Accurate 3D object detection from images can be hindered by inherent depth ambiguity. While knowledge distillation (KD) from privileged sensors such as LiDAR offers a promising direction, it often suffers from a critical cross-sensor domain gap. To address this, we introduce DK3D, a novel depth-aware knowledge distillation framework for 3D detection. Our core strategy involves providing the teacher with privileged ground-truth depth during training. This directly avoids the feature representation mismatch and subsequent inefficient knowledge transfer required when distilling from a LiDAR teacher (sparse, geometric) to a camera-based student (dense, semantic). DK3D introduces specialized modules tailored for two primary student paradigms. For depth-assisted models, we employ a channel-wise projection layer (CPL) and an adversarial scoring block (ASB) to align intermediate features at both the pixel and distribution levels. For depth-independent models, a novel vision-depth association module allows the student to implicitly reason about geometry by fusing depth cues with visual features. Both approaches are further enhanced by target-aware spatial response distillation, which captures complex inter-object spatial relationships. Extensive experiments on the KITTI and nuScenes benchmarks demonstrate that DK3D significantly improves performance for both monocular and multi-view 3D detection, outperforming state-of-the-art methods. As a versatile, plug-and-play framework, DK3D boosts existing models without requiring additional training data or increasing the computational cost at inference.

Monocular 3D object detection usually adopts direct or hierarchical label supervision. Recently, the distillation supervision transfers the spatial knowledge from LiDAR- or stereo-based teacher networks to monocular detectors, but remaining the domain gap. To mitigate this issue and pursue adequate label manipulation, we exploit Foreground Depth map for feature-supervised monocular 3D object detection named FD3D, which develops the high-quality instructive intermediate features to conduct desirable auxiliary feature supervision with only the original image and annotation foreground object-wise depth map (AFOD) as input. Furthermore, we build up our instructive feature generation network to create instructive spatial features based on the sufficient correlation between image features and pre-processed AFOD, where AFOD provides the attention focus only on foreground objects to achieve clearer guidance in the detection task. Moreover, we apply the auxiliary feature supervision from the pixel and distribution level to achieve comprehensive spatial knowledge guidance. Extensive experiments demonstrate that our method achieves state-of-the-art performance on both the KITTI and nuScenes datasets, with no external data and no extra inference computational cost. We also conduct quantitative and qualitative studies to reveal the effectiveness of our designs.

Radar-camera 3D object detection aims at interacting radar signals with camera images for identifying objects of interest and localizing their corresponding 3D bounding boxes. To overcome the severe sparsity and ambiguity of radar signals, we propose a robust framework based on probabilistic denoising diffusion modeling. We design our framework to be easily implementable on different multi-view 3D detectors without the requirement of using LiDAR point clouds during either the training or inference. In specific, we first design our framework with a denoised radar-camera encoder via developing a lightweight denoising diffusion model with semantic embedding. Secondly, we develop the query denoising training into 3D space via introducing the reconstruction training at depth measurement for the transformer detection decoder. Our framework achieves new state-of-the-art performance on the nuScenes 3D detection benchmark but with few computational cost increases compared to the baseline detectors.

Road network persistent surveillance requires a swarm of unmanned ground vehicles (UGVs) to repeatedly surveil a sequence of places called viewpoints on a road network and detect randomly occurring events and problems at viewpoints. In most existing work, UGVs are responsible for detection but not handling emergencies like extinguishing fires, capturing intruders, or managing pollution. Hence, existing methods fail to perform both persistent surveillance and emergency handling. This work copes with both and proposes a cooperative dual-task path planning method for multiple UGVs. UGVs first treat all the viewpoints as candidates and compute their rewards to select their targets. Emergencies have a higher reward, allowing UGVs to solve dual tasks and prioritize emergencies. A heuristic protocol is then employed to choose appropriate UGVs for emergencies. Local path planning is finally considered when UGVs plan paths to targets to avoid deadlock and backtracking. Thus, a necessary and appropriate subswarm of UGVs with the nearest distance to emergencies are selected to handle them, while others continue surveilling viewpoints for potential emergencies, avoiding any long delayed handling of emergencies. The performance of the proposed method is evaluated in three road networks. The simulation and analysis results demonstrate its superiority over the state-of-the-art surveillance methods.

Monocular 3D object detection is a low-cost but challenging task, as it requires generating accurate 3D localization solely from a single image input. Recent developed depth-assisted methods show promising results by using explicit depth maps as intermediate features, which are either precomputed by monocular depth estimation networks or jointly evaluated with 3D object detection. However, inevitable errors from estimated depth priors may lead to misaligned semantic information and 3D localization, hence resulting in feature smearing and suboptimal predictions. To mitigate this issue, we propose ADD, an Attention-based Depth knowledge Distillation framework with 3D-aware positional encoding. Unlike previous knowledge distillation frameworks that adopt stereo- or LiDAR-based teachers, we build up our teacher with identical architecture as the student but with extra ground-truth depth as input. Credit to our teacher design, our framework is seamless, domain-gap free, easily implementable, and is compatible with object-wise ground-truth depth. Specifically, we leverage intermediate features and responses for knowledge distillation. Considering long-range 3D dependencies, we propose 3D-aware self-attention and target-aware cross-attention modules for student adaptation. Extensive experiments are performed to verify the effectiveness of our framework on the challenging KITTI 3D object detection benchmark. We implement our framework on three representative monocular detectors, and we achieve state-of-the-art performance with no additional inference computational cost relative to baseline models. Our code is available at https://github.com/rockywind/ADD.

The monocular depth estimation task has recently revealed encouraging prospects, especially for the autonomous driving task. To tackle the ill-posed problem of 3D geometric reasoning from 2D monocular images, multi-frame monocular methods are developed to leverage the perspective correlation information from sequential temporal frames. However, moving objects such as cars and trains usually violate the static scene assumption, leading to feature inconsistency deviation and misaligned cost values, which would mislead the optimization algorithm. In this work, we present CTA-Depth, a Context-aware Temporal Attention guided network for multi-frame monocular Depth estimation. Specifically, we first apply a multi-level attention enhancement module to integrate multi-level image features to obtain an initial depth and pose estimation. Then the proposed CTA-Refiner is adopted to alternatively optimize the depth and pose. During the CTA-Refiner process, context-aware temporal attention (CTA) is developed to capture the global temporal-context correlations to maintain the feature consistency and estimation integrity of moving objects. In particular, we propose a long-range geometry embedding (LGE) module to produce a long-range temporal geometry prior. Our approach achieves significant improvements (e.g., 13.5% for the Abs Rel metric on the KITTI dataset) over state-of-the-art approaches on three benchmark datasets.

Due to the lack of spatial cues giving merely a single image, many monocular depth estimation methods have been developed to leverage stereo or multi-view images to learn the spatial information of a scene in a self-supervised manner. However, these methods have limited performance gain since they are not able to exploit sufficient 3D geometry cues during inference, where only monocular images are available. In this work, we present V2Depth, a novel coarse-to-fine framework with Virtual View feature simulation for supervised monocular Depth estimation. Specifically, we first design a virtual-view feature simulator by leveraging the technique of novel view synthesis and contrastive learning to generate virtual view feature maps. In this way, we explicitly provide representative spatial geometry for subsequent depth estimation in both the training and inference stages. Then we introduce a 3DVA-Refiner to iteratively optimize the predicted depth map. During the optimization process, 3D-aware virtual attention is developed to capture the global spatial-context correlations to maintain the feature consistency of different views and estimation integrity of the 3D scene such as objects with occlusion relationships. Decisive improvements over state-of-the-art approaches on three benchmark datasets across all metrics demonstrate the superiority of our method.

Locating multiple sources in an unknown environment based on their signal strength is called a multi-source location problem. In recent years, there has been great interest in deploying autonomous devices to solve it. A particle swarm optimizer (PSO) is a widely employed source location method. Yet most work in this field focuses on a flat search space while ignoring height information. An unmanned aerial vehicle (UAV) has a coarser but wider view as it flies higher. Inspired by such facts, this paper focuses on improving the efficiency of locating sources by utilizing height information through UAVs. A novel source location model is designed where their sensing range gradually increases as their flying height rises, but their obtained signal strength fades away. It can be directly deployed to existing PSO-based multi-source location methods and improve their performance, especially in a large-scale environment with sparse sources. UAVs can spontaneously switch their search schemes between a rough search at a higher height and a fine one at a lower height. Experimental results of three PSO-based methods show their significant improvement after deploying our model. Given the same computation resources, its deployment leads to over 30% hike in both location accuracy and speed. This represents a great advance to the field of source location.