GenAI4ELab/papercli-papers huggingface dataset:
MERLiN: Single-Shot Material Estimation and Relighting for Photometric Stereo
Authors: Ashish Tiwari; Satoshi Ikehata; Shanmuganathan Raman
Venue: ECCV 2024 (main)

Abstract: "Photometric stereo typically demands intricate data acquisition setups involving multiple light sources to recover surface normals accurately. In this paper, we propose MERLiN, an attention-based hourglass network that integrates single image-based inverse rendering and relighting within a single unified framework. We evaluate the performance of photometric stereo methods using these relit images and demonstrate how they can circumvent the underlying challenge of complex data acquisition. Our physically-based model is trained on a large synthetic dataset containing complex shapes with spatially varying BRDF and is designed to handle indirect illumination effects to improve material reconstruction and relighting. Through extensive qualitative and quantitative evaluation, we demonstrate that the proposed framework generalizes well to real-world images, achieving high-quality shape, material estimation, and relighting. We assess these synthetically relit images over photometric stereo benchmark methods for their physical correctness and resulting normal estimation accuracy, paving the way towards single-shot photometric stereo through physically-based relighting. This work allows us to address the single image-based inverse rendering problem holistically, applying well to both synthetic and real data and taking a step towards mitigating the challenge of data acquisition in photometric stereo."
Boosting 3D Single Object Tracking with 2D Matching Distillation and 3D Pre-training
Authors: Qiangqiang Wu; Yan Xia; Jia Wan; Antoni Chan
Venue: ECCV 2024 (main)

Abstract: "3D single object tracking (SOT) is an essential task in autonomous driving and robotics. However, learning robust 3D SOT trackers remains challenging due to the limited category-specific point cloud data and the inherent sparsity and incompleteness of LiDAR scans. To tackle these issues, we propose a unified 3D SOT framework that leverages 3D generative pre-training and learns robust 3D matching abilities from 2D pre-trained foundation trackers. Our framework features a consistent target-matching architecture with the widely used 2D trackers, facilitating the transfer of 2D matching knowledge. Specifically, we first propose a lightweight Target-Aware Projection (TAP) module, allowing the pre-trained 2D tracker to work well on the projected point clouds without further fine-tuning. We then propose a novel IoU-guided matching-distillation framework that utilizes the powerful 2D pre-trained trackers to guide 3D matching learning in the 3D tracker, i.e., the 3D template-to-search matching should be consistent with its corresponding 2D template-to-search matching obtained from 2D pre-trained trackers. Our designs are applied to two mainstream 3D SOT frameworks: memory-less Siamese and contextual memory-based approaches, which are respectively named SiamDisst and MemDisst. Extensive experiments show that SiamDisst and MemDisst achieve state-of-the-art performance on KITTI, Waymo Open Dataset and nuScenes benchmarks, while running at above real-time speed of 25 and 90 FPS on a RTX3090 GPU."
Diffusion-Based Image-to-Image Translation by Noise Correction via Prompt Interpolation
Authors: Junsung Lee; Minsoo Kang; Bohyung Han
Venue: ECCV 2024 (main)

Abstract: "We propose a simple but effective training-free approach tailored to diffusion-based image-to-image translation. Our approach revises the original noise prediction network of a pretrained diffusion model by introducing a noise correction term. We formulate the noise correction term as the difference between two noise predictions; one is computed from the denoising network with a progressive interpolation of the source and target prompt embeddings, while the other is the noise prediction with the source prompt embedding. The final noise prediction network is given by a linear combination of the standard denoising term and the noise correction term, where the former is designed to reconstruct must-be-preserved regions while the latter aims to effectively edit regions of interest relevant to the target prompt. Our approach can be easily incorporated into existing image-to-image translation methods based on diffusion models. Extensive experiments verify that the proposed technique achieves outstanding performance with low latency and consistently improves existing frameworks when combined with them."
Real-data-driven 2000 FPS Color Video from Mosaicked Chromatic Spikes
Authors: Siqi Yang; Zhaojun Huang; Yakun Chang; Bin Fan; Zhaofei Yu; Boxin Shi
Venue: ECCV 2024 (main)

Abstract: "The spike camera continuously records scene radiance with high-speed, high dynamic range, and low data redundancy properties, as a promising replacement for frame-based high-speed cameras. Previous methods for reconstructing color videos from monochromatic spikes are constrained in capturing full-temporal color information due to their reliance on compensating colors from low-speed RGB frames. Applying a Bayer-pattern color filter array to the spike sensor yields mosaicked chromatic spikes, which complicates noise distribution in high-speed conditions. By validating that the noise of short-term frames follows a zero-mean distribution, we leverage this hypothesis to develop a self-supervised denoising module trained exclusively on real-world data. Although noise is reduced in short-term frames, the long-term accumulation of incident photons is still necessary to construct HDR frames. Therefore, we introduce a progressive warping module to generate pseudo long-term exposure frames. This approach effectively mitigates motion blur artifacts in high-speed conditions. Integrating these modules forms a real-data-driven reconstruction method for mosaicked chromatic spikes. Extensive experiments conducted on both synthetic and real-world data demonstrate that our approach is effective in reconstructing 2000FPS color HDR videos with significantly reduced noise and motion blur compared to existing methods."
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging
Authors: Peirong Liu; Oula Puonti; Xiaoling Hu; Daniel C. Alexander; Juan E. Iglesias
Venue: ECCV 2024 (main)

Abstract: "Recent learning-based approaches have made astonishing advances in calibrated medical imaging like computerized tomography (CT). Yet, they struggle to generalize in uncalibrated modalities – notably magnetic resonance (MR) imaging, where performance is highly sensitive to the differences in MR contrast, resolution, and orientation. This prevents broad applicability to diverse real-world clinical protocols. We introduce Brain-ID, an anatomical representation learning model for brain imaging. With the proposed “mild-to-severe” intra-subject generation, Brain-ID is robust to the subject-specific brain anatomy regardless of the appearance of acquired images. Trained entirely on synthetic inputs, Brain-ID readily adapts to various downstream tasks through one layer. We present new metrics to validate the intra/inter-subject robustness of Brain-ID features, and evaluate their performance on four downstream applications, covering contrast-independent (anatomy reconstruction, brain segmentation), and contrast-dependent (super-resolution, bias field estimation) tasks (showcase). Extensive experiments on six public datasets demonstrate that Brain-ID achieves state-of-the-art performance in all tasks on different MR contrasts and CT, and more importantly, preserves its performance on low-resolution and small datasets. Code is available at https://github.com/peirong26/Brain-ID."
TTT-MIM: Test-Time Training with Masked Image Modeling for Denoising Distribution Shifts
Authors: Youssef Mansour; Xuyang Zhong; Serdar Caglar; Reinhard Heckel
Venue: ECCV 2024 (main)

Abstract: "Neural networks trained end-to-end give state-of-the-art performance for image denoising. However, when applied to an image outside of the training distribution, the performance often degrades significantly. In this work, we propose a test-time training (TTT) method based on masked image modeling (MIM) to improve denoising performance for out-of-distribution images. The method, termed TTT-MIM, consists of a training stage and a test time adaptation stage. At training, we minimize a standard supervised loss and a self-supervised loss aimed at reconstructing masked image patches. At test-time, we minimize a self-supervised loss to fine-tune the network to adapt to a single noisy image. Experiments show that our method can improve performance under natural distribution shifts, in particular it adapts well to real-world camera and microscope noise. A competitor to our method of training and finetuning is to use a zero-shot denoiser that does not rely on training data. However, compared to state-of-the-art zero-shot denoisers, our method shows superior performance, and is much faster, suggesting that training and finetuning on the test instance is a more efficient approach to image denoising than zero-shot methods in setups where little to no data is available. Our GitHub page is: https://github.com/MLI-lab/TTT Denoising."
RadEdit: stress-testing biomedical vision models via diffusion image editing
Authors: Fernando Pérez-García; Sam Bond-Taylor; Pedro Sanchez; Boris van Breugel; Daniel Coelho de Castro; Harshita Sharma; Valentina Salvatelli; Maria Teodora A Wetscherek; Hannah CM Richardson; Lungren Matthew; Aditya Nori; Javier Alvarez-Valle; Ozan Oktay; Maximilian Ilse
Venue: ECCV 2024 (main)

Abstract: "Biomedical imaging datasets are often small and biased, meaning that real-world performance of predictive models can be substantially lower than expected from internal testing. This work proposes using generative image editing to simulate dataset shifts and diagnose failure modes of biomedical vision models; this can be used in advance of deployment to assess readiness, potentially reducing cost and patient harm. Existing editing methods can produce undesirable changes, with spurious correlations learned due to the co-occurrence of disease and treatment interventions, limiting practical applicability. To address this, we train a text-to-image diffusion model on multiple chest X-ray datasets and introduce a new editing method, , that uses multiple image masks, if present, to constrain changes and ensure consistency in the edited images, minimising bias. We consider three types of dataset shifts: acquisition shift, manifestation shift, and population shift, and demonstrate that our approach can diagnose failures and quantify model robustness without additional data collection, complementing more qualitative tools for explainable AI."
SPAMming Labels: Efficient Annotations for the Trackers of Tomorrow
Authors: Orcun Cetintas; Tim Meinhardt; Guillem Brasó; Laura Leal-Taixé
Venue: ECCV 2024 (main)

Abstract: "Increasing the annotation efficiency of trajectory annotations from videos has the potential to enable the next generation of data-hungry tracking algorithms to thrive on large-scale datasets. Despite the importance of this task, there are currently very few works exploring how to efficiently label tracking datasets comprehensively. In this work, we introduce SPAM, a video label engine that provides high-quality labels with minimal human intervention. SPAM is built around two key insights: i) most tracking scenarios can be easily resolved. To take advantage of this, we utilize a pre-trained model to generate high-quality pseudo-labels, reserving human involvement for a smaller subset of more difficult instances; ii) handling the spatiotemporal dependencies of track annotations across time can be elegantly and efficiently formulated through graphs. Therefore, we use a unified graph formulation to address the annotation of both detections and identity association for tracks across time. Based on these insights, SPAM produces high-quality annotations with a fraction of ground truth labeling cost. We demonstrate that trackers trained on SPAM labels achieve comparable performance to those trained on human annotations while requiring only 3−20% of the human labeling effort. Hence, SPAM paves the way towards highly efficient labeling of large-scale tracking datasets. We release all models and code."
AdaDiffSR: Adaptive Region-aware Dynamic acceleration Diffusion Model for Real-World Image Super-Resolution
Authors: Yuanting Fan; Chengxu Liu; Nengzhong Yin; Changlong Gao; Xueming Qian
Venue: ECCV 2024 (main)

Abstract: "Diffusion models (DMs) have shown promising results on single-image super-resolution and other image-to-image translation tasks. Benefiting from more computational resources and longer inference times, they are able to yield more realistic images. Existing DMs-based super-resolution methods try to achieve an overall average recovery over all regions via iterative refinement, ignoring the consideration that different input image regions require different timesteps to reconstruct. In this work, we notice that previous DMs-based super-resolution methods suffer from wasting computational resources to reconstruct invisible details. To further improve the utilization of computational resources, we propose AdaDiffSR, a DMs-based SR pipeline with dynamic timesteps sampling strategy (DTSS). Specifically, by introducing the multi-metrics latent entropy module (MMLE), we can achieve dynamic perception of the latent spatial information gain during the denoising process, thereby guiding the dynamic selection of the timesteps. In addition, we adopt a progressive feature injection module (PFJ), which dynamically injects the original image features into the denoising process based on the current information gain, so as to generate images with both fidelity and realism. Experiments show that our AdaDiffSR achieves comparable performance over current state-of-the-art DMs-based SR methods while consuming less computational resources and inference time on both synthetic and real-world datasets."
Explicitly Guided Information Interaction Network for Cross-modal Point Cloud Completion
Authors: Xu Hang; Chen Long; Wenxiao Zhang; Yuan Liu; Zhen Cao; Zhen Dong; Bisheng Yang
Venue: ECCV 2024 (main)

Abstract: "∗ Equal contribution Corresponding authorIn this paper, we explore a novel framework, EGIInet (Explicitly Guided Information Interaction Network), a model for View-guided Point cloud Completion (ViPC) task, which aims to restore a complete point cloud from a partial one with a single view image. In comparison with previous methods that relied on the global semantics of input images, EGIInet efficiently combines the information from two modalities by leveraging the geometric nature of the completion task. Specifically, we propose an explicitly guided information interaction strategy supported by modal alignment for point cloud completion. First, in contrast to previous methods which simply use 2D and 3D backbones to encode features respectively, we unified the encoding process to promote modal alignment. Second, we propose a novel explicitly guided information interaction strategy that could help the network identify critical information within images, thus achieving better guidance for completion. Extensive experiments demonstrate the effectiveness of our framework, and we achieved a new state-of-the-art (+16% CD over XMFnet) in benchmark datasets despite using fewer parameters than the previous methods. The pre-trained model and code and are available at https://github.com/WHU-USI3DV/EGIInet."
Towards Real-world Event-guided Low-light Video Enhancement and Deblurring
Authors: Taewoo Kim; Jaeseok Jeong; Hoonhee Cho; Yuhwan Jeong; Kuk-Jin Yoon
Venue: ECCV 2024 (main)

Abstract: "In low-light conditions, capturing videos with frame-based cameras often requires long exposure times, resulting in motion blur and reduced visibility. While frame-based motion deblurring and low-light enhancement have been studied, they still pose significant challenges. Event cameras have emerged as a promising solution for improving image quality in low-light environments and addressing motion blur. They provide two key advantages: capturing scene details well even in low light due to their high dynamic range, and effectively capturing motion information during long exposures due to their high temporal resolution. Despite efforts to tackle low-light enhancement and motion deblurring using event cameras separately, previous work has not addressed both simultaneously. To explore the joint task, we first establish real-world datasets for event-guided low-light enhancement and deblurring using a hybrid camera system based on beam splitters. Subsequently, we introduce an end-to-end framework to effectively handle these tasks. Our framework incorporates a module to efficiently leverage temporal information from events and frames. Furthermore, we propose a module to utilize cross-modal feature information to employ a low-pass filter for noise suppression while enhancing the main structural information. Our proposed method significantly outperforms existing approaches in addressing the joint task. Our project pages are available at https://github.com/intelpro/ELEDNet."
Exploring Pre-trained Text-to-Video Diffusion Models for Referring Video Object Segmentation
Authors: Xuelu Feng; Dongdong Chen; Junsong Yuan; Chunming Qiao; Gang Hua; Zixin Zhu
Venue: ECCV 2024 (main)

Abstract: "In this paper, we explore the visual representations produced from a pre-trained text-to-video (T2V) diffusion model for video understanding tasks. We hypothesize that the latent representation learned from a pretrained generative T2V model encapsulates rich semantics and coherent temporal correspondences, thereby naturally facilitating video understanding. Our hypothesis is validated through the classic referring video object segmentation (R-VOS) task. We introduce a novel framework, termed “VD-IT”, tailored with dedicatedly designed components built upon a fixed pretrained T2V model. Specifically, VD-IT uses textual information as a conditional input, ensuring semantic consistency across time for precise temporal instance matching. It further incorporates image tokens as supplementary textual inputs, enriching the feature set to generate detailed and nuanced masks. Besides, instead of using the standard Gaussian noise, we propose to predict the video-specific noise with an extra noise prediction module, which can help preserve the feature fidelity and elevates segmentation quality. Through extensive experiments, we surprisingly observe that fixed generative T2V diffusion models, unlike commonly used video backbones (e.g., Video Swin Transformer) pretrained with discriminative image/video pre-tasks, exhibit better potential to maintain semantic alignment and temporal consistency. On existing standard benchmarks, our VD-IT achieves highly competitive results, surpassing many existing state-of-the-art methods. The code is available at https://github.com/buxiangzhiren/VD-IT."
TrackNeRF: Bundle Adjusting NeRF from Sparse and Noisy Views via Feature Tracks
Authors: Jinjie Mai; Wenxuan Zhu; Sara Rojas; Jesus Zarzar; Abdullah Hamdi; Guocheng Qian; Bing Li; Silvio Giancola; Bernard Ghanem
Venue: ECCV 2024 (main)

Abstract: "Neural radiance fields (NeRFs) generally require many images with accurate poses for accurate novel view synthesis, which does not reflect realistic setups where views can be sparse and poses can be noisy. Previous solutions for learning NeRFs with sparse views and noisy poses only consider local geometry consistency with pairs of views. Closely following bundle adjustment in Structure-from-Motion (SfM), we introduce TrackNeRF for more globally consistent geometry reconstruction and more accurate pose optimization. TrackNeRF introduces feature tracks, connected pixel trajectories across all visible views that correspond to the same 3D points. By enforcing reprojection consistency among feature tracks, TrackNeRF encourages holistic 3D consistency explicitly. Through extensive experiments, TrackNeRF sets a new benchmark in noisy and sparse view reconstruction. In particular, TrackNeRF shows significant improvements over the state-of-the-art BARF and SPARF by ∼ 8 and ∼ 1 in terms of PSNR on DTU under various sparse and noisy view setups. The code is available at purplehttps://tracknerf.github.io/."
COHO: Context-Sensitive City-Scale Hierarchical Urban Layout Generation
Authors: Liu He; Daniel Aliaga
Venue: ECCV 2024 (main)

Abstract: "The generation of large-scale urban layouts has garnered substantial interest across various disciplines. Prior methods have utilized procedural generation requiring manual rule coding or deep learning needing abundant data. However, prior approaches have not considered the context-sensitive nature of urban layout generation. Our approach addresses this gap by leveraging a canonical graph representation for the entire city, which facilitates scalability and captures the multi-layer semantics inherent in urban layouts. We introduce a novel graph-based masked autoencoder (GMAE) for city-scale urban layout generation. The method encodes attributed buildings, city blocks, communities and cities into a unified graph structure, enabling self-supervised masked training for graph autoencoder. Additionally, we employ scheduled iterative sampling for 2.5D layout generation, prioritizing the generation of important city blocks and buildings. Our approach achieves good realism, semantic consistency, and correctness across the heterogeneous urban styles in 330 US cities. Codes and datasets are released at https://github.com/Arking1995/COHO."
Joint RGB-Spectral Decomposition Model Guided Image Enhancement in Mobile Photography
Authors: Kailai Zhou; Lijing Cai; Yibo Wang; Mengya Zhang; Bihan Wen; Qiu Shen; Xun Cao
Venue: ECCV 2024 (main)

Abstract: "The integration of miniaturized spectrometers into mobile devices offers new avenues for image quality enhancement and facilitates novel downstream tasks. However, the broader application of spectral sensors in mobile photography is hindered by the inherent complexity of spectral images and the constraints of spectral imaging capabilities. To overcome these challenges, we propose a joint RGB-Spectral decomposition model guided enhancement framework, which consists of two steps: joint decomposition and prior-guided enhancement. Firstly, we leverage the complementarity between RGB and Low-resolution Multi-Spectral Images (Lr-MSI) to predict shading, reflectance, and material semantic priors. Subsequently, these priors are seamlessly integrated into the established HDRNet to promote dynamic range enhancement, color mapping, and grid expert learning, respectively. Additionally, we construct a high-quality Mobile-Spec dataset to support our research, and our experiments validate the effectiveness of Lr-MSI in the tone enhancement task. This work aims to establish a solid foundation for advancing spectral vision in mobile photography. The code is available at https://github.com/CalayZhou/JDM-HDRNet."