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🎞️ ECCV2026 · 12 paper notes

📌 Same area in other venues: 📷 CVPR2026 (105) · 🔬 ICLR2026 (116) · 💬 ACL2026 (4) · 🧪 ICML2026 (70) · 🤖 AAAI2026 (117) · 🧠 NeurIPS2025 (121)

Articulat3D: Reconstructing Articulated Digital Twins From Monocular Videos with Geometric and Motion Constraints: Articulat3D proposes a two-stage optimization framework to reconstruct interactive digital twins of articulated objects from casual monocular videos. The first stage performs structured, part-level motion decomposition on 3D point trajectories using low-dimensional motion bases. The second stage constrains the motion to satisfy physical rigid-body rules using explicit kinematic primitives (axes, pivots, and frame-wise scalars of revolute/prismatic joints). It achieves state-of-the-art (SOTA) accuracy on both synthetic and real-world data without requiring static pre-scans.
FedLAS: Feature-Modulated Bidirectional Label Smoothing for Neural Network Calibration: Proposed FeDLaS (Feature-modulated Bidirectional Label Smoothing), which utilizes the L1 norm of hidden layer features as a confidence proxy to bypass the softmax saturation defect. Combined with a bidirectional calibration gating mechanism, it dynamically determines the over-confidence/under-confidence state of each sample in real-time and adaptively modulates label smoothing intensity per sample, significantly reducing ECE and AECE while maintaining Top-1 accuracy.
Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learning: This paper proposes Geometric Gradient Rectification (GGR), a plug-and-play gradient-space rectification framework. Using the supervised gradient as an anchor, GGR projects conflicting unsupervised auxiliary gradients onto a safe half-space. This ensures that auxiliary updates remain first-order non-adversarial compared to the supervised direction on selected parameter blocks, simultaneously enhancing closed-set generalization and open-set robustness without relying on accurate OOD detection.
Geometry-Anchored Transport Framework for Exemplar-Free Class-Incremental Learning: This paper proposes the Geometry-Anchored Transport Framework, which shifts feature transport from a decoupled post-processing paradigm to training-time geometric constraints. By using analytical geometric anchors (closed-form solutions of the Sylvester equation) to suppress macro-anisotropic drift, and combining with topology-aware evolution to constrain local manifold degradation, it stably transports old-class Gaussian statistics under exemplar-free constraints to achieve reliable Mahalanobis evaluation.
Intrinsically Stable Spiking Neural Networks: Overcoming the Performance Barrier in the Absence of Batch Normalization: IS-SNN stabilizes the firing rate of deep spiking neural networks without any activation normalization layers (Batch Normalization) through topology-aware weight standardization and modified residual connections. After training, the standardization operations are folded into static weights, achieving zero normalization overhead during inference. It achieves 68.05% accuracy on ImageNet while reducing FPGA LUT resource consumption by 96.4%.
Lost in the Tail: Addressing Geographic Imbalance in Urban Visual Place Recognition: This paper identifies a severe long-tailed geographic distribution issue (head-to-tail sample ratio up to 300:1) in city-scale visual place recognition datasets. It proposes the DAPR framework: during training, it uses Low-visit Bias Loss for inverse-frequency weighting and classifier bias calibration; during inference, it employs characteristic function distance in the frequency domain for distribution-aware re-ranking, outperforming the previous state-of-the-art hybrid pipeline on the SF-XL benchmark by 18.3% R@1.
Match-Any-Events: Zero-Shot Motion-Robust Feature Matching Across Wide Baselines for Event Cameras: This paper proposes the first event camera wide-baseline feature matching model that achieves zero-shot generalization across datasets. It learns highly efficient multi-temporal-scale event features using a separable spatial-temporal attention backbone (TAg) and sparse-aware token adaptive pruning (SETS). Trained on a large-scale synthetic (E-MegaDepth) and real-world (ECM) wide-baseline dataset, it outperforms the previous state-of-the-art by 37.7% on multiple benchmarks.
NURBS Splatting: A Unified Differentiable Rendering Framework for Vector Graphics: This paper proposes NURBS Splatting, which adaptively samples NURBS curves along the arc length as isotropic Gaussian kernels and differentiably renders them using a tile-based Gaussian splatting rasterizer. This integrates rational weights and non-uniform knot vectors into 2D image-space end-to-end optimization for the first time, significantly outperforming existing polynomial primitive-based methods in tasks such as calligraphy reconstruction, layered image vectorization, and single-stroke image abstraction.
Pointer-CAD v2: Plan-Then-Construct CAD Generation with Dimension-Aware Parametric Precision: Pointer-CAD v2 proposes the Plan-Then-Construct framework, which decouples CAD generation into two stages: "first plan dimension parameters, then construct geometry using point-based parameter retrieval". This thoroughly eliminates the precision loss caused by quantitative parameter discretization in traditional execution sequence methods, significantly outperforming all baselines on vertex, edge, and face-level geometric precision metrics.
Seeing Touch from Motion: A Unified Modality-Aware Visuo-Tactile Policy with Tactile Motion Correlation: Addressing the long-standing issue where optical tactile sensors struggle to distinguish fine-grained contact states, this paper reveals that the dot product of "transient motion" and "cumulative motion" can explicitly differentiate states such as "making contact / stable contact / releasing contact." Based on this, a Tactile Motion Correlation (TMC) representation is proposed. Using a Mixture-of-Transformers, TMC is integrated as an independent modality alongside vision, raw touch, and proprioception, achieving higher success rates across four real-world contact-rich manipulation tasks.
Spectral Gating via Damped Oscillations for Adaptive Implicit Neural Representations: Starting from the steady-state response of a forced damped harmonic oscillator, this paper derives a physically driven activation function, FDHO, which couples the activation amplitude and frequency through a second-order transfer function. This coupling produces an implicit spectral gating mechanism: the self-interaction term of the damping factor gradient constantly closes the spectral gate, whereas the signal term opens the gate only when the target displays coherent frequency components. This enables automatic distinction between signal and noise without any explicit regularization or task-specific hyperparameter tuning, achieving state-of-the-art (SOTA) or competitive performance across tasks like signal fitting, denoising, CT reconstruction, and super-resolution.
Vector Scaffolding: Inter-Scale Orchestration for Differentiable Image Vectorization: This work proposes Vector Scaffolding, a hierarchical optimization framework that addresses the area-boundary gradient imbalance via Interior Gradient Aggregation, densifies curves from coarse to fine according to natural image power laws using Progressive Stratification, and applies a 50x learning rate scaling via Rapid Inflation Scheduling to achieve 2.5x speedup and a ~1.4dB PSNR improvement, reframing differentiable vectorization from "flat pixel matching" to "structured topological construction."