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首次观测 · 记录 #1CVF · CVPR 2026:cvf:207b6ee896d214428f20e38fe95ae948CVF · CVPR 2026

ClipGStream: Clip-Stream Gaussian Splatting for Any Length and Any Motion Multi-View Dynamic Scene Reconstruction

Liang, Jie, Wu, Jiahao, Wang, Chao, Yang, Jiayu, Zheng, Xiaoyun, Xiong, Kaiqiang, Wang, Zhanke, Yan, Jinbo 等 10 位作者

原文摘要

Dynamic 3D scene reconstruction is essential for immersive media such as VR, MR, and XR, yet remains challenging for long multi-view sequences with large-scale motion. Existing dynamic Gaussian approaches are either Frame-Stream, offering scalability but poor temporal stability, or Clip, achieving local consistency at the cost of high memory and limited sequence length. We propose ClipGStream, a hybrid reconstruction framework that performs stream optimization at the clip level rather than the frame level. The sequence is divided into short clips, where dynamic motion is modeled using clip-independent spatio-temporal fields and residual anchor compensation to capture local variations efficiently, while inter-clip inherited anchors and decoders maintain structural consistency across clips. This Clip-Stream design enables scalable, flicker-free reconstruction of long dynamic videos with high temporal coherence and reduced memory overhead. Extensive experiments demonstrate that ClipGStream achieves state-of-the-art reconstruction quality and efficiency.

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:23a81afefd4d54e1a8d874740a36c5c4CVF · CVPR 2026

URScenes: A Multi-scenario Dataset for Unstructured Road Environments

Liu, Runsen, Baoerhan, Aizemaitijiang, Wang, Zhangyu, Wang, Jie, Cui, Jinghao, Yu, Guizhen, Yang, Songyue, Sun, WanCheng 等 11 位作者

原文摘要

As autonomous driving technology transitions from small-scale validation to large-scale deployment, its development in unstructured road environments has become a critical and inevitable trend. Autonomous vehicles increasingly rely on high-quality and diverse datasets for perception systems. However, existing public datasets predominantly focus on clear-weather and urban-road scenarios, leaving a significant gap in the coverage of unstructured road environments. To bridge this gap, we construct URScenes, the first multi-scenario, open-source perception dataset for unstructured road environments. The dataset consists of 472 scenes, each lasting 30 seconds, and provides over 28K annotated samples and 119K sweeps. URScenes, for the first time, covers eight typical scenarios, including rainy, snowy, foggy, dusty, high-glare, night, cloudy, and sunny conditions. Additionally, URScenes supports multi-task perception for 3D object detection, multi-object tracking, and 3D occupancy in unstructured road environments. URScenes also provides a unified annotation system and format conversion tools, enabling easy conversion to popular formats such as nuScenes, KITTI, and Waymo datasets. Finally, this study presents comparative experimental results to assess the performance of state-of-the-art algorithms on the URScenes dataset. The data and development toolkit are available at http://www.sav-lab.com.

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:28157f4ef4a210c92f89e61ed856d585CVF · CVPR 2026

HumanNOVA: Photorealistic, Universal and Rapid 3D Human Avatar Modeling from a Single Image

Hu, Hezhen, Zhao, Wangbo, Guo, Lanqing, Jiang, Hanwen, Liu, Jonathan C., Fan, Zhiwen, Wang, Kai, Wang, Zhangyang 等 9 位作者

原文摘要

In this paper, we present HumanNOVA, a photorealistic, universal, and rapid model for generating 3D human avatars from a single RGB image. Achieving both photorealism and generalization is challenging due to the scarcity of diverse, high-quality 3D human data. To address this, we build a scalable data generation pipeline that follows two strategies. The first one is to leverage existing rigged assets and animate them with extensive poses from daily life. The second strategy is to utilize existing multi-camera captures of humans and employ fitting to generate more diverse views for training. These two strategies enable us to scale up to 100k assets, significantly enhancing both the quantity and the diversity of data for robust model training. In terms of the architecture, HumanNOVA adopts a feed-forward, token-conditioned avatar modeling framework that allows fast inference in less than one second and requires no test-time optimization. Given an input image and an estimated simplified human mesh (SMPL) without detailed geometry or appearance, the model first encodes both inputs into compact token representations. These tokens then act as conditioning signals and are fused through cross-attention to construct a triplane-based 3D avatar representation. Extensive experiments on multiple benchmarks demonstrate the superiority of our approach, both quantitatively and qualitatively, as well as its robustness under diverse input image conditions. Project page at https://HumanNOVA.github.io.

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:41085db8e461998a5162e872ac0aa0d4CVF · CVPR 2026

VLM-3R: Vision-Language Models Augmented with Instruction-Aligned 3D Reconstruction

Fan, Zhiwen, Zhang, Jian, Li, Renjie, Zhang, Junge, Chen, Runjin, Hu, Hezhen, Wang, Kevin, Wang, Peihao 等 19 位作者

原文摘要

The rapid advancement of Large Multimodal Models (LMMs) for 2D images and videos has sparked interest in extending these models to 3D scenes, with the goal of human-like visual-spatial intelligence. However, achieving deep spatial understanding comparable to human capabilities remains challenging for both model design and data acquisition. Existing methods often rely on external depth sensors for geometry capture or off-the-shelf algorithms for pre-constructing 3D maps, which limits their scalability.In this work, we introduce VLM-3R, a framework for Vision-Language Models that couples 3D reconstructive instruction tuning with scalable training data curation and a new benchmark for temporal reasoning. Specifically, VLM-3R processes monocular video frames with a geometry encoder that derives implicit 3D tokens representing scene context (spatial tokens) and camera motion (view tokens). In parallel, we build a scalable data creation pipeline with over 200K 3D reconstructive instruction-tuning question-answer pairs. To evaluate temporal reasoning, we further introduce the Vision-Spatial-Temporal Intelligence benchmark (VSTI-Bench), which contains over 138.6K question-answer pairs across five distinct tasks focused on evolving spatial relationships. Extensive experiments show that VLM-3R supports robust visual-spatial reasoning and improves the understanding of temporal 3D context changes, enabling monocular 3D spatial assistance and embodied reasoning.

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:c889dcfc88ccfdb6c3b278ac7c96ca77CVF · CVPR 2026

Mining Attribute Subspaces for Efficient Fine-tuning of 3D Foundation Models

Jiang, Yu, Jiang, Hanwen, Abdelkader, Ahmed, Chu, Wen-Sheng, Feng, Brandon, Wang, Zhangyang, Huang, Qixing

原文摘要

With the emergence of 3D foundation models, there is growing interest in fine-tuning them for downstream tasks, where LoRA is the dominant fine-tuning paradigm. As 3D datasets exhibit distinct variations in texture, geometry, camera motion, and lighting, there are interesting fundamental questions: 1) Are there LoRA subspaces associated with each type of variation? 2) Are these subspaces disentangled (i.e., orthogonal to each other)? 3) How do we compute them effectively? This paper provides answers to all these questions. We introduce a robust approach that generates synthetic datasets with controlled variations, fine-tunes a LoRA adapter on each dataset, and extracts a LoRA subspace associated with each type of variation. We show that these subspaces are approximately disentangled. Integrating them leads to a reduced LoRA subspace that enables efficient LoRA fine-tuning with improved prediction accuracy for downstream tasks. In particular, we show that such a reduced LoRA subspace, despite being derived entirely from synthetic data, generalizes to real datasets. An ablation study validates the effectiveness of the choices in our approach.

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:deaa6521ad8024314e2b56e3cad92ce9CVF · CVPR 2026

Intrinsic Geometry-Appearance Consistency Optimization for Sparse-View Gaussian Splatting

Xiong, Kaiqiang, Peng, Rui, Wu, Jiahao, Wang, Zhanke, Liang, Jie, Zheng, Xiaoyun, Gao, Feng, Wang, Ronggang

原文摘要

3D Gaussian Splatting (3DGS) represents scenes through primitives with coupled intrinsic properties: geometric attributes (position, covariance, opacity) and appearance attributes (view-dependent color). Faithful reconstruction requires intrinsic geometry-appearance consistency, where geometry accurately captures 3D structure while appearance reflects photometry. However, sparse observations lead to appearance overfitting and underconstrained geometry, causing severe novel-view artifacts. We present ICO-GS (Intrinsic Geometry-Appearance Consistency Optimization for 3DGS), a principled framework that enforces this consistency through tightly coupled geometric regularization and appearance learning. Our approach first regularizes geometry via feature-based multi-view photometric constraints by employing pixel-wise top-k selection to handle occlusions and edge-aware smoothness to preserve sharp structures. Then appearance is coupled with geometry through cycle-consistency depth filtering, which identifies reliable regions to synthesize virtual views that propagate geometric correctness into appearance optimization. Experiments on LLFF, DTU, and Blender show ICO-GS consistently outperforms existing sparse-view baselines, particularly in challenging weakly-textured regions.

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:f0f1167f52aef1dbcb9a8d7fa0046e31CVF · CVPR 2026

Convexity-Aware Noise Calibration: A Self-Supervised Framework for Noise-Level-Unknown Image Denoising

Wang, Zhan, Wang, Leiquan, Wu, Chunlei, Meng, Yu

原文摘要

Image denoising is a fundamental task in computer vision aimed at recovering clean images from noise-corrupted observations. While supervised deep learning methods achieve remarkable performance when trained on paired data with known noise levels, their real-world applicability is limited as noise characteristics are often unknown. Existing unsupervised techniques, such as blind-spot networks or methods based on statistical estimation, either compromise performance due to information loss or suffer from inaccuracies in noise level estimation. To address these challenges, we propose a novel two-stage self-supervised denoising framework that first accurately estimates the noise level directly from noisy images, without requiring clean references or prior noise knowledge. Building upon theoretical insights from Noisier2Noise, we rigorously derive a relationship between the noise level and the variance of the denoised image, enabling robust estimation via a deep learning model and a ternary search strategy. The estimated noise level is then used to synthesize training pairs for supervised denoising. Experiments demonstrate that our method outperforms existing unsupervised approaches and traditional noise estimation techniques, achieving performance competitive with--and in some cases surpassing--supervised methods trained with known noise levels. The proposed framework effectively overcomes the training data pair limitations of supervised approaches for unknown additive white Gaussian noise. Our code is available at https://github.com/zhanzhanblue/CANC.

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 ↗