Monocular 3D object detection (Mono3D) aims to infer object locations and dimensions in 3D space from a single RGB image.Despite recent progress, existing methods remain highly sensitive to camera intrinsics and struggle to generalize across diverse settings, since intrinsic changes reshape how 3D scenes are projected onto the image plane.We propose MonoIA, a unified intrinsic-aware framework that models and adapts to intrinsic variation through a language-grounded representation.The key insight is that intrinsic variation is not a numeric difference but a perceptual transformation that alters apparent scale, perspective, and spatial geometry.To capture this effect, MonoIA employs large language models and vision–language models to generate intrinsic embeddings that encode the visual and geometric implications of camera parameters.These embeddings are hierarchically integrated into the detection network via an Intrinsic Adaptation Module, allowing the model to modulate its feature representations according to camera-specific configurations and maintain consistent 3D detection across intrinsics.This shifts intrinsic modeling from numeric conditioning to semantic representation, enabling robust and unified perception across cameras.Extensive experiments show that MonoIA achieves new state-of-the-art (SoTA) results on standard benchmarks including KITTI, Waymo, and nuScenes (e.g., +1.0% on the KITTI leaderboard), and further improves performance under multi-dataset training (e.g., +4.46% on KITTI Val).

Monocular 3D detection (Mono3D) aims to infer 3D bounding boxes from a single RGB image. Without auxiliary sensors such as LiDAR, this task is inherently ill-posed since the 3D-to-2D projection introduces depth ambiguity. Previous works often predict 3D attributes (e.g., depth, size, and orientation) in parallel, overlooking that these attributes are inherently correlated through the 3D-to-2D projection. However, simply enforcing such correlations through sequential prediction can propagate errors across attributes, especially when objects are occluded or truncated, where inaccurate size or orientation predictions can further amplify depth errors. Therefore, neither parallel nor sequential prediction is optimal. In this paper, we propose MonoCoP, an adaptive framework that learns when and how to leverage inter-attribute correlations with two complementary designs. A Chain-of-Prediction (CoP) explores inter-attribute correlations through feature-level learning, propagation, and aggregation, while an Uncertainty-Guided Selector (GS) dynamically switches between CoP and parallel paradigms for each object based on the predicted uncertainty. By combining their strengths, MonoCoP achieves state-of-the-art (SOTA) performance on KITTI, nuScenes, and Waymo, significantly improving depth accuracy, particularly for distant objects.

Monocular 3D object detection (Mono3D) is a fundamental computer vision task that estimates an object's class, 3D position, dimensions, and orientation from a single image. Its applications, including autonomous driving, augmented reality, and robotics, critically rely on accurate 3D environmental understanding. This thesis addresses the challenge of generalizing Mono3D models to diverse scenarios, including occlusions, datasets, object sizes, and camera parameters. To enhance occlusion robustness, we propose a mathematically differentiable NMS (GrooMeD-NMS). To improve generalization to new datasets, we explore depth equivariant (DEVIANT) backbones. We address the issue of large object detection, demonstrating that it's not solely a data imbalance or receptive field problem but also a noise sensitivity issue. To mitigate this, we introduce a segmentation-based approach in bird's-eye view with dice loss (SeaBird). Finally, we mathematically analyze the extrapolation of Mono3D models to unseen camera heights and improve Mono3D generalization in such out-of-distribution settings.

Monocular 3D object detectors, while effective on data from one ego camera height, struggle with unseen or out-of-distribution camera heights. Existing methods often rely on Plucker embeddings, image transformations or data augmentation. This paper takes a step towards this understudied problem by first investigating the impact of camera height variations on state-of-the-art (SoTA) Mono3D models. With a systematic analysis on the extended CARLA dataset with multiple camera heights, we observe that depth estimation is a primary factor influencing performance under height variations. We mathematically prove and also empirically observe consistent negative and positive trends in mean depth error of regressed and ground-based depth models, respectively, under camera height changes. To mitigate this, we propose Camera Height Robust Monocular 3D Detector (CHARM3R), which averages both depth estimates within the model. CHARM3R improves generalization to unseen camera heights by more than 45%, achieving SoTA performance on the CARLA dataset. Codes and Models at https://github.com/abhi1kumar/CHARM3R

Radar hits reflect from points on both the boundary and internal to object outlines. This results in a complex distribution of radar hits that depends on factors including object category, size, and orientation. Current radar-camera fusion methods implicitly account for this with a black-box neural network. In this paper, we explicitly utilize a radar hit distribution model to assist fusion. First, we build a model to predict radar hit distributions conditioned on object properties obtained from a monocular detector. Second, we use the predicted distribution as a kernel to match actual measured radar points in the neighborhood of the monocular detections, generating matching scores at nearby positions. Finally, a fusion stage combines context with the kernel detector to refine the matching scores. Our method achieves the state-of-the-art radar-camera detection performance on nuScenes. Our source code is available at https://github.com/longyunf/riccardo.

Monocular 3D reconstruction for categorical objects heavily relies on accurately perceiving each object's pose. While gradient-based optimization within a NeRF framework updates initially given poses, this paper highlights that such a scheme fails when the initial pose even moderately deviates from the true pose. Consequently, existing methods often depend on a third-party 3D object to provide an initial object pose, leading to increased complexity and generalization issues. To address these challenges, we present UPNeRF, a Unified framework integrating Pose estimation and NeRF-based reconstruction, bringing us closer to real-time monocular 3D object reconstruction. UPNeRF decouples the object's dimension estimation and pose refinement to resolve the scale-depth ambiguity, and introduces an effective projected-box representation that generalizes well cross different domains. While using a dedicated pose estimator that smoothly integrates into an object-centric NeRF, UPNeRF is free from external 3D detectors. UPNeRF achieves state-of-the-art results in both reconstruction and pose estimation tasks on the nuScenes dataset. Furthermore, UPNeRF exhibits exceptional Cross-dataset generalization on the KITTI and Waymo datasets, surpassing prior methods with up to 50% reduction in rotation and translation error.

Monocular 3D reconstruction for categorical objects heavily relies on accurately perceiving each object's pose. While gradient-based optimization in a NeRF framework updates the initial pose, this paper highlights that scale-depth ambiguity in monocular object reconstruction causes failures when the initial pose deviates moderately from the true pose. Consequently, existing methods often depend on a third-party 3D object to provide an initial object pose, leading to increased complexity and generalization issues. To address these challenges, we present SUP-NeRF, a Streamlined Unification of object Pose estimation and NeRF-based object reconstruction. SUP-NeRF decouples the object's dimension estimation and pose refinement to resolve the scale-depth ambiguity, and introduces a camera-invariant projected-box representation that generalizes cross different domains. While using a dedicated pose estimator that smoothly integrates into an object-centric NeRF, SUP-NeRF is free from external 3D detectors. SUP-NeRF achieves state-of-the-art results in both reconstruction and pose estimation tasks on the nuScenes dataset. Furthermore, SUP-NeRF exhibits exceptional cross-dataset generalization on the KITTI and Waymo datasets, surpassing prior methods with up to 50% reduction in rotation and translation error.

3D sensing is a fundamental task for Autonomous Vehicles. Its deployment often relies on aligned RGB cameras and LiDAR. Despite meticulous synchronization and calibration, systematic misalignment persists in LiDAR projected depthmap. This is due to the physical baseline distance between the two sensors. The artifact is often reflected as background LiDAR incorrectly projected onto the foreground, such as cars and pedestrians. The KITTI dataset uses stereo cameras as a heuristic solution to remove artifacts. However most AV datasets, including nuScenes, Waymo, and DDAD, lack stereo images, making the KITTI solution inapplicable. We propose RePLAy, a parameter-free analytical solution to remove the projective artifacts. We construct a binocular vision system between a hypothesized virtual LiDAR camera and the RGB camera. We then remove the projective artifacts by determining the epipolar occlusion with the proposed analytical solution. We show unanimous improvement in the State-of-The-Art (SoTA) monocular depth estimators and 3D object detectors with the artifacts-free depthmaps.

Several researchers have modified the original Social Force Model (SFM) for pedestrians by including vehicles and simulating interaction behaviors in shared space layouts. The present study is conducted at a signalized intersection where the performance and functionality are defined by spontaneous order. The SFM was modified, and two-wheelers were added along with cars and pedestrians to analyze their interactions. The Hybrid Calibration Method was adopted to calibrate the interaction parameters utilizing an optimization technique and real-time data from the study site. It can be concluded from the parameter values that the strength of interaction reduced as the range between the road agents increased. The findings can aid urban planners in visualizing the interaction behavior of pedestrians, cars, and two-wheelers. Moreover, the feasibility of introducing the shared space concept in India as an alternative to the conventional traffic planning system can also be determined during the designing phase.

Monocular 3D detectors achieve remarkable performance on cars and smaller objects. However, their performance drops on larger objects, leading to fatal accidents. Some attribute the failures to training data scarcity or the receptive field requirements of large objects. In this paper, we highlight this understudied problem of generalization to large objects. We find that modern frontal detectors struggle to generalize to large objects even on nearly balanced datasets. We argue that the cause of failure is the sensitivity of depth regression losses to noise of larger objects. To bridge this gap, we comprehensively investigate regression and dice losses, examining their robustness under varying error levels and object sizes. We mathematically prove that the dice loss leads to superior noise-robustness and model convergence for large objects compared to regression losses for a simplified case. Leveraging our theoretical insights, we propose SeaBird (Segmentation in Bird's View) as the first step towards generalizing to large objects. SeaBird effectively integrates BEV segmentation on foreground objects for 3D detection, with the segmentation head trained with the dice loss. SeaBird achieves SoTA results on the KITTI-360 leaderboard and improves existing detectors on the nuScenes leaderboard, particularly for large objects.

3D sensing for monocular in-the-wild images, e.g., depth estimation and 3D object detection, has become increasingly important. However, the unknown intrinsic parameter hinders their development and deployment. Previous methods for the monocular camera calibration rely on specific 3D objects or strong geometry prior, such as using a checkerboard or imposing a Manhattan World assumption. This work solves the problem from the other perspective by exploiting the monocular 3D prior. Our method is assumption-free and calibrates the complete 4 Degree-of-Freedom (DoF) intrinsic parameters. First, we demonstrate intrinsic is solved from two well-studied monocular priors, i.e., monocular depthmap, and surface normal map. However, this solution imposes a low-bias and low-variance requirement for depth estimation. Alternatively, we introduce a novel monocular 3D prior, the incidence field, defined as the incidence rays between points in 3D space and pixels in the 2D imaging plane. The incidence field is a pixel-wise parametrization of the intrinsic invariant to image cropping and resizing. With the estimated incidence field, a robust RANSAC algorithm recovers intrinsic. We demonstrate the effectiveness of our method by showing superior performance on synthetic and zero-shot testing datasets. Beyond calibration, we demonstrate downstream applications in image manipulation detection restoration, uncalibrated two-view pose estimation, and 3D sensing. Codes, models, and data will be held in https://github.com/ShngJZ/WildCamera.

As a direct depth sensor, radar holds promise as a tool to improve monocular 3D object detection, which suffers from depth errors, due in part to the depth-scale ambiguity. On the other hand, leveraging radar depths is hampered by difficulties in precisely associating radar returns with 3D estimates from monocular methods, effectively erasing its benefits. This paper proposes a fusion network that addresses this radar-camera association challenge. We train our network to predict the 3D offsets between radar returns and object centers, enabling radar depths to enhance the accuracy of 3D monocular detection. By using parallel radar and camera backbones, our network fuses information at both the feature level and detection level, while at the same time leveraging a state-of-the-art monocular detection technique without retraining it. Experimental results show significant improvement in mean average precision and translation error on the nuScenes dataset over monocular counterparts. Our source code is available at https://github.com/longyunf/radiant.

Previous research in $2D$ object detection focuses on various tasks, including detecting objects in generic and camouflaged images. These works are regarded as passive works for object detection as they take the input image as is. However, convergence to global minima is not guaranteed to be optimal in neural networks; therefore, we argue that the trained weights in the object detector are not optimal. To rectify this problem, we propose a wrapper based on proactive schemes, PrObeD, which enhances the performance of these object detectors by learning a signal. PrObeD consists of an encoder-decoder architecture, where the encoder network generates an image-dependent signal termed templates to encrypt the input images, and the decoder recovers this template from the encrypted images. We propose that learning the optimum template results in an object detector with an improved detection performance. The template acts as a mask to the input images to highlight semantics useful for the object detector. Finetuning the object detector with these encrypted images enhances the detection performance for both generic and camouflaged. Our experiments on MS-COCO, CAMO, COD$10$K, and NC$4$K datasets show improvement over different detectors after applying PrObeD. Our models/codes are available at https://github.com/vishal3477/Proactive-Object-Detection.

Recognizing scenes and objects in 3D from a single image is a longstanding goal of computer vision with applications in robotics and AR/VR. For 2D recognition, large datasets and scalable solutions have led to unprecedented advances. In 3D, existing benchmarks are small in size and approaches specialize in few object categories and specific domains, e.g. urban driving scenes. Motivated by the success of 2D recognition, we revisit the task of 3D object detection by introducing a large benchmark, called Omni3D. Omni3D re-purposes and combines existing datasets resulting in 234k images annotated with more than 3 million instances and 98 categories. 3D detection at such scale is challenging due to variations in camera intrinsics and the rich diversity of scene and object types. We propose a model, called Cube R-CNN, designed to generalize across camera and scene types with a unified approach. We show that Cube R-CNN outperforms prior works on the larger Omni3D and existing benchmarks. Finally, we prove that Omni3D is a powerful dataset for 3D object recognition and show that it improves single-dataset performance and can accelerate learning on new smaller datasets via pre-training.

Collecting neighborhood data can both be time- and resource-intensive, especially across broad geographies. In this study, we leveraged 1.4 million publicly available Google Street View (GSV) images from Utah to construct indicators of the neighborhood built environment and evaluate their associations with 2017–2019 health outcomes of approximately one-third of the population living in Utah. The use of electronic medical records allows for the assessment of associations between neighborhood characteristics and individual-level health outcomes while controlling for predisposing factors, which distinguishes this study from previous GSV studies that were ecological in nature. Among 938,085 adult patients, we found that individuals living in communities in the highest tertiles of green streets and non-single-family homes have 10–27% lower diabetes, uncontrolled diabetes, hypertension, and obesity, but higher substance use disorders—controlling for age, White race, Hispanic ethnicity, religion, marital status, health insurance, and area deprivation index. Conversely, the presence of visible utility wires overhead was associated with 5–10% more diabetes, uncontrolled diabetes, hypertension, obesity, and substance use disorders. Our study found that non-single-family and green streets were related to a lower prevalence of chronic conditions, while visible utility wires and single-lane roads were connected with a higher burden of chronic conditions. These contextual characteristics can better help healthcare organizations understand the drivers of their patients’ health by further considering patients’ residential environments, which present both risks and resources.

Modern neural networks use building blocks such as convolutions that are equivariant to arbitrary 2D translations. However, these vanilla blocks are not equivariant to arbitrary 3D translations in the projective manifold. Even then, all monocular 3D detectors use vanilla blocks to obtain the 3D coordinates, a task for which the vanilla blocks are not designed for. This paper takes the first step towards convolutions equivariant to arbitrary 3D translations in the projective manifold. Since the depth is the hardest to estimate for monocular detection, this paper proposes Depth EquiVarIAnt NeTwork (DEVIANT) built with existing scale equivariant steerable blocks. As a result, DEVIANT is equivariant to the depth translations in the projective manifold whereas vanilla networks are not. The additional depth equivariance forces the DEVIANT to learn consistent depth estimates, and therefore, DEVIANT achieves state-of-the-art monocular 3D detection results on KITTI and Waymo datasets in the image-only category and performs competitively to methods using extra information. Moreover, DEVIANT works better than vanilla networks in cross-dataset evaluation. Code and models at https://github.com/abhi1kumar/DEVIANT

We can compress a rectifier network while exactly preserving its underlying functionality with respect to a given input domain if some of its neurons are stable. However, current approaches to determine the stability of neurons with Rectified Linear Unit (ReLU) activations require solving or finding a good approximation to multiple discrete optimization problems. In this work, we introduce an algorithm based on solving a single optimization problem to identify all stable neurons. Our approach is on median 183 times faster than the state-of-art method on CIFAR-10, which allows us to explore exact compression on deeper (5 x 100) and wider (2 x 800) networks within minutes. For classifiers trained under an amount of L1 regularization that does not worsen accuracy, we can remove up to 56% of the connections on the CIFAR-10 dataset. The code is available at the following link, https://github.com/yuxwind/ExactCompression .

Designing the crosswalks at signalized intersections aims to facilitate pedestrians' safe crossing by preventing conflicts with motorized vehicles. However, pedestrian safety remains at high risk due to the signal violations by pedestrians at the intersections in India. The present study examines the violation of pedestrians at twelve signalized intersections in India. The study results show that approximately 44.6% of pedestrians arriving during the red phase violate the signals. The violation prevalence is high in the latter half of the red-light period. With the help of a multiple linear regression model, the relationship between various explanatory variables (i.e., individual characteristics and traffic conditions) and the violation prevalence has been established. Further, the pedestrian-vehicle conflicts during signal violation have been classified based on the yielding behaviour of pedestrians and road users. The yielding behaviour of the road users is found to vary in the beginning, middle, and end of the red phase. The study utilized the conflict indicator 'Post Encroachment Time' (PET) to determine the proximity of conflicts during violation. The severity levels of the conflict were determined with the help of the Swedish Traffic Conflict Technique (STCT) using 'Time to Accident' (TA) and 'Conflicting Speed' (CS). A generalized ordered logit model has been utilized to identify the factors (i.e., traffic conditions, personal characteristics, and situational characteristics) that affect the severity level of pedestrian-vehicle conflicts. The results highlight the need for engineering countermeasures, education, and enforcement to deal with the noncompliance behaviour of pedestrians.

With the rapid urbanization of geographical spaces worldwide, pedestrian safety is a major concern on urban roads. In developing economies like India, an unprecedented increase in accidents involving pedestrians has been observed at intersections. The present study focuses on pedestrian behavior, specifically, violation of red signals while crossing at signalized intersections. With the help of hazard-based duration models, the waiting duration of red-light violators has been analyzed. In addition, the response time of pedestrians during conflict has also been modeled with the help of a hazard-based duration approach. Four signalized intersections from Nagpur City in India were selected for the survival analysis. Kaplan–Meier survival curves have been plotted for both waiting time and response time. With the help of the semi-parametric Cox proportional hazard model, various factors have been identified to describe the survival function of the pedestrians’ crossing. However, the model results were found to be unsatisfactory since the explanatory variables failed in the proportional hazard assumption. Therefore, the parametric accelerated failure time model was utilized to determine the various covariates that affected the waiting time and the response time. The Weibull model was found to be the best fit for waiting duration analysis, while the log-logistic model was considered for the study of response time. The developed models can help understand the external factors and personal features of pedestrians in relation to the risk involved during violation crossings.

The Internet of Things (IoT) consists of three major components: perception or idea generation, secure transmission, and intelligent data analysis. These core components can be applied in different formats for the prevention and control of infectious diseases. The procedure relies on the combination of sensors, artificial intelligence (AI), information technology, and available dynamic networking devices. The IoT networks can establish long-distance communication among hospitals, patients, and medical equipment, which could ultimately improve current medical conditions. The IoT has found many applications in infectious disease management that include early prediction, accurate diagnosis, suggestions on therapeutic intervention, and sharing of research data and policy making. All the components of IoT network gets integrated into skeletal framework and can help in control and prevention of infectious diseases.