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首次观测 · 记录 #1CVF · CVPR 2026:cvf:2a1c831f530cace1faa06255e33bd3ecCVF · CVPR 2026

Bidirectional Cross-Modal Prompting for Event-Frame Asymmetric Stereo

Xu, Ninghui, Tosi, Fabio, Wang, Lihui, Han, Jiawei, Bartolomei, Luca, Yao, Zhiting, Poggi, Matteo, Mattoccia, Stefano

原文摘要

Conventional frame-based cameras capture rich contextual information but suffer from limited temporal resolution and motion blur in dynamic scenes. Event cameras offer an alternative visual representation with higher dynamic range free from such limitations. The complementary characteristics of the two modalities make event-frame asymmetric stereo promising for reliable 3D perception under fast motion and challenging illumination. However, the modality gap often leads to marginalization of domain-specific cues essential for cross-modal stereo matching. In this paper, we introduce Bi-CMPStereo, a novel bidirectional cross-modal prompting framework that fully exploits semantic and structural features from both domains for robust matching. Our approach learns finely aligned stereo representations within a target canonical space and integrates complementary representations by projecting each modality into both event and frame domains. Extensive experiments demonstrate that our approach significantly outperforms state-of-the-art methods in accuracy and generalization.

cs.CV

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

首次观测 · 记录 #1CVF · CVPR 2026:cvf:51fa911d816fef9b08d906414ad436b2CVF · CVPR 2026

Ov3R: Open-Vocabulary Semantic 3D Reconstruction from RGB Videos

Gong, Ziren, Li, Xiaohan, Tosi, Fabio, Han, Jiawei, Mattoccia, Stefano, Cai, Jianfei, Poggi, Matteo

原文摘要

We present Ov3R, a novel framework for open-vocabulary semantic 3D reconstruction from RGB video streams, designed to advance Spatial AI. The system features two key components: CLIP3R, a CLIP-informed 3D reconstruction module that predicts dense point maps from overlapping clips alongside object-level semantics; and 2D-3D OVS, a 2D-3D open-vocabulary semantic module that lifts 2D features into 3D by learning fused descriptors integrating spatial, geometric, and semantic cues. Unlike prior methods, Ov3R incorporates CLIP semantics directly into the reconstruction process, enabling globally consistent geometry and fine-grained semantic alignment. Our framework achieves state-of-the-art performance in both dense 3D reconstruction and open-vocabulary 3D segmentation.

cs.CV

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

首次观测 · 记录 #1CVF · CVPR 2026:cvf:58a1bb8ca902335074b7a285f0f0ec20CVF · CVPR 2026

Image-to-Point Cloud Feature Back-Projection for Multimodal Training of 3D Semantic Segmentation

Han, Jiawei, Poggi, Matteo, Huan, Li, Wang, Changshuo, Liu, Kaiqi, Li, Wei

原文摘要

The effective integration and utilization of multimodal data acquired from image cameras and LiDAR is of paramount importance for perception systems. This paper proposes **I**mage-to-**P**oint Cloud **F**eature Back-**P**rojection (**IPFP**), a novel method for training multimodal fusion networks that back-projects aggregated image-feature centers (from non-projection-aligned image pixels) into the point-cloud feature set via the estimated depth map. Consequently, image features and point cloud features reside within the same three-dimensional space, enabling the natural enrichment of image information into the point cloud during the network forward pass. This process can be selectively enabled when desired -- for instance, at training time -- and turned off in the absence of multimodal data -- for example, at testing time if only LiDAR sensors are available. Experimental results demonstrate that **IPFP** can consistently improve state-of-the-art 3D semantic segmentation models, while retaining the ability to process LiDAR-only data at inference.

cs.CV

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