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Shijia Zhao

7 accepted papers

2026

MSGNav: Unleashing the Power of Multi-modal 3D Scene Graph for Zero-Shot Embodied Navigation

CVPR 2026

Embodied navigation is a fundamental capability for robotic agents operating. Real-world deployment requires open vocabulary generalization and low training overhead, motivating zero-shot methods rather than task-specific RL training. However, existing zero-shot methods that build explicit 3D scene

Cited by 0SourceScholar
2026

OWL: Unsupervised 3D Object Detection by Occupancy Guided Warm-up and Large Model Priors Reasoning

AAAI 2026technical

Unsupervised 3D object detection leverages heuristic algorithms to discover potential objects, offering a promising route to reduce annotation costs in autonomous driving. Existing approaches mainly generate pseudo labels and refine them through self-training iterations. However, these pseudo-label

Cited by 0SourcePDFScholar
2025

AdaCo: Overcoming Visual Foundation Model Noise in 3D Semantic Segmentation via Adaptive Label Correction

AAAI 2025technical

Recently, Visual Foundation Models (VFMs) have shown a remarkable generalization performance in 3D perception tasks. However, their effectiveness in large-scale outdoor datasets remains constrained by the scarcity of accurate supervision signals, the extensive noise caused by variable outdoor cond…

2025

SP3D: Boosting Sparsely-Supervised 3D Object Detection via Accurate Cross-Modal Semantic Prompts

CVPR 2025highlight

Recently, sparsely-supervised 3D object detection has gained great attention, achieving performance close to fully-supervised 3D detectors while requiring only a few annotated instances. Nevertheless, these methods suffer challenges when accurate labels are extremely absent. In this paper, we propos…

2024

Commonsense Prototype for Outdoor Unsupervised 3D Object Detection

CVPR 2024poster

The prevalent approaches of unsupervised 3D object detection follow cluster-based pseudo-label generation and iterative self-training processes. However the challenge arises due to the sparsity of LiDAR scans which leads to pseudo-labels with erroneous size and position resulting in subpar detection…

2024

HINTED: Hard Instance Enhanced Detector with Mixed-Density Feature Fusion for Sparsely-Supervised 3D Object Detection

CVPR 2024poster

Current sparsely-supervised object detection methods largely depend on high threshold settings to derive high-quality pseudo labels from detector predictions. However hard instances within point clouds frequently display incomplete structures causing decreased confidence scores in their assigned pse…

2017

A model of vertebral motion and key point recognition of drilling with force in robot-assisted spinal surgery

IROS 2017poster

Pedicle drilling is a crucial and high-risk process in spinal surgery. Due to the respiration and cardiac cycle, the position of spine would fluctuate during operations, which result in an increase of the difficulty in state recognition of pedicle drilling. To guarantee the safety and validity, a mo…

Cited by 8SourceScholar