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首次观测 · 记录 #1CVF · CVPR 2026:cvf:196990bda043cace87a4a1a9bc5c9a70CVF · CVPR 2026

PromptDepth: Efficient and Promptable Geometric 3D Vision Model for Embodied Intelligence

Wang, Xianyun, Miao, Jiaxu, Xu, Tian, Wang, Siyuan, Li, Yuehao, Hu, Haoyang, Xiao, Jun, Tian, Yonghong 等 9 位作者

原文摘要

Vision models for embodied intelligence require efficient 3D comprehension and interaction with objects within the scene. Existing 3D reconstruction models either overlook instance-level perception or rely on time-consuming offline reasoning, showing a less adaptability in real-time embodied scenario. In this paper, we present PromptDepth, the first promptable vision model that features both geometric 3D understanding and instance-level interaction especially designed for embodied intelligence. PromptDepth is a feed-forward network that quickly yields panoptic, instanced, or tracked depth map from two corresponding frames, enabling the real-time infer sequences from embodied agents. Specifically, following the minimal prediction problem, we design a promptable Dense Prediction Transformer, making it flexible to interact with unified dense prediction according to a specific prompt. Considering the substantial discrepancy between panoptic and instanced depth map, we further introduce a novel Instanced Label Distribution Smoothing (ILDS) loss, followed by Gram Anchoring, to mitigate the inherent conflict between dense and discrete representation. Trained on synthetic data only, our model achieves state-of-the-art results in both depth estimation and interactive segmentation on public benchmarks. Extensive experiments demonstrate superior visual efficiency in embodied tasks compared to current fundamental models. We believe that our efficient and flexible geometric 3D model offers a new foundation for vision tasks in embodied intelligence.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:2e4fd90f152faa489b4d47a3dcebd827CVF · CVPR 2026

Taming the Long Tail: Rebalancing Adversarial Training via Adaptive Perturbation

Zhang, Lilin, Guo, Yimo, Li, Yue, Shi, Jiancheng, Liu, Xianggen

原文摘要

Deep neural networks are highly vulnerable to adversarial examples, i.e.,small perturbations that can significantly degrade model performance. While adversarial training has become the primary defense strategy, most studies focus on balanced datasets, overlooking the challenges posed by real-world long-tail data. Motivated by the fact that perturbations in adversarial examples inherently alter the training distribution, we theoretically investigate their impact. We first revisit adversarial training for long-tail data and identify two key limitations: (i) a skewed training objective caused by class imbalance, and (ii) unstable evolution of adversarial distributions. Furthermore, we show that perturbations can simultaneously address both adversarial vulnerability and class imbalance. Based on these insights, we propose \METHODNAME , a plug-and-play framework that adaptively adjusts perturbations during adversarial training. Extensive experiments demonstrate that \METHODNAME consistently enhances adversarial robustness and class-balance on long-tailed datasets. The code is available at \href https://github.com/zhang-lilin/RobustLT https://github.com/zhang-lilin/RobustLT .

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:504d31e5684b54393db6015bd5460d4cCVF · CVPR 2026

AREA3D: Active Reconstruction Agent with Unified Feed-Forward 3D Perception and Vision-Language Guidance

Xu, Tianling, Gan, Shengzhe, Gu, Leslie, Li, Yuelei, Zhan, Fangneng, Pfister, Hanspeter

原文摘要

Active 3D reconstruction enables an agent to autonomously select viewpoints to build accurate and complete scene geometry efficiently, rather than passively reconstructing scenes from pre-collected images. Existing active reconstruction methods often rely on geometric heuristics, which may result in redundant observations without improving reconstruction quality. To address this, we propose AREA3D, an active reconstruction agent for 3D reconstruction by leveraging feed-forward 3D models and vision-language guidance. The framework decouples view uncertainty modeling from feed-forward reconstruction, enabling precise uncertainty estimation without online optimization. Moreover, the integrated Vision-Language Model provides high-level semantic guidance that guides exploration beyond purely geometric cues. Extensive experiments on both scene-level and object-level benchmarks (Replica and OmniObject3D) demonstrate that AREA3D achieves state-of-the-art reconstruction accuracy, especially in sparse views.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:5594f658c56a8175292b61adc90b25d1CVF · CVPR 2026

Optical Flow Matching: Reframing Optical Flow as Continuous Transport Dynamics

Luo, Ao, Li, Xin, Yang, Fan, Li, Yuezun, Yuan, Zhaoquan, Zhao, Shan, Su, Bing, Wu, Xiao

原文摘要

Modern optical flow estimation, though empowered by recent deep neural architectures, remains rooted in the discrete correspondence paradigm inherited from classical vision. Most networks infer frame-to-frame displacements, capturing where pixels move but not how motion evolves continuously through time. Yet physical motion in the real world follows smooth dynamics governed by underlying velocity fields, as long established in fluid mechanics and transport theory. To bridge this gap, we introduce Optical Flow Matching (OFM), a continuous formulation that learns a time-dependent velocity field to transport pixel coordinates along motion distribution coherent trajectories. A key component of our OFM is Triangle Velocities Synergy (TVS), a neat yet pragmatic geometric mechanism that provides a stable and physically meaningful velocity construction, ensuring that continuous transport remains well-defined. Combined with an Euler-based ODE solver, OFM yields flow fields that are temporally smooth, geometrically consistent, and process-interpretable. Experiments on Sintel, KITTI, and Spring demonstrate that OFM achieves state-of-the-art accuracy, enhanced temporal stability, and notably stronger cross-dataset generalization, advancing optical flow estimation from correspondence inference to continuous dynamical reasoning.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:ba9fddf93861ad90354eb983ead1b01aCVF · CVPR 2026

Chorus: Multi-Teacher Pretraining for Holistic 3D Gaussian Scene Encoding

Li, Yue, Ma, Qi, Yang, Runyi, Ma, Mengjiao, Ren, Bin, Popovic, Nikola, Sebe, Nicu, Gevers, Theo 等 11 位作者

原文摘要

While 3DGS has emerged as a high-fidelity scene representation, encoding rich, general-purpose features directly from its primitives remains under-explored. We address this gap by introducing Chorus, a multi-teacher pretraining framework that learns a holistic feed-forward 3D Gaussian Splatting (3DGS) scene encoder by distilling complementary signals from 2D foundation models. Chorus employs a shared 3D encoder and teacher-specific projectors to learn from language-aligned, generalist, and object-aware teachers, encouraging a shared embedding space that captures signals from high-level semantics to fine-grained structure. We evaluate Chorus on a wide range of tasks: open-vocabulary semantic and instance segmentation, linear and decoder probing, data-efficient supervision, as well as LLM-based Q&A. Besides 3DGS, we also test Chorus on several benchmarks that only support point clouds by pretraining a variant using only Gaussians' centers, colors, estimated normals. Interestingly, this encoder shows strong transfer and outperforms the point clouds baseline while using 39.9xfewer training scenes. Finally, we propose a render-and-distill adaptation that facilitates out-of-domain finetuning. Our code and model is released at this codebase.

cs.CV · cs.CL

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗
首次观测 · 记录 #1CVF · CVPR 2026:cvf:f9a2725e5c533f3aafa02a39db898c7bCVF · CVPR 2026

Breaking Smooth-Motion Assumptions: A UAV Benchmark for Multi-Object Tracking in Complex and Adverse Conditions

Ye, Jingtao, Zhang, Kexin, Ma, Xunchi, Li, Yuechan, Zhu, Guangming, Shen, Peiyi, Jiang, Linhua, Zhang, Xiangdong 等 9 位作者

原文摘要

The rapid movements and agile maneuvers of unmanned aerial vehicles (UAVs) induce significant observational challenges for multi-object tracking (MOT). However, existing UAV-perspective MOT benchmarks often lack these complexities, featuring predominantly predictable camera dynamics and linear motion patterns. To address this gap, we introduce DynUAV, a new benchmark for dynamic UAV-perspective MOT, characterized by intense ego-motion and the resulting complex apparent trajectories. The benchmark comprises 42 video sequences with over 1.7 million bounding box annotations, covering vehicles, pedestrians, and specialized industrial categories such as excavators, bulldozers and cranes. Compared to existing benchmarks, DynUAV introduces substantial challenges arising from ego-motion, including drastic scale changes and viewpoint changes, as well as motion blur. Comprehensive evaluations of state-of-the-art trackers on DynUAV reveal their limitations, particularly in managing the intertwined challenges of detection and association under such dynamic conditions, thereby establishing DynUAV as a rigorous benchmark. We anticipate that DynUAV will serve as a demanding testbed to spur progress in real-world UAV-perspective MOT, and we will make all resources available at https://github.com/kxzhang-lab/DynUAV.

cs.CV

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition · 2026 · 正式发表

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CVF · CVPR 2026 ↗PDF ↗