GenAI4ELab/papercli-papers huggingface dataset:
STAG4D: Spatial-Temporal Anchored Generative 4D Gaussians
Authors: Yifei Zeng; Yanqin Jiang; Siyu Zhu; Yuanxun Lu; Youtian Lin; Hao Zhu; Weiming Hu; Xun Cao; Yao Yao
Venue: ECCV 2024 (main)

Abstract: "Recent progress in pre-trained diffusion models and 3D generation have spurred interest in 4D content creation. However, achieving high-fidelity 4D generation with spatial-temporal consistency remains a challenge. In this work, we propose STAG4D, a novel framework that combines pre-trained diffusion models with dynamic 3D Gaussian splatting for high-fidelity 4D generation. Drawing inspiration from 3D generation techniques, we utilize a multi-view diffusion model to initialize multi-view images anchoring on the input video frames, where the video can be either real-world captured or generated by a video diffusion model. To ensure the temporal consistency of the multi-view sequence initialization, we introduce a simple yet effective fusion strategy to leverage the first frame as a temporal anchor in the self-attention computation. With the almost consistent multi-view sequences , we then apply the score distillation sampling to optimize the 4D Gaussian point cloud. The 4D Gaussian spatting is specially crafted for the generation task, where an adaptive densification strategy is proposed to mitigate the unstable Gaussian gradient for robust optimization. Notably, the proposed pipeline does not require any pre-training or fine-tuning of diffusion networks, offering a more accessible and practical solution for the 4D generation task. Extensive experiments demonstrate that our method outperforms prior 4D generation works in rendering quality, spatial-temporal consistency, and generation robustness, setting a new state-of-the-art for 4D generation from diverse inputs, including text, image, and video."
RGBD GS-ICP SLAM
Authors: Seongbo Ha; Jiung Yeon; Hyeonwoo Yu
Venue: ECCV 2024 (main)

Abstract: "Simultaneous Localization and Mapping (SLAM) with dense representation plays a key role in robotics, Virtual Reality (VR), and Augmented Reality (AR) applications. Recent advancements in dense representation SLAM have highlighted the potential of leveraging neural scene representation and 3D Gaussian representation for high-fidelity spatial representation. In this paper, we propose a novel dense representation SLAM approach with a fusion of Generalized Iterative Closest Point (G-ICP) and 3D Gaussian Splatting (3DGS). In contrast to existing methods, we utilize a single Gaussian map for both tracking and mapping, resulting in mutual benefits. Through the exchange of covariances between tracking and mapping processes with scale alignment techniques, we minimize redundant computations and achieve an efficient system. Additionally, we enhance tracking accuracy and mapping quality through our keyframe selection methods. Experimental results demonstrate the effectiveness of our approach, showing incredibly fast speeds up to 107 FPS (for the entire system) and superior quality of the reconstructed map. The code is available at: https://github.com/Lab-of-AI-and-Robotics/GS-ICP-SLAM Video is: https://youtu.be/ebHh uMMxE"
Efficient NeRF Optimization - Not All Samples Remain Equally Hard
Authors: Juuso Korhonen; Goutham Rangu; Hamed Rezazadegan Tavakoli; Juho Kannala
Venue: ECCV 2024 (main)

Abstract: "We propose an application of online hard sample mining for efficient training of Neural Radiance Fields (NeRF). NeRF models produce state-of-the-art quality for many 3D reconstruction and rendering tasks but require substantial computational resources. The encoding of the scene information within the NeRF network parameters necessitates stochastic sampling. We observe that during the training, a major part of the compute time and memory usage is spent on processing already learnt samples, which no longer affect the model update significantly. We identify the backward pass on the stochastic samples as the computational bottleneck during the optimization. We thus perform the first forward pass in inference mode as a relatively low-cost search for hard samples. This is followed by building the computational graph and updating the NeRF network parameters using only the hard samples. To demonstrate the effectiveness of the proposed approach, we apply our method to Instant-NGP, resulting in significant improvements of the view-synthesis quality over the baseline (1 dB improvement on average per training time, or 2x speedup to reach the same PSNR level) along with ∼40% memory savings coming from using only the hard samples to build the computational graph. As our method only interfaces with the network module, we expect it to be widely applicable."
Revisiting Calibration of Wide-Angle Radially Symmetric Cameras
Authors: Andrea Porfiri Dal Cin; Francesco Azzoni; Giacomo Boracchi; Luca Magri
Venue: ECCV 2024 (main)

Abstract: "Recent learning-based calibration methods yield promising results in estimating parameters for wide field-of-view cameras from single images. Yet, these end-to-end approaches are typically tethered to one fixed camera model, leading to issues: (i) lack of flexibility, necessitating network architectural changes and retraining when changing camera models; (ii) reduced accuracy, as a single model limits the diversity of cameras represented in the training data; (iii) restrictions in camera model selection, as learning-based methods need differentiable loss functions and, thus, undistortion equations with closed-form solutions. In response, we present a novel two-step calibration framework for radially symmetric cameras. Key to our approach is a specialized CNN that, given an input image, outputs an implicit camera representation (VaCR), mapping each image point to the direction of the 3D light ray projecting onto it. The VaCR is used in a subsequent robust non-linear optimization process to determine the camera parameters for any radially symmetric model provided as input. By disentangling the estimation of camera model parameters from the VaCR, which is based only on the assumption of radial symmetry in the model, we overcome the main limitations of end-to-end approaches. Experimental results demonstrate the advantages of the proposed framework compared to state-of-the-art methods. Code is at github.com/andreadalcin/RadiallySymmetricCalib."
Rawformer: Unpaired Raw-to-Raw Translation for Learnable Camera ISPs
Authors: Georgy Perevozchikov; Nancy Mehta; Mahmoud Afifi; Radu Timofte
Venue: ECCV 2024 (main)

Abstract: "Modern smartphone camera quality heavily relies on the image signal processor (ISP) to enhance captured raw images, utilizing carefully designed modules to produce final output images encoded in a standard color space (e.g., sRGB). Neural-based end-to-end learnable ISPs offer promising advancements, potentially replacing traditional ISPs with their ability to adapt without requiring extensive tuning for each new camera model, as is often the case for nearly every module in traditional ISPs. However, the key challenge with the recent learning-based ISPs is the urge to collect large paired datasets for each distinct camera model due to the influence of intrinsic camera characteristics on the formation of input raw images. This paper tackles this challenge by introducing a novel method for unpaired learning of raw-to-raw translation across diverse cameras. Specifically, we propose Rawformer, an unsupervised Transformer-based encoder-decoder method for raw-to-raw translation. It accurately maps raw images captured by a certain camera to the target camera, facilitating the generalization of learnable ISPs to new unseen cameras. Our method demonstrates superior performance on real camera datasets, achieving higher accuracy compared to previous state-of-the-art techniques, and preserving a more robust correlation between the original and translated raw images. The codes and the pretrained models are available at https://github.com/gosha20777/rawformer."
Robust Incremental Structure-from-Motion with Hybrid Features
Authors: Shaohui Liu; Yidan Gao; Tianyi Zhang; Rémi Pautrat; Johannes L Schönberger; Viktor Larsson; Marc Pollefeys
Venue: ECCV 2024 (main)

Abstract: "Structure-from-Motion (SfM) has become a ubiquitous tool for camera calibration and scene reconstruction with many downstream applications in computer vision and beyond. While the state-of-the-art SfM pipelines have reached a high level of maturity in well-textured and well-configured scenes over the last decades, they still fall short of robustly solving the SfM problem in challenging scenarios. In particular, weakly textured scenes and poorly constrained configurations oftentimes cause catastrophic failures or large errors for the primarily keypoint-based pipelines. In these scenarios, line segments are often abundant and can offer complementary geometric constraints. Their large spatial extent and typically structured configurations lead to stronger geometric constraints as compared to traditional keypoint-based methods. In this work, we introduce an incremental SfM system that, in addition to points, leverages lines and their structured geometric relations. Our technical contributions span the entire pipeline (mapping, triangulation, registration) and we integrate these into a comprehensive end-to-end SfM system that we share as an open-source software with the community. We also present the first analytical method to propagate uncertainties for 3D optimized lines via sensitivity analysis. Experiments show that our system is consistently more robust and accurate compared to the widely used point-based state of the art in SfM – achieving richer maps and more precise camera registrations, especially under challenging conditions. In addition, our uncertainty-aware localization module alone is able to consistently improve over the state of the art under both point-alone and hybrid setups."
Revisiting Domain-Adaptive Object Detection in Adverse Weather by the Generation and Composition of High-Quality Pseudo-Labels
Authors: Rui Zhao; Huibin Yan; Shuoyao Wang
Venue: ECCV 2024 (main)

Abstract: "Due to data collection challenges, the mean-teacher learning paradigm has emerged as a critical approach for cross-domain object detection, especially in adverse weather conditions. Despite significant progress, existing methods are still plagued by low-quality pseudo-labels in degraded images. This paper proposes a generation-composition paradigm training framework that includes the tiny-object-friendly loss, i.e., IAoU loss with a joint-filtering and student-aware strategy to improve pseudo-labels generation quality and refine the filtering scheme. Specifically, in the generation phase of pseudo-labels, we observe that bounding box regression is essential for feature alignment and develop the IAoU loss to enhance the precision of bounding box regression, further facilitating subsequent feature alignment. We also find that selecting bounding boxes based solely on classification confidence performs poorly in cross-domain noisy image scenes. Moreover, relying exclusively on predictions from the teacher model could cause the student model to collapse. Accordingly, in the composition phase, we introduce the mean-teacher model with a joint-filtering and student-aware strategy combining classification and regression thresholds from both the student and the teacher models. Our extensive experiments, conducted on synthetic and real-world adverse weather datasets, clearly demonstrate that the proposed method surpasses state-of-the-art benchmarks across all scenarios, particularly achieving a 12.4% improvement of mAP, i.e., Cityscapes to RTTS. Our code will be available at https://github.com/iu110/GCHQ/."
Prediction Exposes Your Face: Black-box Model Inversion via Prediction Alignment
Authors: Yufan Liu; Wanqian Zhang; Dayan Wu; Zheng Lin; jingzi Gu; Weiping Wang
Venue: ECCV 2024 (main)

Abstract: "Model inversion (MI) attack reconstructs the private training data of a target model given its output, posing a significant threat to deep learning models and data privacy. On one hand, most of existing MI methods focus on searching for latent codes to represent the target identity, yet this iterative optimization-based scheme consumes a huge number of queries to the target model, making it unrealistic especially in black-box scenario. On the other hand, some training-based methods launch an attack through a single forward inference, whereas failing to directly learn high-level mappings from prediction vectors to images. Addressing these limitations, we propose a novel Prediction-to-Image (P2I) method for black-box MI attack. Specifically, we introduce the Prediction Alignment Encoder to map the target model’s output prediction into the latent code of StyleGAN. In this way, prediction vector space can be well aligned with the more disentangled latent space, thus establishing a connection between prediction vectors and the semantic facial features. During the attack phase, we further design the Aligned Ensemble Attack scheme to integrate complementary facial attributes of target identity for better reconstruction. Experimental results show that our method outperforms other SOTAs, e.g., compared with RLB-MI, our method improves attack accuracy by 8.5% and reduces query numbers by 99% on dataset CelebA."
Noise Calibration: Plug-and-play Content-Preserving Video Enhancement using Pre-trained Video Diffusion Models
Authors: Qinyu Yang; Haoxin Chen; Yong Zhang; Menghan Xia; Xiaodong Cun; Zhixun Su; Ying Shan
Venue: ECCV 2024 (main)

Abstract: "In order to improve the quality of synthesized videos, currently, one predominant method involves retraining an expert diffusion model and then implementing a noising-denoising process for refinement. Despite the significant training costs, maintaining consistency of content between the original and enhanced videos remains a major challenge. To tackle this challenge, we propose a novel formulation that considers both visual quality and consistency of content. Consistency of content is ensured by a proposed loss function that maintains the structure of the input, while visual quality is improved by utilizing the denoising process of pretrained diffusion models. To address the formulated optimization problem, we have developed a plug-and-play noise optimization strategy, referred to as Noise Calibration. By refining the initial random noise through a few iterations, the content of original video can be largely preserved, and the enhancement effect demonstrates a notable improvement. Extensive experiments have demonstrated the effectiveness of the proposed method. 0 Corresponding author"
UniCal: Unified Neural Sensor Calibration
Authors: Ze Yang; George G Chen; Haowei Zhang; Kevin Ta; Ioan Andrei Bârsan; Daniel Murphy; Sivabalan Manivasagam; Raquel Urtasun
Venue: ECCV 2024 (main)

Abstract: "Self-driving vehicles (SDVs) require accurate calibration of LiDARs and cameras to fuse sensor data accurately for autonomy. Traditional calibration methods typically leverage fiducials captured in a controlled and structured scene and compute correspondences to optimize over. These approaches are costly and require substantial infrastructure and operations, making it challenging to scale for vehicle fleets. In this work, we propose , a unified framework for effortlessly calibrating SDVs equipped with multiple LiDARs and cameras. Our approach is built upon a differentiable scene representation capable of rendering multi-view geometrically and photometrically consistent sensor observations. We jointly learn the sensor calibration and the underlying scene representation through differentiable volume rendering, utilizing outdoor sensor data without the need for specific calibration fiducials. This “drive-and-calibrate” approach significantly reduces costs and operational overhead compared to existing calibration systems, enabling efficient calibration for large SDV fleets at scale. To ensure geometric consistency across observations from different sensors, we introduce a novel surface alignment loss that combines feature-based registration with neural rendering. Comprehensive evaluations on multiple datasets demonstrate that outperforms or matches the accuracy of existing calibration approaches while being more efficient, demonstrating the value of for scalable calibration. For more information, visit waabi.ai/unical."
Mind the Interference: Retaining Pre-trained Knowledge in Parameter Efficient Continual Learning of Vision-Language Models
Authors: Longxiang Tang; Zhuotao Tian; Kai Li; Chunming He; Hantao Zhou; Hengshuang Zhao; Xiu Li; Jiaya Jia
Venue: ECCV 2024 (main)

Abstract: "This study addresses the Domain-Class Incremental Learning problem, a realistic but challenging continual learning scenario where both the domain distribution and target classes vary across tasks. To handle these diverse tasks, pre-trained Vision-Language Models (VLMs) are introduced for their strong generalizability. However, this incurs a new problem: the knowledge encoded in the pre-trained VLMs may be disturbed when adapting to new tasks, compromising their inherent zero-shot ability. Existing methods tackle it by tuning VLMs with knowledge distillation on extra datasets, which demands heavy computation overhead. To address this problem efficiently, we propose the Distribution-aware Interference-free Knowledge Integration (DIKI) framework, retaining pre-trained knowledge of VLMs from a perspective of avoiding information interference. Specifically, we design a fully residual mechanism to infuse newly learned knowledge into a frozen backbone, while introducing minimal adverse impacts on pre-trained knowledge. Besides, this residual property enables our distribution-aware integration calibration scheme, explicitly controlling the information implantation process for test data from unseen distributions. Experiments demonstrate that our DIKI surpasses the current state-of-the-art approach using only 0.86% of the trained parameters and requiring substantially less training time. Code is available at: https://github.com/lloongx/DIKI."
Urban Waterlogging Detection: A Challenging Benchmark and Large-Small Model Co-Adapter
Authors: Suqi Song; Chenxu Zhang; Peng Zhang; Pengkun Li; Fenglong Song; Lei Zhang
Venue: ECCV 2024 (main)

Abstract: "Urban waterlogging poses a major risk to public safety and infrastructure. Conventional methods using water-level sensors need high-maintenance to hardly achieve full coverage. Recent advances employ surveillance camera imagery and deep learning for detection, yet these struggle amidst scarce data and adverse environmental conditions. In this paper, we establish a challenging Urban Waterlogging Benchmark (UW-Bench) under diverse adverse conditions to advance real-world applications. We propose a Large-Small Model co-adapter paradigm (LSM-adapter), which harnesses the substantial generic segmentation potential of large model and the specific task-directed guidance of small model. Specifically, a Triple-S Prompt Adapter module alongside a Dynamic Prompt Combiner are proposed to generate then merge multiple prompts for mask decoder adaptation. Meanwhile, a Histogram Equalization Adap-ter module is designed to infuse the image specific information for image encoder adaptation. Results and analysis show the challenge and superiority of our developed benchmark and algorithm. Project page: https://github.com/zhang-chenxu/LSM-Adapter"
Pseudo-Embedding for Generalized Few-Shot Point Cloud Segmentation
Authors: Chih-Jung Tsai; Hwann-Tzong Chen; Tyng-Luh Liu
Venue: ECCV 2024 (main)

Abstract: "Existing generalized few-shot 3D segmentation (GFS3DS) methods typically prioritize enhancing the training of base-class prototypes while neglecting the rich semantic information within background regions for future novel classes. We introduce a novel GFS3DS learner that strategically leverages background context to improve both base prototype training and few-shot adaptability. Our method employs foundation models to extract semantic features from background points and grounds on text embeddings to cluster background points into pseudo-classes. This approach facilitates clearer base/novel class differentiation and generates pseudo prototypes that effectively mimic novel support samples. Comprehensive experiments on S3DIS and ScanNet datasets demonstrate the state-of-the-art performance of our method in both 1-shot and 5-shot tasks. Our approach significantly advances GFS3DS by unlocking the potential of background context, offering a promising avenue for broader applications. Code: https://github. com/jimtsai23/PseudoEmbed"
WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering
Authors: Pingyi Chen; Chenglu Zhu; Sunyi Zheng; Honglin Li; Lin Yang
Venue: ECCV 2024 (main)

Abstract: "Whole slide imaging is routinely adopted for carcinoma diagnosis and prognosis. Abundant experience is required for pathologists to achieve accurate and reliable diagnostic results of whole slide images (WSI). The huge size and heterogeneous features of WSIs make the workflow of pathological reading extremely time-consuming. In this paper, we propose a novel framework (WSI-VQA) to interpret WSIs by generative visual question answering. WSI-VQA shows universality by reframing various kinds of slide-level tasks in a question-answering pattern, in which pathologists can achieve immunohistochemical grading, survival prediction, and tumor subtyping following human-machine interaction. Furthermore, we establish a WSI-VQA dataset which contains 8672 slide-level question-answering pairs with 977 WSIs. Besides the ability to deal with different slide-level tasks, our generative model which is named Wsi2Text Transformer (W2T) outperforms existing discriminative models in medical correctness, which reveals the potential of our model to be applied in the clinical scenario. Additionally, we also visualize the co-attention mapping between word embeddings and WSIs as an intuitive explanation for diagnostic results. The dataset and related code are available at https://github.com/cpystan/WSI-VQA."
ReMoS: 3D Motion-Conditioned Reaction Synthesis for Two-Person Interactions
Authors: Anindita Ghosh; Rishabh Dabral; Vladislav Golyanik; Christian Theobalt; Philipp Slusallek
Venue: ECCV 2024 (main)

Abstract: "Current approaches for 3D human motion synthesis generate high-quality animations of digital humans performing a wide variety of actions and gestures. However, a notable technological gap exists in addressing the complex dynamics of multi-human interactions within this paradigm. In this work, we present , a denoising diffusion-based model that synthesizes full-body reactive motion of a person in a two-person interaction scenario. Given the motion of one person, we employ a combined spatio-temporal cross-attention mechanism to synthesize the reactive body and hand motion of the second person, thereby completing the interactions between the two. We demonstrate across challenging two-person scenarios such as pair-dancing, Ninjutsu, kickboxing, and acrobatics, where one person’s movements have complex and diverse influences on the other. We also contribute the dataset for two-person interactions containing full-body and finger motions. We evaluate through multiple quantitative metrics, qualitative visualizations, and a user study, and also indicate usability in interactive motion editing applications. More details are available on the project page: https://vcai.mpi-inf.mpg.de/projects/remos"