GenAI4ELab/papercli-papers huggingface dataset:
Flow-Guided Online Stereo Rectification for Wide Baseline Stereo
Authors: Anush Kumar; Fahim Mannan; Omid Hosseini Jafari; Shile Li; Felix Heide
Venue: CVPR 2024 (main)

Abstract: Stereo rectification is widely considered "solved" due to the abundance of traditional approaches to perform rectification. However autonomous vehicles and robots in-the-wild require constant re-calibration due to exposure to various environmental factors including vibration and structural stress when cameras are arranged in a wide-baseline configuration. Conventional rectification methods fail in these challenging scenarios: especially for larger vehicles such as autonomous freight trucks and semi-trucks the resulting incorrect rectification severely affects the quality of downstream tasks that use stereo/multi-view data. To tackle these challenges we propose an online rectification approach that operates at real-time rates while achieving high accuracy. We propose a novel learning-based online calibration approach that utilizes stereo correlation volumes built from a feature representation obtained from cross-image attention. Our model is trained to minimize vertical optical flow as proxy rectification constraint and predicts the relative rotation between the stereo pair. The method is real-time and even outperforms conventional methods used for offline calibration and substantially improves downstream stereo depth post-rectification. We release two public datasets (https://light.princeton.edu/online-stereo-recification/) a synthetic and experimental wide baseline dataset to foster further research.
DNGaussian: Optimizing Sparse-View 3D Gaussian Radiance Fields with Global-Local Depth Normalization
Authors: Jiahe Li; Jiawei Zhang; Xiao Bai; Jin Zheng; Xin Ning; Jun Zhou; Lin Gu
Venue: CVPR 2024 (main)

Abstract: Radiance fields have demonstrated impressive performance in synthesizing novel views from sparse input views yet prevailing methods suffer from high training costs and slow inference speed. This paper introduces DNGaussian a depth-regularized framework based on 3D Gaussian radiance fields offering real-time and high-quality few-shot novel view synthesis at low costs. Our motivation stems from the highly efficient representation and surprising quality of the recent 3D Gaussian Splatting despite it will encounter a geometry degradation when input views decrease. In the Gaussian radiance fields we find this degradation in scene geometry primarily lined to the positioning of Gaussian primitives and can be mitigated by depth constraint. Consequently we propose a Hard and Soft Depth Regularization to restore accurate scene geometry under coarse monocular depth supervision while maintaining a fine-grained color appearance. To further refine detailed geometry reshaping we introduce Global-Local Depth Normalization enhancing the focus on small local depth changes. Extensive experiments on LLFF DTU and Blender datasets demonstrate that DNGaussian outperforms state-of-the-art methods achieving comparable or better results with significantly reduced memory cost a 25x reduction in training time and over 3000x faster rendering speed. Code is available at: https://github.com/Fictionarry/DNGaussian
ColorPCR: Color Point Cloud Registration with Multi-Stage Geometric-Color Fusion
Authors: Juncheng Mu; Lin Bie; Shaoyi Du; Yue Gao
Venue: CVPR 2024 (main)

Abstract: Point cloud registration is still a challenging and open problem. For example when the overlap between two point clouds is extremely low geo-only features may be not sufficient. Therefore it is important to further explore how to utilize color data in this task. Under such circumstances we propose ColorPCR for color point cloud registration with multi-stage geometric-color fusion. We design a Hierarchical Color Enhanced Feature Extraction module to extract multi-level geometric-color features and a GeoColor Superpoint Matching Module to encode transformation-invariant geo-color global context for robust patch correspondences. In this way both geometric and color data can be used thus lead to robust performance even under extremely challenging scenarios such as low overlap between two point clouds. To evaluate the performance of our method we colorize 3DMatch/3DLoMatch datasets as Color3DMatch/Color3DLoMatch and evaluations on these datasets demonstrate the effectiveness of our proposed method. Our method achieves state-of-the-art registration recall of 97.5%/88.9% on them.
HomoFormer: Homogenized Transformer for Image Shadow Removal
Authors: Jie Xiao; Xueyang Fu; Yurui Zhu; Dong Li; Jie Huang; Kai Zhu; Zheng-Jun Zha
Venue: CVPR 2024 (main)

Abstract: The spatial non-uniformity and diverse patterns of shadow degradation conflict with the weight sharing manner of dominant models which may lead to an unsatisfactory compromise. To tackle with this issue we present a novel strategy from the view of shadow transformation in this paper: directly homogenizing the spatial distribution of shadow degradation. Our key design is the random shuffle operation and its corresponding inverse operation. Specifically random shuffle operation stochastically rearranges the pixels across spatial space and the inverse operation recovers the original order. After randomly shuffling the shadow diffuses in the whole image and the degradation appears in a homogenized way which can be effectively processed by the local self-attention layer. Moreover we further devise a new feed forward network with position modeling to exploit image structural information. Based on these elements we construct the final local window based transformer named HomoFormer for image shadow removal. Our HomoFormer can enjoy the linear complexity of local transformers while bypassing challenges of non-uniformity and diversity of shadow. Extensive experiments are conducted to verify the superiority of our HomoFormer across public datasets.
What If the TV Was Off? Examining Counterfactual Reasoning Abilities of Multi-modal Language Models
Authors: Letian Zhang; Xiaotong Zhai; Zhongkai Zhao; Yongshuo Zong; Xin Wen; Bingchen Zhao
Venue: CVPR 2024 (main)

Abstract: Counterfactual reasoning a fundamental aspect of human cognition involves contemplating alternatives to established facts or past events significantly enhancing our abilities in planning and decision-making. In light of the advancements in current multi-modal large language models we explore their effectiveness in counterfactual reasoning. To facilitate this investigation we introduce a novel dataset C-VQA specifically designed to examine the counterfactual reasoning capabilities of modern multi-modal large language models. This dataset is constructed by infusing original questions with counterfactual presuppositions spanning various types such as numerical and boolean queries. It encompasses a mix of real and synthetic data representing a wide range of difficulty levels. Our thorough evaluations of contemporary vision-language models using this dataset have revealed substantial performance drops with some models showing up to a 40% decrease highlighting a significant gap between current models and human-like vision reasoning capabilities. We hope our dataset will serve as a vital benchmark for evaluating the counterfactual reasoning capabilities of models. Code and dataset are publicly available at https://bzhao.me/C-VQA/.
What Do You See in Vehicle? Comprehensive Vision Solution for In-Vehicle Gaze Estimation
Authors: Yihua Cheng; Yaning Zhu; Zongji Wang; Hongquan Hao; Yongwei Liu; Shiqing Cheng; Xi Wang; Hyung Jin Chang
Venue: CVPR 2024 (main)

Abstract: Driver's eye gaze holds a wealth of cognitive and intentional cues crucial for intelligent vehicles. Despite its significance research on in-vehicle gaze estimation remains limited due to the scarcity of comprehensive and well-annotated datasets in real driving scenarios. In this paper we present three novel elements to advance in-vehicle gaze research. Firstly we introduce IVGaze a pioneering dataset capturing in-vehicle gaze collected from 125 individuals and covering a large range of gaze and head within vehicles. Conventional gaze collection systems are inadequate for in-vehicle use. In this dataset we propose a new vision-based solution for in-vehicle gaze collection introducing a refined gaze target calibration method to tackle annotation challenges. Second our research focuses on in-vehicle gaze estimation leveraging the IVGaze. Images of in-vehicle faces often suffer from low resolution prompting our introduction of a gaze pyramid transformer that harnesses transformer-based multilevel features integration. Expanding upon this we introduce the dual-stream gaze pyramid transformer (GazeDPTR). Employing perspective transformation we rotate virtual cameras to normalize images utilizing camera pose to merge normalized and original images for accurate gaze estimation. GazeDPTR showcases state-of-the-art performance on the IVGaze dataset. Thirdly we explore a novel strategy for gaze zone classification by extending the GazeDPTR. A foundational tri-plane and project gaze onto these planes are newly defined. Leveraging both positional features from the projection points and visual attributes from images we achieve superior performance compared to relying solely on visual features thereby substantiating the advantage of gaze estimation. The project is available at https://yihua.zone/work/ivgaze
Driving Everywhere with Large Language Model Policy Adaptation
Authors: Boyi Li; Yue Wang; Jiageng Mao; Boris Ivanovic; Sushant Veer; Karen Leung; Marco Pavone
Venue: CVPR 2024 (main)

Abstract: Adapting driving behavior to new environments customs and laws is a long-standing problem in autonomous driving precluding the widespread deployment of autonomous vehicles (AVs). In this paper we present LLaDA a simple yet powerful tool that enables human drivers and autonomous vehicles alike to drive everywhere by adapting their tasks and motion plans to traffic rules in new locations. LLaDA achieves this by leveraging the impressive zero-shot generalizability of large language models (LLMs) in interpreting the traffic rules in the local driver handbook. Through an extensive user study we show that LLaDA's instructions are useful in disambiguating in-the-wild unexpected situations. We also demonstrate LLaDA's ability to adapt AV motion planning policies in real-world datasets; LLaDA outperforms baseline planning approaches on all our metrics. Please check our website for more details: https://boyiliee.github.io/llada.
UFORecon: Generalizable Sparse-View Surface Reconstruction from Arbitrary and Unfavorable Sets
Authors: Youngju Na; Woo Jae Kim; Kyu Beom Han; Suhyeon Ha; Sung-Eui Yoon
Venue: CVPR 2024 (main)

Abstract: Generalizable neural implicit surface reconstruction aims to obtain an accurate underlying geometry given a limited number of multi-view images from unseen scenes. However existing methods select only informative and relevant views using predefined scores for training and testing phases. This constraint renders the model impractical in real-world scenarios where the availability of favorable combinations cannot always be ensured. We introduce and validate a view-combination score to indicate the effectiveness of the input view combination. We observe that previous methods output degenerate solutions under arbitrary and unfavorable sets. Building upon this finding we propose UFORecon a robust view-combination generalizable surface reconstruction framework. To achieve this we apply cross-view matching transformers to model interactions between source images and build correlation frustums to capture global correlations. Additionally we explicitly encode pairwise feature similarities as view-consistent priors. Our proposed framework significantly outperforms previous methods in terms of view-combination generalizability and also in the conventional generalizable protocol trained with favorable view-combinations. The code is available at https://github.com/Youngju-Na/UFORecon.
FAR: Flexible Accurate and Robust 6DoF Relative Camera Pose Estimation
Authors: Chris Rockwell; Nilesh Kulkarni; Linyi Jin; Jeong Joon Park; Justin Johnson; David F. Fouhey
Venue: CVPR 2024 (main)

Abstract: Estimating relative camera poses between images has been a central problem in computer vision. Methods that find correspondences and solve for the fundamental matrix offer high precision in most cases. Conversely methods predicting pose directly using neural networks are more robust to limited overlap and can infer absolute translation scale but at the expense of reduced precision. We show how to combine the best of both methods; our approach yields results that are both precise and robust while also accurately inferring translation scales. At the heart of our model lies a Transformer that (1) learns to balance between solved and learned pose estimations and (2) provides a prior to guide a solver. A comprehensive analysis supports our design choices and demonstrates that our method adapts flexibly to various feature extractors and correspondence estimators showing state-of-the-art performance in 6DoF pose estimation on Matterport3D InteriorNet StreetLearn and Map-free Relocalization.
eTraM: Event-based Traffic Monitoring Dataset
Authors: Aayush Atul Verma; Bharatesh Chakravarthi; Arpitsinh Vaghela; Hua Wei; Yezhou Yang
Venue: CVPR 2024 (main)

Abstract: Event cameras with their high temporal and dynamic range and minimal memory usage have found applications in various fields. However their potential in static traffic monitoring remains largely unexplored. To facilitate this exploration we present eTraM - a first-of-its-kind fully event-based traffic monitoring dataset. eTraM offers 10 hr of data from different traffic scenarios in various lighting and weather conditions providing a comprehensive overview of real-world situations. Providing 2M bounding box annotations it covers eight distinct classes of traffic participants ranging from vehicles to pedestrians and micro-mobility. eTraM's utility has been assessed using state-of-the-art methods for traffic participant detection including RVT RED and YOLOv8. We quantitatively evaluate the ability of event-based models to generalize on nighttime and unseen scenes. Our findings substantiate the compelling potential of leveraging event cameras for traffic monitoring opening new avenues for research and application. eTraM is available at https://eventbasedvision.github.io/eTraM.
MoCha-Stereo: Motif Channel Attention Network for Stereo Matching
Authors: Ziyang Chen; Wei Long; He Yao; Yongjun Zhang; Bingshu Wang; Yongbin Qin; Jia Wu
Venue: CVPR 2024 (main)

Abstract: Learning-based stereo matching techniques have made significant progress. However existing methods inevitably lose geometrical structure information during the feature channel generation process resulting in edge detail mismatches. In this paper the Motif Channel Attention Stereo Matching Network (MoCha-Stereo) is designed to address this problem. We provide the Motif Channel Correlation Volume (MCCV) to determine more accurate edge matching costs. MCCV is achieved by projecting motif channels which capture common geometric structures in feature channels onto feature maps and cost volumes. In addition edge variations in the reconstruction error map also affect details matching we propose the Reconstruction Error Motif Penalty (REMP) module to further refine the full-resolution disparity estimation. REMP integrates the frequency information of typical channel features from the reconstruction error. MoCha-Stereo ranks 1st on the KITTI-2015 and KITTI-2012 Reflective leaderboards. Our structure also shows excellent performance in Multi-View Stereo. Code is avaliable at https://github.com/ZYangChen/MoCha-Stereo.
Koala: Key Frame-Conditioned Long Video-LLM
Authors: Reuben Tan; Ximeng Sun; Ping Hu; Jui-hsien Wang; Hanieh Deilamsalehy; Bryan A. Plummer; Bryan Russell; Kate Saenko
Venue: CVPR 2024 (main)

Abstract: Long video question answering is a challenging task that involves recognizing short-term activities and reasoning about their fine-grained relationships. State-of-the-art video Large Language Models (vLLMs) hold promise as a viable solution due to their demonstrated emergent capabilities on new tasks. However despite being trained on millions of short seconds-long videos vLLMs are unable to understand minutes-long videos and accurately answer questions about them. To address this limitation we propose a lightweight and self-supervised approach Key frame-conditioned long video-LLM (Koala) that introduces learnable spatiotemporal queries to adapt pretrained vLLMs for generalizing to longer videos. Our approach introduces two new tokenizers that condition on visual tokens computed from sparse video key frames for understanding short and long video moments. We train our proposed approach on HowTo100M and demonstrate its effectiveness on zero-shot long video understanding benchmarks where it outperforms state-of-the-art large models by 3 - 6% in absolute accuracy across all tasks. Surprisingly we also empirically show that our approach not only helps a pretrained vLLM to understand long videos but also improves its accuracy on short-term action recognition.
Extend Your Own Correspondences: Unsupervised Distant Point Cloud Registration by Progressive Distance Extension
Authors: Quan Liu; Hongzi Zhu; Zhenxi Wang; Yunsong Zhou; Shan Chang; Minyi Guo
Venue: CVPR 2024 (main)

Abstract: Registration of point clouds collected from a pair of distant vehicles provides a comprehensive and accurate 3D view of the driving scenario which is vital for driving safety related applications yet existing literature suffers from the expensive pose label acquisition and the deficiency to generalize to new data distributions. In this paper we propose EYOC an unsupervised distant point cloud registration method that adapts to new point cloud distributions on the fly requiring no global pose labels. The core idea of EYOC is to train a feature extractor in a progressive fashion where in each round the feature extractor trained with near point cloud pairs can label slightly farther point cloud pairs enabling self-supervision on such far point cloud pairs. This process continues until the derived extractor can be used to register distant point clouds. Particularly to enable high-fidelity correspondence label generation we devise an effective spatial filtering scheme to select the most representative correspondences to register a point cloud pair and then utilize the aligned point clouds to discover more correct correspondences. Experiments show that EYOC can achieve comparable performance with state-of-the-art supervised methods at a lower training cost. Moreover it outwits supervised methods regarding generalization performance on new data distributions.
HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models
Authors: Tianrui Guan; Fuxiao Liu; Xiyang Wu; Ruiqi Xian; Zongxia Li; Xiaoyu Liu; Xijun Wang; Lichang Chen; Furong Huang; Yaser Yacoob; Dinesh Manocha; Tianyi Zhou
Venue: CVPR 2024 (main)

Abstract: We introduce "HallusionBench" a comprehensive benchmark designed for the evaluation of image-context reasoning. This benchmark presents significant challenges to advanced large visual-language models (LVLMs) such as GPT-4V(ision) Gemini Pro Vision Claude 3 and LLaVA-1.5 by emphasizing nuanced understanding and interpretation of visual data. The benchmark comprises 346 images paired with 1129 questions all meticulously crafted by human experts. We introduce a novel structure for these visual questions designed to establish control groups. This structure enables us to conduct a quantitative analysis of the models' response tendencies logical consistency and various failure modes. In our evaluation on HallusionBench we benchmarked 15 different models highlighting a 31.42% question-pair accuracy achieved by the state-of-the-art GPT-4V. Notably all other evaluated models achieve accuracy below 16%. Moreover our analysis not only highlights the observed failure modes including language hallucination and visual illusion but also deepens an under standing of these pitfalls. Our comprehensive case studies within HallusionBench shed light on the challenges of hallucination and illusion in LVLMs. Based on these insights we suggest potential pathways for their future improvement. The benchmark and codebase can be accessed at https://github.com/tianyilab/HallusionBench.
ID-like Prompt Learning for Few-Shot Out-of-Distribution Detection
Authors: Yichen Bai; Zongbo Han; Bing Cao; Xiaoheng Jiang; Qinghua Hu; Changqing Zhang
Venue: CVPR 2024 (main)

Abstract: Out-of-distribution (OOD) detection methods often exploit auxiliary outliers to train model identifying OOD samples especially discovering challenging outliers from auxiliary outliers dataset to improve OOD detection. However they may still face limitations in effectively distinguishing between the most challenging OOD samples that are much like in-distribution (ID) data i.e. ID-like samples. To this end we propose a novel OOD detection framework that discovers ID-like outliers using CLIP from the vicinity space of the ID samples thus helping to identify these most challenging OOD samples. Then a prompt learning framework is proposed that utilizes the identified ID-like outliers to further leverage the capabilities of CLIP for OOD detection. Benefiting from the powerful CLIP we only need a small number of ID samples to learn the prompts of the model without exposing other auxiliary outlier datasets. By focusing on the most challenging ID-like OOD samples and elegantly exploiting the capabilities of CLIP our method achieves superior few-shot learning performance on various real-world image datasets (e.g. in 4-shot OOD detection on the ImageNet-1k dataset our method reduces the average FPR95 by 12.16% and improves the average AUROC by 2.76% compared to state-of-the-art methods).
Breathing Life Into Sketches Using Text-to-Video Priors
Authors: Rinon Gal; Yael Vinker; Yuval Alaluf; Amit Bermano; Daniel Cohen-Or; Ariel Shamir; Gal Chechik
Venue: CVPR 2024 (main)

Abstract: A sketch is one of the most intuitive and versatile tools humans use to convey their ideas visually. An animated sketch opens another dimension to the expression of ideas and is widely used by designers for a variety of purposes. Animating sketches is a laborious process requiring extensive experience and professional design skills. In this work we present a method that automatically adds motion to a single-subject sketch (hence "breathing life into it") merely by providing a text prompt indicating the desired motion. The output is a short animation provided in vector representation which can be easily edited. Our method does not require extensive training but instead leverages the motion prior of a large pretrained text-to-video diffusion model using a score-distillation loss to guide the placement of strokes. To promote natural and smooth motion and to better preserve the sketch's appearance we model the learned motion through two components. The first governs small local deformations and the second controls global affine transformations. Surprisingly we find that even models that struggle to generate sketch videos on their own can still serve as a useful backbone for animating abstract representations.