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.

Multi-view radar-camera fused 3D object detection provides a farther detection range and more helpful features for autonomous driving, especially under adverse weather. The current radar-camera fusion methods deliver kinds of designs to fuse radar information with camera data. However, these fusion approaches usually adopt the straightforward concatenation operation between multi-modal features, which ignores the semantic alignment with radar features and sufficient correlations across modals. In this paper, we present MVFusion, a novel Multi-View radar-camera Fusion method to achieve semantic-aligned radar features and enhance the cross-modal information interaction. To achieve so, we inject the semantic alignment into the radar features via the semantic-aligned radar encoder (SARE) to produce image-guided radar features. Then, we propose the radar-guided fusion transformer (RGFT) to fuse our radar and image features to strengthen the two modals' correlation from the global scope via the cross-attention mechanism. Extensive experiments show that MVFusion achieves state-of-the-art performance (51.7% NDS and 45.3% mAP) on the nuScenes dataset. We shall release our code and trained networks upon publication.

Monocular 3D object detection reveals an economical but challenging task in autonomous driving. Recently center-based monocular methods have developed rapidly with a great trade-off between speed and accuracy, where they usually depend on the object center's depth estimation via 2D features. However, the visual semantic features without sufficient pixel geometry information, may affect the performance of clues for spatial 3D detection tasks. To alleviate this, we propose MonoPGC, a novel end-to-end Monocular 3D object detection framework with rich Pixel Geometry Contexts. We introduce the pixel depth estimation as our auxiliary task and design depth cross-attention pyramid module (DCPM) to inject local and global depth geometry knowledge into visual features. In addition, we present the depth-space-aware transformer (DSAT) to integrate 3D space position and depth-aware features efficiently. Besides, we design a novel depth-gradient positional encoding (DGPE) to bring more distinct pixel geometry contexts into the transformer for better object detection. Extensive experiments demonstrate that our method achieves the state-of-the-art performance on the KITTI dataset.

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.

The 3D visual perception for vehicles with the surround-view fisheye camera system is a critical and challenging task for low-cost urban autonomous driving. While existing monocular 3D object detection methods perform not well enough on the fisheye images for mass production, partly due to the lack of 3D datasets of such images. In this paper, we manage to overcome and avoid the difficulty of acquiring the large scale of accurate 3D labeled truth data, by breaking down the 3D object detection task into some sub-tasks, such as vehicle’s contact point detection, type classification, re-identification and unit assembling, etc. Particularly, we propose the concept of Multidimensional Vector to include the utilizable information generated in different dimensions and stages, instead of the descriptive approach for the bird’s eye view (BEV) or a cube of eight points. The experiments of real fisheye images demonstrate that our solution achieves state-of-the-art accuracy while being real-time in practice.