Back on Track: Bundle Adjustment for Dynamic Scene Reconstruction

Traditional SLAM systems, which rely on bundle adjustment, struggle with the highly dynamic scenes commonly found in casual videos. Such videos entangle the motion of dynamic elements, undermining the assumption of static environments required by traditional systems. Existing techniques either filter out dynamic elements or model their motion independently. However, the former often results in incomplete reconstructions, while the latter can lead to inconsistent motion estimates. Taking a novel approach, this work leverages a 3D point tracker to separate camera-induced motion from the observed motion of dynamic objects. By considering only the camera-induced component, bundle adjustment can operate reliably on all scene elements. We further ensure depth consistency across video frames with lightweight post-processing based on scale maps. Our framework combines the core of traditional SLAM—bundle adjustment—with a robust learning-based 3D tracker. Integrating motion decomposition, bundle adjustment, and depth refinement, our unified framework, BA-Track, accurately tracks camera motion and produces temporally coherent and scale-consistent dense reconstructions, accommodating both static and dynamic elements. Our experiments on challenging datasets reveal significant improvements in camera pose estimation and 3D reconstruction accuracy.