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新论文 · v1arXiv:2611.15320

Delaunay-Based Nearest Neighbor Search with Grid-Accelerated Queries and Parallel Construction

Pengfei Wang, Shiqing Xin, Changhe Tu

原文摘要

Nearest-neighbor search is a fundamental primitive in computer graphics, with applications in distance field computation, surface reconstruction, and point cloud registration. In many graphics applications, data points lie on a surface manifold embedded in 3D space, whereas queries are distributed throughout the surrounding volume, a setting in which spatial partitioning structures such as k-d trees and grids can become inefficient. Dynamic-programming-based nearest-neighbor search (DP-NNS) offers an alternative: it traverses a successor DAG that records the history of incremental Delaunay construction and often outperforms spatial partitioning structures. However, its sequential construction is slow, and naive parallelization degrades query performance, limiting its practical adoption. We accelerate both queries and construction in DP-NNS. For queries, we introduce a grid-based initialization strategy that precomputes valid starting points on the successor DAG, shortening search paths while preserving exactness and enabling a twofold speedup. For construction, we analyze how parallel insertion affects query cost and develop a parallel algorithm that preserves logarithmic expected query cost under stated geometric assumptions. Our parallel construction algorithm achieves a 10x speedup over the sequential baseline while maintaining nearly the same query cost. We further reorder query points to improve cache locality. Extensive experiments demonstrate that our method outperforms widely used libraries in nearest-neighbor query performance.

cs.GR · cs.CG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.11359

NuJEV: Structured Visual Judgments for Consistent Nuclei Quantification

Yonghuang Wu, Pengfei Wang, Zhenyang Liang, Yize Ma, Pengfei Song, Guoqing Wu, Jinhua Yu

原文摘要

Nuclear quantification requires locating and typing nuclei, beyond generating a count. Inspired by Jev's structured decisions in place of free-form generation, we introduce NuJEV for nuclear perception in Qwen3-VL-2B. We jointly train visual LoRA and a parallel head to predict nuclear presence, centers, and types. One image encoding feeds both the nuclear head and original language decoder. The decoder interprets region and type requests, with counts computed from the predicted nuclei. On 2,732 PanNuke test images, NuJEV achieves a five-query mean absolute error ($\mathrm{MAE}_5$) of 1.844 overall and 5.268 on dense images, with localization/typed F1 of 0.816/0.682. A matched encoder ablation shows 41.0\% lower dense error with visual adaptation. For language requests, NuJEV reduces request MAE by 76.2\% relative to direct generation. Our code will be released as open source.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2604.18215

Memorize When Needed: Decoupled Memory Control for Spatially Consistent Long-Horizon Video Generation

Yanjun Guo, Zhengqiang Zhang, Pengfei Wang, Xinyue Liang, Zhiyuan Ma, Lei Zhang

原文摘要

Spatially consistent long-horizon video generation aims to maintain temporal and spatial consistency along predefined camera trajectories. Existing methods mostly entangle memory modeling with video generation, leading to inconsistent content during scene revisits and diminished generative capacity when exploring novel regions, even when trained on extensive annotated data. To address these limitations, we propose a decoupled framework that separates memory conditioning from generation. Our approach significantly reduces training costs while simultaneously enhancing spatial consistency and preserving the generative capacity for novel scene exploration. Specifically, we employ a lightweight, independent memory branch to learn precise spatial consistency from historical observations. We first introduce a hybrid memory representation to capture complementary temporal and spatial cues from generated frames, then leverage a per-frame cross-attention mechanism to ensure each frame is conditioned exclusively on the most spatially relevant historical information, which is injected into the generative model to ensure spatial consistency. When generating new scenes, a camera-aware gating mechanism is proposed to mediate the interaction between memory and generation modules, enabling memory conditioning only when meaningful historical references exist. Compared with the existing method, our method is highly data-efficient, yet the experiments demonstrate that our approach achieves state-of-the-art performance in terms of both visual quality and spatial consistency. Project page can be found at https://iguoyanjun.github.io/memorize-when-needed-project-page/.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2603.27534

S3KF: Spherical State-Space Kalman Filtering for Panoramic 3D Multi-Object Tracking

Zhongyuan Liu, Shaonan Yu, Jianping Li, Pengfei Wan, Xinhang Xu, Pengfei Wang, Maggie Y. Gao, Lihua Xie

原文摘要

Panoramic multi-object tracking is important for industrial safety monitoring, wide-area robotic perception, and infrastructure-light deployment in large workspaces. In these settings, the sensing system must provide full-surround coverage, metric geometric cues, and stable target association under wide field-of-view distortion and occlusion. Existing image-plane trackers are tightly coupled to the camera projection and become unreliable in panoramic imagery, while conventional Euclidean 3D formulations introduce redundant directional parameters and do not naturally unify angular, scale, and depth estimation. In this paper, we present $\mathbf{S^3KF}$, a panoramic 3D multi-object tracking framework built on a motorized rotating LiDAR and a quad-fisheye camera rig. The key idea is a geometry-consistent state representation on the unit sphere $\mathbb{S}^2$, where object bearing is modeled by a two-degree-of-freedom tangent-plane parameterization and jointly estimated with box scale and depth dynamics. Based on this state, we derive an extended spherical Kalman filtering pipeline that fuses panoramic camera detections with LiDAR depth observations for multimodal tracking. We further establish a map-based ground-truth generation pipeline using wearable localization devices registered to a shared global LiDAR map, enabling quantitative evaluation without motion-capture infrastructure. Experiments on self-collected real-world sequences show decimeter-level planar tracking accuracy, improved identity continuity over a 2D panoramic baseline in dynamic scenes, and real-time onboard operation on a Jetson AGX Orin platform. These results indicate that the proposed framework is a practical solution for panoramic perception and industrial-scale multi-object tracking.The project page can be found at https://kafeiyin00.github.io/S3KF/.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2610.35850

OSWAM: An Effective World Action Model with One-Step Action Generation

Guowen Zhang, Liyi Chen, Xu Hu, Pengfei Wang, Jingfan Chen, Chenhang He, Lei Zhang

原文摘要

Direct-policy World Action Models (WAM) have demonstrated the benefits of decoupling action generation from explicit future visual prediction for efficient inference in autonomous driving and embodied control. However, they still require tens of denoising steps to achieve competitive action quality, incurring substantial latency and hindering real-time deployment. Simply reducing the number of denoising steps will collapse the nonlinear multi-step denoising trajectory into coarse updates, substantially degrading action quality. To reduce the denoising steps while enabling real-time action generation, we present an effective world action model with one-step action generation, namely OSWAM, for autonomous driving and embodied control. We argue that it is the absence of structured action priors in random noise initialization that forces direct-policy WAMs to recover coherent action sequences through iterative denoising. Hence, we construct imagination tokens to initialize the diffusion process, providing physical priors and action-conditioned representations. We further introduce privileged information distillation with future conditioning to enhance future-aware action generation. As a result, OSWAM can efficiently and effectively generate high quality actions in just one diffusion step. Our experiments demonstrate that OSWAM achieves comparable or even better results in both autonomous driving and embodied control than direct-policy WAM and Joint-WAM that require tens of steps. It obtains approximately $3\times$ speedup over direct-policy WAM baselines and supports real-time compiled inference with latency as low as 40ms on an NVIDIA RTX 5090 GPU. The project page is available at https://gwenzhang.github.io/OSWAM/

cs.CV · cs.RO

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arXiv ↗PDF ↗