Zijian Fu

Zijian Fu, Xiangyang Chu, Mengshi Qi et al.

Instruction-conditioned trajectory prediction is an emerging problem in autonomous driving, where a model predicts the future ego trajectory not only from visual scene context and historical motion, but also from a natural-language maneuver instruction. This paper presents our submission to the doScenes Instructed Driving Challenge, built upon OmniDrive, a vision-language-action driving agent with 3D perception, reasoning, and planning capabilities. We adapt OmniDrive to the doScenes setting by training it on instruction-annotated nuScenes scenes and generating a 6-second ego trajectory represented by 12 future waypoints. To improve multi-view visual grounding, we further introduce a DVPE-style divided-view perception module into the OmniDrive perception head. Instead of attending globally to all camera features, the proposed module groups query features and image tokens into divided local view spaces and performs visibility-aware cross-attention within each view. This design reduces irrelevant cross-view interference and helps the model better align language instructions with local driving-relevant visual evidence. The code is publicly available at: https://github.com/feel12348/doscenes-omnidrive.

Mengshi Qi, Changsheng Lv, Zijian Fu et al.

In this paper, we propose SGFormer++, a novel Semantic Graph Transformer for 3D scene graph generation (SGG), which aims to parse point cloud scenes into semantic structural graphs, where nodes denote detected object instances and edges encode their pairwise relationships, with the core challenge lying in modeling complex global scene structure. While existing graph convolutional network (GCN)-based methods suffer from over-smoothing and limited receptive fields, SGFormer++ leverages Transformer layers as its backbone to enable global message passing. Specifically, we introduce two key components tailored for 3D SGG: (1) a Graph Embedding Layer++ that efficiently integrates edge-aware global context with linear computational complexity, and (2) a Semantic Injection Layer++ that enriches visual features with linguistic priors from large language models (LLMs) and vision-language models (VLMs), boosting semantic representation without introducing extra trainable parameters. To further address the practical challenge of incremental SGG (I-SGG), where new relationship categories arrive sequentially, we equip SGFormer++ with a novel Spatial-guided Feature Adapter, which calibrates predicate features using subject-object spatial geometry to counter scale variation, and a Cascaded Binary Prediction Head that mitigates catastrophic forgetting via task-incremental classifier expansion and logit distillation. Extensive experiments on the 3DSSG benchmark demonstrate that SGFormer++ achieves state-of-the-art performance in both standard and incremental settings: it yields a significant 4.49% absolute improvement in Predicate A@1 under the incremental setting. Code and data are available at: https://github.com/Andy20178/SGFormer.

Changsheng Lv, Mengshi Qi, Zijian Fu et al.

In this paper, we introduce a novel method named Robo-SGG, i.e., Layout-Oriented Normalization and Restitution for Robust Scene Graph Generation. Compared to the existing SGG setting, the robust scene graph generation aims to perform inference on a diverse range of corrupted images, with the core challenge being the domain shift between the clean and corrupted images. Existing SGG methods suffer from degraded performance due to compromised visual features e.g., corruption interference or occlusions. To obtain robust visual features, we exploit the layout information, which is domain-invariant, to enhance the efficacy of existing SGG methods on corrupted images. Specifically, we employ Instance Normalization(IN) to filter out the domain-specific feature and recover the unchangeable structural features, i.e., the positional and semantic relationships among objects by the proposed Layout-Oriented Restitution. Additionally, we propose a Layout-Embedded Encoder (LEE) that augments the existing object and predicate encoders within the SGG framework, enriching the robust positional and semantic features of objects and predicates. Note that our proposed Robo-SGG module is designed as a plug-and-play component, which can be easily integrated into any baseline SGG model. Extensive experiments demonstrate that by integrating the state-of-the-art method into our proposed Robo-SGG, we achieve relative improvements of 5.6%, 8.0%, and 6.5% in mR@50 for PredCls, SGCls, and SGDet tasks on the VG-C dataset, respectively, and achieve new state-of-the-art performance in corruption scene graph generation benchmark (VG-C and GQA-C). We will release our source code and model.