Sparse-Aware Vector Quantization for Bandwidth-Efficient Collaborative 3D Semantic Occupancy Prediction

Vision-based 3D semantic occupancy prediction is essential for autonomous driving, yet dense voxel representations waste computation on largely empty space, while BEV and TPV projections compromise fine-grained 3D structure. Fully sparse representations offer an attractive alternative, but existing methods, including SparseOcc, entangle scene completion with semantic prediction by indiscriminately propagating high-dimensional features into empty regions and applying voxel-wise classification. This creates excessive activations, computational overhead, and geometric ambiguity. We present SparseOcc++, a geometry-aware sparse framework that explicitly decouples scene completion from semantic segmentation. SparseOcc++ reformulates completion as signed-distance regression on sparse anchor voxels through a scene completion field (SCF). To model complex outdoor geometry robustly, it combines orthogonal decomposition with discretized distance learning. A geometry-guided propagation module then converts the SCF into a complete volumetric scene and restricts semantic segmentation to geometrically verified regions. Experiments establish new state of the art: SparseOcc++ improves IoU by 2.3 points and is 3.9x faster than SparseOcc on nuScenes, while achieving a 5.9x speedup over OccFormer on SemanticKITTI.

LiDAR-based 3D semantic occupancy prediction, which aims to provide accurate and comprehensive scene representation, is crucial for autonomous driving systems. As point clouds suffer from sparsity and incompleteness, leading to insufficient semantic learning and difficult occupancy perception, existing methods often stack multi-sweep point clouds to obtain dense spatial information. However, such a naive strategy also results in efficiency (e.g., additional computational burden) and robustness (e.g., pose transformation noise) concerns, which hinder their practical applications. In this work, we propose a Dual Range-Voxel Representation (DRVR) that leverages the range-view context and voxel-view geometry of single-sweep point clouds for 3D semantic occupancy prediction, eliminating the concerns associated with the multi-sweeps. Specifically, we use the range-view encoder to extract the compact context of the scene. To fully exploit the spatial information, we design a geometry-aware voxel-view encoder that extracts multi-scale voxel-view features separately and combines them for better geometric occupancy prediction. Moreover, we propose a range-voxel fusion module to cooperate range- and voxel-view features via voxel-to-range and range-to-voxel fusions. Extensive experiments on nuScenes-Occupancy, SemanticKITTI and SemanticPOSS show the superiority of our method. Especially on nuScenes-Occupancy, our single-sweep DRVR achieves 5.4% improvement in mIoU and 2.1x acceleration compared to the multi-sweep method.

Occupancy prediction has garnered increasing attention in recent years for its comprehensive fine-grained environmental representation and strong generalization to open-set objects. Nevertheless, mainstream occupancy prediction methods employ cumbersome voxel features as the scene representation, incurring substantial overheads in both memory and computation. When comparing the occupancy distribution in each spatial dimension, we find that the information entropy of the height dimension is much lower than the other two dimensions that constitute the Bird's Eye View (BEV) plane, which indicates that the height distribution of occupancy is easier to learn and predict. Accordingly, we propose Lightweight Spatial Embedding that can represent complete height information in a more compact way than voxel features, thus significantly enhancing its deployability. First, Single-Channel Occupancy is sampled from the multi-view depth distributions, which is then processed by Spatial-to-Channel mechanism to extract Lightweight Spatial Embeddings of different views by 2D convolution. These embeddings will interact with each other through the Lightweight Cross-View Interaction module to obtain the Unified Embedding, which can directly supplement BEV features with height information. Furthermore, we extract Edge-aware Spatial Embedding and apply Geometric Supervision on Spatial Embeddings, aiming to enhance their capability to represent spatial information. We also propose BEV-CutMix, a feature-level data augmentation strategy, to increase the diversity of the driving scenes. We integrate these innovative components into a pure 2D convolutional model, namely LightOcc. Sufficient experimental results show that LightOcc achieves state-of-the-art performance on multiple benchmarks while demonstrating significant efficiency advantages.