Semantic Causality-Aware Vision-Based 3D Occupancy Prediction
This work addresses a critical bottleneck in 3D vision for applications like autonomous driving by making previously non-trainable components fully learnable, though it is incremental in improving existing modular pipelines.
The paper tackles the problem of cascading errors in vision-based 3D semantic occupancy prediction by introducing a novel causal loss that enables holistic, end-to-end supervision of the modular 2D-to-3D transformation pipeline, achieving state-of-the-art performance on the Occ3D benchmark with improved robustness to camera perturbations.
Vision-based 3D semantic occupancy prediction is a critical task in 3D vision that integrates volumetric 3D reconstruction with semantic understanding. Existing methods, however, often rely on modular pipelines. These modules are typically optimized independently or use pre-configured inputs, leading to cascading errors. In this paper, we address this limitation by designing a novel causal loss that enables holistic, end-to-end supervision of the modular 2D-to-3D transformation pipeline. Grounded in the principle of 2D-to-3D semantic causality, this loss regulates the gradient flow from 3D voxel representations back to the 2D features. Consequently, it renders the entire pipeline differentiable, unifying the learning process and making previously non-trainable components fully learnable. Building on this principle, we propose the Semantic Causality-Aware 2D-to-3D Transformation, which comprises three components guided by our causal loss: Channel-Grouped Lifting for adaptive semantic mapping, Learnable Camera Offsets for enhanced robustness against camera perturbations, and Normalized Convolution for effective feature propagation. Extensive experiments demonstrate that our method achieves state-of-the-art performance on the Occ3D benchmark, demonstrating significant robustness to camera perturbations and improved 2D-to-3D semantic consistency.