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Decomposing a target object from a complex background while reconstructing ischallenging. Most approaches acquire the perception for object instancesthrough the use of manual labels, but the annotation procedure is costly. Therecent advancements in 2D self-supervised learning have brought new prospectsto object-aware representation, yet it remains unclear how to leverage suchnoisy 2D features for clean decomposition. In this paper, we propose aDecomposed Object Reconstruction (DORec) network based on neural implicitrepresentations. Our key idea is to transfer 2D self-supervised features intomasks of two levels of granularity to supervise the decomposition, including abinary mask to indicate the foreground regions and a K-cluster mask to indicatethe semantically similar regions. These two masks are complementary to eachother and lead to robust decomposition. Experimental results show thesuperiority of DORec in segmenting and reconstructing the foreground object onvarious datasets.

DORec: Decomposed Object Reconstruction Utilizing 2D Self-Supervised Features