issue, we propose the AptGCD to deliver apt attention for GCD. It mimics the human brain how leveraging visual perception to refine local attention and comprehend global context by proposing a Meta Visual Prompt (MVP) and Prompt Transformer (PT). MVP is introduced into GCD for the first time, refining channel-level attention, while adaptively self-learning unique inner-patch features as prompts to achieve local visual modeling for our prompt transformer. Yet, relying solely on detailed features can lead to skewed judgments. Hence, PT harmonizes local and global representations, guiding the model's interpretation of features through broader contexts, thereby capturing more useful details with less attention. Extensive experiments on seven datasets demonstrate that AptGCD outperforms current methods, it achieves an average proportional 'New' accuracy improvement of approximately 9.2% over SOTA method on the all four fine-grained datasets, establishing a new standard in the field. The code is available at https://github.com/wendy26zhang/AptGCD.

Generalized Category Discovery (GCD) is an intriguing open-world problem that has garnered increasing attention. Given a dataset that includes both labelled and unlabelled images, GCD aims to categorize all images in the unlabelled subset, regardless of whether they belong to known or unknown classes. In GCD, the common practice typically involves applying a spherical projection operator at the end of the self-supervised pretrained backbone, operating within Euclidean or spherical space. However, both of these spaces have been shown to be suboptimal for encoding samples that possess hierarchical structures. In contrast, hyperbolic space exhibits exponential volume growth relative to radius, making it inherently strong at capturing the hierarchical structure of samples from both seen and unseen categories. Therefore, we propose to tackle the category discovery challenge in the hyperbolic space. We introduce HypCD, a simple Hyperbolic framework for learning hierarchy-aware representations and classifiers for generalized Category Discovery. HypCD first transforms the Euclidean embedding space of the backbone network into hyperbolic space, facilitating subsequent representation and classification learning by considering both hyperbolic distance and the angle between samples. This approach is particularly helpful for knowledge transfer from known to unknown categories in GCD. We thoroughly evaluate HypCD on public GCD benchmarks, by applying it to various baseline and state-of-the-art methods, consistently achieving significant improvements.

Neural networks derived from the M-P model have excelled in various visual tasks. However, as a simplified simulation version of the brain neural pathway, their structures are locked during training, causing over-fitting and over-parameterization. Although recent models have begun using the biomimetic concept and empirical pruning, they still result in irrational pruning, potentially affecting the accuracy of the model. In this paper, we introduce the Glia unit, composed of oligodendrocytes (Oli) and astrocytes (Ast), to emulate the exact workflow of the mammalian brain, thereby enhancing the biological plausibility of neural functions. Oli selects neurons involved in signal transmission during neural communication and, together with Ast, adaptively optimizes the neural structure. Specifically, we first construct the artificial Glia-Neuron (G-N) model, which is formulated at the instance, group, and interaction levels with adaptive and collaborative mechanisms. Then, we construct GliaNet based on our G-N model, whose structure and connections can be continuously optimized during training. Experiments show that our GliaNet advances state-of-the-art on multiple tasks while significantly reducing its parameters.

Recently, multi-class anomaly classification has garnered increasing attention. Previous methods directly cluster anomalies but often struggle due to the lack of anomaly-prior knowledge. Acquiring this knowledge faces two issues: the non-prominent and weak-semantics anomalies. In this paper, we propose AnomalyNCD, a multi-class anomaly classification network compatible with different anomaly detection methods. To address the non-prominence of anomalies, we design main element binarization (MEBin) to obtain anomaly-centered images, ensuring anomalies are learned while avoiding the impact of incorrect detections. Next, to learn anomalies with weak semantics, we design mask-guided representation learning, which focuses on isolated anomalies guided by masks and reduces confusion from erroneous inputs through corrected pseudo labels. Finally, to enable flexible classification at both region and image levels, we develop a region merging strategy that determines the overall image category based on the classified anomaly regions. Our method outperforms the state-of-the-art works on the MVTec AD and MTD datasets. Compared with the current methods, AnomalyNCD combined with zero-shot anomaly detection method achieves a 10.8% F1 gain, 8.8% NMI gain, and 9.5% ARI gain on MVTec AD, and 12.8% F1 gain, 5.7% NMI gain, and 10.8% ARI gain on MTD. Code is available at https://github.com/HUST-SLOW/AnomalyNCD.