OccFusion: Depth Estimation Free Multi-sensor Fusion for 3D Occupancy Prediction

3D occupancy prediction based on multi-sensor fusion, crucial for a reliable autonomous driving system, enables fine-grained understanding of 3D scenes. Previous fusion-based 3D occupancy predictions relied on depth estimation for processing 2D image features. However, depth estimation is an ill-posed problem, hindering the accuracy and robustness of these methods. Furthermore, fine-grained occupancy prediction demands extensive computational resources. We introduce OccFusion, a multi-modal fusion method free from depth estimation, and a corresponding point cloud sampling algorithm for dense integration of image features. Building on this, we propose an active training method and an active coarse to fine pipeline, enabling the model to adaptively learn more from complex samples and optimize predictions specifically for challenging areas such as small or overlapping objects. The active methods we propose can be naturally extended to any occupancy prediction model. Experiments on the OpenOccupancy benchmark show our method surpasses existing state-of-the-art (SOTA) multi-modal methods in IoU across all categories. Additionally, our model is more efficient during both the training and inference phases, requiring far fewer computational resources. Comprehensive ablation studies demonstrate the effectiveness of our proposed techniques.

LongRoPE: Extending LLM Context Window Beyond 2 Million Tokens

Large context window is a desirable feature in large language models (LLMs). However, due to high fine-tuning costs, scarcity of long texts, and catastrophic values introduced by new token positions, current extended context windows are limited to around 128k tokens. This paper introduces LongRoPE that, for the first time, extends the context window of pre-trained LLMs to an impressive 2048k tokens, with up to only 1k fine-tuning steps at within 256k training lengths, while maintaining performance at the original short context window. This is achieved by three key innovations: (i) we identify and exploit two forms of non-uniformities in positional interpolation through an efficient search, providing a better initialization for fine-tuning and enabling an 8x extension in non-fine-tuning scenarios; (ii) we introduce a progressive extension strategy that first fine-tunes a 256k length LLM and then conducts a second positional interpolation on the fine-tuned extended LLM to achieve a 2048k context window; (iii) we readjust LongRoPE on 8k length to recover the short context window performance. Extensive experiments on LLaMA2 and Mistral across various tasks demonstrate the effectiveness of our method. Models extended via LongRoPE retain the original architecture with minor modifications to the positional embedding, and can reuse most pre-existing optimizations.