StixelNExT: Toward Monocular Low-Weight Perception for Object Segmentation and Free Space Detection
This addresses the problem of lightweight perception for autonomous systems by enabling rapid training with minimal data, though it appears incremental in its adaptation of Stixel-World concepts.
The paper tackles monocular object segmentation without manually labeled training data by learning from LiDAR during training and then operating on monocular images alone, achieving recognition of multiple superimposed objects through a 2D multi-layer Stixel-World representation.
In this work, we present a novel approach for general object segmentation from a monocular image, eliminating the need for manually labeled training data and enabling rapid, straightforward training and adaptation with minimal data. Our model initially learns from LiDAR during the training process, which is subsequently removed from the system, allowing it to function solely on monocular imagery. This study leverages the concept of the Stixel-World to recognize a medium level representation of its surroundings. Our network directly predicts a 2D multi-layer Stixel-World and is capable of recognizing and locating multiple, superimposed objects within an image. Due to the scarcity of comparable works, we have divided the capabilities into modules and present a free space detection in our experiments section. Furthermore, we introduce an improved method for generating Stixels from LiDAR data, which we use as ground truth for our network.