91 accepted papers
Fully supervised Video Semantic Segmentation (VSS) relies heavily on densely annotated video data, limiting practical applicability. Alternatively, applying pre-trained Image Semantic Segmentation (ISS) models frame-by-frame avoids annotation costs but ignores crucial temporal coherence. Recent foun
In this paper, we present DSERT-RoLL, a driving dataset that incorporates stereo event, RGB, and thermal cameras together with 4D radar and dual LiDAR, collected across diverse weather and illumination conditions. The dataset provides precise 2D and 3D bounding boxes with track IDs and ego vehicle o
Event cameras provide high-temporal-resolution, motion-centric measurements that remain reliable under fast motion and challenging illumination, making them a promising sensing modality for motion deblurring. However, existing deblurring methods typically require large-scale paired blur--sharp datas
Event cameras provide microsecond latency, making them suitable for 6D object pose tracking in fast, dynamic scenes where conventional RGB and depth pipelines suffer from motion blur and large pixel displacements. We introduce EventTrack6D, an event-depth tracking framework that generalizes to novel
Pose estimation remains challenging under sparse views, especially when visual overlap across images is extremely limited. Recent advances in video generation models offer a promising solution by enabling keyframe interpolation, which can enrich contextual cues and improve pose estimation performanc
Transfer attacks optimize on a surrogate and deploy to a black-box target. While iterative optimization attacks in this paradigm are limited by their per-input cost limits efficiency and scalability due to multistep gradient updates for each input, generative attacks alleviate these by producing adv…
Model merging combines independently fine-tuned checkpoints without joint multi-task training. In the era of foundation-model, fine-tuning with Low-Rank Adaptation (LoRA) is prevalent, making LoRA merging a promising target. Existing approaches can work in homogeneous settings where all target tasks
Dataset distillation compresses large training sets into compact synthetic datasets while preserving downstream performance. As modern systems increasingly operate on paired vision-language inputs, multimodal distillation must preserve representation quality and cross-modal alignment under tight com
Merging multiple Low-Rank Adaptation (LoRA) modules is promising for constructing general-purpose systems, yet challenging because LoRA update directions span different subspaces and contribute unevenly. When merged naively, such mismatches can weaken the directions most critical to certain task los
Recent advances in novel view synthesis (NVS) have predominantly focused on ideal, clear input settings, limiting their applicability in real-world environments with common degradations such as blur, low-light, haze, rain, and snow. While some approaches address NVS under specific degradation types,…
LiDAR semantic segmentation must remain robust under various sensor and environmental corruptions to be reliable in safety-critical applications. Existing test-time adaptation methods, including approaches based on pseudo-labels and normalization statistics, have shown promising results but can stil
We introduce Any6D, a model-free framework for 6D object pose estimation that requires only a single RGB-D anchor image to estimate both the 6D pose and size of unknown objects in novel scenes. Unlike existing methods that rely on textured 3D models or multiple viewpoints, Any6D leverages a joint ob…
Interactive segmentation (IS) allows users to iteratively refine object boundaries with minimal cues, such as positive and negative clicks. While the Segment Anything Model (SAM) has garnered attention in the IS community for its promptable segmentation capabilities, it often struggles in specialize…
Robust 3D object detection across diverse weather con- ditions is crucial for safe autonomous driving, and RADAR is increasingly leveraged for its resilience in adverse weather. Recent advancements have explored 4D RADAR and LiDAR-RADAR fusion to enhance 3D perception capabilities, specifically targ…
Detecting 3D objects in point clouds plays a crucial role in autonomous driving systems. Recently, advanced multi-modal methods incorporating camera information have achieved notable performance. For a safe and effective autonomous driving system, algorithms that excel not only in accuracy but also…
In low-light environments, longer exposure times are commonly used to enhance image visibility; however, this inevitably leads to motion blur. Even with a long exposure time, videos captured in low-light environments still suffer from issues such as low visibility, low contrast, and color distortion…
Human pose estimation is critical for applications such as rehabilitation, sports analytics, and AR/VR systems. However, rapid motion and low-light conditions often introduce motion blur, significantly degrading pose estimation due to the domain gap between sharp and blurred images. Most datasets as…
While data-driven trajectory prediction has enhanced the reliability of autonomous driving systems, it still struggles with rarely observed long-tail scenarios. Prior works addressed this by modifying model architectures, such as using hypernetworks. In contrast, we propose refining the training pro…
Motion planning is a crucial component of autonomous robot driving. While various trajectory datasets exist, effectively utilizing them for a target domain remains challenging due to differences in agent interactions and environmental characteristics. Conventional approaches, such as domain adaptati…
With advancements in sensor and display technologies, high-resolution imagery is becoming increasingly prevalent in diverse applications. As a result, optical flow estimation needs to adapt to larger image resolutions, where even moderate movements lead to substantial pixel displacements, making lon…
Pedestrian trajectory forecasting is crucial in various applications such as autonomous driving and mobile robot navigation. In such applications, camera-based perception enables the extraction of additional modalities (human pose, text) to enhance prediction accuracy. Indeed, we find that textual d…
Safe and effective motion planning is crucial for autonomous robots. Diffusion models excel at capturing complex agent interactions, a fundamental aspect of decision-making in dynamic environments. Recent studies have successfully applied diffusion models to motion planning, demonstrating their comp
Multi-Task Learning (MTL) enables multiple tasks to be learned within a shared network, but differences in objectives across tasks can cause negative transfer, where the learning of one task degrades another task's performance. While pre-trained transformers significantly improve MTL performance, th…
Adverse weather conditions cause diverse and complex degradation patterns, driving the development of All-in-One (AiO) models. However, recent AiO solutions still struggle to capture diverse degradations, since global filtering methods like direct operations on the frequency domain fail to handle hi…
Multi-task learning enables the acquisition of task-generic knowledge by training multiple tasks within a unified architecture. However, training all tasks together in a single architecture can lead to performance degradation, known as negative transfer, which is a main concern in multi-task learnin…
Generalizing neural networks to unseen target domains is a significant challenge in real-world deployments. Test-time training (TTT) addresses this by using an auxiliary self-supervised task to reduce the domain gap caused by distribution shifts between the source and target. However, we find that w…
3D object detection is essential for autonomous systems, enabling precise localization and dimension estimation. While LiDAR and RGB cameras are widely used, their fixed frame rates create perception gaps in high-speed scenarios. Event cameras, with their asynchronous nature and high temporal resolu…
End-to-end autonomous driving (E2E-AD) has emerged as a promising paradigm that unifies perception, prediction, and planning into a holistic, data-driven framework. However, achieving robustness to varying camera viewpoints, a common real-world challenge due to diverse vehicle configurations, remain…
Instance segmentation traditionally relies on dense pixel-level annotations, making it costly and labor-intensive. To alleviate this burden, weakly supervised instance segmentation utilizes cost-effective weak labels, such as image-level tags, points, and bounding boxes. However, existing approaches…
We address the 3D head reconstruction problem and the facial correspondence search problem in a unified framework, named as WarpHE4D. The underlying idea is to establish correspondences between the facial image and the fixed UV texture map by exploiting powerful self-supervised visual representation…
Multi-person pose estimation and tracking have been actively researched by the computer vision community due to their practical applicability. However, existing human pose estimation and tracking datasets have only been successful in typical scenarios, such as those without motion blur or with well-…
Weakly Supervised Semantic Segmentation (WSSS) relies on Class Activation Maps (CAMs) to extract spatial information from image-level labels. With the success of Vision Transformer (ViT) the migration of ViT is actively conducted in WSSS. This work proposes a novel WSSS framework with Class Token In…
Deep neural networks are known to be vulnerable to security risks due to the inherent transferable nature of adversarial examples. Despite the success of recent generative model-based attacks demonstrating strong transferability, it still remains a challenge to design an efficient attack strategy in…
Video deblurring aims to restore sharp frames from blurred video clips. Despite notable progress in video deblurring works it is still a challenging problem because of the loss of motion information during the duration of the exposure time. Since event cameras can capture clear motion information as…
Weakly Supervised Semantic Segmentation (WSSS) aims to learn the concept of segmentation using image-level class labels. Recent WSSS works have shown promising results by using the Segment Anything Model (SAM) a foundation model for segmentation during the inference phase. However we observe that th…
Multi-agent trajectory prediction is crucial for various practical applications, spurring the construction of many large-scale trajectory datasets, including vehicles and pedestrians. However, discrepancies exist among datasets due to external factors and data acquisition strategies. External facto…
Human pose forecasting garners attention for its diverse applications. However challenges in modeling the multi-modal nature of human motion and intricate interactions among agents persist particularly with longer timescales and more agents. In this paper we propose an interaction-aware trajectory-c…
The goal of multi-task learning is to learn diverse tasks within a single unified network. As each task has its own unique objective function conflicts emerge during training resulting in negative transfer among them. Earlier research identified these conflicting gradients in shared parameters betwe…
Trajectory prediction is a challenging problem that requires considering interactions among multiple actors and the surrounding environment. While data-driven approaches have been used to address this complex problem they suffer from unreliable predictions under distribution shifts during test time.…
Semantic Scene Completion (SSC) aims to perform geometric completion and semantic segmentation simultaneously. Despite the promising results achieved by existing studies, the inherently ill-posed nature of the task presents significant challenges in diverse driving scenarios. This paper introduces T…
Video Frame Interpolation (VFI) which aims at generating high-frame-rate videos from low-frame-rate inputs is a highly challenging task. The emergence of bio-inspired sensors known as event cameras which boast microsecond-level temporal resolution has ushered in a transformative era for VFI. Nonethe…
Detecting objects in 3D under various (normal and adverse) weather conditions is essential for safe autonomous driving systems. Recent approaches have focused on employing weather-insensitive 4D radar sensors and leveraging them with other modalities such as LiDAR. However they fuse multi-modal info…
Manual annotation of every point in a point cloud is a costly and labor-intensive process. While weakly supervised point cloud semantic segmentation (WSPCSS) with sparse annotation shows promise the limited information from initial sparse labels can place an upper bound on performance. As a new rese…
Novel View Synthesis (NVS) aims at synthesizing an image from an arbitrary viewpoint using multi-view images and camera poses. Among the methods for NVS, Neural Radiance Fields (NeRF) is capable of NVS for an arbitrary resolution as it learns a continuous volumetric representation. However, radiance…
Video Frame Interpolation (VFI) aims to generate intermediate video frames between consecutive input frames. Since the event cameras are bio-inspired sensors that only encode brightness changes with a micro-second temporal resolution, several works utilized the event camera to enhance the performanc…
Recognizing objects from sparse and noisy events becomes extremely difficult when paired images and category labels do not exist. In this paper, we study label-free event-based object recognition where category labels and paired images are not available. To this end, we propose a joint formulation o…
Recently, event-based stereo matching has been studied due to its robustness in poor light conditions. However, existing event-based stereo networks suffer severe performance degradation when domains shift. Unsupervised domain adaptation (UDA) aims at resolving this problem without using the target…
Point cloud completion is to restore complete 3D scenes and objects from incomplete observations or limited sensor data. Existing fully-supervised methods rely on paired datasets of incomplete and complete point clouds, which are labor-intensive to obtain. Unpaired methods have been proposed, but st…
Understanding the interaction between multiple agents is crucial for realistic vehicle trajectory prediction. Existing methods have attempted to infer the interaction from the observed past trajectories of agents using pooling, attention, or graph-based methods, which rely on a deterministic approa…
Our MATE is the first Test-Time-Training (TTT) method designed for 3D data, which makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data. Like existing TTT methods from the 2D image domain, MATE also leverages test data for adaptation. Its tes…
Motion deblurring from a blurred image is a challenging computer vision problem because frame-based cameras lose information during the blurring process. Several attempts have compensated for the loss of motion information by using event cameras, which are bio-inspired sensors with a high temporal r…
Existing image stitching approaches based on global or local homography estimation are not free from the parallax problem and suffer from undesired artifacts. In this paper, instead of relying on the homography-based warp, we propose a novel deep image stitching framework exploiting the pixel-wise w…
With the success of the 3D deep learning models, various perception technologies for autonomous driving have been developed in the LiDAR domain. While these models perform well in the trained source domain, they struggle in unseen domains with a domain gap. In this paper, we propose a single domain…
Test-time adaptation methods have been gaining attention recently as a practical solution for addressing source-to-target domain gaps by gradually updating the model without requiring labels on the target data. In this paper, we propose a method of test-time adaptation for category-level object pose…
In Weakly Supervised Semantic Segmentation (WSSS), Class Activation Maps (CAMs) usually 1) do not cover the whole object and 2) be activated on irrelevant regions. To address the issues, we propose a novel WSSS framework via adversarial learning of a classifier and an image reconstructor. When an im…
"Weakly supervised semantic segmentation (WSSS) has employed Class Activation Maps (CAMs) to localize the objects. However, the CAMs typically do not fit along the object boundaries and highlight only the most-discriminative regions. To resolve the problems, we propose a Gated Pyramid Pooling (GPP)…
"Providing omnidirectional depth along with RGB information is important for numerous applications. However, as omnidirectional RGB-D data is not always available, synthesizing RGB-D panorama data from limited information of a scene can be useful. Therefore, some prior works tried to synthesize RGB…
"Motion deblurring is a highly ill-posed problem due to the loss of motion information in the blur degradation process. Since event cameras can capture apparent motion with a high temporal resolution, several attempts have explored the potential of events for guiding deblurring. These methods genera…
Event cameras asynchronously output the polarity values of pixel-level log intensity alterations. They are robust against motion blur and can be adopted in challenging light conditions. Owing to these advantages, event cameras have been employed in various vision tasks such as depth estimation, visu…
"Recently, Dual-Pixel (DP) sensors have been adopted in many imaging devices. However, despite their various advantages, DP sensors are used just for faster auto-focus and aesthetic image captures, and research on their usage for 3D facial understanding has been limited due to the lack of datasets a…
Visual degradations caused by motion blur, raindrop, rain, snow, illumination, and fog deteriorate image quality and, subsequently, the performance of perception algorithms deployed in outdoor conditions. While degradation-specific image restoration techniques have been extensively studied, such alg…
The aim of weakly supervised semantic segmentation (WSSS) is to learn semantic segmentation without using dense annotations. WSSS has been intensively studied for 2D images and 3D point clouds. However, the existing WSSS studies have focused on a single domain, i.e. 2D or 3D, even when multi-domain…
Test-time adaptation approaches have recently emerged as a practical solution for handling domain shift without access to the source domain data. In this paper, we propose and explore a new multi-modal extension of test-time adaptation for 3D semantic segmentation. We find that, directly applying ex…
Semantic segmentation provides scene understanding capability by performing pixel-wise classification of objects within an image. However, the sensitivity of such algorithms towards domain changes requires fine-tuning using an annotated dataset for each novel domain, which is expensive to construct…
"Standard frame-based cameras have shortcomings of low dynamic range and motion blur in real applications. On the other hand, event cameras, which are bio-inspired sensors, asynchronously output the polarity values of pixel-level log intensity changes and report continuous stream data even under fas…
The 360deg imaging has recently gained much attention; however, its angular resolution is relatively lower than that of a narrow field-of-view (FOV) perspective image as it is captured using a fisheye lens with the same sensor size. Therefore, it is beneficial to super-resolve a 360deg image. Severa…
Neuromorphic cameras or event cameras mimic human vision by reporting changes in the intensity in a scene, instead of reporting the whole scene at once in a form of an image frame as performed by conventional cameras. Events are streamed data that are often dense when either the scene changes or the…
Learning to estimate object pose often requires ground-truth (GT) labels, such as CAD model and absolute-scale object pose, which is expensive and laborious to obtain in the real world. To tackle this problem, we propose an unsupervised domain adaptation (UDA) for category-level object pose estimati…
Vehicle Re-identification (Re-ID) aims to retrieve all instances of query vehicle images present in an image pool. However viewpoint, illumination, and occlusion variations along with subtle differences between two unique images pose a significant challenge towards achieving an effective system. In…
Event cameras are novel sensors that perceive the per-pixel intensity changes and output asynchronous event streams with high dynamic range and less motion blur. It has been shown that events alone can be used for end-task learning, e.g., semantic segmentation, based on encoder-decoder-like networks…
Event-based cameras sense changes in light intensity, and asynchronously outputs event data. Compared with conventional cameras, event-based cameras have the advantages of low latency, high dynamic range, and low power consumption. Event cameras have been utilized for various vision tasks, such as d
Event cameras sense per-pixel intensity changes and produce asynchronous event streams with high dynamic range and less motion blur, showing advantages over the conventional cameras. A hurdle of training event-based models is the lack of large qualitative labeled data. Prior works learning end-tasks…
Event cameras can report scene movements as an asynchronous stream of data called the events. Unlike traditional cameras, event cameras have very low latency (microseconds vs milliseconds) very high dynamic range (140dB vs 60 dB), and low power consumption, as they report changes of a scene and not…
Owing to the recent advances in the field of deep-learning-based approaches, state-of-the-art performance has been achieved for optical flow estimation. However, non-deep-learning-based improvement in the optical flow estimation performance is still required because many platforms, such as small UGV
Existing vanishing point (VP) estimation methods rely on pre-extracted image lines and/or prior knowledge of the number of VPs. However, in practice, this information may be insufficient or unavailable. To solve this problem, we propose a network that treats a perspective image as input and predicts…
Most existing deep learning-based depth completion methods are only suitable for high (e.g. 64-scanline) resolution LiDAR measurements, and they usually fail to predict a reliable dense depth map with low resolution (4, 8, or 16-scanline) LiDAR. However, it is of great interest to reduce the number
Presence of haze in images obscures underlying information, which is undesirable in applications requiring accurate environment information. To recover such an image, a dehazing algorithm should localize and recover affected regions while ensuring consistency between recovered and its neighboring re…
Weakly supervised semantic segmentation (WSSS) using image-level classification labels usually utilizes the Class Activation Maps (CAMs) to localize objects of interest in images. While pointing out that CAMs only highlight the most discriminative regions of the classes of interest, adversarial eras…
Deep neural networks (DNNs) have achieved impressive performance on handling computer vision problems. However, it has been found that DNNs are vulnerable to adversarial examples. For such reason, adversarial perturbations have been recently studied in several respects. However, most previous works
Anchor boxes act as potential object localization candidates allow single-stage detectors to achieve real-time performance, at the cost of localization accuracy when compared to state-of-the-art two-stage detectors. Therefore, correct selection of the scale and aspect ratio associated with an anchor…
Event cameras sense intensity changes and have many advantages over conventional cameras. To take advantage of event cameras, some methods have been proposed to reconstruct intensity images from event streams. However, the outputs are still in low resolution (LR), noisy, and unrealistic. The low-qua…
An event camera detects per-pixel intensity difference and produces asynchronous event stream with low latency, high dynamic range, and low power consumption. As a trade-off, the event camera has low spatial resolution. We propose an end-to-end network to reconstruct high resolution, high dynamic ra…
Most of existing deep learning-based depth and optical flow estimation methods require the supervision of a lot of ground truth data, and hardly generalize to video frames, resulting in temporal inconsistency. In this letter, we propose a joint framework that estimates disparity and optical flow of
Event cameras have a lot of advantages over traditional cameras, such as low latency, high temporal resolution, and high dynamic range. However, since the outputs of event cameras are the sequences of asynchronous events over time rather than actual intensity images, existing algorithms could not be…
Omni-directional cameras have many advantages overconventional cameras in that they have a much wider field-of-view (FOV). Accordingly, several approaches have beenproposed recently to apply convolutional neural networks(CNNs) to omni-directional images for various visual tasks.However, most of…
To watch 360 videos on normal 2D displays, we need to project the selected part of the 360 image onto the 2D display plane. In this paper, we propose a fully-automated framework for generating content-aware 2D normal-view perspective videos from 360 videos. Especially, we focus on the projection ste…
In this paper, we propose an approach to jointly solve scene layout estimation and global registration problems for accurate indoor 3D reconstruction. Given a sequence of range data, we build a set of scene fragments using KinectFusion and register them through pose graph optimization. Afterwards, w…
Person re-identification is the problem of recognizing people across images or videos from non-overlapping views. Although there has been much progress in person re-identification for the last decade, it still remains a challenging task because of severe appearance changes of a person due to diverse…
Multi-object tracking (MOT) becomes more challenging when objects of interest have similar appearances. In that case, the motion cues are particularly useful for discriminating multiple objects. However, for online 2D MOT in scenes acquired from moving cameras, observable motion cues are complicated…
We propose a new approach to associate supervised learning-based confidence prediction with the stereo matching problem. First of all, we analyze the characteristics of various confidence measures in the regression forest framework to select effective confidence measures using training data. We then…
The widely-used approach for estimating camera orientation is to use points at infinity, i.e., vanishing points (VPs). By enforcing the orthogonal constraint between the VPs, called the Manhattan world constraint, a drift-free camera orientation estimation can be achieved. However, in practical appl…