1.4k citations · 2.6k across the 155 of their papers we have counts for
21 papers · 2 filters
PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields
Sean Wu, Shamik Basu, Tim Broedermann +2
We tackle the ill-posed inverse rendering problem in 3D reconstruction with a Neural Radiance Field (NeRF) approach informed by Physics-Based Rendering (PBR) theory, named PBR-NeRF…
Test-time Training for Hyperspectral Image Super-resolution
Ke Li, Luc Van Gool, Dengxin Dai
The progress on Hyperspectral image (HSI) super-resolution (SR) is still lagging behind the research of RGB image SR. HSIs usually have a high number of spectral bands, so accurate…
MICDrop: Masking Image and Depth Features via Complementary Dropout for Domain-Adaptive Semantic Segmentation
Linyan Yang, Lukas Hoyer, Mark Weber +6
Unsupervised Domain Adaptation (UDA) is the task of bridging the domain gap between a labeled source domain, e.g., synthetic data, and an unlabeled target domain. We observe that c…
VF-NeRF: Learning Neural Vector Fields for Indoor Scene Reconstruction
Albert Gassol Puigjaner, Edoardo Mello Rella, Erik Sandström +2
Implicit surfaces via neural radiance fields (NeRF) have shown surprising accuracy in surface reconstruction. Despite their success in reconstructing richly textured surfaces, exis…
Optimizing against Infeasible Inclusions from Data for Semantic Segmentation through Morphology
Shamik Basu, Luc Van Gool, Christos Sakaridis
State-of-the-art semantic segmentation models are typically optimized in a data-driven fashion, minimizing solely per-pixel or per-segment classification objectives on their traini…
ShapeSplat: A Large-scale Dataset of Gaussian Splats and Their Self-Supervised Pretraining
Qi Ma, Yue Li, Bin Ren +5
3D Gaussian Splatting (3DGS) has become the de facto method of 3D representation in many vision tasks. This calls for the 3D understanding directly in this representation space. To…
PIV3CAMS: a multi-camera dataset for multiple computer vision problems and its application to novel view-point synthesis
Sohyeong Kim, Martin Danelljan, Radu Timofte +2
The modern approaches for computer vision tasks significantly rely on machine learning, which requires a large number of quality images. While there is a plethora of image datasets…
Implicit-Zoo: A Large-Scale Dataset of Neural Implicit Functions for 2D Images and 3D Scenes
Qi Ma, Danda Pani Paudel, Ender Konukoglu +1
Neural implicit functions have demonstrated significant importance in various areas such as computer vision, graphics. Their advantages include the ability to represent complex sha…
Radar Fields: Frequency-Space Neural Scene Representations for FMCW Radar
David Borts, Erich Liang, Tim Brödermann +9
Neural fields have been broadly investigated as scene representations for the reproduction and novel generation of diverse outdoor scenes, including those autonomous vehicles and r…
XTrack: Multimodal Training Boosts RGB-X Video Object Trackers
Yuedong Tan, Zongwei Wu, Yuqian Fu +7
Multimodal sensing has proven valuable for visual tracking, as different sensor types offer unique strengths in handling one specific challenging scene where object appearance vari…
Empowering Image Recovery_ A Multi-Attention Approach
Juan Wen, Yawei Li, Chao Zhang +3
We propose Diverse Restormer (DART), a novel image restoration method that effectively integrates information from various sources (long sequences, local and global regions, featur…
Self-Explainable Affordance Learning with Embodied Caption
Zhipeng Zhang, Zhimin Wei, Guolei Sun +2
In the field of visual affordance learning, previous methods mainly used abundant images or videos that delineate human behavior patterns to identify action possibility regions for…
A Unified and Interpretable Emotion Representation and Expression Generation
Reni Paskaleva, Mykyta Holubakha, Andela Ilic +3
Canonical emotions, such as happy, sad, and fearful, are easy to understand and annotate. However, emotions are often compound, e.g. happily surprised, and can be mapped to the act…
Towards Online Real-Time Memory-based Video Inpainting Transformers
Guillaume Thiry, Hao Tang, Radu Timofte +1
Video inpainting tasks have seen significant improvements in recent years with the rise of deep neural networks and, in particular, vision transformers. Although these models show…
Human Pose Descriptions and Subject-Focused Attention for Improved Zero-Shot Transfer in Human-Centric Classification Tasks
Muhammad Saif Ullah Khan, Muhammad Ferjad Naeem, Federico Tombari +3
We present a novel LLM-based pipeline for creating contextual descriptions of human body poses in images using only auxiliary attributes. This approach facilitates the creation of…
Loopy-SLAM: Dense Neural SLAM with Loop Closures
Lorenzo Liso, Erik Sandström, Vladimir Yugay +2
Neural RGBD SLAM techniques have shown promise in dense Simultaneous Localization And Mapping (SLAM), yet face challenges such as error accumulation during camera tracking resultin…
Cross-Domain Few-Shot Object Detection via Enhanced Open-Set Object Detector
Yuqian Fu, Yu Wang, Yixuan Pan +7
This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples.…
Graph Transformer GANs with Graph Masked Modeling for Architectural Layout Generation
Hao Tang, Ling Shao, Nicu Sebe +1
We present a novel graph Transformer generative adversarial network (GTGAN) to learn effective graph node relations in an end-to-end fashion for challenging graph-constrained archi…
Learning to Prompt with Text Only Supervision for Vision-Language Models
Muhammad Uzair Khattak, Muhammad Ferjad Naeem, Muzammal Naseer +2
Foundational vision-language models such as CLIP are becoming a new paradigm in vision, due to their excellent generalization abilities. However, adapting these models for downstre…
Vanishing-Point-Guided Video Semantic Segmentation of Driving Scenes
Diandian Guo, Deng-Ping Fan, Tongyu Lu +2
The estimation of implicit cross-frame correspondences and the high computational cost have long been major challenges in video semantic segmentation (VSS) for driving scenes. Prio…
MUSES: The Multi-Sensor Semantic Perception Dataset for Driving under Uncertainty
Tim Brödermann, David Bruggemann, Christos Sakaridis +4
Achieving level-5 driving automation in autonomous vehicles necessitates a robust semantic visual perception system capable of parsing data from different sensors across diverse co…