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Modelling individual objects in a scene as Neural Radiance Fields (NeRFs)provides an alternative geometric scene representation that may benefitdownstream robotics tasks such as scene understanding and object manipulation.However, we identify three challenges to using real-world training datacollected by a robot to train a NeRF: (i) The camera trajectories areconstrained, and full visual coverage is not guaranteed - especially whenobstructions to the objects of interest are present; (ii) the poses associatedwith the images are noisy due to odometry or localization noise; (iii) theobjects are not easily isolated from the background. This paper evaluates theextent to which above factors degrade the quality of the learnt implicit objectrepresentation. We introduce a pipeline that decomposes a scene into multipleindividual object-NeRFs, using noisy object instance masks and bounding boxes,and evaluate the sensitivity of this pipeline with respect to noisy poses,instance masks, and the number of training images. We uncover that thesensitivity to noisy instance masks can be partially alleviated with depthsupervision and quantify the importance of including the camera extrinsics inthe NeRF optimisation process.

Implicit Object Mapping With Noisy Data