3D point cloud object detection plays an important role in autonomous driving. However, labeling 3D object boxes is expensive and time-consuming, limiting the number of annotated point clouds used in fully-supervised training. This has led to a rise in semi-supervised 3D object detection research, which aims to improve model performance by leveraging both labeled and unlabeled point clouds. Existing methods typically rely on the Mean Teacher (MT) paradigm, which uses unlabeled instances discovered by the teacher with confidence scores higher than certain thresholds to train the student. However, this leads to a loss of information as it overlooks ambiguous instances from the teacher that could also contain valuable knowledge. To address this issue, we propose a Bi-Stream Knowledge Transfer (BiKT) framework that fully exploits and transfers knowledge from both confident and ambiguous instances to the student network. Specifically, all pseudo labels are allocated into two knowledge streams, the deterministic stream and the noisy stream, and then subsequently guide the student network through bi-level supervision. We also introduce a Dynamic Stream Switching (DSS) algorithm that sets the stream boundary tailored for the current learning status. To further improve the quality of pseudo labels in the knowledge streams, we propose a Diffusive Label Denoising (DLD) module, which is trained by explicitly generating noised instances and then learning to denoise them, as in diffusion models. Experiments show the state-of-the-art performance of our BiKT on the ONCE validation and testing sets, as well as the robust generalization capability when confronted with diverse base detectors, increased amount of unlabeled data, and distinct datasets (e.g., Waymo), unveiling the power of semi-supervised learning in 3D object detection.

Established sampling protocols for 3D point cloud learning, such as Farthest Point Sampling (FPS) and Fixed Sample Size (FSS), have long been relied upon. However, real-world data often suffer from corruptions, such as sensor noise, which violates the benign data assumption in current protocols. As a result, these protocols are highly vulnerable to noise, posing significant safety risks in critical applications like autonomous driving. To address these issues, we propose an enhanced point cloud sampling protocol, PointSP, designed to improve robustness against point cloud corruptions. PointSP incorporates key point reweighting to mitigate outlier sensitivity and ensure the selection of representative points. It also introduces a local-global balanced downsampling strategy, which allows for scalable and adaptive sampling while maintaining geometric consistency. Additionally, a lightweight tangent plane interpolation method is used to preserve local geometry while enhancing the density of the point cloud. Unlike learning-based approaches that require additional model training, PointSP is architecture-agnostic, requiring no extra learning or modification to the network. This enables seamless integration into existing pipelines. Extensive experiments on synthetic and real-world corrupted datasets show that PointSP significantly improves the robustness and accuracy of point cloud classification, outperforming state-of-the-art methods across multiple benchmarks.

Vision-based perception for autonomous driving requires an explicit modeling of a 3D space, where 2D latent representations are mapped and subsequent 3D operators are applied. However, operating on dense latent spaces introduces a cubic time and space complexity, which limits scalability in terms of perception range or spatial resolution. Existing approaches compress the dense representation using projections like Bird's Eye View (BEV) or Tri-Perspective View (TPV). Although efficient, these projections result in information loss, especially for tasks like semantic occupancy prediction. To address this, we propose SparseOcc, an efficient occupancy network inspired by sparse point cloud processing. It utilizes a lossless sparse latent representation with three key innovations. Firstly, a 3D sparse diffuser performs latent completion using spatially decomposed 3D sparse convolutional kernels. Secondly, a feature pyramid and sparse interpolation enhance scales with information from others. Finally, the transformer head is redesigned as a sparse variant. SparseOcc achieves a remarkable 74.9% reduction on FLOPs over the dense baseline. Interestingly, it also improves accuracy, from 12.8% to 14.1% mIOU, which in part can be attributed to the sparse representation's ability to avoid hallucinations on empty voxels.

Knowledge transfer from multi-modal, i.e., LiDAR points and images, to a single LiDAR modal can take advantage of complimentary information from modal-fusion but keep a single modal inference speed, showing a promising direction for point cloud semantic segmentation in autonomous driving. Recent advances in point cloud segmentation distill knowledge from strictly aligned point-pixel fusion features while leaving a large number of unmatched image pixels unexplored and unmatched LiDAR points under-benefited. In this paper, we propose a novel approach, named ProtoTransfer, which not only fully exploits image representations but also transfers the learned multi-modal knowledge to all point cloud features. Specifically, based on the basic multi-modal learning framework, we build up a class-wise prototype bank from the strictly-aligned fusion features and encourage all the point cloud features to learn from the prototypes during model training. Moreover, to exploit the massive unmatched point and pixel features, we use a pseudo-labeling scheme and further accumulate these features into the class-wise prototype bank with a carefully designed fusion strategy. Without bells and whistles, our approach demonstrates superior performance over the published state-of-the-arts on two large-scale benchmarks, i.e., nuScenes and SemanticKITTI, and ranks 2nd on the competitive nuScenes Lidarseg challenge leaderboard.