Segmentation and Recognition Using Structure from Motion Point Clouds

We propose an algorithm for semantic segmentation based on 3D point clouds derived from ego-motion. We motivate five simple cues designed to model specific patterns of motion and 3D world structure that vary with object category. We introduce features that project the 3D cues back to the 2D image plane while modeling spatial layout and context. A randomized decision forest combines many such features to achieve a coherent 2D segmentation and recognize the object categories present. Our main contribution is to show how semantic segmentation is possible based solely on motion-derived 3D world structure. Our method works well on sparse, noisy point clouds, and unlike existing approaches, does not need appearance-based descriptors. Experiments were performed on a challenging new video database containing sequences filmed from a moving car in daylight and at dusk. The results confirm that indeed, accurate segmentation and recognition are possible using only motion and 3D world structure. Further, we show that the motion-derived information complements an existing state-of-the-art appearance-based method, improving both qualitative and quantitative performance. input video frame reconstructed 3D point cloud automatic segmentation Fig. 1. The proposed algorithm uses 3D point clouds estimated from videos such as the pictured driving sequence (with ground truth inset). Having trained on point clouds from other driving sequences, our new motion and structure features, based purely on the point cloud, perform 11-class semantic segmentation of each test frame. The colors in the ground truth and inferred segmentation indicate category labels.