Papers › PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation

PanoFormer: Panorama Transformer for Indoor 360 Depth Estimation

17 Mar 2022arXiv:2203.09283archive 2025-07-28

Zhijie Shen, Chunyu Lin, Kang Liao, Lang Nie, Zishuo Zheng, Yao Zhao

Existing panoramic depth estimation methods based on convolutional neural networks (CNNs) focus on removing panoramic distortions, failing to perceive panoramic structures efficiently due to the fixed receptive field in CNNs. This paper proposes the panorama transformer (named PanoFormer) to estimate the depth in panorama images, with tangent patches from spherical domain, learnable token flows, and panorama specific metrics. In particular, we divide patches on the spherical tangent domain into tokens to reduce the negative effect of panoramic distortions. Since the geometric structures are essential for depth estimation, a self-attention module is redesigned with an additional learnable token flow. In addition, considering the characteristic of the spherical domain, we present two panorama-specific metrics to comprehensively evaluate the panoramic depth estimation models' performance. Extensive experiments demonstrate that our approach significantly outperforms the state-of-the-art (SOTA) methods. Furthermore, the proposed method can be effectively extended to solve semantic panorama segmentation, a similar pixel2pixel task. Code will be available.

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compute_depth_metrics zhijieshen-bjtu/panoformer/PanoFormer/metrics.py official repository unverified MIT (permissive) · 8ee1769c0dd17017 · report
extract_image zhijieshen-bjtu/panoformer/PanoFormer/common.py official repository unverified MIT (permissive) · ed47b6d3840587fc · report
extract_path zhijieshen-bjtu/panoformer/PanoFormer/common.py official repository unverified MIT (permissive) · 6a2f685cb7ea6a39 · report
generate_ref_points zhijieshen-bjtu/panoformer/PanoFormer/network/PSA.py official repository unverified MIT (permissive) · 23b9363b00ceea5b · report
get_k_layer_feature_map zhijieshen-bjtu/panoformer/PanoFormer/network/PSA.py official repository unverified MIT (permissive) · 9e82bf4646d4e34c · report
read_list zhijieshen-bjtu/panoformer/PanoFormer/matterport3d.py official repository unverified MIT (permissive) · e94322673236e329 · report
restore_scale zhijieshen-bjtu/panoformer/PanoFormer/network/PSA.py official repository unverified MIT (permissive) · 94e4147279fb4f23 · report

Tasks

Depth EstimationSemantic Segmentation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Depth Estimation Stanford2D3D Panoramic PanoFormer RMSE 0.3083 #4 of 18 Archive leaderboard report
Depth Estimation Stanford2D3D Panoramic PanoFormer absolute relative error 0.0405 #4 of 18 Archive leaderboard report
Semantic Segmentation Stanford2D3D Panoramic PanoFormer mAcc 64.5 #16 of 25 Archive leaderboard report
Semantic Segmentation Stanford2D3D Panoramic PanoFormer mIoU 48.9% #16 of 25 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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