Temporally Consistent Long-Term Memory for 3D Single Object Tracking

TL;DR

This paper introduces ChronoTrack, a novel long-term 3D single object tracking framework that maintains temporal feature consistency and efficiently utilizes memory to improve long-term tracking accuracy.

Contribution

ChronoTrack is the first to effectively preserve temporal feature consistency and leverage long-term memory with learnable tokens for 3D-SOT.

Findings

Achieves state-of-the-art results on multiple benchmarks.

Operates at real-time speed of 42 FPS on a single GPU.

Effectively models long-term target features with compact memory.

Abstract

3D Single Object Tracking (3D-SOT) aims to localize a target object across a sequence of LiDAR point clouds, given its 3D bounding box in the first frame. Recent methods have adopted a memory-based approach to utilize previously observed features of the target object, but remain limited to only a few recent frames. This work reveals that their temporal capacity is fundamentally constrained to short-term context due to severe temporal feature inconsistency and excessive memory overhead. To this end, we propose a robust long-term 3D-SOT framework, ChronoTrack, which preserves the temporal feature consistency while efficiently aggregating the diverse target features via long-term memory. Based on a compact set of learnable memory tokens, ChronoTrack leverages long-term information through two complementary objectives: a temporal consistency loss and a memory cycle consistency loss. The former enforces feature alignment across frames, alleviating temporal drift and improving the reliability of proposed long-term memory. In parallel, the latter encourages each token to encode diverse and discriminative target representations observed throughout the sequence via memory-point-memory cyclic walks. As a result, ChronoTrack achieves new state-of-the-art performance on multiple 3D-SOT benchmarks, demonstrating its effectiveness in long-term target modeling with compact memory while running at real-time speed of 42 FPS on a single RTX 4090 GPU. The code is available at https://github.com/ujaejoon/ChronoTrack