ChronoTrack enables effective long-term 3D single-object tracking in LiDAR by storing target features in compact learnable memory tokens regularized by temporal consistency and memory-cycle consistency losses, reaching SOTA accuracy at 42 FPS.
Are we ready for autonomous driving? the kitti vision benchmark suite
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A semi-supervised 3D object detection framework with a learnable module for adaptive pseudo-label selection via score fusion, context-aware thresholds, and soft supervision.
VGGT-Long extends VGGT with chunking, overlap alignment, and loop closure to produce consistent kilometer-scale 3D reconstructions from monocular RGB sequences without retraining or extra supervision.
citing papers explorer
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Temporally Consistent Long-Term Memory for 3D Single Object Tracking
ChronoTrack enables effective long-term 3D single-object tracking in LiDAR by storing target features in compact learnable memory tokens regularized by temporal consistency and memory-cycle consistency losses, reaching SOTA accuracy at 42 FPS.
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Learning Adaptive Pseudo-Label Selection for Semi-Supervised 3D Object Detection
A semi-supervised 3D object detection framework with a learnable module for adaptive pseudo-label selection via score fusion, context-aware thresholds, and soft supervision.
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VGGT-Long: Chunk it, Loop it, Align it -- Pushing VGGT's Limits on Kilometer-scale Long RGB Sequences
VGGT-Long extends VGGT with chunking, overlap alignment, and loop closure to produce consistent kilometer-scale 3D reconstructions from monocular RGB sequences without retraining or extra supervision.