We introduce vertex block descent, a block coordinate descent solution for the variational form of implicit Euler through vertex-level Gauss-Seidel iterations. It operates with local vertex position updates that achieve reductions in global variational energy with maximized parallelism. This forms a physics solver that can achieve numerical convergence with unconditional stability and exceptional computation performance. It can also fit in a given computation budget by simply limiting the iteration count while maintaining its stability and superior convergence rate. We present and evaluate our method in the context of elastic body dynamics, providing details of all essential components and showing that it outperforms alternative techniques. In addition, we discuss and show examples of how our method can be used for other simulation systems, including particle-based simulations and rigid bodies.
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In mesh simplification, common requirements like accuracy, triangle quality, and feature alignment are often considered as a trade-off. Existing algorithms concentrate on just one or a few specific aspects of these requirements. For example, the well-known Quadric Error Metrics (QEM) approach [Garland and Heckbert 1997] prioritizes accuracy and can preserve strong feature lines/points as well, but falls short in ensuring high triangle quality and may degrade weak features that are not as distinctive as strong ones. In this paper, we propose a smooth functional that simultaneously considers all of these requirements. The functional comprises a normal anisotropy term and a Centroidal Voronoi Tessellation (CVT) [Du et al. 1999] energy term, with the variables being a set of movable points lying on the surface. The former inherits the spirit of QEM but operates in a continuous setting, while the latter encourages even point distribution, allowing various surface metrics. We further introduce a decaying weight to automatically balance the two terms. We selected 100 CAD models from the ABC dataset [Koch et al. 2019], along with 21 organic models, to compare the existing mesh simplification algorithms with ours. Experimental results reveal an important observation: the introduction of a decaying weight effectively reduces the conflict between the two terms and enables the alignment of weak features. This distinctive feature sets our approach apart from most existing mesh simplification methods and demonstrates significant potential in shape understanding. Please refer to the teaser figure for illustration.
Navigating topological transitions in cellular mechanical systems is a significant challenge for existing simulation methods. While abstract models lack predictive capabilities at the cellular level, explicit network representations struggle with topology changes, and per-cell representations are computationally too demanding for large-scale simulations. To address these challenges, we propose a novel cell-centered approach based on differentiable Voronoi diagrams. Representing each cell with a Voronoi site, our method defines shape and topology of the interface network implicitly. In this way, we substantially reduce the number of problem variables, eliminate the need for explicit contact handling, and ensure continuous geometry changes during topological transitions. Closed-form derivatives of network positions facilitate simulation with Newton-type methods for a wide range of per-cell energies. Finally, we extend our differentiable Voronoi diagrams to enable coupling with arbitrary rigid and deformable boundaries. We apply our approach to a diverse set of examples, highlighting splitting and merging of cells as well as neighborhood changes. We illustrate applications to inverse problems by matching soap foam simulations to real-world images. Comparative analysis with explicit cell models reveals that our method achieves qualitatively comparable results at significantly faster computation times.
Foveated rendering takes advantage of the reduced spatial sensitivity in peripheral vision to greatly reduce rendering cost without noticeable spatial quality degradation. Due to its benefits, it has emerged as a key enabler for real-time high-quality virtual and augmented realities. Interestingly though, a large body of work advocates that a key role of peripheral vision may be motion detection, yet foveated rendering lowers the image quality in these regions, which may impact our ability to detect and quantify motion. The problem is critical for immersive simulations where the ability to detect and quantify movement drives actions and decisions. In this work, we diverge from the contemporary approach towards the goal of foveated graphics, and demonstrate that a loss of high-frequency spatial details in the periphery inhibits motion perception, leading to underestimating motion cues such as velocity. Furthermore, inspired by an interesting visual illusion, we design a perceptually motivated real-time technique that synthesizes controlled spatio-temporal motion energy to offset the loss in motion perception. Finally, we perform user experiments demonstrating our method's effectiveness in recovering motion cues without introducing objectionable quality degradation.
Volume-preserving hyperelastic materials are widely used to model near-incompressible materials such as rubber and soft tissues. However, the numerical simulation of volume-preserving hyperelastic materials is notoriously challenging within this regime due to the non-convexity of the energy function. In this work, we identify the pitfalls of the popular eigenvalue clamping strategy for projecting Hessian matrices to positive semi-definiteness during Newton’s method. We introduce a novel eigenvalue filtering strategy for projected Newton’s method to stabilize the optimization of Neo-Hookean energy and other volume-preserving variants under high Poisson’s ratio (near 0.5) and large initial volume change. Our method only requires a single line of code change in the existing projected Newton framework, while achieving significant improvement in both stability and convergence speed. We demonstrate the effectiveness and efficiency of our eigenvalue projection scheme on a variety of challenging examples and over different deformations on a large dataset.
Real-world fabrics, composed of threads and yarns, often display complex stress-strain relationships, making their homogenization a challenging task for fast simulation by continuum-based models. Consequently, existing homogenized yarn-level models frequently struggle with numerical stability without line search at large time steps, forcing a trade-off between model accuracy and stability. In this paper, we propose a neural-assisted homogenized constitutive model for simulating yarn-level cloth. Unlike analytic models, a neural model is advantageous in adapting to complex dynamic behaviors, and its inherent smoothness naturally mitigates stability issues. We also introduce a sector-based warm-start strategy to accelerate the data collection process in homogenization. This model is trained using collected strain energy datasets and its accuracy is validated through both qualitative and quantitative experiments. Thanks to our model’s stability, our simulator can now achieve two-orders-of-magnitude speedups with large time steps compared to previous models.
This paper introduces a new approach for generating globally consistent normals for point clouds sampled from manifold surfaces. Given that the generalized winding number (GWN) field generated by a point cloud with globally consistent normals is a solution to a PDE with jump boundary conditions and possesses harmonic properties, and the Dirichlet energy of the GWN field can be defined as an integral over the boundary surface, we formulate a boundary energy derived from the Dirichlet energy of the GWN. Taking as input a point cloud with randomly oriented normals, we optimize this energy to restore the global harmonicity of the GWN field, thereby recovering the globally consistent normals. Experiments show that our method outperforms state-of-the-art approaches, exhibiting enhanced robustness to noise, outliers, complex topologies, and thin structures. Our code can be found at https://github.com/liuweizhou319/BIM.
Modal Folding: Discovering Smooth Folding Patterns for Sheet Materials using Strain-Space Modes
PDF ↗Folding can transform mundane objects such as napkins into stunning works of art. However, finding new folding transformations for sheet materials is a challenging problem that requires expertise and real-world experimentation. In this paper, we present Modal Folding—an automated approach for discovering energetically optimal folding transformations, i.e., large deformations that require little mechanical work. For small deformations, minimizing internal energy for fixed displacement magnitudes leads to the well-known elastic eigenmodes. While linear modes provide promising directions for bending, they cannot capture the rotational motion required for folding. To overcome this limitation, we introduce strain-space modes—nonlinear analogues of elastic eigenmodes that operate on per-element curvatures instead of vertices. Using strain-space modes to determine target curvatures for bending elements, we can generate complex nonlinear folding motions by simply minimizing the sheet’s internal energy. Our modal folding approach offers a systematic and automated way to create complex designs. We demonstrate the effectiveness of our method with simulation results for a range of shapes and materials, and validate our designs with physical prototypes.
The consumption of a video requires a considerable amount of energy during the various stages of its life-cycle. With a billion hours of video consumed daily, this contributes significantly to the greenhouse gas emission. Therefore, reducing the end-to-end carbon footprint of the video chain, while preserving the quality of experience at the user side, is of high importance. To contribute in an impactful manner, we propose 3R-INN, a single light invertible network that does three tasks at once: given a high-resolution grainy image, it Rescales it to a lower resolution, Removes film grain and Reduces its power consumption when displayed. Providing such a minimum viable quality content contributes to reducing the energy consumption during encoding, transmission, decoding and display. 3R-INN also offers the possibility to restore either the high-resolution grainy original image or a grain-free version, thanks to its invertibility and the disentanglement of the high frequency, and without transmitting auxiliary data. Experiments show that, while enabling significant energy savings for encoding (78%), decoding (77%) and rendering (5% to 20%), 3R-INN outperforms state-of-the-art film grain synthesis and energy-aware methods and achieves state-of-the-art performance on the rescaling task on different test-sets.
AD3: Introducing a score for Anomaly Detection Dataset Difficulty assessment using VIADUCT dataset
PDF ↗The field of visual Industrial Anomaly Detection (IAD) has brought forth many new semi-supervised learning methods in recent years. At the same time, there have been few new datasets for benchmarking the methods. The most popular dataset is MVTec-AD dataset, because of its diversity of categories and availability of industrial objects. But many methods already achieve AUROC scores of more than 99 % on the MVTec-AD dataset. The defects of the categories that the dataset provides appear to be easily detectable. Furthermore, there is no existing approach to statistically describe the defects that need to be found in IAD datasets. This paper presents a new dataset for visual industrial anomaly detection and a novel approach for Anomaly Detection Dataset Difficulty assessment with the AD3 score. The new dataset named VIADUCT contains 49 categories and 10,986 high resolution images from eleven different sectors. Through the support of several manufacturing companies, numerous real inspection problems are presented through the dataset. It contains a large number of different defects with detailed pixel-wise annotations. The VIADUCT dataset is compared with other state of the art datasets to underline its added value. Therefore, we provide an overview for each dataset regarding the number of categories, images, defect categories and defects. In addition to these obvious comparisons the defects of the datasets are described with the AD3 score. This novel score is used to analyze the size of the defects and the similarity between the defect and its corresponding object. Using seven selected methods from industrial anomaly detection, a benchmark is performed on the new dataset, showing that there is still potential for improvement. It is shown that the VIADUCT dataset is the largest dataset in the field of image-based industrial anomaly detection. In addition to its very small defects which are hard to recognize, the dataset also offers the greatest variance of possible defects and the most defect classes. Describing the datasets with AD3 score it can be found that VIADUCT dataset have the smallest and most inconspicuous defects. With the AD3 score we are able to create a-priori knowledge for every single defect in IAD datasets. The AD3 score correlates with the results of the IAD method benchmark, showing that it can be used to estimate defect detection difficulty. In the future, new objects can be assessed to see whether defects can be recognized using IAD methods before an energy-intensive benchmark is performed. The simple calculation of the AD3 score generates valuable a-priori knowledge and can save resources.
One of the challenges for neural networks in real-life applications is the overconfident errors these models make when the data is not from the original training distribution. Addressing this issue is known as Out-of-Distribution (OOD) detection. Many state-of-the-art OOD methods employ an auxiliary dataset as a surrogate for OOD data during training to achieve improved performance. However, these methods fail to fully exploit the local information embedded in the auxiliary dataset. In this work, we propose the idea of leveraging the information embedded in the gradient of the loss function during training to enable the network to not only learn a desired OOD score for each sample but also to exhibit similar behavior in a local neighborhood around each sample data point. We also develop a novel energy-based sampling method to allow the network to be exposed to more informative OOD samples during the training phase. This is especially important when the auxiliary dataset is large. We demonstrate the effectiveness of our method through extensive experiments on several OOD benchmarks, improving the existing state-of-the-art FPR95 by 4% on our ImageNet experiment. We further provide a theoretical analysis through the lens of certified robustness and Lipschitz analysis to showcase the theoretical foundation of our work. We will publicly release our code after the review process.
Single-photon cameras (SPCs) are emerging as sensors of choice for various challenging imaging applications. One class of SPCs based on the single-photon avalanche diode (SPAD) detects individual photons using an avalanche process; the raw photon data can then be processed to extract scene information under extremely low light, high dynamic range, and rapid motion. Yet, single-photon sensitivity in SPADs comes at a cost --- each photon detection consumes more energy than that of a CMOS camera. This avalanche power significantly limits sensor resolution and could restrict widespread adoption of SPAD-based SPCs. We propose a computational-imaging approach called photon inhibition to address this challenge. Photon inhibition strategically allocates detections in space and time based on downstream inference task goals and resource constraints. We develop lightweight, on-sensor computational inhibition policies that use past photon data to disable SPAD pixels in real-time, to select the most informative future photons. As case studies, we design policies tailored for image reconstruction and edge detection, and demonstrate, both via simulations and real SPC captured data, considerable reduction in photon detections (over 90% of photons) while maintaining task performance metrics. Our work raises the question of ``which photons should be detected?'', and paves the way for future energy-efficient single-photon imaging.
With the advancement of deep neural networks in computer vision, artificial intelligence (AI) is widely employed in real-world applications. However, AI still faces limitations in mimicking high-level human capabilities, such as novel category discovery, for practical use. While some methods utilizing offline continual learning have been proposed for novel category discovery, they neglect the continuity of data streams in real-world settings. In this work, we introduce Online Continuous Generalized Category Discovery (OCGCD), which considers the dynamic nature of data streams where data can be created and deleted in real time. Additionally, we propose a novel method, DEAN, Discovery via Energy guidance and feature AugmentatioN, which can discover novel categories in an online manner through energy-guided discovery and facilitate discriminative learning via energy-based contrastive loss. Furthermore, DEAN effectively pseudo-labels unlabeled data through variational feature augmentation. Experimental results demonstrate that our proposed DEAN achieves outstanding performance in proposed OCGCD scenario. The code will be publicly released after the review.
Event-to-Video (E2V) reconstruction is to recover grayscale video from the neuromorphic event streams, and Spiking Neural Networks (SNNs) are the potential energy-efficient models to solve the reconstruction problem. Event voxels are an efficient representation for compressing event streams for E2V reconstruction, but their temporal latent representation is rarely considered in SNN-based reconstruction. In this paper, we propose a spike-temporal latent representation (STLR) model for SNN-based E2V reconstruction. The STLR solves the temporal latent coding of event voxels for video frame reconstruction. It is composed of two cascaded SNNs: a) Spike-based Voxel Temporal Encoder (SVT) and b) U-shape SNN Decoder. The SVT is a spike-driven spatial unfolding network with a specially designed coding dynamic. It encodes the event voxel into the layer-wise spiking features for latent coding, approximating the fixed point of the Iterative Shrinkage-Thresholding Algorithm. Then, the U-shape SNN decoder reconstructs the video based on the encoded spikes. Experimental results show that the STLR achieves comparable performance to the popular SNNs on IJRR, HQF, and MVSEC, and significantly improves energy efficiency. For example, the STLR achieves comparable performance under 13.20% parameters and 3.33%~ 5.03% energy cost of the PA-EVSNN.
Exploring Vulnerabilities in Spiking Neural Networks: Direct Adversarial Attacks on Raw Event Data
PDF ↗In the field of computer vision, event-based Dynamic Vision Sensors (DVSs) have emerged as a significant complement to traditional pixel-based imaging due to their low power consumption and high temporal resolution. These sensors, particularly when combined with spiking neural networks (SNNs), offer a promising direction for energy-efficient and fast-reacting vision systems. Typically, DVS data are converted into grid-based formats for processing with SNNs, with this transformation process often being an opaque step in the pipeline. As a result, the grid representation becomes an intermediate yet inaccessible stage during the implementation of attacks, highlighting the importance of attacking raw event data. Existing attack methodologies predominantly target grid-based representations, hindered by the complexity of three-valued optimization and the broad optimization space associated with raw event data. Our study addresses this gap by introducing a novel adversarial attack approach that directly targets raw event data. We tackle the inherent challenges of three-valued optimization and the need to preserve data sparsity through a strategic amalgamation of methods: 1) Treating Discrete Event Values as Probabilistic Samples: This allows for continuous optimization by considering discrete event values as probabilistic space samples. 2) Focusing on Specific Event Positions: We prioritize specific event positions that merge original data with additional target label data, enhancing attack precision. 3) Employing a Sparsity Norm: To retain the original data's sparsity, a sparsity norm is utilized, ensuring the adversarial data's comparability. Our empirical findings demonstrate the effectiveness of our combined approach, achieving noteworthy success in targeted attacks and highlighting vulnerabilities in models based on raw event data.
Multimodal models have gained increasing popularity recently. Many works have been proposed to learn the representations for different modalities. The representation can learn shared information from these domains, leading to increased and coherent joint and cross-generation. However, these works mainly considered standard Gaussian or Laplacian as their prior distribution. It can be challenging for the uni-modal and non-informative distribution to capture all the information from multiple data types. Meanwhile, energy-based models (EBM) have shown their effectiveness in multiple tasks due to their expressiveness and flexibility. But its capacity has yet to be discovered for the multimodal generative models. In this paper, we propose a novel framework to train multimodal latent generative models together with the energy-based models. The proposed method can lead to more expressive and informative prior which can better capture the information within multiple modalities. Our experiments showed that our model is effective and can increase generation coherence and latent classification for different multimodal datasets.
In this paper, we propose a novel generative model that utilizes a conditional Energy-Based Model (EBM) for enhancing Variational Autoencoder (VAE), termed Energy-Calibrated VAE (EC-VAE). Specifically, VAEs often suffer from blurry generated samples due to the lack of a tailored training on the samples generated in the generative direction. On the other hand, EBMs can generate high-quality samples but require expensive Markov Chain Monte Carlo (MCMC) sampling. To address these issues, we introduce a conditional EBM for calibrating the generative direction of VAE during training, without requiring it for the generation at test time. In particular, we train EC-VAE upon both the input data and the calibrated samples with adaptive weight to enhance efficacy while avoiding MCMC sampling at test time. Furthermore, we extend the calibration idea of EC-VAE to variational learning and normalizing flows, and apply EC-VAE to an additional application of zero-shot image restoration via neural transport prior and range-null theory. We evaluate the proposed method with two applications, including image generation and zero-shot image restoration, and the experimental results show that our method achieves competitive performance over single-step non-adversarial generation.
Neuromorphic sensors, specifically event cameras, revolutionize visual data acquisition by capturing pixel intensity changes with exceptional dynamic range, minimal latency, and energy efficiency, setting them apart from conventional frame-based cameras. The distinctive capabilities of event cameras have ignited significant interest in the domain of event-based action recognition, recognizing their vast potential for advancement. However, the development in this field is currently slowed by the lack of comprehensive, large-scale datasets, which are critical for developing robust recognition frameworks. To bridge this gap, we introduces DailyDVS-200, a meticulously curated benchmark dataset tailored for the event-based action recognition community. DailyDVS-200 is extensive, covering 200 action categories across real-world scenarios, recorded by 47 participants, and comprises more than 22,000 event sequences. This dataset is designed to reflect a broad spectrum of action types, scene complexities, and data acquisition diversity. Each sequence in the dataset is annotated with 14 attributes, ensuring a detailed characterization of the recorded actions. Moreover, DailyDVS-200 is structured to facilitate a wide range of research paths, offering a solid foundation for both validating existing approaches and inspiring novel methodologies. By setting a new benchmark in the field, we challenge the current limitations of neuromorphic data processing and invite a surge of new approaches in event-based action recognition techniques, which paves the way for future explorations in neuromorphic computing and beyond.
Compared with an extensive list of automotive radar datasets that support autonomous driving, indoor radar datasets are scarce at a smaller scale in the format of low-resolution radar point clouds and usually under an open-space single-room setting. In this paper, we scale up indoor radar data collection using multi-view high-resolution radar heatmap in a multi-day, multi-room, and multi-subject setting, with an emphasis on the diversity of environment and subjects. Referred to as the millimeter-wave multi-view radar (\textbf{MMVR}) dataset, it consists of $345$K multi-view radar frames collected from $25$ human subjects over $6$ different rooms, $446$K annotated bounding boxes/segmentation instances, and $7.59$ million annotated keypoints to support three major perception tasks of object detection, pose estimation, and instance segmentation, respectively. For each task, we report performance benchmarks under two protocols: a single subject in an open space and multiple subjects in several cluttered rooms with two data splits: random split and cross-environment split over $395$ 1-min data segments. We anticipate that MMVR facilitates indoor radar perception development for indoor vehicle (robot/humanoid) navigation, building energy management, and elderly care for better efficiency, user experience, and safety. The MMVR dataset is available at https://doi.org/10.5281/zenodo.12611978.
Spiking neural networks (SNNs) offer an energy-efficient alternative to conventional deep learning by mimicking the event-driven processing of the brain. Incorporating the Transformers with SNNs has shown promise for accuracy, yet it is incompetent to capture high-frequency patterns like moving edge and pixel-level brightness changes due to their reliance on global self-attention operations. Porting frequency representations in SNN is challenging yet crucial for event-driven vision. To address this issue, we propose the Spiking Wavelet Transformer (SWformer), an attention-free architecture that effectively learns comprehensive spatial-frequency features in a spike-driven manner by leveraging the sparse wavelet transform. The critical component is a Frequency-aware Spiking Token Mixer (FSTM) with three branches: 1) spiking wavelet learner for spatial-frequency domain learning, 2) convolution-based learner for spatial feature extraction, and 3) spiking pointwise convolution for cross-channel information aggregation. We also adopt negative spike dynamics to strengthen the frequency representation further. This enables the SWformer to outperform vanilla spiking Transformers in capturing high-frequency visual components, as evidenced by our empirical results. Experiments on both static and neuromorphic datasets demonstrate SWformer's effectiveness in capturing spatial-frequency patterns in a multiplication-free, event-driven fashion, outperforming state-of-the-art SNNs. SWformer achieves an over 50 reduction in energy consumption, a 21.1 reduction in parameter count, and a 2.40 performance improvement on the ImageNet dataset compared to vanilla Spiking Transformers.