Before revision:  The objects have varying poses and arrangements, and are imaged under different illumination conditions and camera poses. The recognition performance of our methods , which are trained using the living room images, remains relatively unaffected on the unseen warehouse images .
After revision:  On the benchmark indoor scenes dataset, sparse PI features show better recognition performance in unseen environments than the features learned using the widely used ResNetV2-56 and EfficientNet-B4 models. Further, they provide slightly higher recall and accuracy values than Faster R-CNN, an end-to-end object detection method, and its state-of-the-art variant, Domain Adaptive Faster R-CNN. The performance of our methods , which are trained using the living room images, remains relatively unaffected on the unseen warehouse images .