作者: DEFT 時間: 2025-3-21 23:39 作者: aristocracy 時間: 2025-3-22 03:44 作者: DAUNT 時間: 2025-3-22 06:39 作者: GET 時間: 2025-3-22 10:19
https://doi.org/10.1007/978-3-0348-6486-2r, are designed only for volumetric rendering-based neural radiance fields and are not straightforwardly applicable to rasterization-based 3D Gaussian splatting methods. Thus, we propose a novel real-time deblurring framework, Deblurring 3D Gaussian Splatting, using a small Multi-Layer Perceptron (M作者: 腐爛 時間: 2025-3-22 13:46
https://doi.org/10.1007/978-3-0348-6486-2n. We observe that the pointwise network structure exhibits robust scalability, upkeeping the performance even with a heavily downsampled . input image. These enable ICELUT, the . purely LUT-based image enhancer, to reach an unprecedented speed of 0.4 ms on GPU and 7 ms on CPU, at least one order fa作者: 腐爛 時間: 2025-3-22 20:53
Alle Marken-Regeln im überblickts to sensor-specific noise as well as spatially varying noise levels, while the sRGB domain denoising adapts to ISP variations and removes residual noise amplified by the ISP. Both denoising networks are connected with a differentiable ISP, which is trained end-to-end and discarded during the infer作者: 簡潔 時間: 2025-3-22 21:45
Einleitung: Die Entzauberung der Marke,Winograd transformations as learnable parameters during network training. Evolving transformations starting from our PSO-derived ones rather than the standard Winograd transformations results in significant numerical error reduction and accuracy improvement. As a consequence, our approach significan作者: oxidant 時間: 2025-3-23 01:22 作者: construct 時間: 2025-3-23 06:10 作者: 使迷惑 時間: 2025-3-23 11:47
Wahrnehmbare Elemente des Marken-Dachsut . (., Big Dogs). To address this issue, we propose a simple, easy-to-implement, two-step training pipeline that we call From Fake to Real (FFR). The first step of FFR pre-trains a model on balanced synthetic data to learn robust representations across subgroups. In the second step, FFR fine-tunes作者: THE 時間: 2025-3-23 16:24 作者: 不真 時間: 2025-3-23 21:17
A Worldview of the Alleviation of Sufferingizes and scales across thousands of shapes and more than ten different data sources. Our essential contribution is?NICP, an ICP-style self-supervised task tailored to neural fields. NICP takes a few seconds, is self-supervised, and works out of the box on pre-trained neural fields. NSR combines NICP作者: 磨坊 時間: 2025-3-23 22:43
Antje Heinrich,Jeannette Brodbecks a visual condition to steer the image generation process within the irregular-canvas. This approach enables the traditionally rectangle canvas-based diffusion model to produce the desired concepts in accordance with the provided geometric shapes. Second, to maintain consistency across multiple let作者: 靈敏 時間: 2025-3-24 04:06
Antje Heinrich,Jeannette Brodbeck models to retrieve the more likely text from the ground truth and its chronologically shuffled version. CAR reveals many cases where current motion-language models fail to distinguish the event chronology of human motion, despite their impressive performance in terms of conventional evaluation metr作者: Exclaim 時間: 2025-3-24 07:29 作者: Urologist 時間: 2025-3-24 12:31
,OneVOS: Unifying Video Object Segmentation with?All-in-One Transformer Framework,y management of multiple objects through the flexible attention mechanism. Furthermore, a Unidirectional Hybrid Attention is proposed through a double decoupling of the original attention operation, to rectify semantic errors and ambiguities of stored tokens in OneVOS framework. Finally, to alleviat作者: 野蠻 時間: 2025-3-24 15:28
,M3DBench: Towards Omni 3D Assistant with?Interleaved Multi-modal Instructions,, composing . in real-world 3D environments. Furthermore, we establish a new benchmark for assessing the performance of large models in understanding interleaved multi-modal instructions. With extensive quantitative and qualitative experiments, we show the effectiveness of our dataset and baseline m作者: 過渡時期 時間: 2025-3-24 22:05 作者: Flat-Feet 時間: 2025-3-24 23:16 作者: conceal 時間: 2025-3-25 04:26
,Taming Lookup Tables for?Efficient Image Retouching,n. We observe that the pointwise network structure exhibits robust scalability, upkeeping the performance even with a heavily downsampled . input image. These enable ICELUT, the . purely LUT-based image enhancer, to reach an unprecedented speed of 0.4 ms on GPU and 7 ms on CPU, at least one order fa作者: emission 時間: 2025-3-25 10:33
,DualDn: Dual-Domain Denoising via?Differentiable ISP,ts to sensor-specific noise as well as spatially varying noise levels, while the sRGB domain denoising adapts to ISP variations and removes residual noise amplified by the ISP. Both denoising networks are connected with a differentiable ISP, which is trained end-to-end and discarded during the infer作者: Longitude 時間: 2025-3-25 15:14 作者: 玷污 時間: 2025-3-25 19:47 作者: 陰謀小團體 時間: 2025-3-25 20:56 作者: ingestion 時間: 2025-3-26 03:16 作者: 使成核 時間: 2025-3-26 07:29
,Cross-Domain Few-Shot Object Detection via?Enhanced Open-Set Object Detector,proposed measures: style, ICV, and IB. Consequently, we propose several novel modules to address these issues. First, the learnable instance features align initial fixed instances with target categories, enhancing feature distinctiveness. Second, the instance reweighting module assigns higher import作者: coltish 時間: 2025-3-26 11:04
,NICP: Neural ICP for?3D Human Registration at?Scale,izes and scales across thousands of shapes and more than ten different data sources. Our essential contribution is?NICP, an ICP-style self-supervised task tailored to neural fields. NICP takes a few seconds, is self-supervised, and works out of the box on pre-trained neural fields. NSR combines NICP作者: stratum-corneum 時間: 2025-3-26 12:57 作者: 啜泣 時間: 2025-3-26 20:47 作者: SPURN 時間: 2025-3-26 22:39
0302-9743 reconstruction; stereo vision; computational photography; neural networks; image coding; image reconstruction; motion estimation..978-3-031-73635-3978-3-031-73636-0Series ISSN 0302-9743 Series E-ISSN 1611-3349 作者: growth-factor 時間: 2025-3-27 02:35 作者: assent 時間: 2025-3-27 05:22
https://doi.org/10.1007/978-3-0348-6486-2olves a motion extrapolation problem. We test our setup on diverse meshes (synthetic and scanned shapes) to demonstrate its superiority in generating realistic and natural-looking animations on unseen body shapes against SoTA alternatives. Supplemental video and code are available at ..作者: 反省 時間: 2025-3-27 10:22
Caitlin O. Mahoney,Laura M. Hardersons. Moreover, its multi-dimensional evaluation framework broadens the analysis with a comprehensive set of metrics, providing deep insights into the capabilities of models. The findings from our research offer strategic directions for future developments in the field. Our codebase is available at ..作者: 蕁麻 時間: 2025-3-27 14:08 作者: Folklore 時間: 2025-3-27 20:05 作者: NATAL 時間: 2025-3-27 22:20
PredBench: Benchmarking Spatio-Temporal Prediction Across Diverse Disciplines,sons. Moreover, its multi-dimensional evaluation framework broadens the analysis with a comprehensive set of metrics, providing deep insights into the capabilities of models. The findings from our research offer strategic directions for future developments in the field. Our codebase is available at ..作者: 串通 時間: 2025-3-28 02:07
Conference proceedings 2025uter Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; reinforceme作者: invade 時間: 2025-3-28 09:22
0302-9743 ce on Computer Vision, ECCV 2024, held in Milan, Italy, during September 29–October 4, 2024...The 2387 papers presented in these proceedings were carefully reviewed and selected from a total of 8585 submissions. They deal with topics such as computer vision; machine learning; deep neural networks; r作者: 范例 時間: 2025-3-28 14:09 作者: justify 時間: 2025-3-28 18:27
Die Mathematik der Compact Discartments. We validate that existing approaches for floor plan generation, while effective in simpler scenarios, cannot yet seamlessly address the challenges posed by MSD. Our benchmark calls for new research in floor plan machine understanding. Code and data are open.作者: 我的巨大 時間: 2025-3-28 20:30 作者: 窒息 時間: 2025-3-28 23:35
AWOL: Analysis WithOut Synthesis Using Language,, imagine creating a specific type of tree using procedural graphics or a new kind of animal from a statistical shape model. Our key idea is to leverage language to control such existing models to produce novel shapes. This involves learning a mapping between the latent space of a vision-language mo作者: inspiration 時間: 2025-3-29 03:50
,OneVOS: Unifying Video Object Segmentation with?All-in-One Transformer Framework,cts aggregation. Recent advanced models either employ a discrete modeling for these components in a sequential manner, or optimize a combined pipeline through substructure aggregation. However, these existing explicit staged approaches prevent the VOS framework from being optimized as a unified whol作者: 修正案 時間: 2025-3-29 08:48
,M3DBench: Towards Omni 3D Assistant with?Interleaved Multi-modal Instructions,er, the majority of existing 3D vision-language datasets and methods are often limited to specific tasks, limiting their applicability in diverse scenarios. The recent advance of .arge .anguage .odels (LLMs) and .ulti-modal .anguage .odels (MLMs) has shown mighty capability in solving various langua作者: Narcissist 時間: 2025-3-29 12:05
,MSD: A Benchmark Dataset for?Floor Plan Generation of?Building Complexes,floor plan datasets predominantly feature simple floor plan layouts, typically representing single-apartment dwellings only. To compensate for the mismatch between current datasets and the real world, we develop . (MSD) – the first large-scale floor plan dataset that contains a significant share of 作者: 有發(fā)明天才 時間: 2025-3-29 15:58 作者: FLEET 時間: 2025-3-29 23:22 作者: 譏諷 時間: 2025-3-30 00:56
,LetsMap: Unsupervised Representation Learning for?Label-Efficient Semantic BEV Mapping,g. However, most BEV mapping approaches employ a fully supervised learning paradigm that relies on large amounts of human-annotated BEV ground truth data. In this work, we address this limitation by proposing the first unsupervised representation learning approach to generate semantic BEV maps from 作者: strdulate 時間: 2025-3-30 07:11 作者: Scintillations 時間: 2025-3-30 09:58 作者: galley 時間: 2025-3-30 15:29 作者: 上坡 時間: 2025-3-30 18:16 作者: Kidnap 時間: 2025-3-30 23:15
,A Task Is Worth One Word: Learning with?Task Prompts for?High-Quality Versatile Image Inpainting,thesis. Existing approaches focus on either context-aware filling or object synthesis using text descriptions. However, achieving both tasks simultaneously is challenging due to differing training strategies. To overcome this challenge, we introduce ., the first high-quality and versatile inpainting作者: convert 時間: 2025-3-31 02:21
,Self-supervised Shape Completion via?Involution and?Implicit Correspondences, learning approaches that do not require any complete 3D shape examples have gained more interests. In this paper, we propose a non-adversarial self-supervised approach for the shape completion task. Our first finding is that completion problems can be formulated as an involutory function trivially,作者: pancreas 時間: 2025-3-31 06:34 作者: Mere僅僅 時間: 2025-3-31 10:15 作者: Cocker 時間: 2025-3-31 15:09 作者: 異端 時間: 2025-3-31 18:58
PredBench: Benchmarking Spatio-Temporal Prediction Across Diverse Disciplines,ogress in this field, there remains a lack of a standardized framework for a detailed and comparative analysis of various prediction network architectures. PredBench addresses this gap by conducting ., upholding ., and implementing .. This benchmark integrates 12 widely adopted methods with 15 diver作者: 阻撓 時間: 2025-3-31 22:43 作者: IVORY 時間: 2025-4-1 03:19 作者: 放縱 時間: 2025-4-1 09:46
Lecture Notes in Computer Sciencehttp://image.papertrans.cn/d/image/242309.jpg作者: Amnesty 時間: 2025-4-1 12:53
Mathematik und intelligente Materialien, imagine creating a specific type of tree using procedural graphics or a new kind of animal from a statistical shape model. Our key idea is to leverage language to control such existing models to produce novel shapes. This involves learning a mapping between the latent space of a vision-language mo
