Weakly Supervised Learning of Rigid 3D Scene Flow

Our method consumes point clouds of two consecutive frames and estimates per-object transformation parameters, ego-motion, and object masks. These outputs can be combined into an object-level scene abstraction and pointwise rigid scene flow.

Abstract

We propose a data-driven scene flow estimation algorithm exploiting the observation that many 3D scenes can be explained by a collection of agents moving as rigid bodies. At the core of our method lies a deep architecture able to reason at the object-level by considering 3D scene flow in conjunction with other 3D tasks. This object level abstraction, enables us to relax the requirement for dense scene flow supervision with simpler binary background segmentation mask and ego-motion. Our mild supervision requirements make our method well suited for recently released massive data collections for autonomous driving, which do not contain dense scene flow annotations. As output, our model provides low-level cues like pointwise flow and higher-level cues such as holistic scene understanding at the level of rigid objects. We further propose a test-time optimization refining the predicted rigid scene flow. We showcase the effectiveness and generalization capacity of our method on four different autonomous driving datasets.

Qualitative results

We train our model in a weakly supervised setting on semanticKITTI dataset and evaluate it on lidarKITTI as well as in a generalization setting on waymo-open.

Qualitative results of our weakly supervised method on lidarKITTI (top) and waymo open (bottom). For improved visibility, the EPE3D (top row b,c ) is clipped to the range between 0.0 m (white) at 0.3m (red). As a result of predicting an unconstrained pointwise sceneflow, the rigid objects (car) in the results of FLOT might get deformed (d).

Citation

@misc{gojcic2021weakly3dsf,
        title = {Weakly {S}upervised {L}earning of {R}igid {3D} {S}cene {F}low}, 
        author = {Gojcic, Zan and Litany, Or and Wieser, Andreas and Guibas, Leonidas J and Birdal, Tolga},
        year = {2021},
        eprint={2102.08945},
        archivePrefix={arXiv},
        primaryClass={cs.CV}
        }

Weakly Supervised Learning of Rigid 3D Scene Flow

Abstract

Qualitative results

lidarKITTI - scene flow

Ours

FLOT

lidarKITTI - EPE3D (red color denotes large error)

Ours

FLOT

waymoOpen - scene flow

Ours (generalization)

Ours (fine-tuned)

waymoOpen - object level abstraction

Ours (fine-tuned)

Ground Truth Instances

Citation

Acknowledgments